Flow-cytometric profiling of large extracellular vesicles as immunophenotypic biomarkers in head and neck squamous cell carcinoma
摘要
Large extracellular vesicles (large EV) released from tumor and benign cells are detectable in blood and hold potential as non-invasive biomarkers. While their diagnostic relevance has been shown in several malignancies, their role in head and neck squamous cell carcinoma (HNSCC) remains insufficiently characterized.
MethodsLarge EV were isolated from peripheral blood of patients with HNSCC (n = 66), non-small cell lung cancer (NSCLC; n = 52; adenocarcinoma n = 39, squamous cell carcinoma (SQCCL) n = 11), and healthy controls (n = 11) by differential centrifugation. Flow cytometry quantified surface expression of EGFR, EPCAM, MUC1, and PD-L1. Associations with clinical parameters were analyzed using t-tests, Spearman correlations, logistic regression, and random forest modeling.
ResultsHNSCC-derived large EV showed significantly higher EGFR and MUC1, but lower EPCAM expression compared to controls. PD-L1 expression increased with advancing tumor stage and was positively associated with metastatic status, whereas EGFR levels declined in metastatic disease. Combined ROC analysis of EGFR, EPCAM, and PD-L1 yielded an AUC of 0.785 (p = 0.003), distinguishing HNSCC from controls. Comparative profiling revealed higher EGFR and EPCAM expression in HNSCC versus NSCLC, while MUC1 predominated in NSCLC, particularly in SQCCL; notably, the NSCLC cohort was predominantly adenocarcinoma. A marker panel comprising EGFR, EPCAM, and MUC1 differentiated HNSCC from SQCCL in this limited subgroup (n = 11) with 96.97% sensitivity, 45.45% specificity, 92.75% positive predictive value and 75.00% negative predictive value.
ConclusionFlow-cytometric profiling of circulating large EV provides a feasible liquid biopsy approach for tumor characterization in HNSCC. PD-L1 expression reflects tumor burden, and combined large EV marker analysis enables differentiation between HNSCC and primary squamous lung carcinoma, supporting its diagnostic utility in clinical oncology.