A prognostic nomogram based on competing endogenous RNA network for clear‐cell renal cell carcinoma. (30th June 2021)

• Record Type: Journal Article
• Authors: Peng, Yun; Wu, Shangrong; Xu, Zihan; Hou, Dingkun; Li, Nan; Zhang, Zheyu; Wang, Lili; Wang, Haitao
• Is Part Of: Cancer medicine. Volume 10:Number 16(2021)
• Page Start: 5499
• Page End: 5512
• Publication Date: 2021-06-30
• Subjects: competing endogenous RNA -- gene signature -- nomograms -- renal cell carcinoma; 616.994005
• DOI: 10.1002/cam4.4109
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Background: Clear‐cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts in understanding the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) was involved in the development of varied tumors. However, a comprehensive analysis of the prognostic model based on lncRNA‐miRNA‐mRNA ceRNA regulatory network of ccRCC with large‐scale sample size and RNA‐sequencing expression data is still limited. Methods: RNA‐sequencing expression data were taken out from GTEx database and TCGA database, a total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC‐specific genes were obtained by WGCNA and differential expression analysis. Following, the communication of mRNAs and lncRNAs with targeted miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was further constructed by univariate Cox regression, Lasso methods, and multivariate Cox regression analysis. Results: A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dysregulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature including eight genes based on this ceRNA was determined followed by the development of a nomogram predicting 1‐, 3‐, and 5‐year survival probability for ccRCC. Conclusion: It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision. Abstract : We constructed a nomogram predicting 1‐, 3‐, and 5‐OS of ccRCC patients with a gene signature based on the ccRCC‐specific dysregulated ceRNA network and some other clinical factors. It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.