The significance of m6A RNA methylation regulators in diagnosis and subtype classification of HBV-related hepatocellular carcinoma

Abstract

Abstract Objective We aim to explore whether m6A modification plays a role in the progression of HBV-related HCC. Methods We performed a random forest model to screen candidate m6A regulators from 23 selected ones. A nomogram model was established to predict the prevalence of HBV-related HCC. To identify m6A modification patterns and m6A-related gene signature, consensus molecular subtyping was used. Immune cell subsets were quantified using the ssGSEA algorithms. PCA algorithms were constructed to calculate the m6A score for individual tumors. Immunofluorescence was used to verify the expression of IGFBP3 and HNRNPC proteins. Results 8 candidate m6A regulators were selected from random forest model. Patients may benefit from the nomogram model according to decision curve analysis. Clinical impact curves demonstrated a strong predictive power of nomogram models. Two distinct m6A modification patterns (clusterA and clusterB) were correlated with different immune infiltration and biological pathways. Patients in clusterA had higher m6A scores than those in clusterB based on the m6A score. IGFBP3 and HNRNPC proteins were highly expressed in tumor tissues. Conclusion Our study highlights the significance of m6A modification in the progress of HBV-related HCC. We may provide new predictive biomarkers and potential immunotherapy targets to identify and treat HBV-related HCC.