Abstract
In recent years, abnormal m6A alteration in hepatocellular carcinoma (HCC) has been a focus on investigating the biological implications. In this study, our objective is to determine whether m6A modification contributes to the progression of HBV-related HCC. To achieve this, we employed a random forest model to screen top 8 characteristic m6A regulators from 19 candidate genes. Subsequently, we developed a nomogram model that utilizes these 8 characteristic m6A regulators to predict the prevalence of HBV-related HCC. According to decision curve analysis, patients may benefit from the nomogram model. The clinical impact curves exhibited a robust predictive capability of the nomogram models. Additionally, consensus molecular subtyping was employed to identify m6A modification patterns and m6A-related gene signature. The quantification of immune cell subsets was accomplished through the implementation of ssGSEA algorithms. PCA algorithms were developed to compute the m6A score for individual tumors. Two distinct m6A modification patterns, namely cluster A and cluster B, exhibited significant correlations with distinct immune infiltration patterns and biological pathways. Notably, patients belonging to cluster B demonstrated higher m6A scores compared to those in cluster A, as determined by the m6A score metric. Furthermore, the expression of IGFBP3 proteins was validated through immunofluorescence, revealing their pronounced lower expression in tumor tissues. In summary, our study underscores the importance of m6A modification in the advancement of HBV-related HCC. This research has the potential to yield novel prognostic biomarkers and therapeutic targets for the identification of HBV-related HCC.