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Updated: May 21, 2026
The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Development and Validation of a Cuproptosis-Related Gene Signature for Prognostic Stratification in Papillary Renal
Background:
Cuproptosis is defined as a novel form of regulated cell death triggered by copper accumulation, with emerging evidence linking cuproptosis-related genes (CRGs) to tumor progression. However, the prognostic relevance of CRGs in papillary renal cell carcinoma (PRCC) remains elusive. This study aimed to construct and validate a cuproptosis-related gene prognostic signature for PRCC, and to explore its potential value in risk stratification, immune infiltration, and pathogenesis.
Methods:
Transcriptomic profiles and clinical data were sourced from the Cancer Genome Atlas Program. Univariate Cox regression assessed the prognostic potential of 17 CRGs, while Lasso-penalized Cox regression identified risk genes for signature construction. Kaplan-Meier curves illustrated survival probabilities, and receiver operating characteristic curves were drawn to evaluate signature predictive performance. External validation was performed using data from the Gene Expression Omnibus. Biological functions were explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, with validation via gene set variation analysis (GSVA). Immune cell infiltration was assessed using QuanTIseq algorithm.
Results:
The PRCC cohort from The Cancer Genome Atlas Program (TCGA) was randomly split into training (n = 183) and validation (n = 75) sets, with GSE2748 (n = 29) from GEO for external validation. Univariate Cox analysis identified 7 CRGs as prognostic factors in PRCC. A risk signature comprising 5 CRGs (DLST, PDHB, SLC25A3, ATP7A, and GLS) was developed using Lasso-penalized Cox regression. Kaplan-Meier curves indicated lower survival probabilities in patients with higher risk scores. ROC analysis demonstrated the cuproptosis risk signature's strong performance for overall survival at 1, 3, and 5 years. GO and KEGG analyses revealed that metabolic processes were the predominant pathways enriched in DEGs between high- and low-risk groups, a finding further validated by GSVA. Immune infiltration analysis highlighted macrophages and neutrophils as the dominant immune cells, with significantly higher Tregs observed in the tumor microenvironment of high-risk patients (p < 0.05).
Conclusions:
This study presents a novel five-gene prognostic signature for PRCC based on 17 CRGs, offering promising performance in risk stratification and providing insights into PRCC pathogenesis, particularly in cellular metabolism.
