Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma

Frontiers in Oncology

Xiaojing Zhang¹  Ziheng Wang¹  Bing Lu¹  Wei Wang²  Shiqi Ren²  Chen Chen³  Chenlin Zhang⁴  Yingjing Wang⁵  Mengjing Sun⁵  Haiyan Hao⁶  Hanyu Shen⁷ 

[1] Department of Clinical Biobank, Nantong University Affiliated Hospital, Nantong, China;Department of Medicine, Nantong University Xinling College, Nantong, China;Department of Oncology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China;Department of Orthopaedics, Qidong Hospital of Chinese Medicine, Nantong, China;Department of Pathology, Medical School of Nantong University, Nantong, China;Department of Urology, Affiliated Hospital of Nantong University, Nantong, China;Medical School of Nantong University, Nantong, China;

关键词: clear cell renal cell carcinoma;    TCGA;    GEO;    immune-related genes;    clinical prognostic model;    tumor microenvironment;   

DOI  :  10.3389/fonc.2020.01496

来源: DOAJ

【 摘 要 】

Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs).Methods: Based on TCGA data, we incorporated IRGs and their expression profiles of 72 normal and 539 ccRCC samples. Univariate Cox analysis was used to evaluate the relationship between overall survival (OS) and IRGs expression. The Lasso Cox regression model identified prognostic genes used to establish a clinical immune prognostic model. The TF–IRG network was used to study the potential molecular mechanisms of action and properties of ccRCC-specific IRGs. Multivariate Cox analysis established a clinical prognostic model of IRGs.Results: We found a significant correlation among 15 differentially expressed IRGs with the OS of patients with ccRCC. Gene function enrichment analysis showed that these IRGs are significantly associated with response to receptor ligand activity. Lasso Cox regression analysis identified 10 genes with the greatest prognostic value. A clinical prognostic model based on six IRGs, which performed well for predicting prognosis, revealed significant associations of patients' survival with age, sex, stage, tumor, node, and metastasis. Moreover, these findings reflect the infiltration of tumors by various immune cells.Conclusion: We identified six clinically significant IRGs and incorporated them into a clinical prognostic model with great significance for monitoring and predicting prognosis of ccRCC.

【 授权许可】

Unknown