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Artificial Intelligence and multiomics beyond PSA screening in African and Middle Eastern prostate cancer patients

2026-05-01Artigo científico Acesso restrito
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datacite.subject.fosCiências Naturais::Ciências Biológicas
dc.contributor.authorAl-Shahrabi, Rula
dc.contributor.authorAlkhnbashi, Omer S.
dc.contributor.authorAlmarri, Rauda S.B.
dc.contributor.authorAhmad, Sarfraz
dc.contributor.authorSoares, Nelson C.
dc.contributor.authorAl Shareef, Zainab
dc.date.accessioned2026-10-01T13:45:16Z
dc.date.available2026-10-01T13:45:16Z
dc.date.issued2026-05-01
dc.description(Special Issue: Published as part of Journal of Proteome Research special issue “Multiomics in Africa and the Middle East)
dc.description.abstractProstate cancer (PCa) remains a major global health burden, with incidence rising as populations age. The molecular, histological, and patient-specific heterogeneity of PCa underscores the urgent need for advanced strategies to improve detection and risk stratification. This review highlights how multiomics integration, including transcriptomics, DNA methylation, proteomics, and metabolomics, combined with artificial intelligence (AI), can validate biological mechanisms across molecular layers, thereby enhancing diagnostic reliability and biological relevance. While prostate-specific antigen (PSA) testing has significantly shaped PCa epidemiology, its limited specificity and sensitivity have led to widespread overdiagnosis and overtreatment, particularly of indolent tumors. These limitations are especially pronounced in underrepresented populations, notably men of African descent and those in the Middle East and North Africa (MENA) region, where PSA-based screening demonstrates reduced effectiveness. Despite advances in biomarker discovery, current datasets lack sufficient ethnic and regional diversity, raising concerns about the clinical validity and equity of AI-driven models. We argue that equitable precision oncology requires not only technological innovation but also the development of inclusive, demographically representative datasets. This review offers a forward-looking perspective on advancing PCa screening and stratification beyond PSA, with a particular emphasis on addressing the unmet clinical needs of African and Middle Eastern patients.eng
dc.description.sponsorshipThe University of Sharjah funded this research through a targeted research project, Grant No. (2301110175).
dc.identifier.citationJ Proteome Res. 2026 May 1;25(5):2221-2233. doi: 10.1021/acs.jproteome.5c00964. Epub 2026 Mar 27
dc.identifier.doi10.1021/acs.jproteome.5c00964
dc.identifier.eissn1535-3907
dc.identifier.issn1535-3893
dc.identifier.pmid41894385
dc.identifier.urihttp://hdl.handle.net/10400.18/11366
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAmerican Chemical Society
dc.relation.hasversionhttps://pubs.acs.org/jprobs/article/25/5/2221/5141605/Artificial-Intelligence-and-Multiomics-Beyond-PSA
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMENA
dc.subjectPSA
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectMulti-Omics
dc.subjectProstate Cancer
dc.subjectProteomics
dc.subjectGenómica Funcional e Estrutural
dc.titleArtificial Intelligence and multiomics beyond PSA screening in African and Middle Eastern prostate cancer patientseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage2233
oaire.citation.issue5
oaire.citation.startPage2221
oaire.citation.titleJournal of Proteome Research
oaire.citation.volume25
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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