Computational protocol: Hsa_circ_0001859 Regulates ATF2 Expression by Functioning as an MiR-204/211 Sponge in Human Rheumatoid Arthritis

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Protocol publication

[…] To explore expression differences between normal and RA cells, we first screened datasets that met the needs of our design by literature mining in the Gene Expression Omnibus (GEO) database. “Rheumatoid” and “arthritis” were searched as keywords in the GEO database, and the list of GSEs is shown in Supplementary Table . GSE2053 is the only dataset generated from human RA sample assays. Datasets from other species or other tissues were filtered out. The GEOquery and limma R packages were then used to screen the differentially expressed genes in RA (RA-DEGs). Functional enrichment analyses conducted by DAVID and GeneCards [, ], which are based on gene ontology (GO) terms and pathway databases, were applied to cluster the top 500 scored RA-DEGs. miRWalk was used to predict the miRNAs that may downregulate the mRNAs of RA-DEGs (RA-miRNAs), and the interactions between candidate circRNAs and miRNAs were predicted by the StarBase v2.0 tool. Molecular interaction networks were constructed according to mRNA profiling analysis, miRNA target prediction, and functional clustering. Algorithms (TargetScan, miRanda, Pictar2, PITA, and RNA22) provided by open-source databases were used to examine the significance of the correlations of expression between interacting molecules. The degree of correlation determined the gene's functional importance in this circRNA-miRNA-mRNA (CMM) network diagram. […]

Pipeline specifications

Software tools GEOquery, limma, DAVID, TargetScan, PicTar, RNA22
Databases GEO GeneCards miRWalk starBase
Application Transcription analysis
Organisms Homo sapiens
Diseases Arthritis, Rheumatoid