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Cholangiocarcinoma and Gallbladder Cancer Studies / Hepatocellular Carcinoma Treatment and Prognosis / Ferroptosis and Cancer Prognosis · Journal article
BMC Gastroenterology · August 14, 2026
Encouraging direction, but not yet definitive.
A 10-gene stemness-related signature was derived using machine learning and validated across four cohorts for prognostic stratification and immunotherapy response prediction in HCC. The signature achieved superior discriminatory performance (C-index 0.8015) compared to conventional parameters and 31 published models, with consistent survival separation in low-risk versus high-risk groups. Functional studies confirmed NCAPG as a stemness-related driver of proliferation and invasion in vitro and tumor growth in vivo, but clinical translation remains unproven.
Retrospective prognostic signature development with machine learning and external validation. Hepatocellular carcinoma patients from TCGA-LIHC cohort and four independent external validation datasets; HCC cell lines for functional studies.. Intervention: 10-gene stemness-related signature (CHAF1A, NCAPG, KIF22, KIF4A, PBK, ANLN, RFC4, MCM2, TRAF2, NSMCE2) for risk stratification; NCAPG knockdown in functional experiments.. Compared with: Conventional clinical parameters and 31 published prognostic models; high-risk versus low-risk groups; control cells (not knocked down) in functional assays..
A 10-gene SRG signature (CHAF1A, NCAPG, KIF22, KIF4A, PBK, ANLN, RFC4, MCM2, TRAF2, NSMCE2) was established Low-risk patients exhibited significantly favorable prognosis across all cohorts (P < 0.05) Risk score achieved average C-index = 0.8015, outperforming 31 published prognostic models
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Clinicians should recognize this signature as a candidate tool for prognostic stratification and immunotherapy response prediction in HCC, but validation in a prospective clinical trial is required before adoption into routine practice. NCAPG emerges as a potential therapeutic target, though in vitro and in vivo data require translation to clinical efficacy studies.
A multi-algorithm machine learning signature study with external validation across four datasets and functional validation of a candidate gene, showing clear prognostic separation but limited by retrospective cohort design and surrogate endpoints rather than prospective clinical outcomes.
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Quoted from the source exactly as published.
Clinicians should recognize this signature as a candidate tool for prognostic stratification and immunotherapy response prediction in HCC, but validation in a prospective clinical trial is required before adoption into routine practice. NCAPG emerges as a potential therapeutic target, though in vitro and in vivo data require translation to clinical efficacy studies.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Cancer stem cells (CSCs) drive drug resistance, recurrence, and heterogeneity in hepatocellular carcinoma (HCC). Although individual stemness-related genes (SRGs) have been explored, comprehensive SRG-based signatures for prognosis and immunotherapy prediction remain underdeveloped. Using an integrated machine learning framework comprising 101 algorithms, we screened SRGs by comparing high- versus low-stemness HCC samples from TCGA-LIHC cohort. The optimal algorithm, StepCox[backward]+Enet[alpha = 0.9], was selected based on the highest average C-index across four independent datasets. A risk stratification system was constructed and validated for survival prediction, immune microenvironment characterization, and immunotherapy response evaluation using TIDE, IPS, TMB analyses, and external ICB therapy cohorts. Functional experiments were performed to validate the role of NCAPG in HCC. A 10-gene SRG signature (CHAF1A, NCAPG, KIF22, KIF4A, PBK, ANLN, RFC4, MCM2, TRAF2, and NSMCE2) was established. Low-risk patients exhibited significantly favorable prognosis across all cohorts ( P < 0.05). The risk score outperformed conventional clinical parameters (average C-index = 0.8015) and 31 published prognostic models. Low-risk tumors displayed an immune-“hot” phenotype with higher immune/stromal scores, elevated CD8 + T cell infiltration, and superior predicted ICB response. High-risk patients showed enrichment of oncogenic pathways and increased sensitivity to multiple chemotherapeutic agents. Knockdown of NCAPG suppressed HCC cell proliferation, migration, invasion, and sphere formation in vitro, and inhibited tumor growth in vivo, accompanied by reduced stemness marker expression. This SRG-based signature provides robust prognostic stratification and immunotherapy response prediction for HCC. NCAPG represents a promising stemness-related therapeutic target.
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