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

Zhen Li, Junhui Liu, Zhichun Liu, Xiaonan Zhu, Rongxin Geng, Rui Ding, Haitao Xu, Shulan Huang
Author Information
  1. Zhen Li: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  2. Junhui Liu: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  3. Zhichun Liu: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  4. Xiaonan Zhu: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  5. Rongxin Geng: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  6. Rui Ding: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  7. Haitao Xu: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.
  8. Shulan Huang: Department of Neurosurgery III, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, P.R. China.

Abstract

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.

Keywords

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MeSH Term

Humans
Plaque, Atherosclerotic
Gene Expression Profiling
Protein Interaction Maps
Atherosclerosis
Carotid Artery Diseases
Computational Biology

Word Cloud

Created with Highcharts 10.0.0PLEKgenesWGCNAdatasetsatheroscleroticanalysisDEGsplaquecellscarotidatherosclerosisprogressionpotentialexpressionGSE41571GSE120521immunohistochemistrydiscoveredmodulesassociatedresultsusedTYROBPITGB2ITGAMLCP2CD86foundcorrelatedBACKGROUNDS:CarotidpronerupturecauseischemicstrokeadvancedstagesdevelopmentresearchaimsprovidemarkerstargetstreatmentMETHODS:performedthoroughusingvarioustechniquesincludingGO/KEGGxCellGSEAmethodsgeneomnibusGSE28829GSE43292utilizedcomprehensivevalidationemployedstudyconsistedFinallyvalidatedstainingclinicalsamplesRESULTS:Usingtechnique636differentiallyexpressedobtained12co-expressionAdditionallytwospecificallytotal330presentcreateprotein-proteinnetworkCytoscapefourdifferentalgorithmsgettop10finallygot6overlappedidentifiedInterestinglyareacurvesAUC0833BesidesstronglypositivelylymphocytesmyeloidespeciallymonocytesmacrophagesnegativelystromalegneuronsmyocytesfibroblastsconsistentCONCLUSIONS:SixconnectedplaquesmayimportantbiomarkertherapeutictargetComprehensiveidentifiescrucialimmunemediatingRNAsequencingbioinformaticsimmunity

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