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. 2024 Dec 10;19(1):127.
doi: 10.1186/s13062-024-00543-5.

An exploration into the diagnostic capabilities of microRNAs for myocardial infarction using machine learning

Mehrdad Samadishadlou  1 , Reza Rahbarghazi  2   3 , Kaveh Kavousi  4 , Farhad Bani  5   6
Affiliations

An exploration into the diagnostic capabilities of microRNAs for myocardial infarction using machine learning

Mehrdad Samadishadlou et al. Biol Direct. .

Abstract

Background: MicroRNAs (miRNAs) have shown potential as diagnostic biomarkers for myocardial infarction (MI) due to their early dysregulation and stability in circulation after MI. Moreover, they 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: This study aimed to identify a miRNA biomarker panel for early-stage MI detection using bioinformatics and machine learning (ML) methods. miRNA expression data were obtained for early-stage MI patients and healthy controls from the Gene Expression Omnibus. Separate datasets were allocated for training and independent testing. Differential expression analysis was performed to identify dysregulated miRNAs in the training set. The least absolute shrinkage and selection operator (LASSO) was applied for feature selection to prioritize relevant miRNAs associated with MI. The selected miRNAs were used to develop ML models including support vector machine, Gradient Boosted, XGBoost, and a hard voting ensemble (HVE).

Results: Differential expression analysis discovered 99 dysregulated miRNAs in the training set. LASSO feature selection prioritized 21 miRNAs. Ten miRNAs were identified in both the LASSO subset and independent test set. The HVE model trained with the selected miRNAs achieved an accuracy of 0.86 and AUC of 0.83 on the independent test set.

Conclusions: An integrated framework for robust miRNA selection from omics data shows promise for developing accurate diagnostic models for early-stage MI detection. The HVE model demonstrated good performance despite differences between training and test datasets.

Keywords: Bioinformatics; Diagnosis; Machine learning; MicroRNA; Myocardial infarction.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by the research ethics committee of Tabriz University of Medical Sciences (approval ID: IR.TBZMED.VCR.REC.1399.388, date of approval: 2021/1/11). Consent for publication: All authors gave consent for the publication of the article Competing interests: The authors declare that they have no Conflict of interest.

Figures

Fig. 1
Fig. 1
Data processing and identification of DEMs. (A) Principal component analysis plots for the training data. (B) Volcano plot of DEMs
Fig. 2
Fig. 2
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment result in DEMs. A Biological process terms (BP), Cellular component terms (CC), and Molecular function terms (MF). B KEGG analysis
Fig. 3
Fig. 3
Selection of the most important miRNAs. A LASSO coefficient profiles for DEMs and B Expression heatmap of 21 LASSO-selected miRNAs
Fig. 4
Fig. 4
A The intersection of LASSO-selected miRNAs and miRNAs was selected on test dataset. B Expression profile of selected miRNAs for training machine learning models
Fig. 5
Fig. 5
ROC curve for A individual miRNAs on the validation set. B Gradient Boosted (GB), XGBoost (XGB), and support vector machine (SVM) with preset hyper-parameters on the validation set. C Hyper-tuned GB, XGB, and SVM and a hard voting ensemble (HVE) made of the three hyper-tuned models on the validation set. D GB, XGB, SVM, and HVE on test set
See this image and copyright information in PMC

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