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Area of Science:

  • Oncology
  • Biochemistry
  • Computational Biology

Background:

  • Endometrial cancer (EC) molecular subtyping is crucial for patient outcomes but currently relies on invasive biopsies and slow genomic analysis.
  • Existing methods for EC diagnosis and subtyping present significant challenges due to their invasive nature and lengthy processing times.

Purpose of the Study:

  • To develop a minimally invasive, high-throughput, and cost-effective platform for endometrial cancer screening and molecular subtyping.
  • To integrate plasma extracellular vesicle (EV) peptidomic profiling with machine learning for improved EC management.

Main Methods:

  • Isolation of EVs from plasma of EC patients and controls.
  • Peptidomic profiling using MALDI-TOF mass spectrometry and LC-MS/MS.
  • Development of machine learning models for EC detection and molecular subtyping (POLE mutant, NSMP, MMRd, P53-abnormal).

Main Results:

  • A machine learning model achieved an AUC of 0.867 for distinguishing EC from controls using MALDI-TOF MS features, CA125, HE4, and clinical data.
  • Multiclassification models for molecular subtyping showed high performance with micro/macro-averaged AUCs of 0.91/0.90.
  • LC-MS/MS identified 7,479 peptides, with FGA, PRSS3, and APOA1 identified as key subtype-specific biomarkers.

Conclusions:

  • This study establishes a novel, minimally invasive platform for endometrial cancer screening and molecular subtyping.
  • The integrated approach of EV peptidomics and machine learning offers a cost-effective and rapid solution for precision oncology in EC.
  • This method has the potential to significantly improve EC management by bridging translational gaps in diagnostics.