International Journal of General Medicine (Oct 2021)
Prognostic Value of Metabolism-Related Genes and Immune Infiltration in Clear Cell Renal Cell Carcinoma
Abstract
Hanwen Li,1– 4 Zengnan Mo1– 4 1Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People’s Republic of China; 2Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, People’s Republic of China; 3Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, 530021, Guangxi, People’s Republic of China; 4Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Key Laboratory of Colleges and Universities, Nanning, 530021, Guangxi, People’s Republic of ChinaCorrespondence: Zengnan MoCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, People’s Republic of ChinaTel +86-13878893666Email [email protected]: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent cancers. Thus, it is warranted to detect the status of metabolism-related genes (MRGs) and infiltrating immune cells in ccRCC progression for the prognosis of ccRCC. This research was designed to establish and verify the prognostic signature of ccRCC using MRGs. In addition, we investigated the potential link between the relative proportion of tumor infiltrated immune cells (TIICs) and ccRCC prognosis.Methods: Sequencing data of metabolism-related gene sets in ccRCC cases were obtained from The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus Database (GEO). The R Programming Language software packages were applied for differential analysis of MRGs. First, a univariate Cox regression model was applied to determine the MRGs linked with overall survival (OS). Then, the multivariate Cox regression model was applied to establish the prognostic signature. Finally, the CIBERSORT algorithm was used to determine the proportion of TIICs.Results: Overall, 286 differentially expressed MRGs were identified in the TCGA dataset. Univariate and multivariate Cox regression models were applied to develop a prognostic signature with six MRGs. The predictive capability of the prognostic signature was further verified by TCGA and GEO database. In addition, RS positively correlated with memory B cells, plasma cells, activated memory CD4+ T cells, follicular helper T cells, regulatory T cells, CD8+ T cells, and M0 macrophages, and were negatively associated with resting memory CD4+ T cells, resting dendritic cells, activated dendritic cells, M2 macrophages, monocytes, resting mast cells, and eosinophils.Conclusion: Herein, a prognostic signature was developed using MRGs for ccRCC prognosis. The proportion of 22 TIICs in ccRCC and the association between TIICs and clinical outcomes were also determined. The identified genes and cells could guide future targeted therapy and immunotherapy.Keywords: altered metabolism, immunization, clear cell renal cell carcinoma, prognostic markers
