Extracellular Vesicle–Derived Adenosine as a Diagnostic Metabolic Biomarker for Pancreatic Ductal Adenocarcinoma
Year: 2025
Type: article
Study design: Observational Study
Abstract: Purpose: Current non-invasive diagnostic tools for pancreatic ductal adenocarcinoma (PDAC) exhibit limited sensitivity and specificity. This study aimed to identify more accurate plasma biomarkers by profiling extracellular vesicles (EVs), which are enriched in tumor-derived metabolites and proteins. Materials and Methods: Plasma samples were collected under strict fasting and standardized processing protocols from patients diagnosed with PDAC, chronic pancreatitis (CP), and tumor-free controls (Ctrl). EVs were isolated from these plasma samples and subjected to comprehensive metabolomic and proteomic profiling to identify disease-specific biomarkers. Results: A biomarker panel comprising adenosine, adenine, and N-acetylneuraminate (AAN) demonstrated outstanding diagnostic performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.968 (95% CI: 0.92-1.00). At a fixed specificity of 96.8%, the panel yielded a sensitivity of 95.0% (95% CI: 85.0%-100.0%), significantly reducing the classification error from 30% with CA19-9 to 5% with the AAN panel. Among individual markers, adenosine alone showed high diagnostic power (AUC=0.952), correlating with increased expression of 5'-nucleotidase ecto (NT5E), indicative of elevated extracellular purine metabolism. Importantly, these features were unique to the EV compartment and outperformed markers derived from total plasma metabolite profiles. Conclusion: The integration of EV metabolomics and proteomics revealed enhanced purine metabolism, particularly elevated extracellular adenosine, as a distinctive hallmark of PDAC. EV-derived adenosine emerges as a highly promising non-invasive biomarker with superior diagnostic accuracy compared to CA19-9.
Source: Cancer Research and Treatment
DOI: 10.4143/crt.2025.510
Authors: Tao Xia, Wenhui Ouyang, Jie Wang, Jinjing Wang, Yiping Mou, Zhengquan Yang, Hezhi Fang, Shanying Gui
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4414487168
