Combining machine learning and single-cell sequencing to identify key immune genes in sepsis
Abstract This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patients (n = 23) and healthy controls (n = 10). 5148 differentially expressed genes were identified using the DESeq2 technique and 5636 differenti...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85799-1 |
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| author | Hao Wang Linghan Len Li Hu Yingchun Hu |
| author_facet | Hao Wang Linghan Len Li Hu Yingchun Hu |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Abstract This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patients (n = 23) and healthy controls (n = 10). 5148 differentially expressed genes were identified using the DESeq2 technique and 5636 differentially expressed genes were identified by the limma method(|Log2 Fold Change|≥2, FDR < 0.05). A total of 1793 immune-related genes were identified from the ImmPort database, with 358 genes identified in both groups. Next, a Biological association network was constructed, and five key hub genes (CD4, HLA-DOB, HLA-DRB1, HLA-DRA, AHNAK) were identified using a combination of three topological analysis algorithms (MCC, Closeness, and MNC) and four machine learning algorithms (Random Forest, LASSO regression, SVM, and XGBoost). immune cell distribution showed that the key genes correlated with multiple immune cell infiltrations. Gene Set Enrichment Analysis (GSEA) revealed that the key genes involved multiple immune response and inflammation-related signaling pathways. Subsequently, diagnostic models were constructed using four machine learning algorithms (Logistic regression, AdaBoost, KNN, and XGBoost) based on the identified key genes. Models with the highest performance were then selected. Ultimately, single-cell sequencing data revealed that the identified key genes were expressed in various immune cells, while Quantitative PCR (qPCR) tests confirmed their reduced expression in the sepsis group. |
| format | Article |
| id | doaj-art-88c27fde4e964d80965edb56da3081aa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-88c27fde4e964d80965edb56da3081aa2025-01-12T12:21:15ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-85799-1Combining machine learning and single-cell sequencing to identify key immune genes in sepsisHao Wang0Linghan Len1Li Hu2Yingchun Hu3Clinical Medical College, Southwest Medical UniversityDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical UniversityDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical UniversityDepartment of Emergency Medicine, The Affiliated Hospital of Southwest Medical UniversityAbstract This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patients (n = 23) and healthy controls (n = 10). 5148 differentially expressed genes were identified using the DESeq2 technique and 5636 differentially expressed genes were identified by the limma method(|Log2 Fold Change|≥2, FDR < 0.05). A total of 1793 immune-related genes were identified from the ImmPort database, with 358 genes identified in both groups. Next, a Biological association network was constructed, and five key hub genes (CD4, HLA-DOB, HLA-DRB1, HLA-DRA, AHNAK) were identified using a combination of three topological analysis algorithms (MCC, Closeness, and MNC) and four machine learning algorithms (Random Forest, LASSO regression, SVM, and XGBoost). immune cell distribution showed that the key genes correlated with multiple immune cell infiltrations. Gene Set Enrichment Analysis (GSEA) revealed that the key genes involved multiple immune response and inflammation-related signaling pathways. Subsequently, diagnostic models were constructed using four machine learning algorithms (Logistic regression, AdaBoost, KNN, and XGBoost) based on the identified key genes. Models with the highest performance were then selected. Ultimately, single-cell sequencing data revealed that the identified key genes were expressed in various immune cells, while Quantitative PCR (qPCR) tests confirmed their reduced expression in the sepsis group.https://doi.org/10.1038/s41598-025-85799-1SepsisRNA sequencingBiomarkersDifferentially expressed genesImmune response |
| spellingShingle | Hao Wang Linghan Len Li Hu Yingchun Hu Combining machine learning and single-cell sequencing to identify key immune genes in sepsis Scientific Reports Sepsis RNA sequencing Biomarkers Differentially expressed genes Immune response |
| title | Combining machine learning and single-cell sequencing to identify key immune genes in sepsis |
| title_full | Combining machine learning and single-cell sequencing to identify key immune genes in sepsis |
| title_fullStr | Combining machine learning and single-cell sequencing to identify key immune genes in sepsis |
| title_full_unstemmed | Combining machine learning and single-cell sequencing to identify key immune genes in sepsis |
| title_short | Combining machine learning and single-cell sequencing to identify key immune genes in sepsis |
| title_sort | combining machine learning and single cell sequencing to identify key immune genes in sepsis |
| topic | Sepsis RNA sequencing Biomarkers Differentially expressed genes Immune response |
| url | https://doi.org/10.1038/s41598-025-85799-1 |
| work_keys_str_mv | AT haowang combiningmachinelearningandsinglecellsequencingtoidentifykeyimmunegenesinsepsis AT linghanlen combiningmachinelearningandsinglecellsequencingtoidentifykeyimmunegenesinsepsis AT lihu combiningmachinelearningandsinglecellsequencingtoidentifykeyimmunegenesinsepsis AT yingchunhu combiningmachinelearningandsinglecellsequencingtoidentifykeyimmunegenesinsepsis |