Native and engineered extracellular vesicles: novel tools for treating liver disease.

Liver diseases are classified as acute liver damage and chronic liver disease, with recurring liver damage causing liver fibrosis and progression to cirrhosis and hepatoma. Liver transplantation is the only effective treatment for end-stage liver diseases; therefore, novel therapies are required. Extracellular vesicles (EVs) are endogenous nanocarriers involved in cell-to-cell communication that play important roles in immune regulation, tissue repair and regeneration. Native EVs can potentially be used for various liver diseases owing to their high biocompatibility, low immunogenicity and tissue permeability and engineered EVs with surface modification or cargo loading could further optimize therapeutic effects. In this review, we firstly introduced the mechanisms and effects of native EVs derived from different cells and tissues to treat liver diseases of different etiologies. Additionally, we summarized the possible methods to facilitate liver targeting and improve cargo-loading efficiency. In the treatment of liver disease, the detailed engineered methods and the latest delivery strategies were also discussed. Finally, we pointed out the limitations and challenges of EVs for future development and applications. We hope that this review could provide a useful reference for the development of EVs and promote the clinical translation.

Identifying optimal candidates for autologous peripheral blood stem cell therapy in patients with decompensated liver cirrhosis: a prognostic scoring system.

BACKGROUND: Stem cell transplantation shows great potential to improve the long-term survival of cirrhosis patients. However, therapeutic effects may not be homogeneous across the whole study population. This study constructed an easy-to-use nomogram to improve prognostic prediction and aid in treatment decision making for cirrhotic patients. METHODS: From August 2005 to April 2019, 315 patients with decompensated cirrhosis receiving autologous peripheral blood stem cell (PBSC) transplantation were enrolled in this study. They were randomly classified into training (2/3) and validation (1/3) groups. A predictive model was developed using Cox proportional hazard models and subsequently validated. The predictive performance of the model was evaluated and also compared with other prognostic models. RESULTS: Age, creatinine, neutrophil-to-lymphocyte ratio, and Child-Turcotte-Pugh class were included in the nomogram as prognostic variables. The nomogram showed high discrimination power concerning the area under receiver operating characteristic curves (3/5-year AUC: 0.742/0.698) and good consistency suggested by calibration plots. Patients could be accurately stratified into poor- and good-outcome groups regarding liver-transplantation free survival after receiving PBSC therapy (P < 0.001). Compared with poor-outcome group, the liver function of patients listed for liver transplantation in the good-outcome group was significantly improved (P < 0.001). Besides, our nomogram achieved a higher C-index (0.685, 95% CI 0.633-0.738) and better clinical utility compared with other conventional prognostic models. CONCLUSIONS: The proposed nomogram facilitated an accurate prognostic prediction for patients with decompensated cirrhosis receiving PBSC transplantation. Moreover, it also held the promise to stratify patients in clinical trials or practice to implement optimal treatment regimens for individuals.

Mesenchymal stem cell-derived exosomes protect against liver fibrosis via delivering miR-148a to target KLF6/STAT3 pathway in macrophages.

BACKGROUND: Despite emerging evidence on the therapeutic potential of mesenchymal stem cells (MSCs) for liver fibrosis, the underlying mechanisms remain unclear. At present, MSC-derived exosomes (MSC-EXOs) are widely accepted as crucial messengers for intercellular communication. This study aimed to explore the therapeutic effects of MSC-EXOs on liver fibrosis and identify the mechanisms underlying the action of MSC-EXOs. METHODS: Carbon tetrachloride was used to induce a liver fibrosis model, which was intravenously administered with MSCs or MSC-EXOs to assess treatment efficacy. The resulting histopathology, fibrosis degree, inflammation and macrophage polarization were analyzed. RAW264.7 and BMDM cells were used to explore the regulatory effects of MSC-EXOs on macrophage polarization. Then, the critical miRNA mediating the therapeutic effects of MSC-EXOs was screened via RNA sequencing and validated experimentally. Furthermore, the target mRNA and downstream signaling pathways were elucidated by luciferase reporter assay, bioinformatics analysis and western blot. RESULTS: MSCs alleviated liver fibrosis largely depended on their secreted exosomes, which were visualized to circulate into liver after transplantation. In addition, MSC-EXOs were found to modulate macrophage phenotype to regulate inflammatory microenvironment in liver and repair the injury. Mechanically, RNA-sequencing illustrates that miR-148a, enriched in the MSC-EXOs, targets Kruppel-like factor 6 (KLF6) to suppress pro-inflammatory macrophages and promote anti-inflammatory macrophages by inhibiting the STAT3 pathway. Administration of miR-148a-enriched MSC-EXOs or miR-148a agomir shows potent ameliorative effects on liver fibrosis. CONCLUSIONS: These findings suggest that MSC-EXOs protect against liver fibrosis via delivering miR-148a that regulates intrahepatic macrophage functions through KLF6/STAT3 signaling and provide a potential therapeutic target for liver fibrosis.

A novel immune checkpoint-related gene signature for hepatocellular carcinoma to predict clinical outcomes and therapeutic response.

Immune checkpoint genes (ICGs) have recently been proven to perform instrumental functions in the maintenance of immune homeostasis and represent a promising therapeutic strategy; however, their expression patterns and prognostic values are not fully elucidated in hepatocellular carcinoma (HCC). In this investigation, we focused on establishing and validating a prognostic gene signature to facilitate decision-making in clinical practice. Clinical information, as well as transcriptome data, was obtained from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox method were employed to build a multi-gene signature in the TCGA database, while the ICGC database was used for validation. Subsequently, utilizing the six-gene signature, we were able to categorize patients into high- and low-risk groups. In two cohorts, survival analysis findings revealed a dismal outlook for the high-risk group. The receiver operating characteristic curves were utilized to estimate the gene signature's prediction ability. Moreover, correlation analysis showed high-risk group was linked to advanced pathological stage, infiltration of immune cells and therapeutic response. In summary, this unique gene profile might serve not only as a useful prognostic indicator but also as a marker of therapy responsiveness in HCC.

Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma.

BACKGROUND: Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients. METHODS: Differently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability. RESULTS: The prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis. CONCLUSIONS: The RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.