The relationship between polymorphism of IGF2BP2 gene rs4402960 and risk of pan-cancer: a meta-analysis and a bioinformatics analysis

Abstract

Objective

To conduct a meta-analysis and a bioinformatics analysis to assess the relationship between IGF2BP2 gene polymorphism and pan-cancer risk.

Methods

PubMed, EMBASE, and Web of Science were conducted to literature searches. The heterogeneity test was used in five genetic models. Odds ratios (OR), 95% confidence intervals (CI), and p-values were used to evaluate the combined effects of various genetic models. Subgroup analysis and Meta-regression analysis were used to analyze the characteristics of heterogeneity. Sensitivity analysis and publication bias were also performed. Transcriptomic information on IGF2BP2 was downloaded and analyzed from the TCGA and GTEx databases. GEPIA (http://gepia.cancer-pku.cn/) was performed to analyze the relationship between IGF2BP2 expression and cancer tissue.

Results

This meta-analysis contained 7 case–control studies, with 5,908 cases and 7,890 controls. There were significant differences in the heterozygous genetic model of IGF2BP2 gene rs4402960 polymorphism (OR = 1.080, 95% CI = 1.003–1.163, p = 0.041). In subgroup analysis based on ethnicity, There was a statistical significant association in Chinese (heterozygous: OR = 1.110, 95% CI = 1.010–1.220, p = 0.030). Bioinformatics analysis found that IGF2BP2 was over-expressed in pan-cancer (p < 0.01). In addition, the Kaplan-Meier estimate showed that there is statistical significance of OS between the low and high IGF2BP2 TPM groups in Lung adenocarcinoma (p <0.001).

Conclusions

To sum up, IGF2BP2 gene polymorphism may be related to cancer risk. IGF2BP2 has diagnostic value in the diagnosis and treatment of pan-cancer.

Keywords:

• IGF2BP2
• genetic polymorphism
• pan-cancer
• meta-analysis
• bioinformatic analysis

Acknowledgments

The results here are based on data generated by the TCGA research network.

Authors’ contributions

Fengke Lu designed the meta-analysis and bioinformatic analysis; Gan Gao and Hongyu Zhang collected and analyzed the data; Wei Zhang performed the quality assessment; Fengke Lu wrote the article; and all the authors approved submission.

Disclosure statement

The authors claim no competing economic interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Data availability statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.