Wang Yajie, Xiao Lin, Pan Yisheng
Department of Gastrointestinal Surgery, Peking University First Hospital, 8 Xishku Street, Xicheng District, Beijing, 100034, People's Republic of China.
Discov Oncol. 2024 Aug 2;15(1):332. doi: 10.1007/s12672-024-01216-5.
This study aimed to elucidate the predictive role of an oxidative stress-related genes (OSRGs) model in colon cancer.
First, OSRGs that were differentially expressed between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA)-(Colorectal Adenocarcinoma) COAD dataset. Then, Lasso COX regression was performed to develop an optimal prognostic model patients were stratified into high- and low-risk groups based on the expression patterns of these genes. The model's validity was confirmed through Kaplan-Meier survival curves and receiver operating characteristic curve (ROC) analysis. Additionally, enrichment analyses were performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to uncover underlying mechanisms.
A totally of 115 differentially expressed OSRGs were identified within the TCGA cohort, with 17 significantly linked to overall survival. These 17 genes were used to formulate a prognostic model that differentiated patients into distinct risk groups, with the high-risk group demonstrating a notably inferior overall survival rate. The risk score, when integrated with clinical and pathological data, emerged as an independent prognostic indicator of colon cancer. Further analyses revealed that the disparity in prognostic outcomes between risk groups could be attributed to the reactive oxygen species pathway and the p53 signaling pathway.
A new prediction model was established based on OSRGs. CYP19A1, NOL3 and UCN were found to be highly expressed in tumor tissues and substantial clinical predictive significance. These findings offer new insights into the role of oxidative stress in colon cancer.
本研究旨在阐明氧化应激相关基因（OSRGs）模型在结肠癌中的预测作用。
首先，使用癌症基因组图谱（TCGA）-（结肠腺癌）COAD数据集鉴定肿瘤组织和正常组织之间差异表达的OSRGs。然后，进行Lasso COX回归以建立最佳预后模型，根据这些基因的表达模式将患者分为高风险和低风险组。通过Kaplan-Meier生存曲线和受试者工作特征曲线（ROC）分析证实了该模型的有效性。此外，使用基因本体论（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA）进行富集分析，以揭示潜在机制。
在TCGA队列中总共鉴定出115个差异表达的OSRGs，其中17个与总生存期显著相关。这17个基因用于构建一个预后模型，将患者分为不同的风险组，高风险组的总生存率明显较低。风险评分与临床和病理数据相结合后，成为结肠癌的独立预后指标。进一步分析表明，风险组之间预后结果的差异可归因于活性氧途径和p53信号通路。
基于OSRGs建立了一种新的预测模型。发现CYP19A1、NOL3和UCN在肿瘤组织中高表达，并具有重要的临床预测意义。这些发现为氧化应激在结肠癌中的作用提供了新的见解。
