Associations between non-coding RNAs genetic polymorphisms with ovarian cancer risk: A systematic review and meta-analysis update with trial sequential analysis

Abstract

Background: This systemic review and meta-analysis seeks to systematically analyze and summarize the association between non-coding RNA polymorphisms and ovarian cancer risk. Methods: We searched PubMed, Web of Science and CNKI for available articles on non-coding RNA polymorphisms in patients with ovarian cancer from inception to March 1, 2023. The quality of each study included in the meta-analysis was rated according to the Newcastle–Ottawa Scale. Odds ratios (ORs) with their 95% confidence intervals (95% CI) were used to assess associations. Chi-square Q-test combined with inconsistency index (I2) was used to test for heterogeneity among studies. Lastly, trial sequential analysis (TSA) software was used to verify the reliability of meta-analysis results, and in-silico miRNA expression were also performed. The meta-analysis was registered with PROSPERO (No. CRD42023422091). Results: A total of 17 case-control studies with 18 SNPs were selected, including 2 studies with H19 rs2107425 and HOTAIR rs4759314, and 5 studies with miR-146a rs2910164 and miR-196a rs11614913. Significant associations were found between H19 rs2107425, miR-146a rs2910164, and miR-196a rs11614913 and ovarian cancer risk. Three genetic models of H19 rs2107425 (CT vs TT (heterozygote model): OR = 1.36, 95% CI = 1.22–1.52, P < .00001; CC + CT vs TT (dominant model): OR = 1.12, 95% CI = 1.02–1.24, P = .02; and CC vs CT + TT (recessive model): OR = 1.23, 95% CI = 1.16–1.31, P < .00001), 2 genetic models of miR-146a rs2910164 (allele model: OR = 1.75, 95% CI = 1.05–2.91, P = .03; and heterozygote model: OR = 0.33, 95% CI = 0.11–0.98, P = .05), 3 genetic models of miR-196a rs11614913 (allele model: OR = 0.70, 95% CI = 0.59–0.82, P < .0001; dominant model: OR = 1.62, 95% CI = 1.18–2.24, P = .0001; and recessive model: OR = 0.70, 95% CI = 0.57–0.87, P = .03) were statistically linked to ovarian cancer risk. Subgroup analysis for miR-146a rs2910164 was performed according to ethnicity. No association was found in any genetic model. The outcomes of TSA also validated the findings of this meta-analysis. Conclusion: This study summarizes that H19 rs2107425, miR-146a rs2910164, and miR-196a rs11614913 polymorphisms are significantly linked with the risk of ovarian cancer, and moreover, large-scale and well-designed studies are needed to validate our result.