Objective: Hepatocellular carcinoma (HCC) is a frequently diagnosed cancer type with low overall survival (OS) rates. Known prognostic biomarkers of HCC are inefficient to monitor disease progression. Therefore, identification of novel patient OS time predictive biomarkers is needed.

Materials and Methods: Cbioportal, OncoLnc, and dbDEMC tools were utilized to analyse DNA-sequencing, mRNA-sequencing and miRNA-sequencing data of HCC patients in The Cancer Genome Atlas (TCGA) database. Integrated molecular interactions network of the novel biomarker candidates were generated using NetworkAnalyst and MiRNet tools.

Results: Next generation sequencing data analyses revealed expression profiles of 11 frequently mutated and differentially expressed genes as well as two differentially expressed miRNAs, which predict OS time. Transcriptional upregulation of GPATCH4 gene (P:0.009) and downregulation of four genes (PPARGC1A P:0.000013, PIK3R1 P:0.002, COL18A1 P:0.009, and A1BG P:0.01) were correlated with poor prognosis of HCC patients, for the first time. Integrated network of these molecules also revealed novel regulatory molecules and interactions associated with prognosis of HCC.

Conclusion: As a resul of this study, in silico data that can benefit the development of novel molecularly targeted diagnostic and therapeutic applications specific to HCC have been obtained.