The Landscape of Iron Metabolism-Related and Methylated Genes in the Prognosis Prediction of Clear Cell Renal Cell Carcinoma

Yanhua Mou; Yao Zhang; Jinchun Wu; Busheng Hu; Chunfang Zhang; Chaojun Duan; Bin Li

doi:10.3389/fonc.2020.00788

The Landscape of Iron Metabolism-Related and Methylated Genes in the Prognosis Prediction of Clear Cell Renal Cell Carcinoma

Front Oncol. 2020 May 22:10:788. doi: 10.3389/fonc.2020.00788. eCollection 2020.

Authors

Yanhua Mou^{1

2}, Yao Zhang³, Jinchun Wu¹, Busheng Hu¹, Chunfang Zhang⁴, Chaojun Duan^{2

4}, Bin Li^{1

5}

Affiliations

¹ Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.
² Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Institute of Medical Sciences, Xiangya Hospital, Central South University, Changsha, China.
³ State Key Laboratory of Reproductive Medicine and Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, China.
⁵ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is characteristics of resistance to chemotherapy and radiotherapy. The prognosis of ccRCC was dismay with immense diversity. Iron metabolism disturbance is a common phenomenon in ccRCC. The purpose of our study is to identify and validate the candidate prognostic gene signature of iron metabolism and methylation closely related to the poor prognosis of ccRCC through comprehensive bioinformatics analysis in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Methods: The prognostic iron metabolism-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. We built a prognostic model using risk score method to predict OS, each ccRCC patient's risk score was calculated, and the resulting score can divide these patients into two categories according to the cut-point risk score. The prognostic significance of the hub genes was further evaluated with the Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was implemented to evaluate the impact of each variable on OS. Furthermore, the prediction power of the 25 gene signatures has been validated using an independent ccRCC cohort from the GEO database. The Gene Set Enrichment Analysis (GSEA) identified the characteristics of hub related oncogenes. Finally, we utilize Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the co-expression network based on these DEGs. Results: In this study, we identified and validated 25 iron metabolism-related and methylated genes as the prognostic signatures, which differentiated ccRCC patients into high and low risk subgroups. The KM analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 25 gene signatures could largely predict OS and DFS for 1, 3, and 5 years in patients with ccRCC. Conclusions: Taken together, we identified the key iron metabolism-related and methylated genes for ccRCC through a comprehensive bioinformatics analysis. This study provides a reliable and robust gene signature for the prognostic predictor of ccRCC patients and maybe provides a promising treatment strategy for this lethal disease.

Keywords: DEGs; GSEA; WGCNA; ccRCC; iron metabolism; methylation.