Comprehensive analysis of competitive endogenous RNAs network: Identification and validation of prediction model composed of mRNA signature and miRNA signature in gastric cancer

Abstract

Gastric cancer (GC), one of the most lethal malig‑ nant tumors, is highly aggressive with a poor prognosis, while the molecular mechanisms underlying it remain largely unknown. Although advanced imaging techniques and comprehensive treatment facilitate the diagnosis and survival of some GC patients, the precise diagnosis and prognosis are still a challenge. The present study used publicly available gene expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus datasets including mRNA, micro (mi)RNA and circular (circ)RNA of GC to establish a competing endogenous RNA network (ceRNA). Further, the present study performed least absolute shrinkage and selector operator regression analysis on the hub RNAs to establish a prediction model with mRNA and miRNA. The ceRNA network contained 109 edges and 56 nodes and the visible network contains 13 miRNAs, 9 circRNAs and 34 mRNAs. The five mRNA‑based signature were CTF1, FKBP5, RNF128, GSTM2 and ADAMTS1. The area under curve (AUC) value of the diagnosis training cohort was 0.9975. The prognosis of the high‑risk group (RiskScore >4.664) was worse compared with that of the low‑risk group (RiskScore ≤4.664; P<0.05) in the training cohort. The five miRNA‑based signature were miR‑145‑5p, miR‑615‑3p, miR‑6507‑5p, miR‑937‑3p and miR‑99a‑3p. The AUC value of the diagnosis training cohort was 0.9975. The prognosis of the high‑risk group (RiskScore >1.621) was worse compared with that of the low‑risk group (RiskScore ≤1.621; P<0.05) in the training cohort. The validation cohorts indicated that both five mRNA