⚡ Quick Summary
This study utilized a machine learning approach to identify clinical diagnostic biomarkers and immune cell infiltration characteristics in acute myocardial infarction (AMI). The findings revealed 134 upregulated and 25 downregulated genes, highlighting the potential of these genes as diagnostic tools and therapeutic targets for AMI.
🔍 Key Details
- 📊 Datasets: GSE61145, GSE34198, GSE66360 from Gene Expression Omnibus
- ⚙️ Technologies: Weighted Gene Co-expression Network Analysis (WGCNA), Support Vector Machine (SVM), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO)
- 📈 Evaluation Metric: Receiver Operating Characteristic (ROC) curves
- 🧬 Key Findings: 134 upregulated and 25 downregulated genes associated with AMI
🔑 Key Takeaways
- 🔬 Machine learning effectively identified hub genes related to AMI.
- 🧬 Immune cell analysis revealed significant infiltration of naive B cells, activated CD4 memory T cells, and resting mast cells in AMI.
- 📊 Ten hub genes were established as potential diagnostic biomarkers.
- 🧪 Immunohistochemistry confirmed upregulation of FOS and IL18RAP in AMI patients.
- 🌟 The study suggests that these hub genes could be targets for clinical treatment of AMI.
- 📈 ROC curves were used to evaluate the risk of AMI patients effectively.
- 💡 The research highlights the role of immune cell infiltration in the progression of AMI.
📚 Background
Acute myocardial infarction (AMI) is a critical condition characterized by the sudden blockage of blood flow to the heart, leading to significant morbidity and mortality. Understanding the underlying mechanisms, including the role of immune cell infiltration, is essential for developing effective diagnostic and therapeutic strategies. Despite advancements in medical technology, the identification of reliable clinical biomarkers for AMI remains a challenge.
🗒️ Study
The study conducted by Jiang et al. aimed to explore the clinical diagnostic and immune cell infiltration characteristics of AMI using a comprehensive machine learning approach. Researchers analyzed three datasets from the Gene Expression Omnibus, employing advanced techniques such as WGCNA and various machine learning algorithms to identify significant genes associated with AMI.
📈 Results
The analysis revealed a total of 134 upregulated and 25 downregulated genes in AMI patients. Functional analysis indicated that these dysregulated genes were primarily involved in cytokine- and immune-related signaling pathways. The study successfully established a diagnostic model using ten hub genes, which were found to correlate with the activation of various immune cells, particularly naive B cells and activated CD4 memory T cells.
🌍 Impact and Implications
The findings of this study have significant implications for the clinical management of AMI. By identifying specific hub genes and understanding their role in immune cell infiltration, healthcare professionals can develop more targeted diagnostic tools and therapeutic strategies. This research paves the way for future studies aimed at improving patient outcomes in AMI through personalized medicine approaches.
🔮 Conclusion
This study highlights the transformative potential of machine learning in identifying diagnostic biomarkers and understanding immune cell dynamics in acute myocardial infarction. The identified hub genes not only serve as promising diagnostic tools but also open new avenues for targeted therapies. Continued research in this area is essential for enhancing the management and treatment of AMI, ultimately leading to better patient care.
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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 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.
Author: [‘Jiang H’, ‘Chen W’, ‘Chen B’, ‘Feng T’, ‘Li H’, ‘Li D’, ‘Wang S’, ‘Li W’]
Journal: Sci Rep
Citation: Jiang H, et al. Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach. Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach. 2025; 15:26315. doi: 10.1038/s41598-025-11957-0