Hypoxia-inducible factors in cancer: an overview of major findings from meta-analyses
Review Article

Hypoxia-inducible factors in cancer: an overview of major findings from meta-analyses

Deli Zou1,2#, Tao Han2,3#, Han Deng1,2#, Xiaodong Shao1, Xiaozhong Guo1*, Xingshun Qi1,2*

1Department of Gastroenterology, Cancer Center, General Hospital of Shenyang Military Area, Shenyang 110840, China2Meta-analysis Study Interest Group, Cancer Center, General Hospital of Shenyang Military Area, Shenyang 110840, China3Department of Oncology, Cancer Center, General Hospital of Shenyang Military Area, Shenyang 110840, China

Contributions: (I) Conception and design: X Qi; (II) Administrative support: X Guo, X Qi; (III) Provision of study materials or patients: X Qi; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These authors contributed equally as co-first authors.

*These authors contributed equally for the senior authorship.

Correspondence to: Dr. Xingshun Qi; Prof. Xiaozhong Guo, Department of Gastroenterology, General Hospital of Shenyang Military Area, No. 83 Wenhua Road, Shenyang 110840, China. Email: xingshunqi@126.com; guo_xiao_zhong@126.com.

Abstract: This paper aims to systematically review the major findings from meta-analyses regarding the impact of hypoxia-inducible factors (HIFs) in various human cancers. A total of 56 eligible meta-analysis papers were identified via the PubMed and EMBASE databases. The associations of HIF-1α gene polymorphism and/or HIF-1α and HIF-2α protein expression with the risk, clinicopathological features, and/or survival were explored in head and neck cancer (n=4), glioma (n=2), oral cancer (n=10), oropharyngeal cancer (n=1), nasopharyngeal cancer (n=1), lung cancer (n=12), breast cancer (n=17), esophageal cancer (n=5), gastric cancer (n=8), colorectal cancer (n=15), pancreatic cancer (n=8), hepatocellular carcinoma (n=5), prostate cancer (n=13), renal cancer (n=13), bladder cancer (n=3), ovarian cancer (n=3), cervical cancer (n=10), endometrial cancer (n=1), and osteosarcoma (n=1). Based on the current evidence, the impact of HIFs should be heterogeneous on various human cancers.

Keywords: Cancer; risk; survival; hypoxia-inducible (HIF); meta-analysis; systematic review


Received: 22 December 2016; Accepted: 10 April 2017; Published: 05 May 2017.

doi: 10.21037/amj.2017.04.08


Introduction

Hypoxia, which refers to a low oxygen condition, is closely associated with the development and progression of cancer (1-3). Hypoxia-inducible factors (HIFs) are important proteins for the regulation of molecular response on hypoxia (4). HIFs consist of two subunits (i.e., α and β). The α subunit is expressed according to the oxygen conditions and determines the transcriptional activity of HIF. In details, the degradation of HIF-1α is enhanced and suppressed in the normoxic and hypoxic conditions, respectively; and high and low expression of HIF-1α increases and decreases the HIF activity, respectively. HIF-1α family contains 3 members (HIF-1α, HIF-2α, and HIF-3α) (5-8). By comparison, the β subunit is constitutively expressed in the nucleus.

Among the HIF-1α family members, HIF-1α is the most widely studied in human cancer (9,10). HIF-1α gene, which is located at the chromosome 14q21-24, consists of 15 exons and 14 introns, codes the cDNA of 3,919 bps, and produces the protein of 826 amino acids. HIF-1α can transactivate more than 70 target genes and is a master regulator of erythropoiesis, blood vessel formation, cell metabolism, and genetic stability. There are two major HIF-1α gene polymorphisms (C1772T and G1790A). Both of them are located at the exon 12 of the HIF-1α gene within the oxygen-dependent degradation domain. HIF-1α C1772T (rs11549465) mutation refers to an amino acid substitution from proline to serine at codon 582 (Pro582Ser or P582S). HIF-1α G1790A (rs11549467) mutation refers to an amino acid substitution from alanine to threonine at codon 588 (Ala588Thr or A588T).

To the best of our knowledge, numerous studies and meta-analyses have explored the role of HIF-1α gene polymorphism and protein expression in cancer. By comparison, less evidence has been accumulated regarding the role of HIF-2α and HIF-3α in cancer. In this paper, we have conducted an overview of meta-analyses to provide more comprehensive recognition of evidence regarding the role of HIFs in cancer.


Methods

Registration

Our study protocol was registered in PROSPERO database. The registration number was CRD42016037401.

Search strategy

We identified the relevant meta-analysis papers via the PubMed and EMBASE databases. We also manually identified the relevant meta-analysis papers. Search items were: “(hypoxia inducible factor) OR HIF” AND “(((cancer) OR tumor) OR neoplasm) OR carcinoma” AND “(meta analysis)”. The last search date was April 6, 2016.

Eligibility criteria

Only meta-analysis papers regarding the role of HIF in cancer were eligible for our study. Duplicates, comments or editorials, narrative reviews, original articles, and irrelevant meta-analysis papers were excluded. Publication language or date was not limited.

Data extraction

We primarily extracted the data from the eligible meta-analysis papers, as follows: first author, publication year, journal, country, databases which were employed for each meta-analysis, date when each meta-analysis was conducted, type of cancer, HIF gene polymorphism or protein expression, number of studies which were included in each meta-analysis, and results of each meta-analysis. If the statistical analyses were performed by using both fixed- and random-effects models, only the results by a random-effects model would be considered.

Evaluation of heterogeneity

If the results were heterogeneous among two or more meta-analyses, we would further identify the reliability according to the following criteria.

First, the number of eligible studies should be considered. A meta-analysis with a larger number of eligible studies would be more reliable.

Second, if the number of eligible studies was similar among them, the number of participants would be considered. A meta-analysis with a larger number of participants would be more reliable.

Third, if the eligible studies were completely overlapped among them, the methods of meta-analysis would be considered. A meta-analysis using a random-effect model would be more reliable.

Fourth, if the controversy or uncertainty remained according to the above-mentioned criteria, the original studies would be extracted and a meta-analysis might be updated. We might also contact with the authors or journal editors, if necessary.


Results

After excluding the irrelevant papers, a total of 55 meta-analysis papers were included in our study (Figure 1). Among them, 53 papers were written by Chinese researchers, 1 paper by UK researchers, and 1 paper by Bangladeshi researchers. The last search date for each meta-analysis ranged from 2009 to 2016. Results of meta-analyses were summarized according to the location of cancer.

Figure 1 The flowchart of inclusion.

Overall cancer

A total of 13 meta-analysis papers explored the role of HIF in overall cancer regardless of location of cancer (11-23) (Table S1). Among them, 5 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (11,13,15,18,22), 5 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12,14,17,19,21), and 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20,23).

Risk

Nine papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of overall cancer (11,12,14,15,17-19,21,22). All of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of overall cancer (11,12,14,15,17-19,21,22).

Seven papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of overall cancer (11,15,16,18,20,22,23). Six of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of overall cancer (11,15,16,18,20,23). But another paper did not show any significant association between them (22). The meta-analyses by Liu P (16) and Zhou Y (23) had a larger number of included studies than those by Yang X (18), Ye Y (20), Anam MT (11), Zhao T (22), and Liu J (15) (26 and 26 versus 24, 21, 19, 12, and 6). Thus, we should support a significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of overall cancer.

Clinicopathological features

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the clinicopathological features of overall cancer (13). It demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the lymph node metastasis and histological grade of overall cancer, but not the tumor size or stage (13).

One paper explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the clinicopathological features of overall cancer (13). It demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the lymph node metastasis and tumor size of overall cancer, but not the histological grade or tumor stage (13).

Head and neck cancer

A total of 4 meta-analysis papers explored the role of HIF in head and neck cancer (12,14,16,23) (Table S2). Among them, 2 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12,14), and another 2 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,23).

Risk

Two papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of head and neck cancer (12,14). One of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of head and neck cancer (12). But another paper did not show any significant association between them (14). The meta-analysis by He P (12) had a larger number of included studies than that by Li Y (14) (5 versus 1). Thus, we should support a significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of head and neck cancer.

Two papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of head and neck cancer (16,23). One of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of head and neck cancer (23). But another paper did not show any significant association between them (16). The meta-analysis by Zhou Y (23) had a larger number of included studies than that by Liu P (16) (6 versus 1). Thus, we should support a significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of head and neck cancer.

Glioma

A total of two meta-analysis papers explored the role of HIF in glioma (14,24) (Table S3). Among them, one paper explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (14), and another paper explored HIF-1α expression alone (24).

Risk

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of glioma (14). It demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of glioma (14).

Clinicopathological features

One paper explored the association of HIF-1α expression with the clinicopathological features of glioma (24). It demonstrated that HIF-1α expression was significantly associated with the tumor stage of glioma (24).

Oral cancer

A total of ten meta-analysis papers explored the role of HIF in oral cancer (14,16,18-20,25-29) (Table S4). Among them, four papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (18,27-29), three papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (14,19,25), two papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20), and one paper explored both HIF-1α and HIF-2α protein expressions (26).

Risk

Seven papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of oral cancer (14,18,19,25,27-29). All of them did not show any significant association between them (14,18,19,25,27-29).

Six papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of oral cancer (16,18,20,27-29). Four of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of oral cancer (16,20,27,28). But another two papers did not show any significant association between them (18,29). The meta-analyses by Sun X (27) and Yan Q (28) had a larger number of included studies than those by Liu P (16), Yang X (Plos One, 2013) (18), Yang X (Tumour Biol, 2014) (29), and Ye Y (20). Thus, we should support a significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of oral cancer.

Prognosis

One paper explored the association of HIF-1α and HIF-2α protein expression with the prognosis of oral cancer (26). It demonstrated that neither HIF-1α nor HIF-2α protein expression was significantly associated with the survival of oral cancer (26).

Oropharyngeal cancer

Only one paper explored the role of HIF in oropharyngeal cancer (30) (Table S5). It explored the association of HIF-1α expression with the prognosis of oropharyngeal cancer (30). It demonstrated that HIF-1α expression was significantly associated with the survival of oropharyngeal cancer (30).

Nasopharyngeal cancer

Only one paper explored the role of HIF in nasopharyngeal cancer (31) (Table S6). It explored the association of HIF-1α expression with the risk and clinicopathological features of nasopharyngeal cancer (31). It demonstrated that HIF-1α expression was significantly associated with the risk, lymph node metastasis, and clinical stage of nasopharyngeal cancer (31).

Lung cancer

A total of 12 meta-analysis papers explored the role of HIF in lung cancer (11,12,14,16,18,23,25,28,32-35) (Table S7). Among them, 4 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (11,18,28,33), 3 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12,14,25), 2 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,23), 1 paper explored both HIF-1α and HIF-2α protein expressions (32), and 2 papers explored HIF-1α protein expression alone (34,35).

Risk

Seven papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of lung cancer (11,12,14,18,25,28,33). Four of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of lung cancer (11,18,28,33). But another 3 papers did not show any significant association between them (12,14,25). The meta-analyses by He P (12), Hu X (25), Li Y (14), Yan Q (28), and Yang X (18) had a larger number of included studies than those by Anam MT (11) and Liao S (33) (3, 3, 3, 3, and 3 versus 2 and 2). Among the meta-analyses by He P (12), Hu X (25), Li Y (14), Yan Q (28), and Yang X (18), the included studies were completely identical (Table S8). Only the meta-analysis by Yang X employed a random-effect model (18). Thus, we should support a significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of lung cancer.

Six papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of lung cancer (11,16,18,23,28,33). All of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of lung cancer (11,16,18,23,28,33).

Clinicopathological features

One paper explored the association of HIF-1α protein expression with the clinicopathological features of lung cancer (34). It demonstrated that HIF-1α protein expression was significantly associated with the stage, pathological type, diameter, lymph node metastasis, and differentiation of lung cancer (34).

Prognosis

Three papers explored the association of HIF-1α protein expression with the prognosis of lung cancer (32,34,35). All of them demonstrated that HIF-1α protein expression was significantly associated with the survival of lung cancer (32,34,35).

One paper explored the association of HIF-2α protein expression with the prognosis of lung cancer (32). It demonstrated that HIF-2α protein expression was significantly associated with the survival of lung cancer (32).

Breast cancer

A total of 17 meta-analysis papers explored the role of HIF in breast cancer (11-14,16-20,22,23,25,28,36-39) (Table S9). Among them, 5 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (11,18,22,28,39), 7 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12-14,17,19,25,36), 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20,23), and 2 papers explored HIF-1α protein expression alone (37,38).

Risk

Eleven papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of breast cancer (11,12,14,17-19,22,25,28,36,39). Three of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of breast cancer (14,18,39). But another 8 papers did not show any significant association between them (11,12,17,19,22,25,28,36). The meta-analyses by He P (12), Ren HT (36), Wu G (17), and Yan Q (28) had a larger number of included studies than those by Hu X (Tumour Biol, 2014) (25), Li Y (14), Yang X (18), Ye Y (19), Zhao T (22), and Anam MT (11) (6, 6, 6, and 6 versus 5, 5, 5, 3, 3, and 2). An abstract paper by Yin W did not report the number of included studies (39). Thus, we should not support any significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of breast cancer.

Eight papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of breast cancer (11,16,18,20,22,23,28,39). Two of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of breast cancer (11,22). But another 6 papers did not show any significant association between them (16,18,20,23,28,39). The meta-analysis by Yan Q (28) had a larger number of included studies than those by Liu P (16), Yang X (18), Zhou Y (23), Anam MT (11), Ye Y (20), and Zhao T (22) (4 versus 3, 3, 3, 2, 2, and 2). An abstract paper by Yin W did not report the number of included studies (39). Thus, we should not support any significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of breast cancer.

Clinicopathological features

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the clinicopathological features of breast cancer (13). It did not show any significant association of HIF-1α rs11549465 (1772 C/T) polymorphism with the lymph node metastasis or histological grade of breast cancer (13).

One paper explored the association of HIF-1α protein expression with the clinicopathological features of breast cancer (37). It demonstrated that HIF-1α protein expression was significantly associated with the pathological differentiation, regional invasive extension, axillary lymph node status, and clinical stage of breast cancer (37).

Prognosis

Two papers explored the association of HIF-1α protein expression with the prognosis of breast cancer (37,38). Both of them demonstrated that HIF-1α protein expression was significantly associated with the survival of breast cancer (37,38).

Digestive cancer

A total of 33 meta-analysis papers explored the role of HIF in digestive cancer (Table S10). Among them, 6 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (15,27-29,40,41), 11 papers explored HIF-1α rs11549465 (1772 C/T)polymorphism alone (11-14,17-19,22,25,42,43), 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20,23), 1 paper explored both HIF-1α and HIF-2α protein expressions (44), 10 papers explored HIF-1α protein expression alone (45-54), and 2 papers explored HIF-2α protein expression alone (55,56).

Overall digestive cancer

A total of 8 meta-analysis papers explored the role of HIF in overall digestive cancer regardless of location of digestive cancer (17,20,27,29,40-43) (Table S10). Among them, 4 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (27,29,40,41), 3 papers explored HIF-1α rs11549465 (1772 C/T)polymorphism alone (17,42,43), and 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (20).

Risk

Seven papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of overall digestive cancer (17,27,29,40-43). Four of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of overall digestive cancer (29,40,42,43). But another 3 papers did not show any significant association between them (17,27,41). The meta-analysis by Sun X (27) had a larger number of included studies than those by Yang X (29), Ni Z (40), Wu G (17), Xu JJ (Genet Mol Res, 2014) (41), Xu J (Genet Mol Res, 2014) (43), and Xu J (Genet Test Mol Biomarkers, 2013) (42) (13 versus 12, 10, 9, 8, 6, and 6). Thus, we should not support any significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of overall digestive cancer.

Five papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of overall digestive cancer (20,27,29,40,41). All of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of overall digestive cancer (20,27,29,40,41).

Esophageal cancer

A total of 5 meta-analysis papers explored the role of HIF in esophageal cancer (14,42,47,49,50) (Table S10). Among them, 2 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (14,42) and 3 papers explored HIF-1α protein expression alone (47,49,50).

Risk

Two papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of esophageal cancer (14,42). Both of them did not show any significant association between them (14,42).

Two papers explored the association of HIF-1α protein expression with the risk of esophageal cancer (47,50). Both of them demonstrated that HIF-1α protein expression was significantly associated with the risk of esophageal cancer (47,50).

Clinicopathological features

Three papers explored the association of HIF-1α protein expression with the clinicopathological features of esophageal cancer (47,49,50). All of them demonstrated that HIF-1α protein expression was significantly associated with the lymphoma node metastasis of esophageal cancer (47,49,50).

Prognosis

Two papers explored the association of HIF-1α protein expression with the prognosis of esophageal cancer (47,49). Both of them demonstrated that HIF-1α protein expression was significantly associated with the survival of esophageal cancer (47,49).

Gastric cancer

A total of 8 meta-analysis papers explored the role of HIF in gastric cancer (14,16,42,46,48,52,54) (Table S10). Among them, 2 papers explored HIF-1α rs11549465 (1772 C/T)polymorphism alone (14,42), 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16), 4 papers explored HIF-1α protein expression alone (46,48,52,54), and 1 paper explored HIF-2α expression alone (56).

Risk

Two papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of gastric cancer (14,42). One of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of gastric cancer (42). But another paper did not show any significant association between them (14). The number of included studies was similar between the two meta-analysis papers by Li Y (14) and Xu J (42) (1 versus 1). The included study was also identical between the two meta-analysis papers (Table S11). After learning the results from the original study (Li K, et al. Biochem Genet, 2009) (57), we should not support any significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of gastric cancer.

One paper explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of gastric cancer (16). It demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of gastric cancer (16).

Clinicopathological features

Three papers explored the association of HIF-1α protein expression with the clinicopathological features of gastric cancer (46,48,54). All of them demonstrated that HIF-1α protein expression was significantly associated with the depth of invasion, lymphatic invasion, vascular invasion, and TNM stage of gastric cancer (46,48,54).

One paper explored the association of HIF-2α protein expression with the clinicopathological features of gastric cancer (56). It demonstrated that HIF-2α protein expression was significantly associated with the tumor infiltration, lymphatic metastasis, and TNM stage of gastric cancer (56).

Prognosis

Four papers explored the association of HIF-1α protein expression with the prognosis of gastric cancer (46,48,52,54). All of them demonstrated that HIF-1α protein expression was significantly associated with the survival of gastric cancer (46,48,52,54).

One paper explored the association of HIF-2α protein expression with the prognosis of gastric cancer (56). It demonstrated that HIF-2α protein expression was significantly associated with the survival of gastric cancer (56).

Colorectal cancer

A total of 15 meta-analysis papers explored the role of HIF in colorectal cancer (11-16,18,19,22,25,27-29,42,44) (Table S10). Among them, 4 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (15,27-29), 9 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (11-14,18,19,22,25,42), 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16), and 1 paper explored both HIF-1α and HIF-2α protein expressions (44).

Risk

Twelve papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of colorectal cancer (11,12,14,15,18,19,22,25,27-29,42,55). All of them did not show any significant association between them (11,12,14,15,18,19,22,25,27-29,42).

Four papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of colorectal cancer (16,27-29). All of them did not show any significant association between them (16,27-29).

Clinicopathological features

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the clinicopathological features of colorectal cancer (13). It did not show any significant association of HIF-1α rs11549465 (1772 C/T) polymorphism with the lymph node metastasis and histological grade of colorectal cancer (13).

One paper explored the association of HIF-1α protein expression with the clinicopathological features of colorectal cancer (44). It demonstrated that HIF-1α protein expression was significantly associated with the Dukes’ stages, lymph node status, depth of invasion, metastasis, and UICC stage of colorectal cancer, but not the differentiation grade (44).

One paper explored the association of HIF-2α protein expression with the clinicopathological features of colorectal cancer (44). It demonstrated that HIF-2α protein expression was significantly associated with the differentiation grade of colorectal cancer, but not the Dukes’ stages, lymph node status, or depth of invasion (44).

Prognosis

One paper explored the association of HIF-1α protein expression with the prognosis of colorectal cancer (44). It demonstrated that HIF-1α protein expression was significantly associated with the survival of colorectal cancer (44).

One paper explored the association of HIF-2α protein expression with the prognosis of colorectal cancer (44). It demonstrated that HIF-2α protein expression was significantly associated with the survival of colorectal cancer (44).

Pancreatic cancer

A total of 8 meta-analysis papers explored the role of HIF in pancreatic cancer (12,14,16,23,27-29,51) (Table S10). Among them, 2 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (27,29), 2 papers explored HIF-1α rs11549465 (1772 C/T)polymorphism alone (12,14), 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,23,28), and 1 paper explored HIF-1α protein expression alone (51).

Risk

Four papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of pancreatic cancer (12,14,27,29). All of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of pancreatic cancer (12,14,27,29).

Five papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of pancreatic cancer (16,23,27-29). All of them demonstrated that HIF-1α rs11549467 (1790 G/A)polymorphism was significantly associated with the risk of pancreatic cancer (16,23,27-29).

Clinicopathological features

One paper explored the association of HIF-1α protein expression with the clinicopathological features of pancreatic cancer (51). It demonstrated that HIF-1α protein expression was significantly associated with the lymph node metastasis and tumor stage of pancreatic cancer, but not the tumor size (51).

Prognosis

One paper explored the association of HIF-1α protein expression with the prognosis of pancreatic cancer (51). It demonstrated that HIF-1α protein expression was significantly associated with the survival of pancreatic cancer (51).

Hepatocellular carcinoma

A total of 5 meta-analysis papers explored the role of HIF in hepatocellular carcinoma (14,16,45,53,55) (Table S10). Among them, 1 paper explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (14), 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16), 2 papers explored HIF-1α protein expression alone (45,53), and 1 paper explored HIF-2α protein expression alone (55).

Risk

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of hepatocellular carcinoma (14). It did not show any significant association between them (14).

One paper explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of hepatocellular carcinoma (16). It demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of hepatocellular carcinoma (16).

Clinicopathological features

One paper explored the association of HIF-1α protein expression with the clinicopathological features of hepatocellular carcinoma (46). It demonstrated that HIF-1α protein expression was significantly associated with the vascular invasion of hepatocellular carcinoma, but not the tumor size or differentiation, liver cirrhosis, or capsule formation (46).

One paper explored the association of HIF-2α protein expression with the clinicopathological features of hepatocellular carcinoma (55). It demonstrated that HIF-2α protein expression was significantly associated with the vein invasion, histological grade, and capsule infiltration of hepatocellular carcinoma, but not the tumor size or liver cirrhosis (55).

Prognosis

Two papers explored the association of HIF-1α protein expression with the prognosis of hepatocellular carcinoma (46,53). Both of them demonstrated that HIF-1α protein expression was significantly associated with the survival of hepatocellular carcinoma (46,53).

One paper explored the association of HIF-2α protein expression with the prognosis of hepatocellular carcinoma (55). It did not show any significant association between HIF-2α protein expression and the survival of hepatocellular carcinoma (55).

Urinary cancer

A total of 15 meta-analysis papers explored the role of HIF in urinary cancer (11,12,14,16-20,22,23,25,28,58-60) (Table S12). Among them, 5 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (11,18,22,28,59), 5 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12,14,17,19,25), 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20,23), 1 paper explored both HIF-1α and HIF-2α protein expressions (58), and 1 paper explored HIF-1α protein expression alone (60).

Overall urinary cancer

One meta-analysis paper explored the association of HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms with the risk of overall urinary cancer (59) (Table S12). It demonstrated that neither of them was significantly associated with the risk of overall urinary cancer (59).

Prostate cancer

A total of 13 meta-analysis papers explored the role of HIF in prostate cancer (11,12,14,16-20,22,23,25,28,59) (Table S12). Among them, 5 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (11,18,22,28,59), 5 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (11,18,22,28,59), and 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20,23).

Risk

Ten papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of prostate cancer (11,12,14,17-19,22,25,59). Five of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of prostate cancer (14,18,19,22,25). Another 5 papers did not show any significant association between them (11,12,17,28,59). The meta-analyses by Anam MT (11), He P (12), Li D (59), Wu G (17), and Yan Q (28) had a larger number of included studies than those by Hu X (25), Yang X (18), Ye Y (19), Li Y (14), and Zhao T (22) (6, 6, 6, 6, and 6 versus 5, 5, 5, 4, and 4). Thus, we should not support any significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of prostate cancer.

Eight papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of prostate cancer (11,16,18,20,22,23,28,59). One of them demonstrated that HIF-1α rs11549467 (1790 G/A)polymorphism was significantly associated with the risk of prostate cancer (59). Another 7 papers did not show any significant association between them (11,16,18,20,22,23,28). The meta-analysis by Li D (59) had a larger number of included studies than those by Anam MT (11), Liu P (16), Yan Q (28), Ye Y (20), Yang X (18), Zhou Y (23), and Zhao T (22) (4 versus 3, 3, 3, 3, 3, 3, and 2). Thus, we should support a significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of prostate cancer.

Renal cancer

A total of 13 meta-analysis papers explored the role of HIF in renal cancer (11,12,14,16,17,19,20,23,25,28,58-60) (Table S12). Among them, 3 papers explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (11,28,59), 5 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12,14,17,19,25), 3 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20,23), 1 paper explored both HIF-1α and HIF-2α nuclear and cytoplasmic expressions (58), and 1 paper explored HIF-1α protein expression alone (60).

Risk

Eight papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of renal cancer (11,12,14,17,19,25,28,59). Two of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of renal cancer (11,12). Another 6 papers did not show any significant association between them (14,17,19,25,28,59). The meta-analyses by Hu X (25), Li D (59), Wu G (17), and Yan Q (28) had a larger number of included studies than those by Anam MT (11), He P (12), Ye Y (19), and Li Y (14) (4, 4, 4, and 4 versus 3, 3, 3, and 2). Thus, we should not support any significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of renal cancer.

Six papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of renal cancer (11,16,20,23,28,59). Three of them demonstrated that HIF-1α rs11549467 (1790 G/A) polymorphism was significantly associated with the risk of renal cancer (11,23,28). Another 3 papers did not show any significant association between them (16,19,59). The meta-analyses by Anam MT (11), Li D (59), and Yan Q (28) had a larger number of included studies than those by Liu P (16), Zhou Y (23), and Ye Y (20) (4, 4, and 4 versus 3, 3, and 2). The included studies were completely identical among the 3 meta-analyses by Anam MT (11), Li D (59), and Yan Q (28) (Table S13). Notably, some statistical results (AA + AG vs. GG and A allele vs. G allele) were completely identical among them (11,28,59). However, the meta-analyses by Anam MT (11) and Yan Q (28) had more statistical results (AA vs. GG, GA vs. GG, and AA vs. GA + GG) than that by Li D (59). Thus, we should support a significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of renal cancer.

Clinicopathological features

One paper explored the association of HIF-1α protein expression with the clinicopathological features of renal cancer (60). It demonstrated that HIF-1α protein expression was significantly associated with the lymph node metastasis and clinical and pathological stage of renal cancer (60).

Prognosis

One paper explored the association of HIF-1α and HIF-2α nuclear and cytoplasmic expressions with the prognosis of renal cancer (58). It demonstrated that neither HIF-1α nor HIF-2α nuclear and cytoplasmic expression was significantly associated with the survival of renal cancer (58).

Bladder cancer

A total of 3 meta-analysis papers explored the role of HIF in bladder cancer (14,16,25) (Table S12). Among them, 2 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (14,25), and 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16).

Risk

Two papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of bladder cancer (14,25). Neither of them demonstrated any significant association between HIF-1α rs11549465 (1772 C/T)polymorphism and the risk of bladder cancer (14,25).

One paper explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of bladder cancer (16). It did not show any significant association between them (16).

Gynecological cancer

A total of 12 meta-analysis papers explored the role of HIF in gynecological cancer (12-14,16,19,20,25,28,61-64) (Table S14). Among them, 1 paper explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (28), 8 papers explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (12-14,19,25,64), 2 papers explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16,20), and 1 paper explored HIF-1α protein expression alone (61-63,65).

Overall gynecological cancer

A total of 3 meta-analysis papers explored the role of HIF in overall gynecological cancer (14,16,65) (Table S14). Among them, 1 paper explored HIF-1α rs11549465 (1772 C/T) polymorphism alone (14), 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (16), and 1 paper explored HIF-1α protein expression alone (65).

Risk

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of overall gynecological cancer (14). It demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of overall gynecological cancer (14).

One paper explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of overall gynecological cancer (16). It did not show any significant association between them (16).

Clinicopathological features

One paper explored the association of HIF-1α protein expression with the clinicopathological features of overall gynecological cancer (65). It demonstrated that HIF-1α protein expression was significantly associated with the pathological and histological type, FIGO stage, and lymph node metastasis of overall gynecological cancer (65).

Prognosis

One paper explored the association of HIF-1α protein expression with the prognosis of overall gynecological cancer (65). It demonstrated that HIF-1α protein expression was significantly associated with the survival of overall gynecological cancer (65).

Ovarian cancer

A total of 3 meta-analysis papers explored the role of HIF in ovarian cancer (62,63,65). All of them explored HIF-1α protein expression alone (62,63,65) (Table S14).

Risk

One paper explored the association of HIF-1α protein expression with the risk of ovarian cancer (63). It demonstrated that HIF-1α protein expression was significantly associated with the risk of ovarian cancer (63).

Clinicopathological features

Three papers explored the association of HIF-1α protein expression with the lymph node metastasis of ovarian cancer (62,63,65). All of them demonstrated that HIF-1α protein expression was significantly associated with the lymph node metastasis of ovarian cancer (62,63,65).

Three papers explored the association of HIF-1α protein expression with the pathological type of ovarian cancer (62,63,65). Two of them demonstrated HIF-1α protein expression was significantly associated with the pathological type of ovarian cancer (62,65). But another paper did not show any significant association between them (63). The meta-analyses by Jin Y (Tumour Biol, 2014) (62) and Jin Y (PLoS One, 2015) (65) had a larger number of included studies than that by Sun C (63) (13 and 13 versus 4). Thus, we should support a significant association between HIF-1α protein expression and the pathological type of ovarian cancer.

Two papers explored the association of HIF-1α protein expression with the FIGO stage of ovarian cancer (62,65). Both of them demonstrated that HIF-1α protein expression was significantly associated with the FIGO stage of ovarian cancer (62,65).

Prognosis

Two papers explored the association of HIF-1α protein expression with the prognosis of ovarian cancer (62,65). Both of them demonstrated that HIF-1α protein expression was significantly associated with the survival of ovarian cancer (62,65).

Cervical cancer

A total of 10 meta-analysis papers explored the role of HIF in cervical cancer (12,13,18-20,25,28,61,64,65) (Table S14). Among them, 1 paper explored both HIF-1α rs11549465 (1772 C/T) and rs11549467 (1790 G/A) polymorphisms (28), 6 papers explored HIF-1α rs11549465 (1772 C/T)polymorphism alone (12,13,18,19,25,64), 1 paper explored HIF-1α rs11549467 (1790 G/A) polymorphism alone (20), and 2 papers explored HIF-1α protein expression alone (61,65).

Risk

Six papers explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of cervical cancer (12,18,19,25,28,64). Five of them demonstrated that HIF-1α rs11549465 (1772 C/T) polymorphism was significantly associated with the risk of cervical cancer (12,18,25,28,64). But another paper did not show any significant association between them (19). The meta-analysis by Zhu J (64) had a larger number of included studies than those by He P (12), Hu X (25), Yan Q (28), Yang X (18), and Ye Y (19) (4 versus 3, 3, 3, 3, and 3). Thus, we should support a significant association between HIF-1α rs11549465 (1772 C/T) polymorphism and the risk of cervical cancer.

Two papers explored the association of HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of cervical cancer (20,28). Neither of them showed any significant association between HIF-1α rs11549467 (1790 G/A) polymorphism and the risk of cervical cancer (20,28).

Clinicopathological features

One paper explored the association of HIF-1α rs11549465 (1772 C/T) polymorphism with the lymph node metastasis of cervical cancer (13). It did not show any significant association between them (13).

Two papers explored the association of HIF-1α protein expression with the FIGO stage of cervical cancer (61,65). Both of them demonstrated that HIF-1α protein expression was significantly associated with the FIGO stage of cervical cancer (61,65).

Two papers explored the association of HIF-1α protein expression with the histological type and lymph node metastasis of cervical cancer (61,65). One of them demonstrated that HIF-1α protein expression was significantly associated with the histological type and lymph node metastasis of cervical cancer (65). But another paper did not show any significant association between them (61). As for the histological type, the meta-analysis by Jin Y (65) had a larger number of included studies than that by Huang M (61) (6 versus 4). As for the lymph node metastasis, the meta-analysis by Jin Y (65) had a larger number of included studies than that by Huang M (61) (8 versus 5). Thus, we should support a significant association between HIF-1α protein expression and the histological type and lymph node metastasis of cervical cancer.

Prognosis

Two papers explored the association of HIF-1α protein expression with the prognosis of cervical cancer (61,65). Both of them demonstrated that HIF-1α protein expression was significantly associated with the survival of cervical cancer (61,65).

Endometrial cancer

Only one paper explored the association of HIF-1α protein expression with the clinicopathological features and prognosis of endometrial cancer (65) (Table S14). It demonstrated that HIF-1α protein expression was significantly associated with the pathological and histological type, FIGO stage, and lymph node metastasis of endometrial cancer, but not the survival (65).

Osteosarcoma

Only one paper explored the association of HIF-1α protein expression with the clinicopathological features and prognosis of osteosarcoma (66) (Table S15). It demonstrated that HIF-1α protein expression was significantly associated with the metastasis, pathologic and tumor grade, and survival of osteosarcoma, but not the histopathology, tumor size, or tumor site (66).


Conclusions

Based on our systematic search strategy, numerous meta-analyses have explored the role of HIF gene polymorphism and protein expression in various human cancers, including head and neck cancer, glioma, oral cancer, oropharyngeal cancer, nasopharyngeal cancer, lung cancer, breast cancer, esophageal cancer, gastric cancer, colorectal cancer, pancreatic cancer, hepatocellular carcinoma, prostate cancer, renal cancer, bladder cancer, ovarian cancer, cervical cancer, endometrial cancer, and osteosarcoma (Figure 2).

Figure 2 A schematic diagram of various human cancers in which the role of HIFs has been explored by meta-analyses. HIFs, hypoxia-inducible factors.

Based on the current evidence, major findings were summarized in Table 1.

Table 1

Summary of major evidence

Cancer Risk Lymph node metastasis/tumor stage Survival
HIF-1α rs11549465 (1772 C/T) polymorphism HIF-1α rs11549467 (1790 G/A) polymorphism HIF-1α expression HIF-2α expression HIF-1α expression HIF-2α expression
Head and neck cancer Y Y
Glioma Y Y
Oral cancer N Y N N
Oropharyngeal cancer Y
Nasopharyngeal cancer Y
Lung cancer Y Y Y/Y Y Y
Breast cancer N N Y Y
Esophageal cancer N Y Y
Gastric cancer N Y Y/Y Y/Y Y Y
Colorectal cancer N N Y/Y N/N Y Y
Pancreatic cancer Y Y Y/Y Y
Hepatocellular carcinoma N Y Y N
Prostate cancer N Y
Renal cancer N Y Y/Y N N
Bladder cancer N N
Ovarian cancer Y/Y Y
Cervical cancer Y N Y
Endometrial cancer N
Osteosarcoma Y Y

Y, There is a significant correlation; N, There is no significant correlation.

First, the evidence regarding the association of HIF-1α gene polymorphism with risk of cancer suggested the following: (I) both HIF-1α rs11549465 (1772 C/T) and HIF-1α rs11549467 (1790 G/A) polymorphisms should be associated with the risk of head and neck cancer and lung cancer; (II) HIF-1α rs11549465 (1772 C/T) polymorphism, rather than HIF-1α rs11549467 (1790 G/A) polymorphism, should be associated with the risk of cervical cancer; (III) HIF-1α rs11549467 (1790 G/A) polymorphism, rather than HIF-1α rs11549465 (1772 C/T) polymorphism, should be associated with the risk of oral cancer, gastric cancer, hepatocellular carcinoma, prostate cancer, and renal cancer; and (IV) neither HIF-1α rs11549465 (1772 C/T) nor HIF-1α rs11549467 (1790 G/A) polymorphism should be associated with the risk of breast cancer, colorectal cancer, and bladder cancer.

Second, the evidence regarding the association of HIF-1α protein expression with the lymph node metastasis of cancer suggested the following: (I) both HIF-1α and HIF-2α expression were associated with the lymph node metastasis of gastric cancer; and (II) HIF-1α expression, rather than HIF-2α expression, was associated with the lymph node metastasis of colorectal cancer.

Third, the evidence regarding the association of HIF-1α protein expression alone with the lymph node metastasis of cancer suggested that HIF-1α expression was associated with the lymph node metastasis of glioma, nasopharyngeal cancer, lung cancer, breast cancer, esophageal cancer, gastric cancer, pancreatic cancer, renal cancer, ovarian cancer, and osteosarcoma.

Fourth, the evidence regarding the association of HIF-1α protein expression with the survival of cancer suggested the following: (I) both HIF-1α and HIF-2α expressions were associated with the survival of lung cancer, gastric cancer, and colorectal cancer; (II) HIF-1α expression, rather than HIF-2α expression, was associated with the survival of hepatocellular carcinoma; and (III) neither HIF-1α nor HIF-2α expression was associated with the survival of renal cancer.

Fifth, the evidence regarding the association of HIF-1α protein expression alone with the survival of cancer suggested that HIF-1α expression was associated with the survival of oropharyngeal cancer, breast cancer, esophageal cancer, pancreatic cancer, ovarian cancer, cervical cancer, and osteosarcoma, but not that of endometrial cancer.

Collectively, the impact of HIFs on the risk, clinicopathological features, and survival of various human cancers should be heterogeneous. The potential explanation might be attributed to the heterogeneity in the cancer biological behavior and effect of hypoxia across the different types of human cancers. Further studies should uncover the potential mechanisms.

Table S1

HIF in overall cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Anam MT Biomark Res [2015] Bangladesh PubMed, PubMed Central, Google Scholar 2014.12 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 22 Risk:
   TT vs. CC: OR =1.52, 95% CI: 0.73–3.18, P=0.2648
   CT vs. CC: OR =1.23, 95% CI: 1.00–1.53, P=0.0536
   TT + CT vs. CC: OR =1.30, 95% CI: 1.06–1.59, P=0.0115
   TT vs. CT + CC: OR =1.64, 95% CI: 0.94–2.85, P=0.0832
   T allele vs. C allele: OR =1.32, 95% CI: 1.07–1.63, P=0.0098
Overall cancer HIF-1α rs11549467 (1790 G/A) polymorphism 19 Risk:
   AA vs. GG: OR =5.10, 95% CI: 3.12–8.33, P<0.0001
   GA vs. GG: OR =1.74, 95% CI: 1.20–2.52, P=0.0033
   AA vs. GA + GG: OR =3.79, 95% CI: 2.34–6.15, P<0.0001
   AA + GA vs. GG: OR =1.82, 95% CI: 1.26–2.62, P=0.0014
   A allele vs. G allele: OR =1.82, 95% CI: 1.31–2.52, P=0.0003
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
36    Dominant model (TT + CT vs. CC): OR =1.23, 95% CI: 1.03–1.47
26    Recessive model (TT vs. CT + CC): OR =2.51, 95% CI: 1.54–4.09
25    Homozygote comparison (TT vs. CC): OR =2.02, 95% CI: 1.21–3.39
36    Heterozygote comparison (CT vs. CC): OR =1.16, 95% CI: 0.97–1.38
Hu X Tumour Biol [2013] China PubMed, Embase, CNKI 2013.2 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 15 Lymph node metastasis:
   OR =1.38, 95% CI: 1.13–1.68, P=0.002
7 Distant metastasis:
   OR =1.39, 95% CI: 0.96–2.02, P=0.082
9 Tumor size:
   T2–4 vs. T1: OR =1.09, 95% CI: 0.83–1.45, P=0.530
   T3–4 vs. T1–2: OR =1.29, 95% CI: 0.93–1.80, P=0.128
5 Stage:
   OR =0.93, 95% CI: 0.66–1.31, P=0.43
9 Histological grade:
   Grades G3 vs. G1: OR =1.07, 95% CI: 0.71–1.60, P=0.759
   Grades G3 vs. G2: OR =1.51, 95% CI: 1.08–2.13, P=0.017
   Grades G2 vs. G1: OR =0.67, 95% CI: 0.46–0.97, P=0.035
HIF-1α rs11549467 (1790 G/A) polymorphism 8 Lymph node metastasis:
   OR =1.33, 95% CI: 1.00–1.78, P=0.050
4 Distant metastasis:
   OR =0.97, 95% CI: 0.58–1.62, P=0.893
5 Tumor size:
   T2–4 vs. T1: OR =1.04, 95% CI: 0.65–1.65, P=0.871
   T3–4 vs. T1–2: OR =1.64, 95% CI: 1.04–2.58, P=0.033
4 Stage:
   OR =1.00, 95% CI: 0.65–1.52, P=0.987
5 Histological grade:
   Grades G3 vs. G1: OR =0.93, 95% CI: 0.56–1.55, P=0.789
   Grades G3 vs. G2: OR =1.12, 95% CI: 0.73–1.70, P=0.609
   Grades G2 vs. G1: OR =0.88, 95% CI: 0.57–1.36, P=0.556
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 28 Risk:
   TT vs. CC: OR =2.15, 95% CI: 1.19–3.88, P=0.011
   CT vs. CC: OR =1.15, 95% CI: 0.96–1.36, P=0.127
   TT/CT vs. CC: OR =1.19, 95% CI: 0.99–1.42, P=0.071
   TT vs. CT/CC: OR =2.21, 95% CI: 1.60–3.05, P=0.010
   T allele vs. C allele: OR =1.20, 95% CI: 1.01–1.44, P=0.043
Liu J Gene [2013] China PubMed, Embase 2012.3 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
8    Allele: OR =1.177, 95% CI=1.011–1.369, P=0.035
7    Genotype: OR =0.975, 95% CI=0.868–1.055, P=0.373
HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
6    Allele: OR =1.254, 95% CI=0.77–2.043, P=0.362
5    Genotype: OR =0.736, 95% CI=0.595–0.910, P=0.005
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Overall cancer HIF-1α rs11549467 (1790 G/A) polymorphism 26 Risk:
   AA vs. GG: OR =4.37, 95% CI: 2.61–7.33, p<0.001
   GA vs. GG: OR =1.39, 95% CI: 1.06–1.82, P=0.017
   AA + GA vs. GG: OR =1.46, 95% CI: 1.11–1.92, P=0.007
   AA vs. GA + GG: OR =3.87, 95% CI: 2.32–6.46, P<0.001
   A allele vs. G allele: OR =1.49, 95% CI: 1.15–1.95, P=0.003
Wu G Tumour Biol [2014] China PubMed, Embase, Google Scholar, Wanfang 2013.6.10 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 38 Risk:
   TT + CT vs. CC: OR =1.18, 95% CI: 1.00–1.38, P=0.048
35    TT vs. CT + CC: OR =1.22, 95% CI: 1.05–1.41, P=0.01
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 34 Risk:
   TT vs. CC: OR =2.45, 95% CI: 1.52–3.96
   CT vs. CC: OR =1.15, 95% CI: 0.92–1.45
   TT + CT vs. CC: OR =1.27, 95% CI: 1.05–1.55
   TT vs. CT + CC: OR =3.18, 95% CI: 1.92–5.29
   T allele vs. C allele: OR =1.42, 95% CI: 1.18–1.70
HIF-1α rs11549467 (1790 G/A) polymorphism 24 Risk:
   AA vs. GG: OR =4.74, 95% CI: 1.78–12.6
   GA vs. GG: OR =1.35, 95% CI: 0.82–2.21
   AA + GA vs. GG: OR =1.65, 95% CI: 1.05–2.60
   AA vs. GA + GG: OR =4.39, 95% CI: 1.61–11.9
   A allele vs. G allele: OR =1.83, 95% CI: 1.13–2.96
Ye Y Cancer Invest [2014] China Medline, Embase, Web of Science 2012.2.20 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 29 Risk:
   TT + CT vs. CC: OR =1.28, 95% CI: 1.06–1.54, P=0.009
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Overall cancer HIF-1α rs11549467 (1790 G/A) polymorphism 21 Risk:
   TT + CT vs. CC: OR =1.79, 95% CI: 1.12–2.86, P=0.01
Zhang Q PLoS One [2013] China PubMed, Embase 2012.12.1 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
15    TT+CT vs. CC: OR =1.39, 95% CI: 1.13–1.71, P=0.002
5    TT vs. CT+CC: OR =1.93, 95% CI: 0.86–4.36, P=0.11
15    T allele vs. C allele: OR =1.36, 95% CI: 1.12–1.64, P=0.002
Zhao T J Exp Clin Cancer Res [2009] China PubMed 2009.6 Overall cancer HIF-1α rs11549465 (1772 C/T) polymorphism 18 Risk:
   T allele vs. C allele: OR =1.29, 95% CI: 1.01–1.65, P=0.04
   TT vs. CT + CC: OR =2.18, 95% CI: 1.32–3.62, P=0.003
   TT + CT vs. CC: OR =1.19, 95% CI: 0.88–1.59, P=0.26
HIF-1α rs11549467 (1790 G/A) polymorphism 12 Risk:
   A allele vs. G allele: OR =1.61, 95% CI: 0.75–3.45, P=0.22
   AA + GA vs. GG: OR =1.56, 95% CI: 0.66–3.65, P=0.31
Zhou Y Cancer Cell Int [2014] China PubMed, Embase, CNKI 2013.12.13 Overall cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
25    AA + GA vs. GG: OR =1.85, 95% CI: 1.27–2.69
26    AA vs. GA + GG: OR =5.69, 95% CI: 3.87–8.37
12    AA vs. GG: OR =6.63, 95% CI: 4.49–9.79
11    GA vs. GG: OR =2.39, 95% CI: 1.53–3.75

Table S2

HIF in head and neck cancer

First author Journal (year) Country Databases Search date Cancer HIF No. studies Results
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Head and neck cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
5    Dominant model (TT + CT vs. CC): OR =1.20, 95% CI: 0.87–1.67
4    Recessive model (TT vs. CT + CC): OR =11.29, 95% CI: 1.24–103.02
3    Homozygote comparison (TT vs. CC): OR =2.24, 95% CI: 1.14–4.39
5    Heterozygote comparison (CT vs. CC): OR =1.03, 95% CI: 0.69–1.62
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Head and neck squamous cell carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   CT vs. CC: OR =1.81, 95% CI: 0.73–4.51, P=0.199
   TT /CT vs. CC: OR =1.81, 95% CI: 0.73–4.51, P=0.199
   T allele vs. C allele: OR =1.73, 95% CI: 0.72–4.15, P=0.217
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Head and neck squamous cell carcinoma HIF-1α rs11549467 (1790 G/A) polymorphism 1 Risk:
   GA vs. GG: OR =0.88, 95% CI: 0.26–3.00, P=0.838
   AA + GA vs. GG: OR =0.88, 95% CI: 0.26–3.00, P=0.838
   A allele vs. G allele: OR =0.88, 95% CI: 0.27–2.94, P=0.841
Zhou Y Cancer Cell Int [2014] China PubMed, Embase, CNKI 2013.12.13 Head and neck cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
6    AA + GA vs. GG: OR =3.57, 95% CI: 0.98–12.99
3    AA vs. GA + GG: OR =58, 95% CI: 1.75–1,924.88
3    AA vs. GG: OR =101.38, 95% CI: 22.09–65.29
3    GA vs. GG: OR =12.53, 95% CI: 2.42–64.76

Table S3

HIF in glioma

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Glioma HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   TT vs. CC: OR =2.23, 95% CI: 0.20–24.92, P=0.514
   CT vs. CC: OR =2.15, 95% CI: 1.08–4.29, P=0.030
   TT/CT vs. CC: OR =2.16, 95% CI: 1.10–4.21, P=0.025
   TT vs. CT/CC: OR =2.01, 95% CI: 0.18–22.45, P=0.569
   T allele vs. C allele: OR =2.05, 95% CI: 1.09–3.83, P=0.025
Liu Q Int J Clin Exp Med [2015] China PubMed, Embase, Wanfang, CNKI 2015 Glioma HIF-1α expression 24 IV + III vs. II+I:
   OR =8.59, 95% CI: 6.56–11.24, P<0.00001
14 IV vs. III:
   OR =2.51, 95% CI: 1.43–4.42, P=0.001
11 IV vs. II:
   OR =9.18, 95% CI: 5.18–16.28, P<0.00001
9 IV vs. I:
   OR = 24.23, 95% CI: 12.21–48.09, P<0.00001
12 III vs. II:
   OR =4.59, 95% CI: 2.96–7.12, P<0.00001
10 III vs. I:
   OR =13.34, 95% CI: 7.53–23.62, P<0.00001
11 II vs. I:
   OR =4.19, 95% CI: 2.59–6.77, P<0.00001

Table S4

HIF in oral cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Hu X Tumour Biol [2014] China PubMed, Embase, CNKI 2013.7 Oral cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   T allele vs. C allele: OR =2.52, 95% CI: 0.71–8.98
   TT vs. CC: OR =1.97, 95% CI: 0.72–5.39
   CT vs. CC: OR =0.92, 95% CI: 0.44–1.89
   TT + CT vs. CC: OR =1.06, 95% CI: 0.64–1.76
   TT vs. CT + CC: OR =22.82, 95% CI: 0.28–1,887.72
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Oral squamous cell carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   TT vs. CC: OR =6.14, 95% CI: 0.25–151.49, P=0.267
   CT vs. CC: OR =1.28, 95% CI: 0.69–2.38, P=0.432
   TT/CT vs. CC: OR =1.35, 95% CI: 0.73–2.49, P=0.334
   TT vs. CT/CC: OR =6.01, 95% CI: 0.24–148.26, P=0.273
   T allele vs. C allele: OR =1.41, 95% CI: 0.78–2.56, P=0.257
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Oral squamous cell carcinoma HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =13.32, 95% CI: 1.57–112.75, P=0.017
   GA vs. GG: OR =2.96, 95% CI: 1.05–8.31, P=0.039
   AA + GA vs. GG: OR =3.15, 95% CI: 1.05–9.47, P=0.041
   AA vs. GA + GG: OR =10.70, 95% CI: 1.25–91.51, P=0.030
   A allele vs. G allele: OR =3.09, 95% CI: 1.07–8.93, P=0.038
Qian J Tumour Biol [2016] China PubMed, Web of Knowledge, Web of Science 2016.1.12 Oral squamous cell carcinoma HIF-1α expression 12 OS:
   RR =1.18, 95% CI: 0.66–2.11
HIF-2α expression 2 OS:
   RR =1.40; 95% CI: 0.93–2.09
Sun X World J Gastroenterol [2015] China PubMed, Embase, CNKI 2013.7.15 Oral cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   CT vs. CC: OR =0.917, 95% CI: 0.444–1.895
   TT + CT vs. CC: OR =1.063, 95% CI: 0.643–1.757
   T allele vs. C allele: OR =2.517, 95% CI: 0.705–8.980
Oral cancer HIF-1α rs11549467 (1790 G/A) polymorphism 4 Risk:
   CT vs. CC: OR =3.165, 95% CI: 1.264–7.924
   TT + CT vs. CC: OR =7.919, 95% CI: 1.582–39.636
   T allele vs. C allele: OR =9.663, 95% CI: 1.312–71.149
Yan Q BMC Cancer [2014] China PubMed, Web of Science 2013.9.20 Oral cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   TT vs. CC: OR =2.01, 95% CI: 0.75–5.41
   CT vs. CC: OR =0.90, 95% CI: 0.55–1.47
   TT + CT vs. CC: OR =1.04, 95% CI: 0.66–1.64
   TT vs. CT + CC: OR =22.82, 95% CI: 0.28–1,887.72
   T allele vs. C allele: OR =2.52, 95% CI: 0.71–8.98
HIF-1α rs11549467 (1790 G/A) polymorphism 4 Risk:
   AA vs. GG: OR =72.11, 95% CI: 2.08–2,502.44
   GA vs. GG: OR =3.17, 95% CI: 1.26–7.92
   AA + GA vs. GG: OR =7.92, 95% CI: 1.58–39.64
   AA vs. GA + GG: OR =58.05, 95% CI: 1.70–1,985.77
   A allele vs. G allele: OR =9.66, 95% CI: 1.31–71.15
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Oral cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =2.01, 95% CI: 0.75–5.41
   CT vs. CC: OR =0.85, 95% CI: 0.24–2.97
   TT + CT vs. CC: OR =1.04, 95% CI: 0.61–1.78
   TT vs. CT + CC: OR =22.8, 95% CI: 0.28–1,888
   T allele vs. C allele: OR =3.93, 95% CI: 0.61–25.4
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =20.7, 95% CI: 0.10–4519
   GA vs. GG: OR =2.21, 95% CI: 0.18–26.9
   AA + GA vs. GG: OR =7.81, 95% CI: 0.27–224
   AA vs. GA + GG: OR =17.5, 95% CI: 0.10–3,257
   A allele vs. G allele: OR =9.34, 95% CI: 0.23–388
Yang X Tumour Biol [2014] China PubMed, Medline, Embase 2013.7 Oral cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk;
   Homozygote codominant: OR =2.01, 95% CI: 0.75–5.41
   Heterozygote codominant: OR =0.85, 95% CI: 0.24–2.97
   Dominant model: OR =1.04, 95% CI: 0.61–1.78
   Recessive model: OR =22.8, 95% CI: 0.28–1,887
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   Homozygote codominant: OR =20.7, 95% CI: 0.10–4519
   Heterozygote codominant: OR =2.21, 95% CI: 0.18–26.9
   Dominant model: OR =7.81, 95% CI: 0.27–225
   Recessive model: OR =17.6, 95% CI: 0.10–3,257
Ye Y Cancer Invest [2014] China Medline, Embase, Web of Science 2012.2.20 Oral carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT + CT vs. CC: OR =1.04, 95% CI: 0.60–1.80, P=0.9
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Oral cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   TT + CT vs. CC: OR =3.15, 95% CI: 1.05–9.47, P=0.04

Table S5

HIF in oropharyngeal cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Rainsbury JW Head & Neck [2013] UK Cochrane, Medline, Zetoc, National Cancer Trials databases, Proquest Dissertations, Theses database, Conference Proceedings Citation Index 2010.7 Oropharyngeal squamous cell carcinoma HIF-1α expression 2 OS: RR =1.27, 95% CI: 0.91–1.77

Table S6

HIF in nasopharyngeal cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Jing S Chinese Journal of Cancer Prevention and Treatment [2015]; Article in Chinese China PubMed, Embase, Cochrane, CBM, CNKI 2014.1.30 Nasopharyngeal carcinoma HIF-1α expression 6 Risk: OR =0.052, 95% CI: 0.012–0.219, P<0.001
8 Sex: OR =1.460, 95% CI: 0.939–2.268, P>0.05
6 Age: OR =1.046, 95%CI: 0.389–2.812, P>0.05
5 T1 + T2 vs. T3 + T4: OR =0.680, 95% CI: 0.423–1.092, P>0.05
7 Lymph node metastasis: OR =0.296, 95% CI: 0.170–0.516, P<0.001
8 Clinical stage: OR =0.298, 95% CI: 0.187–0.474, P<0.001

Table S7

HIF in lung cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Anam MT Biomark Res [2015] Bangladesh PubMed, PubMed Central, Google Scholar 2014.12 Lung cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   TT vs. CC: OR =4.88, 95% CI: 2.42–9.84, P<0.0001
   CT vs. CC: OR =1.56, 95% CI: 0.94–2.61, P=0.088
   TT + CT vs. CC: OR =1.67, 95% CI: 0.79–3.54, P=0.1832
   TT vs. CT + CC: OR =4.04, 95% CI: 2.02–8.08, P<0.0001
   T allele vs. C allele: OR =1.68, 95% CI: 0.77–3.64, P=0.1908
Lung cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   AA vs. GG: OR =5.41, 95% CI: 2.74–10.69, P<0.0001
   GA vs. GG: OR =1.76, 95% CI: 1.25–2.49, P=0.0013
   AA vs. GA + GG: OR =4.51, 95% CI: 2.31–8.81, P<0.0001
   AA + GA vs. GG: OR =2.20, 95% CI: 1.60–3.03, P<0.0001
   A allele vs. G allele: OR =2.31, 95% CI: 1.77–3.02, P<0.0001
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Lung cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
3    Dominant model (TT + CT vs. CC): OR = 1.19, 95% CI: 0.51–2.76
2    Recessive model (TT vs. CT + CC): OR =1.39, 95% CI: 0.09–21.85
2    Homozygote comparison (TT vs. CC): OR =1.42, 95% CI: 0.07–29.73
3    Heterozygote comparison (CT vs. CC): OR =1.13, 95% CI: 0.59–2.19
Hu X Tumour Biol [2014] China PubMed, Embase, CNKI 2013.7 Lung cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   T allele vs. C allele: OR =1.19, 95% CI: 0.50–2.86
   TT vs. CC: OR =1.41, 95% CI: 0.07–30.44
   CT vs. CC: OR =1.13, 95% CI: 0.59–2.19
   TT + CT vs. CC: OR =1.19, 95% CI: 0.51–2.76
   TT vs. CT + CC: OR = 1.38, 95% CI: 0.09–22.18
Li C Asian Pac J Cancer Prev [2013] China PubMed 2012.12.20 Non-small cell lung cancer HIF-1α expression 7 OS: HR=1.50, 95% CI: 1.07–2.10
HIF-2α expression 3 OS: HR=2.02, 95% CI: 1.47–2.77
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Lung cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =1.41, 95% CI: 0.07–30.44*
   CT vs. CC: OR =1.13, 95% CI: 0.59–2.19*
   TT/CT vs. CC: OR =1.19, 95% CI: 0.51–2.76*
   TT vs. CT/CC: OR =1.38, 95% CI: 0.09–22.18*
   T allele vs. C allele: OR =1.19, 95% CI: 0.50–2.86*
Liao S J Recept Signal Transduct Res [2015] China PubMed, Cochrane 2014.9.1 Lung cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   CC vs. CT+TT: OR =0.50, 95% CI: 0.36–0.69, P<0.0001
   TT vs. CT + CC: OR =4.04, 95% CI: 2.02–8.08, P<0.0001
   T allele vs. C allele: OR =1.68, 95% CI: 0.77–3.64, P=0.19
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   GG vs. GA+AA: OR =0.45, 95% CI: 0.33–0.63, P<0.00001
   AA vs. GA+GG: OR =4.52, 95% CI: 2.31–8.83, P<0.0001
   A allele vs. G allele: OR =2.31, 95% CI: 1.77–3.02, P<0.00001
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Lung cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =5.42, 95% CI: 2.75–10.70, P<0.001
   GA vs. GG: OR =1.72, 95% CI: 1.22–2.41, P=0.002
   AA + GA vs. GG: OR =2.41, 95% CI: 1.56–2.94, P<0.001
   AA vs. GA + GG: OR =4.52, 95% CI: 2.31–8.83, P<0.001
   A allele vs. G allele: OR =2.26, 95% CI: 1.74–2.95, P<0.001
Ren W Swiss Med Wkly [2013] China Cochrane, PubMed, Embase, CNKI, CBM, VIP, WanFang 2012.5 Lung cancer HIF-1α expression 4 5–year survival rates: OR = 0.13, 95% CI: 0.03–0.47, P=0.002
7 OS: RR= 1.68, 95% CI: 1.12–2.50, P=0.01
16 Tumor vs. benign tissues: OR =19.00, 95% CI: 12.12–29.78, P=0.00001
20 Male vs. female: OR = 1.00, 95% CI: 0.80–1.26, P=0.99
12 Age (≥60 vs. <60 years): OR = 1.14, 95% CI: 0.85–1.52, P=0.38
7 Diameter (≥5 vs. <5 cm): OR = 1.84, 95% CI: 1.00–3.39, P=0.05
4 Smoking vs. no smoking: OR = 2.16, 95% CI: 0.77–6.05, P=0.14
18 Adenocarcinomas vs. squamous cell carcinoma: OR = 0.78, 95% CI: 0.63–0.98, P=0.03
4 Non-small cell lung cancer vs. small cell lung cancer: OR = 0.24, 95% CI: 0.07–0.77, P=0.02
21 Stage (I–II vs. III–IV): OR = 0.23, 95% CI: 0.14–0.36, P=0.00001
22 Lymph node metastasis (yes vs. no): OR = 3.72, 95% CI: 2.38–5.80, P=0.00001
18 Differentiation (well vs. poorly): OR = 0.47, 95% CI: 0.31–0.70, P=0.0002
Wang Q Gene [2014] China PubMed, Embase, Web of Science 2013.8.31 Non-small cell lung cancer HIF-1α expression 13 OS: HR=1.60, 95% CI: 1.14–2.25, P=0.007
Yan Q BMC Cancer [2014] China PubMed, Web of Science 2013.9.20 Lung cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   TT vs. CC: OR = 1.41, 95% CI: 0.07–30.44
   CT vs. CC: OR =1.13, 95% CI: 0.59–2.19
   TT + CT vs. CC: OR =1.19, 95% CI: 0.51–2.76
   TT vs. CT + CC: OR =3.27, 95% CI: 1.73–6.17
   T allele vs. C allele: OR =1.19, 95% CI: 0.50–2.86
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =5.42, 95% CI: 2.74–10.70
   GA vs. GG: OR =1.72, 95% CI: 1.22–2.41
   AA + GA vs. GG: OR =2.14, 95% CI: 1.56–2.94
   AA vs. GA + GG: OR =4.52, 95% CI: 2.31–8.83
   A allele vs. G allele: OR =2.27, 95% CI: 1.74–2.95
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Lung cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   TT vs. CC: OR =1.41, 95% CI: 0.07–30.4
   CT vs. CC: OR =1.13, 95% CI: 0.59–2.19
   TT + CT vs. CC: OR =1.50, 95% CI: 1.15–1.96
   TT vs. CT + CC: OR =3.27, 95% CI: 1.73–6.17
   T allele vs. C allele: OR =1.19, 95% CI: 0.50–2.86
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =5.42, 95% CI: 2.75–10.7
   GA vs. GG: OR =0.26, 95% CI: 0.01–7.10
   AA + GA vs. GG: OR =0.82, 95% CI: 0.56–1.19
   AA vs. GA + GG: OR =7.11, 95% CI: 3.61–14.0
   A allele vs. G allele: OR =1.48, 95% CI: 1.09–2.00
Zhou Y Cancer Cell Int [2014] China PubMed, Embase, CNKI 2013.12.13 Lung cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
3    AA + GA vs. GG: OR =2.14, 95% CI: 1.56–2.95
2    AA vs. GA + GG: OR =4.5, 95% CI: 2.3–8.81
2    AA vs. GG: OR =5.42, 95% CI: 2.74–10.7
2    GA vs. GG: OR =3.02, 95% CI: 1.48–6.16

Notes: *In the study by Li Y, based on the 95% CI of OR, the statistical difference should not be significant.

Table S8

Characteristics of studies regarding HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of lung cancer

First author Journal (year) No. studies Included studies No. Case No. Control Results Model
He P PLoS One [2013] 3 Kuo WH, et al. Transl Res [2012] 285 300 TT vs. CT + CC: OR =1.39, 95% CI: 0.09–21.85; P value for heterogeneity =0.07 A fixed-effect model was used when P heterogeneity <0.05, otherwise a random effect model was used
Putra AC, et al. Respirology [2011] 83 110
Konac E, et al. Exp Biol Med (Maywood) [2009] 141 156
Hu X Tumour Biol [2014] 3 Kuo WH, et al. Transl Res [2012] 285 300 TT vs. CT + CC: OR =1.38, 95% CI: 0.09–22.18; P value for heterogeneity =0.065 A P value of more than 0.05 for the Q test indicated a lack of heterogeneity, and the fixed-effects model (the Mantel-Haenszel method) was subsequently used to calculate the summary ORs. Otherwise, the random-effects model (the DerSimonian and Laird method) was applied
Putra AC, et al. Respirology [2011] 83 110
Konac E, et al. Exp Biol Med (Maywood) [2009] 141 156
Li Y Int J Clin Exp Med [2015] 3 Kuo WH, et al. Transl Res [2012] 285 300 TT vs. CT/CC: OR =1.38, 95% CI: 0.09-22.18; P value for heterogeneity =0.065 Fixed effects model was used to pool the data when the P value of Q-test ≥0.05; otherwise, random effects model was selected
Putra AC, et al. Respirology [2011] 83 110
Konac E, et al. Exp Biol Med (Maywood) [2009] 141 156
Yan Q BMC Cancer [2014] 3 Kuo WH, et al. Transl Res [2012] 285 300 TT vs. CT + CC: OR =3.27, 95% CI: 1.73–6.17; P value for heterogeneity =0.07 When P > 0.05, the effects were assumed to be homogenous, and the fixed-effect model (the Mantel-Haenszel method) was used. When P<0.05, the random-effect model (the DerSimonian and Laird method) was more appropriate
Putra AC, et al. Respirology [2011] 83 110
Konac E, et al. Exp Biol Med (Maywood) [2009] 141 156
Yang X PLoS One [2013] 3 Kuo WH, et al. Transl Res [2012] 285 300 TT vs. CT + CC: OR =3.27, 95% CI: 1.73–6.17; P value for heterogeneity =0.065 A random-effects model was used when the significant Q statistic (P<0.1) indicated the presence of heterogeneity in the studies. Otherwise, a fixed-effects model was selected
Putra AC, et al. Respirology [2011] 83 110
Konac E, et al. Exp Biol Med (Maywood) [2009] 141 156

Table S9

HIF in breast cancer

First author Journal (year) Country Databases Search date Cancer HIF No. studies Results
Anam MT Biomark Res [2015] Bangladesh PubMed, PubMed Central, Google Scholar 2014.12 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   TT vs. CC: OR =5.18, 95% CI: 0.88–30.38, P=0.0683
   CT vs. CC: OR =1.00, 95% CI: 0.77–1.29, P=0.9964
   TT + CT vs. CC: OR =1.05, 95% CI: 0.81–1.35, P=0.7221
   TT vs. CT + CC: OR =5.18, 95% CI: 0.88–30.36, P=0.0684
   T allele vs. C allele: OR =1.09, 95% CI: 0.86–1.39, P=0.4701
Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   AA vs. GG: OR =0.36, 95% CI: 0.01–8.95, P=0.5332
   GA vs. GG: OR =0.35, 95% CI: 0.10–1.24, P=0.1045
   AA vs. GA + GG: OR =0.37, 95% CI: 0.02–9.29, P=0.5484
   AA + GA vs. GG: OR =0.32, 95% CI: 0.09–1.10, P=0.0702
   A allele vs. G allele: OR =0.30, 95% CI: 0.09–1.00, P=0.0495
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
6    Dominant model (TT + CT vs. CC): OR =1.12, 95% CI: 0.87–1.52
5    Recessive model (TT vs. CT + CC): OR =1.64, 95% CI: 0.56–4.77
5    Homozygote comparison (TT vs. CC): OR =1.69, 95% CI: 0.56–5.14
6    Heterozygote comparison (CT vs. CC): OR =1.10, 95% CI: 0.83–1.46
Hu X Tumour Biol [2013] China PubMed, Embase, CNKI 2013.2 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism 4 Lymph node metastasis: OR =1.31, 95% CI: 0.98–1.75, P=0.069
3 Histological grade:
   Grades G3 vs. G1: OR =1.41, 95% CI: 0.70–2.85, P=0.336
   Grades G3 vs. G2: OR =1.42, 95% CI: 0.91–2.20, P=0.121
   Grades G2 vs. G1: OR =1.12, 95% CI: 0.56–2.24, P=0.745
Hu X Tumour Biol [2014] China PubMed, Embase, CNKI 2013.7 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 5 Risk:
   T allele vs. C allele: OR =1.09, 95% CI: 0.76–1.55
   TT vs. CC: OR =2.16, 95% CI: 0.52–8.85
   CT vs. CC: OR =1.05, 95% CI: 0.79–1.39
   TT + CT vs. CC: OR =1.07, 95% CI: 0.76–1.50
   TT vs. CT + CC: OR =2.15, 95% CI: 0.57–8.01
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 5 Risk:
   TT vs. CC: OR =2.16, 95% CI: 0.52–8.85, P=0.031
   CT vs. CC: OR =1.07, 95% CI: 0.88–1.29, P=0.516
   TT/CT vs. CC: OR =1.07, 95% CI: 0.76–1.50, P=0.254
   TT vs. CT/CC: OR =2.27, 95% CI: 1.06–4.87, P=0.035
   T allele vs. C allele: OR =1.09, 95% CI: 0.76–1.55, P=0.106
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =1.44, 95% CI: 0.38–5.44, P=0.595
   GA vs. GG: OR =0.68, 95% CI: 0.23–2.05, P=0.498
   AA + GA vs. GG: OR =0.63, 95% CI: 0.19–2.10, P=0.451
   AA vs. GA + GG: OR =1.41, 95% CI: 0.37–5.37, P=0.613
   A allele vs. G allele: OR =0.59, 95% CI: 0.17–2.10, P=0.419
Ren HT Med Sci Monit [2014] China PubMed 2013.6 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 6 Risk:
   TT vs. CC: OR =1.64, 95% CI: 0.85–3.19, P=0.14
   CT vs. CC: OR =1.05, 95% CI: 0.87–1.27, P=0.58
   TT + CT vs. CC: OR =1.13, 95% CI: 0.94–1.36, P=0.19
   TT vs. CT + CC: OR =1.62, 95% CI: 0.83–3.15, P=0.16
   T allele vs. C allele: OR =1.10, 95% CI: 0.93–1.30, P=0.28
Sun G Breast J [2014] China NA 2009 Breast cancer HIF-1α protein expression 12 Cancer vs. normal tissues: OR =23.11, 95% CI: 10.07–53.03, P<0.05
12 Pathological differentiation: OR =3.77, 95% CI: 2.78–5.11, P<0.05
7 Regional invasive extension (T3–4 vs. T1–2): OR =1.21, 95% CI: 0.87–1.87, P>0.05
10 Axillary lymph node status (positive vs. negative): OR =3.03, 95% CI: 1.76–5.22, P<0.05
9 Clinical stage: OR =2.82, 95% CI: 1.94–4.10, P<0.05
7 VEGF expression: OR =1.21, 95% CI: 0.87–1.87, P<0.05
4 Overall survival: OR =0.54, 95% CI: 0.35–0.83, P<0.05
Wang W Clinica Chimica Acta [2014] China PubMed, Embase, Web of Science 2013.4.1 Breast cancer HIF-1α expression 7 OS: HR=1.46, 95% CI: 1.12–1.92, P=0.006
8 DFS: HR=1.91, 95% CI: 1.43–2.57, P<0.001
3 DMFS: HR=2.17, 95% CI: 1.16–4.05, P=0.015
3 RFS: HR=1.33, 95% CI: 1.09–1.61, P=0.005
Wu G Tumour Biol [2014] China PubMed, Embase, Google Scholar, Wanfang 2013.6.10 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
6    TT + CT vs. CC: OR =0.99, 95% CI: 0.72–1.36, P=0.951
6    TT vs. CT + CC: OR =1.05, 95% CI: 0.88–1.25, P=0.561
Yan Q BMC Cancer [2014] China PubMed, Web of Science 2013.9.20 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 6 Risk:
   TT vs. CC: OR =1.41, 95% CI: 0.34–5.75
   CT vs. CC: OR =1.01, 95% CI: 0.91–1.33
   TT + CT vs. CC: OR =1.13, 95% CI: 0.94–1.36
   TT vs. CT + CC: OR =1.38, 95% CI: 0.35–5.46
   T allele vs. C allele: OR =1.09, 95% CI: 0.80–1.48
HIF-1α rs11549467 (1790 G/A) polymorphism 4 Risk:
   AA vs. GG: OR =1.44, 95% CI: 0.38–5.44
   GA vs. GG: OR =1.03, 95% CI: 0.70–1.52
   AA + GA vs. GG: OR =1.05, 95% CI: 0.72–1.53
   AA vs. GA + GG: OR =1.41, 95% CI: 0.37–5.40
   A allele vs. G allele: OR =1.07, 95% CI: 0.76–1.52
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 5 Risk:
   TT vs. CC: OR =2.30, 95% CI: 1.08–4.91
   CT vs. CC: OR =1.07, 95% CI: 0.88–1.29
   TT + CT vs. CC: OR =1.12, 95% CI: 0.92–1.35
   TT vs. CT + CC: OR =2.27, 95% CI: 1.06–4.87
   T allele vs. C allele: OR =1.09, 95% CI: 0.76–1.55
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =1.44, 95% CI: 0.38–5.44
   GA vs. GG: OR =1.03, 95% CI: 0.70–1.52
   AA + GA vs. GG: OR =1.05, 95% CI: 0.72–1.53
   AA vs. GA + GG: OR =1.41, 95% CI: 0.37–5.37
   A allele vs. G allele: OR =1.07, 95% CI: 0.75–1.52
Ye Y Cancer Invest [2014] China Medline, Embase, Web of Science 2012.2.20 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk: TT + CT vs. CC: OR =0.91, 95% CI: 0.62–1.32, P=0.51
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk: TT + CT vs. CC: OR =0.32, 95% CI: 0.09–1.10, P=0.07
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk: TT + CT vs. CC: OR =0.32, 95% CI: 0.09–1.10, P=0.07
Yin W Cancer Res (abstract) [2011] China NA NA Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism NA Risk:
   Recessive model: OR =2.273, 95% CI: 1.061–4.872, P=0.035
   Dominant model: OR =1.075, 95% CI: 0.717–1.613, P=0.725
HIF-1α rs11549467 (1790 G/A) polymorphism NA Risk:
   Recessive model: not significant
   Dominant model: not significant
Zhao T J Exp Clin Cancer Res [2009] China PubMed 2009.6 Breast cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   T allele vs. C allele: OR =0.99, 95% CI: 0.79–1.23, P=0.9
   TT vs. CT + CC: OR =1.51, 95% CI: 0.55–4.11, P=0.42
   TT + CT vs. CC: OR =0.96, 95% CI: 0.76–1.21, P=0.75
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   A allele vs. G allele: OR =0.28, 95% CI: 0.08–0.90, P=0.03
   AA + GA vs. GG: OR =0.29, 95% CI: 0.09–0.97, P=0.04
Zhou Y Cancer Cell Int [2014] China PubMed, Embase, CNKI 2013.12.13 Breast cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
3    AA + GA vs. GG: OR =0.63, 95% CI: 0.19–2.08
2    AA vs. GA + GG: OR =1.44, 95% CI: 0.34–6.08
2    AA vs. GG: OR =1.43, 95% CI: 0.37–5.44
2    GA vs. GG: OR =1.45, 95% CI: 0.34–6.17

Table S10

HIF in digestive cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Anam MT Biomark Res [2015] Bangladesh PubMed, PubMed Central, Google Scholar 2014.12 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =1.91, 95% CI: 0.32–11.58, P=0.4801
   CT vs. CC: OR =0.83, 95% CI: 0.50–1.39, P=0.4817
   TT + CT vs. CC: OR =1.24, 95% CI: 0.77–2.01, P=0.3756
   TT vs. CT + CC: OR =1.97, 95% CI: 0.33–11.90, P=0.4603
   T allele vs. C allele: OR =0.94, 95% CI: 0.59–1.49, P=0.7833
Cao S Clin Res Hepatol Gastroenterol [2014] China PubMed, Embase 2013.8 Hepatocellular carcinoma HIF-1α protein expression 4 DFS: OR =2.10, 95% CI: 1.48–2.97
3 Capsule formation: OR =1.25, 95% CI: 0.93–1.69
4 Cirrhosis: OR =1.00, 95% CI: 0.76–1.30
6 Tumor size: OR =0.92, 95% CI: 0.74–1.14
3 Tumor differentiation: OR =0.89, 95% CI: 0.65–1.21
4 Vascular invasion: OR =2.04, 95% CI: 1.31–3.18
5 HCC tissue vs. paraneoplastic tissue: OR =2.50, 95% CI: 0.98–6.36
Chen J PLoS One [2014] China PubMed, Embase, Cochrane, CNKI 2013.6 Gastric cancer HIF-1α protein expression 10 5–year OS: RR=1.508, 95% CI: 1.318–1.725, P<0.001
9 Depth of invasion (T3 and T4 vs. T1 and T2): OR =3.050, 95% CI: 2.067–4.501, P<0.001
11 Lymph node status: OR =3.486, 95% CI: 2.737–4.440, P<0.001
5 Distant metastasis: OR =6.635, 95% CI: 1.855–23.738, P=0.004
10 TNM stage (stages III and IV vs. stage I and II): OR =2.762, 95% CI: 1.941–3.942, P<0.001
6 Vascular invasion: OR =2.368, 95% CI: 1.725–3.252, P<0.001
10 Histological differentiation: OR =2.112, 95% CI: 1.410–3.163, P<0.001
5 Size: OR =1.921, 95% CI: 1.395–2.647, P<0.001
7 Sex: OR =0.905, 95% CI: 0.679–1.205, P=0.495
11 Age: OR =0.846, 95% CI: 0.667–1.072, P=0.166
Chen Z PLoS One [2013] China PubMed, Wanfang, Web of Science NA Colorectal cancer HIF-1α protein expression 9 DFS: HR=2.84, 95% CI: 1.87–4.31
11 OS: HR=2.01, 95% CI: 1.55–2.6
15 Differentiation grade: OR =0.97, 95% CI: 0.67–1.39, P=0.864
5 Dukes’ stages: OR =0.39, 95% CI: 0.17–0.89, P=0.025
15 Lymph node status: OR =0.49, 95% CI: 0.32–0.73, P=0.001
9 Depth of invasion: OR =0.71, 95% CI: 0.51–0.99, P=0.045
5 Metastasis: OR =0.29, 95% CI: 0.11–0.81, P=0.018
9 UICC stage: OR =0.42, 95% CI: 0.3–0.59, P<0.001
HIF-2α protein expression 4 OS: HR=2.07, 95% CI: 1.01–4.26
2 Differentiation grade: OR =0.484, 95% CI: 0.289–0.812, P=0.006
2 Dukes’ stages: OR =0.9, 95% CI: 0.197–4.168, P=0.9
3 Lymph node status: OR =0.95, 95% CI: 0.418–2.16, P=0.904
2 Depth of invasion: OR =0.379, 95% CI: 0.038–3.798, P=0.409
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
2    Dominant model (TT + CT vs. CC): OR =0.26, 95% CI: 0.01–5.09
1    Recessive model (TT vs. CT + CC): OR =1.97, 95% CI: 0.33–11.90
1    Homozygote comparison (TT vs. CC): OR =1.91, 95% CI: 0.32–11.58
2    Heterozygote comparison (CT vs. CC): OR =0.25, 95% CI: 0.01–4.69
Pancreatic cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
2    Dominant model (TT + CT vs. CC): OR = 1.39, 95% CI: 0.54 –3.56
1    Recessive model (TT vs. CT + CC): OR =4.13, 95% CI: 1.57–10.86
1    Homozygote comparison (TT vs. CC): OR =3.39, 95% CI: 1.28–8.97
2    Heterozygote comparison (CT vs. CC): OR =0.51, 95% CI: 0.02–11.53
Hu X Tumour Biol [2013] China PubMed, Embase, CNKI 2013.2 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Lymph node metastasis: OR =1.23, 95% CI: 0.73–2.07, P=0.429
2 Histological grade:
Grades G3 vs. G1: OR =0.58, 95% CI: 0.13–2.53, P=0.47
Grades G3 vs. G2: OR =1.24, 95% CI: 0.32–4.89, P=0.757
Grades G2 vs. G1: OR =0.52, 95% CI: 0.25–1.10, P=0.086
Hu X Tumour Biol [2014] China PubMed, Embase, CNKI 2013.7 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   T allele vs. C allele: OR =0.26, 95% CI: 0.01–6.38
   TT vs. CC: OR =1.91, 95% CI: 0.32–11.58
   CT vs. CC: OR =0.24, 95% CI: 0.01–5.51
   TT + CT vs. CC: OR =1.17, 95% CI: 0.62–2.22
   TT vs. CT + CC: OR =1.97, 95% CI: 0.33–11.90
Jing S Chin J Pathol [2014]Article in Chinese China PubMed, Embase, Cochrane, CBM, CNKI 2014.7.30 Esophageal squamous cell carcinoma HIF-1α protein expression 8 Risk: OR =0.088, 95% CI: 0.061–0.129, P<0.001
10 Tumor differentiation: OR =1.287, 95% CI: 0.904–1.831, P=0.161
4 Histological grade: OR =1.194, 95% CI: 0.307–4.642, P=0.798
8 T1 + T2 vs. T3 + T4: OR =0.421, 95% CI: 0.222–0.798, P=0.008
14 Lymph node metastasis: OR =0.387, 95% CI: 0.207–0.725, P=0.003
8 Tumor stage: OR =0.525, 95% CI: 0.236–1.171, P=0.116
5 Lymphatic vessels invasion: OR =0.560, 95% CI: 0.219–1.431, P=0.226
5 Vascular invasion: OR =0.971, 95% CI: 0.667–1.413, P=0.877
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =1.91, 95% CI: 0.32–11.58
   CT vs. CC: OR =0.34, 95% CI: 0.09–1.34*
   TT/CT vs. CC: OR =0.34, 95% CI: 0.08–1.41*
   TT vs. CT/CC: OR =1.97, 95% CI: 0.33–11.90*
   T allele vs. C allele: OR =0.38, 95% CI: 0.09–1.50*
Esophageal squamous cell carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   CT vs. CC: OR =1.11, 95% CI: 0.46–2.69, P=0.822
   TT/CT vs. CC: OR =1.11, 95% CI: 0.46–2.69, P=0.822
   T allele vs. C allele: OR =1.10, 95% CI: 0.47–2.60, P=0.827
Pancreatic cancer HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   CT vs. CC: OR =2.16, 95% CI: 1.32–3.51, P=0.002TT/CT vs. CC: OR =2.16, 95% CI: 1.32–3.51, P=0.002
   T allele vs. C allele: OR =2.02, 95% CI: 1.27–3.23, P=0.003
Hepatocellular carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   CT vs. CC: OR =2.19, 95% CI: 0.88–5.43, P=0.092
   TT/CT vs. CC: OR =2.19, 95% CI: 0.88–5.43, P=0.092
   T allele vs. C allele: OR =2.14, 95% CI: 0.87–5.23, P=0.096
Gastric cancer HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   CT vs. CC: OR =0.34, 95% CI: 0.11–1.10, P=0.072
   TT/CT vs. CC: OR =0.34, 95% CI: 0.11–1.10, P=0.072
   T allele vs. C allele: OR =0.36, 95% CI: 0.12–1.13, P=0.079
Lin S World J Gastroenterol [2014] China PubMed, Embase, Web of Science 2013.8 Gastric cancer HIF-1α expression 5 5-year OS rate: OR =0.36, 95% CI: 0.21–0.64, P=0.0004
6 Tumor differentiation: OR =0.38, 95% CI: 0.23–0.64, P=0.0003
7 Depth of invasion: OR =0.42, 95% CI: 0.32–0.57, P<0.00001
9 Lymph node metastasis: OR =2.23, 95% CI: 1.46–3.40, P=0.0002
5 Lymphatic invasion: OR =2.50, 95% CI: 1.46–4.28, P=0.0009
5 Vascular invasion: OR =1.80, 95% CI: 1.29–2.51, P=0.0005
6 TNM stages III + IV: OR =0.31; 95% CI: 0.15–0.60, P=0.0006
Liu J Gene [2013] China PubMed, Embase 2012.3 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism NA Risk: OR =1.239, 95% CI =0.985–1.559, P=0.067
HIF-1α rs11549467 (1790 G/A) polymorphism NA Risk: OR =0.867, 95% CI =0.492–1.528, P=0.622
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Colorectal cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   GA vs. GG: OR =0.97, 95% CI: 0.57–1.63, P=0.912
   AA + GA vs. GG: OR =0.97, 95% CI: 0.57–1.63, P=0.912
   A allele vs. G allele: OR =0.97, 95% CI: 0.58–1.62, P=0.914
Pancreatic cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   AA vs. GG: OR =9.30, 95% CI: 1.12–77.61, P=0.039
   GA vs. GG: OR =2.90, 95% CI: 1.82–4.62, P=0.625
   AA + GA vs. GG: OR =2.50, 95% CI: 0.93–6.73, P=0.070
   AA vs. GA + GG: OR =8.65, 95% CI: 1.04–71.65, P=0.045
   A allele vs. G allele: OR =3.12, 95% CI: 2.01–4.84, P<0.001
Hepatocellular carcinoma HIF-1α rs11549467 (1790 G/A) polymorphism 1 Risk:
   GA vs. GG: OR =4.10, 95% CI: 1.91–8.82, P<0.001
   AA + GA vs. GG: OR =4.10, 95% CI: 1.91–8.82, P=0.006
   A allele vs. G allele: OR =3.85, 95% CI:1.83–8.13, P<0.001
Gastric cancer HIF-1α rs11549467 (1790 G/A) polymorphism 1 Risk:
   GA vs. GG: OR =2.93, 95% CI: 1.06–8.06, P=0.038
   AA + GA vs. GG: OR =2.93, 95% CI: 1.06–8.06, P=0.038
   A allele vs. G allele: OR =2.77, 95% CI:1.03–7.45, P=0.043
Ni Z Genes Genom [2015] China NA NA Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism 10 Risk:
   Allele model: OR =1.292, 95% CI =1.107–1.507, P=0.001
   Dominant model: OR =1.277, 95% CI =1.083–1.507, P=0.004
HIF-1α rs11549467 (1790 G/A) polymorphism 9 Risk:
   Allele model: OR =1.920, 95% CI = 1.213–3.038, P=0.005
   Dominant model: OR =1.957, 95% CI =1.219–3.142, P=0.005
Ping W Tumour Biol [2014] China PubMed, Medline, Embase, Cochrane, Web of Science, CBM 2013.9.10 Esophageal squamous cell carcinoma HIF-1α expression 10 OS: HR=1.84, 95% CI: 1.36–2.50, P<0.001
2 DFS: HR= 2.00, 95% CI: 1.05–3.79, P=0.035
9 Gender (male vs. female): HR= 0.82, 95% CI: 0.50–1.35, P=0.429
8 Stage (stage III/IV vs. stage I/II): HR=2.90, 95% CI: 1.90–4.44, P<0.001
11 Lymph node metastasis (yes vs. no): HR=1.93, 95% CI: 1.35–2.76, P<0.001
7 Depth of invasion (T3/T4 vs. T1/T2): HR=2.45, 95% CI: 1.24–4.86, P=0.01
5 Lymphatic invasion (yes vs. no): HR=2.25, 95% CI: 1.3–3.76, P=0.002
5 Vascular invasion (yes vs. no): HR=1.34, 95% CI: 0.79–2.26, P=0.271
8 Histological grade (poor vs. well/moderate): HR=1.20, 95% CI: 0.70–2.07, P=0.507
5 Distant metastasis (M1 vs. M0): HR=1.97, 95% CI: 1.10–3.53, P=0.022
4 Vascular endothelial growth factor (high vs. low): HR=3.67, 95% CI: 1.81–7.46, P<0.001
Sun G J Chin Oncol [2012] Article in Chinese China PubMed, Cochrane 2011.12 Esophageal squamous cell carcinoma HIF-1α protein expression 7 Risk: OR =33.111, 95% CI: 11.912–92.040, P<0.001
11 2–year OS rate: RR=0.320, 95% CI: 0.115–0.887, P=0.0004
3 Tumor differentiation: OR =1.185, 95% CI: 0.859–1.635, P=0.3
8 Clinical stage: OR =0.421, 95% CI: 0.222–0.798, P=0.008
13 Lymphoma node metastasis: OR =2.393, 95% CI: 1.319–4.344, P=0.003
9 Depth of invasion: OR =1.701, 95% CI: 1.076–4.710, P=0.226
Sun X World J Gastroenterol [2015] China PubMed, Embase, CNKI 2013.7.15 Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism 13 Risk:
   CT vs. CC: OR =0.853, 95% CI: 0.502–1.450
   TT + CT vs. CC: OR =1.156, 95% CI: 0.839–1.593
   T allele vs. C allele: OR =1.325, 95% CI: 0.846–2.076
HIF-1α rs11549467 (1790 G/A) polymorphism 10 Risk:
   GA vs. GG: OR =2.677, 95% CI: 1.677–4.273
   AA + GA vs. GG: OR =3.252, 95% CI: 1.661–6.368
   A allele vs. G allele: OR =4.455, 95% CI: 1.938–10.241
Pancreatic cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   CT vs. CC: OR =0.500, 95% CI: 0.018–14.015
   TT + CT vs. CC: OR =1.388, 95% CI: 0.542–3.555
   T allele vs. C allele: OR =1.753, 95% CI: 1.225–2.508
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   CT vs. CC: OR =1.611, 95% CI: 0.241–10.760
   TT + CT vs. CC: OR =2.499, 95% CI: 0.929–6.726
   T allele vs. C allele: OR =3.030, 95% CI: 1.946–4.716
Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   CT vs. CC: OR =0.241, 95% CI: 0.011–5.509
   TT + CT vs. CC: OR =1.118, 95% CI: 0.573–2.182
   T allele vs. C allele: OR =0.262, 95% CI: 0.011–6.380
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   TT + CT vs. CC: OR =0.971, 95% CI: 0.571–1.650
Wu G Tumour Biol [2014] China PubMed, Embase, Google Scholar, Wanfang 2013.6.10 Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
9    TT + CT vs. CC: OR =1.17, 95% CI: 0.78–1.75, P=0.441
7    TT vs. CT + CC: OR =1.04, 95% CI: 0.63–1.71, P=0.879
Xu J Genet Mol Res [2014] China CISCOM, CINAHL, Web of Science, PubMed, Google Scholar, EBSCO, Cochrane, CBM 2013.5.1 Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism 6 Risk:
   TT + CT vs. CC: OR =2.04, 95% CI: 1.06–3.92
   T allele vs. C allele: OR =1.36, 95% CI: 1.15–1.62
Xu J Genet Test Mol Biomarkers [2013] China PubMed, Embase, Web of Science, Cochrane, CBM 2013.5.1 Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism 6 Risk:
   C allele vs. T allele: OR =1.36, 95% CI: 1.15–1.62, P<0.001
   CC vs. TT + CT: OR =2.04, 95% CI: 1.06–3.92, P<0.001
Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   C allele vs. T allele: OR =0.27, 95% CI: 0.01–5.45, P=0.395
   CC vs. TT + CT: OR =1.12, 95% CI: 0.58–2.17, P=0.738
Esophageal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   C allele vs. T allele: OR =1.10, 95% CI: 0.47–2.60, P=0.827
   CC vs. TT + CT: OR =1.11, 95% CI: 0.46–2.69, P=0.822
Gastric cancer HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   C allele vs. T allele: OR =5.17, 95% CI: 1.75–15.26, P=0.003
   CC vs. TT+CT: OR =5.75, 95% CI: 1.91–17.35, P=0.002
Xu JJ Genet Mol Res [2014] China PubMed, Embase, Web of Science, Cochrane, CBM 2013.5.1 Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism 8 Risk:
   TT vs. CC: OR =1.91, 95% CI: 0.32–11.58, P=0.480
   TT vs. CT: OR =2.30, 95% CI: 0.36–14.67, P=0.377
   TT + CT vs. CC: OR =1.23, 95% CI: 0.79–1.91, P=0.367
   TT vs. CT + CC: OR =1.97, 95% CI: 0.33–11.9, P=0.460
   T allele vs. C allele: OR =1.03, 95% CI: 0.56–1.89, P=0.920
HIF-1α rs11549467 (1790 G/A) polymorphism 5 Risk:
   AA + GA vs. GG: OR =2.19, 95% CI: 1.12–4.29, P=0.022
   A allele vs. G allele: OR =2.89, 95% CI: 1.91–4.37, P<0.001
Yan Q BMC Cancer [2014] China PubMed, Web of Science 2013.9.20 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   CT vs. CC: OR =0.24, 95% CI: 0.01–5.51
   CT+ vs. CC: OR =1.12, 95% CI: 0.57–2.18
   T allele vs. C allele: OR =0.26, 95% CI: 0.01–6.38
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk: AA + GA vs. GG: OR =0.97, 95% CI: 0.57–1.63
Pancreatic cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   GA vs. GG: OR =1.61, 95% CI: 0.24–10.76
   AA + GA vs. GG: OR =3.14, 95% CI: 1.99–4.97
   A allele vs. G allele: OR =3.08, 95% CI: 1.98–4.78
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   TT vs. CC: OR =1.91, 95% CI: 0.32–11.6
   CT vs. CC: OR =0.24, 95% CI: 0.01–5.51
   TT + CT vs. CC: OR =1.10, 95% CI: 0.87–1.38
   TT vs. CT + CC: OR =1.97, 95% CI: 0.33–11.9
   T allele vs. C allele: OR =1.36, 95% CI: 0.68–2.70
Yang X Tumour Biol [2014] China PubMed, Medline, Embase 2013.7 Overall digestive tract cancer HIF-1α rs11549465 (1772 C/T) polymorphism 12 Risk:
   Homozygote codominant: OR =2.51, 95% CI: 1.31–4.81
   Heterozygote codominant: OR =0.81, 95% CI: 0.45–1.48
   Dominant model: OR =1.16, 95% CI: 0.82–1.64
   Recessive model: OR =8.73, 95% CI: 1.33–57.1
HIF-1α rs11549467 (1790 G/A) polymorphism 9 Risk:
   Homozygote codominant: OR =14.6, 95% CI: 0.70–305
   Heterozygote codominant: OR =2.26, 95% CI: 0.91–5.59
   Dominant model: OR =3.17, 95% CI: 1.21–8.25
   Recessive model: OR =12.8, 95% CI: 0.65–252
Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   Homozygote codominant: OR =1.91, 95% CI: 0.32–11.6
   Heterozygote codominant: OR =0.24, 95% CI: 0.01–5.51
   Dominant model: OR =1.12, 95% CI: 0.57–2.18
   Recessive model: OR =1.97, 95% CI: 0.33–11.9
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   Heterozygote codominant: OR =1.31, 95% CI: 0.51–3.36
   Dominant model: OR =0.97, 95% CI: 0.57–1.63
Pancreatic cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   Homozygote codominant: OR =3.39, 95% CI: 1.28–8.97
   Heterozygote codominant: OR =0.50, 95% CI: 0.02–14.0
   Dominant model: OR =1.39, 95% CI: 0.54–3.56
   Recessive model: OR =4.13, 95% CI: 1.57–10.9
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   Homozygote codominant: OR =9.30, 95% CI: 1.12–77.6
   Heterozygote codominant: OR =1.60, 95% CI: 0.24–10.52
   Dominant model: OR =3.14, 95% CI: 1.99–4.97
   Recessive model: OR =8.65, 95% CI: 1.05–71.6
Yao Q Saudi Med J [2015] China PubMed, Embase, Web of Science, Elsevier Science Direct, CBM, CNKI 2014.2.28 Hepatocellular carcinoma HIF-2α expression 5 OS: HR=1.640, 95% CI: 0.648–4.151
5 Tumor size: OR =2.173, 95% CI: 0.553–8.533, P=0.226
4 Capsule infiltration: OR =2.738, 95% CI: 1.709–4.386, P<0.001
3 Vein invasion: OR =2.458, 95% CI: 1.053–5.734, P=0.038
4 Liver cirrhosis: OR =1.179, 95% CI: 0.525–2.647, P=0.690
5 Histological grade: OR =0.172, 95% CI: 0.042–0.713, P=0.015
3 Necrosis: OR =2.362, 95% CI: 0.472–11.815, P=0.295
Ye LY Pancreatology [2014] China Medline, Embase, Web of Science, Manual search NA Pancreatic cancer HIF-1α expression 6 OS: HR=1.88, 95% CI: 1.39–2.56, P<0.05
6 Lymph node metastasis: OR =3.16, 95% CI: 1.95–5.11, P<0.05
4 Tumor size: OR =1.58, 95% CI: 0.46–5.47, p>0.05
3 Tumor staging (I–II vs. III–IV): OR =3.66, 95% CI: 2.01–6.69, P<0.05
Ye Y Cancer Invest [2014] China Medline, Embase, Web of Science 2012.2.20 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk: TT + CT vs. CC: OR =0.88, 95% CI: 0.46–1.68, P=0.7
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Overall digestive tract cancer HIF-1α rs11549467 (1790 G/A) polymorphism 5 Risk: TT + CT vs. CC: OR =2.20, 95% CI: 1.12–4.34, P=0.02
Zhang ZG Asian Pac J Cancer Prev [2013] China Cochrane, PubMed, EMBASE, Web of Science, CBM 2013.2 Gastric cancer HIF-1α expression 10 OS: HR =1.34, 95% CI: 1.13–1.58, P=0.0009
5 DFS: HR =1.67, 95% CI: 0.99–2.82, P=0.06
Zhao T J Exp Clin Cancer Res [2009] China PubMed 2009.6 Colorectal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   T allele vs. C allele: OR =0.26, 95% CI: 0.01–6.38, P=0.41
   TT vs. CT + CC: OR =1.97, 95% CI: 0.33–11.90, P=0.46
   TT + CT vs. CC: OR =0.25, 95% CI: 0.01–5.99, P=0.39
Zheng F Medicine [2016] China PubMed, Cochrane, EBSCO NA Gastric cancer HIF-2α expression 2 5-year OS rate: OR =2.08, 95% CI: 1.21–3.58, P=0.0008
4 Tumor infiltration (T3 and T4 vs. T2 and T1): OR =3.08, 95% CI: 1.18–8.04, P=0.022
5 Lymphatic metastasis: OR =3.26, 95% CI: 1.10–9.63, P=0.033
5 TNM stage: OR =2.61, 95% CI: 1.40–4.84, P=0.002
3 Tumor differentiation: OR =2.03, 95% CI: 0.73–5.64, P=0.173
Zheng SS PLoS One [2013] China PubMed, Elsevier, Web of Science 2013.2 Hepatocellular carcinoma HIF-1α protein expression 3 DFS: HR=2.14, 95% CI: 1.39–3.29
6 OS: HR=1.65, 95% CI: 1.38–1.97
Zhou Y Cancer Cell Int [2014] China PubMed, Embase, CNKI 2013.12.13 Pancreatic cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
2    AA + GA vs. GG: OR =2.5, 95% CI: 0.93–6.72
1    AA vs. GA + GG: OR =18.8, 95% CI: 0.96–371.55
1    AA vs. GG: OR =18.3, 95% CI: 0.93–360.19
1    GA vs. GG: OR =29.4, 95% CI: 1.12–772.37
Zhu C Mol Biol Rep [2013] China PubMed, Embase, Web of Science 2012.12.1 Gastric cancer HIF-1α expression 9 Overall mortality risk: HR =2.14, 95% CI: 1.32–3.48
4 TNM stage: OR =1.85, 95% CI: 0.80–4.25
6 Depth of invasion: OR =2.49, 95% CI: 1.28–4.83
7 Lymph node metastasis: OR =2.15, 95% CI: 1.27–3.66
3 Distant metastasis: OR =3.26, 95% CI: 0.17–61.62
6 Grade of differentiation: OR =1.87, 95% CI: 0.95–3.66
5 Vascular invasion: OR =2.23, 95% CI: 1.20–4.14

Notes: *In the study by Li Y, based on the 95% CI of OR, the statistical difference should not be significant.

Table S11

Characteristics of studies regarding HIF-1α rs11549465 (1772 C/T) polymorphism with the risk of gastric cancer

First author Journal (Year) No. studies Included studies No. Case No. Control Results Model
Li Y PLoS One [2013] 1 Li K, et al. Biochem Genet [2009] 87 106 CT vs. CC: OR =0.34, 95% CI: 0.11–1.10, P=0.072 A fixed-effect model was used when P heterogeneity <0.05, otherwise a random effect model was used
TT/CT vs. CC: OR =0.34, 95% CI: 0.11–1.10, P=0.072
T allele vs. C allele: OR =0.36, 95% CI: 0.12–1.13, P=0.079
Xu J Genet Test Mol Biomarkers [2013] 1 Li K, et al. Biochem Genet [2009] 87 106 C allele vs. T allele: OR =5.17, 95% CI: 1.75–15.26, P=0.003 When a significant Q-test with P<0.05 or I2>50% indicated that heterogeneity among studies existed, the random effects model (DerSimonian Laird method) was conducted for the meta-analysis; otherwise, the fixed effects model (Mantel–Haenszel method) was used
CC vs. TT + CT: OR =5.75, 95% CI: 1.91–17.35, P=0.002

Table S12

HIF in urinary cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Anam MT Biomark Res [2015] Bangladesh PubMed, PubMed Central, Google Scholar 2014.12 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 6 Risk:
   TT vs. CC: OR =0.84, 95% CI: 0.47–1.49, P=0.5449
   CT vs. CC: OR =1.34, 95% CI: 0.95–1.87, P=0.0913
   TT + CT vs. CC: OR =1.33, 95% CI: 0.95–1.87, P=0.0982
   TT vs. CT + CC: OR =0.81, 95% CI: 0.47–1.40, P=0.4535
   T allele vs. C allele: OR =1.29, 95% CI: 0.94–1.76, P=0.1178
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =3.35, 95% CI: 0.14–82.30, P=0.4597
   GA vs. GG: OR =1.41, 95% CI: 0.96–2.08, P=0.0822
   AA vs. GA + GG: OR =3.25, 95% CI: 0.13–79.90, P=0.4707
   AA + GA vs. GG: OR =1.41, 95% CI: 0.93–2.15, P=0.1043
   A allele vs. G allele: OR =1.42, 95% CI: 0.93–2.17, P=0.1093
Renal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =0.27, 95% CI: 0.08–0.90, P=0.0335
   CT vs. CC: OR =0.40, 95% CI: 0.12–1.34, P=0.1369
   TT + CT vs. CC: OR =0.43, 95% CI: 0.15–1.20, P=0.1082
   TT vs. CT + CC: OR =1.08, 95% CI: 0.44–2.64, P=0.8703
   T allele vs. C allele: OR =0.84, 95% CI: 0.58–1.22, P=0.3548
HIF-1α rs11549467 (1790 G/A) polymorphism 4 Risk:
   AA vs. GG: OR =5.11, 95% CI: 2.24–11.66, P=0.0001
   GA vs. GG: OR =1.51, 95% CI: 0.45–5.05, P=0.5038
   AA vs. GA + GG: OR =3.05, 95% CI: 1.36–6.84, P=0.0068
   AA + GA vs. GG: OR =1.58, 95% CI: 0.49–5.03, P=0.442
   A allele vs. G allele: OR =1.53, 95% CI: 0.60–3.92, P=0.3747
Fan Y Medicine [2015] China PubMed, Embase, Web of Science, Cochrane, EBSCO, CINAHL, Biological Abstracts 2015.8.15 Renal cell carcinoma HIF-1α nuclear and cytoplasmic expression 5 OS: HR =1.637, 95% CI: 0.898–2.985, P=0.108
6 Cancer-specific survival: HR=1.110, 95% CI: 0.595–2.069, P=0.744
4 PFS: HR =1.113, 95% CI: 0.675–1.836, P=0.674
HIF-2α nuclear and cytoplasmic expression 4 Cancer-specific survival: HR=1.597, 95% CI: 0.667–3.824, P=0.293
3 PFS: HR =0.847, 95% CI: 0.566–1.266, P=0.417
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
6    Dominant model (TT + CT vs. CC): OR =1.36, 95% CI: 0.95–1.96
5    Recessive model (TT vs. CT + CC): OR =1.31, 95% CI: 0.54–3.18
5    Homozygote comparison (TT vs. CC): OR =1.34, 95% CI: 0.54–3.30
6    Heterozygote comparison (CT vs. CC): OR =1.34, 95% CI: 0.93–1.92
Renal cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
3    Dominant model (TT + CT vs. CC): OR = 0.46, 95% CI: 0.13–1.60
3    Recessive model (TT vs. CT + CC): OR =1.55, 95% CI: 1.02–2.37
3    Homozygote comparison (TT vs. CC): OR =0.29, 95% CI: 0.06–1.45
3    Heterozygote comparison (CT vs. CC): OR =0.44, 95% CI: 0.11–1.69
Hu X Tumour Biol [2014] China PubMed, Embase, CNKI 2013.7 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 5 Risk:
   T allele vs. C allele: OR =1.54, 95% CI: 1.04–2.30
   TT vs. CC: OR =1.91, 95% CI: 0.82–4.47
   CT vs. CC: OR =1.54, 95% CI: 0.95–2.49
   TT + CT vs. CC: OR =1.58, 95% CI: 1.00–2.49
   TT vs. CT + CC: OR =1.88, 95% CI: 0.79–4.47
Renal cell carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   T allele vs. C allele: OR =0.92, 95% CI: 0.70–1.19
   TT vs. CC: OR =0.37, 95% CI: 0.12–1.12
   CT vs. CC: OR =0.64, 95% CI: 0.32–1.29
   TT + CT vs. CC: OR =0.65, 95% CI: 0.35–1.23
   TT vs. CT + CC: OR =1.31, 95% CI: 0.77–2.24
Bladder cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk: TT + CT vs. CC: OR =1.12, 95% CI: 0.65–1.92
Li D PLoS One [2013] China PubMed 2012.11.25 Overall urinary cancers HIF-1α gene P582S polymorphism Risk:
11    TT vs. CT + CC: OR = 1.17, 95% CI: 0.67–2.05, P=0.57
11    TT + CT vs. CC: OR =1.10, 95% CI: 0.83–1.45, P=0.52
11    T allele vs. C allele: OR =1.13, 95% CI: 0.90–1.41, P=0.30
HIF-1α gene A588T polymorphism Risk:
9    AA + AG vs. GG: OR =1.40, 95% CI: 0.76–2.58, P=0.28
8    A allele vs. G allele: OR =1.57, 95% CI: 0.89–2.76, P=0.12
Prostate cancer HIF-1α gene P582S polymorphism Risk:
6    TT vs. CT + CC: OR = 1.31, 95% CI: 0.54–3.20, P=0.55
6    TT + CT vs. CC: OR =1.36, 95% CI: 0.95–1.96, P=0.09
6    T allele vs. C allele: OR =1.35, 95% CI: 0.96–1.89, P=0.08
HIF-1α gene A588T polymorphism Risk:
4    AA + AG vs. GG: OR =1.45, 95% CI: 1.00–2.12, P=0.05
4    A allele vs. G allele: OR =1.46, 95% CI: 1.01–2.12, P=0.04
Renal cancer HIF-1α gene P582S polymorphism Risk:
4    TT vs. CT + CC: OR = 1.37, 95% CI: 0.92–2.04, P=0.12
4    TT + CT vs. CC: OR =0.62, 95% CI: 0.33–1.19, P=0.15
4    T allele vs. C allele: OR =0.91, 95% CI: 0.73–1.12, P=0.37
HIF-1α gene A588T polymorphism Risk:
4    AA + AG vs. GG: OR =1.58, 95% CI: 0.49–5.03, P=0.44
4    A allele vs. G allele: OR =1.53, 95% CI: 0.60–3.92, P=0.38
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   TT vs. CC: OR =2.02, 95% CI: 0.60–6.83, P=0.117
   CT vs. CC: OR =1.42, 95% CI: 0.84–2.40, P=0.062
   TT/CT vs. CC: OR =1.46, 95% CI: 0.89–2.40, P=0.031
   TT vs. CT/CC: OR =2.03, 95% CI: 0.58–7.16, P=0.124
   T allele vs. C allele: OR =1.43, 95% CI: 0.93–2.21, P=0.017
Renal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   TT vs. CC: OR =0.67, 95% CI: 0.21–2.15C0.498
   CT vs. CC: OR =0.92, 95% CI: 0.67–1.26, P=0.599
   TT/CT vs. CC: OR =0.90, 95% CI: 0.67–1.22, P=0.509
   TT vs. CT/CC: OR =0.69, 95% CI: 0.22–2.17, P=0.521
   T allele vs. C allele: OR =0.89, 95% CI: 0.67–1.19, P=0.432
Bladder cancer HIF-1α rs11549465 (1772 C/T) polymorphism 1 Risk:
   CT vs. CC: OR =1.11, 95% CI: 0.65–1.92, P=0.697
   TT/CT vs. CC: OR =1.11, 95% CI: 0.65–1.92, P=0.697
   T allele vs. C allele: OR =1.11, 95% CI: 0.65–1.88, P=0.704
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Prostate cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =3.35, 95% CI: 0.14–82.30, P=0.460
   GA vs. GG: OR =1.41, 95% CI: 0.97–2.07, P=0.082
   AA + GA vs. GG: OR =1.44, 95% CI: 0.98–2.10, P=0.104
   AA vs. GA + GG: OR =3.25, 95% CI: 0.13–79.90, P=0.471
   A allele vs. G allele: OR =1.45, 95% CI: 1.00–2.11, P=0.109
Renal cell carcinoma HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =4.70, 95% CI: 0.22–98.24, P=0.319
   GA vs. GG: OR =1.00, 95% CI: 0.69–1.47, P=0.975
   AA + GA vs. GG: OR =1.04, 95% CI: 0.71–1.51, P=0.841
   AA vs. GA + GG: OR =4.78, 95% CI: 0.23–100.04, P=0.313
   A allele vs. G allele: OR =1.07, 95% CI: 0.74–1.55, P=0.706
Tian Y Chinese Journal of Evidence–Based Medicine [2015] Article in Chinese China Cochrane, PubMed, Embase, Ovid, CNKI, VIP, CBM, WanFang 2015.6 Renal cell cancer HIF-1α expression 7 Risk: OR =16.76, 95% CI: 8.53–32.92, p<0.00001
7 Lymph node metastasis: (yes vs. no): OR =4.33, 95% CI: 2.53–7.39, p<0.00001
5 Clinical stage I–II vs. stage III–IV: OR =0.3, 95% CI: 0.18–0.51, p<0.0001
4 Pathological stage G1+G2 vs. G3+G4: OR =0.54, 95% CI: 0.29–0.98, P=0.04
3 Age (≥ 50 vs. <50): OR =1.09, 95% CI: 0.54–2.19, P=0.82
6 Male vs. Female: OR =0.77, 95% CI: 0.48–1.25, P=0.29
Wu G Tumour Biol [2014] China PubMed, Embase, Google Scholar, Wanfang 2013.6.10 Renal carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
4    TT + CT vs. CC: OR =0.62, 95% CI: 0.33–1.19, P=0.15
4    TT vs. CT + CC: OR =0.96, 95% CI: 0.76–1.20, P=0.706
Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
6    TT + CT vs. CC: OR =1.36, 95% CI: 0.95–1.96, P=0.094
6    TT vs. CT + CC: OR =1.27, 95% CI: 0.93–1.73, P=0.126
Yan Q BMC Cancer [2014] China PubMed, Web of Science 2013.9.20 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 6 Risk:
   TT vs. CC: OR =1.34, 95% CI: 0.54–3.31
   CT vs. CC: OR =1.34, 95% CI: 0.93–1.92
   TT + CT vs. CC: OR =1.36, 95% CI: 0.95–1.96
   TT vs. CT + CC: OR =1.31, 95% CI: 0.54–3.20
   T allele vs. C allele: OR =1.35, 95% CI: 0.96–1.89
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   GA vs. GG: OR =1.42, 95% CI: 0.97–2.07
   AA + GA vs. GG: OR =1.44, 95% CI: 0.98–2.10
   A allele vs. G allele: OR =1.45, 95% CI: 0.99–2.11
Renal cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   TT vs. CC: OR =0.28, 95% CI: 0.12–1.28
   CT vs. CC: OR =0.62, 95% CI: 0.31–1.24
   TT + CT vs. CC: OR =0.62, 95% CI: 0.33–1.18
   TT vs. CT + CC: OR =1.37, 95% CI: 0.92–2.04
   T allele vs. C allele: OR =0.91, 95% CI: 0.73–1.12
HIF-1α rs11549467 (1790 G/A) polymorphism 4 Risk:
   AA vs. GG: OR =5.10, 95% CI: 2.21–11.73
   GA vs. GG: OR =1.51, 95% CI: 0.45–5.05
   AA + GA vs. GG: OR =1.58, 95% CI: 0.49–5.04
   AA vs. GA + GG: OR =3.09, 95% CI: 1.38–6.92
   A allele vs. G allele: OR =1.53, 95% CI: 0.60–3.92
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 5 Risk:
   TT vs. CC: OR =3.68, 95% CI: 1.58–8.55
   CT vs. CC: OR =2.02, 95% CI: 1.01–4.07
   TT + CT vs. CC: OR =2.10, 95% CI: 1.08–4.09
   TT vs. CT + CC: OR =3.52, 95% CI: 1.52–8.16
   T allele vs. C allele: OR =2.06, 95% CI: 1.15–3.68
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =3.35, 95% CI: 0.14–82.3
   GA vs. GG: OR =1.41, 95% CI: 0.97–2.07
   AA + GA vs. GG: OR =1.44, 95% CI: 0.98–2.10
   AA vs. GA + GG: OR =3.25, 95% CI: 0.13–79.9
   A allele vs. G allele: OR =1.45, 95% CI: 1.00–2.11
Ye Y Cancer Invest [2014] China Medline, Embase, Web of Science 2012.2.20 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 5 Risk: TT + CT vs. CC: OR =1.59, 95% CI: 1.11–2.28, P=0.01
Renal cell carcinoma HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk: TT + CT vs. CC: OR =1.06, 95% CI: 0.41–2.73, P=0.9
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Prostate cancer HIF-1α rs11549467 (1790 G/A) polymorphism 3* Risk: TT + CT vs. CC: OR =0.98, 95% CI: 0.55–1.76, P=0.95*
Renal cell carcinoma HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk: TT + CT vs. CC: OR =2.47, 95% CI: 0.21–28.92, P=0.47
Zhao T J Exp Clin Cancer Res (2009) China PubMed 2009.6 Prostate cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   T allele vs. C allele: OR =1.78, 95% CI: 1.07–2.94, P=0.03
   TT vs. CT + CC: OR =1.53, 95% CI: 0.90–2.60, P=0.11
   TT + CT vs. CC: OR =1.85, 95% CI: 1.04–3.31, P=0.04
HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   A allele vs. G allele: OR =0.96, 95% CI: 0.49–1.90, P=0.91
   AA + GA vs. GG: OR =0.96, 95% CI: 0.49–1.90, P=0.91
Zhou Y Cancer Cell Int [2014] China PubMed, Embase, CNKI 2013.12.13 Prostate cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
3    AA + GA vs. GG: OR =1.41, 95% CI: 0.93–2.14
1    AA vs. GA + GG: OR =3.24, 95% CI: 0.13–79.9
1    AA vs. GG: OR =3.34, 95% CI: 0.13–82.30
1    GA vs. GG: OR =1.98, 95% CI: 0.07–50.4
Renal cancer HIF-1α rs11549467 (1790 G/A) polymorphism Risk:
3    AA + GA vs. GG: OR =0.94, 95% CI: 0.16–5.29
2    AA vs. GA + GG: OR =2.69, 95% CI: 1.20–6.03
2    AA vs. GG: OR =3.71, 95% CI: 1.72–7.99
1    GA vs. GG: OR =0.81, 95% CI: 0.33–2

Notes: *In the study by Ye Y (Tumori, 2014), the number of included studies regarding prostate cancer should be 3, but not 4. Accordingly, the statistical results should not be reliable.

Table S13

Characteristics of studies regarding HIF-1α rs11549467 (1790 G/A) polymorphism with the risk of renal cancer

First author Journal [year] No. studies Included studies No. Case No. Control Results Model
Anam MT Biomark Res [2015] 4 Qin C, et al. Ann Oncol [2012] 620 623 AA vs. GG: OR =5.11, 95% CI: 2.24–11.66, P=0.0001;
GA vs. GG: OR =1.51, 95% CI: 0.45–5.05, P=0.5038;
AA vs. GA + GG: OR =3.05, 95% CI: 1.36–6.84, P=0.0068;
AA + GA vs. GG: OR =1.58, 95% CI: 0.49–5.03, P=0.442;
A allele vs. G allele: OR =1.53, 95% CI: 0.60–3.92, P=0.3747
Maybe a random-effects model was employed according to the forest plots
Morris MR, et al. Anticancer Res [2009] 325 309
Ollerenshaw M, et al. Cancer Genet Cytogenet [2004] 146 288
Clifford SC, et al. Oncogene [2001] 48 144
Li D PLoS One [2013] 4 Qin C, et al. Ann Oncol [2012] 620 623 AA + AG vs. GG: OR =1.58, 95% CI: 0.49–5.03, P=0.44;
A allele vs. G allele: OR =1.53, 95% CI: 0.60–3.92, P=0.38
The random-effects model (the Dersimonian-Laird method) would be used if the test of heterogeneity was significant; otherwise the fixed-effects model (the Mantel-Haenszel method) would be applied in the analysis
Morris MR, et al. Anticancer Res [2009] 325 309
Ollerenshaw M, et al. Cancer Genet Cytogenet [2004] 146 288
Clifford SC, et al. Oncogene [2001] 48 144
Yan Q BMC Cancer [2014] 4 Qin C, et al. Ann Oncol [2012] 620 623 AA vs. GG: OR =5.10, 95% CI: 2.21–11.73;
GA vs. GG: OR =1.51, 95% CI: 0.45–5.05;
AA vs. GA + GG: OR =3.09, 95% CI: 1.38–6.92;
AA + GA vs. GG: OR =1.58, 95% CI: 0.49–5.04;
A allele vs. G allele: OR =1.53, 95% CI: 0.60–3.92
When P>0.05, the effects were assumed to be homogenous, and the fixed-effect model (the Mantel-Haenszel method) was used. When P<0.05, the random-effect model (the DerSimonian and Laird method) was more appropriate
Morris MR, et al. Anticancer Res [2009] 325 309
Ollerenshaw M, et al. Cancer Genet Cytogenet [2004] 146 288
Clifford SC, et al. Oncogene [2001] 48 144

Table S14

HIF in gynecological cancer

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
He P PLoS One [2013] China PubMed, Embase, CNKI 2013.8.23 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism Risk:
3    Dominant model (TT + CT vs. CC): OR =1.81, 95% CI: 0.79–4.10
2    Recessive model (TT vs. CT + CC): OR =8.80, 95% CI: 2.31–33.52
2    Homozygote comparison (TT vs. CC): OR =11.49, 95% CI: 2.21–59.67
3    Heterozygote comparison (CT vs. CC): OR =1.47, 95% CI: 0.79–2.74
Hu X Tumour Biol [2013] China PubMed, Embase, CNKI 2013.2 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Lymph node metastasis: OR =1.32, 95% CI: 0.60–2.90, P=0.493
Hu X Tumour Biol [2014] China PubMed, Embase, CNKI 2013.7 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   T allele vs. C allele: OR =1.89, 95% CI: 0.84–4.26
   TT vs. CC: OR =11.49, 95% CI: 2.18–60.52
   CT vs. CC: OR =1.47, 95% CI: 0.79–2.74
   TT + CT vs. CC: OR =1.81, 95% CI: 0.79–4.11
   TT vs. CT + CC: OR =8.80, 95% CI: 2.30–33.70
Huang M Int J Gynecol Cancer [2014] China Medline, PubMed, Embase, Web of Science 2013.1 Cervical cancer HIF-1α expression 7 DFS: HR =1.98, 95% CI: 1.22–3.21, P=0.006
7 OS: HR =2.58, 95% CI: 1.86–3.56, P<0.001
5 Lymph node metastasis (yes vs no): OR =2.58, 95% CI: 1.86–3.56, P=0.167
3 Tumor grade (grade 3 vs. grade 1/2): OR =0.99, 95% CI: 0.54–1.82, P=0.969
5 Tumor size (size≥4 cm vs. size <4 cm): OR =2.04, 95% CI: 1.24–3.34, P=0.005
4 FIGO stage (advanced stage vs. earlier stage): OR =1.52, 95% CI: 0.87–2.69, P=0.145
4 Histology type (other type vs. SCC): OR =1.63, 95% CI: 0.85–3.13, P=0.139
3 Anemia (yes vs. no): OR =2.04, 95% CI: 1.07–3.88, P=0.030
Jin Y Tumour Biol [2014] China PubMed, Cochrane, Web of Science, CNKI 2014.2 Epithelial ovarian cancer HIF-1α expression 3 5-year survival rate: OR =11.46, 95% CI: 3.43–38.29, P<0.0001
Pathological type:
13    Cancer vs. benign: OR =9.73, 95% CI: 4.90–19.32, P<0.00001
10    Cancer vs. borderline: OR =2.31, 95% CI: 1.04–5.09, P=0.04
9    Borderline vs. benign: OR =6.29, 95% CI: 2.69–14.73, P<0.0001
NA Histological type:
   Serous vs. others: OR =1.02, 95% CI: 0.79–1.31, P=0.88
   Serous vs. others: OR =1.37, 95% CI: 0.78–2.42, P=0.28
17 FIGO (III–IV vs. I–II): OR =3.01, 95% CI: 1.92–4.74, P<0.00001
10 Histological grade:
   Grades G3 vs. G1: OR =4.52, 95% CI: 2.79–7.31, P<0.00001
   Grades G3 vs. G2: OR =2.02, 95% CI: 1.27–3.19, P=0.003
   Grades G2 vs. G1: OR =2.43, 95% CI: 1.65–3.59, P<0.0001
9 Lymph node metastasis: OR =5.20, 95% CI: 2.10–12.89, P=0.0004
Jin Y PLoS One [2015] China PubMed, Cochrane, Web of Knowledge, clinical trial registries 2014.1 Overall gynecological cancer HIF-1α expression 9 5–year DFS rate: OR =2.93, 95% CI: 1.43–6.01, P=0.003
8 5–year OS rate: OR =5.53, 95% CI: 2.48–12.31, P<0.0001
Pathological type:
21    Cancer vs. Borderline: OR =2.70, 95% CI: 1.69–4.31, P<0.0001
26    Cancer vs. Normal: OR =9.59, 95% CI: 5.97–15.39, P<0.00001
19    Borderline vs. Normal: OR =4.13, 95% CI: 2.43–7.02, P<0.00001
32    FIGO stage: OR =2.66, 95% CI: 1.87–3.79, P<0.00001
Histological type:
22    G3 vs. G1: OR =3.77, 95% CI: 2.76–5.16, P<0.00001
22    G3 vs. G2: OR =1.62, 95% CI: 1.20–2.19, P=0.002
22    G2 vs. G1:OR =2.34, 95% CI: 1.82–3.00, P<0.00001
21 Lymph node metastasis: OR =3.98, 95% CI: 2.10–12.89, P<0.0001
Endometrial cancer HIF-1α expression 4 5-year DFS rate: OR =1.56, 95% CI: 0.36–6.83, P=0.55
2 5-year OS rate: OR =3.67, 95% CI: 0.52–25.63, P=0.19
Pathological type:
4    Cancer vs. Borderline: OR =4.45, 95% CI: 2.57–7.71, P<0.00001
6    Cancer vs. Normal: OR =11.03, 95% CI: 6.55–18.58, P<0.00001
3    Borderline vs. Normal: OR =3.48, 95% CI: 0.75–16.15, P=0.11
11 FIGO stage: OR =2.76, 95% CI: 1.25–6.09, P=0.01
Histological type:
6    G3 vs. G1: OR =2.65, 95% CI: 1.53–4.59, P=0.0005
6    G3 vs. G2: OR =1.15, 95% CI: 0.65–2.01, P=0.63
6    G2 vs. G1: OR =2.19, 95% CI: 1.43–3.37, P=0.0003
4 Lymph node metastasis: OR =4.02, 95% CI: 1.32–12.26, P=0.01
Cervical cancer HIF-1α expression 3 5-year DFS rate: OR =5.28, 95% CI: 2.90–9.63, P<0.00001
3 5-year OS rate: OR =3.28, 95% CI: 1.63–6.60, P=0.008
Pathological type:
7 Cancer vs. borderline: OR =2.36, 95% CI: 1.04–5.38, P=0.04
7 Cancer vs. normal: OR =8.17, 95% CI: 2.80–23.85, P=0.0001
7 Borderline vs. normal: OR =2.40, 95% CI: 1.52–3.78, P=0.0002
4 FIGO stage:
   OR =1.76, 95% CI: 1.03–2.99, P=0.04 (fixed-effect model)
   OR =1.69, 95% CI: 0.90–3.15, P=0.10 (random-effect model)
Histological type:
6    G3 vs. G1: OR =4.29, 95% CI: 2.26–8.14, P<0.00001
6    G3 vs. G2: OR =1.62, 95% CI: 0.91–2.90, P=0.10
6    G2 vs. G1: OR =2.40, 95% CI: 1.46–3.93, P=0.0005
8 Lymph node metastasis: OR =2.94, 95% CI: 1.19–7329, P=0.02
Ovarian cancer HIF-1α expression 2 5-year DFS rate: OR =2.42, 95% CI: 0.80–7.36, P=0.12
3 5-year OS rate: OR =11.46, 95% CI: 3.43–38.29, P<0.0001
Pathological type:
10    Cancer vs. borderline: OR =2.31, 95% CI: 1.04–5.09, P=0.04
13    Cancer vs. normal: OR =9.73, 95% CI: 4.90–19.32, P<0.00001
9    Borderline vs. normal: OR =6.29, 95% CI: 2.69–14.73, P<0.0001
17 FIGO stage: OR =3.01, 95% CI: 1.92–4.74, P<0.00001
Histological type:
10    G3 vs. G1: OR =4.52, 95% CI: 2.79–7.31, P<0.00001
10    G3 vs. G2: OR =2.02, 95% CI: 1.27–3.19, P=0.003
10    G2 vs. G1: OR =2.43, 95% CI: 1.65–3.59, P<0.00001
9 Lymph node metastasis: OR =5.20, 95% CI: 2.10–12.89, P=0.0004
Li Y Int J Clin Exp Med [2015] China PubMed, Web of Knowledge, Medline, Embase, Google Scholar 2014.7 Gynecological cancer HIF-1α rs11549465 (1772 C/T) polymorphism 2 Risk:
   TT vs. CC: OR =9.92, 95% CI: 2.15–45.66, P=0.003
   CT vs. CC: OR =1.16, 95% CI: 0.77–1.75, P=0.488
   TT/CT vs. CC: OR =1.31, 95% CI: 0.58–2.94, P=0.152
   TT vs. CT/CC: OR =8.35, 95% CI: 1.85–37.75, P=0.006
   T allele vs. C allele: OR =1.38, 95% CI: 0.58–3.29, P=0.020
Liu P Neoplasma [2014] China PubMed, Embase, Web of Knowledge, Google Scholar 2013.8 Gynecological cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk:
   AA vs. GG: OR =0.36, 95% CI: 0.01–8.80, P=0.528
   GA vs. GG: OR =1.16, 95% CI: 0.54–2.48, P=0.744
   AA + GA vs. GG: OR =1.08, 95% CI: 0.51–2.28, P=0.791
   AA vs. GA + GG: OR =0.36, 95% CI: 0.01–8.81, P=0.529
   A allele vs. G allele: OR =1.00, 95% CI: 0.48–2.08, P=0.831
Sun C Ai Zheng. Ji Bian. Tu Bian. [2015]; Article in Chinese China CNKI, CBM 2014.3.10 Epithelial ovarian cancer HIF-1α protein expression 6 Risk: OR =0.036, 95% CI: 0.010–0.135, P<0.001
3 Lymph node: OR =0.080, 95% CI: 0.029–0.220, P<0.001
6 Clinical stage: OR =0.258, 95% CI: 0.136–0.490, P<0.001
4 Pathological type: OR =1.779, 95% CI: 0.876–3.616, P=0.111
6 Pathological stage: OR =0.327, 95% CI: 0.084–1.268, P=0.106
3 Age: OR =1.331, 95% CI: 0.341–5.196, P=0.681
Yan Q BMC Cancer [2014] China PubMed, Web of Science 2013.9.20 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =10.11, 95% CI: 2.55–40.05
   CT vs. CC: OR =0.98, 95% CI: 0.72–1.34
   TT + CT vs. CC: OR =1.32, 95% CI: 0.61–2.87
   TT vs. CT + CC: OR =8.55, 95% CI: 2.28–32.13
   T allele vs. C allele: OR =1.41, 95% CI: 0.59–3.35
HIF-1α rs11549467 (1790 G/A) polymorphism 3 Risk:
   AA vs. GG: OR =0.35, 95% CI: 0.04–3.39
   GA vs. GG: OR =0.62, 95% CI: 0.40–0.98
   AA + GA vs. GG: OR =0.60, 95% CI: 0.38–0.94
   AA vs. GA + GG: OR =0.36, 95% CI: 0.04–3.450
   A allele vs. G allele: OR =0.59, 95% CI: 0.38–0.91
Yang X PLoS One [2013] China PubMed, Embase 2013.6.26 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk:
   TT vs. CC: OR =10.1, 95% CI: 3.12–32.6
   CT vs. CC: OR =1.37, 95% CI: 0.92–2.02
   TT + CT vs. CC: OR =1.63, 95% CI: 1.12–2.37
   TT vs. CT + CC: OR =8.26, 95% CI: 2.64–25.9
   T allele vs. C allele: OR =1.89, 95% CI: 0.84–4.26
Ye Y Cancer Invest [2014] China Medline, Embase, Web of Science 2012.2.20 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism 3 Risk: TT + CT vs. CC: OR =1.78, 95% CI: 0.76, 4.18, P=0.18
Ye Y Tumori [2014] China Medline, Embase, Web of Science 2012.2.20 Cervical cancer HIF-1α rs11549467 (1790 G/A) polymorphism 2 Risk: TT + CT vs. CC: OR =0.92, 95% CI: 0.41–2.03, P=0.83
Zhu J Int J Clin Exp Pathol [2014] China PubMed, Embase 2014.1.10 Cervical cancer HIF-1α rs11549465 (1772 C/T) polymorphism 4 Risk:
   TT vs. CC: OR =6.32, 95% CI: 2.28–17.55
   CT vs. CC: OR =1.05, 95% CI: 0.80–1.38
   TT + CT vs. CC: OR =1.13, 95% CI: 0.87–1.47
   TT vs. CT + CC: OR =5.86, 95% CI: 2.13–16.11

Table S15

HIF in osteosarcoma

First author Journal [year] Country Databases Search date Cancer HIF No. studies Results
Ren HY Onco Targets Ther [2016] China PubMed, Embase, Web of Science 2015.8.1 Osteosarcoma HIF-1α expression 2 OS: HR=3.0, 95% CI: 1.46–6.15, P=0.003
3 DFS: HR=2.23, 95% CI: 1.26–3.92, P=0.006
5 Metastasis (yes vs. no): OR =5.06, 95% CI: 2.87–8.92, P<0.00001
2 Pathologic grade (high vs. low): OR =21.33, 95% CI: 4.60-98.88, P<0.0001
4 Tumor stage (high vs. low): OR =10.29, 95% CI: 3.55-29.82, P<0.0001
2 Chemotherapy response (poor vs. good): OR =9.68, 95% CI: 1.87–50.18, P=0.007
4 Tumor size (large vs. small): OR =1.12, 95% CI: 0.22–5.76, P=0.89
3 Tumor site (tibia or femur vs. elsewhere): OR =2.02, 95% CI: 0.10–39.71, P=0.46
2 Histopathology (osteoblastic vs. other types): OR =0.70, 95% CI: 0.28–1.73, P=0.46

Acknowledgements

Funding: This study was partially supported by the grants from the National Natural Science Foundation of China (no. 81500474) for Dr. X Qi.


Footnote

Conflicts of Interest: The authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/amj.2017.04.08). Xingshun Qi serves as an Editor-in-Chief of AME Medical Journal. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.


References

  1. Dhani N, Fyles A, Hedley D, et al. The clinical significance of hypoxia in human cancers. Semin Nucl Med 2015;45:110-21. [Crossref] [PubMed]
  2. Vaupel P, Mayer A. Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev 2007;26:225-39. [Crossref] [PubMed]
  3. Jubb AM, Buffa FM, Harris AL. Assessment of tumour hypoxia for prediction of response to therapy and cancer prognosis. J Cell Mol Med 2010;14:18-29. [Crossref] [PubMed]
  4. Vaupel P. The role of hypoxia-induced factors in tumor progression. Oncologist 2004;9:10-7. [Crossref] [PubMed]
  5. Semenza GL, Nejfelt MK, Chi SM, et al. Hypoxia-inducible nuclear factors bind to an enhancer element located 3' to the human erythropoietin gene. Proc Natl Acad Sci U S A 1991;88:5680-4. [Crossref] [PubMed]
  6. Tian H, McKnight SL, Russell DW. Endothelial PAS domain protein 1 (EPAS1), a transcription factor selectively expressed in endothelial cells. Genes Dev 1997;11:72-82. [Crossref] [PubMed]
  7. Ema M, Taya S, Yokotani N, et al. A novel bHLH-PAS factor with close sequence similarity to hypoxia-inducible factor 1alpha regulates the VEGF expression and is potentially involved in lung and vascular development. Proc Natl Acad Sci U S A 1997;94:4273-8. [Crossref] [PubMed]
  8. Gu YZ, Moran SM, Hogenesch JB, et al. Molecular characterization and chromosomal localization of a third alpha-class hypoxia inducible factor subunit, HIF3alpha. Gene Expr 1998;7:205-13. [PubMed]
  9. Semenza GL. Targeting HIF-1 for cancer therapy. Nat Rev Cancer 2003;3:721-32. [Crossref] [PubMed]
  10. Ke Q, Costa M. Hypoxia-inducible factor-1 (HIF-1). Mol Pharmacol 2006;70:1469-80. [Crossref] [PubMed]
  11. Anam MT, Ishika A, Hossain MB, et al. A meta-analysis of hypoxia inducible factor 1-alpha (HIF1A) gene polymorphisms: association with cancers. Biomark Res 2015;3:29. [Crossref] [PubMed]
  12. He P, Han Q, Liu J, et al. The association between hypoxia-inducible factor-1 alpha gene C1772T polymorphism and cancer risk: a meta-analysis of 37 case-control studies. PLoS One 2013;8:e83441. [Crossref] [PubMed]
  13. Hu X, Lin S, Zheng J, et al. Clinicopathological significance of hypoxia-inducible factor-1 alpha polymorphisms in cancers: evidence from a meta-analysis. Tumour Biol 2013;34:2477-87. [Crossref] [PubMed]
  14. Li Y, Li C, Shi H, et al. The association between the rs11549465 polymorphism in the hif-1alpha gene and cancer risk: a meta-analysis. Int J Clin Exp Med 2015;8:1561-74. [PubMed]
  15. Liu J, Zhang HX. 1790 G/A polymorphism, but not 1772 C/T polymorphism, is significantly associated with cancers: an update study. Gene 2013;523:58-63. [Crossref] [PubMed]
  16. Liu P, Shi H, Yang Y, et al. Update meta-analysis on 1790G/A polymorphism and cancer risk: Evidence from 26 studies. Neoplasma 2014;61:340-51. [Crossref] [PubMed]
  17. Wu G, Yan WF, Zhu YZ, et al. Hypoxia-inducible factor-1alpha (HIF-1alpha) C1772T polymorphism significantly contributes to the risk of malignancy from a meta-analysis. Tumour Biol 2014;35:4113-22. [Crossref] [PubMed]
  18. Yang X, Zhu HC, Zhang C, et al. HIF-1alpha 1772 C/T and 1790 G/A polymorphisms are significantly associated with higher cancer risk: an updated meta-analysis from 34 case-control studies. PLoS One 2013;8:e80396. [Crossref] [PubMed]
  19. Ye Y, Wang M, Hu S, et al. Hypoxia-inducible factor-1alpha C1772T polymorphism and cancer risk: a meta-analysis including 18,334 subjects. Cancer Invest 2014;32:126-35. [Crossref] [PubMed]
  20. Ye Y, Wang M, Li J, et al. Hypoxia-inducible factor-1alpha G polymorphism and the risk of cancer: a meta-analysis. Tumori 2014;100:e257-65. [PubMed]
  21. Zhang Q, Chen Y, Zhang B, et al. Hypoxia-inducible factor-1alpha polymorphisms and risk of cancer metastasis: a meta-analysis. PLoS One 2013;8:e70961. [Crossref] [PubMed]
  22. Zhao T, Lv J, Zhao J, et al. Hypoxia-inducible factor-1alpha gene polymorphisms and cancer risk: a meta-analysis. J Exp Clin Cancer Res 2009;28:159. [Crossref] [PubMed]
  23. Zhou Y, Lin L, Wang Y, et al. The association between hypoxia-inducible factor-1 alpha gene G1790A polymorphism and cancer risk: a meta-analysis of 28 case-control studies. Cancer Cell Int 2014;14:37. [Crossref] [PubMed]
  24. Liu Q, Cao P. Clinical and prognostic significance of HIF-1alpha in glioma patients: a meta-analysis. Int J Clin Exp Med 2015;8:22073-83. [PubMed]
  25. Hu X, Fang Y, Zheng J, et al. The association between HIF-1alpha polymorphism and cancer risk: a systematic review and meta-analysis. Tumour Biol 2014;35:903-16. [Crossref] [PubMed]
  26. Qian J, Wenguang X, Zhiyong W, et al. Hypoxia inducible factor: a potential prognostic biomarker in oral squamous cell carcinoma. Tumour Biol 2016;37:10815-20. [Crossref] [PubMed]
  27. Sun X, Liu YD, Gao W, et al. HIF-1alpha -1790G>A polymorphism significantly increases the risk of digestive tract cancer: a meta-analysis. World J Gastroenterol 2015;21:1641-9. [Crossref] [PubMed]
  28. Yan Q, Chen P, Wang S, et al. Association between HIF-1alpha C1772T/G1790A polymorphisms and cancer susceptibility: an updated systematic review and meta-analysis based on 40 case-control studies. BMC Cancer 2014;14:950. [Crossref] [PubMed]
  29. Yang X, Zhang C, Zhu HC, et al. HIF-1alpha P582S and A588T polymorphisms and digestive system cancer risk-a meta-analysis. Tumour Biol 2014;35:2825-30. [Crossref] [PubMed]
  30. Rainsbury JW, Ahmed W, Williams HK, et al. Prognostic biomarkers of survival in oropharyngeal squamous cell carcinoma: Systematic review and meta-analysis. Head and Neck 2013;35:1048-55. [Crossref] [PubMed]
  31. Jing SW, Zhao ZJ, Jing SH, et al. Relationship between hypoxia inducible factor-1α and clinicopathological features of nasopharyngeal carcinoma: A Meta analysis. Chinese Journal of Cancer Prevention and Treatment 2015;22:548-54.
  32. Li C, Lu HJ, Na FF, et al. Prognostic role of hypoxic inducible factor expression in non-small cell lung cancer: a meta-analysis. Asian Pac J Cancer Prev 2013;14:3607-12. [Crossref] [PubMed]
  33. Liao SH, Liu WZ, Liu T, et al. Potential signaling pathway of hypoxia-inducible factor in lung cancer and its gene polymorphism with lung cancer risk. J Recept Signal Transduct Res 2015;35:233-7. [Crossref] [PubMed]
  34. Ren W, Mi D, Yang K, et al. The expression of hypoxia-inducible factor-1alpha and its clinical significance in lung cancer: a systematic review and meta-analysis. Swiss Med Wkly 2013;143:w13855. [PubMed]
  35. Wang Q, Hu DF, Rui Y, et al. Prognosis value of HIF-1alpha expression in patients with non-small cell lung cancer. Gene 2014;541:69-74. [Crossref] [PubMed]
  36. Ren HT, Wang XJ, Kang HF, et al. Associations between C1772T polymorphism in hypoxia-inducible factor-1alpha gene and breast cancer: a meta-analysis. Med Sci Monit 2014;20:2578-83. [Crossref] [PubMed]
  37. Sun G, Wang Y, Hu W. Correlation between HIF-1alpha expression and breast cancer risk: a meta-analysis. Breast J 2014;20:213-5. [Crossref] [PubMed]
  38. Wang W, He YF, Sun QK, et al. Hypoxia-inducible factor 1alpha in breast cancer prognosis. Clin Chim Acta 2014;428:32-7. [Crossref] [PubMed]
  39. Yin W, Liu G, Lu J, et al. Association of hypoxia-inducible factor-1 with breast cancer risk: A meta-analysis of published studies. Cancer Research 2011;71.
  40. Ni ZH, Liang XJ, Mo JG, et al. Associations of hypoxia inducible factor-1α gene polymorphisms with susceptibility to digestive tract cancers: a case–control study and meta-analysis. Genes and Genomics 2015;37:931-8. [Crossref]
  41. Xu JJ, Zou LY, Yang L, et al. Common polymorphisms in the HIF-1alpha gene confer susceptibility to digestive cancer: a meta-analysis. Genet Mol Res 2014;13:6228-38. [Crossref] [PubMed]
  42. Xu J, Xu L, Li L, et al. HIF-1alpha C1772T polymorphism and gastrointestinal tract cancer risk: a meta-analysis and meta-regression analysis. Genet Test Mol Biomarkers 2013;17:918-25. [Crossref] [PubMed]
  43. Xu J, Xu L, Li LT, et al. HIF1A gene Pro582Ser polymorphism and susceptibility to digestive tract cancers: a meta-analysis of case-control studies. Genet Mol Res 2014;13:5732-44. [Crossref] [PubMed]
  44. Chen Z, He X, Xia W, et al. Prognostic value and clinicopathological differences of HIFs in colorectal cancer: evidence from meta-analysis. PLoS One 2013;8:e80337. [Crossref] [PubMed]
  45. Cao S, Yang S, Wu C, et al. Protein expression of hypoxia-inducible factor-1 alpha and hepatocellular carcinoma: a systematic review with meta-analysis. Clin Res Hepatol Gastroenterol 2014;38:598-603. [Crossref] [PubMed]
  46. Chen J, Li T, Liu Q, et al. Clinical and prognostic significance of HIF-1alpha, PTEN, CD44v6, and survivin for gastric cancer: a meta-analysis. PLoS One 2014;9:e91842. [Crossref] [PubMed]
  47. Jing S, Wang J, Liu Q, et al. Relationship between hypoxia inducible factor-1alpha and esophageal squamous cell carcinoma: a meta analysis. Zhonghua Bing Li Xue Za Zhi 2014;43:593-9. [PubMed]
  48. Lin S, Ma R, Zheng XY, et al. Meta-analysis of immunohistochemical expression of hypoxia inducible factor-1alpha as a prognostic role in gastric cancer. World J Gastroenterol 2014;20:1107-13. [Crossref] [PubMed]
  49. Ping W, Sun W, Zu Y, et al. Clinicopathological and prognostic significance of hypoxia-inducible factor-1alpha in esophageal squamous cell carcinoma: a meta-analysis. Tumour Biol 2014;35:4401-9. [Crossref] [PubMed]
  50. Sun G, Hu W, Zhang J, et al. A meta-analysis of relationship between HIF-1α expression and esophageal squamous cell carinoma. J Chin Oncol 2012;18:686-91.
  51. Ye LY, Zhang Q, Bai XL, et al. Hypoxia-inducible factor 1alpha expression and its clinical significance in pancreatic cancer: a meta-analysis. Pancreatology 2014;14:391-7. [Crossref] [PubMed]
  52. Zhang ZG, Zhang QN, Wang XH, et al. Hypoxia-inducible factor 1 alpha (HIF-1alpha) as a prognostic indicator in patients with gastric tumors: a meta-analysis. Asian Pac J Cancer Prev 2013;14:4195-8. [Crossref] [PubMed]
  53. Zheng SS, Chen XH, Yin X, et al. Prognostic significance of HIF-1alpha expression in hepatocellular carcinoma: a meta-analysis. PLoS One 2013;8:e65753. [Crossref] [PubMed]
  54. Zhu CL, Huang Q, Liu CH, et al. Prognostic value of HIF-1alpha expression in patients with gastric cancer. Mol Biol Rep 2013;40:6055-62. [Crossref] [PubMed]
  55. Yao Q, Lv Y, Pan T, et al. Prognostic significance and clinicopathological features of hypoxic inducible factor-2alpha expression in hepatocellular carcinoma. Saudi Med J 2015;36:170-5. [Crossref] [PubMed]
  56. Zheng F, Du F, Zhao J. Clinicopathological Differences and Prognostic Value of Hypoxia-Inducible Factor-2alpha Expression for Gastric Cancer: Evidence From Meta-Analysis. Medicine (Baltimore) 2016;95:e2871. [Crossref] [PubMed]
  57. Li K, Zhang Y, Dan Z, et al. Association of the hypoxia inducible factor-1alpha gene polymorphisms with gastric cancer in Tibetans. Biochem Genet 2009;47:625-34. [Crossref] [PubMed]
  58. Fan Y, Li H, Ma X, et al. Prognostic Significance of Hypoxia-Inducible Factor Expression in Renal Cell Carcinoma: A PRISMA-compliant Systematic Review and Meta-Analysis. Medicine (Baltimore) 2015;94:e1646. [Crossref] [PubMed]
  59. Li D, Liu J, Zhang W, et al. Association between HIF1A P582S and A588T polymorphisms and the risk of urinary cancers: a meta-analysis. PLoS One 2013;8:e63445. [Crossref] [PubMed]
  60. Tian YJ, Guo Q, Chen ZH, et al. Correlation between HIF-1α protein expression and renal cell cancer risk: A meta-analysis. Chinese Journal of Evidence-Based Medicine 2015;15:1035-41.
  61. Huang M, Chen Q, Xiao J, et al. Overexpression of hypoxia-inducible factor-1alpha is a predictor of poor prognosis in cervical cancer: a clinicopathologic study and a meta-analysis. Int J Gynecol Cancer 2014;24:1054-64. [Crossref] [PubMed]
  62. Jin Y, Wang H, Liang X, et al. Pathological and prognostic significance of hypoxia-inducible factor 1alpha expression in epithelial ovarian cancer: a meta-analysis. Tumour Biol 2014;35:8149-59. [Crossref] [PubMed]
  63. Sun C, Jing S, Wang J, et al. Relationship between hypoxia inducible factor-1α and clinicopathology of epithelial ovarian cancer: Meta-analysis. Carcinogenesis, Teratogenesis & Mutagenesis 2015;27:49-53, 58.
  64. Zhu J, Cheng X, Xie R, et al. Genetic association between the HIF-1alpha P582S polymorphism and cervical cancer risk: a meta analysis. Int J Clin Exp Pathol 2014;7:6085-90. [PubMed]
  65. Jin Y, Wang H, Ma X, et al. Clinicopathological characteristics of gynecological cancer associated with hypoxia-inducible factor 1alpha expression: a meta-analysis including 6,612 subjects. PLoS One 2015;10:e0127229. [Crossref] [PubMed]
  66. Ren HY, Zhang YH, Li HY, et al. Prognostic role of hypoxia-inducible factor-1 alpha expression in osteosarcoma: a meta-analysis. Onco Targets Ther 2016;9:1477-87. [Crossref] [PubMed]
doi: 10.21037/amj.2017.04.08
Cite this article as: Zou D, Han T, Deng H, Shao X, Guo X, Qi X. Hypoxia-inducible factors in cancer: an overview of major findings from meta-analyses. AME Med J 2017;2:48.

Download Citation