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Novel biomarkers identified by weighted gene co-expression network analysis for atherosclerosis

Neue Biomarker durch gewichtete Gen-Koexpressions-Netzwerk-Analyse für Arteriosklerose entdeckt

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Abstract

Background

This study aimed to screen out the potential diagnostic biomarkers for atherosclerosis (AS).

Methods

We downloaded the gene expression profiles GSE66360, GSE28829, GSE41571, GSE71226, and GSE100927 from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the “limma” package in R. Weighted gene co-expression network analysis (WGCNA) was applied to reveal the correlation between genes in different samples. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The interaction pairs of proteins were retained by the STRING database, and the protein–protein interaction (PPI) network was visualized with the hub genes. Finally, the R packages “ggpubr” and “preprocessCore” were used to analyze immune cell infiltration.

Results

In total, 40 overlapping genes both in GSE66360 and GSE28829 were found to be related to the occurrence of AS. Further, the top 10 network hub genes including TYROBP, CSF1R, TLR2, CD14, CCL4, FCER1G, CD163, TREM1, PLEK, and C5AR1 were identified as significant key genes. Moreover, four genes (TYROBP, CSF1R, FCGR1B, and CD14) were verified that could efficiently diagnose AS. Finally, the gene TYROBP was found to have a strong correlation with immune-infiltrating cells.

Conclusion

Our study identified four genes (TYROBP, CSF1R, FCGR1B, and CD14) that may be effective biomarkers for AS, with the potential to guide the clinical diagnosis of AS.

Zusammenfassung

Hintergrund

Ziel der vorliegenden Studie war es, die potenziellen diagnostischen Biomarker für Arteriosklerose (AS) zu ermitteln.

Methoden

Dazu führten die Autoren einen Download der Genexpressionsprofile GSE66360, GSE28829, GSE41571, GSE71226 und GSE100927 von der Datenbank Gene Expression Omnibus (GEO) durch. Die unterschiedlich exprimierten Gene („differentially expressed genes“, DEG) wurden unter Verwendung der Software R „limma“ identifiziert. Unter Einsatz der gewichteten Gen-Koexpressions-Netzwerk-Analyse („weighted gene co-expression network analysis“, WGCNA) wurde die Korrelation zwischen Genen in verschiedenen Proben ermittelt. Anschließend wurden Signalweg-Anreicherungsanalysen mithilfe der Datenbanken Gene Ontology (GO) und Kyoto Encyclopedia of Genes and Genomes (KEGG) durchgeführt. Die in Wechselwirkung stehenden Proteinpaare wurden in der STRING-Datenbank erfasst, und das Protein-Protein-Interaktions(PPI)-Netzwerk wurde mit den Hub-Genen visualisiert. Schließlich wurden R „ggpubr“ und R „preprocessCore“ verwendet, um die Immunzellinfiltration zu analysieren.

Ergebnisse

Bei 40 sowohl in GSE66360 als auch in GSE28829 überlappenden Genen wurde festgestellt, dass sie mit dem Auftreten einer AS in Zusammenhang stehen. Darüber hinaus wurden die wichtigsten 10 Netzwerk-Hub-Gene einschließlich TYROBP, CSF1R, TLR2, CD14, CCL4, FCER1G, CD163, TREM1, PLEK und C5AR1 als wesentliche Schlüsselgene identifiziert. Außerdem wurden 4 Gene (TYROBP, CSF1R, FCGR1B und CD14) erfasst, die bedeutsam für die Diagnosestellung einer AS waren. Schließlich wurde bei dem Gen TYROBP festgestellt, dass es eine starke Korrelation mit immuninfiltrierenden Zellen aufweist.

Schlussfolgerung

In der vorliegenden Studie wurden 4 Gene entdeckt, die wirksame Biomarker für AS darstellen könnten und das Potenzial besitzen, zur klinischen Diagnose einer AS zu führen.

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References

  1. Briggs RC, Atkinson JB, Miranda RN (2005) Variable expression of human myeloid specific nuclear antigen MNDA in monocyte lineage cells in atherosclerosis. J Cell Biochem 95:293–301. https://doi.org/10.1002/jcb.20435

    Article  CAS  PubMed  Google Scholar 

  2. Chang TT, Yang HY, Chen C, Chen JW (2020) CCL4 inhibition in atherosclerosis: effects on plaque stability, endothelial cell adhesiveness, and macrophages activation. Int J Mol Sci 21:6567. https://doi.org/10.3390/ijms21186567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11. https://doi.org/10.1186/1752-0509-8-s4-s11

    Article  PubMed  PubMed Central  Google Scholar 

  4. Etzerodt A, Moestrup SK (2013) CD163 and inflammation: biological, diagnostic, and therapeutic aspects. Antioxid Redox Signal 18:2352–2363. https://doi.org/10.1089/ars.2012.4834

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Geovanini GR, Libby P (2018) Atherosclerosis and inflammation: overview and updates. Clin Sci (Lond) 132:1243–1252. https://doi.org/10.1042/cs20180306

    Article  CAS  PubMed  Google Scholar 

  6. Guo L, Akahori H, Harari E, Smith SL, Polavarapu R et al (2018) CD163+ macrophages promote angiogenesis and vascular permeability accompanied by inflammation in atherosclerosis. J Clin Invest 128:1106–1124. https://doi.org/10.1172/jci93025

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hagan N, Kane JL, Grover D, Woodworth L, Madore C, Saleh J et al (2020) CSF1R signaling is a regulator of pathogenesis in progressive MS. Cell Death Dis 11:904. https://doi.org/10.1038/s41419-020-03084-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kim S, Karin M (2011) Role of TLR2-dependent inflammation in metastatic progression. Ann N Y Acad Sci 1217:191–206. https://doi.org/10.1111/j.1749-6632.2010.05882.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559. https://doi.org/10.1186/1471-2105-9-559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Liu H, Yang Y, Ge Y, Liu J, Zhao Y (2019) TERC promotes cellular inflammatory response independent of telomerase. Nucleic Acids Res 47:8084–8095. https://doi.org/10.1093/nar/gkz584

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Liu Y, Liu N, Liu Q (2021) Constructing a ceRNA-immunoregulatory network associated with the development and prognosis of human atherosclerosis through weighted gene co-expression network analysis. Aging 13:3080–3100. https://doi.org/10.18632/aging.202486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. McKinney C, Broen JC, Vonk MC, Beretta L, Hesselstrand R, Hunzelmann N et al (2012) Evidence that deletion at FCGR3B is a risk factor for systemic sclerosis. Genes Immun 13:458–460. https://doi.org/10.1038/gene.2012.15

    Article  CAS  PubMed  Google Scholar 

  13. Medina I, Cougoule C, Drechsler M, Bermudez B, Koenen RR, Sluimer J et al (2015) Hck/Fgr kinase deficiency reduces plaque growth and stability by blunting monocyte recruitment and intraplaque motility. Circulation 132:490–501. https://doi.org/10.1161/circulationaha.114.012316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Newman JL, Stone JR (2019) Immune checkpoint inhibition alters the inflammatory cell composition of human coronary artery atherosclerosis. Cardiovasc Pathol 43:107148. https://doi.org/10.1016/j.carpath.2019.107148

    Article  CAS  PubMed  Google Scholar 

  15. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM et al (2020) Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol 76:2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010

    Article  PubMed  PubMed Central  Google Scholar 

  16. Soehnlein O, Libby P (2021) Targeting inflammation in atherosclerosis—From experimental insights to the clinic. Nat Rev Drug Discov. https://doi.org/10.1038/s41573-021-00198-1

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wolf D, Ley K (2019) Immunity and inflammation in atherosclerosis. Circ Res 124:315–327. https://doi.org/10.1161/circresaha.118.313591

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Xue Y, Guo Y, Luo S, Zhou W, Xiang J, Zhu Y, Xiang Z, Shen J (2020) Aberrantly methylated-differentially expressed genes identify novel atherosclerosis risk subtypes. Front Genet 11:569572. https://doi.org/10.3389/fgene.2020.569572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhou J, Zhang C, Wu X, Xie Q, Li L, Chen Y, Yan H, Ren P, Huang X (2019) Identification of genes and pathways related to atherosclerosis comorbidity and depressive behavior via RNA-seq and bioinformation analysis in ApoE(-/-) mice. Ann Transl Med 7:733. https://doi.org/10.21037/atm.2019.11.118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhu Y, Xian X, Wang Z, Bi Y, Chen Q, Han X, Tang D, Chen R (2018) Research progress on the relationship between atherosclerosis and inflammation. Biomolecules 8:80. https://doi.org/10.3390/biom8030080

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Clinical Medicine–Internal Medicine, School of Medicine of Nantong University, Nantong University, No. 19 Qixiu Road, 22600, Nantong City, Jiangsu Province, China

    Jiajun Ni

  2. Department of Cardiology, Qidong Hospital Affiliated to Nantong University (Qidong People’s Hospital), No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China

    Jiajun Ni & Kaijian Huang

  3. Endocrinology department, Qidong Hospital Affiliated to Nantong University (Qidong People’s Hospital), No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China

    Jialin Xu

  4. Department of Cardiology, Affiliated Hospital of Nantong University, No. 568 Minle Middle Road, 226200, Qidong City, Nantong City, Jiangsu Province, China

    Qi Lu & Chu Chen

Authors
  1. Jiajun Ni
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  2. Kaijian Huang
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Contributions

JN, KH, and JX performed the literature search; JN and QL analyzed the data. This work was performed by JN and QL. The manuscript was written by JN and edited by CC.

Corresponding author

Correspondence to Qi Lu.

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Conflict of interest

J. Ni, K. Huang, J. Xu, Q. Lu and C. Chen declare that they have no competing interests.

For this article no studies with human participants or animals were performed by any of the authors. All studies mentioned were in accordance with the ethical standards indicated in each case.

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Ni, J., Huang, K., Xu, J. et al. Novel biomarkers identified by weighted gene co-expression network analysis for atherosclerosis. Herz (2023). https://doi.org/10.1007/s00059-023-05204-3

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  • DOI: https://doi.org/10.1007/s00059-023-05204-3

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