ArticleScientific reports2025
Identification of biomarkers for renal cell carcinoma in plasma samples measured using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Ochratoxin A and Clear Cell Renal Cell Carcinoma: Exploring Potential Molecular Links Through Network Toxicology and Machine Learning.International journal of molecular sciences · 2026Article
- Molecular Mechanisms of Aspartame-Induced Kidney Renal Papillary Cell Carcinoma Revealed by Network Toxicology and Molecular Docking Techniques.International journal of molecular sciences · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Metabolomics has been applied in several studies on cancer, but few studies have screened potential biomarkers for renal cell carcinoma (RCC) by integrating two mass mass spectrometers. This study aims to identify differentially expressed metabolites in plasma samples from patients with RCC compared with healthy individuals, which could be used as potential biomarkers to detect RCC. Plasma samples from 48 patients diagnosed with RCC and 22 healthy individuals were analyzed. Two mass spectrometers were utilized: liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Ceramide m40:0 was significantly less abundant in plasma samples from patients with RCC (P < 0.05), whereas kynureine metabolites and glucose metabolites were significantly more abundant in plasma samples from patients with RCC compared with healthy controls (P < 0.05). A selection of 21 metabolites was utilized to construct an optimal diagnostic model, which achieved an area under the receiver operating characteristic curve (AUC) value of 0.860 (95% confidence interval 0.736-0.960). Significant differences were identified in the metabolic pathways of nicotinate and nicotinamide metabolism, pantothenate and coenzyme A biosynthesis, and beta-alanine metabolism between the RCC group and the control group (P < 0.05). The combined use of LC-MS and GC-MS effectively identified differentially expressed metabolites and dysregulated pathways based on analysis of plasma samples from patients with RCC compared with healthy individuals. The diagnostic model demonstrated robust performance with an AUC of 0.860, highlighting its reliability for RCC identification.
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