ArticleAsian Pacific journal of cancer prevention : APJCP2025
Extracellular Vesicles as a Platform for Predicting Cancer Risk in Patients with Adenomas and Polyps of the Colon with Obesity.
Article in Asian Pacific journal of cancer prevention : APJCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The aim of the study was to investigate the level of heat shock proteins (HSPs) and matrix metalloproteinases (MMPs) on circulating extracellular vesicles (EVs) with the assessment to use EV markers to predict cancer risk in patients with colon adenomas and polyps. MATERIALS AND
methodsMultiparametric staining with flow cytometric visualization was performed to assess the expression of MMPs and HSPs on EVs isolated from blood plasma. Vesicles were characterized using transmission electron microscopy, nanoparticle tracking analysis, and flow cytometry. Groups of patients with colorectal cancer (CRC) n=40 (26 women and 14 men, T2-4N0-2M0, mean age 59.6±1.61 years) and patients with villous adenomas of the colon (CA) (n=20, 9 men and 11 women, mean age 57.5±10.55 years, with obesity, defined as a body mass index ≥30 kg/m² were formed. Logistic regression was used to calculate the cancer risk.
resultsThe logistic regression model for predicting the cancer risk in patients with CA with obesity included both CD9-positive EVs and the adipocyte-derived FABP4-positive EVs. Clinical parameters were not significant as indicators for predicting the cancer risk. The sensitivity and specificity of this model were 85.4% and 79.2%, respectively.
conclusionThe advantages of the calculated mathematical model are as follows: 1) the parameters included in the model are estimated in the blood plasma EVs, thereby not requiring invasive procedures (colonoscopy, biopsy sampling); 2) the model takes into account the presence of metabolic disorders, since the model includes subpopulations of CD9-positive vesicles and FABP4-positive (adipocyte-derived) EVs as predictors of cancer risk; 3) the calculation of cancer risk using this model can be repeated. This version of the application of the model can consider changes in cancer risk and allows optimization of management tactics for CA patients.
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