Characterization of the Prognostic m6A-Related lncRNA Signature in Gastric Cancer

N6-methyladenosine (m(6)A) is a common form of mRNA modification regulated by m6A RNA methylation regulators and play an important role in the progression of gastric cancer (GC). However, the prognostic role of m(6)A-related lncRNA in gastric cancer has not been fully explored. This study aims at exploring the biological function and prognostic roles of the m(6)A-related lncRNA signature in gastric cancer. A total of 800 m6A-related lncRNAs were identified through Pearson correlation analysis between m6A regulators and all lncRNAs. Eleven m6A-related lncRNA signatures were identified through a survival analysis and the Kaplan-Meier (KM) curve analysis results suggest that patients in the low-risk group have a better overall survival (OS) and disease-free survival (DFS) outcome than the high-risk group. Also, the lncRNA signature can serve as an independent prognostic factor for OS and DFS. The gene set enrichment analysis (GSEA) result suggests that patients in the high-risk group were mainly enriched in the ECM receptor interaction, focal adhesion, and cytokine-cytokine receptor interaction pathway, while the low-risk group was characterized by the base excision repair pathway. We further constructed an individualized prognostic prediction model via the nomogram based on the independent prognostic factor for the OS and DFS, respectively. In addition, some candidate drugs aimed at GC risk group differentiation were identified using the Connective Map (CMAP) database. Lastly, four subgroups (C1, C2, C3, and C4) were identified based on the m6A-related lncRNA expression, through a consensus clustering algorithm. Among them, C1 and C2 have a greater likelihood to respond to immune checkpoint inhibitor immunotherapy, suggesting that the C1 and C2 subgroup might benefit from immunotherapy. In conclusion, the m6A-related lncRNA signature can independently predict the OS and DFS of GC and may aid in development of personalized immunotherapy strategies.