Analysis of gene expression profile identifies potential biomarkers for atherosclerosis

Liu,Luran; Liu,Yan; Liu,Chang; Zhang,Zhuobo; Du,Yaojun; Zhao,Hao

doi:10.3892/mmr.2016.5650

Analysis of gene expression profile identifies potential biomarkers for atherosclerosis

Authors:
- Luran Liu
- Yan Liu
- Chang Liu
- Zhuobo Zhang
- Yaojun Du
- Hao Zhao
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Affiliations: Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
Published online on: August 19, 2016 https://doi.org/10.3892/mmr.2016.5650
Pages: 3052-3058
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study aimed to identify potential biomarkers for atherosclerosis via analysis of gene expression profiles. The microarray dataset no. GSE20129 was downloaded from the Gene Expression Omnibus database. A total of 118 samples from the peripheral blood of female patients was used, including 47 atherosclerotic and 71 non‑atherosclerotic patients. The differentially expressed genes (DEGs) in the atherosclerosis samples were identified using the Limma package. Gene ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses for DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. The recursive feature elimination (RFE) algorithm was applied for feature selection via iterative classification, and support vector machine classifier was used for the validation of prediction accuracy. A total of 430 DEGs in the atherosclerosis samples were identified, including 149 up‑ and 281 downregulated genes. Subsequently, the RFE algorithm was used to identify 11 biomarkers, whose receiver operating characteristic curves had an area under curve of 0.92, indicating that the identified 11 biomarkers were representative. The present study indicated that APH1B, JAM3, FBLN2, CSAD and PSTPIP2 may have important roles in the progression of atherosclerosis in females and may be potential biomarkers for early diagnosis and prognosis as well as treatment targets for this disease.

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