Integrating subpathway analysis to identify candidate agents for hepatocellular carcinoma

Area of Science:

• Oncology
• Bioinformatics
• Pharmacology

Background:

• Hepatocellular carcinoma (HCC) presents significant global health challenges due to its invasiveness and treatment resistance.
• There is an urgent need for novel therapeutic strategies to combat HCC.
• Current treatments for HCC often face limitations in efficacy.

Purpose of the Study:

• To develop a bioinformatics method for identifying novel small molecular drugs targeting HCC.
• To integrate HCC-specific subpathways with drug-affected subpathways for drug discovery.
• To identify and validate potential new therapeutic agents for hepatocellular carcinoma.

Main Methods:

• Utilized RNA-sequencing data from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes in HCC.
• Identified significant HCC-related subpathways and subpathways affected by small molecular drugs from the CMap database.
• Integrated these datasets to pinpoint novel drug candidates and experimentally validated prenylamine's efficacy in HCC cells.

Main Results:

• Identified 1,763 differentially expressed genes and 104 significant HCC-related subpathways.
• Discovered 40 novel small molecular drugs targeting HCC-involved subpathways, including prenylamine.
• Prenylamine demonstrated induction of HCC cell apoptosis, affecting key proteins like Bax, Bcl-2, and cytochrome c, and increasing caspase-3 activity.

Conclusions:

• The developed bioinformatics method successfully identified novel small molecular drug candidates for HCC treatment.
• Prenylamine shows potential as a therapeutic agent for HCC, warranting further investigation.
• These findings offer new insights into potential therapeutic approaches for hepatocellular carcinoma.