A novel m7G-related lncRNA risk model for predicting prognosis and evaluating the tumor immune microenvironment in colon carcinoma

Abstract

N7-Methylguanosine (m7G) modifications are a common type of posttranscriptional RNA modifications. Its function in the tumor microenvironment (TME) has garnered widespread focus in the past few years. Long non-coding RNAs (lncRNAs) played an essential part in tumor development and are closely associated with the tumor immune microenvironment. In this study, we employed a comprehensive bioinformatics approach to develop an m7G-associated lncRNA prognostic model based on the colon adenocarcinoma (COAD) database from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation analysis was performed to identify m7G-related lncRNAs. Differential gene expression analysis was used to screen lncRNAs. Then, we gained 88 differentially expressed m7G-related lncRNAs. Univariate Cox analysis and Lasso regression analysis were performed to build an eight-m7G-related-lncRNA (ELFN1-AS1, GABPB1-AS1, SNHG7, GS1-124K5.4, ZEB1-AS1, PCAT6, C1RL-AS1, MCM3AP-AS1) risk model. Consensus clustering analysis was applied to identify the m7G-related lncRNA subtypes. We also verified the risk prediction effect of a gene signature in the GSE17536 test set (177 patients). A nomogram was constructed to predict overall survival rates. Furthermore, we analyzed differentially expressed genes (DEGs) between high-risk and low-risk groups. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with the analyzed DEGs. At last, single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, MCP-COUNTER, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were utilized to discover the relationship between the risk model and the TME. Consequently, the m7G-related lncRNA risk model for COAD patients could be a viable prognostic tool and treatment target.