Endometrial cancer (EC) is the second most frequent gynecological malignant tumor in postmenopausal women. Pathogenic mechanisms related to the onset and development of disease are still unknown. In this study, we aimed to characterize the EV proteome by combining Data-Independent Acquisition (DIA) acquisition, in albumin-depleted serum EVs to identify dysregulated proteins and enzymes associated with the disease. A deep proteomics analysis with advanced computational tools allowed us to identify a large number of proteins in serum albumin-depleted extracellular vesicles (EVs) from 10 patients with EC compared to 10 healthy controls. This is the largest number of proteins identified in EC serum EVs. After quantification and statistical analysis, we identified 373 significantly (p < 0.05) dysregulated proteins involved in neutrophil and platelet degranulation pathways. A more detailed bioinformatics analysis revealed 61 dysregulated enzymes related to metabolic and catabolic pathways linked to tumor invasion. Our bioinformatic analysis identified 49 metabolic and catabolic pathways related to tumor growth. MTD project_tag Cancer (B/D-HPP)