Tumor-derived exosomes have been recognized as promising biomarkers for early-stage cancer diagnosis, tumor prognosis monitoring and individual medical treatment. However, it is a huge challenge to separate exosomes from trace biological samples in clinics for disease diagnosis. Herein, we propose a simple, quick, and label-free method for isolating circulating exosomes from serum of patients. The strategy synergistically integrates chitosan electrostatic-adsorption, micro-patterned substrates, and microfluidic shuttle flow control to enable the capture/release of circulating exosomes in a simple manner. Using this microchip, we can isolate exosomes from trace samples (10 μl) with relative purity over 90% and high RNA recovery ratio over 84% within 15 minutes, which is impossible for traditional ultracentrifugation methods. We then validate the application of the microchip using 24 serum samples from clinical breast cancer and breast fibroma patients. The isolated exosomes are subjected to miRNA sequencing and RT-PCR, followed by pathway prediction analysis. The results showed that exosomes were relevant to the invasion and metastasis of breast cancer cells and hsa-miR-18a-3p might have the potential to become a new biomarker for distinguishing breast cancer from breast fibroma (AUC = 0.83, P value = 0.019). This established method is simple, quick and easy to operate with integration. And it may pave a new way for clinical research on exosomes and tumor relevant diagnosis.

Breast cancer is one of the most commonly diagnosed malignancies and the leading cause of cancer death in women worldwide. Although many factors associated with breast cancer have been identified, the definite etiology of breast cancer is still unclear. In addition, early diagnosis of breast cancer remains challenging. Exosomes are membrane-bound nanovesicles secreted by most types of cells and contain a series of biologically important molecules, such as lipids, proteins, and miRNAs, etc. Emerging evidence shows that exosomes can affect the status of cells by transmitting substances and messages among cells and are involved in various physiological and pathological processes. In breast cancer, exosomes play a significant role in breast tumorigenesis and progression through transfer miRNAs which can be potential biomarkers for early diagnosis of breast cancer. This review discusses the potential utility of exosomal miRNAs in breast cancer progression such as tumorigenesis, metastasis, immune regulation and drug resistance, and further in breast cancer diagnosis.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models’ overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73–0.74; IBS, 0.16–0.17; 3-year AUC, 0.73–0.78; 5-year AUC, 0.72–0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Arthritis is caused by inflammation, infection, degeneration, trauma, or other factors that affect approximately 250 million people all over the world. Early diagnosis and prediction are essential for treatment. Exosomes are nanoscale vesicles that participate in the process of joint disease. Serum is the mainly used sources in the study of arthritis-related exosomes, while whether serum exosomes can reflect the contents of synovial fluid exosomes is still unknown. In this work, we separated exosomes from serum and the synovial fluid of osteoarthritis patients and compared their miRNA expression utilizing miRNA sequencing. The results revealed that 31 upregulated and 33 downregulated miRNAs were found in synovial fluid compared to serum. Transcriptome analysis showed that these differentially expressed miRNAs were mainly associated with intercellular processes and metabolic pathways. Our results show that serum-derived exosomes cannot fully represent the exosomes of synovial fluid, which may be helpful for the study of joint diseases and the discovery of early diagnostic biomarkers of arthritis.

The aim of the present study was to investigate the stimulation of osteoinduced mesenchymal stem cells (MSCs) into chondrogenically predifferentiated MSCs and chondrocytes in a mechanical environment. A novel two‑layer microfluidic chip was used to mimic the interstitial flow in the superficial zones of articular cartilage. The morphology, proliferation rate and the expression of collagen I, collagen II and aggrecan of chondrocytes and chondro‑MSCs were investigated. The results revealed that the cells in the bottom layer were influenced by the top layer's osteoinduced MSCs and the bottom layer's shear flow. The expression of collagen I, which may signify the effect of the shear stress on the dedifferentiation change, was weakened by the stimulation of osteoinduced MSCs on the top layer. The expression of collagen II and aggrecan was increased in the fluidic environment by osteoinduced MSCs. These results indicate that osteoinduced MSCs have a significant effect on the phenotype of chondro‑MSCs and chondrocytes in the fluidic microenvironment. The present study described a simple and promising way to rapidly evaluate cell responses to other cells in a fluidic environment, which may help to better promote the utilization of MSCs and chondrocytes in tissue engineering.

Exosomes are membrane-bound nanovesicles secreted by most types of cells, which contain a series of biologically important molecules, such as miRNAs, proteins, and lipids, etc. Emerging evidence show that exosomes can affect the physiological status of cells and are involved in various pathological processes. However, due to their small size and density close to body fluids, it is challenging to separate exosomes from a small volume of biological samples in a simple manner. Herein, we propose a new strategy for isolating circulating exosomes from biological samples in a portable device. This method synergistically integrates chitosan electrostatic-adsorption, scaffold substrates, and shuttle flow to enable the highly effective capture of circulating exosomes with a recovery rate of over 80% within 20 min, which is much better than the performance of traditional ultracentrifugation (5–25%, 3 h). Besides, the isolated exosomes from samples could be lysed in situ and further subjected to RNA concentration detection and protein analysis. In particular, all the necessary procedures for exosome separation could be integrated into a single device without the need for bulky equipment. This established device is portable and easy to operate, which provides a promising platform for the study of exosome biology and clinical diagnosis.