Liquid chromatography-mass spectrometry-based metabolic panels characteristic for patients with prostate cancer and prostate-specific antigen levels of 4–10 ng/mL

Background: Prostate cancer is the second most common malignant tumor among men worldwide. This study explores potential metabolic biomarkers and pathways through metabolomics and evaluates the diagnostic performance of a metabolic panel in distinguishing prostate cancer, benign prostatic hyperplasia (BPH) and prostatitis. Methods: Liquid chromatography-mass spectrometry (LC-MS) was used to perform untargeted metabolomic analysis on serum samples from 30 prostate cancer patients, 30 BPH patients, and 30 prostatitis patients. Based on the identified metabolites, LASSO regression was applied for variable selection, and logistic regression and support vector machine models were developed. Results: The LASSO algorithm’s ability to select variables effectively reduced redundant features and minimized model overfitting. Receiver Operating Characteristic (ROC) analysis demonstrated strong diagnostic performance, with an area under the curve of 0.852 for prostate cancer versus BPH and 0.891 for prostate cancer versus prostatitis. Enrichment analysis revealed that fatty acid metabolism, particularly the biosynthesis of unsaturated fatty acids, is a key metabolic feature of prostate cancer. Conclusions: This study demonstrates that metabolites selected through the LASSO algorithm, combined with machine learning models, enhance the early diagnosis of prostate cancer and exhibit excellent performance in distinguishing it from BPH and prostatitis. These findings lay a foundation for precision medicine and disease screening, with potential applications in early intervention and personalized treatment in clinical practice.

Prostate cancer; Metabolomics; LASSO regression; Machine learning models; Fatty acid metabolism

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