Small extracellular vesicle-based one-step high-throughput microfluidic platform for epithelial ovarian cancer diagnosis.

Abstract

Background: Ovarian cancer (OC) is diagnosed at advanced stages, resulting in limited treatment options for patients. While early detection of OC has been investigated, the invasiveness of approaches, high sample requirements, or false-positive rates undermined its benefits. Here, we present a "one-step" high-throughput microfluidic platform for epithelial ovarian cancer (EOC) detection that integrates small extracellular vesicle (sEV) capture, in situ lysis, and protein biomarker detection.

Results: We identified 1,818 differentially expressed proteins (DEPs) through proteomic analysis of sEVs from patients' serum, combined with cell lines. Through multi-step screening of DEPs, we identified EOC biomarkers to customize the microfluidic platform. We used the microfluidic platform to test the expression of EOC biomarkers with 2 µL of serum from 209 participants in a prospective cohort. Based on the test results, an EOC detection model (P9) was constructed, which achieved a sensitivity of 92.3% (95% CI, 75.9-97.9%) for stage I, 90.0% (95% CI, 69.9-97.2%) for stage II at a specificity of 98.8% (95% CI, 93.6-99.8%) in the training set. The specificities reached 98.8% (95% CI, 93.6-99.8%) in the training set and 100.0% (95% CI, 91.6-100.0%) in the validation set of a held-out group of 105 participants. A model combining the P9 and patient's CA125 value exhibited 100.0% (95% CI, 95.6-100%) specificity in both training and validation, without compromising sensitivity.

Conclusions: We developed a non-invasive high-throughput microfluidic platform for EOC sEV-derived biomarker detection. It significantly reduced false positives and sample volume. Given its convenience and low cost, this platform could advance OC early detection to benefit of women.