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

• Published in: International journal of molecular sciences Vol. 21; no. 17; p. 6364
• Main Authors: Zhang, Huayu, Bredewold, Edwin O W, Vreeken, Dianne, Duijs, Jacques M G J, de Boer, Hetty C, Kraaijeveld, Adriaan O, Jukema, J Wouter, Pijls, Nico H, Waltenberger, Johannes, Biessen, Erik A L, van der Veer, Eric P, van Zonneveld, Anton Jan, van Gils, Janine M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.09.2020; MDPI
• Subjects: Adult; Age; Arteries; Arteriosclerosis; Atherogenesis; Atherosclerosis; Bayesian analysis; Biomarkers - blood; Blood vessels; Cardiovascular diseases; Cardiovascular Diseases - blood; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - genetics; Case-Control Studies; Cell membranes; Cohort Studies; Datasets; Discriminant analysis; Disease; EphA2 protein; Ephrins - blood; Ephrins - genetics; Female; Gender; Gene expression; Humans; Learning algorithms; Ligands; Machine Learning; machine-learning methods; Male; Middle Aged; Monocytes; Monocytes - metabolism; Multidimensional methods; Netrin-1 - blood; Netrin-1 - genetics; neuroimmune guidance cues; Noise; Peripheral blood; Semaphorins - blood; Semaphorins - genetics; Stochasticity; Transcriptome; monocytes; neuroimmune guidance cues; cardiovascular diseases; machine-learning methods
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms21176364

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, Naïve 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.