Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms
Abstract Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with im...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86442-9 |
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| author | Zhichao Wang Long Cheng Guanghui Li Huiyan Cheng |
| author_facet | Zhichao Wang Long Cheng Guanghui Li Huiyan Cheng |
| author_sort | Zhichao Wang |
| collection | DOAJ |
| description | Abstract Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R. Differential expression analysis was performed using the DESeq2 and limma packages, followed by analysis of variance to identify immune-related differentially expressed genes (DEGs). Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. Our analysis identified 34 immune source–related DEGs. Using the identified PE- and immune source–related genes, we constructed a diagnostic forecasting model employing several ML algorithms. We identified six types of statistically significant immune cells in patients with PE and discovered a strong relationship between biomarkers and immune cells. Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning–derived immune-related biomarkers. Our results indicate that these biomarkers may outperform previously reported molecular signatures in predicting PE and provide insights into the mechanisms underlying immune dysregulation in PE. Further validation in larger cohorts could lead to their clinical application in PE prediction and treatment. |
| format | Article |
| id | doaj-art-cbdddca2d7e2461ea38131bd2d33d1f4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-cbdddca2d7e2461ea38131bd2d33d1f42025-01-19T12:19:40ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-86442-9Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithmsZhichao Wang0Long Cheng1Guanghui Li2Huiyan Cheng3Department of Pediatric Surgery, First Hospital of Jilin UniversityDepartment of Intensive Care Unit, First Hospital of Jilin UniversityDepartment of Vascular Surgery, First Hospital of Jilin UniversityDepartment of Gynecology and Obstetrics, First Hospital of Jilin UniversityAbstract Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R. Differential expression analysis was performed using the DESeq2 and limma packages, followed by analysis of variance to identify immune-related differentially expressed genes (DEGs). Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. Our analysis identified 34 immune source–related DEGs. Using the identified PE- and immune source–related genes, we constructed a diagnostic forecasting model employing several ML algorithms. We identified six types of statistically significant immune cells in patients with PE and discovered a strong relationship between biomarkers and immune cells. Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning–derived immune-related biomarkers. Our results indicate that these biomarkers may outperform previously reported molecular signatures in predicting PE and provide insights into the mechanisms underlying immune dysregulation in PE. Further validation in larger cohorts could lead to their clinical application in PE prediction and treatment.https://doi.org/10.1038/s41598-025-86442-9Drug targetsImmune infiltrationMolecular markersMachine learning (ML)Preeclampsia (PE)Therapeutic references |
| spellingShingle | Zhichao Wang Long Cheng Guanghui Li Huiyan Cheng Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms Scientific Reports Drug targets Immune infiltration Molecular markers Machine learning (ML) Preeclampsia (PE) Therapeutic references |
| title | Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms |
| title_full | Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms |
| title_fullStr | Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms |
| title_full_unstemmed | Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms |
| title_short | Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms |
| title_sort | development of immune derived molecular markers for preeclampsia based on multiple machine learning algorithms |
| topic | Drug targets Immune infiltration Molecular markers Machine learning (ML) Preeclampsia (PE) Therapeutic references |
| url | https://doi.org/10.1038/s41598-025-86442-9 |
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