LCK, FOXC1 and hsa-miR-146a-5p as potential immune effector molecules associated with rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) is a type of systemic immune disease characterized by chronic inflammatory disease of the joints. However, the aetiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify potential immune effector molecules involved in RA. The three microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen 115 overlapping differentially expressed genes (DEGs). Subsequently, we constructed a protein–protein interaction (PPI) network encoded by these DEGs and identified 10 genes closely associated with RA – LCK, GZMA, GZMB, CD2, LAG3, IL-15, TNFRSF4, CD247, CCR5 and CCR7. Furthermore, in the miRNA–hub gene networks, we screened out hsa-miR-146a-5p, which is the miRNA controlling the largest number of hub genes. Finally, we found some transcription factors that closely interact with hub genes, such as FOXC1, GATA2, YY1, RUNX2, SREBF1, CEBPB and NFIC. This study successfully predicted that LCK, FOXC1 and hsa-miR-146a-5p can be used as potential immune effector molecules of RA. Our study may have potential implications for future prediction of disease progression in patients with symptomatic RA, and has important significance for the pathogenesis and targeted therapy of RA.

Keywords:

• Rheumatoid arthritis
• GEO dataset
• functional enrichment
• protein–protein interaction
• bioinformatics
• immune effector molecules

Acknowledgements

We thank Prof. Wei Wu for their valuable comments on an earlier draft of the manuscript.

Ethics approval and consent to participants

All methods were performed in accordance with the relevant guidelines and regulations (e.g., Declaration of Helsinki).

Consent for publication

The authors consent to the above manuscript being published in Biomarkers.

Author contributions

WW, XMC and LX conceived and designed the article. XMC and LX wrote the manuscript. XMC, YJ and RHZ developed the methodology. XMC, LX and YJ analyzed and discussed the data. All authors read and approved the manuscript.

Disclosure statement

The authors declare that they have no competing interests.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the [GEO] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi TO DATASETS ALONG WITH THE GSE1919, GSE10500, and GSE55457]. All data generated or analyzed during this study are included in this article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [number 81860644].