Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach
Abstract Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three datasets (GSE61145, GSE34198, and GSE6636...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-11957-0 |
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| author | Huali Jiang Weijie Chen Benfa Chen Tao Feng Heng Li Dan Li Shanhua Wang Weijie Li |
| author_facet | Huali Jiang Weijie Chen Benfa Chen Tao Feng Heng Li Dan Li Shanhua Wang Weijie Li |
| author_sort | Huali Jiang |
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| description | Abstract Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three datasets (GSE61145, GSE34198, and GSE66360) were used from Gene Expression Omnibus. Dysregulated expression of genes was screened and functionally analyzed. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify significant module genes associated with AMI. Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. Subsequently, receiver operating characteristic curves (ROC) were generated to evaluate the risk of AMI patients. Finally, immune cell infiltration were assessed by CIBERSORT, correlation analysis and immunohistochemistry. A total of 134 upregulated and 25 downregulated genes were identified. Functional analysis showed that the dysregulated genes were involved in cytokine- and immune-related signaling. Ten hub genes were used to establish a diagnostic model. Immune cell infiltration analysis showed that ten genes were correlated with activation of various immune cells; specifically, naive B cells, activated CD4 memory T cells, and resting mast cells were significantly associated with AMI. Immunohistochemical staining indicated that FOS and IL18RAP were significantly upregulated in AMI, CD4 naive T and neutrophils were significantly infiltrated in the microenvironment of AMI. The hub genes involved in activating immune cell infiltration and developing AMI could act as promising diagnostic biomarkers and targets for clinical treatment of AMI. |
| format | Article |
| id | doaj-art-a28622260bd6470a868df48cdb28a200 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a28622260bd6470a868df48cdb28a2002025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-11957-0Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approachHuali Jiang0Weijie Chen1Benfa Chen2Tao Feng3Heng Li4Dan Li5Shanhua Wang6Weijie Li7Department of Cardiovascularology, Dongguan Key Laboratory of Prevention and Treatment for Chronic Cardiovascular Diseases, Dongguan Tungwah HospitalDepartment of Cardiovascularology, Dongguan Songshan Lake Tungwah HospitalDepartment of Cardiovascularology, Dongguan Key Laboratory of Prevention and Treatment for Chronic Cardiovascular Diseases, Dongguan Tungwah HospitalDepartment of Cardiology, Zhongshan People’s HospitalDepartment of Cardiovascularology, Dongguan Songshan Lake Tungwah HospitalDepartment of Cardiovascularology, The Sixth People’s Hospital of Huizhou City, Huiyang Hospital Affiliated to Southern Medical UniversityDepartment of Cardiovascularology, Dongguan Key Laboratory of Prevention and Treatment for Chronic Cardiovascular Diseases, Dongguan Tungwah HospitalDepartment of Cardiovascularology, Dongguan Key Laboratory of Prevention and Treatment for Chronic Cardiovascular Diseases, Dongguan Tungwah HospitalAbstract Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three datasets (GSE61145, GSE34198, and GSE66360) were used from Gene Expression Omnibus. Dysregulated expression of genes was screened and functionally analyzed. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify significant module genes associated with AMI. Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. Subsequently, receiver operating characteristic curves (ROC) were generated to evaluate the risk of AMI patients. Finally, immune cell infiltration were assessed by CIBERSORT, correlation analysis and immunohistochemistry. A total of 134 upregulated and 25 downregulated genes were identified. Functional analysis showed that the dysregulated genes were involved in cytokine- and immune-related signaling. Ten hub genes were used to establish a diagnostic model. Immune cell infiltration analysis showed that ten genes were correlated with activation of various immune cells; specifically, naive B cells, activated CD4 memory T cells, and resting mast cells were significantly associated with AMI. Immunohistochemical staining indicated that FOS and IL18RAP were significantly upregulated in AMI, CD4 naive T and neutrophils were significantly infiltrated in the microenvironment of AMI. The hub genes involved in activating immune cell infiltration and developing AMI could act as promising diagnostic biomarkers and targets for clinical treatment of AMI.https://doi.org/10.1038/s41598-025-11957-0Acute myocardial infarctionImmune cell infiltrationMachine learningDiagnosisBioinformatics |
| spellingShingle | Huali Jiang Weijie Chen Benfa Chen Tao Feng Heng Li Dan Li Shanhua Wang Weijie Li Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach Scientific Reports Acute myocardial infarction Immune cell infiltration Machine learning Diagnosis Bioinformatics |
| title | Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach |
| title_full | Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach |
| title_fullStr | Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach |
| title_full_unstemmed | Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach |
| title_short | Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach |
| title_sort | identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach |
| topic | Acute myocardial infarction Immune cell infiltration Machine learning Diagnosis Bioinformatics |
| url | https://doi.org/10.1038/s41598-025-11957-0 |
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