Contributions and Prognostic Values of N6-Methyladenosine RNA Methylation Regulators in Hepatocellular Carcinoma - Qi, Li-Wen; Jia, Jian-Hui; Jiang, Chen-Hao; Hu, Jian-Ming (Frontiers in Genetics, 2021)
Introduction The methylation at position N6 of adenine is called N6-methyladenosine (m6A). This transcriptional RNA modification exerts a very active and important role in RNA metabolism and in other biological processes. However, the activities of m6A associated with malignant liver hepatocellular carcinoma (LIHC) are unknown and are worthy of study. Materials and Methods Using the data of University of California, Santa Cruz (UCSC), the expression of M6A methylation regulators in pan-cancer was evaluated as a screening approach to identify the association of M6A gene expression and 18 cancer types, with a specific focus on LIHC. LIHC datasets of The Cancer Genome Atlas (TCGA) were used to explore the expression of M6A methylation regulators and their clinical significance. Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the underlying mechanism based on the evaluation of aberrant expression of m6A methylation regulators. Results The expression alterations of m6A-related genes varied across cancer types. In LIHC, we found that in univariate Cox regression analysis, up-regulated m6A modification regulators were associated with worse prognosis, except for ZC3H13. Kaplan–Meier survival curve analysis indicated that higher expression of methyltransferase-like protein 3 (METTL3) and YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) genes related to the worse survival rate defined by disease-related survival (DSS), overall survival (OS), progression-free interval (PFI), and disease-free interval (DFI). Up-regulated m6A methylation regulator group (cluster2) obtained by consensus clustering was associated with poor prognosis. A six-gene prognostic signature established using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm performed better in the early (I + II; T1 + T2) stages than in the late (III + IV; T3 + T4) stages of LIHC. Using the gene signature, we constructed a risk score and found that it was an independent predictive factor for prognosis. Using GSEA, we identified processes involved in DNA damage repair and several biological processes associated with malignant tumors that were closely related to the high-risk group. Conclusion In summary, our study identified several genes associated with m6A in LIHC, especially METTL3 and YTHDF1, and confirmed that a risk signature comprised of m6A-related genes was able to forecast prognosis.