Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome
Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-b...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-06-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.897390/full |
| _version_ | 1811334902282452992 |
|---|---|
| author | Tingqian Ming Mingyou Dong Xuemin Song Xingqiao Li Qian Kong Qing Fang Jie Wang Xiaojing Wu Zhongyuan Xia |
| author_facet | Tingqian Ming Mingyou Dong Xuemin Song Xingqiao Li Qian Kong Qing Fang Jie Wang Xiaojing Wu Zhongyuan Xia |
| author_sort | Tingqian Ming |
| collection | DOAJ |
| description | Sepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS. |
| first_indexed | 2024-04-13T17:16:05Z |
| format | Article |
| id | doaj.art-dc5db609610749979c1d9db3145bb9b1 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-13T17:16:05Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-dc5db609610749979c1d9db3145bb9b12022-12-22T02:38:08ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-06-011310.3389/fimmu.2022.897390897390Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress SyndromeTingqian Ming0Mingyou Dong1Xuemin Song2Xingqiao Li3Qian Kong4Qing Fang5Jie Wang6Xiaojing Wu7Zhongyuan Xia8Department of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, ChinaCollege of Medical Laboratory Science, Youjiang Medical College for Nationalities, Baise, ChinaDepartment of Anesthesiology and Critical Care Medicine, Zhongnan Hospital, Wuhan University, Wuhan, ChinaSchool of Computer, Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, ChinaDepartment of Anesthesiology and Critical Care Medicine, Zhongnan Hospital, Wuhan University, Wuhan, ChinaDepartment of Otolaryngology-Head and Neck Surgery, Renmin Hospital, Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital, Wuhan University, Wuhan, ChinaSepsis is a series of clinical syndromes caused by immunological response to severe infection. As the most important and common complication of sepsis, acute respiratory distress syndrome (ARDS) is associated with poor outcomes and high medical expenses. However, well-described studies of analysis-based researches, especially related bioinformatics analysis on revealing specific targets and underlying molecular mechanisms of sepsis and sepsis-induced ARDS (sepsis/se-ARDS), still remain limited and delayed despite the era of data-driven medicine. In this report, weight gene co-expression network based on data from a public database was constructed to identify the key modules and screen the hub genes. Functional annotation by enrichment analysis of the modular genes also demonstrated the key biological processes and signaling pathway; among which, extensive immune-involved enrichment was remarkably associated with sepsis/se-ARDS. Based on the differential expression analysis, least absolute shrink and selection operator, and multivariable logistic regression analysis of the screened hub genes, SIGLEC9, TSPO, CKS1B and PTTG3P were identified as the candidate biomarkers for the further analysis. Accordingly, a four-gene-based model for diagnostic prediction assessment was established and then developed by sepsis/se-ARDS risk nomogram, whose efficiency was verified by calibration curves and decision curve analyses. In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. Receiver operating characteristic curve analysis proved the great diagnostic and predictive performance of these models, and the multivariable logistic regression of the model was still found to be the best as further verified again by the internal test, training, and external validation cohorts. During the development of sepsis/se-ARDS, the expressions of the identified biomarkers including SIGLEC9, TSPO, CKS1B and PTTG3P were all regulated remarkably and generally exhibited notable correlations with the stages of sepsis/se-ARDS. Moreover, the expression levels of these four genes were substantially correlated during sepsis/se-ARDS. Analysis of immune infiltration showed that multiple immune cells, neutrophils and monocytes in particular, might be closely involved in the process of sepsis/se-ARDS. Besides, SIGLEC9, TSPO, CKS1B and PTTG3P were considerably correlated with the infiltration of various immune cells including neutrophils and monocytes during sepsis/se-ARDS. The discovery of relevant gene co-expression network and immune signatures might provide novel insights into the pathophysiology of sepsis/se-ARDS.https://www.frontiersin.org/articles/10.3389/fimmu.2022.897390/fullgene co-expression network analysissepsisARDSdiagnostic biomarkerprediction modelimmune cell infiltration |
| spellingShingle | Tingqian Ming Mingyou Dong Xuemin Song Xingqiao Li Qian Kong Qing Fang Jie Wang Xiaojing Wu Zhongyuan Xia Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome Frontiers in Immunology gene co-expression network analysis sepsis ARDS diagnostic biomarker prediction model immune cell infiltration |
| title | Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome |
| title_full | Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome |
| title_fullStr | Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome |
| title_full_unstemmed | Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome |
| title_short | Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Respiratory Distress Syndrome |
| title_sort | integrated analysis of gene co expression network and prediction model indicates immune related roles of the identified biomarkers in sepsis and sepsis induced acute respiratory distress syndrome |
| topic | gene co-expression network analysis sepsis ARDS diagnostic biomarker prediction model immune cell infiltration |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.897390/full |
| work_keys_str_mv | AT tingqianming integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT mingyoudong integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT xueminsong integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT xingqiaoli integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT qiankong integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT qingfang integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT jiewang integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT xiaojingwu integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome AT zhongyuanxia integratedanalysisofgenecoexpressionnetworkandpredictionmodelindicatesimmunerelatedrolesoftheidentifiedbiomarkersinsepsisandsepsisinducedacuterespiratorydistresssyndrome |