Skip to main content

Advertisement

SpringerLink
Log in

Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies

  • Research
  • Published:
EPMA Journal Aims and scope Submit manuscript

Abstract

Background

Atherosclerosis is a major contributor to morbidity and mortality worldwide. Although several molecular markers associated with atherosclerosis have been developed in recent years, the lack of robust evidence hinders their clinical applications. For these reasons, identification of novel and robust biomarkers will directly contribute to atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). This integrative analysis aimed to identify critical genetic markers of atherosclerosis and further explore the underlying molecular immune mechanism attributing to the altered biomarkers.

Methods

Gene Expression Omnibus (GEO) series datasets were downloaded from GEO. Firstly, differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were then employed to screen and determine key genetic markers, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Subsequently, cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) and a single-cell RNA sequencing (scRNA-seq) data were performed to explore relationships between signatures and immune cells. Lastly, we validated the biomarkers’ expression in human and mice experiments.

Results

A total of 611 overlapping differentially expressed genes (DEGs) included 361 upregulated and 250 downregulated genes. Based on the enrichment analysis, DEGs were mapped in terms related to immune cell involvements, immune activating process, and inflaming signals. After using multiple machine-learning strategies, dehydrogenase/reductase 9 (DHRS9) and protein tyrosine phosphatase receptor type J (PTPRJ) were identified as critical biomarkers and presented their high diagnostic accuracy for atherosclerosis. From CIBERSORT analysis, both DHRS9 and PTPRJ were significantly related to diverse immune cells, such as macrophages and mast cells. Further scRNA-seq analysis indicated DHRS9 was specifically upregulated in macrophages of atherosclerotic lesions, which was confirmed in atherosclerotic patients and mice.

Conclusions

Our findings are the first to report the involvement of DHRS9 in the atherogenesis, and the proatherogenic effect of DHRS9 is mediated by immune mechanism. In addition, we confirm that DHRS9 is localized in macrophages within atherosclerotic plaques. Therefore, upregulated DHRS9 could be a novel potential target for the future predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in atherosclerosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The data involved in this study had been described in detail in the “Materials and methods.” The data analyzed during the human and mice experiments are available from the corresponding author on reasonable request.

Code availability

All software applications used are included in this article.

Abbreviations

ABI :

Ankle brachial index

ASCVD :

Atherosclerotic cardiovascular diseases

AUC :

Area under the curve

BP :

Biological process

CC :

Cellular component

CIBERSORT :

Cell-type identification by estimating relative subsets of RNA transcript

c-IMT :

Carotid artery intima medial thickness

DAPI :

4’,6-Diamidino-2-phenylindole

DEGs :

Differentially expressed genes

DHRS9 :

Dehydrogenase/reductase 9

GAPDH :

Anti-glyceraldehyde-3-phosphate dehydrogenase

GEO :

Gene Expression Omnibus

GO :

Gene ontology

GSE :

Gene Expression Omnibus series

Hcy :

Homocysteine

KEGG :

Kyoto encyclopedia of genes and genomes

LASSO :

Least absolute shrinkage and selection operator

lncRNA :

Long non-coding RNA

MF :

Molecular function

miRNA :

MicroRNA

NCBI :

National Center of Biotechnology Information-GEO

NCD :

Normal chow diet

PCA :

Principal component analysis

PPPM :

Predictive, preventive, and personalized medicine

PTPRJ :

Protein tyrosine phosphatase receptor type J

PWV :

Pulse wave velocity

RF :

Random forests

ROC :

Receiver operating characteristic

scRNA-seq :

Single-cell RNA sequencing

TF :

Transcription factor

UMAP :

Uniform Manifold Approximation and Projection

WD :

Western diet

WGCNA :

Weighted gene co-expression network analysis

References

  1. Gallino A, Aboyans V, Diehm C, Cosentino F, Stricker H, Falk E, et al. Non-coronary atherosclerosis. Eur Heart J. 2014;35:1112–9. https://doi.org/10.1093/eurheartj/ehu071.

    Article  PubMed  Google Scholar 

  2. Ross R. Atherosclerosis–an inflammatory disease. N Engl J Med. 1999;340:115–26. https://doi.org/10.1056/NEJM199901143400207.

    Article  CAS  PubMed  Google Scholar 

  3. Timmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, et al. European Society of Cardiology: Cardiovascular Disease Statistics 2019 (Executive Summary). Eur Heart J Qual Care Clin Outcomes. 2020;6:7–9. https://doi.org/10.1093/ehjqcco/qcz065.

    Article  PubMed  Google Scholar 

  4. Murray CJ, Lopez AD. Measuring the global burden of disease. N Engl J Med. 2013;369:448–57. https://doi.org/10.1056/NEJMra1201534.

    Article  CAS  PubMed  Google Scholar 

  5. Golubnitschaja O, Costigliola V. General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive. Prev Personal Med EPMA J. 2012;3:14. https://doi.org/10.1186/1878-5085-3-14.

    Article  Google Scholar 

  6. Golubnitschaja O, Watson ID, Topic E, Sandberg S, Ferrari M, Costigliola V. Position paper of the EPMA and EFLM: a global vision of the consolidated promotion of an integrative medical approach to advance health care. EPMA J. 2013;4:12. https://doi.org/10.1186/1878-5085-4-12.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016;7:23. https://doi.org/10.1186/s13167-016-0072-4.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ridker PM. A test in context: high-sensitivity c-reactive protein. J Am Coll Cardiol. 2016;67:712–23. https://doi.org/10.1016/j.jacc.2015.11.037.

    Article  PubMed  Google Scholar 

  9. Kaptoge S, Seshasai SR, Gao P, Freitag DF, Butterworth AS, Borglykke A, et al. Inflammatory cytokines and risk of coronary heart disease: new prospective study and updated meta-analysis. Eur Heart J. 2014;35:578–89. https://doi.org/10.1093/eurheartj/eht367.

    Article  CAS  PubMed  Google Scholar 

  10. Ridker PM. From C-reactive protein to interleukin-6 to interleukin-1: moving upstream to identify novel targets for atheroprotection. Cir Res. 2016;118:145–56. https://doi.org/10.1161/CIRCRESAHA.115.306656.

    Article  CAS  Google Scholar 

  11. Koklesova L, Mazurakova A, Samec M, Biringer K, Samuel SM, Büsselberg D, et al. Homocysteine metabolism as the target for predictive medical approach, disease prevention, prognosis, and treatments tailored to the person. EPMA J. 2021;12:1–29. https://doi.org/10.1007/s13167-021-00263-0.

    Article  Google Scholar 

  12. Ganz P, Heidecker B, Hveem K, Jonasson C, Kato S, Segal MR, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. 2016;315:2532–41. https://doi.org/10.1001/jama.2016.5951.

    Article  CAS  PubMed  Google Scholar 

  13. Stitziel NO, Stirrups KE, Masca NG, Erdmann J, Ferrario PG, König IR, et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N Engl J Med. 2016;374:1134–44. https://doi.org/10.1056/NEJMoa1507652.

    Article  CAS  PubMed  Google Scholar 

  14. Döring Y, Noels H, van der Vorst EPC, Neideck C, Egea V, Drechsler M, et al. Vascular CXCR4 limits atherosclerosis by maintaining arterial integrity: evidence from mouse and human studies. Circulation. 2017;136:388–403. https://doi.org/10.1161/CIRCULATIONAHA.117.027646.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Döring Y, van der Vorst EPC, Duchene J, Jansen Y, Gencer S, Bidzhekov K, et al. CXCL12 derived from endothelial cells promotes atherosclerosis to drive coronary artery disease. Circulation. 2019;139:1338–40. https://doi.org/10.1161/CIRCULATIONAHA.118.037953.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Fu Q, Zhao M, Wang D, Hu H, Guo C, Chen W, et al. Coronary plaque characterization assessed by optical coherence tomography and plasma trimethylamine-N-oxide levels in patients with coronary artery disease. Am J Cardiol. 2016;118:1311–5. https://doi.org/10.1016/j.amjcard.2016.07.071.

    Article  CAS  PubMed  Google Scholar 

  17. Lu Y, Zhang X, Hu W, Yang Q. The identification of candidate biomarkers and pathways in atherosclerosis by integrated bioinformatics analysis. Comput Math Methods Med. 2021;2021:6276480. https://doi.org/10.1155/2021/6276480.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Martinez E, Martorell J, Riambau V. Review of serum biomarkers in carotid atherosclerosis. J Vasc Surg. 2020;71:329–41. https://doi.org/10.1016/j.jvs.2019.04.488.

    Article  PubMed  Google Scholar 

  19. Tibaut M, Caprnda M, Kubatka P, Sinkovič A, Valentova V, Filipova S, et al. Markers of atherosclerosis: part 1 - serological markers. Heart Lung Circ. 2019;28:667–77. https://doi.org/10.1016/j.hlc.2018.06.1057.

    Article  PubMed  Google Scholar 

  20. Zhang J. Biomarkers of endothelial activation and dysfunction in cardiovascular diseases. Rev Cardiovasc Med. 2022;23:73. https://doi.org/10.31083/j.rcm2302073.

    Article  PubMed  Google Scholar 

  21. Kobiyama K, Ley K. Atherosclerosis Cir Res. 2018;123:1118–20. https://doi.org/10.1161/CIRCRESAHA.118.313816.

    Article  CAS  Google Scholar 

  22. Libby P, Lichtman AH, Hansson GK. Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity. 2013;38:1092–104. https://doi.org/10.1016/j.immuni.2013.06.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Libby P. Inflammation in atherosclerosis. Nature. 2002;420:868–74. https://doi.org/10.1038/nature01323.

    Article  CAS  PubMed  Google Scholar 

  24. Wolf D, Ley K. Immunity and inflammation in atherosclerosis. Cir Res. 2019;124:315–27. https://doi.org/10.1161/CIRCRESAHA.118.313591.

    Article  CAS  Google Scholar 

  25. Soehnlein O, Libby P. Targeting inflammation in atherosclerosis - from experimental insights to the clinic. Nat Rev Drug Discov. 2021;20:589–610. https://doi.org/10.1038/s41573-021-00198-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Shen Y, Xu LR, Tang X, Lin CP, Yan D, Xue S, et al. Identification of potential therapeutic targets for atherosclerosis by analysing the gene signature related to different immune cells and immune regulators in atheromatous plaques. BMC Med Genomics. 2021;14:145. https://doi.org/10.1186/s12920-021-00991-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhao B, Wang D, Liu Y, Zhang X, Wan Z, Wang J, et al. Six-gene signature associated with immune cells in the progression of atherosclerosis discovered by comprehensive bioinformatics analyses. Cardiovasc Ther. 2020;2020:1230513. https://doi.org/10.1155/2020/1230513.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Alsaigh T, Evans D, Frankel D, Torkamani AJb. Decoding the transcriptome of atherosclerotic plaque at single-cell resolution. 2020 BioRxiv. Preprint. https://doi.org/10.1101/2020.03.03.968123

  29. Fernandez DM, Rahman AH, Fernandez NF, Chudnovskiy A, Amir ED, Amadori L, et al. Single-cell immune landscape of human atherosclerotic plaques. Nat Med. 2019;25:1576–88. https://doi.org/10.1038/s41591-019-0590-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Depuydt MAC, Prange KHM, Slenders L, Örd T, Elbersen D, Boltjes A, et al. Microanatomy of the human atherosclerotic plaque by single-cell transcriptomics. Cir Res. 2020;127:1437–55. https://doi.org/10.1161/CIRCRESAHA.120.316770.

    Article  CAS  Google Scholar 

  31. Steenman M, Espitia O, Maurel B, Guyomarch B, Heymann MF, Pistorius MA, et al. Identification of genomic differences among peripheral arterial beds in atherosclerotic and healthy arteries. Sci Rep. 2018;8:3940. https://doi.org/10.1038/s41598-018-22292-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ayari H, Bricca G. Identification of two genes potentially associated in iron-heme homeostasis in human carotid plaque using microarray analysis. J Biosci. 2013;38:311–5. https://doi.org/10.1007/s12038-013-9310-2.

    Article  CAS  PubMed  Google Scholar 

  33. 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;125:1673–83. https://doi.org/10.1161/CIRCULATIONAHA.111.046755.

    Article  CAS  PubMed  Google Scholar 

  34. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5:R80. https://doi.org/10.1186/gb-2004-5-10-r80.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 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: e47. https://doi.org/10.1093/nar/gkv007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wickham H. ggplot2: elegant graphics for data analysis. Springer-Verlag New York.

  37. Kolde R. pheatmap: Pretty Heatmaps. R package version 1.0.12. 2019.

  38. Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 2011;12:35. https://doi.org/10.1186/1471-2105-12-35.

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 2015;31:2912–4. https://doi.org/10.1093/bioinformatics/btv300.

    Article  CAS  PubMed  Google Scholar 

  41. Breiman L. Random Forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.

    Article  Google Scholar 

  42. Tibshirani R. Regression shrinkage and selection via the lasso. J R Statist Soc B. 1996;58:267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.

    Article  Google Scholar 

  43. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. https://doi.org/10.1186/1471-2105-12-77.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014;42:D92-7. https://doi.org/10.1093/nar/gkt1248.

    Article  CAS  PubMed  Google Scholar 

  46. Zhang Q, Liu W, Zhang HM, Xie GY, Miao YR, Xia M, et al. hTFtarget: a comprehensive database for regulations of human transcription factors and their targets. Genomics Proteomics Bioinforma. 2020;18:120–8. https://doi.org/10.1016/j.gpb.2019.09.006.

    Article  Google Scholar 

  47. Tokar T, Pastrello C, Rossos AEM, Abovsky M, Hauschild AC, Tsay M, et al. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Res. 2018;46:D360-d70. https://doi.org/10.1093/nar/gkx1144.

    Article  CAS  PubMed  Google Scholar 

  48. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. https://doi.org/10.1101/gr.1239303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20. https://doi.org/10.1038/nbt.4096.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20:163–72. https://doi.org/10.1038/s41590-018-0276-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Carter HE, Schofield D, Shrestha R. Productivity costs of cardiovascular disease mortality across disease types and socioeconomic groups. Open heart. 2019;6:e000939. https://doi.org/10.1136/openhrt-2018-000939.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ergin A, Muntner P, Sherwin R, He J. Secular trends in cardiovascular disease mortality, incidence, and case fatality rates in adults in the United States. Am J Med. 2004;117:219–27. https://doi.org/10.1016/j.amjmed.2004.03.017.

    Article  PubMed  Google Scholar 

  53. Adams A, Bojara W, Schunk K. Early diagnosis and treatment of coronary heart disease in symptomatic subjects with advanced vascular atherosclerosis of the carotid artery (type III and IV b findings using ultrasound). Cardiol Res. 2017;8:7–12. https://doi.org/10.14740/cr516w.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Abdolmaleki F, Gheibi Hayat SM, Bianconi V, Johnston TP, Sahebkar A. Atherosclerosis and immunity: a perspective. Trends Cardiovasc Med. 2019;29:363–71. https://doi.org/10.1016/j.tcm.2018.09.017.

    Article  CAS  PubMed  Google Scholar 

  55. Du M, Wang X, Mao X, Yang L, Huang K, Zhang F, et al. Absence of Interferon regulatory factor 1 protects against atherosclerosis in apolipoprotein E-deficient mice. Theranostics. 2019;9:4688–703. https://doi.org/10.7150/thno.36862.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sozen E, Karademir B, Yazgan B, Bozaykut P, Ozer NK. Potential role of proteasome on c-jun related signaling in hypercholesterolemia induced atherosclerosis. Redox Biol. 2014;2:732–8. https://doi.org/10.1016/j.redox.2014.02.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Everts HB, Silva KA, Montgomery S, Suo L, Menser M, Valet AS, et al. Retinoid metabolism is altered in human and mouse cicatricial alopecia. J Invest Dermatol. 2013;133:325–33. https://doi.org/10.1038/jid.2012.393.

    Article  CAS  PubMed  Google Scholar 

  58. Chazenbalk G, Chen YH, Heneidi S, Lee JM, Pall M, Chen YD, et al. Abnormal expression of genes involved in inflammation, lipid metabolism, and Wnt signaling in the adipose tissue of polycystic ovary syndrome. J Clin Endocrinol Metab. 2012;97:E765–70. https://doi.org/10.1210/jc.2011-2377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Zhang D, Li Z, Zhang R, Yang X, Zhang D, Li Q, et al. Identification of differentially expressed and methylated genes associated with rheumatoid arthritis based on network. Autoimmunity. 2020;53:303–13. https://doi.org/10.1080/08916934.2020.1786069.

    Article  CAS  PubMed  Google Scholar 

  60. Hu L, Chen HY, Han T, Yang GZ, Feng D, Qi CY, et al. Downregulation of DHRS9 expression in colorectal cancer tissues and its prognostic significance. Tumour Biol. 2016;37:837–45. https://doi.org/10.1007/s13277-015-3880-6.

    Article  CAS  PubMed  Google Scholar 

  61. Li HB, Zhou J, Zhao F, Yu J, Xu L. Prognostic Impact of DHRS9 Overexpression in pancreatic cancer. Cancer Manag Research. 2020;12:5997–6006. https://doi.org/10.2147/CMAR.S251897.

    Article  CAS  Google Scholar 

  62. Shimomura H, Sasahira T, Nakashima C, Shimomura-Kurihara M, Kirita T. Downregulation of DHRS9 is associated with poor prognosis in oral squamous cell carcinoma. Pathology. 2018;50:642–7. https://doi.org/10.1016/j.pathol.2018.06.002.

    Article  CAS  PubMed  Google Scholar 

  63. Belyaeva OV, Wirth SE, Boeglin WE, Karki S, Goggans KR, Wendell SG, et al. Dehydrogenase reductase 9 (SDR9C4) and related homologs recognize a broad spectrum of lipid mediator oxylipins as substrates. J Biol Chem. 2022;298:101527. https://doi.org/10.1016/j.jbc.2021.101527.

    Article  CAS  PubMed  Google Scholar 

  64. Riquelme P, Amodio G, Macedo C, Moreau A, Obermajer N, Brochhausen C, et al. DHRS9 is a stable marker of human regulatory macrophages. Transplantation. 2017;101:2731–8. https://doi.org/10.1097/TP.0000000000001814.

    Article  CAS  PubMed  Google Scholar 

  65. Hutchinson JA, Riquelme P, Geissler EK, Fändrich F. Human regulatory macrophages. Methods Mol Biol. 2011;677:181–92. https://doi.org/10.1007/978-1-60761-869-0_13.

    Article  CAS  PubMed  Google Scholar 

  66. Gertow K, Nobili E, Folkersen L, Newman JW, Pedersen TL, Ekstrand J, et al. 12- and 15-lipoxygenases in human carotid atherosclerotic lesions: associations with cerebrovascular symptoms. Atherosclerosis. 2011;215:411–6. https://doi.org/10.1016/j.atherosclerosis.2011.01.015.

    Article  CAS  PubMed  Google Scholar 

  67. Mallat Z, Nakamura T, Ohan J, Lesèche G, Tedgui A, Maclouf J, et al. The relationship of hydroxyeicosatetraenoic acids and F2-isoprostanes to plaque instability in human carotid atherosclerosis. J Clin Invest. 1999;103:421–7. https://doi.org/10.1172/JCI3985.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Burleigh ME, Babaev VR, Oates JA, Harris RC, Gautam S, Riendeau D, et al. Cyclooxygenase-2 promotes early atherosclerotic lesion formation in LDL receptor-deficient mice. Circulation. 2002;105:1816–23. https://doi.org/10.1161/01.cir.0000014927.74465.7f.

    Article  CAS  PubMed  Google Scholar 

  69. Ylä-Herttuala S, Rosenfeld ME, Parthasarathy S, Glass CK, Sigal E, Witztum JL, et al. Colocalization of 15-lipoxygenase mRNA and protein with epitopes of oxidized low density lipoprotein in macrophage-rich areas of atherosclerotic lesions. Proc Natl Acad Sci U S A. 1990;87:6959–63. https://doi.org/10.1073/pnas.87.18.6959.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Lorenz MW, Markus HS, Bots ML, Rosvall M, Sitzer M. Prediction of clinical cardiovascular events with carotid intima-media thickness: a systematic review and meta-analysis. Circulation. 2007;115:459–67. https://doi.org/10.1161/CIRCULATIONAHA.106.628875.

    Article  PubMed  Google Scholar 

  71. Ato D. Pitfalls in the ankle-brachial index and brachial-ankle pulse wave velocity. Vasc Health Risk Manag. 2018;14:41–62. https://doi.org/10.2147/VHRM.S159437.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364:937–52. https://doi.org/10.1016/S0140-6736(04)17018-9.

    Article  PubMed  Google Scholar 

  73. Bogiatzi C, Gloor G, Allen-Vercoe E, Reid G, Wong RG, Urquhart BL, et al. Metabolic products of the intestinal microbiome and extremes of atherosclerosis. Atherosclerosis. 2018;273:91–7. https://doi.org/10.1016/j.atherosclerosis.2018.04.015.

    Article  CAS  PubMed  Google Scholar 

  74. Bubnov R, Babenko L, Lazarenko L, Kryvtsova M, Shcherbakov O, Zholobak N, et al. Can tailored nanoceria act as a prebiotic? Report on improved lipid profile and gut microbiota in obese mice. EPMA J. 2019;10:317–35. https://doi.org/10.1007/s13167-019-00190-1.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Park D, Lee JH, Han S. Underweight: another risk factor for cardiovascular disease?: A cross-sectional 2013 Behavioral Risk Factor Surveillance System (BRFSS) study of 491,773 individuals in the USA. Medicine. 2017;96:e8769. https://doi.org/10.1097/MD.0000000000008769.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Golubnitschaja O, Liskova A, Koklesova L, Samec M, Biringer K, Büsselberg D, et al. Caution, “normal” BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021. EPMA J. 2021;12:243–64. https://doi.org/10.1007/s13167-021-00251-4.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Authors thank National Natural Science Foundation of China (Grant no: NSFC 82170857) for its support for this study.

Funding

This work was supported by the National Natural Science Foundation of China (Grant no: NSFC 82170857).

Author information

Author notes

  1. Jinling Xu and Hui Zhou contributed equally to this work.

Authors and Affiliations

  1. The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Jinling Xu, Yangyang Cheng & Guangda Xiang

  2. Department of Endocrinology, General Hospital of Central Theater Command, Wuhan, 430070, Hubei, China

    Jinling Xu, Yangyang Cheng & Guangda Xiang

  3. Department of General Surgery, Central South University, The Third Xiangya Hospital, Changsha, 410013, Hunan, China

    Hui Zhou

Authors
  1. Jinling Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yangyang Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangda Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JX and HZ were responsible for data acquisition and analysis. YC conducted the human and mice experiments. JX drafted the manuscript. GX designed the study and revised the article. All the authors read and approved the final manuscript for submission.

Corresponding author

Correspondence to Guangda Xiang.

Ethics declarations

Ethics approval

The protocol for collecting human tissue samples was approved by Ethics Committee of the General Hospital of Central Theater Command (approval no. [2021]004–02), and all procedures were performed in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was provided by all participants before their inclusion in the study. The animal experimental protocols were permitted by the Animal Ethics Committee of the General Hospital of Central Theater Command (approval no.2021013).

Consent to participate

All individuals were informed about the purposes of the study and have signed their consent for publishing the data.

Consent for publication

After reviewing the manuscript, all authors agreed with its publication in the current form.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 673 KB)

Supplementary Figure 1

PPPM strategies of DHRS9 in atherosclerosis. Abbreviations: DHRS9 dehydrogenase/reductase 9; PPPM: predictive, preventive, and personalized medicine (DOCX 18 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, J., Zhou, H., Cheng, Y. et al. Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies. EPMA Journal 13, 433–449 (2022). https://doi.org/10.1007/s13167-022-00289-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13167-022-00289-y

Keywords

Search

Navigation