MicroRNA-29c Acting on FOS Plays a Significant Role in Nonalcoholic Steatohepatitis Through the Interleukin-17 Signaling Pathway

Multiomics Analysis and Machine Learning-based Identification of Molecular Signatures for Diagnostic Classification in Liver Disease Types Along the Microbiota-gut-liver Axis

Background

Liver disease, responsible for around two million deaths annually, remains a pressing global health challenge. Microbial interactions within the microbiota-gut-liver axis play a substantial role in the pathogenesis of various liver conditions, including early chronic liver disease (eCLD), chronic liver disease (CLD), acute liver failure (ALF), acute-on-chronic liver failure (ACLF), non-alcoholic fatty liver disease (NAFLD), steatohepatitis, and cirrhosis. This study aimed to identify key molecular signatures involved in liver disease progression by analyzing transcriptomic and gut microbiome data, and to evaluate their diagnostic utility using machine learning models.

Methods

Transcriptomic analysis identified differentially expressed genes (DEGs) that, when integrated with regulatory elements microRNAs, transcription factors, receptors, and the gut microbiome highlight disease-specific molecular interactions. To assess the diagnostic potential of these molecular signatures, a two-step analysis involving principal component analysis (PCA) and Random Forest classification was conducted, achieving accuracies of 75% for ALF and 89% for NAFLD. Additionally, machine learning algorithms, including K-neighbors, multi-layer perceptron (MLP), decision tree, Random Forest, logistic regression, gradient boosting, CatBoost, Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), were applied to gene expression data for ALF and NAFLD.

Results

Key genes including CLDN14, EGFR, GSK3B, MYC, and TJP2, alongside regulatory miRNAs let-7a-5p, miR-124-3p, and miR-195-5p and transcription factors NFKB1 and SP1 may be suggested as critical to liver disease progression. Additionally, gut microbiota members, Dictyostelium discoideum and Eikenella might be novel candidates associated with liver disease, highlighting the importance of the gut-liver axis. The Random Forest model reached 75% accuracy and 83% area under the curve for ALF, while NAFLD classification achieved 100% accuracy, precision, recall, and area under the curve underscoring robust diagnostic potential.

Conclusion

This study establishes a solid foundation for further research and therapeutic advancement by identifying key biomolecules and pathways critical to liver disease. Additional experimental validation is needed to confirm clinical applicability.

Comprehensive profiling of candidate biomarkers and immune infiltration landscape in metabolic dysfunction-associated steatohepatitis

Background

The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing, with an incompletely understood pathophysiology involving multiple factors, particularly innate and adaptive immune responses. Given the limited pharmacological treatments available, identification of novel immune metabolic targets is urgently needed. In this study, we aimed to identify hub immune-related genes and potential biomarkers with diagnostic and predictive value for MASH patients.

Methods

The GSE164760 dataset from the Gene Expression Omnibus was utilized for analysis, and the R package was used to identify differentially expressed genes. Immune-related differentially expressed genes (IR-DEGs) were identified by comparing the overlap of differentially expressed genes with well-known immune-related genes. Furthermore, the biological processes and molecular functions of the IR-DEGs were analyzed. To characterize the hub IR-DEGs, we employed a protein-protein interaction network. The diagnostic and predictive values of these hub IR-DEGs in MASH were confirmed using GSE48452 and GSE63067 datasets. Finally, the significance of the hub IR-DEGs was validated using a mouse model of MASH.

Results

A total of 91 IR-DEGs were identified, with 61 upregulated and 30 downregulated genes. Based on the protein-protein interaction network, FN1, RHOA, FOS, PDGFRα, CCND1, PIK3R1, CSF1, and FGF3 were identified as the hub IR-DEGs. Moreover, we found that these hub genes are closely correlated with immune cells. Notably, the validation across two independent cohorts as well as a murine MASH model confirmed their high diagnostic potential.

Conclusion

The hub IR-DEGs, such as FN1, RHOA, FOS, PDGFRα, CCND1, PIK3R1, CSF1, and FGF3, may enhance the diagnosis and prognosis of MASH by modulating immune homeostasis.

Elucidation of the molecular mechanism of type 2 diabetes mellitus affecting the progression of nonalcoholic steatohepatitis using bioinformatics and network pharmacology: A review

Increasing evidence suggests that patients with diabetes are at increased risk of developing nonalcoholic steatohepatitis (NASH), but the underlying mechanisms that affect the progression of NASH remain unclear. In this study, we used bioinformatics and network pharmacology methods to explore the differentially expressed genes of NASH and the related genes of type 2 diabetes mellitus, and a total of 46 common targets were obtained. Gene ontology showed that the common targets were mainly involved in biological processes such as glucocorticoid, hormone, and bacterium responses. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis signal pathways were mainly in colorectal cancer, amphetamine addition, the peroxisome proliferator-activated receptor signaling pathway, and the toll-like receptor signaling pathway. The protein-protein interaction network identified 8 hub genes, and the co-expression network was analyzed to obtain 7 related functions and mutual proportions of hub genes. A total of 120 transcription factors were predicted for hub genes. Hub genes were closely related to immune cells, including neutropils and eosinophils. In addition, we identified 15 potential candidate drugs based on hub genes that are promising for the treatment of NASH. Type 2 diabetes mellitus can affect the progression of NASH by changing hormone levels and inflammatory responses through multiple targets and signaling pathways. Eight hub genes are expected to be potential targets for subsequent treatment.

Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study

OBJECTIVE

The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition.

METHODS

We used GEO database data, including 74 non-endometriosis and 74 varying-degree EMs patients. Our approach involved identifying significant gene modules, exploring gene intersections, identifying core genes, and screening for potential EMs biomarkers using weighted gene co-expression network analysis (WGCNA) and various machine learning approaches. We also performed gene set enrichment analysis (GSEA) to understand relevant pathways. This comprehensive approach helps investigate EMs genetics and potential biomarkers.

RESULTS

Nine genes were identified at the intersection, suggesting their involvement in EMs. GSEA linked DEGs to pathways like complement and coagulation cascades, DNA replication, chemokines, apical plasma membrane processes, and diseases such as Hepatitis B, Human T-cell leukemia virus 1 infection, and COVID-19. Five feature genes (FOS, CFD, CCNA1, CA4, CST1) were selected by machine learning for an effective EMs diagnostic nomogram. GSEA indicated their roles in mismatch repair, cell cycle regulation, complement and coagulation cascades, and IL-17 inflammation. Notable differences in immune cell proportions (CD4 T cells, CD8 T cells, DCs, macrophages) were observed between normal and disease groups, suggesting immune involvement.

CONCLUSIONS

This study suggests the potential involvement of phase separation-related genes in the pathogenesis of endometriosis (EMs) and identifies promising biomarkers for diagnosis. These findings have implications for further research and the development of new therapeutic strategies for EMs.

Two sides of the same coin: Non-alcoholic fatty liver disease and atherosclerosis

The prevalence of non-alcoholic fatty liver disease (NAFLD) and atherosclerosis remain high, which is primarily due to widespread adoption of a western diet and sedentary lifestyle. NAFLD, together with advanced forms of this disease such as non-alcoholic steatohepatitis (NASH) and cirrhosis, are closely associated with atherosclerotic-cardiovascular disease (ASCVD). In this review, we discussed the association between NAFLD and atherosclerosis and expounded on the common molecular biomarkers underpinning the pathogenesis of both NAFLD and atherosclerosis. Furthermore, we have summarized the mode of function and potential clinical utility of existing drugs in the context of these diseases.

FOS Inhibits the Differentiation of Intramuscular Adipocytes in Goats

Goat intramuscular fat (IMF) deposition is precisely regulated by many key genes as well as transcription factors. Nevertheless, the potential of the regulators of goat IMF deposition remains undefined. In this work, we reported that the transcription factor FOS is expressed at a low level at the early differentiation stage and at a high level in late differentiation. The overexpression of FOS inhibited intramuscular adipocyte lipid accumulation and significantly downregulated the expressions of PPARγ, C/EBPβ, C/EBPα, AP2, SREBP1, FASN, ACC, HSL, and ATGL. Consistently, the knockdown of FOS, facilitated by two distinct siRNAs, significantly promoted intramuscular adipocyte lipid accumulation. Moreover, our analysis revealed multiple potential binding sites for FOS on the promoters of PPARγ, C/EBPβ, and C/EBPα. The expression changes in PPARγ, C/EBPβ, and C/EBPα during intramuscular adipogenesis were opposite to that of FOS. In summary, FOS inhibits intramuscular lipogenesis in goats and potentially negatively regulates the expressions of PPARγ, C/EBPβ, and C/EBPα genes. Our research will provide valuable data for the underlying molecular mechanism of the FOS regulation network of intramuscular lipogenesis.

The progress of autoimmune hepatitis research and future challenges

Autoimmune hepatitis (AIH) is a chronic liver inflammatory disease with various immune system manifestations, showing a global trend of increased prevalence. AIH is diagnosed through histological abnormalities, clinical manifestations, and biochemical indicators. The biochemical markers involve interfacial hepatitis, transaminase abnormalities, positive autoantibodies, etc. Although AIH pathogenesis is unclear, gene mutations and immunological factors could be the leading factors. AIH usually presents as a chronic liver disease and sometimes as acute hepatitis, making it challenging to distinguish it from drug-related hepatitis due to similar clinical symptoms. Normalizing transaminases and serum IgG levels is essential in assessing the remission status of AIH treatment. Glucocorticoids and azathioprine are the first-line AIH treatment, with lifelong maintenance therapy in some patients. The quality of life and survival can be improved after appropriate treatment. However, certain limitations jeopardize the quality of treatment, including long treatment cycles, side effects, poor patient compliance, and inability to inhibit liver fibrosis and cirrhosis. Accurate AIH animal models will help us understand the pathophysiology of the disease while providing fresh perspectives for avoiding and treating AIH. This review will help us understand AIH better, from the cellular and molecular causes to the clinical features, and will provide insight into new therapy techniques with fewer side effects.

Decoding the role of immune T cells: A new territory for improvement of metabolic-associated fatty liver disease

Metabolic-associated fatty liver disease (MAFLD) is a new emerging concept and is associated with metabolic dysfunction, generally replacing the name of nonalcoholic fatty liver disease (NAFLD) due to heterogeneous liver condition and inaccuracies in definition. The prevalence of MAFLD is rising by year due to dietary changes, metabolic disorders, and no approved therapy, affecting a quarter of the global population and representing a major economic problem that burdens healthcare systems. Currently, in addition to the common causative factors like insulin resistance, oxidative stress, and lipotoxicity, the role of immune cells, especially T cells, played in MAFLD is increasingly being emphasized by global scholars. Based on the diverse classification and pathophysiological effects of immune T cells, we comprehensively analyzed their bidirectional regulatory effects on the hepatic inflammatory microenvironment and MAFLD progression. This interaction between MAFLD and T cells was also associated with hepatic-intestinal immune crosstalk and gut microbiota homeostasis. Moreover, we pointed out several T-cell-based therapeutic approaches including but not limited to adoptive transfer of T cells, fecal microbiota transplantation, and drug therapy, especially for natural products and Chinese herbal prescriptions. Overall, this study contributes to a better understanding of the important role of T cells played in MAFLD progression and corresponding therapeutic options and provides a potential reference for further drug development.

Update on genetics and epigenetics in metabolic associated fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is becoming the most frequent chronic liver disease worldwide. Metabolic (dysfunction) associated fatty liver disease (MAFLD) is suggested to replace the nomenclature of NAFLD. For individuals with metabolic dysfunction, multiple NAFLD-related factors also contribute to the development and progression of MAFLD including genetics and epigenetics. The application of genome-wide association study (GWAS) and exome-wide association study (EWAS) uncovers single-nucleotide polymorphisms (SNPs) in MAFLD. In addition to the classic SNPs in PNPLA3, TM6SF2, and GCKR, some new SNPs have been found recently to contribute to the pathogenesis of liver steatosis. Epigenetic factors involving DNA methylation, histone modifications, non-coding RNAs regulations, and RNA methylation also play a critical role in MAFLD. DNA methylation is the most reported epigenetic modification. Developing a non-invasion biomarker to distinguish metabolic steatohepatitis (MASH) or liver fibrosis is ongoing. In this review, we summarized and discussed the latest progress in genetic and epigenetic factors of NAFLD/MAFLD, in order to provide potential clues for MAFLD treatment.

Regulatory T cells and their associated factors in hepatocellular carcinoma development and therapy

Liver cancer is the third leading cause of cancer-related death worldwide with primary type hepatocellular carcinoma (HCC). Factors, including carcinogens, infection of hepatitis viruses, alcohol abuse, and non-alcoholic fatty liver disease (NAFLD), can induce HCC initiation and promote HCC progression. The prevalence of NAFLD accompanying the increased incidence of obesity and type 2 diabetes becomes the most increasing factor causing HCC worldwide. However, the benefit of current therapeutic options is still limited. Intrahepatic immunity plays critically important roles in HCC initiation, development, and progression. Regulatory T cells (Tregs) and their associated factors such as metabolites and secreting cytokines mediate the immune tolerance of the tumor microenvironment in HCC. Therefore, targeting Tregs and blocking their mediated factors may prevent HCC progression. This review summarizes the functions of Tregs in HCC-inducing factors including alcoholic and NAFLD, liver fibrosis, cirrhosis, and viral infections. Overall, a better understanding of the role of Tregs in the development and progression of HCC provides treatment strategies for liver cancer treatment.