Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning
Abstract Sepsis-induced acute respiratory distress syndrome (ARDS) is one of the leading causes of death in critically ill patients, and macrophages play very important roles in the pathogenesis and treatment of sepsis-induced ARDS. The aim of this study was to screen macrophage-related biomarkers f...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-37162-5 |
| _version_ | 1797795714607087616 |
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| author | Qiuyue Li Hongyu Zheng Bing Chen |
| author_facet | Qiuyue Li Hongyu Zheng Bing Chen |
| author_sort | Qiuyue Li |
| collection | DOAJ |
| description | Abstract Sepsis-induced acute respiratory distress syndrome (ARDS) is one of the leading causes of death in critically ill patients, and macrophages play very important roles in the pathogenesis and treatment of sepsis-induced ARDS. The aim of this study was to screen macrophage-related biomarkers for the diagnosis and treatment of sepsis-induced ARDS by bioinformatics and machine learning algorithms. A dataset including gene expression profiles of sepsis-induced ARDS patients and healthy controls was downloaded from the gene expression omnibus database. The limma package was used to screen 325 differentially expressed genes, and enrichment analysis suggested enrichment mainly in immune-related pathways and reactive oxygen metabolism pathways. The level of immune cell infiltration was analysed using the ssGSEA method, and then 506 macrophage-related genes were screened using WGCNA; 48 showed differential expression. PPI analysis was also performed. SVM-RFE and random forest map analysis were used to screen 10 genes. Three key genes, SGK1, DYSF and MSRB1, were obtained after validation with external datasets. ROC curves suggested that all three genes had good diagnostic efficacy. The nomogram model consisting of the three genes also had good diagnostic efficacy. This study provides new targets for the early diagnosis of sepsis-induced ARDS. |
| first_indexed | 2024-03-13T03:22:12Z |
| format | Article |
| id | doaj.art-7192397037dd414abc1aa52c2b489eac |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T03:22:12Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7192397037dd414abc1aa52c2b489eac2023-06-25T11:17:12ZengNature PortfolioScientific Reports2045-23222023-06-0113111610.1038/s41598-023-37162-5Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learningQiuyue Li0Hongyu Zheng1Bing Chen2Department of Emergency Medicine, The Second Hospital of Tianjin Medical UniversityDepartment of Maxillofacial Surgery, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Emergency Medicine, The Second Hospital of Tianjin Medical UniversityAbstract Sepsis-induced acute respiratory distress syndrome (ARDS) is one of the leading causes of death in critically ill patients, and macrophages play very important roles in the pathogenesis and treatment of sepsis-induced ARDS. The aim of this study was to screen macrophage-related biomarkers for the diagnosis and treatment of sepsis-induced ARDS by bioinformatics and machine learning algorithms. A dataset including gene expression profiles of sepsis-induced ARDS patients and healthy controls was downloaded from the gene expression omnibus database. The limma package was used to screen 325 differentially expressed genes, and enrichment analysis suggested enrichment mainly in immune-related pathways and reactive oxygen metabolism pathways. The level of immune cell infiltration was analysed using the ssGSEA method, and then 506 macrophage-related genes were screened using WGCNA; 48 showed differential expression. PPI analysis was also performed. SVM-RFE and random forest map analysis were used to screen 10 genes. Three key genes, SGK1, DYSF and MSRB1, were obtained after validation with external datasets. ROC curves suggested that all three genes had good diagnostic efficacy. The nomogram model consisting of the three genes also had good diagnostic efficacy. This study provides new targets for the early diagnosis of sepsis-induced ARDS.https://doi.org/10.1038/s41598-023-37162-5 |
| spellingShingle | Qiuyue Li Hongyu Zheng Bing Chen Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning Scientific Reports |
| title | Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning |
| title_full | Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning |
| title_fullStr | Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning |
| title_full_unstemmed | Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning |
| title_short | Identification of macrophage-related genes in sepsis-induced ARDS using bioinformatics and machine learning |
| title_sort | identification of macrophage related genes in sepsis induced ards using bioinformatics and machine learning |
| url | https://doi.org/10.1038/s41598-023-37162-5 |
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