Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis

Abstract

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.

Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine‐learning and bioinformatics strategies

“…The WGCNA analysis was well‐described in our previous study 46 . In briefly, Figure 3A,B indicated that the best soft‐power value for IBD merged dataset (GSE75214 + GSE36807) was 16 according to scale independence and average connectivity.…”

“…The WGCNA analysis was well‐described in our previous study. 46 In briefly, Figure 3A,B indicated that the best soft‐power value for IBD merged dataset (GSE75214 + GSE36807) was 16 according to scale independence and average connectivity. After modules merging, a total of 12 co‐expression modules were recognized, each corresponding to one colour (Figure 3D,E ).…”

Online application for the diagnosis of atherosclerosis by six genes

“…Cheng et al identified eight pivotal genes by analyzing hub differentially expressed genes [ 19 ]. Chunjiang et al selected three hub genes for diagnosing carotid plaque progression [ 20 ]. Di et al used bioinformatics analysis to identify seven genes essential to late-stage carotid atherosclerosis markers [ 21 ].…”