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image of Single-Cell RNA Sequencing to Identify Natural Killer Cell-Linked Genetic Markers and Regulatory Biomolecules in Coronary Heart Disease

Abstract

Introduction

Bacterial and viral infections have been linked to an increased risk of coronary heart disease (CHD), potentially through natural killer (NK) cell-mediated innate immune mechanisms. This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk transcriptomics data to identify NK cell-associated genetic biomarkers that could aid in the diagnosis and assessment of CHD.

Methods

Publicly available single-cell and bulk RNA-seq datasets were analyzed to identify differentially expressed genes (DEGs). Functional enrichment analysis, protein-protein interaction (PPI) network construction, and biomarker validation were performed using standard bioinformatics pipelines.

Results

A total of 106 shared DEGs were identified through integrated cross-comparative analysis. Enrichment analysis revealed involvement in immune activation, signal transduction, T-cell receptor signaling, and TYROBP signaling pathways. PPI network analysis identified key hub proteins, including CDK1 and PTPRC, as potential biomarkers. Regulatory analysis revealed transcription factors (TP53, YY1, and RELA) and post-transcriptional miRNAs (hsa-miR-195-5p, hsa-miR-34a-5p, and hsa-miR-16-5p) that may influence CHD-associated gene expression. Several small molecules were also predicted to interact with these targets, suggesting potential therapeutic applications.

Discussion

The findings underscore the role of NK cell-mediated immune pathways in CHD pathogenesis. Hub genes such as CDK1 (involved in cell cycle regulation) and PTPRC (an immune signaling regulator) show promise as diagnostic biomarkers. The discovery of regulatory factors and druggable targets supports a complex, multi-level mechanism involving transcriptional and immune modulation.

Conclusion

This integrative study identifies novel NK cell-related molecular signatures and therapeutic targets, offering promising avenues for CHD diagnosis and the development of personalized treatment strategies.

© 2025 Bentham Science Publishers
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2025-04-25
2025-08-18

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References

  1. Naghavi M. Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: A systematic analysis for the global burden of disease study 2013. Lancet 2015 385 9963 117 171 10.1016/S0140‑6736(14)61682‑2 25530442
    [Google Scholar]
  2. Moran A. E. Temporal trends in ischemic heart disease mortality in 21 world regions, 1980 to 2010: The global burden of disease 2010 study. Circulation 2014 129 14 1483 1492 10.1161/CIRCULATIONAHA.113.004042 24573352
    [Google Scholar]
  3. Moran A. E. The global burden of ischemic heart disease in 1990 and 2010: The global burden of disease 2010 study. Circulation 2014 129 14 1493 1501 10.1161/CIRCULATIONAHA.113.004046 24573351
    [Google Scholar]
  4. Samuel M. Colchicine for secondary prevention of cardiovascular disease: A systematic review and meta-analysis of randomized controlled trials. Can J Cardiol 2021 37 5 776 785 10.1016/j.cjca.2020.10.006 33075455
    [Google Scholar]
  5. Roth G. A. Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation 2015 132 17 1667 1678 10.1161/CIRCULATIONAHA.114.008720 26503749
    [Google Scholar]
  6. Collet J.P. Zeitouni M. Procopi N. Hulot J.S. Silvain J. Kerneis M. Thomas D. Lattuca B. Barthelemy O. Lavie-Badie Y. Esteve J.B. Payot L. Brugier D. Lopes I. Diallo A. Vicaut E. Montalescot G. Long-term evolution of premature coronary artery disease. J. Am. Coll. Cardiol. 2019 74 15 1868 1878 10.1016/j.jacc.2019.08.1002 31601367
    [Google Scholar]
  7. Fang Y. Zhang M. Shen G. Zhang R. Potential of immune-related genes as promising biomarkers for premature coronary heart disease through high throughput sequencing and integrated bioinformatics analysis. Front Cardiovasc Med 2022 9 893502 10.3389/fcvm.2022.893502 36093144
    [Google Scholar]
  8. Roberts R. Stewart A.F.R. Genes and coronary artery disease: Where are we? J. Am. Coll. Cardiol. 2012 60 18 1715 1721 10.1016/j.jacc.2011.12.062 23040572
    [Google Scholar]
  9. Wang H. Liu Z. Shao J. Lin L. Jiang M. Wang L. Lu X. Zhang H. Chen Y. Zhang R. Immune and inflammation in acute coronary syndrome: Molecular mechanisms and therapeutic implications. J. Immunol. Res. 2020 2020 1 11 10.1155/2020/4904217 32908939
    [Google Scholar]
  10. Ridker P. M. From CANTOS to CIRT to COLCOT to clinic: Will all atherosclerosis patients soon be treated with combination lipid-lowering and inflammation-inhibiting agents? Circulation 2020 141 10 787 789 10.1161/CIRCULATIONAHA.119.045256 32150469
    [Google Scholar]
  11. Zhou Z. Chen J. Wu T. Liu Q. Song H. Sun S. Profiling of plasma circulating miRNA in coronary heart disease patients detected by next-generation small RNA sequencing. Int J Clin Exp Med 2017 10 5 8060 8068
    [Google Scholar]
  12. Antonarakis S.E. Beckmann J.S. Mendelian disorders deserve more attention. Nat. Rev. Genet. 2006 7 4 277 282 10.1038/nrg1826 16534515
    [Google Scholar]
  13. Garcia C.K. Autosomal recessive hypercholesterolemia caused by mutations in a putative LDL receptor adaptor protein. Science 2001 292 5520 1394 1398 10.1126/science.1060458 11326085
    [Google Scholar]
  14. Dai X. Wiernek S. Evans J.P. Runge M.S. Genetics of coronary artery disease and myocardial infarction. World J. Cardiol. 2016 8 1 1 23 10.4330/wjc.v8.i1.1 26839654
    [Google Scholar]
  15. Kalinina N. Agrotis A. Antropova Y. Ilyinskaya O. Smirnov V. Tararak E. Bobik A. Smad expression in human atherosclerotic lesions: Evidence for impaired TGF-β/Smad signaling in smooth muscle cells of fibrofatty lesions. Arterioscler. Thromb. Vasc. Biol. 2004 24 8 1391 1396 10.1161/01.ATV.0000133605.89421.79 15166010
    [Google Scholar]
  16. Lowe S.W. Sherr C.J. Tumor suppression by Ink4a–Arf: Progress and puzzles. Curr. Opin. Genet. Dev. 2003 13 1 77 83 10.1016/S0959‑437X(02)00013‑8 12573439
    [Google Scholar]
  17. Kraml P. The role of iron in the pathogenesis of atherosclerosis. Physiol. Res 2017 66 Suppl 1 S55 S67 10.33549/physiolres.933589 28379030
    [Google Scholar]
  18. Jabir N.R. Firoz C.K. Ahmed F. Kamal M.A. Hindawi S. Damanhouri G.A. Almehdar H.A. Tabrez S. Reduction in CD16/CD56 and CD16/CD3/CD56 natural killer cells in coronary artery disease. Immunol. Invest. 2017 46 5 526 535 10.1080/08820139.2017.1306866 28414590
    [Google Scholar]
  19. Liao J. Wang J. Liu Y. Li J. Duan L. Transcriptome sequencing of lncRNA, miRNA, mRNA and interaction network constructing in coronary heart disease. BMC Med. Genomics 2019 12 1 124 10.1186/s12920‑019‑0570‑z 31443660
    [Google Scholar]
  20. Mantri M. Scuderi G.J. Abedini-Nassab R. Wang M.F.Z. McKellar D. Shi H. Grodner B. Butcher J.T. De Vlaminck I. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat. Commun. 2021 12 1 1771 10.1038/s41467‑021‑21892‑z 33741943
    [Google Scholar]
  21. Sultana A. Alam M.S. Liu X. Sharma R. Singla R.K. Gundamaraju R. Shen B. Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches. Transl. Oncol. 2023 27 101571 10.1016/j.tranon.2022.101571 36401966
    [Google Scholar]
  22. Ma W.F. Hodonsky C.J. Turner A.W. Wong D. Song Y. Mosquera J.V. Ligay A.V. Slenders L. Gancayco C. Pan H. Barrientos N.B. Mai D. Alencar G.F. Owsiany K. Owens G.K. Reilly M.P. Li M. Pasterkamp G. Mokry M. van der Laan S.W. Khomtchouk B.B. Miller C.L. Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets. Atherosclerosis 2022 340 12 22 10.1016/j.atherosclerosis.2021.11.025 34871816
    [Google Scholar]
  23. Shi X. Zhang L. Li Y. Xue J. Liang F. Ni H. Wang X. Cai Z. Shen L. Huang T. He B. Integrative analysis of bulk and single-cell RNA sequencing data reveals cell types involved in heart failure. Front. Bioeng. Biotechnol. 2022 9 779225 10.3389/fbioe.2021.779225 35071201
    [Google Scholar]
  24. 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 5 411 420 10.1038/nbt.4096 29608179
    [Google Scholar]
  25. Davis S. Meltzer P.S. GEOquery: A bridge between the gene expression omnibus (GEO) and bioconductor. Bioinformatics 2007 23 14 1846 1847 10.1093/bioinformatics/btm254 17496320
    [Google Scholar]
  26. Lytal N. Ran D. An L. Normalization methods on single-cell RNA-seq data: An empirical survey. Front. Genet. 2020 11 41 10.3389/fgene.2020.00041 32117453
    [Google Scholar]
  27. Cole M.B. Risso D. Wagner A. DeTomaso D. Ngai J. Purdom E. Dudoit S. Yosef N. Performance assessment and selection of normalization procedures for single-cell RNA-Seq. Cell Syst. 2019 8 4 315 328.e8 10.1016/j.cels.2019.03.010 31022373
    [Google Scholar]
  28. Becht E. McInnes L. Healy J. Dutertre C.A. Kwok I.W.H. Ng L.G. Ginhoux F. Newell E.W. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 2019 37 1 38 44 10.1038/nbt.4314 30531897
    [Google Scholar]
  29. Macosko E.Z. Basu A. Satija R. Nemesh J. Shekhar K. Goldman M. Tirosh I. Bialas A.R. Kamitaki N. Martersteck E.M. Trombetta J.J. Weitz D.A. Sanes J.R. Shalek A.K. Regev A. McCarroll S.A. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015 161 5 1202 1214 10.1016/j.cell.2015.05.002 26000488
    [Google Scholar]
  30. Al Mahi N. Najafabadi M.F. Pilarczyk M. Kouril M. Medvedovic M. GREIN: An interactive web platform for re-analyzing GEO RNA-seq data. Sci. Rep. 2019 9 1 1 9 30626917
    [Google Scholar]
  31. Rahman M.H. Peng S. Hu X. Chen C. Rahman M.R. Uddin S. Quinn J.M.W. Moni M.A. A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases. Int. J. Environ. Res. Public Health 2020 17 3 1035 10.3390/ijerph17031035 32041280
    [Google Scholar]
  32. Shahjaman M. Manir Hossain Mollah M. Rezanur Rahman M. Islam S.M.S. Nurul Haque Mollah M. Robust identification of differentially expressed genes from RNA-seq data. Genomics 2020 112 2 2000 2010 10.1016/j.ygeno.2019.11.012 31756426
    [Google Scholar]
  33. Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995 57 1 289 300 10.1111/j.2517‑6161.1995.tb02031.x
    [Google Scholar]
  34. Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014 15 12 550 10.1186/s13059‑014‑0550‑8 25516281
    [Google Scholar]
  35. García-Campos M.A. Espinal-Enríquez J. Hernández-Lemus E. Pathway analysis: State of the art. Front. Physiol. 2015 6 383 10.3389/fphys.2015.00383 26733877
    [Google Scholar]
  36. Pathan M. Keerthikumar S. Ang C.S. Gangoda L. Quek C.Y.J. Williamson N.A. Mouradov D. Sieber O.M. Simpson R.J. Salim A. Bacic A. Hill A.F. Stroud D.A. Ryan M.T. Agbinya J.I. Mariadason J.M. Burgess A.W. Mathivanan S. FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 2015 15 15 2597 2601 10.1002/pmic.201400515 25921073
    [Google Scholar]
  37. Jiao X. Sherman B.T. Huang D.W. Stephens R. Baseler M.W. Lane H.C. Lempicki R.A. DAVID-WS: A stateful web service to facilitate gene/protein list analysis. Bioinformatics 2012 28 13 1805 1806 10.1093/bioinformatics/bts251 22543366
    [Google Scholar]
  38. Rahman H. Peng S. Chen C. Lio’ P. Moni M.A. Genetic effect of type 2 diabetes to the progression of neurological diseases. bioRxiv 480400 2018 10.1101/480400
    [Google Scholar]
  39. Mahmud S.M.H. Al-Mustanjid M. Akter F. Rahman M.S. Ahmed K. Rahman M.H. Chen W. Moni M.A. Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients. Brief. Bioinform. 2021 22 5 bbab115 10.1093/bib/bbab115 33847347
    [Google Scholar]
  40. Fox A.D. Hescott B.J. Blumer A.C. Slonim D.K. Connectedness of PPI network neighborhoods identifies regulatory hub proteins. Bioinformatics 2011 27 8 1135 1142 10.1093/bioinformatics/btr099 21367871
    [Google Scholar]
  41. Szklarczyk D. Morris J.H. Cook H. Kuhn M. Wyder S. Simonovic M. Santos A. Doncheva N.T. Roth A. Bork P. Jensen L.J. von Mering C. The STRING database in 2017: Quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2017 45 D1 D362 D368 10.1093/nar/gkw937 27924014
    [Google Scholar]
  42. Chin C.H. Chen S.H. Wu H.H. Ho C.W. Ko M.T. Lin C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014 8 Suppl 4 Suppl 4 S11 10.1186/1752‑0509‑8‑S4‑S11 25521941
    [Google Scholar]
  43. Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 13 11 2498 2504 10.1101/gr.1239303 14597658
    [Google Scholar]
  44. Gomez C. Goponenko A.V. Soulakova J.N. Constructing UpSet plot for survey data with weights using SAS and R software. Commun. Stat. Simul. Comput. 2023 52 6 2320 2326 10.1080/03610918.2021.1904142
    [Google Scholar]
  45. Mitsis T. Efthimiadou A. Bacopoulou F. Vlachakis D. Chrousos G. Eliopoulos E. Transcription factors and evolution: An integral part of gene expression (Review). World Acad Sci J. 2020 2 1 3 8 10.3892/wasj.2020.32
    [Google Scholar]
  46. Lin C.C. Chen Y.J. Chen C.Y. Oyang Y.J. Juan H.F. Huang H.C. Crosstalk between transcription factors and microRNAs in human protein interaction network. BMC Syst. Biol. 2012 6 1 18 10.1186/1752‑0509‑6‑18 22413876
    [Google Scholar]
  47. Fornes O. Castro-Mondragon J.A. Khan A. van der Lee R. Zhang X. Richmond P.A. Modi B.P. Correard S. Gheorghe M. Baranašić D. Santana-Garcia W. Tan G. Chèneby J. Ballester B. Parcy F. Sandelin A. Lenhard B. Wasserman W.W. Mathelier A. JASPAR 2020: Update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2019 48 D1 gkz1001 10.1093/nar/gkz1001 31701148
    [Google Scholar]
  48. Zhou G. Soufan O. Ewald J. Hancock R.E.W. Basu N. Xia J. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019 47 W1 W234 W241 10.1093/nar/gkz240 30931480
    [Google Scholar]
  49. Chou C.H. Shrestha S. Yang C.D. Chang N.W. Lin Y.L. Liao K.W. Huang W.C. Sun T.H. Tu S.J. Lee W.H. Chiew M.Y. Tai C.S. Wei T.Y. Tsai T.R. Huang H.T. Wang C.Y. Wu H.Y. Ho S.Y. Chen P.R. Chuang C.H. Hsieh P.J. Wu Y.S. Chen W.L. Li M.J. Wu Y.C. Huang X.Y. Ng F.L. Buddhakosai W. Huang P.C. Lan K.C. Huang C.Y. Weng S.L. Cheng Y.N. Liang C. Hsu W.L. Huang H.D. miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018 46 D1 D296 D302 10.1093/nar/gkx1067 29126174
    [Google Scholar]
  50. Le T.D. Liu L. Liu B. Tsykin A. Goodall G.J. Satou K. Li J. Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics 2013 14 1 92 10.1186/1471‑2105‑14‑92 23497388
    [Google Scholar]
  51. Nagamine N. Shirakawa T. Minato Y. Torii K. Kobayashi H. Imoto M. Sakakibara Y. Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLOS Comput. Biol. 2009 5 6 e1000397 10.1371/journal.pcbi.1000397 19503826
    [Google Scholar]
  52. Grondin C.J. Davis A.P. Wiegers T.C. King B.L. Wiegers J.A. Reif D.M. Hoppin J.A. Mattingly C.J. Advancing exposure science through chemical data curation and integration in the comparative toxicogenomics database. Environ. Health Perspect. 2016 124 10 1592 1599 10.1289/EHP174 27170236
    [Google Scholar]
  53. Mandrekar S.J. Sargent D.J. Clinical trial designs for predictive biomarker validation: Theoretical considerations and practical challenges. J. Clin. Oncol. 2009 27 24 4027 4034 10.1200/JCO.2009.22.3701 19597023
    [Google Scholar]
  54. Faraggi D. Reiser B. Schisterman E.F. ROC curve analysis for biomarkers based on pooled assessments. Stat. Med. 2003 22 15 2515 2527 10.1002/sim.1418 12872306
    [Google Scholar]
  55. Thompson M. L. Zucchini W. On the statistical analysis of ROC curves. Stat Med 1989 8 10 1277 1290 10.1002/sim.4780081011 2814075
    [Google Scholar]
  56. Liu D. Glaser A.P. Patibandla S. Blum A. Munson P.J. McCoy J.P. Raghavachari N. Cannon R.O. III Transcriptional profiling of CD133+ cells in coronary artery disease and effects of exercise on gene expression. Cytotherapy 2011 13 2 227 236 10.3109/14653249.2010.491611 21235297
    [Google Scholar]
  57. Deng J.T. Wang X.L. Chen Y.X. O’Brien E.R. Gui Y. Walsh M.P. The effects of knockdown of rho-associated kinase 1 and zipper-interacting protein kinase on gene expression and function in cultured human arterial smooth muscle cells. PLoS One 2015 10 2 e0116969 10.1371/journal.pone.0116969 25723491
    [Google Scholar]
  58. Li L. Yang X. Jiang F. Dusting G.J. Wu Z. Transcriptome-wide characterization of gene expression associated with unruptured intracranial aneurysms. Eur. Neurol. 2009 62 6 330 337 10.1159/000236911 19752560
    [Google Scholar]
  59. Hak Ł. Myśliwska J. Więckiewicz J. Szyndler K. Trzonkowski P. Siebert J. Myśliwski A. NK cell compartment in patients with coronary heart disease. Immun. Ageing 2007 4 1 3 10.1186/1742‑4933‑4‑3 17488493
    [Google Scholar]
  60. Thomas P.D. The gene ontology and the meaning of biological function. Methods in Molecular Biology. Humana Press Inc. 2017 1446 15 24 10.1007/978‑1‑4939‑3743‑1_2
    [Google Scholar]
  61. Sasso F.C. Torella D. Carbonara O. Ellison G.M. Torella M. Scardone M. Marra C. Nasti R. Marfella R. Cozzolino D. Indolfi C. Cotrufo M. Torella R. Salvatore T. Increased vascular endothelial growth factor expression but impaired vascular endothelial growth factor receptor signaling in the myocardium of type 2 diabetic patients with chronic coronary heart disease. J. Am. Coll. Cardiol. 2005 46 5 827 834 10.1016/j.jacc.2005.06.007 16139132
    [Google Scholar]
  62. Wang S. S. Wu L. J. Li J. J. H. Xiao H. B. He Y. Yan Y. X. A meta-analysis of dysregulated miRNAs in coronary heart disease. Life Sci 2018 215 170 181 10.1016/j.lfs.2018.11.016 30423308
    [Google Scholar]
  63. Wirleitner B. Immune activation and degradation of tryptophan in coronary heart disease. Eur J Clin Invest 2003 33 7 550 554 10.1046/j.1365‑2362.2003.01186.x 12814390
    [Google Scholar]
  64. Saotome M. Katoh H. Satoh H. Hayashi H. Hajnoczky G. Mitochondrial remodeling in coronary heart disease. Res Rep Clin Cardiol. 2014 5 111 122 10.2147/RRCC.S43364
    [Google Scholar]
  65. Chen X. Sun A. Zou Y. Ge J. Lazar J.M. Jiang X.C. Impact of sphingomyelin levels on coronary heart disease and left ventricular systolic function in humans. Nutr. Metab. 2011 8 1 25 10.1186/1743‑7075‑8‑25 21521522
    [Google Scholar]
  66. Miller C.L. Anderson D.R. Kundu R.K. Raiesdana A. Nürnberg S.T. Diaz R. Cheng K. Leeper N.J. Chen C.H. Chang I.S. Schadt E.E. Hsiung C.A. Assimes T.L. Quertermous T. Disease-related growth factor and embryonic signaling pathways modulate an enhancer of TCF21 expression at the 6q23.2 coronary heart disease locus. PLoS Genet. 2013 9 7 e1003652 10.1371/journal.pgen.1003652 23874238
    [Google Scholar]
  67. Hou L. Huang J. Lu X. Wang L. Fan Z. Gu D. Polymorphisms of tumor necrosis factor alpha gene and coronary heart disease in a Chinese Han population: Interaction with cigarette smoking. Thromb. Res. 2009 123 6 822 826 10.1016/j.thromres.2008.07.016 18814905
    [Google Scholar]
  68. Moorjani S. Roy M. Torres A. Bétard C. Gagné C. Lambert M. Brun D. Davignon J. Lupien P. Mutations of low-density-lipoprotein-receptor gene, variation in plasma cholesterol, and expression of coronary heart disease in homozygous familial hypercholesterolaemia. Lancet 1993 341 8856 1303 1306 10.1016/0140‑6736(93)90815‑X 8098448
    [Google Scholar]
  69. Malik I. Soluble adhesion molecules and prediction of coronary heart disease: A prospective study and meta-analysis. The Lancet 2001 358 9286 971 976 10.1016/S0140‑6736(01)06104‑9 11583751
    [Google Scholar]
  70. Fioranelli M. Bottaccioli A.G. Bottaccioli F. Bianchi M. Rovesti M. Roccia M.G. Stress and inflammation in coronary artery disease: A review psychoneuroendocrineimmunology-based. Front. Immunol. 2018 9 2031 10.3389/fimmu.2018.02031 30237802
    [Google Scholar]
  71. Li Y. Lin M. Wang K. Zhan Y. Gu W. Gao G. Huang Y. Chen Y. Huang T. Wang J. A module of multifactor‐mediated dysfunction guides the molecular typing of coronary heart disease. Mol. Genet. Genomic Med. 2020 8 10 e1415 10.1002/mgg3.1415 32743916
    [Google Scholar]
  72. Wu D. Huo M. Chen X. Zhang Y. Qiao Y. Mechanism of tanshinones and phenolic acids from Danshen in the treatment of coronary heart disease based on co-expression network. BMC Complementary Medicine and Therapies 2020 20 1 28 10.1186/s12906‑019‑2712‑4 32020855
    [Google Scholar]
  73. Zhang L. Li G. Liang B. Su X. Xie H. Sun H. Wu G. Integrative analyses of immune-related biomarkers and associated mechanisms in coronary heart disease. BMC Med. Genomics 2022 15 1 219 10.1186/s12920‑022‑01375‑w 36266609
    [Google Scholar]
  74. Liu L. The therapeutic effect and targets of cellulose polysaccharide on coronary heart disease (CHD) and the construction of a prognostic signature based on network pharmacology. Front Nutr 2022 9 986639 10.3389/fnut.2022.986639 36299990
    [Google Scholar]
  75. Zheng P.F. Chen L.Z. Guan Y.Z. Liu P. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease. Sci. Rep. 2021 11 1 6711 10.1038/s41598‑021‑86207‑0 33758323
    [Google Scholar]
  76. Saigusa R. Vallejo J. Gulati R. Suthahar S.S.A. Suryawanshi V. Alimadadi A. Makings J. Durant C.P. Freuchet A. Roy P. Ghosheh Y. Pandori W. Pattarabanjird T. Drago F. Taylor A. McNamara C.A. Shemesh A. Lanier L.L. Hedrick C.C. Ley K. Sex differences in coronary artery disease and diabetes revealed by scRNA-Seq and CITE-Seq of human CD4+ T cells. Int. J. Mol. Sci. 2022 23 17 9875 10.3390/ijms23179875 36077273
    [Google Scholar]
  77. Yan S. Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods. Mol. Med. Rep. 2018 18 3 2789 2797 10.3892/mmr.2018.9277 30015926
    [Google Scholar]
  78. Liu J. Guo Z. Zhang Y. Wu T. Ma Y. Lai W. Guo Z. LCK inhibitor attenuates atherosclerosis in ApoE mice via regulating T cell differentiation and reverse cholesterol transport. J. Mol. Cell. Cardiol. 2020 139 87 97 10.1016/j.yjmcc.2020.01.003 31972265
    [Google Scholar]
  79. Deng S. Yan T. Jendrny C. Nemecek A. Vincetic M. Gödtel-Armbrust U. Wojnowski L. Dexrazoxane may prevent doxorubicin-induced DNA damage via depleting both Topoisomerase II isoforms. BMC Cancer 2014 14 1 842 10.1186/1471‑2407‑14‑842 25406834
    [Google Scholar]
  80. Lou Y. Li P.H. Liu X.Q. Wang T.X. Liu Y.L. Chen C.C. Ma K.L. ITGAM-mediated macrophages contribute to basement membrane damage in diabetic nephropathy and atherosclerosis. BMC Nephrol. 2024 25 1 72 10.1186/s12882‑024‑03505‑1 38413872
    [Google Scholar]
  81. Sun Z. Lin J. Sun X. Yun Z. Zhang X. Xu S. Duan J. Yao K. Bioinformatics combining machine learning and single-cell sequencing analysis to identify common mechanisms and biomarkers of rheumatoid arthritis and ischemic heart failure. Heliyon 2025 11 2 e41641 10.1016/j.heliyon.2025.e41641 39897930
    [Google Scholar]
  82. Hu J. Luo T. Xi D. Guo K. Hu L. Zhao J. Chen S. Guo Z. Silencing ZAP70 prevents HSP65-induced reverse cholesterol transport and NF-κB activation in T cells. Biomed. Pharmacother. 2018 102 271 277 10.1016/j.biopha.2018.03.082 29567540
    [Google Scholar]
  83. Zhou Y. Yang Y. Hong L. Shao L. Lai H. Zhu F. Lan J. Downregulation of microRNA-29-3p following percutaneous coronary intervention: An implication of YY1/IRAK1 pathway in the post-vascular injury inflammation. Acta Cardiol Sin. 2023 39 5 742 754 10.6515/ACS.202309_39(5).20230215A 37720410
    [Google Scholar]
  84. Xu W. Xu J. Sun B. Chen H. Wang Y. Huang F. Xi P. Jiang J. The effect of PPARG gene polymorphisms on the risk of coronary heart disease: A meta-analysis. Mol. Biol. Rep. 2013 40 2 875 884 10.1007/s11033‑012‑2128‑4 23065280
    [Google Scholar]
  85. Connelly J.J. GATA2 is associated with familial early-onset coronary artery disease. PLoS Genet 2006 2 8 e139 10.1371/journal.pgen.0020139 16934006
    [Google Scholar]
  86. Vogel U. Jensen M.K. Due K.M. Rimm E.B. Wallin H. Nielsen M.R.S. Pedersen A.P.T. Tjønneland A. Overvad K. The NFKB1 ATTG ins/del polymorphism and risk of coronary heart disease in three independent populations. Atherosclerosis 2011 219 1 200 204 10.1016/j.atherosclerosis.2011.06.018 21726863
    [Google Scholar]
  87. Caamaño J. Saavedra N. Jaramillo P.C. Lanas C. Lanas F. Salazar L.A. TP53 codon 72 polymorphism is associated with coronary artery disease in Chilean subjects. Med. Princ. Pract. 2011 20 2 171 176 10.1159/000321206 21252575
    [Google Scholar]
  88. Wang C. Li Q. Yang H. Gao C. Du Q. Zhang C. Zhu L. Li Q. MMP9, CXCR1, TLR6, and MPO participant in the progression of coronary artery disease. J. Cell. Physiol. 2020 235 11 8283 8292 10.1002/jcp.29485 32052443
    [Google Scholar]
  89. Dégano I.R. Camps-Vilaró A. Subirana I. García-Mateo N. Cidad P. Muñoz-Aguayo D. Puigdecanet E. Nonell L. Vila J. Crepaldi F.M. de Gonzalo-Calvo D. Llorente-Cortés V. Pérez-García M.T. Elosua R. Fitó M. Marrugat J. Association of circulating microRNAs with coronary artery disease and usefulness for reclassification of healthy individuals: The REGICOR study. J. Clin. Med. 2020 9 5 1402 10.3390/jcm9051402 32397522
    [Google Scholar]
  90. Choudhury R.R. Gupta H. Bhushan S. Singh A. Roy A. Saini N. Role of miR-128-3p and miR-195-5p as biomarkers of coronary artery disease in Indians: A pilot study Sci Rep 2024 14 1 11881 10.21203/rs.3.rs‑3344850/v1 38789551
    [Google Scholar]
  91. Zhang F. Cheng N. Du J. Zhang H. Zhang C. MicroRNA-200b-3p promotes endothelial cell apoptosis by targeting HDAC4 in atherosclerosis. BMC Cardiovasc. Disord. 2021 21 1 172 10.1186/s12872‑021‑01980‑0 33845782
    [Google Scholar]
  92. D’Antona S. Porro D. Gallivanone F. Bertoli G. Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways. Comput. Biol. Med. 2024 174 108346 10.1016/j.compbiomed.2024.108346 38581999
    [Google Scholar]
  93. Coban N. Erkan A.F. Ozuynuk-Ertugrul A.S. Ekici B. Investigation of miR-26a-5p and miR-19a-3p expression levels in angiographically confirmed coronary artery disease. Acta Cardiol. 2023 78 8 945 956 10.1080/00015385.2023.2227484 37376990
    [Google Scholar]
  94. Braza-Boïls A. Marí-Alexandre J. Molina P. Arnau M.A. Barceló-Molina M. Domingo D. Girbes J. Giner J. Martínez-Dolz L. Zorio E. Deregulated hepatic micro RNA s underlie the association between non‐alcoholic fatty liver disease and coronary artery disease. Liver Int. 2016 36 8 1221 1229 10.1111/liv.13097 26901384
    [Google Scholar]
  95. Özgür Yurttaş N. Eşkazan A.E. Dasatinib‐induced pulmonary arterial hypertension. Br. J. Clin. Pharmacol. 2018 84 5 835 845 10.1111/bcp.13508 29334406
    [Google Scholar]
  96. Zittermann A. Schleithoff S.S. Frisch S. Götting C. Kuhn J. Koertke H. Kleesiek K. Tenderich G. Koerfer R. Circulating calcitriol concentrations and total mortality. Clin. Chem. 2009 55 6 1163 1170 10.1373/clinchem.2008.120006 19359534
    [Google Scholar]
  97. Kerola A.M. Rollefstad S. Semb A.G. Atherosclerotic cardiovascular disease in rheumatoid arthritis: Impact of inflammation and antirheumatic treatment. Eur. Cardiol. 2021 16 e18 10.15420/ecr.2020.44 34040652
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936362436250417105150
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Single-Cell RNA Sequencing to Identify Natural Killer Cell-Linked Genetic Markers and Regulatory Biomolecules in Coronary Heart Disease
Bentham Science Publishers ; https://doi.org/10.2174/0115748936362436250417105150
/content/journals/cbio/10.2174/0115748936362436250417105150
/content/journals/cbio/10.2174/0115748936362436250417105150
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  • Article Type:     Research Article
Keywords: biomarkers ; CDK1 ; single cell RNA-sequencing ; Coronary heart disease ; natural killer cell ; PTPRC

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