Zhan Yating, Weng Min, Guo Yangyang, Lv Dingfeng, Zhao Feng, Yan Zejun, Jiang Junhui, Xiao Yanyi, Yao Lili
Department of Blood Transfusion, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Department of Urology, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Front Immunol. 2024 Dec 17;15:1465638. doi: 10.3389/fimmu.2024.1465638. eCollection 2024.
Several studies indicate that smoking is one of the major risk factors for bladder cancer. Nicotine and its metabolites, the main components of tobacco, have been found to be strongly linked to the occurrence and progression of bladder cancer. However, the function of nicotine metabolism-related genes (NRGs) in bladder urothelial carcinoma (BLCA) are still unclear.
NRGs were collected from MSigDB to identify the clusters associated with nicotine metabolism. Prognostic differentially expressed genes (DEGs) were filtered via differentially expression analysis and univariate Cox regression analysis. Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. Subsequently, the clinical application of signature in terms of prognosis, tumor microenvironment (TME) as well as immunotherapy was comprehensively evaluated. Finally, the biology function of the signature gene was further verified via CCK-8, transwell migration and colony formation.
Three clusters associated with nicotine metabolism were discovered with distinct prognosis and immunological patterns. A four gene-signature was developed by random survival forest (RSF) method with highest average Harrell's concordance index (C-index) of 0.763. The signature exhibited a reliable and accurate performance in prognostic prediction across TCGA-train, TCGA-test and GSE32894 cohorts. Furthermore, the signature showed highly correlation with clinical characteristics, TME and immunotherapy responses. Suppression of MKRN1 was found to reduce the migration and proliferation of bladder cancer cell. In addition, enhanced migration and proliferation caused by nicotine was blocked down by loss of MKRN1.
The novel nicotine metabolism-related signature may provide valuable insights into clinical prognosis and potential benefits of immunotherapy in bladder cancer patients.
多项研究表明，吸烟是膀胱癌的主要危险因素之一。尼古丁及其代谢产物作为烟草的主要成分，已被发现与膀胱癌的发生和发展密切相关。然而，尼古丁代谢相关基因（NRGs）在膀胱尿路上皮癌（BLCA）中的作用仍不清楚。
从MSigDB中收集NRGs以识别与尼古丁代谢相关的簇。通过差异表达分析和单变量Cox回归分析筛选出预后差异表达基因（DEGs）。基于10种机器学习算法的集成机器学习组合用于构建稳健的特征。随后，全面评估了该特征在预后、肿瘤微环境（TME）以及免疫治疗方面的临床应用。最后，通过CCK-8、Transwell迁移和集落形成进一步验证了特征基因的生物学功能。
发现了三个与尼古丁代谢相关的簇，具有不同的预后和免疫模式。采用随机生存森林（RSF）方法开发了一个四基因特征，平均Harrell一致性指数（C-index）最高为0.763。该特征在TCGA训练、TCGA测试和GSE32894队列的预后预测中表现出可靠且准确的性能。此外，该特征与临床特征、TME和免疫治疗反应高度相关。发现抑制MKRN1可降低膀胱癌细胞的迁移和增殖。此外，MKRN1的缺失可阻断尼古丁引起的迁移和增殖增强。
新的尼古丁代谢相关特征可能为膀胱癌患者的临床预后和免疫治疗的潜在益处提供有价值的见解。
