Uncovering Prognostic Biomarkers Underlying Hepatocellular Carcinoma Through Integrative Multi-Omics and a...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Hepatocellular carcinoma (HCC) is a major global cause of cancer mortality.
• Despite therapeutic advances, HCC prognosis remains poor due to late detection, recurrence, and heterogeneity.
• There is a critical need for robust molecular biomarkers to enhance HCC prognosis and treatment.

Purpose of the Study:

• To identify prognostic biomarkers and therapeutic targets in Hepatocellular Carcinoma (HCC).
• To utilize an integrative transcriptomic and systems biology approach for discovering key genes in HCC.

Main Methods:

• Examined mRNA-sequencing data from the TCGA-HCC cohort to identify differentially expressed genes (DEGs).
• Applied Weighted Gene Co-expression Network Analysis (WGCNA) to find key gene modules and hub genes.
• Utilized Protein-Protein Interaction Network (PPIN) analysis and survival analysis to refine candidate genes.

Main Results:

• Identified five genes (TTK, CENPA, NUF2, KIF2C, CDCA8) significantly associated with poor overall survival in HCC patients.
• Pathway enrichment analysis linked these genes to mitotic checkpoint and kinetochore signaling.
• NUF2 showed frequent genomic alterations and significant correlations with immune cell infiltration.

Conclusions:

• This study highlights TTK, CENPA, NUF2, KIF2C, and CDCA8 as prognostically relevant hub genes in HCC.
• These identified genes hold potential as prognostic biomarkers and therapeutic targets for Hepatocellular Carcinoma.
• The findings underscore the importance of an integrative approach in uncovering molecular drivers of HCC.