Identifying genes related with rheumatoid arthritis via system biology analysis

Liu, Tao; Lin, Xinmei; Yu, Haoyang

doi:10.1016/j.gene.2015.06.058

Cited by 9 publications

(7 citation statements)

References 57 publications

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“…The RA association of rs2013109 in the RNASE2 gene was shown this way [ 51 ]. Currently, the search for RA candidate genes is performed by bioinformatic analysis of published expression profiles obtained with high-density microchip technology (for instance, Affymetrix) [ 52 , 53 , 54 , 55 ]. Whole-genome or whole-exome sequencing, utilising high-performance HTS platforms, could also help to identify rare and population-specific pathogenic genetic variants.…”

Section: Predisposition To Ra Depends On the Methods Of Analysis Ofmentioning

confidence: 99%

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Mikhaylenko

Немцова

Bure

et al. 2020

IJMS

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Rheumatoid arthritis (RA) is the most common inflammatory arthropathy worldwide. Possible manifestations of RA can be represented by a wide variability of symptoms, clinical forms, and course options. This multifactorial disease is triggered by a genetic predisposition and environmental factors. Both clinical and genealogical studies have demonstrated disease case accumulation in families. Revealing the impact of candidate gene missense variants on the disease course elucidates understanding of RA molecular pathogenesis. A multivariate genomewide association study (GWAS) based analysis identified the genes and signalling pathways involved in the pathogenesis of the disease. However, these identified RA candidate gene variants only explain 30% of familial disease cases. The genetic causes for a significant proportion of familial RA have not been determined until now. Therefore, it is important to identify RA risk groups in different populations, as well as the possible prognostic value of some genetic variants for disease development, progression, and treatment. Our review has two purposes. First, to summarise the data on RA candidate genes and the increased disease risk associated with these alleles in various populations. Second, to describe how the genetic variants can be used in the selection of drugs for the treatment of RA.

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Section: Predisposition To Ra Depends On the Methods Of Analysis Ofmentioning

confidence: 99%

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Mikhaylenko

Немцова

Bure

et al. 2020

IJMS

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“…Synovial joint diseases are a substantial burden to society, including arthritis, trauma, and congenital anomalies. Individual growth and transcriptional factors have been studied primarily to understand joint development and arthritis [ 1 , 2 ]. However, little is known regarding the genes that regulate stem/progenitor cells in homeostasis or pathological conditions of postnatal synovial joints.…”

Section: Introductionmentioning

confidence: 99%

Profiling of Stem/Progenitor Cell Regulatory Genes of the Synovial Joint by Genome-Wide RNA-Seq Analysis

Zhou

Ricupero

et al. 2018

BioMed Research International

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Synovial joints suffer from arthritis and trauma that may be severely debilitative. Despite robust investigations in the roles of individual genes in synovial joint development and arthritis, little is known about global profiles of genes that regulate stem/progenitor cells of a synovial joint. The temporomandibular joint is a poorly understood synovial arthrosis with few clinical treatment options. Here, we isolated the articular and mature zones of the mandibular condyle by laser capture microdissection, performed genome-wide profiling, and analyzed molecular signaling pathways relevant to stem/progenitor cell functions. A total of 804 genes were differentially expressed between the articular and mature zones. Pathway analyses revealed 29 enriched signaling pathways, including the PI3K-Akt, Wnt, and Toll-like receptor signaling pathways that may regulate stem/progenitor cell homeostasis and differentiation into the chondrocyte lineage. Upstream regulator analyses further predicted potential upstream key regulators such as Xbp1, Nupr1, and Hif1a, and associated underlying mechanism networks were described. Among the multiple candidates of growth and transcriptional factors that may regulate stem/progenitor cells, we immunolocalized Sox9, Ihh, Frzb, Dkk1, Lgr5, and TGFβ3 in the articular and mature zones. These findings provide a comprehensive genetic mapping of growth and transcriptional genes in the articular and mature zones of a synovial joint condyle. Differentially expressed genes may play crucial roles in the regulation of stem/progenitor cells in development, homeostasis, and tissue regeneration.

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“…In this analysis, we have added several new views, hints and insights about gene expression in RA. Some of these approaches had been tried on former, limited microarray data in the one or other similar way [18, 19, 57–60]. Compared with these results, it makes sense to to repeat the same or similar types of analyses with recent RNA-seq data.…”

Section: Discussionmentioning

confidence: 99%

“…We have combined and compared 236 RNA-seq synovial biopsy samples from the papers of Walsh et al and Guo et al [15, 16] and microarray data from the papers of Liu et al, Teixeira et al, Niu et al and Yoshida et al [18, 58–60] in this study. This RNA-seq sample collection was chosen, because it is consistent, large and there are open questions for which insights or at least hints are in this data.…”

Section: Methodsmentioning

confidence: 99%

Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns

et al. 2019

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The era of next-generation sequencing has mounted the foundation of many gene expression studies. In rheumatoid arthritis research, this has led to the discovery of important candidate genes which offered novel insights into mechanisms and their possible roles in the cure of the disease. In the last years, data generation has outstripped data analysis and while many studies focused on specific aspects of the disease, a global picture of the disease is not yet accomplished. Here, we analyzed and compared a collection of gene expression information from healthy individuals and from patients suffering under different arthritis conditions from published studies containing the following clinical conditions: early and established rheumatoid arthritis, osteoarthritis and arthralgia. We show comprehensive overviews of this data collection and give new insights specifically on gene expression in the early stage, into sex-dependent gene expression, and we describe general differences in expression of different biotypes of genes. Many genes that are related to cytoskeleton changes (actin filament related genes) are differently expressed in early rheumatoid arthritis in comparison to healthy subjects; interestingly, eight of these genes reverse their expression ratio significantly between men and women compared early rheumatoid arthritis and healthy subjects. There are some slighter changes between men and woman between the conditions early and established rheumatoid arthritis. Another aspect are miRNAs and other gene biotypes which are not only promising candidates for diagnoses but also change their expression grossly in average at rheumatoid arthritis and arthralgia compared to the healthy condition. With a selection of intersecting genes, we were able to generate simple classification models to distinguish between healthy and rheumatoid arthritis as well as between early rheumatoid arthritis to other arthritides based on gene expression.

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Identifying genes related with rheumatoid arthritis via system biology analysis

Cited by 9 publications

References 57 publications

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Profiling of Stem/Progenitor Cell Regulatory Genes of the Synovial Joint by Genome-Wide RNA-Seq Analysis

Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns

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