Urinary fatty acid biomarkers for prostate cancer detection

• Published in: PloS one Vol. 19; no. 2; p. e0297615
• Main Authors: Noriega Landa, Elizabeth, Quaye, George E., Su, Xiaogang, Badmos, Sabur, Holbrook, Kiana L., Polascik, Thomas J., Adams, Eric S., Deivasigamani, Sriram, Gao, Qin, Annabi, Michael H., Habib, Ahsan, Lee, Wen-Yee
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.02.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Biological markers; Cancer; Care and treatment; Demographic aspects; Diagnosis; Fatty acids; Glucose metabolism; Health aspects; Immune system; Prostate cancer; Prostate-specific antigen; Testing; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0297615

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60–75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients’ PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67–0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46–0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.