Dataset Information

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 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.

SUBMITTER: Platzer A

PROVIDER: S-EPMC6657850 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Platzer Alexander A Nussbaumer Thomas T Karonitsch Thomas T Smolen Josef S JS Aletaha Daniel D

PloS one 20190725 7

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 ...[more]

PMID: 31344123

Similar Datasets

Project description:Rheumatoid arthritis (RA) is a chronic synovial autoinflammatory disease that destructs the cartilage and bone, leading to disability. The functional regulation of major immunity-related pathways like nuclear factor kappa B (NF-κB), which is involved in the chronic inflammatory reactions underlying the development of RA, remains to be explored. Therefore, this study has adopted statistical and knowledge-based systemic investigations (like gene correlation, semantic similarity, and topological parameters based on graph theory) to study the gene expression status of NF-κB protein family (NKPF) and its regulators in synovial tissues to trace the molecular pathways through which these regulators contribute to RA. A complex protein-protein interaction map (PPIM) of 2,742 genes and 37,032 interactions was constructed from differentially expressed genes (p ≤ 0.05). PPIM was further decomposed into a Regulator Allied Protein Interaction Network (RAPIN) based on the interaction between genes (5 NKPF, 31 seeds, 131 hubs, and 652 bottlenecks). Pathway network analysis has shown the RA-specific disturbances in the functional connectivity between seed genes (RIPK1, ATG7, TLR4, TNFRSF1A, KPNA1, CFLAR, SNW1, FOSB, PARVA, CX3CL1, and TRPC6) and NKPF members (RELA, RELB, NFKB2, and REL). Interestingly, these genes are known for their involvement in inflammation and immune system (signaling by interleukins, cytokine signaling in immune system, NOD-like receptor signaling, MAPK signaling, Toll-like receptor signaling, and TNF signaling) pathways connected to RA. This study, for the first time, reports that SNW1, along with other NK regulatory genes, plays an important role in RA pathogenesis and might act as potential biomarker for RA. Additionally, these genes might play important roles in RA pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative data analysis and network-based methods could accelerate the identification of novel drug targets for RA from high-throughput genomic data.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets