Five miRNAs identified in fucosylated extracellular vesicles as non-invasive diagnostic signatures for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a prevalent and aggressive cancer that presents significant challenges for early detection. This study introduces the GlyExo-Capture method for isolating fucosylated extracellular vesicles (Fu-EVs) from serum. We analyze microRNA (miRNA) profiles from Fu-EVs in 88 HCC patients and 179 non-HCC controls using next-generation sequencing (NGS) and identify five miRNAs (hsa-let-7a, hsa-miR-21, hsa-miR-125a, hsa-miR-200a, and hsa-miR-150) as biomarkers for HCC diagnosis. The five-miRNA panel demonstrates exceptional HCC diagnostic performance, with a sensitivity of 0.90 and specificity of 0.92 in a combined cohort of 194 HCC and 412 non-HCC controls, significantly surpassing the performance of alpha-fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP). Notably, the miRNA model achieves recall rates of 85.7% and 90.8% for stage 0 and stage A early-stage HCC, respectively, identifies 88.1% of AFP-negative HCC cases, and effectively differentiates HCC from other cancers. This study provides a high-throughput, rapid, and non-invasive approach for early HCC detection.

Rapid isolation method for extracellular vesicles based on Fe3O4@ZrO2.

Extracellular vesicles (EVs) are pivotal in intercellular communication, disease mechanisms. Despite numerous methods for EVs isolation, challenges persist in yield, purity, reproducibility, cost, time, and automation. We introduce a EVs isolation technique using Fe3O4@ZrO2 beads, leveraging ZrO2-phosphate interaction. The results indicated that EVs were efficiently separated from large volumes of samples in 30 minutes without preconcentration. Our method demonstrated capture efficiency (74%-78%) compared to ultracentrifugation, purity (97%), and reproducibility (0.3%-0.5%), with excellent linearity (R2 > 0.99). EVs from urine samples showed altered expression of miRNAs. The logistic regression model achieved an AUC of 0.961, sensitivity of 0.92, and specificity of 0.94. With potential for automation, this magnetic bead-based method holds promise for clinical applications, offering an efficient and reliable tool for EVs research and clinical studies.

In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data.

MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA-miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.

Storage Stability of Blood Samples for miRNAs in Glycosylated Extracellular Vesicles.

Extracellular vesicle (EV) miRNAs are promising biomarkers for clinical diagnosis. However, their stability is a crucial concern affecting reliability and accuracy. Factors such as sample collection, processing, storage conditions, and experimental procedures impact EV miRNA stability. Studying EV miRNA stability aims to find optimal handling and storage methods, ensuring integrity and functionality throughout research. In this study, we used RT-qPCR and GlyExo-Capture technology, which can specifically capture glycosylated EVs by lectin, to assess the stability of glycosylated EV miRNAs. We found that slow acceleration centrifugation and two-step centrifugation methods were suitable for subsequent experiments. To ensure uniformity, we recommend using the two-step centrifugation method. We also studied blood storage before serum separation and recommend separation within 2 h at 4 °C or 25 °C. For separated serum samples, higher temperatures accelerated miRNA degradation, and the storage duration should be adjusted based on laboratory conditions. Short-term storage at -20 °C is acceptable for up to 3 months while avoiding repeated freeze-thaw cycles. We developed protective agents to extend the storage time at 25 °C, meeting clinical requirements. Additionally, Lakebio's cfRNA storage tubes effectively preserved the stability of miRNAs in plasma glycosylated EVs. Understanding EV miRNA stability provides insights into optimizing sample handling, storage strategies, and enhancing reliability in clinical applications.

A New Single Gene Differential Biomarker for Mycobacterium tuberculosis Complex and Non-tuberculosis Mycobacteria.

BACKGROUND: Tuberculosis (TB) and non-tuberculous mycobacteriosis are serious threats to health worldwide. A simple non-sequencing method is needed for rapid diagnosis, especially in less experienced hospitals, but there is no specific biomarker commonly used for all mycobacteria. The ku gene of the prokaryotic error-prone non-homologous end joining system (NHEJ) has the potential to be a highly specific detection biomarker for mycobacteria. METHODS: A total of 7294 mycobacterial genomes and 14 complete genomes of other families belonging to Corynebacteriales with Mycobacteriaceae were downloaded and analyzed for the existence and variation of the ku gene. Mycobacterium tuberculosis complex (MTBC) and non-tuberculosis mycobacteria (NTM)- specific primers were designed and the actual amplification and identification efficiencies were tested with 150 strains of 40 Mycobacterium species and 10 kinds of common respiratory pathogenic bacteria. RESULTS: The ku gene of the NHEJ system was ubiquitous in all genome sequenced Mycobacterium species and absent in other families of Corynebacteriales. On the one hand, as a single gene non-sequencing biomarker, its specific primers could effectively distinguish mycobacteria from other bacteria, MTBC from NTM, which would make the clinical detection of mycobacteria easy and have great clinical practical value. On the other hand, the sequence of ku gene can effectively distinguish NTM to species level with high resolution. CONCLUSION: The Ku protein existed before the differentiation of Mycobacterium species, which was an important protein involved in maintaining of the genome's integrity and related to the special growth stage of mycobacteria. It was rare in prokaryotes. These features made it a highly special differential biomarker for Mycobacterium.