Development a glycosylated extracellular vesicle-derived miRNA Signature for early detection of esophageal squamous...

Esophageal squamous cell carcinoma (ESCC) is often diagnosed at an advanced stage due to the lack of non-invasive early detection tools, which significantly impacts patient prognosis. Given that glycosylation alterations especially high sialylation and fucosylation, frequently occur during cellular malignant transformation, but their roles are not elucidated. We examined alterations in disease-specific glycosylated extracellular vesicles (EVs)-derived miRNAs in the serum of ESCC patients, evaluating their utility as diagnostic biomarkers.

A total of 371 ESCC and 303 healthy controls (HCs) were recruited in this multi-stage, multicentre case-control study. Fucosylated (Fuc-) and sialylated (Sia-) EVs were isolated utilizing Lentil lectin (LCA) and wheat germ lectin (WGA)-coated magnetic beads, respectively. The glycosylated EVs-derived miRNAs-based signature (Riskscore^Fuc-&Sia-) was established through logistic regression in a training cohort and subsequently validated in an internal and an external multicentre cohort.

The Riskscore^Fuc-&Sia- effectively identified ESCC across all stages, demonstrating high AUC values in training (0.980), internal validation (0.957), and external multicentre validation (0.973) cohorts, markedly higher than carcinoembryonic antigen (CEA) (AUC = 0.769, training cohort; AUC = 0.749, internal validation cohort; AUC = 0.765, external validation cohort). Notably, this score exhibited robust accuracy in detecting CEA (-) ESCC cases (CEA < 5 ng/ml) (AUC = 0.974, training & internal cohort; AUC = 0.973, external multicentre validation cohort). Additionally, it displayed strong efficacy in differentiating early-stage ESCC patients (AUC = 0.982, training cohort; AUC = 0.977, external multicentre validation cohort).

Our study illustrates the effectiveness of glycosylated EVs capture strategy for isolating tumour-specific EVs. The unique glycosylated EVs-derived miRNAs-based signature shows the optimal potential as a biomarker for early detection of ESCC.