Research Paper Volume 13, Issue 24 pp 25980—26002
Identification of a competing endogenous RNA network related to immune signature in clear cell renal cell carcinoma
- 1 Department of Urology, West China Hospital of Sichuan University, Chengdu, China
- 2 Department of Oncology, Chengdu Fifth People’s Hospital of Chengdu University of TCM, Chengdu, China
- 3 Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- 4 Department of Traumatology, Chongqing University Central Hospital, Chongqing, China
- 5 Department of Hepatobiliary Surgery, Daping Hospital, Army Medical University, Chongqing, China
- 6 Department of Integrated Care Management Center, West China Hospital of Sichuan University, Chengdu, China
Received: July 23, 2021 Accepted: December 8, 2021 Published: December 27, 2021
https://doi.org/10.18632/aging.203784How to Cite
Copyright: © 2021 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Clear cell renal cell carcinoma (ccRCC) is a fatal cancer of the urinary system. Long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) involving the ccRCC progression. However, the relationship between the ceRNA network and immune signature is largely unknown. In this study, the ccRCC-related gene expression profiles retrieved from the TCGA database were used first to identify the differentially expressed genes through differential gene expression analysis and weighted gene co-expression network analysis. The interaction among differentially expressed lncRNAs, miRNAs, and mRNAs were matched using public databases. As a result, a ceRNA network was developed that contained 144 lncRNAs, 23 miRNAs, as well as 62 mRNAs. Four of 144 lncRNAs including LINC00943, SRD5A3-AS1, LINC02345, and U62317.3 were identified through LASSO regression and Cox regression analyses, and were used to create a prognostic risk model. Then, the ccRCC samples were divided into the high- and low-risk groups depending on their risk scores. ROC curves, Kaplan-Meier survival analysis, and the survival risk plots indicated that the predictive performance of our developed risk model was accurate. Moreover, the CIBERSORT algorithm was used to measure the infiltration levels of immune cells in the ccRCC samples. The further genomic analysis illustrated a positive correlation between most immune checkpoint blockade-related genes and the risk score. In conclusion, the present findings effectually contribute to the comprehensive understanding of the ccRCC pathogenesis, and may offer a reference for developing novel therapeutic and prognostic biomarkers.
Abbreviations
ccRCC: Clear cell renal cell carcinoma; lncRNAs: Long non-coding RNAs; ceRNA: Competing endogenous RNA; TCGA: The cancer genome atlas; WGCNA: Weighted gene co-expression network analysis; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; FDR: False discovery rate; FC: Fold change; LASSO: Least absolute shrinkage and selection operator; ICB: Immune checkpoint blockade; RCC: Renal cell carcinoma; ncRNAs: Non-coding RNAs; miRNAs: MicroRNAs; MREs: miRNA response elements; mRNAs: messenger RNAs; TME: Tumor microenvironment; Tregs: Regulatory T cells; TOM: Topological overlap matrix; 1-TOM: Dissimilarity; DEGs: Differentially expressed genes; MF: Molecular function; CC: Cellular component; BP: Biological process; ROC: Receiver operator characteristic; OS: Overall survival; AUC: Area under the curve; PCA: Principal component analysis; t-SNE: T-distributed stochastic neighbor embedding; MM: Module membership; GS: Gene significance; CTLA4: Cytotoxic T lymphocyte-associated antigen 4; PD-1: Programmed cell death protein 1; PD-L1: PD-1 ligand 1; CAR: Chimeric antigen receptor.