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Identifying gene clusters as signature biomarkers for predicting methotrexate effectiveness and potential side effects during Rheumatoid Arthritis therapy
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| Author:
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ANAS KHALEEL, AFNAN MEQBEL, ROWAN ALEJIELAT, YAZAN S. BATARSEH, ABDALLAH A. ELBAKKOUSH, BAYAN Y. GHANIM, CRISTINA I. BATARSEH, NIDAL A. QINNA
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| Abstract:
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Background: Analysis of gene expression based on primary experimental databases is a powerful systematic biological tool that can be implemented to facilitate the understanding of therapy prognosis and potential molecular interactions. Such an approach was adopted in the current research to explore key genes and functional relationships rising during clinical Rheumatoid Arthritis (RA) therapy by Methotrexate (MTX). Methods: The whole gene expression data GSE45867 from synovial biopsies of RA patients before and after MTX therapy was obtained from the Gene Expression Omnibus (GEO) database in The National Center for Biotechnology Information (NCBI). Gene clusters and biological characterizations were identified by Gene set enrichment analysis (GSEA) tool while co-expression genes and key molecular pathways of interaction were confirmed via Gene Multiple Association Network Integration Algorithm (GeneMANIA), The Search Tool for The Retrieval of Interacting Genes (STRING) and Reactome bioinformatics tools. Results: Data showed that MTX-treated RA patients had differentially expressed genes that are involved in MAPK kinase activation and Toll-like receptor pathways. Two distinct networks were discovered by the STRING database; a cluster of genes including STON2, ARRB1, CNKSR2, DLG3, FAT3, VANGL2 and another including NFIX, JUN, MAP3K8, APAF1, NAIP. Analysis of differentially expressed genes initiated by MTX treatment revealed significant gene clusters and networks that are involved in important inflammatory pathways. Conclusion: Such pathways and networks might serve as prognostic biomarkers for RA and can be further developed as predictive gene signatures for MTX activity and adverse effects.
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| Keyword:
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Gene networks; Bioinformatics; Synoviocyte; Rheumatoid
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.01.319
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| Download:
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