ArticleJournal of translational medicine2025
G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in hepatocellular carcinoma: insights from spatial transcriptome and machine learning.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Podophyllotoxin sensitizes triple-negative breast cancer cells to CD47-targeted immunotherapy.Cell insight · 2026Article
- Multi-omics analysis and experimental validation uncovers prognosis significance of IKBIP in patients with hepatocellular carcinoma: a multicenter cohort study.BMC gastroenterology · 2026Article
- Development of a G2/M checkpoint subtype classifier for guiding precision therapy in liver hepatocellular carcinoma patients.Cancer cell international · 2026Article
- United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes.iScience · 2026Article
- Lactylation-related genes signature panel in hepatocellular carcinoma reveals the prognostic and therapeutic optimization.Molecular & cellular oncology · 2026Article
- Construction of a novel G2/M checkpoint-related gene signature for prognosis in hepatocellular carcinoma based on machine learning.Discover oncology · 2025Article
- Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.PloS one · 2025Article
- WISP1 drives esophageal squamous cell carcinoma progression via modulation of cancer-associated fibroblasts and immune microenvironment.Frontiers in immunology · 2025Article
- Single-Gene Mutations in Hepatocellular Carcinoma: Applications and Challenges in Precision Medicine.International journal of medical sciences · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
backgroundHepatocellular carcinoma (HCC) treatment remains challenging, particularly for immune checkpoint inhibitors (ICIs) non-response patients. Spatial transcriptome (ST) data and machine learning algorithms offer new insights into understanding HCC heterogeneity and ICIs resistance mechanisms.
methodsUtilizing ST data from HCC patients on ICIs treatment, we analyzed pathway activity and immune infiltration. We combined 167 machine learning models to develop a G2M-checkpoint related signature (G2MRS) based on differential gene expression. The four cohorts and in-house cohort was used to validate G2MRS, and KPNA2's role was further examined through in vitro experiments in two different liver cancer cell lines.
resultsOur analysis revealed a distinct suppressive immune barrier structure (SIBS) in ICIs non-response patients, associated with upregulated G2M-checkpoint levels. G2MRS, consisting of KPNA2, CENPA, and UCK2, accurately predicted HCC prognosis and ICIs response. Further in vitro experiments demonstrated KPNA2's role in regulating migration, proliferation, and apoptosis in liver cancer.
conclusionsThis study highlights the importance of spatial heterogeneity and machine learning in refining HCC prognosis and ICIs response prediction. G2MRS and KPNA2 emerge as promising biomarkers for personalized HCC management.
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