Abstract
Introduction
Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women.
Methods
First, the Gene Expression Omnibus database’s gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses.
Results
According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy.
Conclusion
The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease.
Key Points • Our research point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. • PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. • These findings may aid in the development of novel diagnostic and treatment techniques for RA in men and women. |
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References
England BR, Thiele GM, Anderson DR, Mikuls TR (2018) Increased cardiovascular risk in rheumatoid arthritis: mechanisms and implications. BMJ 361:k1036. https://doi.org/10.1136/bmj.k1036
Ngo ST, Steyn FJ, McCombe PA (2014) Gender differences in autoimmune disease. Front Neuroendocrinol 35:347–369. https://doi.org/10.1016/j.yfrne.2014.04.004
Zhou Y, Zhang C, Zhou Z et al (2022) Identification of key genes and pathways associated with PIEZO1 in bone-related disease based on bioinformatics. Int J Mol Sci 23:5250. https://doi.org/10.3390/ijms23095250
Zhou S, Lu H, Xiong M (2021) Identifying immune cell infiltration and effective diagnostic biomarkers in rheumatoid arthritis by bioinformatics analysis. Front Immunol 12:3291. https://doi.org/10.3389/fimmu.2021.726747
Yu C, Liu C, Jiang J et al (2020) Gender differences in rheumatoid arthritis: Interleukin-4 plays an important role. J Immunol Res 2020:1–12. https://doi.org/10.1155/2020/4121524
Barrett T, Wilhite SE, Ledoux P et al (2012) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41:D991–D995. https://doi.org/10.1093/nar/gks1193
Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R Package for comparing biological themes among gene clusters. Omi A J Integr Biol 16:284–287. https://doi.org/10.1089/omi.2011.0118
Zhou Y, Zhou B, Pache L et al (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523. https://doi.org/10.1038/s41467-019-09234-6
Mering Cv (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31:258–261. https://doi.org/10.1093/nar/gkg034
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (1971) Cytoscape: a software environment for integrated models. Genome Res 13:426. https://doi.org/10.1101/gr.1239303.metabolite
Chin C-H, Chen S-H, Wu H-H et al (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8:S11. https://doi.org/10.1186/1752-0509-8-S4-S11
Hu H, Luan L, Yang K, Li S (2018) Burden of rheumatoid arthritis from a societal perspective: a prevalence-based study on cost of this illness for patients in China. Int J Rheum Dis 21:1572–1580. https://doi.org/10.1111/1756-185X.13028
Iikuni N, Sato E, Hoshi M et al (2009) The influence of sex on patients with rheumatoid arthritis in a large observational cohort. J Rheumatol 36:508–511. https://doi.org/10.3899/jrheum.080724
Favalli EG, Biggioggero M, Crotti C et al (2019) Sex and management of rheumatoid arthritis. Clin Rev Allergy Immunol 56:333–345. https://doi.org/10.1007/s12016-018-8672-5
Nakano K, Boyle DL, Firestein GS (2013) Regulation of DNA methylation in rheumatoid arthritis synoviocytes. J Immunol 190:1297–1303. https://doi.org/10.4049/jimmunol.1202572
Nakano K, Whitaker JW, Boyle DL et al (2013) DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis 72:110–117. https://doi.org/10.1136/annrheumdis-2012-201526
Franz JK, Pap T, Hummel KM et al (2000) Expression of sentrin, a novel antiapoptotic molecule, at sites of synovial invasion in rheumatoid arthritis. Arthritis Rheum 43:599. https://doi.org/10.1002/1529-0131(200003)43:3%3c599::AID-ANR17%3e3.0.CO;2-T
Maeshima K, Stanford SM, Hammaker D et al (2016) Abnormal PTPN11 enhancer methylation promotes rheumatoid arthritis fibroblast-like synoviocyte aggressiveness and joint inflammation. JCI Insight 1:(7). https://doi.org/10.1172/jci.insight.86580
Tak PP, Zvaifler NJ, Green DR, Firestein GS (2000) Rheumatoid arthritis and p53: how oxidative stress might alter the course of inflammatory diseases. Immunol Today 21:78–82. https://doi.org/10.1016/S0167-5699(99)01552-2
Koster MJ, Warrington KJ (2021) VEXAS within the spectrum of rheumatologic disease. Semin Hematol 58:218–225. https://doi.org/10.1053/j.seminhematol.2021.10.002
Verstappen M, van Steenbergen HW, de Jong PHP, van der Helm-van Mil AHM (2022) Unraveling heterogeneity within ACPA-negative rheumatoid arthritis: the subgroup of patients with a strong clinical and serological response to initiation of DMARD treatment favor disease resolution. Arthritis Res Ther 24:4. https://doi.org/10.1186/s13075-021-02671-z
Nishikawa M, Myoui A, Tomita T et al (2003) Prevention of the onset and progression of collagen-induced arthritis in rats by the potent p38 mitogen-activated protein kinase inhibitor FR167653. Arthritis Rheum 48:2670–2681. https://doi.org/10.1002/art.11227
Behl T, Upadhyay T, Singh S et al (2021) Polyphenols targeting MAPK mediated oxidative stress and inflammation in rheumatoid arthritis. Molecules 26:6570. https://doi.org/10.3390/molecules26216570
Huang Q, Xiao X, Yu J et al (2022) Tectoridin exhibits anti-rheumatoid arthritis activity through the inhibition of the inflammatory response and the MAPK pathway in vivo and in vitro. Arch Biochem Biophys 727:109328. https://doi.org/10.1016/j.abb.2022.109328
Cybulsky AV (2017) Endoplasmic reticulum stress, the unfolded protein response and autophagy in kidney diseases. Nat Rev Nephrol 13:681–696. https://doi.org/10.1038/nrneph.2017.129
Funding
This work was supported by the Scientific Research Fund of Sichuan Health and Health Committee (no. 20PJ311).
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Tingting Wang designed the study. Fanxin Zeng and Jianhong Wu supervised the study. Tingting Wang, Fanxin Zeng, Xue Li, Yuanli Wei, Shilin Li, Dongmei Wang, and Weihua Zhang analyze the data. Tingting Wang, Huanhuan Xie, Lingli Wei, Siying Xiong, Caizhen Liu, and Xue Li did the digital visualization. Tingting Wang and Jianhong Wu wrote and revised the manuscript. All authors read and approved the final manuscript. All the authors agreed to publish the article. Tingting Wang and Fanxin Zeng contributed equally to this work and should be regarded as co-first authors.
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Wang, T., Zeng, F., Li, X. et al. Identification of key genes and pathways associated with sex differences in rheumatoid arthritis based on bioinformatics analysis. Clin Rheumatol 42, 399–406 (2023). https://doi.org/10.1007/s10067-022-06387-6
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DOI: https://doi.org/10.1007/s10067-022-06387-6