Integrative Bioinformatics Analysis for Targeting Hub Genes in Hepatocellular Carcinoma Treatment

Background:
The damage in the liver and hepatocytes is where the primary liver cancer
begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods
for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.


Objective:
This study aimed to identify potential drug target(s) hubs mediating HCC progression
using computational approaches through gene expression and protein-protein interaction datasets.


Methodology:
Four datasets related to HCC were acquired from the GEO database, and Differentially
Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using
the Fun Rich tool, functional associations among the genes were identified. Further, protein-
protein interaction networks were predicted using STRING, and hub genes were identified using
Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival
analysis and functional enrichment studies for the identified hub genes. The up-regulating
genes were further studied for immunohistopathological studies using HPA to identify gene/protein
expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database
were employed to find the reported drug status and targets. Finally, STITCH was performed to
identify the functional association between the drugs and the identified hub genes.


Results:
The GEO2R analysis for the considered datasets identified 735 upregulating and 284
downregulating DEGs. Functional gene associations were identified through the Fun Rich tool.
Further, PPIN network analysis was performed using STRING. A comparative study was carried
out between the experimental evidence and the other seven data evidence in STRING, revealing
that most proteins in the network were involved in protein-protein interactions. Further, through
Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were
identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The
identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM,
KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and
CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional
enrichment studied for the hub genes. The HPA database studies further found that upregulating
genes were involved in changes in protein expression in Normal vs HCC tissues. These findings
indicated that hub genes were certainly involved in the progression of HCC. STITCH
database studies uncovered that existing drug molecules, including sorafenib, regorafenib,
cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub
genes could also be considered as potential and promising drug targets as they are involved in the
gene-chemical interaction networks.


Conclusion:
The present study involved various integrated bioinformatics approaches, analyzing
gene expression and protein-protein interaction datasets, resulting in the identification of 20 topranked
hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK,
TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A,
NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered
that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can
be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets.
The current study underscores the significance of targeting these hub genes and utilizing existing
molecules to generate new molecules to combat liver cancer effectively and can be further
explored in terms of drug discovery research to develop treatments for HCC.