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Related Experiment Video

Updated: Jun 13, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Integrated Single-Cell RNA-Seq and Machine Learning to Construct an EMT Infiltration Scoring Model for Prostate

Zhipeng Xie1, Yingjie Sun1, Yuheng Tang1

  • 1Tumor and Aging Laboratory, Kunming University of Science and Technology, Kunming 650500, China.

International Journal of Molecular Sciences
|June 12, 2026
PubMed
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This summary is machine-generated.

This study characterizes epithelial-mesenchymal transition (EMT) heterogeneity in prostate cancer (PCa) using single-cell data. A novel EMT infiltration model predicts recurrence and guides personalized treatment strategies for aggressive PCa.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Prostate cancer (PCa) poses a significant global health challenge, with aggressive forms often developing despite current treatments.
  • Epithelial-mesenchymal transition (EMT) is crucial for tumor invasion and metastasis but its heterogeneity in PCa is not fully understood.

Purpose of the Study:

  • To characterize EMT dynamics in malignant prostate cancer cells at the single-cell level.
  • To develop a prognostic model for predicting PCa recurrence and guiding therapeutic strategies.

Main Methods:

  • Single-cell transcriptomic analysis to identify malignant cells and quantify EMT activity.
  • Gene expression profiling, gene set enrichment analysis, and LASSO regression to identify key EMT genes.
  • Construction and validation of an EMT infiltration scoring model using bulk transcriptomic data.
Keywords:
biochemical recurrencecopy number variation (CNV)epithelial–mesenchymal transitionimmune checkpointmachine learningprostate cancersingle-cell analysistumor immune microenvironment

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Main Results:

  • Identified key EMT-associated genes and characterized EMT heterogeneity in PCa.
  • Developed a robust EMT infiltration model that accurately predicts early biochemical recurrence in validation cohorts.
  • Demonstrated associations between EMT scores and clinicopathological features, immune infiltration, and genomic instability.

Conclusions:

  • The study systematically delineates EMT heterogeneity in PCa at the single-cell level.
  • The developed EMT infiltration model offers a valuable tool for prognostic prediction and personalized treatment strategies in PCa.
  • Findings provide insights for precision risk stratification and individualized therapy in prostate cancer.