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Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes.

Menggang Zhang1,2,3,4, Shuijun Zhang1,2,3,4, Wenzhi Guo5,6,7,8

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This study identifies 13 prognostic genes and develops a RiskScore to predict immunotherapy response in hepatocellular carcinoma (HCC) patients. This approach aids in pre-screening patients for effective cancer treatment.

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Area of Science:

  • Oncology
  • Immunotherapy
  • Genomics

Background:

  • Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide.
  • Immunotherapy offers treatment promise for HCC, but patient response varies significantly.
  • Predictive biomarkers are needed to identify patients likely to benefit from HCC immunotherapy.

Purpose of the Study:

  • To identify prognostic genes for hepatocellular carcinoma (HCC).
  • To develop a molecular subtyping system for HCC based on gene expression.
  • To create a prognostic model (RiskScore) to predict immunotherapy response in HCC.

Main Methods:

  • Univariate Cox regression analysis of 87 apoptosis-related genes to identify prognostic markers.
  • ConsensusClusterPlus for molecular subtyping (C1, C2, C3) of HCC patients.
  • Lasso regression to build a prognostic model and RiskScore using differentially expressed genes between subtypes.
  • Quantitative polymerase chain reaction (qPCR) for gene expression validation.

Main Results:

  • Identified 13 prognostic genes associated with HCC.
  • Classified HCC patients into three distinct molecular subtypes (C1, C2, C3).
  • Developed a RiskScore model that effectively predicts immunotherapy response in HCC patients.
  • RiskScore emerged as a significant prognostic factor in Cox regression analysis.

Conclusions:

  • The identified molecular subtypes and RiskScore can predict patient response to immunotherapy in HCC.
  • The developed RiskScore shows potential for clinical application in guiding HCC treatment decisions.
  • This study provides a foundation for further experimental research into HCC immunotherapy biomarkers.