A mA regulators-related classifier for prognosis and tumor microenvironment characterization in hepatocellular carcinoma.

BACKGROUND

Increasing evidence have highlighted the biological significance of mRNA N-methyladenosine (mA) modification in regulating tumorigenicity and progression. However, the potential roles of mA regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification.

METHOD

RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify mA regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a mArisk score model was constructed. The correlations of mArisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC).

RESULTS

Two distinct mA modification patterns were identified based on 23 mA regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed mArisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the mArisk score showed excellent prognostic performance. Patients with a high mArisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the mArisk score and clinicopathological features. The application of the mArisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit.

CONCLUSION

Our findings reveal the crucial role of mA modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of mArisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.