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

Endometrial cancer (EC) molecular subtyping is critical
for prognosis
and treatment but remains hindered by 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 MALDI-TOF MS features, the levels of CA125 and HE4,
and clinical features related to cancer risk achieved an AUC of 0.867
in distinguishing EC from the controls. For molecular subtyping (POLE
mutant, NSMP, MMRd, P53-abnormal), a multiclassification model demonstrated
micro/macro-averaged AUCs of 0.91/0.90. LC-MS/MS identified 7,479
peptides, with fibrinogen α chain (FGA), protease serine 3 (PRSS3),
and apolipoprotein A-I (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.