Classifiers for predicting coronary artery disease based on gene expression profiles in peripheral blood mononuclear cells
Open accessAbstract
OBJECTIVE: Coronary artery disease (CAD) is a serious global health concern. Current diagnostic methods for CAD involve risk to the patient and are costly, so better diagnostic tools are needed. We defined four classifiers based on gene expression profiles in peripheral blood mononuclear cells and determined their potential for CAD detection. METHODS: We downloaded a CAD-related data set (GSE113079) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in peripheral blood mononuclear cells between CAD samples and healthy controls. DEGs were analyzed for functional enrichment. To create a robust CAD classifier, DEGs were identified by feature selection using the principal component analysis. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and support vector machine (SVM) models were created. Gene set variation analysis (GSVA) score and gene set enrichment analysis (GSEA) were also conducted. The performance of the models was evaluated in terms of the area under receiver operating characteristic curves (AUC). RESULTS: In the training set, we found 135 up-regulated genes and 104 down-regulated genes in CAD patients compared with controls. The DEGs were involved in some pathways associated with CAD, such as pathways involving calcium and interleukin-17 signaling. Twenty genes were identified as optimal features and used to generate the logistic classifier based on LASSO. The AUC for the classifier was 1.00 in the training set and 0.997 in the test set. Using the 20 DEGs, SVM and random forest classifiers were also generated and showed high diagnostic efficacy, with respective AUCs of 0.997 and 1.00 against the training set. A GSVA score was also established using the top 20 significant DEGs, which showed an AUC of 0.971 in the training set and 0.989 in the test set. Furthermore, GSEA showed autophagy and the proteasome to be major pathways involving the DEGs. CONCLUSION: We identified a set of genes specific for CAD whose expression can be measured non-invasively. Using these genes, we defined four diagnostic classifiers using multiple methods.
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Liu et al. [148] achieved an AUC of 97.1% in the training set and 98.9% in the testing set for coronary artery disease classification.
“They used data from the GEO dataset GSE113079, achieving an AUC of 97.1% in the training set and 98.9% in the testing set.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [148]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
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- Authors
- Liu, J., Wang, X., Lin, J., Li, S., Deng, G., & Wei, J.
- Affiliations
- Guangxi Medical University (CN)
- ORCID
- Venue
- International Journal of General Medicine
- Publisher
- Informa UK Limited
- Published
- 2021-09
- Volume
- Volume 14
- Pages
- 5651-5663
- ISSN
- 1178-7074 (ISSN-L)
- PMC
- PMC8450378
- OpenAlex
- W3199326502
- Cited by
- 8
- References
- 47
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 3 · 2022: 2
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of General Medicine (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- 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
- coronary artery disease, peripheral blood mononuclear cells, gene expression profiling, diagnostic classifier, differentially expressed genes, LASSO logistic regression, random forest, support vector machine, gene set enrichment analysis, autophagy, proteasome, calcium signaling, IL-17 signaling, non-invasive diagnosis
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