Integrative analysis of DNA methylation and gene expression identify a six epigenetic driver signature for predicting prognosis in hepatocellular carcinoma.

DNA methylation is a crucial regulator of gene transcription in the etiology and pathogenesis of hepatocellular carcinoma (HCC). Thus, it is reasonable to identify DNA methylation-related prognostic markers. Currently, we aimed to make an integrative epigenetic analysis of HCC to identify the effectiveness of epigenetic drivers in predicting prognosis for HCC patients. By the software pipeline TCGA-Assembler 2, RNA-seq, and methylation data were downloaded and processed from The Cancer Genome Atlas. A bioconductor package MethylMix was utilized to incorporate gene expression and methylation data on all 363 samples and identify 589 epigenetic drivers with transcriptionally predictive. By univariate survival analysis, 72 epigenetic drivers correlated with overall survival (OS) were selected for further analysis in our training cohort. By the robust likelihood-based survival model, six epi-drivers (doublecortin domain containing 2, flavin containing monooxygenase 3, G protein-coupled receptor 171, Lck interacting transmembrane adaptor 1, S100 calcium binding protein P, small nucleolar RNA host gene 6) serving as prognostic markers was identified and then a DNA methylation signature for HCC (MSH) predicting OS was identified to stratify patients into low-risk and high-risk groups in the training cohort (p < 0.001). The capability of MSH was also assessed in the validation cohort (p = 0.002). Furthermore, a receiver operating characteristic curve confirmed MSH as an effective prognostic model for predicting OS in HCC patients in training area under curve (AUC = 0.802) and validation (AUC = 0.691) cohorts. Finally, a nomogram comprising MSH and pathologic stage was generated to predict OS in the training cohort, and it also operated effectively in the validation cohort (concordance index: 0.674). In conclusion, MSH, a six epi-drivers based signature, is a potential model to predict prognosis for HCC patients.