Comprehensive Analysis of the Molecular Mechanism for Gastric Cancer Based on Competitive Endogenous RNA Network

作     者：Hong-Jin Wu Wei-Wei Dai Li-Bo Wang Jie Zhang Cheng-Long Wang Hong-Jin Wu;Wei-Wei Dai;Li-Bo Wang;Jie Zhang;Cheng-Long Wang

作者机构：Central Laboratory for Science and TechnologyLonghua Hospital Shanghai University of Traditional Chinese MedicineShanghaiChina 

出 版 物：《World Journal of Traditional Chinese Medicine》 (世界中医药杂志（英文）)

年 卷 期：2023年第9卷第1期

页      面：29-42页

核心收录：

学科分类：1007[医学-药学(可授医学、理学学位)] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by the Shanghai Natural Science Foundation of China(Grant No.16ZR1447300)。 

主　　题：Competitive endogenous RNAs network gastric cancer prognostic biomarkers The Cancer Genome Atlas 

摘      要：Objective: To explore the regulatory mechanism of competitive endogenous RNAs(ce RNA) in gastric cancer(GC) and to predict the prognosis of GC. Materials and Methods: Expression profiles of long noncoding RNAs(lnc RNAs), micro RNAs(mi RNAs), and m RNAs were obtained from The Cancer Genome Atlas platform. Differentially expressed RNAs(DERNAs) were screened to construct a lnc RNA-mi RNA-m RNA ce RNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the ce RNA network-related differentially expressed m RNAs(DEm RNAs). Next, the DERNAs were subjected to Cox regression and survival analyses to identify crucial prognostic factors for patients with GC. Results: We detected 1029 differentially expressed lnc RNAs, 104 differentially expressed mi RNAs, and 1659 DEm RNAs in patients with GC. Next, we performed bioinformatic analysis to construct the lnc RNA-mi RNA-m RNA ce RNA network, which included 10 mi RNAs, 65 lnc RNAs, and 10 m RNAs. Subsequently, Kaplan Meier(K-M) analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group, and the area under the curve value of the receiver operating characteristic curve revealed that the polygenic model had good predictive ability. The results indicated that ADAMTS9-AS1, ATAD2, and CADM2 might be potential therapeutic targets and prognostic biomarkers for GC. Conclusions: Our study has implications for predicting prognosis and monitoring surveillance of GC and provides a new theoretical and experimental basis for the clinical prognosis of GC.