目的: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。
方法:从基因表达综合(GEO)和癌症基因组图谱(TCGA)数据库获得肝癌的RNA-seq和临床数据。生物信息学分析用于鉴定与预后相关的差异表达基因(DEGs)。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患者分为低风险和高风险组。通过主成分分析(PCA)评估签名的预测性能,卡普兰·迈耶(KM)生存分析,接收机工作特性(ROC)曲线,列线图,基因突变,药物敏感性分析,免疫学相关分析,和富集分析。构建单细胞图谱以说明核心基因的分布。免疫组织化学(IHC),实时定量PCR(qRT-PCR),采用Westernblot验证核心基因的表达。通过一系列体外实验验证了一个核心基因的功能,包括细胞活力,菌落形成,伤口愈合,跨井迁移,和入侵检测。在相关信号通路的背景下分析结果。
结果:生物信息学分析确定了15个与预后相关的FAMGs。构建了4个基因签名,根据特征将患者分为高危组和低危组。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的预后。该模型能够有效预测免疫微环境,对化疗的药物敏感性,和每个组的基因突变。单细胞图谱表明模型中的FAMG分布在肿瘤细胞中。富集分析显示细胞周期,脂肪酸β氧化和PPAR信号通路是最重要的通路。在四个关键的预后相关FAMG中,精胺合成酶(SMS)被选择并验证为影响细胞周期的潜在癌基因,肝癌中PPAR-γ信号通路与脂肪酸β氧化.
结论:基于FAMGs的风险特征可以作为预测HCC预后的独立预后指标,也可以作为基因突变的评估标准。豁免权,肝癌患者的化疗药物治疗。同时,靶向脂肪酸代谢可通过相关信号通路治疗肝癌。
OBJECTIVE: This study aimed to explore novel targets for hepatocellular carcinoma (HCC) treatment by investigating the role of fatty acid metabolism.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.