Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness

Sheila M. Valle Cortés, Jaileene Pérez Morales, Mariely Nieves Plaza, Darielys Maldonado, Swizel M. Tevenal Baez, Marc A. Negrón Blas, Cayetana Lazcano Etchebarne, José Feliciano, Gilberto Ruiz Deyá, Juan C. Santa Rosario and Pedro Santiago Cardona ()
Additional contact information
Sheila M. Valle Cortés: Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA
Jaileene Pérez Morales: Knight Cancer Institute, Oregon Health & Science University, Oncological Sciences Division, Portland, OR 97239, USA
Mariely Nieves Plaza: Universidad Central del Caribe, Department of Medicine, Bayamón, PR 00960, USA
Darielys Maldonado: Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA
Swizel M. Tevenal Baez: Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA
Marc A. Negrón Blas: Universidad Autónoma de Guadalajara, Department of Medicine, Zapopan 45129, Mexico
Cayetana Lazcano Etchebarne: Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA
José Feliciano: Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA
Gilberto Ruiz Deyá: Ponce Research Institute, Ponce Health Sciences University, Surgery Division, Ponce, PR 00716, USA
Juan C. Santa Rosario: CorePlus Servicios Clínicos y Patológicos, Carolina, PR 00983, USA
Pedro Santiago Cardona: Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA

Stats, 2024, vol. 7, issue 3, 1-19

Abstract: Prostate cancer (PCa) poses a significant challenge because of the difficulty in identifying aggressive tumors, leading to overtreatment and missed personalized therapies. Although only 8% of cases progress beyond the prostate, the accurate prediction of aggressiveness remains crucial. Thus, this study focused on studying retinoblastoma phosphorylated at Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for identifying aggressive PCa using a logistic regression model and a classification and regression tree (CART). Using immunohistochemistry (IHC), we targeted the expression of these biomarkers in PCa tissues and correlated their expression with clinicopathological data of the tumor. The results showed a negative correlation between E-cadherin and β-catenin with aggressive tumor behavior, whereas Phospho-Rb S249 and N-cadherin positively correlated with increased tumor aggressiveness. Furthermore, patients were stratified based on Gleason scores and E-cadherin staining patterns to evaluate their capability for early identification of aggressive PCa. Our findings suggest that the classification tree is the most effective method for measuring the utility of these biomarkers in clinical practice, incorporating β-catenin, tumor grade, and Gleason grade as relevant determinants for identifying patients with Gleason scores ≥ 4 + 3. This study could potentially benefit patients with aggressive PCa by enabling early disease detection and closer monitoring.

Keywords: prostate cancer; aggressive; E-cadherin; N-cadherin; ?-catenin; retinoblastoma; phosphorylation; epithelial to mesenchymal transition (EMT); CART (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/7/3/53/pdf (application/pdf)
https://www.mdpi.com/2571-905X/7/3/53/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:7:y:2024:i:3:p:53-893:d:1460822

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().