Ask about this productRelated genes to: Snf8 antibody
- Gene:
- SNF8 NIH gene
- Name:
- SNF8 subunit of ESCRT-II
- Previous symbol:
- -
- Synonyms:
- EAP30, VPS22, Dot3
- Chromosome:
- 17q21.32
- Locus Type:
- gene with protein product
- Date approved:
- 2005-08-02
- Date modifiied:
- 2019-01-31
Related products to: Snf8 antibody
Related articles to: Snf8 antibody
- Metabolic dysfunction-associated steatotic liver disease (MASLD), marked by excess fat in the liver, has become the most prevalent chronic liver disease worldwide, affecting over 30% of adults. Its advanced form, metabolic dysfunction associated steatohepatitis (MASH), includes liver ballooning, and inflammation, and can progress to cirrhosis and hepatocellular carcinoma (HCC). Despite the increasing burden, effective pharmacotherapies for MASLD/MASH are still lacking. Programmed cell death mechanisms, such as autophagy and ferroptosis, are critical in the pathology of MASLD, influencing liver inflammation, fibrosis, and malignant transformation. This study employed six machine learning models—Random Forest, Logistic Regression, Extra Trees Classifier, Linear Discriminant Analysis, and Light Gradient Boosting Machine—to identify significant drug targets using Febuxostat, Perindopril, Amlodipine, and Atorvastatin, evaluated through molecular, biochemical, immunohistochemical, and pathological markers. We identified genes associated with MASH using microarray datasets from the Gene Expression Omnibus database, followed by protein-protein interaction and functional enrichment analyses to select genes related to ferroptosis, autophagy, and their epigenetic regulators (miRNAs-LncRNAs) in MASH-induced rats. Quantitative real-time PCR validated the expression of selected networks (mRNAs-miRNAs-LncRNAs). Additionally, we measured biochemical, inflammatory, and liver pathology markers to ensure the model’s robustness. Our results identified 16 out of 29 valuable therapeutic targets with an accuracy of 88.74% and an AUC of 0.9745, including LPCAT3, HGS, TSG101, SNF8, rno-miR-27a-5p, rno-miR-329-5p, CTBP1-AS2, ALT, AST, ALP, GGT, D. Bilirubin, Albumin, TMAO, GPX4, and TGFβ1. - Source: PubMed
Publication date: 2026/04/27
Matboli MarwaElanwar AlyKhaled RadwaKhaled AbdelrahmanEltantawy Eman Hamdy BadrHamam Ghada GalalAhmed Manar YehiaFouad ManarTarek Ahmed AsmaaElmasry MaryamEl-Shafei Marwa MAboulhoda Basma EmadDiab Gouda IbrahimAboughaleb Ibrahim H - Mitochondrial dynamics (MD) are crucial in various inflammatory disorders, yet the specific mechanisms involved in psoriasis remain inadequately understood. Thus, this study aims to discover potential biomarkers and explore the mechanisms related to MD in psoriasis by employing bioinformatics methods in conjunction with the Mendelian randomization (MR) approach. In this investigation, datasets associated with psoriasis, specifically (GSE14905, GSE13355, and ukb-a-100), alongside genes pertinent to MD (MDRGs), were employed. The initial step involved the identification of significant module genes associated with MD through weighted gene co-expression network analysis. Subsequently, the identified module genes were cross-referenced with differentially expressed genes discerned between psoriasis and control groups to extract differentially expressed MDRGs. Additionally, MR analysis was conducted to identify potential candidate genes. The definitive potential biomarkers were determined through protein-protein interaction (PPI) networks, machine learning methodologies, receiver operating characteristic analysis, and expression profiling. Finally, gene set enrichment analysis, alongside immune infiltration and immune response assessments, was executed to elucidate the underlying mechanisms by which the potential biomarkers function in the context of psoriasis. There were 3136 key module genes through weighted gene co-expression network analysis and 643 differentially expressed MDRGs by crossing key module genes and 4310 differentially expressed genes. Afterward, 56 candidate genes with causal relationship to psoriasis were selected by MR analysis. Then 19 hub genes from PPI network were used to further screen 6 feature genes by machine learning, and they had a better ability to distinguish psoriasis (area under the curve > 0.7). C1orf43, SNF8, STOML2, and MRPS16 were identified as potential biomarkers in psoriasis, and were co-enriched in pyrimidine metabolism, DNA_replication, and proteasome. Eventually, there were 11 differential immune cells (memory B cells, activated dendritic cells, etc) and 13 differential immune responses (antigen processing and presentation, antimicrobials, etc) between psoriasis and control samples in psoriasis (P < .05). C1orf43, SNF8, STOML2, and MRPS16 were identified as potential biomarkers linked to MD in psoriasis, which provide promising leads for further investigation. These biomarkers require experimental validation to confirm their role in the pathogenesis of psoriasis and their potential as therapeutic targets. - Source: PubMed
Zhang BaolanShi JingJiang JianhangZhang Litao - DisoLipPred is a state-of-the-art predictor of intrinsically disordered lipid-binding residues in protein sequences. This method relies on a modern deep neural network model, produces accurate results, and is available as a convenient web server. We provide a practical and detailed introduction to the DisoLipPred's web server. We describe the underlying predictive process, which is fully automated and performed on the server side, and offer instructions for interactions with DisoLipPred's web interface. We also discuss how to obtain, read, and interpret results produced by this server using a case study that analyzes results generated for the vacuolar-sorting protein SNF8. The web server is freely available at http://biomine.cs.vcu.edu/servers/DisoLipPred/ . - Source: PubMed
Zhao BiKurgan Lukasz - Metabolic dysfunction-associated steatotic liver disease (MASLD) constitutes a global health threat with its ability to develop into liver cirrhosis and hepatocellular carcinoma (HCC). Emerging data suggests that oxidative stress and regulated cell death are major driving forces for liver inflammation in MASH. Febuxostat (Feb.), one of the Xanthine oxidase (XO) inhibitors, has shown promise in significantly improving the prognosis of MASH by reducing inflammatory cytokines and cell death. However, the underlying molecular mechanisms remain unclear. In this study, we evaluated the therapeutic effects of febuxostat on MASH through the modulation of cell death, inflammation, and intestinal permeability, focusing on hepatic mRNAs (HGS, SNF8, TSG101) and their epigenetic regulators (rno-miR-6216, rno-miR-1224). MASH was induced in Wistar rats via a High-sucrose high-fat (HSHF) diet over 14 weeks, followed by febuxostat treatment at doses of 1.5, 3, and 6 mg/kg/day for 4 weeks. Febuxostat treatment significantly improved liver function and lipid profiles, reduced hepatic steatosis, intralobular inflammation, and ballooning, and restored normal expression of the hepatic RNA panel by downregulating HGS, SNF8, and TSG101 mRNAs and their epigenetic regulators. Furthermore, febuxostat decreased serum levels of inflammatory (IL6), fibrosis (TGFB1), and cell death (TSG101) markers while reducing apoptosis and regulated cell death via modulation of Caspase-3 and LC3B expression. Improvements in intestinal permeability were evident via reductions in serum haptoglobin (Hpt) and TMAO and restoration of occludin expression. These findings highlight febuxostat as a promising therapeutic candidate for MASH by targeting key molecular mechanisms of liver inflammation and gut-liver axis dysfunction. - Source: PubMed
Publication date: 2025/04/29
Matboli MarwaSaad MahaAhmed Manar FouadHelmy Hasanin AmanyEllithy Ghada MAbdelwahab Marian SamirEltantawy Eman Hamdy BadrHamam Ghada GalalHamoud Amany EEl-Shafei Marwa MSamir Nehal - The global rise in Metabolic dysfunction-associated steatotic liver disease (MASLD)/Metabolic dysfunction-associated steatohepatitis (MASH) highlights the urgent necessity for noninvasive biomarkers to detect these conditions early. To address this, we endeavored to construct a diagnostic model for MASLD/MASH using a combination of bioinformatics, molecular/biochemical data, and machine learning techniques. Initially, bioinformatics analysis was employed to identify RNA molecules associated with MASLD/MASH pathogenesis and enriched in ferroptosis and exophagy. This analysis unveiled specific networks related to ferroptosis (GPX4, LPCAT3, ACSL4, miR-4266, and LINC00442) and exophagy (TSG101, HGS, SNF8, miR-4498, miR-5189-5p, and CTBP1-AS2). Subsequently, serum samples from 400 participants (151 healthy, 150 MASH, and 99 MASLD) underwent biochemical and molecular analysis, revealing significant dyslipidemia, impaired liver function, and disrupted glycemic indicators in MASLD/MASH patients compared to healthy controls. Molecular analysis indicated increased expression of LPCAT3, ACSL4, TSG101, HGS, and SNF8, alongside decreased GPX4 levels in MASH and MASLD patients compared to controls. The expression of epigenetic regulators from both networks (miR-4498, miR-5189-5p, miR-4266, LINC00442, and CTBP1-AS2) significantly differed among the studied groups. Finally, supervised machine learning models, including Neural Networks and Random Forest, were applied to molecular signatures and clinical/biochemical data. The Random Forest model exhibited superior performance, and molecular features effectively distinguished between the three studied groups. Clinical features, particularly BMI, consistently served as discriminatory factors, while biochemical features exhibited varying discriminant behavior across MASH, MASLD, and control groups. Our study underscores the significant potential of integrating diverse data types to enable early detection of MASLD/MASH, offering a promising approach for non-invasive diagnostic strategies. - Source: PubMed
Publication date: 2024/10/11
Matboli MarwaHamady ShaimaaSaad MahaKhaled RadwaKhaled AbdelrahmanBarakat Eman MfSayed Sayed AhmedAgwa SaraH AYoussef Ibrahim