2019
DOI: 10.1371/journal.pone.0219698
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Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns

Abstract: 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 coll… Show more

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Cited by 16 publications
(9 citation statements)
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“…Such aberrantly methylated genes in RA and SLE may be used as biomarkers or predictors of disease progression, and severity (31). Moreover, by analyzing the gene expression in RA and SLE, we can get new insights into sex-bias in the inflammatory functions, gene biotypes and co-expression patterns (32). However, until now, a combined analysis of gene expression and methylation profiling microarray in immune cells of RA and SLE has not been performed.…”
Section: Introductionmentioning
confidence: 99%
“…Such aberrantly methylated genes in RA and SLE may be used as biomarkers or predictors of disease progression, and severity (31). Moreover, by analyzing the gene expression in RA and SLE, we can get new insights into sex-bias in the inflammatory functions, gene biotypes and co-expression patterns (32). However, until now, a combined analysis of gene expression and methylation profiling microarray in immune cells of RA and SLE has not been performed.…”
Section: Introductionmentioning
confidence: 99%
“…First of all, when data are made available for reuse, citations to the initial report increase. 90 In addition, genomic data potentially has value beyond the initial purpose and re-analysis of publicly available sequencing data with novel bioinformatic tools can lead to novel insights, for example, in RA, 91 to examine HLA and proteasome expression in different tissues 92 or public HTS data can be used to provide supportive information in addition to own sequencing experiments, as in the case of uncovering distinct subsets of patients with SLE using machine learning methods. 93 However, clinically useful and translational reanalysis requires (1) the searchability of this data, which is only guaranteed if the data are deposited one of the above-mentioned repositories and (2) the availability of detailed patient characteristics along with clinical information linked to the respective sequencing sample (ie, data characterisation challenge).…”
Section: Discussionmentioning
confidence: 99%
“…Although the key genes described in their study did not overlap with that identified in our current study, it is notable that TNFSF10 (the key gene identified in the current study) was also among the top 10 characteristic genes (without heterogeneity between two datasets) reported by Xing et al ( 2011 ); Ma et al ( 2017 ) used Bayesian network in conjunction with Monte Carlo simulation to predict genes with novel roles (which was not directly associated with RA therapeutic target, TNF-α) in RA treatment, whereby dozens of genes were predicted to play novel roles in RA by impacting Disease Activity Score 28 (DAS28) or joint health. Platzer et al ( 2019 ) adopted machine learning to generate gene expression-based models for distinguishing between HC/RA and early RA/other related arthritis (e.g., arthralgia) based on several DEGs identified by single-variable comparisons. By comparing these machine learning-based reports, we did not find intersection between our currently identified key genes and those identified in the abovementioned machine learning-based reports, which might be attributed to different research goals and the scales of datasets.…”
Section: Discussionmentioning
confidence: 99%