Mesenchymal stromal cells: promising treatment for liver cirrhosis

Abstract Liver fibrosis is a wound-healing process that occurs in response to severe injuries and is hallmarked by the excessive accumulation of extracellular matrix or scar tissues within the liver. Liver fibrosis can be either acute or chronic and is induced by a variety of hepatotoxic causes, including lipid deposition, drugs, viruses, and autoimmune reactions. In advanced fibrosis, liver cirrhosis develops, a condition for which there is no successful therapy other than liver transplantation. Although liver transplantation is still a viable option, numerous limitations limit its application, including a lack of donor organs, immune rejection, and postoperative complications. As a result, there is an immediate need for a different kind of therapeutic approach. Recent research has shown that the administration of mesenchymal stromal cells (MSCs) is an attractive treatment modality for repairing liver injury and enhancing liver regeneration. This is accomplished through the cell migration into liver sites, immunoregulation, hepatogenic differentiation, as well as paracrine mechanisms. MSCs can also release a huge variety of molecules into the extracellular environment. These molecules, which include extracellular vesicles, lipids, free nucleic acids, and soluble proteins, exert crucial roles in repairing damaged tissue. In this review, we summarize the characteristics of MSCs, representative clinical study data, and the potential mechanisms of MSCs-based strategies for attenuating liver cirrhosis. Additionally, we examine the processes that are involved in the MSCs-dependent modulation of the immune milieu in liver cirrhosis. As a result, our findings lend credence to the concept of developing a cell therapy treatment for liver cirrhosis that is premised on MSCs. MSCs can be used as a candidate therapeutic agent to lengthen the survival duration of patients with liver cirrhosis or possibly reverse the condition in the near future

Mitochondrial dysfunction: A promising therapeutic target for liver diseases

The liver is an important metabolic and detoxification organ and hence demands a large amount of energy, which is mainly produced by the mitochondria. Liver tissues of patients with alcohol-related or non-alcohol-related liver diseases contain ultrastructural mitochondrial lesions, mitochondrial DNA damage, disturbed mitochondrial dynamics, and compromised ATP production. Overproduction of mitochondrial reactive oxygen species induces oxidative damage to mitochondrial proteins and mitochondrial DNA, decreases mitochondrial membrane potential, triggers hepatocyte inflammation, and promotes programmed cell death, all of which impair liver function. Mitochondrial DNA may be a potential novel non-invasive biomarker of the risk of progression to liver cirrhosis and hepatocellular carcinoma in patients infected with the hepatitis B virus. We herein present a review of the mechanisms of mitochondrial dysfunction in the development of acute liver injury and chronic liver diseases, such as hepatocellular carcinoma, viral hepatitis, drug-induced liver injury, alcoholic liver disease, and non-alcoholic fatty liver disease. This review also discusses mitochondrion-centric therapies for treating liver diseases

Table2_Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies.xlsx

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF.Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells.Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen production-associated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells.Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF.</p

Table1_Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies.xls

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF.Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells.Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen production-associated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells.Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF.</p

Table2_Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis.XLSX

Hepatitis B virus (HBV) infection remains the leading cause of liver fibrosis (LF) worldwide, especially in China. Identification of decisive diagnostic biomarkers for HBV-associated liver fibrosis (HBV-LF) is required to prevent chronic hepatitis B (CHB) from progressing to liver cancer and to more effectively select the best treatment strategy. We obtained 43 samples from CHB patients without LF and 81 samples from CHB patients with LF (GSE84044 dataset). Among these, 173 differentially expressed genes (DEGs) were identified. Functional analysis revealed that these DEGs predominantly participated in immune-, extracellular matrix-, and metabolism-related processes. Subsequently, we integrated four algorithms (LASSO regression, SVM-RFE, RF, and WGCNA) to determine diagnostic biomarkers for HBV-LF. These analyses and receive operating characteristic curves identified the genes for phosphatidic acid phosphatase type 2C (PPAP2C) and versican (VCAN) as potentially valuable diagnostic biomarkers for HBV-LF. Single-sample gene set enrichment analysis (ssGSEA) further confirmed the immune landscape of HBV-LF. The two diagnostic biomarkers also significantly correlated with infiltrating immune cells. The potential regulatory mechanisms of VCAN underlying the occurrence and development of HBV-LF were also analyzed. These collective findings implicate VCAN as a novel diagnostic biomarker for HBV-LF, and infiltration of immune cells may critically contribute to the occurrence and development of HBV-LF.</p

Table1_Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis.XLSX

Hepatitis B virus (HBV) infection remains the leading cause of liver fibrosis (LF) worldwide, especially in China. Identification of decisive diagnostic biomarkers for HBV-associated liver fibrosis (HBV-LF) is required to prevent chronic hepatitis B (CHB) from progressing to liver cancer and to more effectively select the best treatment strategy. We obtained 43 samples from CHB patients without LF and 81 samples from CHB patients with LF (GSE84044 dataset). Among these, 173 differentially expressed genes (DEGs) were identified. Functional analysis revealed that these DEGs predominantly participated in immune-, extracellular matrix-, and metabolism-related processes. Subsequently, we integrated four algorithms (LASSO regression, SVM-RFE, RF, and WGCNA) to determine diagnostic biomarkers for HBV-LF. These analyses and receive operating characteristic curves identified the genes for phosphatidic acid phosphatase type 2C (PPAP2C) and versican (VCAN) as potentially valuable diagnostic biomarkers for HBV-LF. Single-sample gene set enrichment analysis (ssGSEA) further confirmed the immune landscape of HBV-LF. The two diagnostic biomarkers also significantly correlated with infiltrating immune cells. The potential regulatory mechanisms of VCAN underlying the occurrence and development of HBV-LF were also analyzed. These collective findings implicate VCAN as a novel diagnostic biomarker for HBV-LF, and infiltration of immune cells may critically contribute to the occurrence and development of HBV-LF.</p

Image1_Comprehensive bioinformatics and machine learning analysis identify VCAN as a novel biomarker of hepatitis B virus-related liver fibrosis.tif

Hepatitis B virus (HBV) infection remains the leading cause of liver fibrosis (LF) worldwide, especially in China. Identification of decisive diagnostic biomarkers for HBV-associated liver fibrosis (HBV-LF) is required to prevent chronic hepatitis B (CHB) from progressing to liver cancer and to more effectively select the best treatment strategy. We obtained 43 samples from CHB patients without LF and 81 samples from CHB patients with LF (GSE84044 dataset). Among these, 173 differentially expressed genes (DEGs) were identified. Functional analysis revealed that these DEGs predominantly participated in immune-, extracellular matrix-, and metabolism-related processes. Subsequently, we integrated four algorithms (LASSO regression, SVM-RFE, RF, and WGCNA) to determine diagnostic biomarkers for HBV-LF. These analyses and receive operating characteristic curves identified the genes for phosphatidic acid phosphatase type 2C (PPAP2C) and versican (VCAN) as potentially valuable diagnostic biomarkers for HBV-LF. Single-sample gene set enrichment analysis (ssGSEA) further confirmed the immune landscape of HBV-LF. The two diagnostic biomarkers also significantly correlated with infiltrating immune cells. The potential regulatory mechanisms of VCAN underlying the occurrence and development of HBV-LF were also analyzed. These collective findings implicate VCAN as a novel diagnostic biomarker for HBV-LF, and infiltration of immune cells may critically contribute to the occurrence and development of HBV-LF.</p

Hypertension and NAFLD risk: Insights from the NHANES 2017-2018 and Mendelian randomization analyses

Abstract. Background:. Hypertension and non-alcoholic fatty liver disease (NAFLD) share several pathophysiologic risk factors, and the exact relationship between the two remains unclear. Our study aims to provide evidence concerning the relationship between hypertension and NAFLD by analyzing data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 and Mendelian randomization (MR) analyses. Methods:. Weighted multivariable-adjusted logistic regression was applied to assess the relationship between hypertension and NAFLD risk by using data from the NHANES 2017-2018. Subsequently, a two-sample MR study was performed using the genome-wide association study (GWAS) summary statistics to identify the causal association between hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), and NAFLD. The primary inverse variance weighted (IVW) and other supplementary MR approaches were conducted to verify the causal association between hypertension and NAFLD. Sensitivity analyses were adopted to confirm the robustness of the results. Results:. A total of 3144 participants were enrolled for our observational study in NHANES. Weighted multivariable-adjusted logistic regression analysis suggested that hypertension was positively related to NAFLD risk (odds ratio [OR] = 1.677; 95% confidence interval [CI], 1.159-2.423). SBP ≥130 mmHg and DBP ≥80 mmHg were also significantly positively correlated with NAFLD. Moreover, hypertension was independently connected with liver steatosis (β = 7.836 [95% CI, 2.334-13.338]). The results of MR analysis also supported a causal association between hypertension (OR = 7.203 [95% CI, 2.297-22.587]) and NAFLD. Similar results were observed for the causal exploration between SBP (OR = 1.024 [95% CI, 1.003-1.046]), DBP (OR = 1.047 [95% CI, 1.005-1.090]), and NAFLD. The sensitive analysis further confirmed the robustness and reliability of these findings (all P >0.05). Conclusion:. Hypertension was associated with an increased risk of NAFLD

Additional file 1 of Meta-analysis on last ten years of clinical injection of bone marrow-derived and umbilical cord MSC to reverse cirrhosis or rescue patients with acute-on-chronic liver failure

Additional file 1: Table S1 Results of administration route and cell type of MSCs therapy on MELD score. Table S2 Results of administration route and cell type of MSCs therapy on ALB level. Table S3 Results of administration route and cell type of MSCs therapy on TBIL level.