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European Journal of Medical Research

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Open Access 01-12-2023 | Arterial Occlusive Disease | Research

Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis

Authors: Chunjiang Liu, Liming Tang, Yue Zhou, Xiaoqi Tang, Gang Zhang, Qin Zhu, Yufei Zhou

Published in: European Journal of Medical Research | Issue 1/2023

Abstract

Background

Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs).

Methods

Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein–protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed.

Results

Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971–1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes.

Conclusion

The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.

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Wong HS, Chang CM, Kao CC, Hsu YW, Liu X, Chang WC, et al. V-J combinations of T-cell receptor predict responses to erythropoietin in end-stage renal disease patients. J Biomed Sci. 2017;24(1):43. https://doi.org/10.1186/s12929-017-0349-5.CrossRefPubMedPubMedCentral

Raggi P, Bellasi A, Ferramosca E, Islam T, Muntner P, Block GA. Association of pulse wave velocity with vascular and valvular calcification in hemodialysis patients. Kidney Int. 2007;71(8):802–7. https://doi.org/10.1038/sj.ki.5002164.CrossRefPubMed

Pencak P, Czerwieńska B, Ficek R, Wyskida K, Kujawa-Szewieczek A, Olszanecka-Glinianowicz M, et al. Calcification of coronary arteries and abdominal aorta in relation to traditional and novel risk factors of atherosclerosis in hemodialysis patients. BMC Nephrol. 2013;14:10. https://doi.org/10.1186/1471-2369-14-10.CrossRefPubMedPubMedCentral

London GM. Vascular disease and atherosclerosis in uremia. Nefrologia. 2005;25(Suppl 2):91–5.PubMed

Bechler SL, Si Y, Yu Y, Ren J, Liu B, Lynn DM. Reduction of intimal hyperplasia in injured rat arteries promoted by catheter balloons coated with polyelectrolyte multilayers that contain plasmid DNA encoding PKCδ. Biomaterials. 2013;34(1):226–36. https://doi.org/10.1016/j.biomaterials.2012.09.010.CrossRefPubMed

de Ruijter J, van Sambeek M, van de Vosse F, Lopata R. Automated 3D geometry segmentation of the healthy and diseased carotid artery in free-hand, probe tracked ultrasound images. Med Phys. 2020;47(3):1034–47. https://doi.org/10.1002/mp.13960.CrossRefPubMed

Okamoto Y, Ishii S, Croce K, Katsumata H, Fukushima M, Kihara S, et al. Adiponectin inhibits macrophage tissue factor, a key trigger of thrombosis in disrupted atherosclerotic plaques. Atherosclerosis. 2013;226(2):373–7. https://doi.org/10.1016/j.atherosclerosis.2012.12.012.CrossRefPubMed

Avramovski P, Janakievska P, Sotiroski K, Sikole A. Accelerated progression of arterial stiffness in dialysis patients compared with the general population. Korean J Intern Med. 2013;28(4):464–74. https://doi.org/10.3904/kjim.2013.28.4.464.CrossRefPubMedPubMedCentral

Shen Y, Yuan Z, Yin A, Liu Y, Xiao Y, Wu Y, et al. Antiatherogenic effect of pioglitazone on uremic apolipoprotein E knockout mice by modulation of the balance of regulatory and effector T cells. Atherosclerosis. 2011;218(2):330–8. https://doi.org/10.1016/j.atherosclerosis.2011.07.112.CrossRefPubMed

10.

Daskalopoulou SS, Daskalopoulos ME, Perrea D, Nicolaides AN, Liapis CD. Carotid artery atherosclerosis: what is the evidence for drug action? Curr Pharm Des. 2007;13(11):1141–59. https://doi.org/10.2174/138161207780619019.CrossRefPubMed

11.

Yoo SM, Choi JH, Lee SY, Yoo NC. Applications of DNA microarray in disease diagnostics. J Microbiol Biotechnol. 2009;19(7):635–46.PubMed

12.

Xin G, Zhou G, Zhang W, Zhang X. Construction and validation of predictive model to identify critical genes associated with advanced kidney disease. Int J Genomics. 2020;2020:7524057. https://doi.org/10.1155/2020/7524057.CrossRefPubMedPubMedCentral

13.

Bao MH, Zhang RQ, Huang XS, Zhou J, Guo Z, Xu BF, et al. Transcriptomic and proteomic profiling of human stable and unstable carotid atherosclerotic plaques. Front Genet. 2021;12:755507. https://doi.org/10.3389/fgene.2021.755507.CrossRefPubMedPubMedCentral

14.

Yao M, Zhang C, Gao C, Wang Q, Dai M, Yue R, et al. Exploration of the shared gene signatures and molecular mechanisms between systemic lupus erythematosus and pulmonary arterial hypertension: evidence from transcriptome data. Front Immunol. 2021;12:658341. https://doi.org/10.3389/fimmu.2021.658341.CrossRefPubMedPubMedCentral

15.

Clough E, Barrett T. The gene expression omnibus database. Methods Mol Biol. 2016;1418:93–110. https://doi.org/10.1007/978-1-4939-3578-9_5.CrossRefPubMedPubMedCentral

16.

Scherer A, Günther OP, Balshaw RF, Hollander Z, Wilson-McManus J, Ng R, et al. Alteration of human blood cell transcriptome in uremia. BMC Med Genomics. 2013;6:23. https://doi.org/10.1186/1755-8794-6-23.CrossRefPubMedPubMedCentral

17.

Lee K, Santibanez-Koref M, Polvikoski T, Birchall D, Mendelow AD, Keavney B. Increased expression of fatty acid binding protein 4 and leptin in resident macrophages characterises atherosclerotic plaque rupture. Atherosclerosis. 2013;226(1):74–81. https://doi.org/10.1016/j.atherosclerosis.2012.09.037.CrossRefPubMedPubMedCentral

18.

Jin H, Goossens P, Juhasz P, Eijgelaar W, Manca M, Karel JMH, et al. Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage. Clin Transl Med. 2021;11(6):e458. https://doi.org/10.1002/ctm2.458.CrossRefPubMedPubMedCentral

19.

Döring Y, Manthey HD, Drechsler M, Lievens D, Megens RT, Soehnlein O, et al. Auto-antigenic protein-DNA complexes stimulate plasmacytoid dendritic cells to promote atherosclerosis. Circulation. 2012;125(13):1673–83. https://doi.org/10.1161/circulationaha.111.046755.CrossRefPubMed

20.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. https://doi.org/10.1093/nar/gkv007.CrossRefPubMedPubMedCentral

21.

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

22.

Resource TGO. 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):D330-d338. https://doi.org/10.1093/nar/gky1055.CrossRef

23.

Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353-d361. https://doi.org/10.1093/nar/gkw1092.CrossRefPubMed

24.

Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. https://doi.org/10.1089/omi.2011.0118.CrossRefPubMedPubMedCentral

25.

Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605-d612. https://doi.org/10.1093/nar/gkaa1074.CrossRefPubMed

26.

Otasek D, Morris JH, Bouças J, Pico AR, Demchak B. Cytoscape Automation: empowering workflow-based network analysis. Genome Biol. 2019;20(1):185. https://doi.org/10.1186/s13059-019-1758-4.CrossRefPubMedPubMedCentral

27.

Su W, Zhao Y, Wei Y, Zhang X, Ji J, Yang S. Exploring the Pathogenesis of Psoriasis Complicated With Atherosclerosis via Microarray Data Analysis. Front Immunol. 2021;12:667690. https://doi.org/10.3389/fimmu.2021.667690.CrossRefPubMedPubMedCentral

28.

Huang ML, Hung YH, Lee WM, Li RK, Jiang BR. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. ScientificWorldJournal. 2014;2014:795624. https://doi.org/10.1155/2014/795624.CrossRefPubMedPubMedCentral

29.

Yang C, Delcher C, Shenkman E, Ranka S. Machine learning approaches for predicting high cost high need patient expenditures in health care. Biomed Eng Online. 2018;17(Suppl 1):131. https://doi.org/10.1186/s12938-018-0568-3.CrossRefPubMedPubMedCentral

30.

Blanchet L, Vitale R, van Vorstenbosch R, Stavropoulos G, Pender J, Jonkers D, et al. Constructing bi-plots for random forest: Tutorial. Anal Chim Acta. 2020;1131:146–55. https://doi.org/10.1016/j.aca.2020.06.043.CrossRefPubMed

31.

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. https://doi.org/10.1038/nmeth.3337.CrossRefPubMedPubMedCentral

32.

Hu K. Become competent within one day in generating boxplots and violin plots for a novice without prior R experience. Methods Protoc. 2020. https://doi.org/10.3390/mps3040064.CrossRefPubMedPubMedCentral

33.

Chang CT, Shen MY, Hsieh JY, Chang CM, Liao HY, Chen FY, et al. Increased electronegativity of high-density lipoprotein in uremia patients impairs its functional properties and is associated with the risk of coronary artery disease. Atherosclerosis. 2018;278:147–55. https://doi.org/10.1016/j.atherosclerosis.2018.09.009.CrossRefPubMed

34.

Sarnak MJ, Amann K, Bangalore S, Cavalcante JL, Charytan DM, Craig JC, et al. Chronic kidney disease and coronary artery disease: JACC state-of-the-art review. J Am Coll Cardiol. 2019;74(14):1823–38. https://doi.org/10.1016/j.jacc.2019.08.1017.CrossRefPubMed

35.

François, Dialysis MJSi. THE CLINICAL EPIDEMIOLOGY OF CARDIOVASCULAR DISEASES IN CHRONIC KIDNEY DISEASE: Uremia-Related Metabolic Cardiac Risk Factors in Chronic Kidney Disease. 2003.

36.

Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart disease and stroke statistics-2022 update: a report from the american heart association. Circulation. 2022;145(8):e153–639. https://doi.org/10.1161/cir.0000000000001052.CrossRefPubMed

37.

Patel M, Faulkner L, Katz DR, Brickell PM. The c-fgr proto-oncogene: expression in Epstein-Barr-virus-infected B lymphocytes and in cells of the myelomonocytic and granulocytic lineages. Pathobiology. 1991;59(4):289–92. https://doi.org/10.1159/000163665.CrossRefPubMed

38.

Gutkind JS, Robbins KC. Translocation of the FGR protein-tyrosine kinase as a consequence of neutrophil activation. Proc Natl Acad Sci USA. 1989;86(22):8783–7. https://doi.org/10.1073/pnas.86.22.8783.CrossRefPubMedPubMedCentral

39.

Crainiciuc G, Palomino-Segura M, Molina-Moreno M, Sicilia J, Aragones DG, Li JLY, et al. Behavioural immune landscapes of inflammation. Nature. 2022;601(7893):415–21. https://doi.org/10.1038/s41586-021-04263-y.CrossRefPubMed

40.

Medina I, Cougoule C, Drechsler M, Bermudez B, Koenen RR, Sluimer J, et al. Hck/Fgr kinase deficiency reduces plaque growth and stability by blunting monocyte recruitment and intraplaque motility. Circulation. 2015;132(6):490–501. https://doi.org/10.1161/circulationaha.114.012316.CrossRefPubMedPubMedCentral

41.

Balta E, Hardt R, Liang J, Kirchgessner H, Orlik C, Jahraus B, et al. Spatial oxidation of L-plastin downmodulates actin-based functions of tumor cells. Nat Commun. 2019;10(1):4073. https://doi.org/10.1038/s41467-019-11909-z.CrossRefPubMedPubMedCentral

42.

Ge X, Liu W, Zhao W, Feng S, Duan A, Ji C, et al. Exosomal transfer of LCP1 promotes osteosarcoma cell tumorigenesis and metastasis by activating the JAK2/STAT3 signaling pathway. Mol Ther Nucleic Acids. 2020;21:900–15. https://doi.org/10.1016/j.omtn.2020.07.025.CrossRefPubMedPubMedCentral

43.

Zeng Q, Li L, Feng Z, Luo L, Xiong J, Jie Z, et al. LCP1 is a prognostic biomarker correlated with immune infiltrates in gastric cancer. Cancer Biomark. 2021;30(1):105–25. https://doi.org/10.3233/cbm-200006.CrossRefPubMed

44.

De Clercq S, Boucherie C, Vandekerckhove J, Gettemans J, Guillabert A. L-plastin nanobodies perturb matrix degradation, podosome formation, stability and lifetime in THP-1 macrophages. PLoS ONE. 2013;8(11):e78108. https://doi.org/10.1371/journal.pone.0078108.CrossRefPubMedPubMedCentral

45.

Timofeeva AV, Goriunova LE, Khaspekov GL, Il’inskaia OP, Sirotkin VN, Andreeva ER, et al. Comparative transcriptome analysis of human aorta atherosclerotic lesions and peripheral blood leukocytes from essential hypertension patients. Kardiologiia. 2009;49(9):27–38.PubMed

46.

Li K, Wu KY, Wu W, Wang N, Zhang T, Choudhry N, et al. C5aR1 promotes acute pyelonephritis induced by uropathogenic E. coli. JCI Insight. 2017. https://doi.org/10.1172/jci.insight.97626.CrossRefPubMedPubMedCentral

47.

Speidl WS, Katsaros KM, Kastl SP, Zorn G, Huber K, Maurer G, et al. Coronary late lumen loss of drug eluting stents is associated with increased serum levels of the complement components C3a and C5a. Atherosclerosis. 2010;208(1):285–9. https://doi.org/10.1016/j.atherosclerosis.2009.07.030.CrossRefPubMed

48.

van Tuijl J, Joosten LAB, Netea MG, Bekkering S, Riksen NP. Immunometabolism orchestrates training of innate immunity in atherosclerosis. Cardiovasc Res. 2019;115(9):1416–24. https://doi.org/10.1093/cvr/cvz107.CrossRefPubMedPubMedCentral

49.

Masias C, Vasu S, Cataland SR. None of the above: thrombotic microangiopathy beyond TTP and HUS. Blood. 2017;129(21):2857–63. https://doi.org/10.1182/blood-2016-11-743104.CrossRefPubMed

50.

Wezel A, de Vries MR, Lagraauw HM, Foks AC, Kuiper J, Quax PH, et al. Complement factor C5a induces atherosclerotic plaque disruptions. J Cell Mol Med. 2014;18(10):2020–30. https://doi.org/10.1111/jcmm.12357.CrossRefPubMedPubMedCentral

51.

Vaziri ND, Pahl MV, Crum A, Norris K. Effect of uremia on structure and function of immune system. J Ren Nutr. 2012;22(1):149–56. https://doi.org/10.1053/j.jrn.2011.10.020.CrossRefPubMedPubMedCentral

52.

He B, Lu Z, Jiang H. Interferon regulatory factors: New targets for intervention of cardiovascular diseases. Int J Cardiol. 2015;181:355–6. https://doi.org/10.1016/j.ijcard.2014.12.084.CrossRefPubMed

53.

Kalil RS, Flanigan M, Stanford W, Haynes WG. Dissociation between progression of coronary artery calcification and endothelial function in hemodialysis patients: a prospective pilot study. Clin Nephrol. 2012;78(1):1–9. https://doi.org/10.5414/cn106830.CrossRefPubMed

54.

Gao J, Shi L, Gu J, Zhang D, Wang W, Zhu X, et al. Difference of immune cell infiltration between stable and unstable carotid artery atherosclerosis. J Cell Mol Med. 2021;25(23):10973–9. https://doi.org/10.1111/jcmm.17018.CrossRefPubMedPubMedCentral

55.

Chinetti-Gbaguidi G, Colin S, Staels B. Macrophage subsets in atherosclerosis. Nat Rev Cardiol. 2015;12(1):10–7. https://doi.org/10.1038/nrcardio.2014.173.CrossRefPubMed

56.

Bories GFP, Leitinger N. Macrophage metabolism in atherosclerosis. FEBS Lett. 2017;591(19):3042–60. https://doi.org/10.1002/1873-3468.12786.CrossRefPubMed

57.

Cai Y, Wen J, Ma S, Mai Z, Zhan Q, Wang Y, et al. Huang-Lian-Jie-Du decoction attenuates atherosclerosis and increases plaque stability in high-fat diet-induced ApoE(-/-) mice by inhibiting M1 macrophage polarization and promoting M2 macrophage polarization. Front Physiol. 2021;12:666449. https://doi.org/10.3389/fphys.2021.666449.CrossRefPubMedPubMedCentral

58.

Zhang X, Liu MH, Qiao L, Zhang XY, Liu XL, Dong M, et al. Ginsenoside Rb1 enhances atherosclerotic plaque stability by skewing macrophages to the M2 phenotype. J Cell Mol Med. 2018;22(1):409–16. https://doi.org/10.1111/jcmm.13329.CrossRefPubMed

Title: Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis
Authors: Chunjiang Liu
Liming Tang
Yue Zhou
Xiaoqi Tang
Gang Zhang
Qin Zhu
Yufei Zhou
Publication date: 01-12-2023
Publisher: BioMed Central
Keywords: Arterial Occlusive Disease
Biomarkers
Published in: European Journal of Medical Research / Issue 1/2023
Electronic ISSN: 2047-783X
DOI: https://doi.org/10.1186/s40001-023-01043-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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