ArticleBMC urology2024
Establishment and validation of a novel peroxisome-related gene prognostic risk model in kidney clear cell carcinoma.
Article in BMC urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Peroxisomal activity drives aggressive bladder cancer phenotypes and reveals erythorbic acid as a potential therapeutic modulator.Frontiers in oncology · 2026Article
- FNDC5 and ACOX1 as Biomarkers of Peroxisomal Activity with Contrast Outcomes in Colon Adenocarcinoma.Biological procedures online · 2025Article
- Identification and validation of a 9-RBPs-related gene signature associated with prognosis and immune infiltration in bladder cancer based on bioinformatics analysis and machine learning.Translational andrology and urology · 2025Article
- Article
- A Novel Peroxisome-Related Gene Signature Predicts Breast Cancer Prognosis and Correlates with T Cell Suppression.Breast cancer (Dove Medical Press) · 2024Article
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4 authors.
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Abstract
backgroundKidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC.
methodsThe significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry.
resultsFourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients.
conclusionsThe peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.
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