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Integrating bioinformatic resources to identify characteristics of rheumatoid arthritis-related usual interstitial pneumonia.


ABSTRACT:

Background

Rheumatoid arthritis (RA) is often accompanied by a common extra-articular manifestation known as RA-related usual interstitial pneumonia (RA-UIP), which is associated with a poor prognosis. However, the mechanism remains unclear. To identify potential mechanisms, we conducted bioinformatics analysis based on high-throughput sequencing of the Gene Expression Omnibus (GEO) database.

Results

Weighted gene co-expression network analysis (WGCNA) analysis identified 2 RA-positive related modules and 4 idiopathic pulmonary fibrosis (IPF)-positive related modules. A total of 553 overlapped differentially expressed genes (DEG) were obtained, of which 144 in the above modules were further analyzed. The biological process of "oxidative phosphorylation" was found to be the most relevant with both RA and IPF. Additionally, 498 up-regulated genes in lung tissues of RA-UIP were screened out and enriched by 7 clusters, of which 3 were closely related to immune regulation. The analysis of immune infiltration showed a characteristic distribution of peripheral immune cells in RA-UIP, compared with IPF-UIP in lung tissues.

Conclusions

These results describe the complex molecular and functional landscape of RA-UIP, which will help illustrate the molecular pathological mechanism of RA-UIP and identify new biomarkers and therapeutic targets for RA-UIP in the future.

SUBMITTER: Qiu Y 

PROVIDER: S-EPMC10413595 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Integrating bioinformatic resources to identify characteristics of rheumatoid arthritis-related usual interstitial pneumonia.

Qiu Yulu Y   Liu Chang C   Shi Yumeng Y   Hao Nannan N   Tan Wenfeng W   Wang Fang F  

BMC genomics 20230810 1


<h4>Background</h4>Rheumatoid arthritis (RA) is often accompanied by a common extra-articular manifestation known as RA-related usual interstitial pneumonia (RA-UIP), which is associated with a poor prognosis. However, the mechanism remains unclear. To identify potential mechanisms, we conducted bioinformatics analysis based on high-throughput sequencing of the Gene Expression Omnibus (GEO) database.<h4>Results</h4>Weighted gene co-expression network analysis (WGCNA) analysis identified 2 RA-pos  ...[more]

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