Identification of <i>ITGAX</i> and <i>CCR1</i> as potential biomarkers of atherosclerosis via Gene Set Enrichment Analysis

Yan, Sheng; Meng, Ling‐Bing; Guo, Xiaoyong; Chen, Zuoguan; Zhang, Yuanmeng; Li, Yongjun

doi:10.1177/03000605211039480

Cited by 8 publications

(7 citation statements)

References 31 publications

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“…While the AQP series is closely related to autoimmune diseases such as optic neuromyelitis optica, dry syndrome, and rheumatoid arthritis, SLE is an autoimmune disease with few studies related to AQP9 and SLE, and the correlation between AQP9 and SLE could be considered in future studies to provide new targets for the clinical treatment of SLE. Shen et al 16 found, through a comprehensive bioinformatics study, that CCR1 expression was significantly higher in atherosclerotic plaque samples than in healthy samples 16 and a study confirmed that the use of pharmacological antagonists of CCR1 reduces the risk of cardiovascular disease. 17 Other studies have shown that CCR1 is upregulated in the skin of SLE patients, which leads to SLE skin tissue damage.…”

Section: Discussionmentioning

confidence: 94%

“…18 IL1RN is an endogenous natural antagonist of IL-1 and usually acts by competing for the binding site of IL-1R with IL-1. 19 Yan et al 16 determined that IL1RN was upregulated in AS tissues by bioinformatics methods and further verified its expression level was significantly higher than that of the control group by experiments. Similarly, a correlation meta-analysis showed that genetic polymorphisms in IL1RN are strongly associated with susceptibility to SLE, 20 however, additional research is required to determine how these polymorphisms contribute to the pathogenesis of SLE.…”

Section: Discussionmentioning

confidence: 96%

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The exploration of shared genes and molecular mechanisms of systemic lupus erythematosus and atherosclerosis

Fan

Zhu

et al. 2022

Lupus

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Objective Despite widespread recognition, the mechanisms underlying the relationship between systemic lupus erythematosus (SLE) and atherosclerosis (AS) are still unclear. Our study aimed to explore the shared genetic signature and molecular mechanisms of SLE and AS using a bioinformatics approach. Methods Gene expression profiles of GSE50772 (contains peripheral blood mononuclear cells from 61 SLE patients and 20 normal samples) and GSE100927 (contains 69 AS plaque tissue samples and 35 control samples) were downloaded from the Gene Expression Database (GEO) before the differentially expressed genes were obtained using the “limma” package in R. The differential genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the DAVID online platform to annotate their functions. The intersection targets of PPI and WGCNA were used as key shared genes for SLE and AS with their diagnostic value as shared genes being verified through ROC curves. Finally, Cytoscape 3.7.2 software was used to construct a miRNA-mRNA network map associated with the shared genes. Results A total of 246 DEGs were identified, including 189 upregulated genes and 57 downregulated genes, which were mainly enriched in signaling pathways such as TNF signaling pathway, IL-17 signaling pathway, and NF-kB signaling pathway. The molecular basis for the relationship between SLE and AS may be the aforementioned signaling pathways. Following ROC curve validation, the intersection of PPI and WGCNA, as well as AQP9, CCR1, CD83, CXCL1, and FCGR2A, resulted in the identification of 15 shared genes. Conclusion The study provided a new perspective on the common molecular mechanisms between SLE and AS, and the key genes and pathways that were identified as being part of these pathways may offer fresh perspectives and suggestions for further experimental research.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 96%

The exploration of shared genes and molecular mechanisms of systemic lupus erythematosus and atherosclerosis

Fan

Zhu

et al. 2022

Lupus

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“…ITGAX is involved in early AS lesion formation and is highly expressed in AS tissues, and several bioinformatic analyses have suggested it as a potential biomarker for AS 34 , 35 . The mice with double knockouts of ITGAX and Apoe demonstrated lower monocyte adhesion and AS plaque macrophage content 36 .…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes for atherosclerosis in different arterial beds

Wu,

Pan,

Zhou

et al. 2024

Sci Rep

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Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

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“…Signaling pathways include immune system [54], metabolism of lipids [55], phospholipid metabolism [56] and disease [57] plays an important role in CAD. DOCK4 [58], CEACAM1 [59], STAT1 [60], ARG1 [61], TLR4 [62], ADAM9 [63], VNN1 [64], ABCA1 [65], STAT2 [66], TLR5 [67], PTGS2 [68], RNF213 [69], ZDHHC17 [70], JAK2 [71], TLR8 [72], NOTCH2 [73], PDGFC (platelet derived growth factor C) [74], CMPK2 [75], TLR2 [76], CYP1B1 [77], CCR1 [78], HDAC9 [79], IL1RN [80], GCH1 [81], LYST (lysosomal trafficking regulator) [82], PELI1 [83], EGR1 [84], SNX10 [85], CA2 [86], ZEB2 [87], HIF1A [88], PLA2G7 [89], CCR2 [90], GAB1 [91], IRAK3 [92], LDLR (low density lipoprotein receptor) [93], TLR6 [94], SIRT1 [95], NOD2 [96], ATP10D [97], ELOVL6 [98], VCAN (versican) [99], TET2 [100], TET3 [101], ZBTB20 [102], HS3ST1 [103], PF4 [104], DNAJC2 [105], NFIA (nuclear factor I A) [106], CCR7 [107], PRDX2 [108], ADK (adenosine kinase) [109], TCF7 [110], LGALS3 [111], OLFM2 [112], HDAC11 [113] and ARPC1B [114] are significantly related to the atherosclerosis. Studies have revealed that KCNJ2 [115], TLR4 [116], JAK2 [117], TLR2 [118], EGR1 [119], GAB1 [120], ZBTB11 [121], BIN1 [122], TCF4 [123], PPP1R13L [124], TRPM4 [125], LGALS3 [126] and SNTA1 [127] plays a key role in cardiac arrhythmia.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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Identification of ITGAX and CCR1 as potential biomarkers of atherosclerosis via Gene Set Enrichment Analysis

Cited by 8 publications

References 31 publications

The exploration of shared genes and molecular mechanisms of systemic lupus erythematosus and atherosclerosis

The exploration of shared genes and molecular mechanisms of systemic lupus erythematosus and atherosclerosis

Identification of key genes for atherosclerosis in different arterial beds

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

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