Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia
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Authors
Spick, MMuazzam, Ammara
Pandha, H
Michael, A
Gethings, LA
Hughes, C J
Munjoma, N
Plumb, R S
Wilson, I D
Whetton, Anthony D
Townsend, Paul A
Geifman, N
Affiliation
Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom.Issue Date
2023
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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.Citation
Spick M, Muazzam A, Pandha H, Michael A, Gethings LA, Hughes CJ, et al. Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia. Heliyon. 2023 Dec;9(12):e22604. PubMed PMID: 38076065. Pubmed Central PMCID: PMC10709398. Epub 2023/12/11. eng.Journal
HeliyonDOI
10.1016/j.heliyon.2023.e22604PubMed ID
38076065Additional Links
https://dx.doi.org/10.1016/j.heliyon.2023.e22604Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.heliyon.2023.e22604
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