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Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through
Chunjiang Liu1, Liming Tang1, Yue Zhou1
1Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital (Shaoxing Hospital of Zhejiang University), Shaoxing, 312000, China.
Insights
Researchers identified three key genes (FGR, LCP1, C5AR1) and a diagnostic tool for unstable carotid plaques in uremia patients. This aids in diagnosing cardiovascular complications associated with uremia.
Area of Science:
- Genomics and Bioinformatics
- Cardiovascular Research
- Nephrology
Background:
- Uremia presents significant medical challenges globally, increasing public health concerns.
- Patients with uremia experience accelerated atherosclerosis, leading to plaque instability and clinical events.
- Cardiovascular and cerebrovascular complications are heightened in uremic individuals.
Purpose of the Study:
- To identify diagnostic biomarkers for uremic patients presenting with unstable carotid plaques (USCPs).
- To develop a predictive model for USCP in the context of uremia.
Main Methods:
- Utilized four microarray datasets from the NCBI Gene Expression Omnibus.
- Applied Limma package and Weighted Gene Co-expression Network Analysis (WGCNA) to identify differentially expressed genes (DEGs) in uremia and USCP.
- Employed protein-protein interaction (PPI) networks and three machine learning algorithms to pinpoint diagnostic genes, followed by nomogram and ROC curve analysis.
Main Results:
- Identified 99 uremia-related DEGs in USCP from the intersection of uremia and USCP DEGs.
- Selected three hub genes (FGR, LCP1, and C5AR1) using PPI networks and machine learning algorithms.
- Developed a nomogram with high diagnostic performance (AUC: 0.989) for USCP in uremia, alongside observations of dysregulated immune cell infiltration.
Conclusions:
- Successfully identified three candidate hub genes (FGR, LCP1, C5AR1) and a diagnostic nomogram for USCP in uremic patients.
- The findings offer a basis for future research into diagnostic markers for USCP in uremia.
- Immune cell infiltration analysis suggests a critical role for macrophages in the pathogenesis of USCP.
Background:
Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs).
Methods:
Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein-protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed.
Results:
Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971-1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes.
Conclusion:
The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.
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