Genome-wide identification of a novel miRNA-based signature to predict recurrence in patients with gastric cancer.

ABSTRACT: The current tumor node metastasis (TNM) staging system is inadequate for identifying high-risk gastric cancer (GC) patients. Using a systematic and comprehensive-biomarker discovery and validation approach, we attempted to build a microRNA (miRNA)-recurrence classifier (MRC) to improve the prognostic prediction of GC. We identified 312 differentially expressed miRNAs in 446 GC tissues compared to 45 normal controls by analyzing high-throughput data from The Cancer Genome Atlas (TCGA). Using a Cox regression model, we developed an 11-miRNA signature that could successfully discriminate high-risk patients in the training set (n = 372; P < 0.0001). Quantitative real-time polymerase chain reaction-based validation in an independent clinical cohort (n = 88) of formalin-fixed paraffin-embedded c

SUBMITTER: Yang Y 

PROVIDER: S-EPMC6275280 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature