Development and validation of RNA binding protein-applied prediction model for gastric cancer.

Abstract

RNA-binding proteins (RBPs) have been reported to be associated with the occurrence and progression of multiple cancers, but the role in gastric adenocarcinoma remains poorly understood. The present study aims to uncover potential RBPs associated with the survival of gastric adenocarcinoma, as well as corresponding biologic properties and signaling pathways of these RBPs. RNA sequencing and clinical data of GC were obtained from The Cancer Genome Atlas (n=373) and the Gene Expression Omnibus (GSE84437, n=433) database. Tumor samples in TCGA were randomly divided into the training and internal testing group by R software. A total of 238 DERBPs were selected for univariate and multivariate Cox regression analyses. Five pivotal RBP genes (RNASE2, METTL1, ANG, YBX2 and LARP6) were screened out and were used to construct a new prognostic model. Survival relevance and prediction accuracy of model were tested via Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in internal and external testing groups. Further analysis has also showed that this model could serve as an independent prognosis-related parameter. A prognostic nomogram has been eventually developed, and presents a good performance of prediction.