Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

Background Atherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm. Methods Clinicopathological parameters and transcriptomi...

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Veröffentlicht in:BMC bioinformatics Jg. 24; H. 1; S. 196 - 14
Hauptverfasser: Rao, Li, Peng, Bo, Li, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 12.05.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
ACOXL
Algorithms
Arteriosclerosis
Atherosclerosis
Atherosclerosis - diagnosis
Atherosclerosis - genetics
Bioinformatics
Biological markers
Biomarkers
Biomedical and Life Sciences
Calibration
Cell growth
Cell proliferation
Cerebrovascular diseases
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Correlation analysis
Datasets
Decision analysis
Decision trees
Diagnosis
Diagnostic systems
Epithelial cells
Epithelium
Factorization
Gene expression
Genes
Genetic aspects
Humans
IL17C
Immune infiltration
Ischemia
Learning algorithms
Life Sciences
Machine Learning
Medical diagnosis
Medical prognosis
Medical research
Medicine, Experimental
Microarrays
Mitochondria
Performance prediction
Prediction models
Prognosis
Random Forest
Support vector machines
Survival analysis
Transcriptomics
Vascular diseases
China
ACOXL
IL17C
Atherosclerosis
Machine learning
Immune infiltration
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background Atherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm. Methods Clinicopathological parameters and transcriptomics data were obtained from 4 datasets (GSE21545, GSE20129, GSE43292, GSE100927). A nonnegative matrix factorization algorithm was used to classify arteriosclerosis patients in GSE21545 dataset. Then, we identified prognosis-related differentially expressed genes (DEGs) between the subtypes. Multiple machine learning methods to detect pivotal markers. Discrimination, calibration and clinical usefulness of the predicting model were assessed using area under curve, calibration plot and decision curve analysis respectively. The expression level of the feature genes was validated in GSE20129, GSE43292, GSE100927. Results 2 molecular subtypes of atherosclerosis was identified, and 223 prognosis-related DEGs between the 2 subtypes were identified. These genes are not only related to epithelial cell proliferation, mitochondrial dysfunction, but also to immune related pathways. Least absolute shrinkage and selection operator, random forest, support vector machine- recursive feature elimination show that IL17C and ACOXL were identified as diagnostic markers of atherosclerosis. The prediction model displayed good discrimination and good calibration. Decision curve analysis showed that this model was clinically useful. Moreover, IL17C and ACOXL were verified in other 3 GEO datasets, and also have good predictive performance. Conclusion IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05244-w