Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

Abstract Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumours. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. To fill this shortcoming, we hope to build a more accurate predictive model to guide prognostic assessments of HCC. We use the TCGA to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve. A multi-omics model was built and evaluated by decision curve analysis (DCA), C-index, and ROC analysis. Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. Five single-omic models were constructed, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a quite predictive solid ability with a c-index over 0.80. In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment and treatment decision-making.