ArticleRheumatology international2015
Predicting the potential ankylosing spondylitis-related genes utilizing bioinformatics approaches.
Article in Rheumatology international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 35 citations in OpenAlex.
- Article
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- Identification of potential biomarkers related to the Bitong mixture in osteoarthritis based on bioinformatics and network pharmacology, and exploration of the mechanism involved.Frontiers in immunology · 2026Article
- From locus to gene: causal inference and experimental validation prioritize PSMA4 as a candidate target in lung adenocarcinoma.American journal of cancer research · 2026Article
- Comprehensive network pharmacology and experimental study to investigate the effect and mechanism of solasonine on breast carcinoma treatment.Cancer cell international · 2025Article
- Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model.Frontiers in physiology · 2023Article
- Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation.Frontiers in neuroscience · 2023Article
- Circulating microRNA sequencing revealed miRNome patterns in hematology and oncology patients aiding the prognosis of invasive aspergillosis.Scientific reports · 2022Article
- Dysregulation of ribosome-related genes in ankylosing spondylitis: a systems biology approach and experimental method.BMC musculoskeletal disorders · 2021Article
- Aberrant Methylation of miR-34b and IL-12B mRNA Promoters Contributes to the Reduced Severity of Ankylosing Spondylitis.Biochemical genetics · 2021Article
- Co-expression Network Analysis Reveals Key Genes Related to Ankylosing spondylitis Arthritis Disease: Computational and Experimental Validation.Iranian journal of biotechnology · 2021Article
- Article
- MicroRNAs in ankylosing spondylitis: Function, potential and challenges.Journal of translational autoimmunity · 2020Review
- Development and Validation of an RNA-Binding Protein-Based Prognostic Model for Ovarian Serous Cystadenocarcinoma.Frontiers in genetics · 2020Article
- Identification of differential modules in ankylosing spondylitis using systemic module inference and the attract method.Experimental and therapeutic medicine · 2018Article
- Comparative transcriptomic analysis of skeletal muscle tissue during prenatal stages in Tongcheng and Yorkshire pig using RNA-seq.Functional & integrative genomics · 2018Article
- Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2017Article
- Interactions of the Immune System with Skin and Bone Tissue in Psoriatic Arthritis: A Comprehensive Review.Clinical reviews in allergy & immunology · 2016Review
- The Role of MicroRNAS in Ankylosing Spondylitis.Medicine · 2016Review
- Induction of Targeted Differentiation of Dermal Mesenchymal Stem Cells Into Neural Lineage According to Peroxiredoxin II Expression.In vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given that ankylosing spondylitis (AS) occurs in approximately 5 out of 1,000 adults of European descent and the unclear pathogenesis, the aim of the research was to further predict the molecular mechanism of this disease. The Affymetrix chip data GSE25101 were available from Gene Expression Omnibus database. First of all, differentially expressed genes (DEGs) were identified by Limma package in R. Moreover, DAVID was used to perform gene set enrichment analysis of DEGs. In addition, miRanda, miRDB, miRWalk, RNA22 and TargetScan were applied to predict microRNA-target associations. Meanwhile, STRING 9.0 was utilized to collect protein-protein interactions (PPIs) with confidence score >0.4. Then, the PPI networks for up- and down-regulated genes were constructed, and the clustering analysis was undergone using ClusterONE. Finally, protein-domain enrichment analysis of modules was conducted using DAVID. Total 145 DEGs were identified, including 103 up-regulated and 42 down-regulated genes. These DEGs were significantly enriched in phosphorylation (p = 1.21E-05) and positive regulation of gene expression (p = 1.25E-03). Furthermore, one module was screened out from the up-regulated network, which contained 39 nodes and 205 edges. Moreover, the nodes in the module were significantly enriched in ribosomal protein (RPL17, ribosomal protein L17 and MRPL22, mitochondrial ribosomal protein L22) and proteasome (PSMA6, proteasome subunit, alpha type 6, PSMA4)-related domains. Our findings that might explore the potential pathogenesis of AS and RPL17, MRPL22, PSMA6 and PSMA4 have the potential to be the biomarkers for the disease.
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