Prognostic N6-methyladenosine (m6A)-related lncRNA patterns to aid therapy in pancreatic ductal adenocarcinoma - Wang, Yuxin; Ji, Yutian; Xu, Qianhui; Huang, Wen (Frontiers in Genetics, 2022)
Background: Mounting researches have suggested the indispensable roles of N6-methyladenosine (m6A) RNA modification in carcinogenesis. Nevertheless, it was little to know the potential function of m6A-related lncRNAs in samples clustering, underlying mechanism and anticancer immunity of pancreatic ductal adenocarcinoma (PDAC). Methods: PDAC samples data were obtained from TCGA-PAAD project and total 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 lncRNAs subtypes by consensus clustering. Next, least absolute shrinkage and selection operator (LASSO) algorithm was applied for constructing m6A-related lncRNAs scoring system further quantify the m6A-related lncRNAs patterns in individual sample. Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual sample. To decode the comprehensive landscape of TME, CIBERSORT method and ESTIMATE algorithm were analyzed. The half-maximal inhibitory concentration (IC50) of chemotherapeutic agents was predicted with R package pRRophetic. Finally, quantitative real-time polymerase chain reaction was used to determine TRPC7-AS1 mRNA expression in PDAC. Results: Two distinct m6A-related lncRNAs patterns with different clinical outcome TEM features, and biological enrichment were identified based on 45 prognostic m6A-related lncRNAs. The identification of m6A-related lncRNAs patterns within individual sample based on risk score contributed into revealing biological signatures, clinical outcome, TEM characterization, and chemotherapeutic effect. Prognostic risk-clinical nomogram was constructed and confirmed to estimate m6A-related lncRNAs patterns in individual sample. Finally, the biological roles of TRPC7-AS1 were revealed in PDAC. Conclusion: This work comprehensively elucidated that m6A-related lncRNAs patterns served as an indispensable player in prognostic prediction and TEM features. Quantitative identification of m6A-related lncRNAs patterns in individual tumor will contribute into samples stratification further optimizing therapeutic strategies.