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Related Concept Videos

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Related Experiment Video

Updated: Jan 10, 2026

Mass Cytometry Analysis of Systemic and Local Immune Responses in Hepatocellular Carcinoma
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Decoding the cholesterol-apoptosis axis in HCC: a machine learning-based multi-omics integration and single-cell

Yang Li1, Jun Shi2, Aiqing Zhao3

  • 1Department of Emergency, Ningbo Medical Center Lihuili Hospital, The Affiliated Lihuili Hospital of Ningbo University, Ningbo, 315040, Zhejiang, China.

Discover Oncology
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies six key genes related to apoptosis and cholesterol metabolism as crucial prognostic markers for liver hepatocellular carcinoma (LIHC). This discovery aids in early risk assessment and personalized treatment strategies for LIHC patients.

Keywords:
ApoptosisCholesterol metabolismLiver hepatocellular carcinomaPrognostic modelSingle-cell sequence

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Liver hepatocellular carcinoma (LIHC) is a prevalent cancer with increasing incidence, posing significant health and economic challenges.
  • Early diagnosis of LIHC is difficult, leading to late-stage detection and reduced treatment effectiveness.

Purpose of the Study:

  • To investigate the roles of apoptosis and cholesterol metabolism in LIHC.
  • To identify novel prognostic biomarkers and develop a risk prediction model for LIHC.

Main Methods:

  • Utilized multi-omics data and machine learning for gene expression analysis.
  • Employed LASSO regression to identify hub genes and constructed a prognostic risk score model.
  • Conducted immune cell infiltration, gene enrichment, and single-cell analyses.

Main Results:

  • Identified apoptosis- and cholesterol metabolism-related differentially expressed genes (ACMRDEGs).
  • Six hub genes (EPHX2, FABP5, SQLE, ADH4, HMGCS2, CYP7A1) were validated as significant prognostic biomarkers for overall survival (OS) in LIHC.
  • Revealed significant immune cell infiltration differences and enriched cholesterol/alcohol metabolism pathways in LIHC, with single-cell analysis detailing cellular heterogeneity.

Conclusions:

  • Developed and validated the first integrated six-gene prognostic model for LIHC based on apoptosis and cholesterol metabolism pathways.
  • This model shows potential for improving risk stratification and guiding personalized therapeutic strategies in LIHC clinical practice.