Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease
is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better
clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the
disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy
of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and
applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade,
chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we
integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions
and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and
E metabolism are key processes in the development of HCC and therefore need to be further explored for the
development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are
important in HCC in humans and warrant further studies.