Screening of potential gene markers for predicting carotid atheroma plaque formation using bioinformatics approaches

Comprehensive analysis identifies crucial genes associated with immune cells mediating progression of carotid atherosclerotic plaque

BACKGROUNDS

Carotid atherosclerosis is prone to rupture and cause ischemic stroke in advanced stages of development. Our research aims to provide markers for the progression of atherosclerosis and potential targets for its treatment.

METHODS

We performed a thorough analysis using various techniques including DEGs, GO/KEGG, xCell, WGCNA, GSEA, and other methods. The gene expression omnibus datasets GSE28829 and GSE43292 were utilized for this comprehensive analysis. The validation datasets employed in this study consisted of GSE41571 and GSE120521 datasets. Finally, we validated PLEK by immunohistochemistry staining in clinical samples.

RESULTS

Using the WGCNA technique, we discovered 636 differentially expressed genes (DEGs) and obtained 12 co-expression modules. Additionally, we discovered two modules that were specifically associated with atherosclerotic plaque. A total of 330 genes that were both present in DEGs and WGCNA results were used to create a protein-protein network in Cytoscape. We used four different algorithms to get the top 10 genes and finally got 6 overlapped genes (TYROBP, ITGB2, ITGAM, PLEK, LCP2, CD86), which are identified by GSE41571 and GSE120521 datasets. Interestingly, the area under curves (AUC) of PLEK is 0.833. Besides, we found PLEK is strongly positively correlated with most lymphocytes and myeloid cells, especially monocytes and macrophages, and negatively correlated with most stromal cells (e.g, neurons, myocytes, and fibroblasts). The expression of PLEK were consistent with the immunohistochemistry results.

CONCLUSIONS

Six genes (TYROBP, ITGB2, ITGAM, PLEK, LCP2, CD86) were found to be connected with carotid atherosclerotic plaques and PLEK may be an important biomarker and a potential therapeutic target.

Bioinformatic Analysis Revealed the Essential Regulatory Genes and Pathways of Early and Advanced Atherosclerotic Plaque in Humans

Atherosclerosis (AS) is a lipid-induced, chronic inflammatory, autoimmune disease affecting multiple arteries. Although much effort has been put into AS research in the past decades, it is still the leading cause of death worldwide. The complex genetic network regulation underlying the pathogenesis of AS still needs further investigation to provide effective targeted therapy for AS. We performed a bioinformatic microarray data analysis at different atherosclerotic plaque stages from the Gene Expression Omnibus database with accession numbers GSE43292 and GSE28829. Using gene set enrichment analysis, we further confirmed the immune-related pathways that play an important role in the development of AS. We are reporting, for the first time, that the metabolism of the three branched-chain amino acids (BCAAs; leucine, isoleucine, and valine) and short-chain fatty acids (SCFA; propanoate, and butanoate) are involved in the progression of AS using microarray data of atherosclerotic plaque tissue. Immune and muscle system-related pathways were further confirmed as highly regulated pathways during the development of AS using gene expression pattern analysis. Furthermore, we also identified four modules mainly involved in histone modification, immune-related processes, macroautophagy, and B cell activation with modular differential connectivity in the dataset of GSE43292, and three modules related to immune-related processes, B cell activation, and nuclear division in the dataset of GSE28829 also display modular differential connectivity based on the weighted gene co-expression network analysis. Finally, we identified eight key genes related to the pathways of immune and muscle system function as potential therapeutic biomarkers to distinguish patients with early or advanced stages in AS, and two of the eight genes were validated using the gene expression dataset from gene-deficient mice. The results of the current study will improve our understanding of the molecular mechanisms in the progression of AS. The key genes and pathways identified could be potential biomarkers or new drug targets for AS management.

Identification of key genes in ruptured atherosclerotic plaques by weighted gene correlation network analysis

The rupture of atherosclerotic plaques is essential for cardiovascular and cerebrovascular events. Identification of the key genes related to plaque rupture is an important approach to predict the status of plaque and to prevent the clinical events. In the present study, we downloaded two expression profiles related to the rupture of atherosclerotic plaques (GSE41571 and GSE120521) from GEO database. 11 samples in GSE41571 were used to identify the differentially expressed genes (DEGs) and to construct the weighted gene correlation network analysis (WGCNA) by R software. The gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment tool in DAVID website, and the Protein-protein interactions in STRING website were used to predict the functions and mechanisms of genes. Furthermore, we mapped the hub genes extracted from WGCNA to DEGs, and constructed a sub-network using Cytoscape 3.7.2. The key genes were identified by the molecular complex detection (MCODE) in Cytoscape. Further validation was conducted using dataset GSE120521 and human carotid endarterectomy (CEA) plaques. Results: In our study, 868 DEGs were identified in GSE41571. Six modules with 236 hub genes were identified through WGCNA analysis. Among these six modules, blue and brown modules were of the highest correlations with ruptured plaques (with a correlation of 0.82 and −0.9 respectively). 72 hub genes were identified from blue and brown modules. These 72 genes were the most likely ones being related to cell adhesion, extracellular matrix organization, cell growth, cell migration, leukocyte migration, PI₃K-Akt signaling, focal adhesion, and ECM-receptor interaction. Among the 72 hub genes, 45 were mapped to the DEGs (logFC > 1.0, p-value < 0.05). The sub-network of these 45 hub genes and MCODE analysis indicated 3 clusters (13 genes) as key genes. They were LOXL1, FBLN5, FMOD, ELN, EFEMP1 in cluster 1, RILP, HLA-DRA, HLA-DMB, HLA-DMA in cluster 2, and SFRP4, FZD6, DKK3 in cluster 3. Further expression detection indicated EFEMP1, BGN, ELN, FMOD, DKK3, FBLN5, FZD6, HLA-DRA, HLA-DMB, HLA-DMA, and RILP might have potential diagnostic value.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

IGFBP6 Is Downregulated in Unstable Carotid Atherosclerotic Plaques According to an Integrated Bioinformatics Analysis and Experimental Verification

Aims: To investigate the differentially expressed genes (DEGs) and molecular interaction in unstable atherosclerotic carotid plaques.

Methods: Gene expression datasets GSE41571, GSE118481, and E-MTAB-2055 were analyzed. Co-regulated DEGs in at least two datasets were analyzed with the enrichment of Gene Ontology Biological Process (GO-BP), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) networks, interrelationships between miRNAs/transcriptional factors, and their target genes and drug-gene interactions. The expression of notable DEGs in human carotid artery plaques and plasma was further identified.

Results: The GO-BP enrichment analysis revealed that genes associated with inflammatory response, and extracellular matrix organization were altered. The KEGG enrichment analysis revealed that upregulated DEGs were enriched in the tuberculous, lysosomal, and chemokine signaling pathways, whereas downregulated genes were enriched in the focal adhesion and PI3K/Akt signaling pathway. Collagen type I alpha 2 chain (COL1A2), adenylate cyclase 3 (ADCY3), C-X-C motif chemokine receptor 4 (CXCR4), and TYRO protein tyrosine kinase binding protein (TYROBP) might play crucial roles in the PPI networks. In drug–gene interactions, colony-stimulating factor-1 receptor had the most drug interactions. Insulin-like growth factor binding protein 6 (IGFBP6) was markedly downregulated in unstable human carotid plaques and plasma. Under a receiver operating characteristic curve analysis, plasma IGFBP6 had a significant discriminatory power (AUC, 0.894; 95% CI, 0.810–0.977), with a cutoff value of 142.08 ng/mL.

Conclusions: The genes COL1A2, ADCY3, CXCR4, and TYROBP are promising targets for the prevention of unstable carotid plaque formation. IGFBP6 may be an important biomarker for predicting vulnerable plaques.