Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism
Abstract Background Sphingolipid metabolism (SM) is linked to acute myocardial infarction (AMI), but its role remains unclear. This study explored SM-related genes (SMRGs) in AMI to support clinical diagnosis. Methods We analyzed datasets GSE48060 and GSE123342 to identify differentially expressed g...
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BMC
2025-08-01
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| Series: | Hereditas |
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| Online Access: | https://doi.org/10.1186/s41065-025-00515-3 |
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| author | Ling Sun Lingyan He Hai-Hua Pan Chang-Lin Zhai |
| author_facet | Ling Sun Lingyan He Hai-Hua Pan Chang-Lin Zhai |
| author_sort | Ling Sun |
| collection | DOAJ |
| description | Abstract Background Sphingolipid metabolism (SM) is linked to acute myocardial infarction (AMI), but its role remains unclear. This study explored SM-related genes (SMRGs) in AMI to support clinical diagnosis. Methods We analyzed datasets GSE48060 and GSE123342 to identify differentially expressed genes (DEGs) and key module genes. Protein-protein interaction (PPI) network analysis and machine learning were used to screen potential biomarkers, which were validated via receiver operating characteristic (ROC) curves and expression assessment. Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. Single-cell RNA sequencing (scRNA-seq) identified key cell types and their functions. Biomarkers were validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results Intersection of 95 DEGs and 2,196 module genes yielded 20 genes, with ANXA3 and SOCS3 identified as biomarkers. The ANN model showed superior diagnostic performance compared to individual markers. Biomarkers were enriched in the toll-like receptor (TLR) signaling pathway. Immune infiltration analysis revealed differences in five immune cell types between AMI and control groups. ANXA3 correlated positively with neutrophils and negatively with resting memory CD4 T cells. Drugs targeting ANXA3 included ethanolamine, difluocortolone, and fluocinolone acetonide, with strong binding affinity. scRNA-seq identified B cells and monocytes as key cells; ANXA3 and SOCS3 expression increased during monocyte differentiation before decreasing, while B cells showed no significant changes. Conclusion ANXA3 and SOCS3 were identified as SM-related biomarkers in AMI, providing insights for clinical diagnosis. |
| format | Article |
| id | doaj-art-dce5ea7d9ec34dbbbcae513b91c73a38 |
| institution | Kabale University |
| issn | 1601-5223 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| series | Hereditas |
| spelling | doaj-art-dce5ea7d9ec34dbbbcae513b91c73a382025-08-20T03:43:27ZengBMCHereditas1601-52232025-08-01162112110.1186/s41065-025-00515-3Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolismLing Sun0Lingyan He1Hai-Hua Pan2Chang-Lin Zhai3Zhejiang Chinese Medical UniversityZhejiang Chinese Medical UniversityDepartment of Cardiology, The First Hospital of Jiaxing Affiliated Hospital of Jiaxing UniversityZhejiang Chinese Medical UniversityAbstract Background Sphingolipid metabolism (SM) is linked to acute myocardial infarction (AMI), but its role remains unclear. This study explored SM-related genes (SMRGs) in AMI to support clinical diagnosis. Methods We analyzed datasets GSE48060 and GSE123342 to identify differentially expressed genes (DEGs) and key module genes. Protein-protein interaction (PPI) network analysis and machine learning were used to screen potential biomarkers, which were validated via receiver operating characteristic (ROC) curves and expression assessment. Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. Single-cell RNA sequencing (scRNA-seq) identified key cell types and their functions. Biomarkers were validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results Intersection of 95 DEGs and 2,196 module genes yielded 20 genes, with ANXA3 and SOCS3 identified as biomarkers. The ANN model showed superior diagnostic performance compared to individual markers. Biomarkers were enriched in the toll-like receptor (TLR) signaling pathway. Immune infiltration analysis revealed differences in five immune cell types between AMI and control groups. ANXA3 correlated positively with neutrophils and negatively with resting memory CD4 T cells. Drugs targeting ANXA3 included ethanolamine, difluocortolone, and fluocinolone acetonide, with strong binding affinity. scRNA-seq identified B cells and monocytes as key cells; ANXA3 and SOCS3 expression increased during monocyte differentiation before decreasing, while B cells showed no significant changes. Conclusion ANXA3 and SOCS3 were identified as SM-related biomarkers in AMI, providing insights for clinical diagnosis.https://doi.org/10.1186/s41065-025-00515-3Acute myocardial infarctionSphingolipid metabolismMachine learningImmune microenvironmentSingle cell RNA analysis |
| spellingShingle | Ling Sun Lingyan He Hai-Hua Pan Chang-Lin Zhai Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism Hereditas Acute myocardial infarction Sphingolipid metabolism Machine learning Immune microenvironment Single cell RNA analysis |
| title | Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism |
| title_full | Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism |
| title_fullStr | Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism |
| title_full_unstemmed | Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism |
| title_short | Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism |
| title_sort | identification and validation of anxa3 and socs3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism |
| topic | Acute myocardial infarction Sphingolipid metabolism Machine learning Immune microenvironment Single cell RNA analysis |
| url | https://doi.org/10.1186/s41065-025-00515-3 |
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