Prognostic model based on m6A-associated lncRNAs in esophageal cancer

This research aimed to build an m6A-associated lncRNA prognostic model of esophageal cancer that can be used to predict outcome in esophageal cancer patients.

RNA sequencing transcriptome data and clinical information about patients with esophageal cancer were obtained according to TCGA. Twenty-four m6A-associated genes were selected based on previous studies. m6A-associated lncRNAs were determined through Pearson correlation analysis. Three m6A-associated lncRNA prognostic signatures were built through analysis of the training set using univariate, LASSO, and multivariate Cox regression. To validate the stabilization of the risk signature, Kaplan-Meier and ROC curve analyses were performed on the testing and complete sets. The prognoses of EC patients were predicted quantitatively by building a nomogram. GSEA was conducted to analyze the underlying signaling pathways and biological processes. To identify the underlying mechanisms through which the lncRNAs act, we constructed a PPI network and a ceRNA network and conducted GO and KEGG pathway analyses. EC samples were evaluated using the ESTIMATE algorithm to compute stromal, immune, and estimate scores. The ssGSEA algorithm was used to quantitatively infer immune cell infiltration and immune functions. The TIDE algorithm was performed to simulate immune evasion and predict the response to immunotherapy.

We identified and validated an m6A-associated lncRNA risk model in EC that could correctly and reliably predict the OS of EC patients. The ceRNA network, PPI network, and GO and KEGG pathway analyses confirmed and the underlying mechanisms and functions provided enlightenment regarding therapeutic strategies for EC. Immunotherapy responses were better in the low-risk subgroup, and PD-1 and CTLA4 checkpoint immunotherapy benefited the patients in the low-risk subgroup.

We constructed a new m6A-related lncRNA prognostic risk model of EC, based on three m6A-related lncRNAs: LINC01612, AC025166.1 and AC016876.2, that can predict the prognoses of EC patients.