ArticleEndocrine, metabolic & immune disorders drug targets2026
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Article in Endocrine, metabolic & immune disorders drug targets, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
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Abstract
introductionThis study developed a prognostic model for hepatocellular carcinoma (HCC) based on cancer stem cell (CSC)-related modules identified by high-dimensional WGCNA (hdWGCNA).
methodsThe scRNA-seq data were filtered, dimensionally reduced, and clustered for downstream analysis. CSC-related modules were identified by hdWGCNA. Based on the TCGA liver hepatocellular carcinoma (LIHC) data, we developed a prognostic RiskScore model using univariate Cox, LASSO, and stepwise regression analyses and validated in the ICGC-LIRI-JP dataset. Correlations between the model and the tumor immune microenvironment (TME) and drug sensitivity were analyzed.
resultsNine cell subpopulations, including CSCs, were identified and were significantly enriched in tumors. hdWGCNA identified six CSC-related key modules. The prognostic RiskScore model, developed based on TXNIP, FTCD, and HAGH, exhibited an AUC > 0.6 in both cohorts. A high RiskScore was correlated with an immunosuppressive TME characterized by downregulated neutrophils, NK cells, and eosinophils, and higher sensitivity to chemotherapeutic agents such as Pyrimethamine and Vinorelbine. DISCUSSION: This study identified CSC-related modules associated with HCC prognosis, TME, and drug response, providing a potential mechanistic basis for the clinical application of the model.
conclusionPotential prognostic and targeted therapy biomarkers were identified. The CSC-related module-based prognostic model may offer a new tool for personalized HCC treatment.
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