本研究旨在通过研究脂肪酸代谢的作用来探索肝细胞癌（HCC）治疗的新靶点。HCC的RNA-seq和临床数据来自基因表达综合（GEO）和癌症基因组图谱（TCGA）数据库。采用生物信息学分析来识别与预后相关的差异表达基因（DEG）。然后使用最小绝对收缩和选择算子 (LASSO) Cox 回归构建签名，将 TCGA 数据库中的 HCC 患者分为低风险组和高风险组。通过主成分分析（PCA）、Kaplan Meier（KM）生存分析、受试者工作特征（ROC）曲线、列线图、基因突变、药物敏感性分析、免疫相关分析和富集分析来评估特征的预测性能。构建单细胞图谱来说明核心基因的分布。采用免疫组织化学（IHC）、实时定量PCR（qRT-PCR）和蛋白质印迹来验证核心基因的表达。一个核心基因的功能通过一系列体外测定进行验证，包括细胞活力、集落形成、伤口愈合、跨孔迁移和侵袭测定。在相关信号通路的背景下对结果进行了分析。生物信息学分析确定了 15 个与预后相关的 FAMG。构建了4个基因签名，根据签名将患者分为高风险组和低风险组。在训练组 (P < 0.001) 和验证组 (P = 0.020) 中，与低风险组相比，高风险组的预后较差。此外，风险评分被确定为 OS 的独立预测因子（P < 0.001，HR = 8.005）。将风险评分和临床病理特征纳入列线图可以有效预测患者预后。该模型能够有效预测各组的免疫微环境、化疗药物敏感性以及基因突变。单细胞图谱表明模型中的 FAMG 分布在肿瘤细胞中。富集分析表明，细胞周期、脂肪酸β氧化和PPAR信号通路是最重要的通路。在四个与预后相关的关键FAMG中，精胺合酶（SMS）被选择并验证为影响HCC细胞周期、PPAR-γ信号通路和脂肪酸β氧化的潜在癌基因。基于FAMG的风险特征可以作为独立的预后指标预测HCC预后，也可作为HCC患者基因突变、免疫、化疗药物治疗的评价标准。同时，靶向脂肪酸代谢可通过相关信号通路用于治疗 HCC。版权所有 © 2024。Elsevier B.V. 出版。

This study aimed to explore novel targets for hepatocellular carcinoma (HCC) treatment by investigating the role of fatty acid metabolism.RNA-seq and clinical data of HCC were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatic analyses were employed to identify differentially expressed genes (DEGs) related to prognosis. A signature was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to classify HCC patients from the TCGA database into low-risk and high-risk groups. The predictive performance of the signature was evaluated through principal components analysis (PCA), Kaplan Meier (KM) survival analysis, receiver operating characteristics (ROC) curves, nomogram, genetic mutations, drug sensitivity analysis, immunological correlation analysis, and enrichment analysis. Single-cell maps were constructed to illustrate the distribution of core genes. Immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blot were employed to verify the expression of core genes. The function of one core gene was validated through a series of in vitro assays, including cell viability, colony formation, wound healing, trans-well migration, and invasion assays. The results were analyzed in the context of relevant signaling pathways.Bioinformatic analyses identified 15 FAMGs that were related to prognosis. A 4-gene signature was constructed, and patients were divided into high- and low-risk groups according to the signature. The high-risk group exhibited a poorer prognosis compared to the low-risk group in both the training (P < 0.001) and validation (P = 0.020) sets. Furthermore, the risk score was identified as an independent predictor of OS (P < 0.001, HR = 8.005). The incorporation of the risk score and clinicopathologic features into a nomogram enabled the effective prediction of patient prognosis. The model was able to effectively predict the immune microenvironment, drug sensitivity to chemotherapy, and gene mutation for each group. Single-cell maps demonstrated that FAMGs in the model were distributed in tumor cells. Enrichment analyses revealed that the cell cycle, fatty acid β oxidation and PPAR signaling pathways were the most significant pathways. Among the four key prognostically related FAMGs, Spermine Synthase (SMS) was selected and validated as a potential oncogene affecting cell cycle, PPAR-γ signaling pathway and fatty acid β oxidation in HCC.The risk characteristics based on FAMGs could serve as independent prognostic indicators for predicting HCC prognosis and could also serve as evaluation criteria for gene mutations, immunity, and chemotherapy drug therapy in HCC patients. Meanwhile, targeted fatty acid metabolism could be used to treat HCC through related signaling pathways.Copyright © 2024. Published by Elsevier B.V.