Abstract
Background: Mounting research studies have suggested the indispensable roles of N6-methyladenosine (m6A) RNA modification in carcinogenesis. Nevertheless, it was little known about the potential function of m6A-related lncRNAs in sample clustering, underlying mechanism, and anticancer immunity of pancreatic ductal adenocarcinoma (PDAC). Methods: PDAC sample data were obtained from TCGA-PAAD project, and a total of 23 m6A regulators were employed based on published articles. Pearson correlation and univariate Cox regression were analyzed to determine m6A-related lncRNAs with prognostic significance to identify distinct m6A-related lncRNA subtypes by consensus clustering. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was applied for constructing an m6A-related lncRNA scoring system, further quantifying the m6A-related lncRNA patterns in individual samples. Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual samples. To decode the comprehensive landscape of TME, the CIBERSORT method and ESTIMATE algorithm were analyzed. The half-maximal inhibitory concentration (IC(50)) of chemotherapeutic agents was predicted with the R package pRRophetic. Finally, a quantitative real-time polymerase chain reaction was used to determine TRPC7-AS1 mRNA expression in PDAC. Results: Two distinct m6A-related lncRNA patterns with different clinical outcomes, TEM features, and biological enrichment were identified based on 45 prognostic m6A-related lncRNAs. The identification of m6A-related lncRNA patterns within individual samples based on risk scores contributed to revealing biological signatures, clinical outcomes, TEM characterization, and chemotherapeutic effects. A prognostic risk-clinical nomogram was constructed and confirmed to estimate m6A-related lncRNA patterns in individual samples. Finally, the biological roles of TRPC7-AS1 were revealed in PDAC. Conclusion: This work comprehensively elucidated that m6A-related lncRNA patterns served as an indispensable player in prognostic prediction and TEM features. Quantitative identification of m6A-related lncRNA patterns in individual tumors will contribute to sample stratification for further optimizing therapeutic strategies.