RNA Sequencing of Whole Blood in Premature Coronary Artery Disease: Identification of Novel Biomarkers and Involvement of T Cell Imbalance

Libby P, Theroux P. Pathophysiology of coronary artery disease. Circulation. 2005;111(25):3481–8. https://doi.org/10.1161/CIRCULATIONAHA.105.537878.

Article  PubMed  Google Scholar 

Zhao D, Liu J, Wang M, et al. Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol. 2019;16(4):203–12. https://doi.org/10.1038/s41569-018-0119-4.

Article  PubMed  Google Scholar 

Andersson C, Vasan RS. Epidemiology of cardiovascular disease in young individuals. Nat Rev Cardiol. 2018;15(4):230–40. https://doi.org/10.1038/nrcardio.2017.154.

Article  PubMed  Google Scholar 

Aggarwal A, Srivastava S, Velmurugan M. Newer perspectives of coronary artery disease in young. World J Cardiol. 2016;8(12):728–34. https://doi.org/10.4330/wjc.v8.i12.728.

Article  PubMed  PubMed Central  Google Scholar 

Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med. 2010;362(10):886–95. https://doi.org/10.1056/NEJMoa0907272.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Farrehi PM, Bernstein SJ, Rasak M, et al. Frequency of negative coronary arteriographic findings in patients with chest pain is related to community practice patterns. Am J Manag Care. 2002;8(7):643–8.

PubMed  Google Scholar 

Minha S, Behar S, Krakover R, et al. Characteristics and outcome of patients with acute coronary syndrome and normal or near-normal coronary angiography. Coron Artery Dis. 2010;21(4):212–6. https://doi.org/10.1097/MCA.0b013e328338cd5c.

Article  PubMed  Google Scholar 

From AM, Kane G, Bruce C, et al. Characteristics and outcomes of patients with abnormal stress echocardiograms and angiographically mild coronary artery disease (<50% stenoses) or normal coronary arteries. J Am Soc Echocardiogr. 2010;23(2):207–14. https://doi.org/10.1016/j.echo.2009.11.023.

Article  PubMed  Google Scholar 

Shukor MFA, Musthafa QA, Mohd Yusof YA, et al. Biomarkers for premature coronary artery disease (PCAD): a case control study. Diagnostics (Basel, Switzerland). 2023;13(2) https://doi.org/10.3390/diagnostics13020188.

Wei A, Liu J, Wang L, et al. Correlation of triglyceride-glucose index and dyslipidaemia with premature coronary heart diseases and multivessel disease: a cross-sectional study in Tianjin, China. BMJ open. 2022;12(9):e065780. https://doi.org/10.1136/bmjopen-2022-065780.

Article  PubMed  PubMed Central  Google Scholar 

Wu Z, Liu L, Wang W, et al. Triglyceride-glucose index in the prediction of adverse cardiovascular events in patients with premature coronary artery disease: a retrospective cohort study. Cardiovasc Diabetol. 2022;21(1):142. https://doi.org/10.1186/s12933-022-01576-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bao J, Zheng S, Huang J, et al. Mental health is correlated with lipoprotein(a) levels in male patients with premature coronary heart disease. Ann Palliat Med. 2021;10(6):6482–92. https://doi.org/10.21037/apm-21-1024.

Article  PubMed  Google Scholar 

Afanasieva OI, Tyurina AV, Klesareva EA, et al. Lipoprotein(a), immune cells and cardiovascular outcomes in patients with premature coronary heart disease. J Pers Med. 2022;12(2) https://doi.org/10.3390/jpm12020269.

Shi YP, Cao YX, Jin JL, et al. Lipoprotein(a) as a predictor for the presence and severity of premature coronary artery disease: a cross-sectional analysis of 2433 patients. Coron Artery Dis. 2021;32(1):78–83. https://doi.org/10.1097/mca.0000000000000940.

Article  PubMed  Google Scholar 

Haji Aghajani M, Toloui A, Ahmadzadeh K, et al. Premature coronary artery disease and plasma levels of interleukins; a systematic scoping review and meta-analysis, Arch. Acad Emerg Med. 2022;10(1):e51. https://doi.org/10.22037/aaem.v10i1.1605.

Article  Google Scholar 

Joehanes R, Johnson AD, Barb JJ, et al. Gene expression analysis of whole blood, peripheral blood mononuclear cells, and lymphoblastoid cell lines from the Framingham Heart Study. Physiol Genomics. 2012;44(1):59–75. https://doi.org/10.1152/physiolgenomics.00130.2011.

Article  CAS  PubMed  Google Scholar 

Barrett TJ, Lee AH, Smilowitz NR, et al. Whole-blood transcriptome profiling identifies women with myocardial infarction with nonobstructive coronary artery disease. Circ Genom Precis Med. 2018;11(12):e002387. https://doi.org/10.1161/CIRCGEN.118.002387.

Article  PubMed  PubMed Central  Google Scholar 

Chen JX, He S, Wang YJ, et al. Comprehensive analysis of mRNA expression profiling and identification of potential diagnostic biomarkers in coronary artery disease. ACS Omega. 2021;6(37):24016–26. https://doi.org/10.1021/acsomega.1c03171.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chiesa M, Piacentini L, Bono E, et al. Whole blood transcriptome profile at hospital admission discriminates between patients with ST-segment elevation and non-ST-segment elevation acute myocardial infarction. Sci Rep. 2020;10(1):8731. https://doi.org/10.1038/s41598-020-65527-7.

Article  CAS  PubMed  PubMed Central  Google Scholar 

McCaffrey TA, Toma I, Yang Z, et al. RNA sequencing of blood in coronary artery disease: involvement of regulatory T cell imbalance. BMC Med Genet. 2021;14(1):216. https://doi.org/10.1186/s12920-021-01062-2.

Article  CAS  Google Scholar 

Andreini D, Melotti E, Vavassori C, et al. Whole-blood transcriptional profiles enable early prediction of the presence of coronary atherosclerosis and high-risk plaque features at coronary CT angiography. Biomedicines. 2022;10(6) https://doi.org/10.3390/biomedicines10061309.

Reagent, T. TRIzol™ Reagent User Guide. Thermofisher.com/support. https://assets.thermofisher.cn/TFS-Assets/LSG/manuals/trizol_reagent.pdf. 2023;14:1–4.

Pertea M, Kim D, Pertea GM, et al. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11(9):1650–67. https://doi.org/10.1038/nprot.2016.095.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yates A, Akanni W, Amode MR, et al. Ensembl 2016. Nucleic Acids Res. 2016;44(D1):D710–6. https://doi.org/10.1093/nar/gkv1157.

Article  CAS  PubMed  Google Scholar 

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. https://doi.org/10.1038/s41467-019-09234-6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Villanueva, RAM, Chen, ZJ, ggplot2: elegant graphics for data analysis (2nd ed.), Measurement: interdisciplinary research and perspectives. 17(3)(2019) 160-167, https://doi.org/10.1080/15366367.2019.1565254.

Yu G, Wang LG, Han Y, et al. 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.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stacklies W, Redestig H, Scholz M, et al. pcaMethods--a bioconductor package providing PCA methods for incomplete data. Bioinformatics. 2007;23(9):1164–7. https://doi.org/10.1093/bioinformatics/btm069.

Article  CAS  PubMed  Google Scholar 

Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8. https://doi.org/10.1006/meth.2001.1262.

Article  CAS  PubMed  Google Scholar 

Ariansen I, Strand BH, Kjøllesdal MKR, et al. The educational gradient in premature cardiovascular mortality: examining mediation by risk factors in cohorts born in the 1930s, 1940s and 1950s. Eur J Prev Cardiol. 2019;26(10):1096–103. https://doi.org/10.1177/2047487319826274.

Article  PubMed  Google Scholar 

Arora S, Stouffer GA, Kucharska-Newton AM, et al. Twenty year trends and sex differences in young adults hospitalized with acute myocardial infarction. Circulation. 2019;139(8):1047–56. https://doi.org/10.1161/circulationaha.118.037137.

Article  PubMed  PubMed Central  Google Scholar 

Gupta A, Wang Y, Spertus JA, et al. Trends in acute myocardial infarction in young patients and differences by sex and race, 2001 to 2010. J Am Coll Cardiol. 2014;64(4):337–45. https://doi.org/10.1016/j.jacc.2014.04.054.

Article  PubMed  PubMed Central  Google Scholar 

Vikulova DN, Grubisic M, Zhao Y, et al. Premature atherosclerotic cardiovascular disease: trends in incidence, risk factors, and sex-related differences, 2000 to 2016. J Am Heart Assoc. 2019;8(14):e012178. https://doi.org/10.1161/JAHA.119.012178.

Article  PubMed  PubMed Central  Google Scholar 

Konishi H, Miyauchi K, Kasai T, et al. Long-term prognosis and clinical characteristics of young adults (≤40 years old) who underwent percutaneous coronary intervention. J Cardiol. 2014;64(3):171–4. https://doi.org/10.1016/j.jjcc.2013.12.005.

Article  PubMed  Google Scholar 

Gupta R, Misra A, Vikram NK, et al. Younger age of escalation of cardiovascular risk factors in Asian Indian subjects. BMC Cardiovasc Disord. 2009;9:28. https://doi.org/10.1186/1471-2261-9-28.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Christus T, Shukkur AM, Rashdan I, et al. Coronary artery disease in patients aged 35 or less - a different beast? Heart Views. 2011;12(1):7–11. https://doi.org/10.4103/1995-705X.81550.

Article  CAS  PubMed  PubMed Central 

留言 (0)

沒有登入
gif