Table4_A novel prognostic model based on urea cycle-related gene signature for colorectal cancer.xlsx

BackgroundColorectal cancer (CRC) is the second leading cause of cancer-related deaths in the world. This study aimed to develop a urea cycle (UC)-related gene signature that provides a theoretical foundation for the prognosis and treatment of patients with CRC.
MethodsDifferentially expressed UC-related genes in CRC were confirmed using differential analysis and Venn diagrams. Univariate Cox and least absolute shrinkage and selection operator regression analyses were performed to identify UC-related prognostic genes. A UC-related signature was created and confirmed using distinct datasets. Independent prognostic predictors were authenticated using Cox analysis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts algorithm and Spearman method were applied to probe the linkage between UC-related prognostic genes and tumor immune-infiltrating cells. The Human Protein Atlas database was used to determine the protein expression levels of prognostic genes in CRC and normal tissues. Verification of the expression levels of UC-related prognostic genes in clinical tissue samples was performed using real-time quantitative polymerase chain reaction (qPCR).
ResultsA total of 49 DEUCRGs in CRC were mined. Eight prognostic genes (TIMP1, FABP4, MMP3, MMP1, CD177, CA2, S100P, and SPP1) were identified to construct a UC-related gene signature. The signature was then affirmed using an external validation set. The risk score was demonstrated to be a credible independent prognostic predictor using Cox regression analysis. Functional enrichment analysis revealed that focal adhesion, ECM-receptor interaction, IL-17 signaling pathway, and nitrogen metabolism were associated with the UC-related gene signature. Immune infiltration and correlation analyses revealed a significant correlation between UC-related prognostic genes and differential immune cells between the two risk subgroups. Finally, the qPCR results of clinical samples further confirmed the results of the public database.
ConclusionTaken together, this study authenticated UC-related prognostic genes and developed a gene signature for the prognosis of CRC, which will be of great significance in the identification of prognostic molecular biomarkers, clinical prognosis prediction, and development of treatment strategies for patients with CRC.

背景：结直肠癌（Colorectal cancer, CRC）是全球范围内第二大癌症相关死亡病因。本研究旨在构建一种尿素循环（Urea Cycle, UC）相关基因特征模型，为结直肠癌患者的预后评估与临床治疗提供理论依据。
方法：首先通过差异表达分析与韦恩图，筛选并确认结直肠癌组织中差异表达的尿素循环相关基因；随后采用单因素Cox回归与最小绝对收缩与选择算子回归分析，筛选出与预后相关的尿素循环基因；基于独立数据集构建并验证尿素循环相关基因特征模型；通过Cox回归分析验证该模型为独立预后预测因子；采用基于RNA转录本相对亚群丰度估算的细胞类型鉴定算法（Cell-type Identification by Estimating Relative Subsets of RNA Transcripts）与斯皮尔曼相关分析法，探究尿素循环相关预后基因与肿瘤免疫浸润细胞之间的关联；借助人类蛋白质图谱（Human Protein Atlas）数据库，检测预后基因在结直肠癌与正常组织中的蛋白表达水平；通过实时定量聚合酶链反应（real-time quantitative polymerase chain reaction, qPCR）验证临床组织样本中尿素循环相关预后基因的表达水平。
结果：本研究共筛选得到49个结直肠癌差异表达尿素循环相关基因（differentially expressed UC-related genes, DEUCRGs）；最终筛选出8个预后相关基因（TIMP1、FABP4、MMP3、MMP1、CD177、CA2、S100P及SPP1）用于构建尿素循环相关基因特征模型，并通过外部验证数据集对该模型进行了验证；Cox回归分析证实，风险评分是可靠的独立预后预测因子；功能富集分析显示，黏着斑、细胞外基质-受体相互作用、白细胞介素-17信号通路及氮代谢过程与该尿素循环相关基因特征模型密切相关；免疫浸润与相关性分析表明，尿素循环相关预后基因与两个风险亚组间的差异免疫细胞存在显著关联；最后，临床样本的qPCR验证结果进一步证实了公共数据库的分析结论。
结论：综上，本研究明确了结直肠癌中与预后相关的尿素循环基因，并构建了用于结直肠癌预后评估的基因特征模型，该模型可为结直肠癌患者预后分子生物标志物的筛选、临床预后预测及治疗策略制定提供重要参考价值。