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Circulating extracellular vesicles (EVs) are a novel and emerging biomarker for nonalcoholic steatohepatitis (NASH). It has been demonstrated that total circulating EVs and hepatocyte‐derived EVs are elevated in male mice with diet‐induced NASH. How hepatocyte‐derived EVs change over time and other cellular sources of EVs in NASH have not been determined. Our objective was to define the quantitative evolution of hepatocyte‐derived, macrophage‐derived, neutrophil‐derived, and platelet‐derived EVs in male and female mice with dietary NASH. Fluorescently labeled antibodies and a nanoscale flow cytometer were used to detect plasma levels of EVs. Asialoglycoprotein receptor 1 (ASGR1) and cytochrome P450 family 2 subfamily E member 1 (CYP2E1) are markers of hepatocyte‐derived EVs; galectin 3 is a marker of macrophage‐derived EVs; common epitope on lymphocyte antigen 6 complex, locus G/C1 (Ly‐6G and Ly‐6C) is a marker of neutrophil‐derived EVs; and clusters of differentiation 61 (CD61) is a marker of platelet‐derived EVs. Nonalcoholic fatty liver disease activity score (NAS) was calculated using hematoxylin and eosin‐stained liver sections, and magnetic resonance imaging (MRI) was used for measurement of the fat fraction and elastography. Hepatocyte‐derived EVs increased in both male and female mice at 12 and 10 weeks of feeding, respectively, and remained elevated at 24 weeks in both male and female mice and at 48 weeks in male mice and 36 weeks in female mice. Macrophage‐ and neutrophil‐derived EVs were significantly elevated at 24 weeks of dietary feeding concomitant with the histologic presence of inflammatory foci in the liver. In fat‐, fructose‐, and cholesterol‐ (FFC) fed male mice, platelet‐derived EVs were elevated at 12, 24, and 48 weeks, whereas in female mice, platelet derived EVs were significantly elevated at 24 weeks. Hepatocyte‐, macrophage‐ and neutrophil‐derived EVs correlated well with the histologic NAS. Conclusion: Circulating cell‐type‐specific EVs may be a novel biomarker for NASH diagnosis and longitudinal follow up.

Objective: Recently, ribosome binding protein 1 (RRBP1) is reported to be involved in tumorigenesis. However, the expression and clinical significance of RRBP1 in prostate cancer (PCa) remains unknown. This study is aimed to investigate the expression and clinical significance of RRBP1 in PCa. Materials and methods: RRBP1 expression was firstly detected in 6 cases of PCa and matched adjacent non-cancerous prostate tissues by reverse transcription-quantitative PCR (RT-qPCR) and Western blot. Then, RRBP1 expression was further detected in 127 cases of PCa and 40 cases of non-cancerous prostate tissues by immunohistochemistry (IHC). The relationship of RRBP1 with clinical-pathological characters and patients’ prognosis was analyzed in PCa. Results: RT-qPCR and Western blot analysis showed that RRBP1 expression levels in PCa tissues were significantly higher compared with those in matched adjacent non-cancerous prostate tissues. IHC results shown that the high-expression rate of RRBP1 in PCa was 69.3%, which was significantly greater than those in non-cancerous prostate tissues (15.0%, P <0.001). RRBP1 expression was significantly associated with T stage, lymph node metastasis, PSA and Gleason score in PCa. Survival analysis indicated that patients with RRBP1 low-expression presented longer survival time compared with those with RRBP1 high-expression. Moreover, RRBP1 as well as T stage, lymph node metastasis and Gleason score could serve as independent prognostic factors in PCa. Conclusion: RRBP1 is highly expressed in PCa and correlates with prognosis, which may serve as a potential biomarker in PCa.

Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy.

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