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Updated: May 10, 2026
08:05
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
An integrative proteomics and interaction network-based classifier for prostate cancer diagnosis
Fu-neng Jiang1, Hui-chan He, Yan-qiong Zhang
1School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
Plos One
|June 6, 2013
Summary
This study developed a new prostate cancer (PCa) diagnostic tool by integrating protein expression and network analysis. The classifier shows high accuracy and identifies PTEN as a prognostic marker for PCa recurrence.
Area of Science:
- Proteomics
- Bioinformatics
- Cancer Diagnostics
Background:
- Prostate cancer (PCa) diagnosis is challenging due to its heterogeneity.
- Existing PCa markers lack validation and overlap.
- Improved diagnostic tools are crucial for patient prognosis.
Purpose of the Study:
- To develop an integrative classifier for enhanced PCa diagnosis.
- To combine differential protein expression with protein interaction network features.
- To identify novel prognostic markers for PCa.
Main Methods:
- Proteomics (2D-DIGE, MS) identified 60 differentially expressed proteins.
- Network analysis pinpointed hub proteins (PTEN, SFPQ, HDAC1).
- Support Vector Machine (SVM) modeling used gene expression data for classifier construction.
Main Results:
- The classifier achieved high diagnostic accuracy (85.96–90.18%) with an ROC curve near 1.0.
- PTEN, SFPQ, and HDAC1 proteins were validated in PCa tissues.
- PTEN emerged as an independent prognostic marker for recurrence-free survival.
Conclusions:
- An integrative classifier combining proteomics and network topology shows promise for PCa diagnosis.
- PTEN is identified as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.
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