Identification of TXNIP, FTCD, and HAGH as Key Genes in a Cancer Stem Cell-Driven Prognostic Model for Hepatocellular Carcinoma
Xieling Yin, Yuan Zhou, Shi Chen, Chunyang Ma, Hongfei Wu, Hui Cao, Wei Shi, Chi LiangIntroduction:
This study developed a prognostic model for hepatocellular carcinoma (HCC) based on cancer stem cell (CSC)-related modules identified by high-dimensional WGCNA (hdWGCNA).
Methods:
The 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 it in the ICGC-LIRI-JP dataset. Correlations between the model and the tumor immune microenvironment (TME) and drug sensitivity were analyzed.
Results:
Nine 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.
Conclusion:
Potential prognostic and targeted therapy biomarkers were identified. The CSC-related module-based prognostic model may offer a new tool for personalized HCC treatment.