Pancreatic cancer (PC) is a lethal disease developing from either exocrine or endocrine cells. Efforts to assist early diagnosis focus on liquid biopsy methods, and especially on the detection of Extracellular Vesicles (EVs) secreted from cancer cells in their microenvironment and accumulated in systemic circulation. Multiple studies explore how EVs size, surface biomarkers or content can determine their unique role and function in the recipient cell's gene expression, metabolism and behavior affecting cancer development. This study aimed to develop a machine learning-driven (ML) pipeline utilizing clinical variables and EV-based features to predict the presence of pancreatic tumors of different nature (exocrine/endocrine) in patients' plasma compared to patients with benign lesions or age-matched non-oncological patients.
All available plasma samples (N=126) and variables were collected prior to surgery. EVs were detected and characterized by flow cytometry-immunostaining. Data including size and a unique set of biomarkers (CD45, CD63 and EphA2) were combined with hematological/biochemical data and processed under two use cases, each formulated as a 3-class classification problem for patient risk stratification. The first use case aimed at classifying patients as with benign lesions or exocrine/endocrine neoplasms. The second use case aimed to distinguish patients with exocrine/endocrine neoplasms from non-oncological patients. Various ML methods were applied, including Logistic Regression, Random Forest, Support Vector Machines, and Extreme Gradient Boosting. Evaluation metrics, as area under the receiver operating characteristic curve (AUC-ROC), were computed, and Shapley values were utilized to determine features with the greatest impact on the discrimination of outcome groups.
Analyses identified hematological and biochemical features, among significant predictors. Models demonstrated substantial accuracy and AUC-ROC values based on plasma EVs subpopulations, which scored over 0.90 in accuracy of the Random Forest and XGBoost algorithms, presenting 0.96 +/- 0.03 accuracy in the first use case and 0.93 +/- 0.04 in the second.
By leveraging advanced analytical ML-driven approaches and integrating diverse data types, this study achieved significant accuracy, assisting patient's risk estimation and supporting the feasibility for early detection of pancreatic cancer. Going beyond currently used biomarkers such as CEA, or CA19.9, EV-based features represent an added value offering increased diagnostic capacity.
Copyright © 2025 Angelioudaki, Iosif, Kourou, Tzingounis, Kigka, Skreka, Costopoulos, Memos, Kataki, Konstadoulakis and Fotiadis.

Exosomes, nanoscale vesicles with high biocompatibility, were engineered to express human epidermal growth factor receptor 2 (HER2)-binding peptides and carry miR-34a, targeting HER2 and programmed death-ligand 1 (PD-L1)-positive breast cancer cells.
An in vivo xenograft breast cancer model was established by subcutaneously injecting breast cancer cells of both HER2 and PD-L1 positivity (SK-BR3 cells) into the buttocks of BALB/c nude mice. miR-34a-loaded HER2-targeting exosomes, termed tEx[34a], were engineered by transfecting human adipose-derived mesenchymal stem cells with the pDisplay vector to express HER2-binding peptides (P51 peptide). Purified exosomes were then loaded with miR-34a, a tumor-suppressor miRNA, using the Exo-Fect transfection kit, creating tEx[34a] for targeted cancer therapy.
Intravenous administration of miR-34a-loaded HER2-targeting exosomes, referred to as tEx[34a], demonstrated superior targetability compared to other materials, such as natural exosomes, miR-34a-loaded exosomes, and unloaded HER2-targeting exosomes. In vivo experiments using mouse breast cancer xenograft models revealed that the administration of tEx[34a] resulted in the smallest tumor size and lowest tumor weight when compared to all other groups. Notably, tEx[34a] treatment significantly reduced PD-L1 expression in breast cancer tissue compared to the other groups. Furthermore, tEx[34a] administration led to the highest upregulation of pro-apoptotic markers (Bax, PARP, and BIM) and the lowest downregulation of the anti-apoptotic marker Bcl-xL, as confirmed through various methods including RT-PCR, Western blot analysis, and immunofluorescence.
MiR-34a-loaded HER2-targeting exosomes demonstrate strong anticancer efficacy by selectively binding to HER2-positive breast cancer cells and effectively suppressing PD-L1 expression.
© The Authors 2025.

In this study, we explored the potential of genetically engineered exosomes as vehicles for precise drug delivery in gastric cancer therapy. A novel antitumor strategy using biocompatible exosomes (Ex) was devised by genetically engineering adipose-derived stem cells to express an MKN45-binding peptide (DE532) on their surfaces. 17-(Dimethylaminoethylamino)-17-demethoxygeldanamycin (17-DMAG) was encapsulated in engineered exosomes, resulting in 17-DMAG-loaded DE532 exosomes. In both in vitro and in vivo experiments using mouse gastric cancer xenograft models, we demonstrated that 17-DMAG-loaded DE532 Ex exhibited superior targetability over DE532 Ex, 17-DMAG-loaded Ex, and Ex. Administration of the 17-DMAG-loaded DE532 Ex yielded remarkable antitumor effects, as evidenced by the smallest tumor size, lowest tumor growth rate, and lowest excised tumor weight. Further mechanistic examinations revealed that the 17-DMAG-loaded DE532 Ex induced the highest upregulation of the pro-apoptotic marker B-cell lymphoma-2-like protein 11 and the lowest downregulation of the anti-apoptotic marker B-cell lymphoma-extra large. Concurrently, the 17-DMAG-loaded DE532 Ex demonstrated the lowest suppression of antioxidant enzymes, such as superoxide dismutase 2 and catalase, within tumor tissues. These findings underscore the potential of 17-DMAG-loaded DE532 exosomes as a potent therapeutic strategy for gastric cancer, characterized by precise targetability and the potential to minimize adverse effects.

Wharton's jelly-derived mesenchymal stem cell (WJ-MSC)-derived exosomes contain a diverse cargo and exhibit remarkable biological activity, rendering them suitable for regenerative and immune-modulating functions. However, the quantity of secretion is insufficient. A large body of prior work has investigated the use of various growth factors to enhance MSC-derived exosome production. In this study, we evaluated the utilization of thermostable basic fibroblast growth factor (TS-bFGF) with MSC culture and exosome production. MSCs cultured with TS-bFGF displayed superior proliferation, as evidenced by cell cycle analysis, compared with wild-type bFGF (WT-bFGF). Stemness was assessed through mRNA expression level and colony-forming unit (CFU) assays. Furthermore, nanoparticle tracking analysis (NTA) measurements revealed that MSCs cultured with TS-bFGF produced a greater quantity of exosomes, particularly under three-dimensional culture conditions. These produced exosomes demonstrated substantial anti-inflammatory and wound-healing effects, as confirmed by nitric oxide (NO) assays and scratch assays. Taken together, we demonstrate that utilization of TS-bFGF for WJ-MSC-derived exosome production not only increases exosome yield but also enhances the potential for various applications in inflammation regulation and wound healing.

Exosomes are small vesicles that are secreted from cells to dispose of undegraded materials and mediate intercellular communication. A major source of exosomes is intraluminal vesicles within multivesicular endosomes that undergo exocytic fusion with the plasma membrane. An alternative fate of multivesicular endosomes is fusion with lysosomes, resulting in degradation of the intraluminal vesicles. The factors that determine whether multivesicular endosomes fuse with the plasma membrane or with lysosomes are unknown. In this study, we show that impairment of endolysosomal fusion by disruption of a pathway involving the BLOC-one-related complex (BORC), the small GTPase ARL8, and the tethering factor HOPS increases exosome secretion by preventing the delivery of intraluminal vesicles to lysosomes. These findings demonstrate that endolysosomal fusion is a critical determinant of the amount of exosome secretion and suggest that suppression of the BORC-ARL8-HOPS pathway could be used to boost exosome yields in biotechnology applications.
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