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

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 196 - 14
• Main Authors: Rao, Li, Peng, Bo, Li, Tao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.05.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: 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
• DOI: 10.1186/s12859-023-05244-w

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.