Zhen Zhang

Background Ulcerative colitis (UC) is a chronic inflammatory disease characterized by mucosal immune activation and epithelial barrier breakdown. The pathogenic mechanisms underlying UC remain incompletely understood, and identifying key molecular drivers of disease onset and progression may provide new therapeutic opportunities. Methods A total of 1,446 samples were integrated to identify key genes through machine learning and SHAP analysis. Immune infiltration, cytokine activity, barrier gene expression, and clinical outcomes were analyzed. Functional and mechanistic validations were further performed in lipopolysaccharide (LPS)–stimulated HT29 cells. Results FBXO6, COL1A2, and NPY emerged as robust diagnostic biomarkers that stratify UC patients into molecular subgroups with distinct inflammatory states and therapeutic responses. High FBXO6 expression correlated with macrophage-driven inflammation, barrier disruption, and reduced responsiveness to biologics. Mechanistically, FBXO6 directly bound SLC3A2 and induced its degradation through K48-linked ubiquitination, thereby promoting ferroptosis and amplifying mucosal inflammation. Conclusion A previously unrecognized ubiquitin-mediated FBXO6-SLC3A2 axis was identified as a critical driver of ferroptosis and epithelial barrier disruption in UC. Targeting FBXO6 to preserve SLC3A2 stability is proposed as a potential therapeutic strategy to restore epithelial integrity and improve treatment responsiveness in patients with UC.

Background: The pathogenesis of ulcerative colitis (UC) is thought to involve abnormal regulation of cell death. However, key cell death-related genes (CDGs) that drive disease progression have not been fully characterized. The identification of these CDGs is thought to potentially reveal new therapeutic targets. Methods: Machine learning (ML) and Mendelian randomization (MR) methods were integrated to identify CDGs with causal effects in UC progression. The validation included immune-related analysis, drug response assessment (infliximab/vedolizumab/golimumab), patient stratification based on consensus clustering, and functional validation. Results: Six key CDG genes (VNN1, PTGDS, MMP9, IL13RA2, S100A8, and IL1B) were identified by ML. VNN1 and MMP9 were confirmed by MR to be pathogenic risk factors for UC progression. All six genes were significantly associated with immune cell infiltration, pro-inflammatory cytokines, and intestinal barrier dysfunction. Compared with non-responders, the expression of these six CDGs was significantly downregulated in biologic therapy responders. Based on these genes, patients with UC were classified into two groups: the C1 group with severe disease activity and the C2 group with reduced Mayo scores and enhanced treatment sensitivity. Additionally, knocking down VNN1 functionally alleviated intestinal inflammation. Conclusion: These six genes can be used to assess the severity of UC and predict treatment outcomes.

Dysregulated programmed cell death pathways mechanistically contribute to hepatic inflammation and fibrogenesis in non-alcoholic steatohepatitis (NASH). Identification of cell death genes may offer insights into diagnostic and therapeutic strategies for NASH. Data from multiple NASH cohorts were integrated, and 12 machine learning algorithms were applied to identify key dysregulated cell death-related genes and develop a binary classification model for NASH. Spearman's rank correlation coefficients quantified associations between these genes and clinical markers, immune infiltration profiles, and signature genes encoding pro-inflammatory mediators, metabolic regulators, and fibrotic drivers. Gene set enrichment analysis (GSEA) was performed to delineate the mechanistic underpinnings of these key genes. Consensus clustering analysis was then used to stratify patients with NASH into distinct phenotypic subgroups based on expression levels of these genes. A NASH prediction model, developed using the random forest (RF) algorithm, demonstrated high diagnostic accuracy across multiple cohorts. Four key genes, enriched in lipid metabolism and inflammation pathways, were identified. Their transcriptional levels were significantly correlated with the non-alcoholic fatty liver disease activity score (NAS), hepatic inflammatory infiltration, molecular signatures of metabolic dysregulation (lipid homeostasis regulators), and fibrosis progression. These genes also enabled accurate classification of patients with NASH into clusters reflecting varying disease severity. A binary classification model, developed using the RF algorithm, accurately identified patients with NASH. The four cell death genes, identified through 12 machine learning algorithms, represent potential biomarkers and therapeutic targets for NASH. These genes contribute to inflammation-related immune cell activation, lipid metabolism dysregulation, and liver fibrosis, highlighting the complex interplay between cell death and NASH progression.

Background: Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets. Methods: Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms. Results: The Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo–keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity. Conclusion: These three genes are pivotal mitochondrial genes implicated in NASH progression.

Background: Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis. Methods: To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used. Results: The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures. Conclusion: ZFP36 is a key FRG involved in NASH pathogenesis.

Background In hepatocytes, mitochondrial dysfunction drives aberrant fatty acid metabolism, oxidative stress, and cell apoptosis, promoting the occurrence and progression of NASH. Given the pivotal role of mitochondrial dysfunction in the advancement of NASH, the identification of mitochondrial core genes within NASH may offer potential targets for NASH treatment. Methods According to 101 machine learning algorithms assembled from 10 different machine learning algorithms, mitochondrial core genes were identified in NASH patients. The relationship between mitochondrial core genes and inflammation, lipid metabolism, liver fibrosis, and immune infiltration was investigated. Results AKR1B10, TYMS, and TREM2 were identified. A predictive model constructed using these three mitochondrial genes exhibited excellent diagnostic performance for NASH in the GEO cohorts. AKR1B10, TYMS, and TREM2 were significantly upregulated in NASH, F3-F4 stage liver fibrosis patients, and NAFLD-HCC patients. The expression levels of AKR1B10, TYMS, and TREM2 were positively correlated with pro-inflammatory genes, lipid synthesis genes, liver fibrosis genes, NAS score, pro-inflammatory immune signatures, and M1 macrophage content. Conversely, they were significantly negatively correlated with fatty acid oxidation genes and M2 macrophage content. Moreover, the biological and mitochondrial pathways enriched when AKR1B10, TYMS, and TREM2 were upregulated were related to NASH progression. NASH patients were further classified into Cluster 1 and Cluster 2. Pro-inflammatory genes, lipid synthesis genes, liver fibrosis genes, NAS score, pro-inflammatory immune signatures, and M1 macrophage content were significantly upregulated in Cluster 1. Conversely, fatty acid oxidation genes and M2 macrophage content were significantly downregulated in Cluster 1. Conclusion AKR1B10, TYMS, and TREM2 are associated with the severity of NASH. High expression of AKR1B10, TYMS, and TREM2 indicates a more severe condition in NASH patients.