MicroRNAs (miRNAs) are a class of short, single-stranded, noncoding RNAs, with a length of about 18–22 nucleotides. Extracellular vesicles (EVs) are derived from cells and play a vital role in the development of diseases and can be used as biomarkers for liquid biopsy, as they are the carriers of miRNA. Existing studies have found that most of the functions of miRNA are mainly realized through intercellular transmission of EVs, which can protect and sort miRNAs. Meanwhile, detection sensitivity and specificity of EV-derived miRNA are higher than those of conventional serum biomarkers. In recent years, EVs have been expected to become a new marker for liquid biopsy. This review summarizes recent progress in several aspects of EVs, including sorting mechanisms, diagnostic value, and technology for isolation of EVs and detection of EV-derived miRNAs. In addition, the study reviews challenges and future research avenues in the field of EVs, providing a basis for the application of EV-derived miRNAs as a disease marker to be used in clinical diagnosis and even for the development of point-of-care testing (POCT) platforms.

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MicroRNA (miRNA) in extracellular vesicles (EVs) has great potential to be a promising marker in liquid biopsy. However, the present EV isolation methods, such as ultracentrifugation, have complicated and long-time operation, which impedes research on EV miRNA. The downstream complex miRNA extraction process will also significantly increase the detection cycle and loss. We first established a simple automated technique to efficiently extract target miRNAs in EVs from plasma based on Fe3O4@TiO2 beads with high affinity and capture efficiency. We combined a heat-lysis method for quick and simple EV miRNA extraction and detection. The results indicated that our method has more RNA yield than TRIzol or a commercial kit and could complete EV enrichment and miRNA extraction in 30 min. Through the detection of miRNA-21, healthy people and lung cancer patients were distinguished, which verified the possibility of the application in clinical detection. The automated isolation technology for EV miRNA has good repeatability and high throughput, with great application potential in clinical diagnosis.

Non-invasive and cost-effective diagnosis of gastric cancer is essential to improve outcomes. Aim of the study was to establish a neural network model based on patient demographic data and serum biomarker panels to aid gastric cancer diagnosis. A total of 295 patients hospitalized in Nanjing Drum Tower hospital diagnosed with gastric cancer based on tissue biopsy, and 423 healthy volunteers were included in the study. Demographical information and tumor biomarkers were obtained from Hospital Information System (HIS) as original data. Pearson's correlation analysis was applied on 574 individuals’ data (training set, 229 patients and 345 healthy volunteers) to analyze the relationship between each variable and the final diagnostic result. And independent sample t test was used to detect the differences of the variables. Finally, a neural network model based on 14 relevant variables was constructed. The model was tested on the validation set (144 individuals including 66 patients and 78 healthy volunteers). The predictive ability of the proposed model was compared with other common machine learning models including logistic regression and random forest. Tumor markers contributing significantly to gastric cancer screening included CA199, CA125, AFP, and CA242 were identified, which might be considered as important inspection items for gastric cancer screening. The accuracy of the model on validation set was 86.8% and the F1-score was 85.0%, which were better than the performance of other models under the same condition. A non-invasive and low-cost artificial neural network model was developed and proved to be a valuable tool to assist gastric cancer diagnosis.

Background: Exosomes have great potential as new biomarkers in liquid biopsy. However, due to the limitations of exosome extraction and component analysis procedures, further clinical applications of exosomes are hampered. Carcinoembryonic antigen (CEA) is a commonly used broad-spectrum tumor marker that is strongly expressed in a variety of malignancies. Results: In this study, CEA+ exosomes were directly separated from serum using immunomagnetic beads, and the nucleic acid to protein ultraviolet absorption ratio (NPr) of CEA+ exosomes was determined. It was found that the NPr of CEA+ exosomes in tumor group was higher than that of healthy group. We further analyzed the exosome-derived nucleic acid components using fluorescent staining and found that the concentration ratio of double-stranded DNA to protein (dsDPr) in CEA+ exosomes was also significantly different between the two groups, with a sensitivity of 100% and a specificity of 41.67% for the diagnosis of pan-cancer. The AUC of dsDPr combined with NPr was 0.87 and the ACU of dsDPr combined with CA242 could reach 0.94, showing good diagnostic performance for pan-cancer. Conclusions: This study demonstrates that the dsDPr of CEA+ exosomes can effectively distinguish exosomes derived from tumor patients and healthy individuals, which can be employed as a simple and cost-effective non-invasive screening technology to assist tumor diagnosis.