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PXD074950-1

PXD074950 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleProteomic profiling of plasma-derived extracellular vesicles using Mag-Net for biomarker discovery in pancreatic ductal adenocarcinoma
DescriptionPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with a five-year survival rate of less than 10%. In South Africa, PDAC was the seventh leading cause of cancer deaths among men and the sixth among women in 2020. Approximately 85% of patients are diagnosed at an advanced stage, highlighting the urgent need for ongoing research to identify reliable biomarkers that could improve clinical outcomes. Circulating proteins are present not only as soluble molecules but also encapsulated within extracellular vesicles (EVs), which protect and transport biologically active cargo in plasma. This study compared the plasma proteomes of patients with PDAC with those of patients with benign biliary pathologies (BBP) and healthy controls (HC). We employed Mag-Net, a magnetic bead-based method that combines the enrichment of membrane-bound vesicles with the depletion of high-abundance plasma proteins. On average, 2,339 protein groups were identified across all sample groups. Comparative analyses revealed both unique and overlapping dysregulated proteins among the PDAC, BBP, and HC cohorts. PDAC was characterised by coordinated extracellular matrix (ECM) remodelling, chronic immune-inflammatory activation, and metabolic disruption, particularly affecting lipid homeostasis. This molecular profile was marked by elevated ECM-associated and matricellular proteins (such as THBS2, TGFBI, MFAP4, ANPEP, and ITIH4) alongside increased inflammatory mediators (including ORM1, C1QC, CTSS, SPP1, FBLN2, LTBP2, SDC1, HP, SAA1, PTX3, and LRG1). This overlap suggests that dynamic ECM remodelling within the tumour microenvironment is mirrored systemically by the release or shedding of matrix-associated proteins into the circulation. Notably, LRG1 demonstrated the strongest association with disease severity and clustered with acute-phase proteins, complement components, and pancreatic injury markers, supporting its role within an inflammation-stromal remodelling network characteristic of aggressive PDAC. Collectively, these findings demonstrate that plasma proteomics provides valuable discriminatory and severity-related information. While several candidate proteins show promise for inclusion in multi-analyte panels, further validation in larger, independent cohorts is necessary to establish their diagnostic and translational utility for early detection, risk stratification, and improved differential diagnosis of PDAC.
HostingRepositoryPRIDE
AnnounceDate2026-08-06
AnnouncementXMLSubmission_2026-08-06_01:21:23.008.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterRethabile Mokoena
SpeciesList scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606;
ModificationListacetylated residue; monohydroxylated residue; iodoacetamide derivatized residue
InstrumenttimsTOF Pro
Dataset History
RevisionDatetimeStatusChangeLog Entry
02026-02-26 11:42:03ID requested
12026-08-06 01:21:23announced
Publication List
Buthelezi S, Elebo N, Naicker P, Mokoena R, Dubazana S, Govender I, Mamputha S, Ellero A, Stoychev S, Mazibuko J, Ojo D, Candy G, Devar J, Cacciatore S, Omoshoro-Jones J, Nweke EE, Proteomic Profiling of Extracellular Vesicle-Enriched Plasma Using Mag-Net for Biomarker Discovery in Pancreatic Ductal Adenocarcinoma. J Proteome Res, 25(8):4120-4131(2026) [pubmed]
10.1021/acs.jproteome.6c00172;
Keyword List
submitter keyword: Pancreatic ductal adenocarcinoma (PDAC)
biomarker
plasma
extracellular vesicles
Mag-Net
proteomics
Contact List
Rethabile Mokoena
contact affiliationPrteomics Facility, Council of Scientific and Industrial Research (CSIR), Pretoria, South Africa
contact emailrmokoena1@csir.co.za
lab head
Rethabile Mokoena
contact affiliationCouncil of Scientific and Industrial Research (CSIR)
contact emailrmokoena1@csir.co.za
dataset submitter
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