Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning
Abstract Non-alcoholic fatty liver disease (NAFLD) is a global health challenge with complex pathogenesis and limited diagnostic biomarkers. Palmitoylation, a post-translational modification, has emerged as a critical regulator in metabolic disorders, yet its role in NAFLD remains underexplored. Thi...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-13477-3 |
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| author | Zheng Liu Xiaohong Wang Mingzhu Xiu Rui Luo Xiaomin Shi Yizhou Wang Yusong Ye Ruiyu Wang Sha Liu Muhan Lv Xiaowei Tang |
| author_facet | Zheng Liu Xiaohong Wang Mingzhu Xiu Rui Luo Xiaomin Shi Yizhou Wang Yusong Ye Ruiyu Wang Sha Liu Muhan Lv Xiaowei Tang |
| author_sort | Zheng Liu |
| collection | DOAJ |
| description | Abstract Non-alcoholic fatty liver disease (NAFLD) is a global health challenge with complex pathogenesis and limited diagnostic biomarkers. Palmitoylation, a post-translational modification, has emerged as a critical regulator in metabolic disorders, yet its role in NAFLD remains underexplored. This study integrated bioinformatics analysis and machine learning to identify palmitoylation-related biomarkers for NAFLD. Transcriptomic datasets from human liver tissues were analyzed to identify differentially expressed genes (DEGs) and co-expression modules via WGCNA. Intersection analysis revealed 60 palmitoylation-related DEGs (PR-DEGs). Seven machine learning models were employed, with Neural Network (NNET) and Decision Tree (DT) outperforming others, identifying three hub genes: TYMS, WNT5A, and ZFP36. A nomogram integrating these genes demonstrated robust diagnostic accuracy (AUC = 0.976). The pivotal role of these genes in diagnosing NAFLD was confirmed using the validation dataset (AUC = 0.903). Functional enrichment linked these genes to TNF signaling, lipid metabolism, and immune pathways. Single-cell RNA-seq analysis highlighted their expression in hepatocytes and immune cells, with altered intercellular communication patterns. Immune infiltration analysis revealed significant shifts in monocytes, dendritic cells, and macrophages in NAFLD. Regulatory network analysis highlighted that hsa-let-7b-5p might be pivotal co-regulator of the three hub gene expressions. Finally, the top 10 potential gene-targeted drugs were screened. This study unveils novel palmitoylation-related biomarkers and provides insights into NAFLD pathogenesis, offering diagnostic and therapeutic avenues. |
| format | Article |
| id | doaj-art-a76388caa1e14a4d91a7c3cb71a4dac6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a76388caa1e14a4d91a7c3cb71a4dac62025-08-20T03:45:51ZengNature PortfolioScientific Reports2045-23222025-08-0115111610.1038/s41598-025-13477-3Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learningZheng Liu0Xiaohong Wang1Mingzhu Xiu2Rui Luo3Xiaomin Shi4Yizhou Wang5Yusong Ye6Ruiyu Wang7Sha Liu8Muhan Lv9Xiaowei Tang10Department of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityDepartment of Gastroenterology, the Affiliated Hospital of Southwest Medical UniversityAbstract Non-alcoholic fatty liver disease (NAFLD) is a global health challenge with complex pathogenesis and limited diagnostic biomarkers. Palmitoylation, a post-translational modification, has emerged as a critical regulator in metabolic disorders, yet its role in NAFLD remains underexplored. This study integrated bioinformatics analysis and machine learning to identify palmitoylation-related biomarkers for NAFLD. Transcriptomic datasets from human liver tissues were analyzed to identify differentially expressed genes (DEGs) and co-expression modules via WGCNA. Intersection analysis revealed 60 palmitoylation-related DEGs (PR-DEGs). Seven machine learning models were employed, with Neural Network (NNET) and Decision Tree (DT) outperforming others, identifying three hub genes: TYMS, WNT5A, and ZFP36. A nomogram integrating these genes demonstrated robust diagnostic accuracy (AUC = 0.976). The pivotal role of these genes in diagnosing NAFLD was confirmed using the validation dataset (AUC = 0.903). Functional enrichment linked these genes to TNF signaling, lipid metabolism, and immune pathways. Single-cell RNA-seq analysis highlighted their expression in hepatocytes and immune cells, with altered intercellular communication patterns. Immune infiltration analysis revealed significant shifts in monocytes, dendritic cells, and macrophages in NAFLD. Regulatory network analysis highlighted that hsa-let-7b-5p might be pivotal co-regulator of the three hub gene expressions. Finally, the top 10 potential gene-targeted drugs were screened. This study unveils novel palmitoylation-related biomarkers and provides insights into NAFLD pathogenesis, offering diagnostic and therapeutic avenues.https://doi.org/10.1038/s41598-025-13477-3Non-alcoholic fatty liver diseasePalmitoylationBiomarkerBioinformaticsMachine learning |
| spellingShingle | Zheng Liu Xiaohong Wang Mingzhu Xiu Rui Luo Xiaomin Shi Yizhou Wang Yusong Ye Ruiyu Wang Sha Liu Muhan Lv Xiaowei Tang Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning Scientific Reports Non-alcoholic fatty liver disease Palmitoylation Biomarker Bioinformatics Machine learning |
| title | Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning |
| title_full | Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning |
| title_fullStr | Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning |
| title_full_unstemmed | Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning |
| title_short | Identification of palmitoylated biomarkers in non-alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning |
| title_sort | identification of palmitoylated biomarkers in non alcoholic fatty liver disease via integrated bioinformatics analysis and machine learning |
| topic | Non-alcoholic fatty liver disease Palmitoylation Biomarker Bioinformatics Machine learning |
| url | https://doi.org/10.1038/s41598-025-13477-3 |
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