MicroRNA neural networks improve diagnosis of acute coronary syndrome (ACS). (February 2021)

• Record Type: Journal Article
• Authors: Kayvanpour, Elham; Gi, Weng-Tein; Sedaghat-Hamedani, Farbod; Lehmann, David H.; Frese, Karen S.; Haas, Jan; Tappu, Rewati; Samani, Omid Shirvani; Nietsch, Rouven; Kahraman, Mustafa; Fehlmann, Tobias; Müller-Hennessen, Matthias; Weis, Tanja; Giannitsis, Evangelos; Niederdränk, Torsten; Keller, Andreas; Katus, Hugo A.; Meder, Benjamin
• Is Part Of: Journal of molecular and cellular cardiology. Volume 151(2021)
• Page Start: 155
• Page End: 162
• Publication Date: 2021-02
• Subjects: Acute coronary syndrome -- microRNA -- Deep learning -- High-sensitive troponin; Cardiology -- Periodicals; Heart Diseases -- Periodicals; Molecular Biology -- Periodicals; Cardiologie -- Périodiques; Cardiology; Electronic journals; Periodicals; 616.12
• DOI: 10.1016/j.yjmcc.2020.04.014
• Languages: English
• ISSNs: 0022-2828

Abstract

Abstract: Background: Cardiac troponins are the preferred biomarkers of acute myocardial infarction. Despite superior sensitivity, serial testing of Troponins to identify patients suffering acute coronary syndromes is still required in many cases to overcome limited specificity. Moreover, unstable angina pectoris relies on reported symptoms in the troponin-negative group. In this study, we investigated genome-wide miRNA levels in a prospective cohort of patients with clinically suspected ACS and determined their diagnostic value by applying an in silico neural network. Methods: PAXgene blood and serum samples were drawn and hsTnT was measured in patients at initial presentation to our Chest-Pain Unit. After clinical and diagnostic workup, patients were adjudicated by senior cardiologists in duty to their final diagnosis: STEMI, NSTEMI, unstable angina pectoris and non-ACS patients. ACS patients and a cohort of healthy controls underwent deep transcriptome sequencing. Machine learning was implemented to construct diagnostic miRNA classifiers. Results: We developed a neural network model which incorporates 34 validated ACS miRNAs, showing excellent classification results. By further developing additional machine learning models and selecting the best miRNAs, we achieved an accuracy of 0.96 (95% CI 0.96–0.97), sensitivity of 0.95, specificity of 0.96 and AUC of 0.99. The one-point hsTnT value reached an accuracy of 0.89, sensitivity of 0.82, specificity of 0.96, and AUC of 0.96. Conclusions: Here we show the concept of neural network based biomarkers for ACS. This approach also opens the possibility to include multi-modal data points to further increase precision and perform classification of other ACS differential diagnoses. Graphical abstract: Unlabelled Image Highlights: miRNAs may be used as early diagnostic biomarkers of acute coronary syndrome (ACS). Artificial intelligence extracted precise information from large molecular dataset. 34 previously published miRNA associated with myocardial infarction were validated. Neural network model of 10 miRNA showed the highest accuracy in diagnosis of ACS.