Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis
Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes invo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Mediators of Inflammation |
| Online Access: | http://dx.doi.org/10.1155/2020/3432587 |
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| author | Fang-Chen Gong Ran Ji Yu-Ming Wang Zhi-Tao Yang Ying Chen En-Qiang Mao Er-Zhen Chen |
| author_facet | Fang-Chen Gong Ran Ji Yu-Ming Wang Zhi-Tao Yang Ying Chen En-Qiang Mao Er-Zhen Chen |
| author_sort | Fang-Chen Gong |
| collection | DOAJ |
| description | Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes—LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN—as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis. |
| format | Article |
| id | doaj-art-cebaaf5323cd4ec1b600a1dde61094f4 |
| institution | Kabale University |
| issn | 0962-9351 1466-1861 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Mediators of Inflammation |
| spelling | doaj-art-cebaaf5323cd4ec1b600a1dde61094f42025-02-03T01:20:30ZengWileyMediators of Inflammation0962-93511466-18612020-01-01202010.1155/2020/34325873432587Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics AnalysisFang-Chen Gong0Ran Ji1Yu-Ming Wang2Zhi-Tao Yang3Ying Chen4En-Qiang Mao5Er-Zhen Chen6Department of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Emergency in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes—LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN—as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.http://dx.doi.org/10.1155/2020/3432587 |
| spellingShingle | Fang-Chen Gong Ran Ji Yu-Ming Wang Zhi-Tao Yang Ying Chen En-Qiang Mao Er-Zhen Chen Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis Mediators of Inflammation |
| title | Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis |
| title_full | Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis |
| title_fullStr | Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis |
| title_full_unstemmed | Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis |
| title_short | Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis |
| title_sort | identification of potential biomarkers and immune features of sepsis using bioinformatics analysis |
| url | http://dx.doi.org/10.1155/2020/3432587 |
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