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Updated: Aug 27, 2025
Comparing Metastatic Clear Cell Renal Cell Carcinoma Model Established in Mouse Kidney and on Chicken Chorioallantoic Membrane
Published on: February 8, 2020
A metabolic reprogramming-related prognostic risk model for clear cell renal cell carcinoma: From construction to
Qian Zhang1, Lei Ding1, Tianren Zhou1
1Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Abstract:
Metabolic reprogramming is one of the characteristics of clear cell renal cell carcinoma (ccRCC). Although some treatments associated with the metabolic reprogramming for ccRCC have been identified, remain still lacking. In this study, we identified the differentially expressed genes (DEGs) associated with clinical traits with a total of 965 samples via DEG analysis and weighted correlation network analysis (WGCNA), screened the prognostic metabolism-related genes, and constructed the risk score prognostic models. We took the intersection of DEGs with significant difference coexpression modules and received two groups of intersection genes that were connected with metabolism via functional enrichment analysis. Then we respectively screened prognostic metabolic-related genes from the genes of the two intersection groups and constructed the risk score prognostic models. Compared with the predicted effect of clinical grade and stage for ccRCC patients, finally, we selected the model constructed with genes of ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, and FBP1. The risk scores of the prognostic model were significantly related to overall survival (OS) and could serve as an independent prognostic factor. The Kaplan-Meier analysis and ROC curves revealed that the model efficiently predicts prognosis in the TCGA-KIRC cohort and the validation cohort. Then we investigated the potential underlying mechanism and sensitive drugs between high- and low-risk groups. The six key genes were significantly linked with worse OS and were downregulated in ccRCC, we confirmed the results in clinical samples. These results demonstrated the efficacy and robustness of the risk score prognostic model, based on the characteristics of metabolic reprogramming in ccRCC, and the key genes used in constructing the model also could develop into targets of molecular therapy for ccRCC.
Insights
This study identifies key metabolic genes in clear cell renal cell carcinoma (ccRCC) to create a prognostic model. The model accurately predicts patient survival and highlights potential therapeutic targets for ccRCC.
Area of Science:
- Oncology
- Metabolic pathways
- Genomics
Background:
- Metabolic reprogramming is a hallmark of clear cell renal cell carcinoma (ccRCC).
- Current treatments targeting metabolic alterations in ccRCC are limited.
- Identifying novel prognostic markers is crucial for ccRCC management.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) associated with clinical traits in ccRCC.
- To screen prognostic metabolism-related genes and construct a risk score prognostic model.
- To validate the model's predictive capability and explore underlying mechanisms and therapeutic targets.
Main Methods:
- Differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were performed on 965 ccRCC samples.
- Functional enrichment analysis identified metabolism-related gene sets.
- A risk score prognostic model was constructed using selected key genes (ABAT, ALDH6A1, CHDH, EPHX2, ETNK2, FBP1).
Main Results:
- A prognostic model comprising six key genes was developed, demonstrating significant association with overall survival (OS).
- The model served as an independent prognostic factor, validated in independent cohorts.
- The identified key genes were downregulated in ccRCC and linked to worse OS.
Conclusions:
- The developed risk score model is effective and robust for predicting ccRCC prognosis.
- The six key genes represent potential molecular targets for ccRCC therapy.
- Understanding metabolic reprogramming is vital for advancing ccRCC treatment strategies.
