Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients
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Abstract
Background: Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. Methods: A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. Results: Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. Conclusion: AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.
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Support vector machine (SVM) is a commonly employed algorithm in disease diagnosis.
“The SVM [9] is another commonly employed algorithm in disease diagnosis [42,43,44].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [44]
Zhang et al. [44] achieved an AUC over 90% for classifying acute myocardial infarction and stable coronary artery disease.
“This algorithm was applied to classify patients with Acute Myocardial Infarction (AMI) and stable coronary artery disease (SCAD), using GEO datasets GSE60993, GSE62646, GSE48060, and GSE59867, achieving an AUC over 90%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [44]
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- Authors
- Zhang, L., Liu, Y., Wang, K., Ou, X., Zhou, J., Zhang, H., Huang, M., Du, Z., & Qiang, S.
- Affiliations
- Tianjin University of Traditional Chinese Medicine (CN)
- Nanjing University of Chinese Medicine (CN)
- Corresponding author
- Du, Zhenfang
- Qiang, Sheng
- Venue
- Journal of Translational Medicine
- Published
- 2023-10-27
- Volume
- 21
- Issue
- 1
- Pages
- 761-761
- ISSN
- 1479-5876 (ISSN-L)
- PMC
- PMC10612217
- OpenAlex
- W4387996474
- Cited by
- 18
- References
- 76
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 6
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Translational Medicine (publishedVersion, cc-by)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Suzhou Science & Technology Development Plan (SYSD2019222)
- Zhangjiagang science and technology plan project (ZKS2135)
- Youth science and technology project of Zhangjiagang Municipal Health Commission (ZJGQNKJ202211)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- acute myocardial infarction, leukocyte transcriptome, diagnostic biomarkers, AQP9, SOCS3, stable coronary artery disease, monocytes, neutrophils, differentially expressed genes, gene expression profiling, machine learning classification, genetic markers, Killip class, coronary artery stenosis, myocardial infarction diagnosis, microarray data
- MeSH terms
- Machine Learning, Coronary Artery Disease, Humans, Leukocytes, Monocytes, Myocardial Infarction, RNA
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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