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Gregersen, Peter K.

doi:10.1186/ar9

Cited by 74 publications

(7 citation statements)

References 71 publications

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“…Consistent with the results of previous studies, our study indicates that RA is a disease involving a complex gene network and multiple gene contributors [ 28 ]. In this study, 39 genes were selected in the PPI network and 9 hub genes were identified after modular analysis of the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA.…”

Section: Discussionsupporting

confidence: 93%

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

Liu

Chen

2021

J Orthop Surg Res

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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.

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Section: Discussionsupporting

confidence: 93%

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

Liu

Chen

2021

J Orthop Surg Res

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“…Like other autoimmune diseases, RA has a strong genetic component with a heritability of ~60% based on twin data (1–3). To elucidate the genetic and molecular basis of RA, tremendous efforts have been made in the mapping of quantitative trait loci (QTL) associated with the disease.…”

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confidence: 99%

Genetic and Molecular Basis of Quantitative Trait Loci of Arthritis in Rat: Genes and Polymorphisms

Xiong

Jiao

Hasty

et al. 2008

The Journal of Immunology

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Rheumatoid arthritis (RA) is an autoimmune disease, the pathogenesis of which is affected by multiple genetic and environmental factors. To understand the genetic and molecular basis of RA, a large number of quantitative trait loci (QTL) that regulate experimental autoimmune arthritis have been identified using various rat models for RA. However, identifying the particular responsible genes within these QTL remains a major challenge. Using currently available genome data and gene annotation information, we systematically examined RA-associated genes and polymorphisms within and outside QTL over the whole rat genome. By the whole genome analysis of genes and polymorphisms, we found that there are significantly more RA-associated genes in QTL regions as contrasted with non-QTL regions. Further experimental studies are necessary to determine whether these known RA-associated genes or polymorphisms are genetic components causing the QTL effect.

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“…Citrullination [ 19 ] leads to the activation of complex immune responses and specific ACPA generation, found in approximately 75% of RA patients [ 20 ]. The risk of developing RA also depends on hereditary factors [ 21 ], and over 100 genetic loci are involved in an increased risk of RA [ 22 ]. Interestingly, twin studies have found no difference in heritability in subsets of ACPA-positive and ACPA-negative RA [ 23 ].…”

Section: Role Of Autoantibodies In Predicting the Response To Ra Tmentioning

confidence: 99%

Biomarkers to Personalize the Treatment of Rheumatoid Arthritis: Focus on Autoantibodies and Pharmacogenetics

et al. 2020

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Rheumatoid arthritis (RA) is a chronic inflammatory disease that is very complex and heterogeneous. If not adequately treated, RA patients are likely to manifest excess of morbidity and disability with an important impact on the quality of life. Pharmacological treatment is based on the administration of the disease-modifying antirheumatic drugs (DMARDs), subdivided into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs), and biological (bDMARDs). bDMARDs are now frequently administered in patients, both as alternative treatment and together with csDMARDs. Unfortunately, there is a therapeutic response variability both to old and new drugs. Therefore, to identify pre-therapeutic and on-treatment predictors of response is a priority. This review aims to summarize recent advances in understanding the causes of the variability in treatment response in RA, with particular attention to predictive potential of autoantibodies and DMARD pharmacogenetics. In recent years, several biomarkers have been proposed to personalize the therapy. Unfortunately, a magic bullet does not exist, as many factors concur to disease susceptibility and treatment outcomes, acting around the patient’s congenital background. Models integrating demographic, clinical, biochemical, and genetic data are needed to enhance the predictive capacity of specific factors singularly considered to optimize RA treatment in light of multidisciplinary patient management.

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Untitled

Cited by 74 publications

References 71 publications

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

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

Genetic and Molecular Basis of Quantitative Trait Loci of Arthritis in Rat: Genes and Polymorphisms

Biomarkers to Personalize the Treatment of Rheumatoid Arthritis: Focus on Autoantibodies and Pharmacogenetics

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