ArticleMolecular medicine reports2016
Analysis of gene expression profile identifies potential biomarkers for atherosclerosis.
Article in Molecular medicine reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 35 citations in OpenAlex.
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- Prognostic value of nutrition for contrast-induced nephropathy in patients undergoing peripheral vascular intervention.Biomarkers in medicine · 2024Observational
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Fibulin-2 Facilitates Malignant Progression of Hepatocellular Carcinoma.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Article
- The landscape of expression and alternative splicing variation across human traits.Cell genomics · 2023Article
- Malnutrition and the risk for contrast-induced acute kidney injury in patients with coronary artery disease.International urology and nephrology · 2022Article
- Adipocyte-specific deletion of HuR induces spontaneous cardiac hypertrophy and fibrosis.American journal of physiology. Heart and circulatory physiology · 2021Article
- A 2 miRNAs-based signature for the diagnosis of atherosclerosis.BMC cardiovascular disorders · 2021Article
- Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles.Frontiers in molecular biosciences · 2021Article
- A transcriptome profile in gallbladder cancer based on annotation analysis of microarray studies.Molecular medicine reports · 2021Article
- Article
- Transcriptome Profiling in Systems Vascular Medicine.Frontiers in pharmacology · 2017Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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