Network analysis of miRNA targeting m6A-related genes in patients with esophageal cancer

Background: We investigated the miRNA-m6A related gene network and identified a miRNA-based prognostic signature in patients with esophageal cancer using integrated genomic analysis.

Methods: We obtained expression data for m6A-related genes and miRNAs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Survival analysis was conducted to identify potential prognostic biomarkers. LASSO Cox regression was performed to construct the overall survival (OS) associated prediction signature. We used the Kaplan-Meier (K-M) curve and receiver operating characteristic (ROC) curves to explore the signature's efficiency and accuracy. Interactions between the m6A-related genes and miRNAs were identified in starBase3.0 and used to construct the miRNA-m6A related gene network.

Results: We found that HNRNPC, YTHDF, ZC3H13, YTHDC2, and METTL14 were dysregulated in esophageal cancer tissues. Multivariate Cox regression analysis revealed that HNRNPC may be an independent risk factor for OS. Five hundred twenty-two potential upstream miRNAs were obtained from starBase3.0. Four miRNAs (miR-186, miR-320c, miR-320d, and miR-320b) were used to construct a prognostic signature, which could serve as a prognostic predictor independent from routine clinicopathological features. Finally, we constructed a key miRNA-m6A related gene network and used one m6A-related gene and four miRNAs associated with the prognosis. The results of our bioinformatics analysis were successfully validated in the human esophageal carcinoma cell lines KYSE30 and TE-1.

Conclusion: Our study identified a 4-miRNA prognostic signature and established a key miRNA-m6A related gene network. These tools may reliably assist with esophageal cancer patient prognosis.