Identification of dysregulated genes in rheumatoid arthritis based on bioinformatics analysis

Hao, Ruihu; Du, H; Guo, Lin; Tian, Fengde; An, Ning; Yang, Tiejun; Wang, Changcheng; Wang, Bo; Zhou, Zihao

doi:10.7717/peerj.3078

Cited by 13 publications

(8 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The role of miRNA regulation in bone diseases has been reported, including osteoporosis and arthritis that are associated with altered bone homeostasis [ 20 , 21 ]. The study of gene associations using the next generation sequencing (NGS) technique and bioinformatics approaches has evolved, providing high-throughput genomic profiling and further understanding and analysis of functional annotations of identified genes and/or miRNAs [ 22 , 23 ]. In this study, we used various bioinformatics tools and databases to assist in identifying potential miRNA–mRNA interactions in osteoblasts of RA population, including miRmap [ 24 ], Gene Expression Omnibus (GEO) [ 25 ], Ingenuity ® Pathway Analysis (IPA) [ 26 ], and Database for Annotation, Visualization and Integrated Discovery (DAVID) [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Deduction of Novel Genes Potentially Involved in Osteoblasts of Rheumatoid Arthritis Using Next-Generation Sequencing and Bioinformatic Approaches

Chen

Chang

Hsu

et al. 2017

IJMS

View full text Add to dashboard Cite

The role of osteoblasts in peri-articular bone loss and bone erosion in rheumatoid arthritis (RA) has gained much attention, and microRNAs are hypothesized to play critical roles in the regulation of osteoblast function in RA. The aim of this study is to explore novel microRNAs differentially expressed in RA osteoblasts and to identify genes potentially involved in the dysregulated bone homeostasis in RA. RNAs were extracted from cultured normal and RA osteoblasts for sequencing. Using the next generation sequencing and bioinformatics approaches, we identified 35 differentially expressed microRNAs and 13 differentially expressed genes with potential microRNA–mRNA interactions in RA osteoblasts. The 13 candidate genes were involved mainly in cell–matrix adhesion, as classified by the Gene Ontology. Two genes of interest identified from RA osteoblasts, A-kinase anchoring protein 12 (AKAP12) and leucin rich repeat containing 15 (LRRC15), were found to express more consistently in the related RA synovial tissue arrays in the Gene Expression Omnibus database, with the predicted interactions with miR-183-5p and miR-146a-5p, respectively. The Ingenuity Pathway Analysis identified AKAP12 as one of the genes involved in protein kinase A signaling and the function of chemotaxis, interconnecting with molecules related to neovascularization. The findings indicate new candidate genes as the potential indicators in evaluating therapies targeting chemotaxis and neovascularization to control joint destruction in RA.

show abstract

Section: Introductionmentioning

confidence: 99%

Deduction of Novel Genes Potentially Involved in Osteoblasts of Rheumatoid Arthritis Using Next-Generation Sequencing and Bioinformatic Approaches

Chen

Chang

Hsu

et al. 2017

IJMS

View full text Add to dashboard Cite

show abstract

“…There is currently no study that explains the association of "RNA transport" with RA. However, a recent study that analyzed dysregulated genes in RA also found that DEGs were enriched in "RNA transport" among other pathways 53 . This implies that dysregulation of "RNA transport" may play an important role in RA.…”

Section: Results Of Analysis On Ra Datamentioning

confidence: 99%

pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

Ülgen

Özışık

Sezerman

2018

Preprint

View full text Add to dashboard Cite

PathfindR is a tool for pathway enrichment analysis utilizing active subnetworks. It identifies gene sets that form active subnetworks in a protein-protein interaction network using a list of genes provided by the user. It then performs pathway enrichment analyses on the identified gene sets. Further, using the R package pathview, it maps the user data on the enriched pathways and renders pathway diagrams with the mapped genes. Because many of the enriched pathways are usually biologically related, pathfindR also offers functionality to cluster these pathways and identify representative pathways in the clusters. PathfindR is built as a stand-alone package but it can easily be integrated with other tools, such as differential expression/methylation analysis tools, for building fully automated pipelines. In this article, an overview of pathfindR is provided and an example application on a rheumatoid arthritis dataset is presented and discussed.

show abstract

“…Whole-exome sequencing defined LCK as linked to familial RA and highlighted LCK variation in the T cell receptor (TCR) signaling pathway leading to T cell activation, resulting in T cell differentiation, survival, and effector functions [ 30 ]. Bioinformatics analysis showed that HLA-DRA was dysregulated in RA patients [ 31 , 32 ]. Furthermore, HLA-E was involved in susceptibility to RA and anti-TNF treatment in RA patients [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning

Liu

Chen

2021

J Orthop Surg Res

View full text Add to dashboard Cite

Background Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA. Methods The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes. Results Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC. Conclusion The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.

show abstract

Identification of dysregulated genes in rheumatoid arthritis based on bioinformatics analysis

Cited by 13 publications

References 44 publications

Deduction of Novel Genes Potentially Involved in Osteoblasts of Rheumatoid Arthritis Using Next-Generation Sequencing and Bioinformatic Approaches

Deduction of Novel Genes Potentially Involved in Osteoblasts of Rheumatoid Arthritis Using Next-Generation Sequencing and Bioinformatic Approaches

pathfindR: An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks

A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning

Contact Info

Product

Resources

About