结直肠癌（CRC）是影响胃肠道的最常见恶性肿瘤。大量研究表明，基底膜 (BM) 可能在疾病的发生和进展中发挥着至关重要的作用。结直肠癌患者的 RNA 表达模式和临床病理信息数据来源于癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据库。使用单变量 Cox 回归和组合机器学习技术制定了用于预测总体生存 (OS) 的 BM 相关风险特征。还研究了不同风险分类下的生存结果、功能途径、肿瘤微环境（TME）以及对免疫治疗和化疗的反应。通过逆转录聚合酶链反应（RT-PCR）和人类蛋白质图谱（HPA）数据库评估模型基因的表达趋势。包含UNC5C、TINAG、TIMP1、SPOCK3、MMP1、AGRN、UNC5A的九个基因风险特征、ADAMTS4 和 ITGA7 是为了预测 CRC 患者的结果而构建的。使用RT-PCR和HPA数据库验证这些候选基因的表达谱，发现这些候选基因的表达谱与TCGA数据集中差异基因表达的结果一致。使用 GEO 队列确认了签名的有效性。根据临床病理特征、TME特征、富集功能和药物敏感性的差异，将患​​者分为不同的危险组。最后，基于风险评分的预后列线图模型被发现可有效识别高危患者并预测 OS。构建了基底膜相关风险特征并发现可有效预测 CRC 患者的预后。版权© 2024 盛笑、欣欣、云翔、志杰、小飞。

Colorectal cancer (CRC) is the most common malignancy affecting the gastrointestinal tract. Extensive research indicates that basement membranes (BMs) may play a crucial role in the initiation and progression of the disease.Data on the RNA expression patterns and clinicopathological information of patients with CRC were sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A BM-linked risk signature for the prediction of overall survival (OS) was formulated using univariate Cox regression and combined machine learning techniques. Survival outcomes, functional pathways, the tumor microenvironment (TME), and responses to both immunotherapy and chemotherapy within varying risk classifications were also investigated. The expression trends of the model genes were evaluated by reverse transcription polymerase chain reaction (RT-PCR) and the Human Protein Atlas (HPA) database.A nine-gene risk signature containing UNC5C, TINAG, TIMP1, SPOCK3, MMP1, AGRN, UNC5A, ADAMTS4, and ITGA7 was constructed for the prediction of outcomes in patients with CRC. The expression profiles of these candidate genes were verified using RT-PCR and the HPA database and were found to be consistent with the findings on differential gene expression in the TCGA dataset. The validity of the signature was confirmed using the GEO cohort. The patients were stratified into different risk groups according to differences in clinicopathological characteristics, TME features, enrichment functions, and drug sensitivities. Lastly, the prognostic nomogram model based on the risk score was found to be effective in identifying high-risk patients and predicting OS.A basement membrane-related risk signature was constructed and found to be effective for predicting the prognosis of patients with CRC.Copyright © 2024 Shengxiao, Xinxin, Yunxiang, Zhijie and Xiaofei.