A Novel RNA Methylation-Related Prognostic Signature and its Tumor Microenvironment Characterization in Hepatocellular Carcinoma

Abstract

Introduction Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system. RNA methylation plays an important role in tumorigenesis and metastasis, which could alter gene expression and even function at multiple levels, such as RNA splicing, stability, translocation, and translation. In this study, we aimed to conduct a comprehensive analysis of RNA methylation-related genes (RMGs) in HCC and their relationship with survival and clinical features. Methods A retrospective analysis was performed using publicly available HCC-related datasets. The differentially expressed genes (DEGs) between HCC and controls were identified from TCGA-LlHC and intersected with RMGs to obtain differentially expressed RNA methylation-related genes (DERMGs). Regression analysis was used to screen for prognostic genes and construct risk models. Simultaneously, clinical, immune infiltration and therapeutic efficacy analyses were performed. Finally, multivariate cox regression was used to identify independent risk factors, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression levels of the core genes of the model. Results A 21-gene risk model for HCC was established with excellent performance based on ROC curves and survival analysis. Risk scores correlated with tumor grade, pathologic T, and TNM stage. Immune infiltration analysis showed correlations with immune scores, 11 immune cells, and 30 immune checkpoints. Low-risk patients showed a higher susceptibility to immunotherapy. The risk score and TNM stage were independent prognostic factors. qRT-PCR confirmed higher expression of PRDM9, ALPP, and GAD1 in HCC. Conclusions This study identified RNA methylation-related signature genes in HCC and constructed a risk model that predicts patient outcomes and reflects the immune microenvironment. Prognostic genes are involved in complex regulatory mechanisms, which may be useful for cancer diagnosis, prognosis, and therapy.