Machine learning-driven multi-omics integration uncovers a senescence associated molecular axis in HCC.

Area of Science:

• Oncology
• Molecular Biology
• Immunology

Background:

• Hepatocellular carcinoma (HCC) is characterized by significant molecular diversity and abnormal cellular senescence.
• Understanding the molecular drivers of HCC progression and immune evasion is critical.

Purpose of the Study:

• To systematically analyze the senescence-associated molecular landscape in HCC.
• To identify key regulators driving HCC progression and immune evasion.
• To develop a prognostic signature for HCC.

Main Methods:

• Integrated multi-cohort transcriptomic data with machine learning models.
• Performed proteomic, metabolomic, and single-cell RNA sequencing (scRNA-seq).
• Validated findings in a clinical HCC cohort using RT-qPCR, Western blotting, immunohistochemistry, and multiplex immunofluorescence (mIF).
• Conducted in vitro functional assays in HepG2 cells.

Main Results:

• A 12-gene signature accurately predicted patient survival.
• MCM7 was identified as a central senescence-associated driver.
• MCM7 expression correlated with a proliferative CD8+ T cell subset and immune evasion.
• MCM7 knockdown inhibited proliferation and increased senescence markers (p16, p21) in HepG2 cells.

Conclusions:

• An MCM7-driven senescence-associated axis influences HCC progression and immune dysfunction.
• This framework provides a prognostic tool for HCC stratification.
• The findings suggest novel therapeutic strategies targeting MCM7 in HCC.