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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Identification of immune-relevant candidate genes in atherosclerosis by WGCNA and single-cell analysis
1Vasculocardiology Department, The Third People's Hospital of Datong, Datong, China.
Insights
This study identifies five immune-related biomarkers (SYK, PTPRC, ITGAL, FGR, IL10RA) for atherosclerosis (AS) diagnosis. These findings offer new insights for developing early detection and treatment strategies for AS, a leading cause of death.
Area of Science:
- Cardiovascular Biology
- Immunology
- Bioinformatics
Background:
- Atherosclerosis (AS) is a major global cause of mortality, often diagnosed late due to asymptomatic early stages.
- Identifying reliable biomarkers is crucial for improving early diagnosis, treatment, and reducing mortality rates.
Purpose of the Study:
- To identify novel immune-related biomarkers for atherosclerosis (AS).
- To develop diagnostic and prognostic models for AS using bioinformatics approaches.
- To explore potential therapeutic targets for AS.
Main Methods:
- Downloaded and analyzed gene expression datasets (GSE43292, GSE100927) from atherosclerotic and normal arterial tissues.
- Utilized CIBERSORT for immune cell infiltration analysis, differential gene expression analysis, and weighted gene co-expression network analysis (WGCNA).
- Constructed and validated machine learning models (Random Forest, SVM, GLM) and a nomogram for AS risk assessment.
Main Results:
- Identified five key immune-related genes (SYK, PTPRC, ITGAL, FGR, IL10RA) significantly associated with AS.
- Developed predictive models demonstrating good validity for AS diagnosis and risk stratification.
- Identified potential therapeutic agents targeting the identified biomarkers.
Conclusions:
- The study proposes SYK, PTPRC, ITGAL, FGR, and IL10RA as promising immune-related biomarkers for AS.
- The developed models offer potential for improved early diagnosis and risk assessment of AS.
- These findings provide a foundation for further experimental validation and therapeutic development in AS.
Abstract:
Atherosclerosis (AS) is a systemic disease closely related to inflammatory cell infiltration and immune cell activation, often leading to myocardial infarction and stroke and is the leading cause of death worldwide. AS is asymptomatic in its early stages, which leads to a low rate of early diagnosis of the disease and often delays treatment. Therefore, it is extremely important to explore potential biomarkers and molecular mechanisms for the diagnosis and treatment of AS, not only to improve early diagnosis and early treatment of patients but also to reduce the risk of death. The datasets GSE43292 and GSE100927 containing atherosclerotic plaques and normal arterial tissues (including 101 cases of atherosclerotic samples and 66 cases of normal tissue samples) were downloaded from the Gene Expression Omnibus database. The relationship between gene expression and immune cells was analyzed by the CIBERSORT package. Then the differentially expressed genes, weighted gene co-expression network analysis, and immune-related genes (IRGs) set were used to screen out the differentially expressed IRGs. These differentially expressed IRGs were then analyzed by constructing random forest model, support vector machine model, and generalized linear model. Next, a nomogram was established to assess disease risk, the calibration curve, decision curve analysis curve, and clinical impact curve were used to assess the validity of these models. The molecular mechanisms of these biomarkers were analyzed using single-gene gene set enrichment analysis. Potential target drugs for these molecules were identified in the Drug-Gene Interaction database. We screened 5 potential immune-relevant biomarkers (SYK, PTPRC, ITGAL, FGR, and IL10RA) associated with AS, constructed diagnostic models, and predicted potential therapeutic agents. Our findings, derived from integrated bioinformatics analysis, provide novel candidate genes and insights for the future diagnosis and treatment of AS, which warrant further experimental validation.
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