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Related Experiment Video

Updated: May 27, 2025

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
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G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in

Xingte Chen1, Shiji Wu1, Hongxin He2

  • 1Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.

Journal of Translational Medicine
|February 18, 2025
PubMed
Summary

Hepatocellular carcinoma (HCC) treatment is improved by a new G2M-checkpoint related signature (G2MRS) that predicts response to immune checkpoint inhibitors (ICIs). This signature, including KPNA2, offers insights into HCC heterogeneity and personalized therapy.

Keywords:
G2M-checkpointHepatocellular carcinomaImmune checkpoint inhibitorsMachine learningSpatial transcriptomeSuppressive immune barrier structure

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Area of Science:

  • Oncology
  • Genomics
  • Immunotherapy

Background:

  • Hepatocellular carcinoma (HCC) treatment presents challenges, especially for patients unresponsive to immune checkpoint inhibitors (ICIs).
  • Spatial transcriptome (ST) data and machine learning (ML) offer novel approaches to understand HCC heterogeneity and ICIs resistance.

Purpose of the Study:

  • To leverage ST data and ML to identify biomarkers for predicting HCC prognosis and ICIs response.
  • To investigate the role of G2M-checkpoint related pathways in HCC immune evasion.
  • To validate the efficacy of a novel G2M-checkpoint related signature (G2MRS) and its components.

Main Methods:

  • Analysis of ST data from HCC patients undergoing ICIs treatment, focusing on pathway activity and immune infiltration.
  • Development of a G2MRS using 167 ML models based on differential gene expression.
  • Validation of G2MRS across four external cohorts and an in-house cohort, with in vitro experiments on KPNA2.

Main Results:

  • Identification of a suppressive immune barrier structure (SIBS) in ICIs non-responders, linked to elevated G2M-checkpoint activity.
  • G2MRS, comprising KPNA2, CENPA, and UCK2, demonstrated high accuracy in predicting HCC prognosis and ICIs response.
  • In vitro studies confirmed KPNA2's significant role in regulating liver cancer cell migration, proliferation, and apoptosis.

Conclusions:

  • Spatial heterogeneity and ML are crucial for improving HCC prognosis and ICIs response prediction.
  • G2MRS and KPNA2 show potential as predictive biomarkers for personalized HCC management strategies.