ArticleFrontiers in genetics2022
A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and IdentifiesCancers · 2026Article
- Phosphoethanolamine: a translational journey from biological process and physiopathological effects to therapeutical innovation - a mini-review.Frontiers in pharmacology · 2026Review
- HOGA1 Suppresses Renal Cell Carcinoma Growth via Inhibiting the Wnt/β-Catenin Signalling Pathway.Journal of cellular and molecular medicine · 2025Article
- Comprehensive bioinformatics analysis of metabolism‑related microRNAs in high myopia in young and old adults with age‑related cataracts.Molecular medicine reports · 2025Article
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- ETNK2 Low-Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment.Journal of oncology · 2023Article
- The emerging role of long non-coding RNAs in renal cell carcinoma progression and clinical therapyFrontiers in pharmacology · 2023Review
- Distinctive modulation of hepcidin in cancer and its therapeutic relevance.Frontiers in oncology · 2023Review
- Identification of Prognostic Gene Signatures for Survival of Patients With Phaeochromocytoma, Paraganglioma, and Other Tumor Types.Cancer diagnosis & prognosisArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10-15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (
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