Wang Chengwei, Zhang Xi, Zhu Shiqing, Hu Bintao, Deng Zhiyao, Feng Huan, Liu Bo, Luan Yang, Liu Zhuo, Wang Shaogang, Liu Jihong, Wang Tao, Wu Yue
Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Heliyon. 2024 Aug 13;10(16):e36156. doi: 10.1016/j.heliyon.2024.e36156. eCollection 2024 Aug 30.
Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.
免疫细胞浸润和肿瘤相关免疫分子在肿瘤发生和肿瘤进展中起关键作用。免疫相互作用对透明细胞肾细胞癌（ccRCC）分子特征和预后的影响仍不清楚。应用机器学习算法对来自癌症基因组图谱数据库的转录组数据进行分析，以确定ccRCC患者的免疫表型和免疫特征。这些算法包括单样本基因集富集分析和细胞类型鉴定。利用生物信息学技术，我们研究了参与ccRCC免疫相互作用的免疫相关基因（IRG）的预后潜力和调控网络。确定了15个IRG（CCL7、CHGA、CMA1、CRABP2、IFNE、ISG15、NPR3、PDIA2、PGLYRP2、PLA2G2A、SAA1、TEK、TGFA、TNFSF14和UCN2）为与总生存期相关的预后IRG，并用于构建预后模型。1年时受试者工作特征曲线下面积为0.927；3年时为0.822；5年时为0.717，表明预测准确性良好。发现分子调控网络控制ccRCC中的免疫相互作用。此外，我们开发了一个包含该模型和具有高预后潜力的临床特征的列线图。通过系统研究ccRCC免疫相互作用的复杂调控机制、分子特征和预后潜力，我们为理解ccRCC的分子机制以及为未来研究确定新的预后标志物和治疗靶点提供了一个重要框架。
