Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock
Abstract This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed genes were identified using the R 4.3.2 (...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-95028-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850208712976760832 |
|---|---|
| author | Peng Lyu Na Xie Xu-peng Shao Shuai Xing Xiao-yue Wang Li-yun Duan Xue Zhao Jia-min Lu Rong-fei Liu Duo Zhang Wei Lu Kai-liang Fan |
| author_facet | Peng Lyu Na Xie Xu-peng Shao Shuai Xing Xiao-yue Wang Li-yun Duan Xue Zhao Jia-min Lu Rong-fei Liu Duo Zhang Wei Lu Kai-liang Fan |
| author_sort | Peng Lyu |
| collection | DOAJ |
| description | Abstract This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed genes were identified using the R 4.3.2 ( https://www.r-project.org/ ), followed by gene set enrichment analysis. Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. The inflammatory and immune status of pediatric septic shock was evaluated through cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The correlation between diagnostic markers and infiltrating immune cells was further examined. Overall, we detected 12 differentially expressed genes. CD177, MCEMP1, MMP8, and OLAH were examined as diagnostic indicators for pediatric septic shock, revealing statistically significant differences (P < 0.01) and diagnostic efficacy in the validation cohort. The immune cell infiltration analysis suggests that various immune cells may contribute to the onset of pediatric septic shock. Furthermore, all diagnostic characteristics may exhibit varying degrees of correlation with immune cells. This study identifies four potential biomarkers—CD177, MCEMP1, MMP8, and OLAH—that provide diagnostic value and novel insights into immune dysregulation in pediatric septic shock. Through the integration of bioinformatics and machine learning methodologies, we offer a novel perspective on the immune mechanisms involved in pediatric septic shock, potentially facilitating more targeted and personalized therapies for individual patients. |
| format | Article |
| id | doaj-art-aa5fc1701e504ddd8fe334dd4eb9b3a2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aa5fc1701e504ddd8fe334dd4eb9b3a22025-08-20T02:10:10ZengNature PortfolioScientific Reports2045-23222025-03-0115111010.1038/s41598-025-95028-4Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shockPeng Lyu0Na Xie1Xu-peng Shao2Shuai Xing3Xiao-yue Wang4Li-yun Duan5Xue Zhao6Jia-min Lu7Rong-fei Liu8Duo Zhang9Wei Lu10Kai-liang Fan11Department of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineDepartment of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineDepartment of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineDepartment of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineDepartment of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineFirst Clinical College, Shandong University of Traditional Chinese MedicineDepartment of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineFirst Clinical College, Shandong University of Traditional Chinese MedicineFirst Clinical College, Shandong University of Traditional Chinese MedicineFirst Clinical College, Shandong University of Traditional Chinese MedicineFirst Clinical College, Shandong University of Traditional Chinese MedicineDepartment of Emergency, the Affiliated Hospital of Shandong University of Traditional Chinese MedicineAbstract This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed genes were identified using the R 4.3.2 ( https://www.r-project.org/ ), followed by gene set enrichment analysis. Thereafter, the genes were identified utilizing machine-learning algorithms. The receiver operating characteristic curve was employed to assess the discrimination and effectiveness of the hub genes. The inflammatory and immune status of pediatric septic shock was evaluated through cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The correlation between diagnostic markers and infiltrating immune cells was further examined. Overall, we detected 12 differentially expressed genes. CD177, MCEMP1, MMP8, and OLAH were examined as diagnostic indicators for pediatric septic shock, revealing statistically significant differences (P < 0.01) and diagnostic efficacy in the validation cohort. The immune cell infiltration analysis suggests that various immune cells may contribute to the onset of pediatric septic shock. Furthermore, all diagnostic characteristics may exhibit varying degrees of correlation with immune cells. This study identifies four potential biomarkers—CD177, MCEMP1, MMP8, and OLAH—that provide diagnostic value and novel insights into immune dysregulation in pediatric septic shock. Through the integration of bioinformatics and machine learning methodologies, we offer a novel perspective on the immune mechanisms involved in pediatric septic shock, potentially facilitating more targeted and personalized therapies for individual patients.https://doi.org/10.1038/s41598-025-95028-4Pediatric septic shockBioinformaticsMachine-LearningBiomarkersImmune cell infiltration |
| spellingShingle | Peng Lyu Na Xie Xu-peng Shao Shuai Xing Xiao-yue Wang Li-yun Duan Xue Zhao Jia-min Lu Rong-fei Liu Duo Zhang Wei Lu Kai-liang Fan Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock Scientific Reports Pediatric septic shock Bioinformatics Machine-Learning Biomarkers Immune cell infiltration |
| title | Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock |
| title_full | Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock |
| title_fullStr | Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock |
| title_full_unstemmed | Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock |
| title_short | Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock |
| title_sort | integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock |
| topic | Pediatric septic shock Bioinformatics Machine-Learning Biomarkers Immune cell infiltration |
| url | https://doi.org/10.1038/s41598-025-95028-4 |
| work_keys_str_mv | AT penglyu integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT naxie integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT xupengshao integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT shuaixing integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT xiaoyuewang integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT liyunduan integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT xuezhao integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT jiaminlu integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT rongfeiliu integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT duozhang integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT weilu integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock AT kailiangfan integratingbioinformaticsandmachinelearningforcomprehensiveanalysisandvalidationofdiagnosticbiomarkersandimmunecellinfiltrationcharacteristicsinpediatricsepticshock |