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Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer Molecular Subtypes

ABSTRACT: Endometrial cancer (EC) molecular subtyping is critical for prognosis and treatment but remains hindered by the reliance on invasive tissue biopsies and time-consuming genomic sequencing. Here, we present a minimally invasive approach integrating MALDI-TOF mass spectrometry and LC-MS/MS-based peptidomic profiling of plasma extracellular vesicles (EVs) with machine learning for rapid EC screening and subtyping. EVs were isolated from EC patients and controls, and their peptidome fingerprints were analyzed. A machine learning model utilizing 12 discriminative features achieved 100% accuracy and an AUC of 1.0 in distinguishing EC from controls. For molecular subtyping (POLE-mutant, NSMP, MMRd, P53-abnormal), a multiclassification model demonstrated 80% accuracy with micro-/macro-averaged AUCs of 0.93/0.95. LC-MS/MS identified 7,479 peptides, with fibrinogen α chain (FGA), protease serine 3 (PRSS3), and apolipoprotein AI (APOA1) emerging as key biomarkers linked to specific subtypes. This study establishes a high-throughput, cost-effective platform for EC management, bridging translational gaps in precision oncology.

ORGANISM(S): Homo Sapiens

SUBMITTER: Xiaojun Chen

PROVIDER: PXD066573 | iProX | Thu Jul 24 00:00:00 BST 2025

REPOSITORIES: iProX

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