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Predicting the potential ankylosing spondylitis-related genes utilizing bioinformatics approaches
1Department of Arthritis Emergency, Guanghua Integrative Medicine Hospital, Changning District, Shanghai, China, zhh-happyhome@163.com.
Rheumatology International
|November 30, 2014
Summary
Ankylosing spondylitis (AS) molecular mechanisms were predicted by analyzing gene expression data. Key genes involved in ribosomal and proteasome pathways were identified as potential biomarkers for AS diagnosis and treatment.
Area of Science:
- Genomics and Bioinformatics
- Immunology and Rheumatology
Background:
- Ankylosing spondylitis (AS) affects approximately 5 in 1,000 adults of European descent.
- The precise molecular pathogenesis of AS remains unclear, necessitating further investigation.
Purpose of the Study:
- To predict the molecular mechanisms underlying ankylosing spondylitis (AS).
- To identify potential molecular biomarkers for AS.
Main Methods:
- Utilized Affymetrix chip data (GSE25101) from the Gene Expression Omnibus database.
- Identified differentially expressed genes (DEGs) using Limma package in R.
- Performed gene set enrichment analysis, microRNA-target prediction, and protein-protein interaction (PPI) network construction and analysis.
Main Results:
- Identified 145 DEGs (103 up-regulated, 42 down-regulated) significantly enriched in phosphorylation and gene expression regulation.
- Constructed and analyzed PPI networks, identifying a key module within up-regulated genes.
- The identified module was enriched in ribosomal protein (RPL17, MRPL22) and proteasome (PSMA6, PSMA4) related domains.
Conclusions:
- The study provides insights into the potential molecular pathogenesis of AS.
- RPL17, MRPL22, PSMA6, and PSMA4 are proposed as potential biomarkers for ankylosing spondylitis.
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