Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts
Abstract Background 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. Methods Data from mu...
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BMC
2025-05-01
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| Series: | Lipids in Health and Disease |
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| Online Access: | https://doi.org/10.1186/s12944-025-02588-5 |
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| author | Renao Jiang Longfei Dai Xinjian Xu Zhen Zhang |
| author_facet | Renao Jiang Longfei Dai Xinjian Xu Zhen Zhang |
| author_sort | Renao Jiang |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
| format | Article |
| id | doaj-art-26b9b8fac15b4e538a8a03d58c12c08b |
| institution | Kabale University |
| issn | 1476-511X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | Lipids in Health and Disease |
| spelling | doaj-art-26b9b8fac15b4e538a8a03d58c12c08b2025-08-20T03:52:55ZengBMCLipids in Health and Disease1476-511X2025-05-0124111410.1186/s12944-025-02588-5Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohortsRenao Jiang0Longfei Dai1Xinjian Xu2Zhen Zhang3Department of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityAbstract Background 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. Methods 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. Results 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. Conclusions 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.https://doi.org/10.1186/s12944-025-02588-5NASHCell deathMachine learningPrediction model |
| spellingShingle | Renao Jiang Longfei Dai Xinjian Xu Zhen Zhang Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts Lipids in Health and Disease NASH Cell death Machine learning Prediction model |
| title | Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts |
| title_full | Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts |
| title_fullStr | Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts |
| title_full_unstemmed | Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts |
| title_short | Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts |
| title_sort | multiple machine learning algorithms identify 13 types of cell death critical genes in large and multiple non alcoholic steatohepatitis cohorts |
| topic | NASH Cell death Machine learning Prediction model |
| url | https://doi.org/10.1186/s12944-025-02588-5 |
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