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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.
Pubmed ID: 27573188 RIS Download
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Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.
View all literature mentionsSoftware to graphically browse results of clustering and other analyses from Cluster.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
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