There is an unmet need for improved diagnostic testing and risk prediction for cases of
prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum
proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa,
benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving
biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for
prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of
biomarkers differentiating between BPH and
prostate cancer were identified, including
proteins in MAP
Kinase pathways, as well as in
lipids containing
oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.