2. Academic Validation
  3. Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

  • BMC Med Genomics. 2023 May 12;16(1):100. doi: 10.1186/s12920-023-01531-w.
Zhipeng Zheng # 1,  Dong Yuan # 2,  Cheng Shen # 2,  Zhiyuan Zhang # 1 3,  Jun Ye 4 5,  Li Zhu 6 7 8
Affiliations

Affiliations

  • 1 Dalian Medical University, Dalian, 116000, China.
  • 2 Nanjing University of Chinese Medicine, Nanjing, 210023, China.
  • 3 The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, 225300, China.
  • 4 Dalian Medical University, Dalian, 116000, China. [email protected].
  • 5 The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, 225300, China. [email protected].
  • 6 Dalian Medical University, Dalian, 116000, China. [email protected].
  • 7 Nanjing University of Chinese Medicine, Nanjing, 210023, China. [email protected].
  • 8 The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, 225300, China. [email protected].
  • # Contributed equally.
PMID: 37173673 DOI: 10.1186/s12920-023-01531-w
Abstract

Background: Atherosclerosis is the main pathological change in atherosclerotic Cardiovascular Disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis.

Methods: Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman's rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells.

Results: A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis.

Conclusions: ​This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis.

Keywords

Atherosclerosis; Bioinformatics; Gene expression profiling; Hub genes; Robust rank aggregation.

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