A Novel m1A-Score Model Correlated With the Immune Microenvironment Predicts Prognosis in Hepatocellular Carcinoma

RNA methylation plays crucial roles in gene expression and has been indicated to be involved in tumorigenesis, while it is still unclear whether m1A modifications have potential roles in the prognosis of hepatocellular carcinoma (HCC). In this study, we comprehensively analyzed RNA sequencing (RNA-seq) data and clinical information using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We collected 10 m1A regulators and performed consensus clustering to determine m1A modification patterns in HCC. The CIBERSORT method was utilized to evaluate the level of immune cell infiltration. Principal component analysis was used to construct the m1A-score model. In the TCGA-LIHC cohort, the expression of all 10 m1A regulators was higher in tumor tissues than in normal control tissues, and 8 of 10 genes were closely related to the prognosis of HCC patients. Two distinct m1A methylation modification patterns (Clusters C1 and C2) were identified by the 10 regulators and were associated with different overall survival, TNM stage and tumor microenvironment (TME) characteristics. Based on the differentially expressed genes (DEGs) between C1 and C2, we identified three gene clusters (Clusters A, B and C). C1 with a better prognosis was mainly distributed in Cluster C, while Cluster A contained the fewest samples of C1. An m1A-score model was constructed using five m1A regulators related to prognosis. Patients with higher m1A scores showed a poorer prognosis than those with lower scores in the TCGA-LIHC and GSE14520 datasets. In conclusions, our study showed the vital role of m1A modification in the TME and progression of HCC. Quantitative evaluation of the m1A modification patterns of individual patients facilitates the development of more effective biomarkers for predicting the prognosis of patients with HCC.