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Related Concept Videos

Proteomics01:33

Proteomics

9.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Updated: Jan 15, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness.

Sheila M Valle Cortés1, Jaileene Pérez Morales2, Mariely Nieves Plaza3

  • 1Ponce Research Institute, Ponce Health Sciences University, Biochemistry and Cancer Biology Divisions, Ponce, PR 00716, USA.

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|October 6, 2025
PubMed
Summary
This summary is machine-generated.

Identifying aggressive prostate cancer (PCa) is key. Biomarkers like E-cadherin and Phospho-Rb S249, analyzed with classification trees, help predict tumor aggressiveness for better patient monitoring.

Keywords:
CARTE-cadherinN-cadherinaggressiveepithelial to mesenchymal transition (EMT)phosphorylationprostate cancerretinoblastomaβ-catenin

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Area of Science:

  • Oncology
  • Molecular Pathology
  • Biomarker Discovery

Background:

  • Prostate cancer (PCa) diagnosis is challenged by difficulty in identifying aggressive tumors, leading to overtreatment.
  • Accurate prediction of PCa aggressiveness is crucial for personalized therapy and avoiding unnecessary interventions.

Purpose of the Study:

  • To investigate the utility of retinoblastoma phosphorylated at Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for aggressive PCa.
  • To correlate biomarker expression with clinicopathological data for improved diagnostic accuracy.

Main Methods:

  • Immunohistochemistry (IHC) was used to assess biomarker expression in PCa tissues.
  • Logistic regression and Classification and Regression Tree (CART) models were employed to analyze biomarker correlations with tumor aggressiveness and clinicopathological data.

Main Results:

  • E-cadherin and β-catenin showed a negative correlation with aggressive tumor behavior.
  • Phospho-Rb S249 and N-cadherin positively correlated with increased PCa aggressiveness.
  • CART analysis identified β-catenin, tumor grade, and Gleason grade as key determinants for identifying patients with Gleason scores ≥ 4 + 3.

Conclusions:

  • Biomarkers including E-cadherin, β-catenin, Phospho-Rb S249, and N-cadherin show potential for identifying aggressive prostate cancer.
  • Classification and Regression Tree (CART) models offer an effective method for clinical utility assessment of these biomarkers.
  • Early detection of aggressive PCa through these biomarkers can guide patient monitoring and treatment strategies.