A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

Abstract

Background: Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy.

Methods: FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8-10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers.

Findings: Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism.

Interpretation: In this pilot study, transcriptomic models could predict if FCT increased following 8-10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

Keywords: Optical coherence tomography; Personalized medicine; Predictive modeling; Statin.

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1

Central figure illustrating the components of study. (a) An example OCT image of an atherosclerotic plaque, before and after 8–12 weeks of high intensity rosuvastatin therapy. (b) The study workflow. Blood transcriptomics and OCT imaging were performed at 69 patients at baseline and follow-up periods. 35 patients had increased FCT (responders), and 34 patients did not (non-responders). (c) Predictive modeling of FCT response. We combined clinical variables and transcriptomic data and used two machine learning methods to predict responder type. (d) Graphical explanation of extensive sensitivity testing. We iteratively performed the strategy depicted in panel (c) upon randomly selected subsets of our dataset in order to understand the variability of the results.

Fig. 2

Predictive Model Receiver Operating Characteristic Curves. The receiver operating characteristic (ROC) curves for the elastic net and K top scoring pairs predictive models are shown in (a). ROC scores were computed for KTSP by dividing the number of votes by number of potential votes (i.e. gene pairs) in the classifier as the predicted probability. Sensitivity testing using elastic net (b) and KTSP (c) showed performance is highly robust to sampling error.

Fig. 3

Heatmap of 18 Genes Selected by K-Top-Scoring-Pairs Algorithm (KTSP). Patient samples and genes were grouped using hierarchical clustering. Gene expression values were normalized for plotting by dividing the gene's microarray signal intensity minus the mean signal intensity for that gene by the standard deviation of signal intensity for that gene (Z score).

Fig. 4

Genes Shared Between Elastic Net and KTSP predictive models. Venn diagram showing the overlap of genes included in the elastic net and KTSP algorithms. 12 of 18 KTSP genes were also selected by the elastic net algorithm.

References

1. 1. Narula J., Nakano M., Virmani R. Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques. J Am Coll Cardiol. 2013;61(10):1041–1051.
   2. Narula J, Nakano M, Virmani R, et al. Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques. J Am Coll Cardiol 2013; 61(10): 1041-51.
2. 1. Burke A.P., Farb A., Malcom G.T., Liang Y.H., Smialek J., Virmani R. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med. 1997;336(18):1276–1282.
   2. Burke AP, Farb A, Malcom GT, Liang YH, Smialek J, Virmani R. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med 1997; 336(18): 1276-82.
3. 1. Otsuka F., Joner M., Prati F., Virmani R., Narula J. Clinical classification of plaque morphology in coronary disease. Nat Rev Cardiol. 2014;11(7):379–389.
   2. Otsuka F, Joner M, Prati F, Virmani R, Narula J. Clinical classification of plaque morphology in coronary disease. Nat Rev Cardiol 2014; 11(7): 379-89.
4. 1. Feldman M.D., Milner T.E. Improving plaque classification with optical coherence tomography. JACC Cardiovasc Imaging. 2017;11(11):1677–1678.
   2. Feldman MD, Milner TE. Improving Plaque Classification With Optical Coherence Tomography. JACC Cardiovasc Imaging 2017.
5. 1. Kini A.S., Vengrenyuk Y., Yoshimura T. Fibrous cap thickness by optical coherence tomography in vivo. J Am Coll Cardiol. 2017;69(6):644–657.
   2. Kini AS, Vengrenyuk Y, Yoshimura T, et al. Fibrous Cap Thickness by Optical Coherence Tomography In Vivo. J Am Coll Cardiol 2017; 69(6): 644-57.
6. 1. Kini A.S., Vengrenyuk Y., Shameer K. Intracoronary imaging, cholesterol efflux, and transcriptomes after intensive statin treatment: the YELLOW II study. J Am Coll Cardiol. 2017;69(6):628–640.
   2. Kini AS, Vengrenyuk Y, Shameer K, et al. Intracoronary Imaging, Cholesterol Efflux, and Transcriptomes After Intensive Statin Treatment: The YELLOW II Study. J Am Coll Cardiol 2017; 69(6): 628-40.
7. 1. Yamagishi M., Terashima M., Awano K. Morphology of vulnerable coronary plaque: insights from follow-up of patients examined by intravascular ultrasound before an acute coronary syndrome. J Am Coll Cardiol. 2000;35(1):106–111.
   2. Yamagishi M, Terashima M, Awano K, et al. Morphology of vulnerable coronary plaque: insights from follow-up of patients examined by intravascular ultrasound before an acute coronary syndrome. J Am Coll Cardiol 2000; 35(1): 106-11.
8. 1. Jang I.K., Bouma B.E., Kang D.H. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol. 2002;39(4):604–609.
   2. Jang IK, Bouma BE, Kang DH, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol 2002; 39(4): 604-9.
9. 1. Gardner C.M., Tan H., Hull E.L. Detection of lipid core coronary plaques in autopsy specimens with a novel catheter-based near-infrared spectroscopy system. JACC Cardiovasc Imaging. 2008;1(5):638–648.
   2. Gardner CM, Tan H, Hull EL, et al. Detection of lipid core coronary plaques in autopsy specimens with a novel catheter-based near-infrared spectroscopy system. JACC Cardiovasc Imaging 2008; 1(5): 638-48.
10. 1. Kini A.S., Baber U., Kovacic J.C. Changes in plaque lipid content after short-term intensive versus standard statin therapy: the YELLOW trial (reduction in yellow plaque by aggressive lipid-lowering therapy) J Am Coll Cardiol. 2013;62(1):21–29.
    2. Kini AS, Baber U, Kovacic JC, et al. Changes in plaque lipid content after short-term intensive versus standard statin therapy: the YELLOW trial (reduction in yellow plaque by aggressive lipid-lowering therapy). J Am Coll Cardiol 2013; 62(1): 21-9.
11. 1. Sato A., Hoshi T., Kakefuda Y. In vivo evaluation of fibrous cap thickness by optical coherence tomography for positive remodeling and low-attenuation plaques assessed by computed tomography angiography. Int J Cardiol. 2015;182:419–425.
    2. Sato A, Hoshi T, Kakefuda Y, et al. In vivo evaluation of fibrous cap thickness by optical coherence tomography for positive remodeling and low-attenuation plaques assessed by computed tomography angiography. Int J Cardiol 2015; 182: 419-25.
12. 1. Sun J., Zhao X.Q., Balu N. Carotid plaque lipid content and fibrous cap status predict systemic CV outcomes: the MRI substudy in AIM-HIGH. JACC Cardiovasc Imaging. 2017;10(3):241–249.
    2. Sun J, Zhao XQ, Balu N, et al. Carotid Plaque Lipid Content and Fibrous Cap Status Predict Systemic CV Outcomes: The MRI Substudy in AIM-HIGH. JACC Cardiovasc Imaging 2017; 10(3): 241-9.
13. 1. Eken S.M., Jin H., Chernogubova E. MicroRNA-210 enhances fibrous cap stability in advanced atherosclerotic lesions. Circ Res. 2017;120(4):633–644.
    2. Eken SM, Jin H, Chernogubova E, et al. MicroRNA-210 Enhances Fibrous Cap Stability in Advanced Atherosclerotic Lesions. Circ Res 2017; 120(4): 633-44.
14. 1. Wang Z., Cho Y.S., Soeda T. Three-dimensional morphological response of lipid-rich coronary plaques to statin therapy: a serial optical coherence tomography study. Coron Artery Dis. 2016;27(5):350–356.
    2. Wang Z, Cho YS, Soeda T, et al. Three-dimensional morphological response of lipid-rich coronary plaques to statin therapy: a serial optical coherence tomography study. Coron Artery Dis 2016; 27(5): 350-6.
15. 1. Park S.J., Kang S.J., Ahn J.M. Effect of statin treatment on modifying plaque composition: a double-blind, randomized study. J Am Coll Cardiol. 2016;67(15):1772–1783.
    2. Park SJ, Kang SJ, Ahn JM, et al. Effect of Statin Treatment on Modifying Plaque Composition: A Double-Blind, Randomized Study. J Am Coll Cardiol 2016; 67(15): 1772-83.
16. 1. Oesterle A., Laufs U., Liao J.K. Pleiotropic effects of statins on the cardiovascular system. Circ Res. 2017;120(1):229–243.
    2. Oesterle A, Laufs U, Liao JK. Pleiotropic Effects of Statins on the Cardiovascular System. Circ Res 2017; 120(1): 229-43.
17. 1. Komukai K., Kubo T., Kitabata H. Effect of atorvastatin therapy on fibrous cap thickness in coronary atherosclerotic plaque as assessed by optical coherence tomography: the EASY-FIT study. J Am Coll Cardiol. 2014;64(21):2207–2217.
    2. Komukai K, Kubo T, Kitabata H, et al. Effect of atorvastatin therapy on fibrous cap thickness in coronary atherosclerotic plaque as assessed by optical coherence tomography: the EASY-FIT study. J Am Coll Cardiol 2014; 64(21): 2207-17.
18. 1. Kataoka Y., St John J., Wolski K. Atheroma progression in hyporesponders to statin therapy. Arterioscler Thromb Vasc Biol. 2015;35(4):990–995.
    2. Kataoka Y, St John J, Wolski K, et al. Atheroma progression in hyporesponders to statin therapy. Arterioscler Thromb Vasc Biol 2015; 35(4): 990-5.
19. 1. Wei K.K., Zhang L.R. Interactions between CYP3A5*3 and POR*28 polymorphisms and lipid lowering response with atorvastatin. Clin Drug Investig. 2015;35(9):583–591.
    2. Wei KK, Zhang LR. Interactions between CYP3A5*3 and POR*28 polymorphisms and lipid lowering response with atorvastatin. Clin Drug Investig 2015; 35(9): 583-91.
20. 1. Kataoka Y., Hammadah M., Puri R. Plaque microstructures in patients with coronary artery disease who achieved very low low-density lipoprotein cholesterol levels. Atherosclerosis. 2015;242(2):490–495.
    2. Kataoka Y, Hammadah M, Puri R, et al. Plaque microstructures in patients with coronary artery disease who achieved very low low-density lipoprotein cholesterol levels. Atherosclerosis 2015; 242(2): 490-5.
21. 1. Trompet S., Postmus I., Slagboom P.E. Non-response to (statin) therapy: the importance of distinguishing non-responders from non-adherers in pharmacogenetic studies. Eur J Clin Pharmacol. 2016;72(4):431–437.
    2. Trompet S, Postmus I, Slagboom PE, et al. Non-response to (statin) therapy: the importance of distinguishing non-responders from non-adherers in pharmacogenetic studies. Eur J Clin Pharmacol 2016; 72(4): 431-7.
22. 1. Dudley J.T., Tibshirani R., Deshpande T., Butte A.J. Disease signatures are robust across tissues and experiments. Mol Syst Biol. 2009;5:307.
    2. Dudley JT, Tibshirani R, Deshpande T, Butte AJ. Disease signatures are robust across tissues and experiments. Mol Syst Biol 2009; 5: 307.
23. 1. R Development Core Team . R Foundation for Statistical Computing; Vienna, Austria: 2017. R: A Language and Environment for Statistical Computing.
    2. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2017.
24. 1. Du P., Kibbe W.A., Lin S.M. nuID: a universal naming scheme of oligonucleotides for illumina, affymetrix, and other microarrays. Biol Direct. 2007;2:16.
    2. Du P, Kibbe WA, Lin SM. nuID: a universal naming scheme of oligonucleotides for illumina, affymetrix, and other microarrays. Biol Direct 2007; 2: 16.
25. 1. Du P., Kibbe W.A., Lin S.M. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547–1548.
    2. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics 2008; 24(13): 1547-8.
26. 1. Lin S.M., Du P., Huber W., Kibbe W.A. Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res. 2008;36(2):e11.
    2. Lin SM, Du P, Huber W, Kibbe WA. Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res 2008; 36(2): e11.
27. 1. Leek J.T., Johnson W.E., Parker H.S., Jaffe A.E., Storey J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–883.
    2. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012; 28(6): 882-3.
28. 1. Kim K., Bolotin E., Theusch E., Huang H., Medina M.W., Krauss R.M. Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation. Genome Biol. 2014;15(9):460.
    2. Kim K, Bolotin E, Theusch E, Huang H, Medina MW, Krauss RM. Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation. Genome Biol 2014; 15(9): 460.
29. 1. Friedman J., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
    2. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 2010; 33(1): 1-22.
30. 1. Afsari B., Fertig E.J., Geman D., Marchionni L. switchBox: an R package for k-top scoring pairs classifier development. Bioinformatics. 2015;31(2):273–274.
    2. Afsari B, Fertig EJ, Geman D, Marchionni L. switchBox: an R package for k-Top Scoring Pairs classifier development. Bioinformatics 2015; 31(2): 273-4.
31. 1. Geman D., d'Avignon C., Naiman D.Q., Winslow R.L. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol. 2004;3
    2. Geman D, d'Avignon C, Naiman DQ, Winslow RL. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol 2004; 3: Article19.
32. 1. Afsari B., Braga-Neto U.M., Geman D. Rank discriminants for predicting phenotypes from Rna expression. Ann Appl Stat. 2014;8(3):1469–1491.
    2. Afsari B, Braga-Neto UM, Geman D. Rank Discriminants for Predicting Phenotypes from Rna Expression. Ann Appl Stat 2014; 8(3): 1469-91.
33. 1. Mendelson M.M., Marioni R.E., Joehanes R. Association of body mass index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a Mendelian randomization approach. PLoS Med. 2017;14(1):e1002215.
    2. Mendelson MM, Marioni RE, Joehanes R, et al. Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach. PLoS Med 2017; 14(1): e1002215.
34. 1. Miao L., Yin R.X., Pan S.L., Yang S., Yang D.Z., Lin W.X. Weighted gene co-expression network analysis identifies specific modules and hub genes related to hyperlipidemia. Cell Physiol Biochem. 2018;48(3):1151–1163.
    2. Miao L, Yin RX, Pan SL, Yang S, Yang DZ, Lin WX. Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia. Cell Physiol Biochem 2018; 48(3): 1151-63.
35. 1. Chen J., Yu L., Zhang S., Chen X. Network analysis-based approach for exploring the potential diagnostic biomarkers of acute myocardial infarction. Front Physiol. 2016;7:615.
    2. Chen J, Yu L, Zhang S, Chen X. Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction. Front Physiol 2016; 7: 615.
36. 1. Lo A., Chernoff H., Zheng T., Lo S.H. Why significant variables aren't automatically good predictors. Proc Natl Acad Sci U S A. 2015;112(45):13892–13897.
    2. Lo A, Chernoff H, Zheng T, Lo SH. Why significant variables aren't automatically good predictors. Proc Natl Acad Sci U S A 2015; 112(45): 13892-7.
37. 1. Ridker P.M., Narula J. Will reducing inflammation reduce vascular event rates? JACC Cardiovasc Imaging. 2018;11(2 Pt 2):317–319.
    2. Ridker PM, Narula J. Will Reducing Inflammation Reduce Vascular Event Rates? JACC Cardiovasc Imaging 2018; 11(2 Pt 2): 317-9.
38. 1. Ridker P.M., Danielson E., Fonseca F.A. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med. 2008;359(21):2195–2207.
    2. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008; 359(21): 2195-207.
39. 1. Chamaria S., Johnson K.W., Vengrenyuk Y. Intracoronary imaging, cholesterol efflux, and transcriptomics after intensive statin treatment in diabetes. Sci Rep. 2017;7(1):7001.
    2. Chamaria S, Johnson KW, Vengrenyuk Y, et al. Intracoronary Imaging, Cholesterol Efflux, and Transcriptomics after Intensive Statin Treatment in Diabetes. Sci Rep 2017; 7(1): 7001.
40. 1. Tang D., Yang C., Huang S. Cap inflammation leads to higher plaque cap strain and lower cap stress: an MRI-PET/CT-based FSI modeling approach. J Biomech. 2017;50:121–129.
    2. Tang D, Yang C, Huang S, et al. Cap inflammation leads to higher plaque cap strain and lower cap stress: An MRI-PET/CT-based FSI modeling approach. J Biomech 2017; 50: 121-9.
41. 1. Ridker P.M., Everett B.M., Thuren T. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377(12):1119–1131.
    2. Ridker PM, Everett BM, Thuren T, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med 2017; 377(12): 1119-31.
42. 1. Tall A.R., Westerterp M. Inflammasomes, neutrophil extracellular traps, and cholesterol. J Lipid Res. 2019;60(4):721–727.
    2. Tall AR, Westerterp M. Inflammasomes, Neutrophil Extracellular Traps, and Cholesterol. J Lipid Res 2019.
43. 1. Soehnlein O., Steffens S., Hidalgo A., Weber C. Neutrophils as protagonists and targets in chronic inflammation. Nat Rev Immunol. 2017;17(4):248–261.
    2. Soehnlein O, Steffens S, Hidalgo A, Weber C. Neutrophils as protagonists and targets in chronic inflammation. Nat Rev Immunol 2017; 17(4): 248-61.
44. 1. Doring Y., Soehnlein O., Weber C. Neutrophil extracellular traps in atherosclerosis and atherothrombosis. Circ Res. 2017;120(4):736–743.
    2. Doring Y, Soehnlein O, Weber C. Neutrophil Extracellular Traps in Atherosclerosis and Atherothrombosis. Circ Res 2017; 120(4): 736-43.
45. 1. Geng S., Zhang Y., Lee C., Li L. Novel reprogramming of neutrophils modulates inflammation resolution during atherosclerosis. Sci Adv. 2019;5(2)
    2. Geng S, Zhang Y, Lee C, Li L. Novel reprogramming of neutrophils modulates inflammation resolution during atherosclerosis. Sci Adv 2019; 5(2): eaav2309.
46. 1. Gomez D., Baylis R.A., Durgin B.G. Interleukin-1beta has atheroprotective effects in advanced atherosclerotic lesions of mice. Nat Med. 2018;24(9):1418–1429.
    2. Gomez D, Baylis RA, Durgin BG, et al. Interleukin-1beta has atheroprotective effects in advanced atherosclerotic lesions of mice. Nat Med 2018; 24(9): 1418-29.
47. 1. Stone N.J., Robinson J.G., Lichtenstein A.H. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 Pt B):2889–2934.
    2. Stone NJ, Robinson JG, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014; 63(25 Pt B): 2889-934.
48. 1. Nasu K., Terashima M., Habara M. Impact of cholesterol metabolism on coronary plaque vulnerability of target vessels: a combined analysis of virtual histology intravascular ultrasound and optical coherence tomography. JACC Cardiovasc Interv. 2013;6(7):746–755.
    2. Nasu K, Terashima M, Habara M, et al. Impact of cholesterol metabolism on coronary plaque vulnerability of target vessels: a combined analysis of virtual histology intravascular ultrasound and optical coherence tomography. JACC Cardiovasc Interv 2013; 6(7): 746-55.
49. 1. Ozaki Y., Tanaka A., Komukai K. High-density lipoprotein cholesterol level is associated with fibrous cap thickness in acute coronary syndrome. Circ J. 2013;77(12):2982–2989.
    2. Ozaki Y, Tanaka A, Komukai K, et al. High-density lipoprotein cholesterol level is associated with fibrous cap thickness in acute coronary syndrome. Circ J 2013; 77(12): 2982-9.
50. 1. Chhatriwalla A.K., Rader D.J. Intracoronary imaging, reverse cholesterol transport, and transcriptomics: precision medicine in CAD? J Am Coll Cardiol. 2017;69(6):641–643.
    2. Chhatriwalla AK, Rader DJ. Intracoronary Imaging, Reverse Cholesterol Transport, and Transcriptomics: Precision Medicine in CAD? J Am Coll Cardiol 2017; 69(6): 641-3.
51. 1. Zhang Z., Tang Z., Ma X. TAOK1 negatively regulates IL-17-mediated signaling and inflammation. Cell Mol Immunol. 2018;15(8):794–802.
    2. Zhang Z, Tang Z, Ma X, et al. TAOK1 negatively regulates IL-17-mediated signaling and inflammation. Cell Mol Immunol 2018; 15(8): 794-802.
52. 1. Chen F.C., Brozovich F.V. Gene expression profiles of vascular smooth muscle show differential expression of mitogen-activated protein kinase pathways during captopril therapy of heart failure. J Vasc Res. 2008;45(5):445–454.
    2. Chen FC, Brozovich FV. Gene expression profiles of vascular smooth muscle show differential expression of mitogen-activated protein kinase pathways during captopril therapy of heart failure. J Vasc Res 2008; 45(5): 445-54.
53. 1. Li X., Xue Y., Liu X. ZRANB2/SNHG20/FOXK1 axis regulates vasculogenic mimicry formation in glioma. J Exp Clin Cancer Res. 2019;38(1):68.
    2. Li X, Xue Y, Liu X, et al. ZRANB2/SNHG20/FOXK1 Axis regulates Vasculogenic mimicry formation in glioma. J Exp Clin Cancer Res 2019; 38(1): 68.
54. 1. Wang X., Du X., Li H., Zhang S. Identification of the zinc finger protein ZRANB2 as a novel maternal lipopolysaccharide-binding protein that protects embryos of Zebrafish against gram-negative bacterial infections. J Biol Chem. 2016;291(8):4019–4034.
    2. Wang X, Du X, Li H, Zhang S. Identification of the Zinc Finger Protein ZRANB2 as a Novel Maternal Lipopolysaccharide-binding Protein That Protects Embryos of Zebrafish against Gram-negative Bacterial Infections. J Biol Chem 2016; 291(8): 4019-34.
55. 1. Prakash S.K., LeMaire S.A., Guo D.C. Rare copy number variants disrupt genes regulating vascular smooth muscle cell adhesion and contractility in sporadic thoracic aortic aneurysms and dissections. Am J Hum Genet. 2010;87(6):743–756.
    2. Prakash SK, LeMaire SA, Guo DC, et al. Rare copy number variants disrupt genes regulating vascular smooth muscle cell adhesion and contractility in sporadic thoracic aortic aneurysms and dissections. Am J Hum Genet 2010; 87(6): 743-56.
56. 1. Singhal P., Luk A., Rao V., Butany J. Molecular basis of cardiac myxomas. Int J Mol Sci. 2014;15(1):1315–1337.
    2. Singhal P, Luk A, Rao V, Butany J. Molecular basis of cardiac myxomas. Int J Mol Sci 2014; 15(1): 1315-37.
57. 1. Chen J.H., Kuo H.C., Lee K.F., Tsai T.H. Global proteomic analysis of brain tissues in transient ischemia brain damage in rats. Int J Mol Sci. 2015;16(6):11873–11891.
    2. Chen JH, Kuo HC, Lee KF, Tsai TH. Global proteomic analysis of brain tissues in transient ischemia brain damage in rats. Int J Mol Sci 2015; 16(6): 11873-91.
58. 1. Xu W., Zhu Q., Liu S. Calretinin participates in regulating steroidogenesis by PLC-Ca(2+)-PKC pathway in Leydig cells. Sci Rep. 2018;8(1):7403.
    2. Xu W, Zhu Q, Liu S, et al. Calretinin Participates in Regulating Steroidogenesis by PLC-Ca(2+)-PKC Pathway in Leydig Cells. Sci Rep 2018; 8(1): 7403.
59. 1. Calderon-Dominguez M., Gil G., Medina M.A., Pandak W.M., Rodriguez-Agudo D. The StarD4 subfamily of steroidogenic acute regulatory-related lipid transfer (START) domain proteins: new players in cholesterol metabolism. Int J Biochem Cell Biol. 2014;49:64–68.
    2. Calderon-Dominguez M, Gil G, Medina MA, Pandak WM, Rodriguez-Agudo D. The StarD4 subfamily of steroidogenic acute regulatory-related lipid transfer (START) domain proteins: new players in cholesterol metabolism. Int J Biochem Cell Biol 2014; 49: 64-8.
60. 1. Riegelhaupt J.J., Waase M.P., Garbarino J., Cruz D.E., Breslow J.L. Targeted disruption of steroidogenic acute regulatory protein D4 leads to modest weight reduction and minor alterations in lipid metabolism. J Lipid Res. 2010;51(5):1134–1143.
    2. Riegelhaupt JJ, Waase MP, Garbarino J, Cruz DE, Breslow JL. Targeted disruption of steroidogenic acute regulatory protein D4 leads to modest weight reduction and minor alterations in lipid metabolism. J Lipid Res 2010; 51(5): 1134-43.
61. 1. Borthwick F., Allen A.M., Taylor J.M., Graham A. Overexpression of STARD3 in human monocyte/macrophages induces an anti-atherogenic lipid phenotype. Clin Sci (Lond) 2010;119(7):265–272.
    2. Borthwick F, Allen AM, Taylor JM, Graham A. Overexpression of STARD3 in human monocyte/macrophages induces an anti-atherogenic lipid phenotype. Clin Sci (Lond) 2010; 119(7): 265-72.
62. 1. Rodriguez-Agudo D., Calderon-Dominguez M., Ren S. Subcellular localization and regulation of StarD4 protein in macrophages and fibroblasts. Biochim Biophys Acta. 2011;1811(10):597–606.
    2. Rodriguez-Agudo D, Calderon-Dominguez M, Ren S, et al. Subcellular localization and regulation of StarD4 protein in macrophages and fibroblasts. Biochim Biophys Acta 2011; 1811(10): 597-606.
63. 1. Upadhyay R.K. Emerging risk biomarkers in cardiovascular diseases and disorders. J Lipids. 2015;2015(97):1453.
    2. Upadhyay RK. Emerging risk biomarkers in cardiovascular diseases and disorders. J Lipids 2015; 2015: 971453.
64. 1. Byron S.A., Van Keuren-Jensen K.R., Engelthaler D.M., Carpten J.D., Craig D.W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016;17(5):257–271.
    2. Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet 2016; 17(5): 257-71.
65. 1. Johnson K.W., Torres Soto J., Glicksberg B.S. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–2679.
    2. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial Intelligence in Cardiology. J Am Coll Cardiol 2018; 71(23): 2668-79.
66. 1. Shameer K., Johnson K.W., Glicksberg B.S., Dudley J.T., Sengupta P.P. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156–1164.
    2. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart 2018; 104(14): 1156-64.
67. 1. Johnson K.W., Shameer K., Glicksberg B.S. Enabling precision cardiology through multiscale biology and systems medicine. JACC Basic Transl Sci. 2017;2(3):311–327.
    2. Johnson KW, Shameer K, Glicksberg BS, et al. Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine. JACC Basic Transl Sci 2017; 2(3): 311-27.