Integrative module analysis of HCC gene expression landscapes

Area of Science:

• Oncology
• Molecular Biology
• Genetics

Background:

• Hepatocellular carcinoma (HCC) is a prevalent global cancer with poorly understood initiation and progression mechanisms.
• Understanding molecular drivers is crucial for developing effective treatments.

Purpose of the Study:

• To identify hub genes and key biological processes involved in hepatocellular carcinoma (HCC) progression.
• To explore potential regulatory mechanisms and therapeutic targets for HCC.

Main Methods:

• Analyzed three HCC gene expression datasets from the Gene Expression Omnibus database (480 patients).
• Constructed protein-protein interaction (PPI) networks and identified differentially expressed genes.
• Utilized weighted gene correlation network analysis, topological overlapping matrix, and hierarchical clustering via STRING.
• Validated findings using clinical data from The Cancer Genome Atlas and constructed transcription factor and microRNA-mRNA networks.

Main Results:

• Identified 657 differentially expressed genes and six candidate hub genes crucial for HCC progression.
• Confirmed oxidation-reduction and cell cycle processes are significantly involved in HCC.
• Validated six highly expressed hub genes (cyclin B2, CDC20, MAD2L1, MCM2, CENPF, Bub1) associated with cell division.

Conclusions:

• Six identified hub genes are critical for HCC progression and cell division.
• These genes represent potential biomarkers and therapeutic targets for clinical gene therapy in HCC.