Author
Listed:
- 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)
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.
Suggested Citation
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 Rui, 2024.
"Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness,"
Stats, MDPI, vol. 7(3), pages 1-19, August.
Handle:
RePEc:gam:jstats:v:7:y:2024:i:3:p:53-893:d:1460822
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