Integrative proteomic profiling of tumor and plasma extracellular vesicles identifies a diagnostic biomarker panel for colorectal cancer.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 40311616.
- Also identified by DOI 10.1016/j.xcrm.2025.102090 and PMC identifier 12147850.
- Licence recorded as CC BY-NC-ND.
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Abstract
The lack of reliable non-invasive biomarkers for early colorectal cancer (CRC) diagnosis underscores the need for improved diagnostic tools. Extracellular vesicles (EVs) have emerged as promising candidates for liquid-biopsy-based cancer monitoring. Here, we propose a comprehensive workflow that integrates staged mass spectrometry (MS)-based discovery and verification with ELISA-based validation to identify EV protein biomarkers for CRC. Our approach, applied to 1,272 individuals, yields a machine learning model, ColonTrack, incorporating EV proteins HNRNPK, CTTN, and PSMC6. ColonTrack effectively distinguishes CRC from non-CRC cases and identifies early-stage CRC with high accuracy (combined area under the curve [AUC] >0.97, sensitivity ∼0.94, specificity ∼0.93). Our analysis of EV protein profiles from tissue and plasma demonstrates ColonTrack's potential as a robust non-invasive biomarker panel for CRC diagnosis and early detection.
Medical subject headings
- Colorectal Neoplasms
- Extracellular Vesicles
- Biomarkers, Tumor
- Proteomics