The impact of de novo lipogenesis on predicting survival and clinical therapy: an exploration based on a multigene prognostic model in hepatocellular carcinoma

作者：Xin Zhou, Guangzu Cui, Erya Hu, Xinwen Wang, Diya Tang, Xiangyang Zhang, Jiayao Ma, Li Yin, Haicong Liu, Qingping Peng, Ying Han, Yihong Chen, Shan Zeng, Yan Zhang, Hong Shen · 发表于：Journal of Translational Medicine · 年份：2025 · DOI：10.1186/s12967-025-06704-y · 被引用次数：8 · 研究领域：Ferroptosis and cancer prognosis、Cancer, Lipids, and Metabolism、Lipid metabolism and disorders

BACKGROUND: Hepatocellular carcinoma (HCC) ranks among the most aggressive malignancies worldwide, with poor outcomes attributed to delayed diagnosis and therapeutic limitations. Emerging evidence suggests that de novo lipogenesis (DNL) plays a crucial role in HCC progression and its interaction with the immune microenvironment. METHODS: We systematically analyzed DNL-related gene expression profiles from TCGA, GEO, ICGC-LIRI datasets, and our Xiangya HCC cohort (n = 106) to construct a prognostic risk model. Through LASSO-Cox regression analysis, we identified six signature genes (G6PD, LCAT, SERPINE1, SOAT2, CYP2C9, and UGT1A10) that effectively stratified patients into distinct risk groups. We evaluated clinical characteristics, immune cell infiltration patterns, and differential therapeutic responses between high-risk and low-risk groups. Comprehensive validation included immunohistochemical analysis and Western blotting to assess expression levels of key model genes, along with multiplex immunofluorescence staining and single-cell RNA sequencing(scRNA-seq) to characterize immune microenvironmental differences between risk groups. RESULTS: We successfully established a robust six-gene prognostic signature (G6PD, LCAT, SERPINE1, SOAT2, CYP2C9, and UGT1A10) based on de novo lipogenesis pathways, which demonstrated excellent predictive performance (AUC: 0.78-0.82). The model revealed significant differences in immune infiltration patterns between risk groups, with the high-r...