A personalized approach identifies disturbed pathways and key genes in hepatitis C virus-cirrhosis with hepatocellular carcinoma.

OBJECTIVE

This work aimed to identify disturbed pathways in hepatitis C virus (HCV)-cirrhosis with hepatocellular carcinoma (HCC) based on individualized pathway aberrance score (iPAS) method.

MATERIALS AND METHODS

First of all, gene expression data and pathway data were recruited and preprocessed. Next, iPAS method, which contained three steps (gene-level statistics based on average Z algorithm, pathway-level statistics and pathway significant analysis based on Wilcoxon-test), was performed to identify differential pathways in HCV-cirrhosis with HCC. Then, a protein-protein interaction (PPI) network was conducted based on the genes enriched in the differential pathways. Finally, topological analysis of the PPI network combined with cancer genes was conducted to identify hub disease genes.

RESULTS

After a systematic operation by the iPAS method, a total of 34 differential pathways were identified (p-value < 0.01). From the PPI network that was constructed using the 243 genes in the differential pathways, a total of 24 hub genes were obtained by conducting degree centrality, and 4 hub cancer genes (UBC, MAPK1, NOTCH1 and RHOA) were identified. An in-depth analysis indicated that NF-kB is activated and signals survival pathway contained the most cancer genes (number = 7), in which there was a hub cancer gene UBC. In addition, as we set the p-value in ascending order, we found that opioid signaling pathway was the most significant pathway (p = 1.59E-06), and hub cancer gene MAPK1 was enriched in this pathway.

CONCLUSIONS

The altered pathways and several key genes identified by this method were predicted to play important roles in HCV-cirrhosis with HCC and might be potentially novel predictive and prognostic markers for HCV-cirrhosis with HCC.