Journal of Extracellular Vesicles, 14 May, 2025, DOI：https://doi.org/10.1002/jev2.70078

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning

Ye Yuan, Hai Jiang, Rui Xue, Xiao-Jun Feng, Bi-Feng Liu, Lian Li, Bo Peng, Chen-Shuo Ren, Shi-Min Li, Na Li, Min Li, Dian-Bing Wang, Xian-En Zhang

Abstract

Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in plasma extracellular vesicles (EVs) from patients with non-small cell lung cancer (NSCLC) than from healthy individuals. Utilizing CD155 as a bait protein on the EV membrane, CD155+ TDEVs are enriched from NSCLC patient plasma EVs. In the discovery cohort, 281 differentially expressed proteins are identified in TDEVs of the NSCLC group compared with the healthy control group. In the verification cohort, 49 candidate biomarkers are detected using targeted proteomic analysis. Of these, a biomarker panel of seven frequently and stably detected proteins—MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4, and APOD—demonstrates good diagnostic performance, achieving an area under the curve (AUC) of 1.0 with 100% sensitivity and specificity in receiver operating characteristic (ROC) curve analysis, and 92.3% sensitivity and 88.9% specificity in confusion matrix analysis. Western blotting results confirm upregulation trends for MVP, GYS1, SERPINA3, HECTD3, SERPING1 and APOD, and TPM4 is downregulated in EVs of NSCLC patients compared with healthy individuals. These findings highlight the potential of this biomarker panel for the clinical diagnosis of NSCLC.

文章链接：https://isevjournals.onlinelibrary.wiley.com/doi/10.1002/jev2.70078