Identification of 8 Feature Genes Related to Clear Cell Renal Cell Carcinoma Progression Based on Co-Expression Analysis

• Published in: Kidney & blood pressure research Vol. 47; no. 2; pp. 113 - 124
• Main Authors: Yu, Xiaoxia, Wu, Hua, Wang, Hongmei, Dong, He, Gao, Bihu
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.02.2022; Karger Publishers
• Subjects: Biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Cancer therapies; Carcinoma, Renal Cell - genetics; Cell activation; Cell proliferation; Clear cell-type renal cell carcinoma; Correlation analysis; Encyclopedias; Gene expression; Gene Expression Regulation, Neoplastic; Genes; Genomes; Humans; Immune system; Infiltration; Kidney cancer; Kidney Neoplasms - genetics; Leukocyte migration; Lung cancer; Lymphocytes; Lymphocytes T; Medical prognosis; Metabolism; Modules; Multivariate analysis; Network analysis; Patients; Prognosis; Protein interaction; Proteins; Regression analysis; Research Article; Risk; Risk groups; Survival analysis; Biomarker; Clear cell renal cell carcinoma; Weighted gene correlation network analysis; Prognosis
• ISSN: 1420-4096; 1423-0143
• DOI: 10.1159/000520832

Background: The aim of the study was to screen biomarkers related to clear cell renal cell carcinoma (ccRCC) progression and prognosis. Methods: 1,026 ccRCC-related genes were dug from 494 ccRCC samples in TCGA based on weighted gene co-expression network analysis, and 7 modules were identified. Afterward, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted on modules of interest. Genes in these modules were taken as the input to construct a protein-protein interaction network. Thereafter, 30 genes with the highest connectivity were taken as core genes. Univariate Cox regression, LASSO Cox regression, and multivariate Cox regression analyses were performed on core genes. Univariate and multivariate Cox regression analyses were performed on patients’ clinical characteristics and risk scores. Results: Stage displayed significantly strong correlations with green module and red module (p < 0.001). Genes in modules participated in biological functions including T-cell proliferation and regulation of lymphocyte activation. GSEA showed that high- and low-risk groups exhibited significant enrichment differences in pathways related to immunity, cell migration, and invasion. Immune infiltration analysis also presented a strong correlation between the expression of these 8 genes and immune cell infiltration in ccRCC samples. It was displayed that risk score could be an independent factor to assess patients’ prognosis. Conclusion: We determined biomarkers relevant to ccRCC progression, offering candidate targets for ccRCC treatment.