Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning
Background: To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis. Methods: The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least...
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023001332 |
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| author | Li Ke Yasu Lu Han Gao Chang Hu Jiahao Zhang Qiuyue Zhao Zhongyi Sun Zhiyong Peng |
| author_facet | Li Ke Yasu Lu Han Gao Chang Hu Jiahao Zhang Qiuyue Zhao Zhongyi Sun Zhiyong Peng |
| author_sort | Li Ke |
| collection | DOAJ |
| description | Background: To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis. Methods: The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The diagnostic and prognostic abilities of the markers were evaluated by plotting receiver operating characteristic (ROC) curves and Kaplan–Meier survival curves. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to further elucidate the molecular mechanisms and immune-related processes. Finally, the potential biomarkers were validated in a septic mouse model by qRT-PCR and western blotting. Results: Eleven DEGs were identified between the sepsis and control samples, including YOD1, GADD45A, BCL11B, IL1R2, UGCG, TLR5, S100A12, ITK, HP, CCR7 and C19orf59 (all AUC>0.9). Furthermore, the survival analysis identified YOD1, GADD45A, BCL11B and IL1R2 as the prognostic biomarkers of sepsis. According to GSEA, four DEGs were significantly associated with immune-related processes. In addition, ssGSEA demonstrated a significant difference in the enriched immune cell populations between the sepsis and control groups (all P < 0.05). Moreover, YOD1, GADD45A and IL1R2 were upregulated, and BCL11B was downregulated in the heart, liver, lungs, and kidneys of the septic mice model. Conclusions: We identified four potential immune-releated diagnostic and prognostic gene markers for sepsis that offer new insights into its underlying mechanisms. |
| first_indexed | 2024-03-08T21:31:21Z |
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| language | English |
| last_indexed | 2024-03-08T21:31:21Z |
| publishDate | 2023-01-01 |
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| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-84627c48283f424abe4b56c7c2c387062023-12-21T07:31:18ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012123162331Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learningLi Ke0Yasu Lu1Han Gao2Chang Hu3Jiahao Zhang4Qiuyue Zhao5Zhongyi Sun6Zhiyong Peng7Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, ChinaDepartment of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, ChinaDepartment of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, ChinaDepartment of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, ChinaDepartment of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, ChinaDepartment of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, ChinaDepartment of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China; Correspondence to: Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, Hubei, China.Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China; Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China; Correspondence to: Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, Hubei, China.Background: To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis. Methods: The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The diagnostic and prognostic abilities of the markers were evaluated by plotting receiver operating characteristic (ROC) curves and Kaplan–Meier survival curves. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to further elucidate the molecular mechanisms and immune-related processes. Finally, the potential biomarkers were validated in a septic mouse model by qRT-PCR and western blotting. Results: Eleven DEGs were identified between the sepsis and control samples, including YOD1, GADD45A, BCL11B, IL1R2, UGCG, TLR5, S100A12, ITK, HP, CCR7 and C19orf59 (all AUC>0.9). Furthermore, the survival analysis identified YOD1, GADD45A, BCL11B and IL1R2 as the prognostic biomarkers of sepsis. According to GSEA, four DEGs were significantly associated with immune-related processes. In addition, ssGSEA demonstrated a significant difference in the enriched immune cell populations between the sepsis and control groups (all P < 0.05). Moreover, YOD1, GADD45A and IL1R2 were upregulated, and BCL11B was downregulated in the heart, liver, lungs, and kidneys of the septic mice model. Conclusions: We identified four potential immune-releated diagnostic and prognostic gene markers for sepsis that offer new insights into its underlying mechanisms.http://www.sciencedirect.com/science/article/pii/S2001037023001332SepsisMachine learningDiagnosisPrognosisBiomarker |
| spellingShingle | Li Ke Yasu Lu Han Gao Chang Hu Jiahao Zhang Qiuyue Zhao Zhongyi Sun Zhiyong Peng Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning Computational and Structural Biotechnology Journal Sepsis Machine learning Diagnosis Prognosis Biomarker |
| title | Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning |
| title_full | Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning |
| title_fullStr | Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning |
| title_full_unstemmed | Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning |
| title_short | Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning |
| title_sort | identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning |
| topic | Sepsis Machine learning Diagnosis Prognosis Biomarker |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023001332 |
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