Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer

Abstract

In recent years, molecular and genetic research hotspots of gastric cancer have been investigated, including microRNAs, long noncoding RNAs (lncRNAs) and messenger RNA (mRNAs). The study on the role of lncRNAs may help to develop personalized treatment and identify potential prognostic biomarkers in gastric cancer. The RNA-seq and miRNA-seq data of gastric cancer were downloaded from the TCGA database. Differential analysis of RNA expression between gastric cancer samples and normal samples was performed using the edgeR package. The ceRNA regulatory network was visualized using Cytoscape. KEGG pathway analysis of mRNAs in the ceRNA network was performed using the clusterProfiler package. CIBERSORT was used to distinguish 22 immune cell types and the prognosis-related genes and immune cells were determined using Kaplan-Meier and Cox proportional hazard analyses. To estimate these nomograms, we used receiver operating characteristic and calibration curve studies. The ceRNA regulation network of gastric cancer was built in this study, and the genes in the network were analyzed for prognosis. A total of 980 lncRNAs were differentially expressed, of which 774 were upregulated and 206 were downregulated. A survival study identified 15 genes associated with gastric cancer prognosis, including VCAN-AS1, SERPINE1, AL139002.1, LINC00326, AC018781.1, C15orf54, hsa-miR-145. Monocytes and Neutrophils were associated with the survival rate of gastric cancer. Our research uncovers new ceRNA network for the detection, treatment, and monitoring of gastric cancer.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

“…Among them, in the ceRNA network, the lncRNAs VCAN-AS1, AL139002.1, LINC00326, AC018781.1, and C15orf54 acted as hub nodes. In particular, this study also found an association between TIME, (including monocytes, and neutrophils) and lncRNA-based ceRNA regulatory network [ 45 ]. In recent years, with the rapid progress of artificial intelligence, deep learning has been continuously developing.…”

“…Competitive endogenous RNAs (ceRNA) is a novel regulatory mechanism that has been found to be involved in a variety of progression processes in cancer [ 116 ]. Integrative multi-omics strategies using Cytoscape were applied to examine the relationship between the prognosis and biomarkers in the STAD-linked ceRNA network by Kaplan–Meier survival analysis [ 45 ]. Fifteen genes were associated with the outcome of GC patients, involving AC011374.1, AC018781.1, ADAMTS9-AS1, AL139002.1, AL391152.1, HOTTIP, FLRT1, NKX2-1-AS1, ADAMTS9-AS2, LINC00326, VCAN-AS1, SERPINE1, POU6F2-AS2, IGF2-AS, and miR-145.…”