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Significance of neutrophil extracellular traps-related gene in the diagnosis and classification of atherosclerosis

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Abstract

Atherosclerosis (AS) is a pathological process associated with various cardiovascular diseases. Upon different stimuli, neutrophils release reticular complexes known as neutrophil extracellular traps (NETs). Numerous researches have indicated a strong correlation between NETs and AS. However, its role in cardiovascular disease requires further investigation. By utilizing a machine learning algorithm, we examined the genes associated with NETs that were expressed differently in individuals with AS compared to normal controls. As a result, we identified four distinct genes. A nomogram model was built to forecast the incidence of AS. Additionally, we conducted analysis on immune infiltration, functional enrichment and consensus clustering in AS samples. The findings indicated that individuals with AS could be categorized into two groups, exhibiting notable variations in immune infiltration traits among the groups. Furthermore, to measure the NETs model, the principal component analysis algorithm was developed and cluster B outperformed cluster A in terms of NETs. Additionally, there were variations in the expression of multiple chemokines between the two subtypes. By studying AS NETs, we acquired fresh knowledge about the molecular patterns and immune mechanisms implicated, which could open up new possibilities for AS immunotherapy.

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Data availability

Both the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the published article contain the data that substantiate the findings of this research. All analysis methods and software packages were indicated in the section titled “Materials and methods”.

References

  1. Herrington W, Lacey B, Sherliker P et al (2016) Epidemiology of atherosclerosis and the potential to reduce the global burden of atherothrombotic disease. Circ Res 118:535–546

    Article  CAS  PubMed  Google Scholar 

  2. Gallino A, Aboyans V, Diehm C et al (2014) Non-coronary atherosclerosis. Eur Heart J 35:1112–1119

    Article  PubMed  Google Scholar 

  3. Badimon L, Vilahur G (2014) Thrombosis formation on atherosclerotic lesions and plaque rupture. J Intern Med 276:618–632

    Article  CAS  PubMed  Google Scholar 

  4. Arts EEA, Popa C, Den Broeder AA et al (2015) Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis 74:668–674

    Article  CAS  PubMed  Google Scholar 

  5. Bathon JM, Centola M, Liu X et al (2023) Identification of novel biomarkers for the prediction of subclinical coronary artery atherosclerosis in patients with rheumatoid arthritis: an exploratory analysis. Arthritis Res Ther 25:213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Filep JG (2022) Targeting neutrophils for promoting the resolution of inflammation. Front Immunol 13:866747

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang YW, Deng NH, Tian KJ et al (2022) Development of hydrogen sulfide donors for anti-atherosclerosis therapeutics research: challenges and future priorities. Front Cardiovasc Med 9:909178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Roy P, Orecchioni M, Ley K (2022) How the immune system shapes atherosclerosis: roles of innate and adaptive immunity. Nat Rev Immunol 22:251–265

    Article  CAS  PubMed  Google Scholar 

  9. Brinkmann V, Reichard U, Goosmann C et al (2004) Neutrophil extracellular traps kill bacteria. Science 303:1532–1535

    Article  CAS  PubMed  Google Scholar 

  10. Megens RTA, Vijayan S, Lievens D et al (2012) Presence of luminal neutrophil extracellular traps in atherosclerosis. Thromb Haemost 107:597–598

    Article  CAS  PubMed  Google Scholar 

  11. Knight JS, Luo W, O’Dell AA et al (2014) Peptidylarginine deiminase inhibition reduces vascular damage and modulates innate immune responses in murine models of atherosclerosis. Circ Res 114:947–956

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Carmona-Rivera C, Zhao W, Yalavarthi S et al (2015) Neutrophil extracellular traps induce endothelial dysfunction in systemic lupus erythematosus through the activation of matrix metalloproteinase-2. Ann Rheum Dis 74:1417–1424

    Article  CAS  PubMed  Google Scholar 

  13. Warnatsch A, Ioannou M, Wang Q et al (2015) Neutrophil extracellular traps license macrophages for cytokine production in atherosclerosis. Science 349:316–320

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang Y, Jian W, He L et al (2020) Externalized histone H4: a novel target that orchestrates chronic inflammation by inducing lytic cell death. Acta Biochim Biophys Sin 52:336–338

    Article  CAS  PubMed  Google Scholar 

  15. Döring Y, Manthey HD, Drechsler M et al (2012) Auto-antigenic protein-DNA complexes stimulate plasmacytoid dendritic cells to promote atherosclerosis. Circulation 125:1673–1683

    Article  PubMed  Google Scholar 

  16. Zhang R, Brennan ML, Fu X et al (2001) Association between myeloperoxidase levels and risk of coronary artery disease. JAMA 286(17):2136–2142

    Article  CAS  PubMed  Google Scholar 

  17. Mangold A, Alias S, Scherz T et al (2015) Coronary neutrophil extracellular trap burden and deoxyribonuclease activity in ST-elevation acute coronary syndrome are predictors of ST-segment resolution and infarct size. Circ Res 116:1182–1192

    Article  CAS  PubMed  Google Scholar 

  18. Helseth R, Shetelig C, Andersen GØ et al (2019) Neutrophil extracellular trap components associate with infarct size, ventricular function, and clinical outcome in STEMI. Mediat Inflamm. https://doi.org/10.1155/2019/7816491

    Article  Google Scholar 

  19. Langseth MS, Opstad TB, Bratseth V et al (2018) Markers of neutrophil extracellular traps are associated with adverse clinical outcome in stable coronary artery disease. Eur J Prev Cardiol 25:762–769

    Article  PubMed  Google Scholar 

  20. Kithcart AP, Libby P (2018) Casting NETs to predict cardiovascular outcomes. Eur J Prev Cardiol 25:759–761

    Article  PubMed  Google Scholar 

  21. Steenman M, Espitia O, Maurel B et al (2018) Identification of genomic differences among peripheral arterial beds in atherosclerotic and healthy arteries. Sci Rep 8:3940

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ayari H, Bricca G (2013) Identification of two genes potentially associated in iron-heme homeostasis in human carotid plaque using microarray analysis. J Biosci 38:311–315

    Article  CAS  PubMed  Google Scholar 

  23. Jin H, Goossens P, Juhasz P et al (2021) Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage. Clin Transl Med 11:e458

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wu J, Zhang F, Zheng X et al (2022) Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-related genes. Front Immunol 13:1047367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ritchie ME, Phipson B, Wu DI et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e47

    Article  PubMed  PubMed Central  Google Scholar 

  26. Engebretsen S, Bohlin J (2019) Statistical predictions with glmnet. Clin Epigenet 11:1–3

    Article  Google Scholar 

  27. Lin X, Li C, Zhang Y et al (2017) Selecting feature subsets based on SVM-RFE and the overlapping ratio with applications in bioinformatics. Molecules 23:52

    Article  PubMed  PubMed Central  Google Scholar 

  28. Alderden J, Pepper GA, Wilson A et al (2018) Predicting pressure injury in critical care patients: a machine-learning model. Am J Crit Care 27:461–468

    Article  PubMed  PubMed Central  Google Scholar 

  29. Iasonos A, Schrag D, Raj GV et al (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26:1364–1370

    Article  PubMed  Google Scholar 

  30. Robin X, Turck N, Hainard A et al (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12:1–8

    Article  Google Scholar 

  31. Zhang Y, Liu N, Wang S (2018) A differential privacy protecting K-means clustering algorithm based on contour coefficients. PLoS ONE 13:e0206832

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform 14:1–15

    Article  Google Scholar 

  33. Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262

    Article  CAS  PubMed  Google Scholar 

  34. Zhang B, Wu Q, Li B et al (2020) m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. Mol Cancer 19:1–21

    Article  PubMed  PubMed Central  Google Scholar 

  35. Roth GA, Johnson C, Abajobir A et al (2017) Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol 70:1–25

    Article  PubMed  PubMed Central  Google Scholar 

  36. Döring Y, Soehnlein O, Weber C (2017) Neutrophil extracellular traps in atherosclerosis and atherothrombosis. Circ Res 120:736–743

    Article  PubMed  Google Scholar 

  37. Tian Z, Li X, Jiang D (2023) Analysis of immunogenic cell death in atherosclerosis based on scRNA-seq and bulk RNA-seq data. Int Immunopharmacol 119:110130

    Article  CAS  PubMed  Google Scholar 

  38. Tang C, Deng L, Luo Q et al (2023) Identification of oxidative stress-related genes and potential mechanisms in atherosclerosis. Front Genet 13:998954

    Article  PubMed  PubMed Central  Google Scholar 

  39. Urban CF, Ermert D, Schmid M et al (2009) Neutrophil extracellular traps contain calprotectin, a cytosolic protein complex involved in host defense against Candida albicans. PLoS Pathog 5:e1000639

    Article  PubMed  PubMed Central  Google Scholar 

  40. 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

    Article  CAS  PubMed  Google Scholar 

  41. Lin JD, Nishi H, Poles J et al (2019) Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression. JCI Insight. https://doi.org/10.1172/jci.insight.124574

    Article  PubMed  PubMed Central  Google Scholar 

  42. Boshuizen MCS, de Winther MPJ (2015) Interferons as essential modulators of atherosclerosis. Arterioscler Thromb Vasc Biol 35:1579–1588

    Article  CAS  PubMed  Google Scholar 

  43. Manthey HD, Thomas AC, Shiels IA et al (2011) Complement C5a inhibition reduces atherosclerosis in ApoE–/–mice. FASEB J 25:2447–2455

    Article  CAS  PubMed  Google Scholar 

  44. An G, Li B, Liu X et al (2016) Overexpression of complement component C5a accelerates the development of atherosclerosis in ApoE-knockout mice. Oncotarget 7:56060

    Article  PubMed  PubMed Central  Google Scholar 

  45. Libby P (2017) Interleukin-1 beta as a target for atherosclerosis therapy: biological basis of CANTOS and beyond. J Am Coll Cardiol 70:2278–2289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Keshari RS, Jyoti A, Dubey M et al (2012) Cytokines induced neutrophil extracellular traps formation: implication for the inflammatory disease condition. PLoS ONE 7:e48111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fu L, Liu Z, Liu Y (2023) Fibrinogen-like protein 2 in inflammatory diseases: a future therapeutic target. Int Immunopharmacol 116:109799

    Article  CAS  PubMed  Google Scholar 

  48. Herrero-Fernandez B, Gomez-Bris R, Somovilla-Crespo B et al (2019) Immunobiology of atherosclerosis: a complex net of interactions. Int J Mol Sci 20:5293

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yang Y, Yi X, Cai Y et al (2022) Immune-associated gene signatures and subtypes to predict the progression of atherosclerotic plaques based on machine learning. Front Pharmacol 13:865624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yan Y, Thakur M, van der Vorst EPC et al (2021) Targeting the chemokine network in atherosclerosis. Atherosclerosis 330:95–106

    Article  CAS  PubMed  Google Scholar 

  51. Georgakis MK, Malik R, Björkbacka H et al (2019) Circulating monocyte chemoattractant protein-1 and risk of stroke: meta-analysis of population-based studies involving 17 180 individuals. Circ Res 125:773–782

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Combadière C, Potteaux S, Rodero M et al (2008) Combined inhibition of CCL2, CX3CR1, and CCR5 abrogates Ly6Chi and Ly6Clo monocytosis and almost abolishes atherosclerosis in hypercholesterolemic mice. Circulation 117:1649–1657

    Article  PubMed  Google Scholar 

  53. Boring L, Gosling J, Cleary M et al (1988) Decreased lesion formation in CCR2−/− mice reveals a role for chemokines in the initiation of atherosclerosis. Nature 394(6696):894–897

    Article  Google Scholar 

  54. Gutwein P, Abdel-Bakky MS, Schramme A et al (2009) CXCL16 is expressed in podocytes and acts as a scavenger receptor for oxidized low-density lipoprotein. Am J Pathol 174:2061–2072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. dos Santos SM, Blankenbach K, Scholich K et al (2015) Platelets from flowing blood attach to the inflammatory chemokine CXCL16 expressed in the endothelium of the human vessel wall. Thromb Haemost 114:297–312

    Article  Google Scholar 

  56. Linke B, Dos Santos SM, Picard-Willems B et al (2019) CXCL16/CXCR6-mediated adhesion of human peripheral blood mononuclear cells to inflamed endothelium. Cytokine 122:154081

    Article  PubMed  Google Scholar 

  57. van den Borne P, Quax PHA, Hoefer IE et al (2011) The multifaceted functions of CXCL10 in cardiovascular disease. BioMed Res Int. https://doi.org/10.1155/2014/893106

    Article  Google Scholar 

  58. Segers D, Lipton JA, Leenen PJM et al (2011) Atherosclerotic plaque stability is affected by the chemokine CXCL10 in both mice and humans. Int J Inflamm. https://doi.org/10.4061/2011/936109

    Article  Google Scholar 

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Funding

As part of the project, we received funds from the Hebei Province Education Department (Project No. QN2016145), as well as the University-level Science Funds in CDMC (202118 and 2023108), and the Chengde Medical University’s Fundamental Research Funds (KY202220). Thanks for these help, we were also honoured to be rated as a national college student scientific research project (Project No. 2023004).

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Contributions

The study’s planning and manuscript writing were done by LS and BZ. The data was collected and examined by RL, YD and YC.The final paper was examined and approved by YX and XH. QX oversaw all aspects of the project, including data interpretation and article review. After evaluating the findings, all authors gave their approval to the final version of the paper.

Corresponding author

Correspondence to Qian Xu.

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Throughout the study, the authors assert that there were no conflicts of interest, either in terms of relationships or financial ties, that could be perceived as potential.

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Song, L., Zhang, B., Li, R. et al. Significance of neutrophil extracellular traps-related gene in the diagnosis and classification of atherosclerosis. Apoptosis 29, 605–619 (2024). https://doi.org/10.1007/s10495-023-01923-4

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