Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis
Abstract Objective Emerging evidence suggests a potential relationship between lipid metabolism and idiopathic pulmonary fibrosis (IPF). This study aimed to identify lipid-related genes implicated in IPF pathogenesis. Methods Lipid-associated genes were retrieved from the GeneCards database and anal...
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BMC
2025-07-01
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| Series: | Orphanet Journal of Rare Diseases |
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| Online Access: | https://doi.org/10.1186/s13023-025-03876-0 |
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| author | Xingren Liu Junmei Song Shujin Guo Yi Liao Jun Zou Liqing Yang Caiyu Jiang |
| author_facet | Xingren Liu Junmei Song Shujin Guo Yi Liao Jun Zou Liqing Yang Caiyu Jiang |
| author_sort | Xingren Liu |
| collection | DOAJ |
| description | Abstract Objective Emerging evidence suggests a potential relationship between lipid metabolism and idiopathic pulmonary fibrosis (IPF). This study aimed to identify lipid-related genes implicated in IPF pathogenesis. Methods Lipid-associated genes were retrieved from the GeneCards database and analyzed using unsupervised consensus clustering to classify IPF samples. Weighted gene co-expression network analysis (WGCNA) was performed on the identified clusters to determine core modules and hub genes associated with IPF. Machine learning algorithms were applied to these hub genes to construct diagnostic and prognostic models, which were validated across multiple datasets. Single-cell sequencing was used to investigate the distribution of potential pathogenic genes, and their functional roles were further validated through cellular experiments. Results Two distinct clusters were identified, showing significant differences in lung function parameters and fibrosis-related gene expression. WGCNA revealed that the blue module was strongly associated with IPF and served as the core module. Genes from this module were used to construct diagnostic and prognostic models, which demonstrated strong predictive performance across multiple validation datasets. Single-cell sequencing revealed that KLF4 was highly expressed in lung epithelial cells. Functional assays indicated that knockdown of KLF4 did not affect the proliferation of human alveolar type II epithelial cells but significantly enhanced their migratory capacity, thereby promoting the fibrotic process. Conclusion This study successfully constructed lipid-related diagnostic and prognostic models for IPF and identified KLF4 as a potential causative gene. These findings provide a foundation for further exploration of lipid metabolism in IPF pathogenesis and potential therapeutic strategies targeting KLF4. |
| format | Article |
| id | doaj-art-ad6518fcad5f424aa7dc2d8a8b78c536 |
| institution | DOAJ |
| issn | 1750-1172 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj-art-ad6518fcad5f424aa7dc2d8a8b78c5362025-08-20T03:06:05ZengBMCOrphanet Journal of Rare Diseases1750-11722025-07-0120111610.1186/s13023-025-03876-0Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosisXingren Liu0Junmei Song1Shujin Guo2Yi Liao3Jun Zou4Liqing Yang5Caiyu Jiang6Department of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaUltrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cardiovascular Disease, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Health Management & Institute of Health Management, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaAbstract Objective Emerging evidence suggests a potential relationship between lipid metabolism and idiopathic pulmonary fibrosis (IPF). This study aimed to identify lipid-related genes implicated in IPF pathogenesis. Methods Lipid-associated genes were retrieved from the GeneCards database and analyzed using unsupervised consensus clustering to classify IPF samples. Weighted gene co-expression network analysis (WGCNA) was performed on the identified clusters to determine core modules and hub genes associated with IPF. Machine learning algorithms were applied to these hub genes to construct diagnostic and prognostic models, which were validated across multiple datasets. Single-cell sequencing was used to investigate the distribution of potential pathogenic genes, and their functional roles were further validated through cellular experiments. Results Two distinct clusters were identified, showing significant differences in lung function parameters and fibrosis-related gene expression. WGCNA revealed that the blue module was strongly associated with IPF and served as the core module. Genes from this module were used to construct diagnostic and prognostic models, which demonstrated strong predictive performance across multiple validation datasets. Single-cell sequencing revealed that KLF4 was highly expressed in lung epithelial cells. Functional assays indicated that knockdown of KLF4 did not affect the proliferation of human alveolar type II epithelial cells but significantly enhanced their migratory capacity, thereby promoting the fibrotic process. Conclusion This study successfully constructed lipid-related diagnostic and prognostic models for IPF and identified KLF4 as a potential causative gene. These findings provide a foundation for further exploration of lipid metabolism in IPF pathogenesis and potential therapeutic strategies targeting KLF4.https://doi.org/10.1186/s13023-025-03876-0Idiopathic pulmonary fibrosisLipidDiagnosisPrognosis |
| spellingShingle | Xingren Liu Junmei Song Shujin Guo Yi Liao Jun Zou Liqing Yang Caiyu Jiang Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis Orphanet Journal of Rare Diseases Idiopathic pulmonary fibrosis Lipid Diagnosis Prognosis |
| title | Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis |
| title_full | Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis |
| title_fullStr | Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis |
| title_full_unstemmed | Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis |
| title_short | Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis |
| title_sort | machine learning identifies lipid associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis |
| topic | Idiopathic pulmonary fibrosis Lipid Diagnosis Prognosis |
| url | https://doi.org/10.1186/s13023-025-03876-0 |
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