Metabolomic profiling of renal cell carcinoma to identify prognostic markers of metastasis
Year: 2026
Type: article
Abstract: Background Patients with clear cell renal cell carcinoma (ccRCC) undergo surveillance for recurrent or metastatic disease following nephrectomy. The primary aim of this study was identification of prognostic biomarkers for metastatic disease. Methods We conducted a retrospective case control study of ccRCC patients treated with surgical resection. At 3 years follow up tumors were classified as cases or non-metastatic tumors based on presence or absence of metastases. Fresh frozen ccRCC tumors and adjacent normal tissues were analyzed by combining a chemical labeling method with liquid chromatography-mass spectrometry (LC-MS). Three different chemical groups were analyzed: Carboxyl, Amine-Phenol, and Hydroxyl. Metabolites were identified using a chemical library for cross-reference. LC-MS results were analyzed with machine learning approaches and statistical models. Unsupervised machine learning (UMAP, Non-negative Matrix Factorization) was used for exploratory analysis of the data. Supervised machine learning, using both linear and nonlinear Support Vector Machines (SVM), was used to construct classification models. Repeated cross-validation was used to avoid overfitting and gain representative classification performance of the model. Results Machine learning and SVM classification models showed good classification performance with an area under the Precision-Recall curve (AUPRC) of 0.92 for the best-performing model. Metabolites showing statistically significant changes associated with metastasis included the following: 4-Hydroxybutanoic acid, Glutamyl-Glutamic acid, 3-Aminosalicylic acid, Indoxyl sulfate, 3,4-Dihydroxyphenylpropanoic acid, and N-ethylglycine. Conclusions Metabolites which are prognostic for ccRCC metastasis were identified. Impact: Prognostic biomarkers could inform the frequency and intensity of post-operative surveillance of ccRCC. Identification of such biomarkers could aid clinicians in the management of patients with ccRCC.
Source: PLoS ONE
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W7225782942