Lu Jie, Shi Lili, Zhang Caiming, Zhang Fabiao, Cai Miaoguo
Department of Hepatobiliary Surgery, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, China.
Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, China.
J Chemother. 2024 Jul 30:1-17. doi: 10.1080/1120009X.2024.2385266.
Hepatocellular carcinoma (HCC), as a malignancy derived from liver tissue, is typically associated with poor prognosis. Increasing evidence suggests a connection between pyrimidine metabolism and HCC progression. The purpose of this study was to establish a model applied to the prediction of HCC patients' overall survival. Transcriptomic data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) website. Pyrimidine metabolism-related genes (PMRGs) were collected from the Gene Set Enrichment Analysis (GSEA) website. Differential gene expression analysis was carried out on the HCC data, followed by an intersection of the differentially expressed genes (DEGs) and PMRGs. Subsequently, a prognostic model incorporating nine genes was established using univariate/multivariate Cox regression and Least absolute shrinkage and selection operator (LASSO) regression. Survival analysis demonstrated that the high-risk group defined by this model had considerably shorter overall survival than the low-risk group in both TCGA and Gene Expression Omnibus (GEO) datasets. Receiver operating characteristic (ROC) analysis indicated the good predictive capability of the model. CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms revealed significantly higher levels of Macrophages M0 and lower levels of natural killer (NK)_cells in the high-risk group compared to the low-risk group. The immunophenoscore (IPS) and the tumor immune dysfunction and exclusion (TIDE) score demonstrated that the model could significantly differentiate patients who would be more suitable for immunotherapy. Moreover, the CellMiner database was utilized to predict anti-tumor drugs significantly associated with the model genes. Collectively, the potential prognostic significance of pyrimidine metabolism in HCC was revealed in this study. The prognostic model aids in evaluating the survival time and immune status of HCC patients.
肝细胞癌（HCC）作为一种源自肝组织的恶性肿瘤，通常预后较差。越来越多的证据表明嘧啶代谢与HCC进展之间存在关联。本研究的目的是建立一个用于预测HCC患者总生存期的模型。从癌症基因组图谱（TCGA）网站下载HCC患者的转录组数据。从基因集富集分析（GSEA）网站收集嘧啶代谢相关基因（PMRG）。对HCC数据进行差异基因表达分析，随后将差异表达基因（DEG）与PMRG进行交集分析。随后，使用单变量/多变量Cox回归和最小绝对收缩和选择算子（LASSO）回归建立了一个包含9个基因的预后模型。生存分析表明，在TCGA和基因表达综合数据库（GEO）数据集中，该模型定义的高危组的总生存期明显短于低危组。受试者工作特征（ROC）分析表明该模型具有良好的预测能力。CIBERSORT和单样本基因集富集分析（ssGSEA）算法显示，与低危组相比，高危组中M0巨噬细胞水平显著升高，自然杀伤（NK）细胞水平降低。免疫表型评分（IPS）和肿瘤免疫功能障碍与排除（TIDE）评分表明，该模型可以显著区分更适合免疫治疗的患者。此外，利用CellMiner数据库预测与模型基因显著相关的抗肿瘤药物。总的来说，本研究揭示了嘧啶代谢在HCC中的潜在预后意义。该预后模型有助于评估HCC患者的生存时间和免疫状态。
