Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis
Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Anal...
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Frontiers Media S.A.
2025-03-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1576467/full |
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| author | Yanwei Cheng Haoran Peng Qiao Chen Lijun Xu Lijie Qin |
| author_facet | Yanwei Cheng Haoran Peng Qiao Chen Lijun Xu Lijie Qin |
| author_sort | Yanwei Cheng |
| collection | DOAJ |
| description | Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes. |
| format | Article |
| id | doaj-art-88ed3cdae3d64658928797147f5c4e23 |
| institution | DOAJ |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj-art-88ed3cdae3d64658928797147f5c4e232025-08-20T02:49:50ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-03-011610.3389/fphar.2025.15764671576467Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsisYanwei Cheng0Haoran Peng1Qiao Chen2Lijun Xu3Lijie Qin4Department of Emergency, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, ChinaDepartment of Neurology, People’s Hospital of Henan University, Henan Provincial People’s Hospital, Zhengzhou, Henan, ChinaNursing Department, Air Force Medical Center, PLA, Beijing, ChinaDepartment of Emergency, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, ChinaDepartment of Emergency, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, People’s Hospital of Henan University, Zhengzhou, ChinaSepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.https://www.frontiersin.org/articles/10.3389/fphar.2025.1576467/fullsepsisbiomarkerstranscriptomicsmachine learningtherapeutic targetsimmune regulation |
| spellingShingle | Yanwei Cheng Haoran Peng Qiao Chen Lijun Xu Lijie Qin Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis Frontiers in Pharmacology sepsis biomarkers transcriptomics machine learning therapeutic targets immune regulation |
| title | Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis |
| title_full | Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis |
| title_fullStr | Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis |
| title_full_unstemmed | Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis |
| title_short | Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis |
| title_sort | machine learning based transcriptmics analysis reveals bmx grb10 and gadd45a as crucial biomarkers and therapeutic targets in sepsis |
| topic | sepsis biomarkers transcriptomics machine learning therapeutic targets immune regulation |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1576467/full |
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