Abstract

Background

   MicroRNAs (miRNAs) play a crucial role in regulating adaptive and
   maladaptive responses in cardiovascular diseases, making them
   attractive targets for potential biomarkers. However, their potential
   as novel biomarkers for diagnosing cardiovascular diseases requires
   systematic evaluation.

Methods

   In this study, we aimed to identify a key set of miRNA biomarkers using
   integrated bioinformatics and machine learning analysis. We combined
   and analyzed three gene expression datasets from the Gene Expression
   Omnibus (GEO) database, which contains peripheral blood mononuclear
   cell (PBMC) samples from individuals with myocardial infarction (MI),
   stable coronary artery disease (CAD), and healthy individuals.
   Additionally, we selected a set of miRNAs based on their area under the
   receiver operating characteristic curve (AUC-ROC) for separating the
   CAD and MI samples. We designed a two-layer architecture for sample
   classification, in which the first layer isolates healthy samples from
   unhealthy samples, and the second layer classifies stable CAD and MI
   samples. We trained different machine learning models using both
   biomarker sets and evaluated their performance on a test set.

Results

   We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the
   differentially expressed miRNAs, and a set including hsa-miR-186-5p,
   hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as
   the optimum set of miRNAs selected by their AUC-ROC. Both biomarker
   sets could distinguish healthy from not-healthy samples with complete
   accuracy. The best performance for the classification of CAD and MI was
   achieved with an SVM model trained using the biomarker set selected by
   AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test
   data.

Conclusions

   Our study demonstrated that miRNA signatures derived from PBMCs could
   serve as valuable novel biomarkers for cardiovascular diseases.

   Keywords: MicroRNA, Machine learning, Myocardial infarction,
   Bioinformatics, Biomarker

Introduction

   Cardiovascular diseases (CVDs) are the leading cause of human
   mortality, accounting for 32% of all global deaths. It is estimated
   that approximately 85% of CVD mortality is due to myocardial infarction
   (MI) [[37]1]. MI is an acute coronary syndrome characterized by sudden
   blockage and stenosis of the coronary artery and subsequent myocardial
   ischemia, leading to extensive cardiomyocyte damage and necrosis
   [[38]2].

   Over the last 50 years, numerous attempts have been made to use
   biomarkers to facilitate diagnosis, assess the risk, follow-up therapy,
   and determine therapeutic efficacy in CVD candidates. Based on released
   guidelines, cardiac troponins (cTns) are used as a highly sensitive and
   accurate approach for detecting MI. Despite these inherent advantages,
   the high sensitivity of cTn-based assays has also led to more
   false-positive results [[39]3], necessitating the advent and
   development of new modalities with pathological value. To improve the
   diagnostic value of existing MI biomarkers, a combination of
   complementary biological markers, such as microRNAs (miRNAs) and other
   genetic factors, has been proposed. Previous research supports the
   notion that miRNAs exhibit great potential as alternative biomarkers
   for CVD detection and follow-up [[40]4]. It has been suggested that
   miRNAs possess 18-22 nucleotides and play a crucial role in the
   regulation of gene expression. Evidence indicates that miRNAs are
   involved in the pathogenesis of cardiac tissue injury [[41]5]. Several
   biological processes, such as angiogenesis, cardiomyocyte growth and
   contractility, lipid metabolism, plaque formation, and cardiac rhythm,
   are regulated by miRNAs [[42]6]. Circulating and tissue-specific miRNAs
   have shown promise as diagnostic and prognostic biomarkers across a
   range of cardiovascular diseases, including MI and other conditions
   such as CAD, heart failure, atrial fibrillation, cardiac hypertrophy,
   and fibrosis [[43]7, [44]8]. The use of miRNAs as diagnostic and
   prognostic biomarkers in CVDs is supported by their stability and rapid
   release into circulation after myocardial injury [[45]7]. In CAD,
   altered expression of miRNAs like miR-1, miR-133a, miR-208a/b, and
   miR-499, which are abundantly expressed in the heart, has been reported
   in patients compared to healthy controls. Additional miRNAs including
   miR-21, miR-208a/b, miR-133a/b, and the miR-30 family are frequently
   dysregulated in acute coronary syndrome (ACS) versus stable CAD
   [[46]9]. Furthermore, miRNAs like miR-3113-5p, miR-223-3p, miR-499a-5p,
   and miR-133a-3p demonstrate potential as biomarkers to identify
   patients at risk of sudden cardiac death [[47]10]. Moreover, miRNAs
   have shown diagnostic potential in other CVDs. For instance, miR-21 has
   been associated with cardiac injury and has been implicated in the
   pathology and recurrence of MI. Elevated levels of miR-21 have been
   observed in ACS patients and have been linked to cardiomyocyte
   apoptosis and cardiac hypertrophy. Similarly, miR-26 has been
   implicated in the pathology and recurrence of MI [[48]11]. In addition
   to their diagnostic potential, miRNAs have also shown promise as
   prognostic biomarkers for adverse myocardial effects, sudden death, and
   risk assessment in MI and other CVDs. For example, miR-101 and miR-150
   have been associated with flawed left ventricular contractility after
   MI, while miR-16 and miR-27a have been linked to an increased risk of
   adverse left ventricular remodeling [[49]7, [50]9]. These miRNAs may
   provide valuable prognostic information and aid in risk stratification
   for post-MI complications.

   Numerous studies have investigated the potential of miRNAs as
   biomarkers for MI, revealing promising findings. For instance, miR-1
   has been proposed as a potential biomarker for MI [[51]9]. This miRNA
   has shown increased expression levels in patients with MI, suggesting
   its potential diagnostic value. Additionally, other miRNAs, such as
   miR-19b-3p, miR-208a, miR-223-3p, miR-483-5p, and miR-499a-5p, have
   demonstrated promising diagnostic accuracy for MI within a short time
   window after the onset of symptoms [[52]10]. A recent systematic review
   compared the peak time and diagnostic accuracy of miRNAs and
   conventional biomarkers in MI. The results revealed miR-1-3p,
   miR-19b-3p, miR-208a, miR-223-3p, miR-483-5p, and miR-499a-5p had
   superior peak times within 4 h and better accuracy versus cTn and
   Creatine kinase-MB, indicating their promise for early diagnosis. The
   strengths of miRNAs included their early peak expression, satisfactory
   sensitivity and specificity, and higher accuracy especially within the
   first few hours of symptom onset compared to conventional biomarkers
   [[53]12].

   It has been postulated that the function and diagnostic properties of
   miRNAs are beyond the myocardium in patients with CVD. Specifically,
   the expression of miRNAs can vary in different biofluids and cell
   components such as serum and peripheral blood mononuclear cells (PBMCs)
   [[54]13]. PBMCs are a fraction of white blood cells, including
   monocytes, lymphocytes, macrophages, and other cells of the immune
   system [[55]14]. Emerging data indicate that PBMCs can be used as a
   valid source of biomarkers for monitoring various pathological
   conditions. Of note, the alteration of mRNAs and miRNAs under
   pathological conditions provides valuable information about different
   kinds of disorders. PBMCs can recapitulate the conditions of target
   tissues, thus providing a highly sensitive and specific source of
   biomarkers [[56]15]. Combined with these conditions, these cells are
   repositories of dysregulated genes and miRNA expression profiles in
   CVDs [[57]14, [58]15].

   In recent years, the advent and application of machine learning (ML)
   has been an exciting prospect for advancing scientific research.
   Although the concept of ML and its initial algorithms were conceived
   many years ago, recent improvements in computing power and access to
   vast amounts of data have demonstrated that ML techniques outperform
   classical statistical methods in various fields. Furthermore, the
   progress made in omics technologies has enabled the analysis of massive
   and intricate biological datasets, consisting of hundreds to thousands
   of samples, which makes it possible for ML to extract valuable
   biological insights and information from such data [[59]16].
   Consequently, ML provides innovative methods for merging and
   interpreting diverse types of omics data, leading to the identification
   of new biomarkers. These biomarkers can aid in precise disease
   prediction, patient stratification, and the development of novel
   therapeutic approaches [[60]17].

   In this study, we aimed to identify potential miRNA biomarkers in
   patients with MI by combining and analyzing three different microarray
   datasets from PBMCs. The integration of omics data with bioinformatics
   and ML techniques could be a promising tool in the discovery of new and
   more accurate biomarkers for monitoring MI. Additionally, this approach
   can deepen our understanding of the underlying mechanisms of MI and aid
   in the development of valid diagnostic biomarkers and patient
   stratification.

Methods

Microarray data collection

   Microarray datasets were obtained from the Gene Expression Omnibus
   (GEO) database ([61]https://www.ncbi.nlm.nih.gov/geo/). To obtain
   robust classification performance between MI, healthy control, and CAD
   samples, sufficiently large sample sizes for each group are required.
   For this purpose, the [62]GSE59867 dataset was selected, as it contains
   sizable numbers of both MI and CAD samples. To provide an equally large
   set of healthy controls, the [63]GSE56609 and [64]GSE54475 datasets
   containing healthy samples were also included. Combining these three
   datasets enabled comparative analysis between MI, CAD, and healthy
   control groups with adequate statistical power. All samples were
   produced using Affymetrix Human Gene 1.0 ST Array platform
   ([65]GPL6244). This platform contains 189 miRNA probes based on the
   annotation data from the GEO database. Only healthy, CAD, and
   early-stage MI samples were selected from these datasets for further
   analysis. Early-stage MI samples were analyzed to enable detection of
   miRNA biomarkers specific to the initial ischemia and infarction event,
   before extensive myocardial necrosis and remodeling occurs. Using
   samples from the early phase enhances identification of miRNA signals
   related to plaque rupture and MI onset versus stable CAD. Additionally,
   early-stage samples allow investigation of mechanisms initiating
   myocardial injury. The basic information for the three datasets
   evaluated in this study is provided in Table  [66]1. Bioinformatics
   analyses including preprocessing, differential expression analysis, and
   functional and pathway enrichment analyses were conducted using R, ver.
   4.2.0 [[67]18], and RStudio [[68]19]. All plots and graphics of these
   sections were created using the ggplot2 R package [[69]20].

Table 1.

   Sample information on the GEO microarray dataset
   Dataset      Platform    Healthy CAD MI  References