Machine learning models for predicting prostate cancer recurrence and identifying potential molecular biomarkers

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorInstituto Oswaldo Cruz (IOC)-
Autor(es): dc.creatorAntunes, Maria Eliza-
Autor(es): dc.creatorAraújo, Thaise Gonçalves-
Autor(es): dc.creatorTill, Tatiana Martins-
Autor(es): dc.creatorPantaleão, Eliana-
Autor(es): dc.creatorMancera, Paulo F. A.-
Autor(es): dc.creatorOliveira, Marta Helena de-
Data de aceite: dc.date.accessioned2025-08-21T15:59:13Z-
Data de disponibilização: dc.date.available2025-08-21T15:59:13Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3389/fonc.2025.1535091-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303063-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303063-
Descrição: dc.descriptionProstate cancer (PCa) recurrence affects between 20% and 40% of patients, being a significant challenge for predicting clinical outcomes and increasing survival rates. Although serum PSA levels, Gleason score, and tumor staging are sensitive for detecting recurrence, they present low specificity. This study compared the performance of three supervised machine learning models, Naive Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN) for classifying PCa recurrence events using a dataset of 489 patients from The Cancer Genome Atlas (TCGA). Besides comparing the models performance, we searched for analyzing whether the incorporation of specific genes expression in the predictor set would enhance the prediction of PCa recurrence, then suggesting these genes as potential biomarkers of patient prognosis. The models showed accuracy above 60% and sensitivity above 65% in all combinations. ANN models were more consistent in their performance across different predictor sets. Notably, SVM models showed strong results in precision and specificity, particularly considering the inclusion of genes selected by feature selection (NETO2, AR, HPN, and KLK3), without compromising sensitivity. However, the relatively high standard deviations observed in some metrics indicate variability across simulations, suggesting a gap for additional studies via different datasets. These findings suggest that genes are potential biomarkers for predicting PCa recurrence in the dataset, representing a promising approach for early prognosis even before the main treatment.-
Descrição: dc.descriptionGraduate Program in Biometrics Instituto de Biociências de Botucatu (IBB) Universidade Estadual Paulista (UNESP), São Paulo-
Descrição: dc.descriptionDepartment of Biodiversity and Biostatistics Instituto de Biociências de Botucatu (IBB) Universidade Estadual Paulista (UNESP), São Paulo-
Descrição: dc.descriptionInstitute of Biotechnology Universidade Federal de Uberlândia (UFU, Patos de Minas-
Descrição: dc.descriptionLaboratory of Clinical and Experimental Pathophysiology Instituto Oswaldo Cruz (IOC), Rio de Janeiro-
Descrição: dc.descriptionSchool of Computing Universidade Federal de Uberlândia (UFU, Patos de Minas-
Descrição: dc.descriptionInstitute of Mathematics and Statistics Universidade Federal de Uberlândia (UFU, Patos de Minas-
Descrição: dc.descriptionGraduate Program in Biometrics Instituto de Biociências de Botucatu (IBB) Universidade Estadual Paulista (UNESP), São Paulo-
Descrição: dc.descriptionDepartment of Biodiversity and Biostatistics Instituto de Biociências de Botucatu (IBB) Universidade Estadual Paulista (UNESP), São Paulo-
Idioma: dc.languageen-
Relação: dc.relationFrontiers in Oncology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectmolecular markers-
Palavras-chave: dc.subjectnext generation sequencing-
Palavras-chave: dc.subjectprognostic-
Palavras-chave: dc.subjectsupervised learning-
Título: dc.titleMachine learning models for predicting prostate cancer recurrence and identifying potential molecular biomarkers-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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