Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods

Huayu Zhang, Edwin O. W. Bredewold, Dianne Vreeken, Jacques M. G. J. Duijs, Hetty C. de Boer, Adriaan O. Kraaijeveld, J. Wouter Jukema, Nico H. Pijls, Johannes Waltenberger, Erik A. L. Biessen, Eric P. van der Veer, Anton Jan van Zonneveld, Janine M. van Gils*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)

Abstract

Atherosclerosis is the underlying pathology in a major part of cardiovascular disease, the leading cause of mortality in developed countries. The infiltration of monocytes into the vessel walls of large arteries is a key denominator of atherogenesis, making monocytes accountable for the development of atherosclerosis. With the development of high-throughput transcriptome profiling platforms and cytometric methods for circulating cells, it is now feasible to study in-depth the predicted functional change of circulating monocytes reflected by changes of gene expression in certain pathways and correlate the changes to disease outcome. Neuroimmune guidance cues comprise a group of circulating- and cell membrane-associated signaling proteins that are progressively involved in monocyte functions. Here, we employed the CIRCULATING CELLS study cohort to classify cardiovascular disease patients and healthy individuals in relation to their expression of neuroimmune guidance cues in circulating monocytes. To cope with the complexity of human datasets featured by noisy data, nonlinearity and multidimensionality, we assessed various machine-learning methods. Of these, the linear discriminant analysis, Naive Bayesian model and stochastic gradient boost model yielded perfect or near-perfect sensibility and specificity and revealed that expression levels of the neuroimmune guidance cues SEMA6B, SEMA6D and EPHA2 in circulating monocytes were of predictive values for cardiovascular disease outcome.

Original languageEnglish
Article number6364
Number of pages18
JournalInternational journal of molecular sciences
Volume21
Issue number17
DOIs
Publication statusPublished - Sep 2020

Keywords

  • cardiovascular diseases
  • monocytes
  • machine-learning methods
  • neuroimmune guidance cues
  • PROMOTES ATHEROSCLEROSIS
  • INFLAMMATION
  • MECHANISMS
  • NETRIN-1
  • PLAQUES

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