Extracellular vesicles (EVs) are recognized as promising biomarkers for several diseases. However, their conventional isolation methods have several drawbacks, such as poor yields, low purity, and time-consuming operations. Therefore, a simple, low-cost, and rapid microfluidic platform has been extensively developed to meet the requirement in biomedical applications. Herein, a modular microfluidic platform is demonstrated to isolate and enrich EVs directly from plasma, in a combination of continuous capture and purification of EVs. The EVs were selectively captured by target-specific antibody-coated beads in a horseshoe-shaped orifice micromixer (HOMM) chip within 2 min. A fish-trap-shaped microfilter unit was subsequently used to elute and purify the affinity-induced captured EVs from the microbeads. The ability of the modular chip to capture, enrich, and release EVs was demonstrated in 5 min (100 μL sample) at high throughput (100 μL min⁻¹). The two chips can be modularized or individually operated, depending on the clinical applications such as diagnostics and therapeutics. For the diagnostic applications, the EVs on microbeads can be directly subjected to the molecular analysis whereas the pure EVs should be released from the microbeads for the therapeutic treatments. This study reveals that the fabricated modular chip can be appropriately employed as a platform for EV-related research tools.

Background and objectives: Breast cancer is the most common cancer among women worldwide. Early stage diagnosis is important for predicting increases in treatment success rates and decreases in patient mortality. Recently, circulating biomarkers such as circulating tumor cells, circulating tumor DNA, exosomes, and circulating microRNAs have been examined as blood-based markers for the diagnosis of breast cancer. Although miR-202 has been studied for its function or expression in breast cancer, its potential diagnostic value in a clinical setting remains elusive and miR-202 has not been investigated in South Korea. In this study, we aimed to evaluate the diagnostic utility of miR-202 in plasma samples of breast cancer patients in South Korea. Materials and Methods: We investigated miR-202 expression in the plasma of 30 breast cancer patients during diagnosis along with 30 healthy controls in South Korea by quantitative reverse transcription PCR. Results: The results showed that circulating miR-202 levels were significantly elevated in the breast cancer patients compared with those in healthy controls (p < 0.001). The sensitivity and specificity of circulating miR-202 were 90.0% and 93.0%, respectively. Additionally, circulating miR-202 showed high positivity at early stage. The positive rate of miR-202 was as follows: 100% (10/10) for stage I, 90% (9/10) for stage II, and 80% (8/10) for stage III. miR-202 was also a predictor of a 9.6-fold high risk for breast cancer (p < 0.001). Conclusions: Additional alternative molecular biomarkers for diagnosis and management of pre-cancer patients are needed. Circulating miR-202 might be potential diagnostic tool for detecting early stage breast cancer.

Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.