Prognostic significance of pyrimidine metabolism-related genes as risk biomarkers in hepatocellular carcinoma.

IF 1.9 4区医学 Q3 INFECTIOUS DISEASES

Journal of Chemotherapy Pub Date : 2024-07-30 DOI:10.1080/1120009X.2024.2385266

Jie Lu, Lili Shi, Caiming Zhang, Fabiao Zhang, Miaoguo Cai

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引用次数: 0

Abstract

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.

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嘧啶代谢相关基因作为肝细胞癌风险生物标志物的预后意义。

肝细胞癌（HCC）是一种源自肝组织的恶性肿瘤，通常预后不良。越来越多的证据表明，嘧啶代谢与 HCC 进展之间存在联系。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。HCC患者的转录组数据从癌症基因组图谱（TCGA）网站下载。嘧啶代谢相关基因（PMRGs）来自基因组富集分析（Gene Set Enrichment Analysis，GSEA）网站。对 HCC 数据进行了差异基因表达分析，然后对差异表达基因（DEGs）和 PMRGs 进行了交叉分析。随后，利用单变量/多变量考克斯回归和最小绝对收缩和选择算子（LASSO）回归建立了包含九个基因的预后模型。生存分析表明，在TCGA和Gene Expression Omnibus（GEO）数据集中，该模型定义的高危组的总生存期大大短于低危组。接收者操作特征（ROC）分析表明该模型具有良好的预测能力。CIBERSORT和单样本基因组富集分析（ssGSEA）算法显示，与低风险组相比，高风险组的巨噬细胞M0水平明显较高，而自然杀伤（NK）细胞水平较低。免疫表观评分（IPS）和肿瘤免疫功能障碍与排斥评分（TIDE）表明，该模型能明显区分出更适合接受免疫疗法的患者。此外，还利用 CellMiner 数据库预测了与模型基因显著相关的抗肿瘤药物。总之，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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemotherapy 医学-药学

CiteScore

3.70

自引率

0.00%

发文量

144

审稿时长

6-12 weeks

期刊介绍： The Journal of Chemotherapy is an international multidisciplinary journal committed to the rapid publication of high quality, peer-reviewed, original research on all aspects of antimicrobial and antitumor chemotherapy. The Journal publishes original experimental and clinical research articles, state-of-the-art reviews, brief communications and letters on all aspects of chemotherapy, providing coverage of the pathogenesis, diagnosis, treatment, and control of infection, as well as the use of anticancer and immunomodulating drugs. Specific areas of focus include, but are not limited to: · Antibacterial, antiviral, antifungal, antiparasitic, and antiprotozoal agents; · Anticancer classical and targeted chemotherapeutic agents, biological agents, hormonal drugs, immunomodulatory drugs, cell therapy and gene therapy; · Pharmacokinetic and pharmacodynamic properties of antimicrobial and anticancer agents; · The efficacy, safety and toxicology profiles of antimicrobial and anticancer drugs; · Drug interactions in single or combined applications; · Drug resistance to antimicrobial and anticancer drugs; · Research and development of novel antimicrobial and anticancer drugs, including preclinical, translational and clinical research; · Biomarkers of sensitivity and/or resistance for antimicrobial and anticancer drugs; · Pharmacogenetics and pharmacogenomics; · Precision medicine in infectious disease therapy and in cancer therapy; · Pharmacoeconomics of antimicrobial and anticancer therapies and the implications to patients, health services, and the pharmaceutical industry.