Skip to main content
Download PDF
Download ePub
  • Research article
  • Open access
  • Published:

The association between IGF1 gene rs1520220 polymorphism and cancer susceptibility: a meta-analysis based on 12,884 cases and 58,304 controls

Environmental Health and Preventive Medicine volume 23, Article number: 38 (2018) Cite this article

Abstract

Background

The rs1520220 polymorphism in the insulin-like growth factor 1 (IGF1) gene has been reported to affect cancer susceptibly in several studies. However, the results of the relevant studies are inconsistent. We conduct a current meta-analysis to investigate the association between rs1520220 and cancer susceptibly.

Methods

Three databases (PubMed, Embase, and Web of Science) were searched for studies regarding the relationship between rs1520220 and cancer susceptibly. Odds ratios (ORs) and the related 95% confidence intervals (CIs) were employed to assess the strength of the associations. A stratified analysis was performed according to cancer type, ethnicity, and quality score, and when results were obtained from no fewer than two studies, these results were pooled.

Results

There was no positive association between rs1520220 and overall cancer risk. However, the analysis stratified by ethnicity revealed that rs1520220 significantly increased cancer susceptibility in Asian populations (allele model OR = 1.10, 95%Cl = 1.00–1.21, p = 0.040; homozygote model OR = 1.22, 95%Cl = 1.01–1.47, p = 0.040; dominant model OR = 1.19, 95%Cl = 1.01–1.39, p = 0.033). No significantly association was detected in Caucasian populations. The analysis stratified by cancer type suggested that rs1520220 was not associated with susceptibility to breast cancer.

Conclusions

The results of our meta-analysis demonstrate that the role of IGF1 rs1520220 in cancer susceptibility varies by ethnicity and cancer type and that rs1520220 increases cancer susceptibility in Asian populations.

Background

The occurrence of cancer depends on both genetic and environmental factors [1, 2]. Relevant environmental factors include pollution, tobacco and alcohol intake, overweight, and infection [3]. Studies based on twins have found that genetic factors are also an important risk factor for cancer [2, 4]. Recently, the role of SNPs in the occurrence and development of cancer has attracted increasing attention [5]. The SNPs that are associated with cancer risk may act as biomarkers for cancer diagnosis [6, 7].

IGF1 is a growth factor that involves in many important biological and pathological processes [8, 9]. The important functions of IGF1 are promoting cell proliferation and inhibiting apoptosis [10]. IGF1 has also been reported to be involved in cancer development [11]. Plasma IGF1 levels depend on many factors, such as BMI, but gene is also an important factor [12, 13]. Many studies have reported that several IGF1 SNPs affect plasma IGF1 levels and thus influence the risk of cancer [14, 15].

rs1520220 is located in intron 3 of IGF1 gene which might lead to alternative splicing and a subsequent change in protein function [16]. It has been reported that rs1520220 G to C substitution leads to increased plasma IGF1 level, increasing cancer risk as a result [13, 17]. However, the studies regarding the relationship between rs1520220 and cancer susceptibility are inconsistent [18,19,20,21,22,23,24,25]. For instance, Al-Zahrani et al. reported that rs1520220 increased susceptibility to breast cancer [18], but Li et al. suggested that rs1520220 was not related to susceptibility to breast cancer [25]. Considering the disagreement between these studies, we performed a meta-analysis of the associations between rs1520220 and cancer susceptibility to review these results and draw a more accurate conclusion.

Methods

Search strategy

We searched for relevant studies in three databases: PubMed, Embase, and Web of Science. The search conditions limited the language to English and the data of publication prior to February 28, 2018. The following keywords were used: “IGF1 or IGF-1 or insulin-like growth factor 1 or rs1520220,” “cancer or tumor or carcinoma,” and “SNP or polymorphism or variant or mutation.” We also checked the references of the identified articles to ensure that we obtained all potentially relevant studies.

Inclusion and exclusion criteria

The inclusion criteria of this meta-analysis are as follows: studies must (1) concern the relationship between rs1520220 and cancer susceptibility, (2) be case-control or cohort study, and (3) contain sufficient genotyping data to allow for the pooling of the results (the GG, GC, and CC genotype frequencies in the case and control groups were provided directly or could be calculated from the provided data). The exclusion criteria are as follows: (1) when subjects of two studies overlap, the one containing fewer subjects was excluded, and (2) reviews and meta-analyses are excluded.

Data extraction

The following information was extracted from the included studies by two authors independently: first author’s name, year of publication, country, cancer type, ethnicity, genotyping methods, control source, genotype distributions of cases and controls, and Hardy-Weinberg equilibrium (HWE) for controls. Disagreements were resolved via discussion.

Quality score

We assessed the quality of the included studies based on the following five factors [26]: case source, control source, specimens used for determining genotypes, HWE in controls, and total sample size (Additional file 1: Table S1). A perfect score was 15.

Statistical analysis

We estimated the strengths of the associations using pooled ORs with corresponding 95% CIs. Five genetic models are employed: the allele model (C vs. G), the homozygote model (CC vs. GG), the heterozygote model (GC vs. GG), the dominant model (CC + GC vs. GG), and the recessive model (CC vs. GC + GG). The heterogeneity was evaluated using a Q test and quantified by I2 [27]. When heterogeneity not exists (P > 0.1), the fixed-effects model was used [28]. Otherwise, the random-effects model was applied [29]. Hardy-Weinberg equilibrium (HWE) for controls was assessed using a chi-squared test. P values less than 0.05 were considered to indicate significant disequilibrium. Stratified analyses were conducted by ethnicity, cancer type, and quality score. Only results synthesized from no fewer than two studies are shown. Sensitivity analyses were performed via metainf command which investigates the influence of each individual study on the overall meta-analysis summary estimate by omitting each study in turn [30]. Publication bias was assessed using Begg’s test and Egger’s test [31, 32]. All statistical analyses were performed using the STATA software (Version 12.0; Stata Corporation, College Station, TX, USA).

Results

Characteristics of the studies

We obtained 2086 relevant articles through database searching after removing duplicates. Then, by screening the titles and abstracts, we excluded 1953 articles, and 133 articles remained. We read the full texts of these 133 articles and ultimately identified eight articles that meet the inclusion criteria (Fig. 1), which involved 12,884 cases and 58,304 controls. The characteristics of the included studies are shown in Table 1. Among these eight studies, four were carried out in Asian populations, two were carried out in Caucasian populations, and two were carried out in mix populations. Four of them concerned breast cancer, and four concerned other cancers including testicular germ cell tumors (TGCT), stomach cancer, pancreatic cancer, and colorectal cancer. Seven of the included studies had quality scores of no less than 12. The distributions of the genotypes and allele frequencies in the cases and controls are shown in Table 2.

Fig. 1
figure 1

The flow diagram of included/excluded studies

Full size image
Table 1 Characteristics of the studies included in the meta-analysis
Full size table
Table 2 IGF1 rs1520220 polymorphism genotype distribution and allele frequency in cases and controls
Full size table

Meta-analysis

We investigate the role of rs1520220 polymorphisms in cancer susceptibility via pooled OR and 95%Cl. Only results synthesized from no fewer than two studies are shown. In the overall analysis, we did not find positive associations between rs1520220 and cancer susceptibility (Table 3).

Table 3 Meta-analysis of the association between IGF1 rs1520220 polymorphism and cancer susceptibility
Full size table

In the analysis stratified by ethnicity, we found that rs1520220 was significantly associated with increased cancer susceptibility in Asian populations (Table 3 and Fig. 2, allele model OR = 1.10, 95%Cl = 1.00–1.21, p = 0.040; homozygote model OR = 1.22, 95%Cl = 1.01–1.47, p = 0.040; dominant model OR = 1.19, 95%Cl = 1.01–1.39, p = 0.033). Thus, no significant association was detected in Caucasian populations (Table 3 and Fig. 2).

Fig. 2
figure 2

Stratification analyses by ethnicity between IGF1 rs1520220 polymorphism and cancer susceptibility. a Allele model. b Homozygous model, c Heterozygous model. d Dominant model. e Recessive model. The squares and horizontal lines correspond to the study specific OR and 95% CI. The area of the squares reflects the weight. The diamond represents the summary OR and 95% CI. The fixed-effects model was used

Full size image

In the analysis stratified by cancer type, the results show that rs1520220 was not associated with susceptibility to breast cancer (Table 3). Also, the results synthesized from studies that scored no less than 12 did not exhibited any differences from the results of the overall analysis (Table 3).

Sensitivity analysis

We performed sensitive analysis and found there was not an individual study that affected the results of the overall analysis (Fig. 3 and Additional file 1: Table S2), indicating that in this meta-analysis, our results are relatively stable.

Fig. 3
figure 3

Sensitivity analyses between IGF1 rs1520220 polymorphism and cancer susceptibility. a Allele model. b Homozygous model. c Heterozygous model. d Dominant model. e Recessive model. a, b, and e, the random-effects model was used. For c and d, the fixed-effects model was used

Full size image

Publication bias

Begg’s test and Egger’s test were performed to assess the publication bias among the included studies. No publication bias was detected in the present meta-analysis (Fig. 4 and Table 4).

Fig. 4
figure 4

Funnel plot for publication bias test. a Allele model. b Homozygous model. c Heterozygous model. d Dominant model. e Recessive model

Full size image
Table 4 Publication bias analysis
Full size table

Discussion

Many SNPs have been reported to be associated with cancer susceptibility and thus may have the potentiality as biomarkers for clinical diagnosis [5,6,7]. Thus far, with the improvement of living standards in more and more developing countries, obesity and lifestyle-related cancers have been on the increase [33, 34]. IGF1 has been reported to relate to be associated with the cancer susceptibility, especially cancers caused by obesity, due to its important role in cell proliferation [35].

Several IGF1 SNPs have been reported to be associated with cancer susceptibility [36,37,38,39]. These SNPs include rs1520220, rs6214, rs6220, rs35767, and rs5742612. Of these, rs6214 and rs6220 are located in the 3′-UTR region of the IGF1 gene. It has been reported that rs6214 is associated with increased esophageal adenocarcinoma (EAC) and head and neck cancer (HNC) susceptibility in women [39] and that rs6220 is associated with increased prostate cancer susceptibility [40]. The rs35767 and rs5742612 SNPs are located in the promoter region of the IGF1 gene. It has been reported that rs35767 is significantly associated with increased susceptibility of childhood acute lymphoblastic leukemia (ALL) [41] and that rs5742612 is associated with increased susceptibility to prostate cancer [42].

rs1520220 is an SNP that is located in the intron of the IGF1 gene, and it has a minor allele frequency (MAF) about 10~ 40% in the populations included in the human 1000 Genomic Project phase 3 (Additional file 1: Table S3). We paid special attention to rs1520220 because it has been reported to be associated with plasma IGF1 levels in many studies and thus associated with cancer susceptibility [13, 17, 18].

In this meta-analysis, we systematically searched for literature on IGF1 SNPs and cancer in three important databases (PubMed, Embase, and Web of Science). After removing duplicate documents, 2086 related articles were initially obtained, which ensured the maximum possible recall rate. Through meta-analysis, we found that rs1520220 was not related to cancer susceptibility in the overall analysis based on the present epidemiology studies. Thus, in the analysis stratified by ethnicity, we revealed that rs1520220 increased cancer susceptibility in Asian populations.

The present studies regarding the effect of IGF1 rs1520220 polymorphism on serum IGF1 are inconsistent [13, 18]. In brief, rs1520220 may influence circulating IGF1 expression by altering the secondary structure of the RNA or DNA [16], and this effect may be enhanced by dietary factors [43]. Therefore, we infer that rs1520220 affects cancer susceptibility in Asians but not other populations due to the combined effects of genetic and environmental factors. The mechanism via which rs1520220 affects serum levels must be investigated in the future.

Our meta-analysis has several limitations. Firstly, we found that rs1520220 increased cancer susceptibility in Asians. The molecular mechanism via which the rs1520220 C allele increases plasma IGF1 levels and thus cancer risk remains unclear. Secondly, we did not consider potential external factors, such as gender, age, diet, and tobacco and alcohol intake habits or gene-gene interactions. Thirdly, the number of studies included in the meta-analysis is limited. We only included studies written in English, and important-related studies in other languages may have been overlooked.

Conclusion

The present meta-analysis showed that IGF1 rs1520220 is not significantly associated with overall cancer susceptibility. However, we did find that rs1520220 significantly increased cancer susceptibility in Asian populations. We also suggest that rs1520220 was not associated with susceptibility to breast cancer. There is a need for additional well-designed epidemiology and molecular biology studies to verify these conclusions and provide new insights into the role of SNPs in the etiology of cancer.

Abbreviations

3′-UTR:

three prime untranslated region

ALL:

Acute lymphoblastic leukemia

CI:

Confidence interval

EAC:

Esophageal adenocarcinoma

HB:

Hospital-based

HNC:

Head and neck cancer

HWE:

Hardy-Weinberg equilibrium

IGF1:

Insulin-like growth factor 1

MAF:

Minor allele frequency

OR:

Odds ratio

PB:

Population-based

SNP:

Single nucleotide polymorphism

TGCT:

Testicular germ cell tumors

References

  1. Ekman P. Genetic and environmental factors in prostate cancer genesis: identifying high-risk cohorts. Eur Urol. 1999;35:362–9. https://doi.org/10.1159/000019910.

    Article  PubMed  CAS  Google Scholar 

  2. Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, Pukkala E, Skytthe A, Hemminki K. Environmental and heritable factors in the causation of cancer––analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000;343:78–85. https://doi.org/10.1056/NEJM200007133430201.

    Article  PubMed  CAS  Google Scholar 

  3. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108. https://doi.org/10.3322/caac.21262.

    Article  PubMed  Google Scholar 

  4. Sturm RA. Skin colour and skin cancer––MC1R, the genetic link. Melanoma Res. 2002;12:405–16.

    Article  PubMed  CAS  Google Scholar 

  5. Lei H, Hemminki K, Johansson R, Altieri A, Enquist K, Henriksson R, Lenner P, Forsti A. PAI-1-675 4G/5G polymorphism as a prognostic biomarker in breast cancer. Breast Cancer Res Treat. 2008;109:165–75. https://doi.org/10.1007/s10549-007-9635-3.

    Article  PubMed  CAS  Google Scholar 

  6. Zhu DQ, Zou Q, Hu CH, Su JL, Zhou GH, Liu P. XRCC1 genetic polymorphism acts a potential biomarker for lung cancer. Tumour Biol. 2015;36:3745–50. https://doi.org/10.1007/s13277-014-3014-6.

    Article  PubMed  CAS  Google Scholar 

  7. Matsuoka H, Makimura C, Koyama A, Fujita Y, Tsurutani J, Sakai K, Sakamoto R, Nishio K, Nakagawa K. Prospective replication study implicates the catechol-O-methyltransferase Val(158)Met polymorphism as a biomarker for the response to morphine in patients with cancer. Biomed Rep. 2017;7:380–4. https://doi.org/10.3892/br.2017.963.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tao Y, Pinzi V, Bourhis J, Deutsch E. Mechanisms of disease: signaling of the insulin-like growth factor 1 receptor pathway––therapeutic perspectives in cancer. Nat Clin Pract Oncol. 2007;4:591–602. https://doi.org/10.1038/ncponc0934.

    Article  PubMed  CAS  Google Scholar 

  9. Pollak M. Insulin and insulin-like growth factor signalling in neoplasia. Nat Rev Cancer. 2008;8:915–28. https://doi.org/10.1038/nrc2536.

    Article  PubMed  CAS  Google Scholar 

  10. Pollak MN, Schernhammer ES, Hankinson SE. Insulin-like growth factors and neoplasia. Nat Rev Cancer. 2004;4:505–18. https://doi.org/10.1038/nrc1387.

    Article  PubMed  CAS  Google Scholar 

  11. Samani AA, Yakar S, LeRoith D, Brodt P. The role of the IGF system in cancer growth and metastasis: overview and recent insights. Endocr Rev. 2007;28:20–47. https://doi.org/10.1210/er.2006-0001.

    Article  PubMed  CAS  Google Scholar 

  12. Chen WF, Wong MS. Genistein enhances insulin-like growth factor signaling pathway in human breast cancer (MCF-7) cells. J Clin Endocrinol Metab. 2004;89:2351–9. https://doi.org/10.1210/jc.2003-032065.

    Article  PubMed  CAS  Google Scholar 

  13. Wang Q, Liu L, Li H, Tao P, Qi Y, Li J. Effects of high-order interactions among IGFBP-3 genetic polymorphisms, body mass index and soy isoflavone intake on breast cancer susceptibility. PLoS One. 2016;11:e0162970. https://doi.org/10.1371/journal.pone.0162970.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Tempfer CB, Hefler LA, Schneeberger C, Huber JC. How valid is single nucleotide polymorphism (SNP) diagnosis for the individual risk assessment of breast cancer? Gynecol Endocrinol. 2006;22:155–9. https://doi.org/10.1080/09513590600629175.

    Article  PubMed  CAS  Google Scholar 

  15. Poole EM, Tworoger SS, Hankinson SE, Baer HJ. Genetic variability in IGF-1 and IGFBP-3 and body size in early life. BMC Public Health. 2012;12:659. https://doi.org/10.1186/1471-2458-12-659.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Lu L, Risch E, Deng Q, Biglia N, Picardo E, Katsaros D, Yu H. An insulin-like growth factor-II intronic variant affects local DNA conformation and ovarian cancer survival. Carcinogenesis. 2013;34:2024–30. https://doi.org/10.1093/carcin/bgt168.

    Article  PubMed  CAS  Google Scholar 

  17. Gu F, Schumacher FR, Canzian F, Allen NE, Albanes D, Berg CD, Berndt SI, Boeing H, Bueno-de-Mesquita HB, Buring JE, et al. Eighteen insulin-like growth factor pathway genes, circulating levels of IGF-I and its binding protein, and risk of prostate and breast cancer. Cancer Epidemiol Biomark Prev. 2010;19:2877–87. https://doi.org/10.1158/1055-9965.EPI-10-0507.

    Article  CAS  Google Scholar 

  18. Al-Zahrani A, Sandhu MS, Luben RN, Thompson D, Baynes C, Pooley KA, Luccarini C, Munday H, Perkins B, Smith P, et al. IGF1 and IGFBP3 tagging polymorphisms are associated with circulating levels of IGF1, IGFBP3 and risk of breast cancer. Hum Mol Genet. 2006;15:1–10. https://doi.org/10.1093/hmg/ddi398.

    Article  PubMed  CAS  Google Scholar 

  19. Chia VM, Sakoda LC, Graubard BI, Rubertone MV, Chanock SJ, Erickson RL, McGlynn KA. Risk of testicular germ cell tumors and polymorphisms in the insulin-like growth factor genes. Cancer Epidemiol Biomark Prev. 2008;17:721–6. https://doi.org/10.1158/1055-9965.EPI-07-0768.

    Article  CAS  Google Scholar 

  20. Patel AV, Cheng I, Canzian F, Le Marchand L, Thun MJ, Berg CD, Buring J, Calle EE, Chanock S, Clavel-Chapelon F, et al. IGF-1, IGFBP-1, and IGFBP-3 polymorphisms predict circulating IGF levels but not breast cancer risk: findings from the breast and prostate cancer cohort consortium (BPC3). PLoS One. 2008;3:e2578. https://doi.org/10.1371/journal.pone.0002578.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Ennishi D, Shitara K, Ito H, Hosono S, Watanabe M, Ito S, Sawaki A, Yatabe Y, Yamao K, Tajima K, et al. Association between insulin-like growth factor-1 polymorphisms and stomach cancer risk in a Japanese population. Cancer Sci. 2011;102:2231–5. https://doi.org/10.1111/j.1349-7006.2011.02062.x.

    Article  PubMed  CAS  Google Scholar 

  22. Nakao M, Hosono S, Ito H, Watanabe M, Mizuno N, Yatabe Y, Yamao K, Ueda R, Tajima K, Tanaka H, et al. Interaction between IGF-1 polymorphisms and overweight for the risk of pancreatic cancer in Japanese. Int J Mol Epidemiol Genet. 2011;2:354–66.

    PubMed  PubMed Central  CAS  Google Scholar 

  23. Qian B, Zheng H, Yu H, Chen K. Genotypes and phenotypes of IGF-I and IGFBP-3 in breast tumors among Chinese women. Breast Cancer Res Treat. 2011;130:217–26. https://doi.org/10.1007/s10549-011-1552-9.

    Article  PubMed  CAS  Google Scholar 

  24. Simons CC, Schouten LJ, Godschalk RW, van Engeland M, van den Brandt PA, van Schooten FJ, Weijenberg MP. Genetic variants in the insulin-like growth factor pathway and colorectal cancer risk in the Netherlands cohort study. Sci Rep. 2015;5:14126. https://doi.org/10.1038/srep14126.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Li H, Zhao M, Wang Q, Liu L, Qi YN, Li JY. Genetic polymorphisms of insulin-like growth factor 1 and insulin-like growth factor binding protein 3, xenoestrogen, phytoestrogen, and premenopausal breast cancer. Curr Oncol. 2016;23:e17–23. https://doi.org/10.3747/co.23.2835.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Tian X, Dai S, Sun J, Jiang S, Jiang Y. Association between TP53 Arg72Pro polymorphism and leukemia risk: a meta-analysis of 14 case-control studies. Sci Rep. 2016;6 https://doi.org/10.1038/srep24097.

  27. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58. https://doi.org/10.1002/sim.1186.

    Article  PubMed  Google Scholar 

  28. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–48.

    PubMed  CAS  Google Scholar 

  29. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88.

    Article  PubMed  CAS  Google Scholar 

  30. Steichen T. METANINF: Stata module to evaluate influence of a single study in meta-analysis estimation, Statistical Software Components S419201, Boston College Department of Economics. 2001. https://ideas.repec.org/c/boc/bocode/s419201.html.

  31. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–101.

    Article  PubMed  CAS  Google Scholar 

  32. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Hendriks SH, Schrijnders D, van Hateren KJ, Groenier KH, Siesling S, Maas A, Landman GWD, Bilo HJG, Kleefstra N. Association between body mass index and obesity-related cancer risk in men and women with type 2 diabetes in primary care in the Netherlands: a cohort study (ZODIAC-56). BMJ Open. 2018;8:e018859. https://doi.org/10.1136/bmjopen-2017-018859.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Tarasiuk A, Mosinska P, Fichna J. The mechanisms linking obesity to colon cancer: an overview. Obes Res Clin Pract. 2018; https://doi.org/10.1016/j.orcp.2018.01.005.

  35. LeRoy EC, Moore JH, Hu C, Martinez ME, Lance P, Duggan D, Thompson PA. Genes in the insulin and insulin-like growth factor pathway and odds of metachronous colorectal neoplasia. Hum Genet. 2011;129:503–12. https://doi.org/10.1007/s00439-010-0942-0.

    Article  PubMed  CAS  Google Scholar 

  36. Birmann BM, Tamimi RM, Giovannucci E, Rosner B, Hunter DJ, Kraft P, Mitsiades C, Anderson KC, Colditz GA. Insulin-like growth factor-1- and interleukin-6-related gene variation and risk of multiple myeloma. Cancer Epidemiol Biomark Prev. 2009;18:282–8. https://doi.org/10.1158/1055-9965.EPI-08-0778.

    Article  CAS  Google Scholar 

  37. Feik E, Baierl A, Hieger B, Fuhrlinger G, Pentz A, Stattner S, Weiss W, Pulgram T, Leeb G, Mach K, et al. Association of IGF1 and IGFBP3 polymorphisms with colorectal polyps and colorectal cancer risk. Cancer Causes Control. 2010;21:91–7. https://doi.org/10.1007/s10552-009-9438-4.

    Article  PubMed  Google Scholar 

  38. Dong X, Li Y, Tang H, Chang P, Hess KR, Abbruzzese JL, Li D. Insulin-like growth factor axis gene polymorphisms modify risk of pancreatic cancer. Cancer Epidemiol. 2012;36:206–11. https://doi.org/10.1016/j.canep.2011.05.013.

    Article  PubMed  CAS  Google Scholar 

  39. Ong J, Salomon J, te Morsche RH, Roelofs HM, Witteman BJ, Dura P, Lacko M, Peters WH. Polymorphisms in the insulin-like growth factor axis are associated with gastrointestinal cancer. PLoS One. 2014;9:e90916. https://doi.org/10.1371/journal.pone.0090916.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Johansson M, McKay JD, Wiklund F, Rinaldi S, Verheus M, van Gils CH, Hallmans G, Balter K, Adami HO, Gronberg H, et al. Implications for prostate cancer of insulin-like growth factor-I (IGF-I) genetic variation and circulating IGF-I levels. J Clin Endocrinol Metab. 2007;92:4820–6. https://doi.org/10.1210/jc.2007-0887.

    Article  PubMed  CAS  Google Scholar 

  41. Chokkalingam AP, Metayer C, Scelo G, Chang JS, Schiffman J, Urayama KY, Ma X, Hansen HM, Feusner JH, Barcellos LF, et al. Fetal growth and body size genes and risk of childhood acute lymphoblastic leukemia. Cancer Causes Control. 2012;23:1577–85. https://doi.org/10.1007/s10552-012-0035-6.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Qian J, Zhou H, Chen J, Ding Q, Cao Q, Qin C, Shao P, Li P, Cai H, Meng X, et al. Genetic polymorphisms in IGF-I and IGFBP-3 are associated with prostate cancer in the Chinese population. PLoS One. 2014;9:e85609. https://doi.org/10.1371/journal.pone.0085609.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Wang Q, Liu L, Li H, McCullough LE, Qi YN, Li JY, Zhang J, Miller E, Yang CX, Smith JS. Genetic and dietary determinants of insulin-like growth factor (IGF)-1 and IGF binding protein (BP)-3 levels among Chinese women. PLoS One. 2014;9:e108934. https://doi.org/10.1371/journal.pone.0108934.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Funding

This work was supported by grants from the National Natural Science Foundation of China (grant no. 81601826).

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Author information

Authors and Affiliations

  1. Transfusion Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Gui-Ping Xu

  2. Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China

    Wei-Xian Chen & Li-Fang Wu

  3. Department of Oncology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Wen-Yue Xie

Authors
  1. Gui-Ping Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-Xian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-Yue Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li-Fang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GPX and LFW conceived and designed the experiments. GPX and LFW performed the experiments. GPX, LFW, and WYX analyzed the data. LFW and WXC contributed the reagents/materials/analysis tools. LFW and GPX wrote the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Li-Fang Wu.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional file

Additional file 1:

Table S1. Quality score assessment. Table S2. Sensitivity analyses for IGF1 rs1520220 polymorphism and cancer susceptibility. Table S3. MAFs of IGF1 rs1520220 polymorphism in the populations from the 1000 Genomes Project Phase 3. (DOCX 30 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, GP., Chen, WX., Xie, WY. et al. The association between IGF1 gene rs1520220 polymorphism and cancer susceptibility: a meta-analysis based on 12,884 cases and 58,304 controls. Environ Health Prev Med 23, 38 (2018). https://doi.org/10.1186/s12199-018-0727-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12199-018-0727-y

Keywords

Environmental Health and Preventive Medicine

ISSN: 1347-4715

Contact us