Yongqiang Wang1, Huimin Zhang2, Ju Wang1. 1. Department of Gastrointestinal Surgery, Inner Mongolia People's Hospital, Hohhot, China. 2. Department of gastroenterology, Inner Mongolia People's Hospital, Hohhot, China.
Abstract
BACKGROUND: Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play a predictive role in the prognosis of gastric cancer (GC). The present study aims to construct a lncRNA-based model via mining data of The Cancer Genome Atlas (TCGA). METHODS: Differentially expressed lncRNAs were first identified, followed by univariate Cox analysis, Robust likelihood-based survival model and multivariate Cox analysis to construct a signature composed of lncRNAs. RESULTS: A three-lncRNA based predictive signature (OVAAL, FLJ16779, FAM230D) was established to stratify GC patients into high- and low-risk groups. Patients in the high-risk group had markedly shorter overall survival (OS) than those in the low-risk group, which was verified by the ROC curve. Then, we validated the predictive power of the scoring system in other two cohorts. Multivariate Cox analysis also indicated that the 3-lncRNA signature was an independent prognostic factor for survival prediction in GC patients. Moreover, Gene Set Enrichment Analysis (GSEA) revealed that diverse metabolic pathways significantly clustered in the low-risk group, which might explain how the 3-lncRNA signature promoted gastric carcinogenesis. CONCLUSIONS: We established a robust three-lncRNA model to predict the OS of GC patients, which might benefit the clinical decision making for personalized treatment and prognostic prediction for GC patients. 2020 Journal of Gastrointestinal Oncology. All rights reserved.
BACKGROUND: Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play a predictive role in the prognosis of gastric cancer (GC). The present study aims to construct a lncRNA-based model via mining data of The Cancer Genome Atlas (TCGA). METHODS: Differentially expressed lncRNAs were first identified, followed by univariate Cox analysis, Robust likelihood-based survival model and multivariate Cox analysis to construct a signature composed of lncRNAs. RESULTS: A three-lncRNA based predictive signature (OVAAL, FLJ16779, FAM230D) was established to stratify GC patients into high- and low-risk groups. Patients in the high-risk group had markedly shorter overall survival (OS) than those in the low-risk group, which was verified by the ROC curve. Then, we validated the predictive power of the scoring system in other two cohorts. Multivariate Cox analysis also indicated that the 3-lncRNA signature was an independent prognostic factor for survival prediction in GC patients. Moreover, Gene Set Enrichment Analysis (GSEA) revealed that diverse metabolic pathways significantly clustered in the low-risk group, which might explain how the 3-lncRNA signature promoted gastric carcinogenesis. CONCLUSIONS: We established a robust three-lncRNA model to predict the OS of GC patients, which might benefit the clinical decision making for personalized treatment and prognostic prediction for GC patients. 2020 Journal of Gastrointestinal Oncology. All rights reserved.
Entities:
Keywords:
Gastric carcinoma (GC); biomarker; gene set enrichment analysis; long non-coding RNA (lncRNAs)
Authors: Narasimha M Beeraka; Hao Gu; Nannan Xue; Yang Liu; Huiming Yu; Junqi Liu; Kuo Chen; Vladimir N Nikolenko; Ruitai Fan Journal: Exp Biol Med (Maywood) Date: 2022-01-22