Research Paper Volume 13, Issue 6 pp 8276—8289
Identification and validation of a two-gene metabolic signature for survival prediction in patients with kidney renal clear cell carcinoma
- 1 Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- 2 Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong, China
Received: September 2, 2020 Accepted: October 22, 2020 Published: March 3, 2021
https://doi.org/10.18632/aging.202636How to Cite
Copyright: © 2021 Guo 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
Metabolic reprogramming contributes to the high mortality of advanced stage kidney renal clear cell carcinoma (KIRC), the most common renal cancer subtype. This study aimed to identify a metabolism-related gene (MRG) signature to improve survival prediction in KIRC patients. We downloaded RNA sequencing data and corresponding clinical information for KIRC and control samples from The Cancer Genome Atlas database and identified, based on an MRG dataset in the Molecular Signatures Database, 123 MRGs with differential expression in KIRC. Following Cox regression analysis and least absolute shrinkage and selection operator selection, RRM2 and ALDH6A1 were identified as prognosis-related genes and used to construct a prognostic signature with independent prognostic significance. After risk score-based patient separation, stratified survival analysis indicated that high-risk patients showed poorer overall survival than low-risk patients. We then constructed a clinical nomogram that showed a concordance index of 0.774 and good performance based upon calibration curves. Gene set enrichment analysis revealed several metabolic pathways significantly enriched in the target genes. The two-gene metabolic signature identified herein may represent a highly valuable tool for KIRC prognosis prediction, and might also help identify new metabolism-related biomarkers and therapeutic targets for KIRC.
Abbreviations
AJCC: The American Joint Committee on Cancer; AUC: area under the ROC curve; BP: biological process; CC: cellular component; C-index: concordance index; DCA: decision curve analysis; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSEA: Gene set enrichment analysis; HR: hazard ratio; KEGG: Kyoto Encyclopedia of Genes and Genomes; KIRC: kidney renal clear cell carcinoma; LASSO: least absolute shrinkage and selection operator; MF: molecular function; MRG: metabolism-related gene; OS: overall survival; PCA: principal component analysis; RCC: renal cell carcinoma; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas.