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BACKGROUND & AIMS: Early detection of esophageal squamous cell carcinoma (ESCC) will facilitate curative treatment. We aimed to establish a microRNA (miRNA) signature derived from salivary extracellular vesicles and particles (EVPs) for early ESCC detection and prognostication.

METHODS: Salivary EVP miRNA expression was profiled in a pilot cohort (n = 54) using microarray. Area under the receiver operator characteristic curve (AUROC) and least absolute shrinkage and selector operation regression analyses were used to prioritize miRNAs that discriminated patients with ESCC from controls. Using quantitative reverse transcription polymerase chain reaction, the candidates were measured in a discovery cohort (n = 72) and cell lines. The prediction models for the biomarkers were derived from a training cohort (n = 342) and validated in an internal cohort (n = 207) and an external cohort (n = 226).

RESULTS: The microarray analysis identified 7 miRNAs for distinguishing patients with ESCC from control subjects. Because 1 was not always detectable in the discovery cohort and cell lines, the other 6 miRNAs formed a panel. A signature of this panel accurately identified patients with all-stage ESCC in the training cohort (AUROC = 0.968) and was successfully validated in 2 independent cohorts. Importantly, this signature could distinguish patients with early-stage (stage Ⅰ/Ⅱ) ESCC from control subjects in the training cohort (AUROC = 0.969, sensitivity = 92.00%, specificity = 89.17%) and internal (sensitivity = 90.32%, specificity = 91.04%) and external (sensitivity = 91.07%, specificity = 88.06%) validation cohorts. Moreover, a prognostic signature based on the panel was established and efficiently predicted the high-risk cases with poor progression-free survival and overall survival.

CONCLUSIONS: The salivary EVP-based 6-miRNA signature can serve as noninvasive biomarkers for early detection and risk stratification of ESCC. Chinese Clinical Trial Registry, ChiCTR2000031507.

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This article was published in Gastroenterology.

The published version is available at

Copyright © 2023 The Authors. CC BY-NC-ND 4.0.