Genomic Insights into Rheumatoid Arthritis through computational Profiling for Hub Genes
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Abstract
Rheumatoid arthritis (RA) is a complex autoimmune disorder predominantly affecting joints, with its etiology and response to treatment still not fully understood despite extensive historical documentation. This study aims to shed light on the differentially expressed genes (DEGs) associated with RA progression, potentially identifying new drug targets and management strategies. This study analyzed gene expression data (GSE193193) from the Gene Expression Omnibus (GEO) database, identifying 3672 significant DEGs out of 36107 initially retrieved genes. Among these, 283 genes were up-regulated and 360 were down-regulated. Gene enrichment analysis was performed to uncover relevant gene ontology terms and pathways. Subsequently, network construction and analysis, along with hub gene prediction using Cytoscape's MCODE and CytoHubba plugins, were conducted. Key genes identified in this study include HBB, ALAS2, GATA1, AHSP, HBG1, HBG2, HBD, KLF1, SLC4A1, EPB42, ZMYND10, DNAJC7, HYDIN, LRRC6, FN1, NCAM1, FASLG, CTCF, SMAD4, and STAT1. These genes are implicated not only in RA but also in other diseases, presenting them as potential therapeutic targets. Additionally, three transcription factors (GATA1, NFKB1, and RELA) and one miRNA (has-mir-27a-3p) were identified as key regulators of these hub genes. In conclusion, this study not only enhances our understanding of the molecular mechanisms underlying RA but also identifies several critical DEGs and regulatory factors that could serve as promising targets for therapeutic intervention. The identification of these genes and regulatory elements paves the way for the development of targeted treatments, which could significantly improve disease management and patient outcomes. Future research focusing on these identified targets may lead to innovative strategies for combating RA and potentially other autoimmune disorders, thereby offering new hope to patients affected by these conditions.
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References
Akbari, F., Peymani, M., Salehzadeh, A., & Ghaedi, K. (2020). Integrative in silico and in vitro transcriptomics analysis revealed new lncRNAs related to intrinsic apoptotic genes in colorectal cancer. Cancer Cell International, 20(1), Article 546. https://doi.org/10.1186/s12935-020-01633-w
Bader, G. D., & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4(2), 1-27. https://doi.org/10.1186/1471-2105-4-2
Bullock, J., Rizvi, S. A. A., Saleh, A. M., Ahmed, S. S., Do, D. P., Ansari, R. A. & Ahmed, J. (2018). Rheumatoid arthritis: A brief overview of the treatment. Medical Principles and Practice, 27(6), 501-507. https://doi.org/10.1159/000493390
Cassotta, M., Pistollato, F., & Battino, M. (2020). Rheumatoid arthritis research in the 21st century: Limitations of traditional models, new technologies, and opportunities for a human biology-based approach. ALTEX, 37(2), 223-242. https://doi.org/10.14573/altex.1910011
Chae, D.-K., Ban, E., Yoo, Y. S., Kim, E. E., Baik, J. H., & Song, E. J. (2017). MIR-27a regulates the TGF-β signaling pathway by targeting SMAD2 and SMAD4 in lung cancer. Molecular Carcinogenesis, 56(8), 1992-1998. https://doi.org/10.1002/mc.22655
Chauhan, K., Jandu, J. S.,Goyal, A. & Al-Dhahir, M. A. (2022). Rheumatoid arthritis. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK441999/
Conigliaro, P., Scrivo, R., Valesini, G., & Perricone, R. (2011). Emerging role for NK cells in the pathogenesis of inflammatory arthropathies. Autoimmunity Reviews, 10(10), 577-581. https://doi.org/10.1016/j.autrev.2011.04.017
Crowson, C. S., Matteson, E. L., Myasoedova, E., Michet, C. J., Ernste, F. C., Warrington, K. J., Davis III, J. M., Hunder, G. G., Therneau, T. M., & Gabriel, S. E. (2011). The lifetime risk of adult-onset rheumatoid arthritis and other inflammatory autoimmune rheumatic diseases. Arthritis & Rheumatism, 63(3), 633-639. https://doi.org/10.1002/art.30155
Cui, H., Banerjee, S., Xie, N., Ge, J., Liu, R.-M., Matalon, S., Thannickal. V. J., & Liu, G. (2016). MicroRNA-27a-3p is a negative regulator of lung fibrosis by targeting myofibroblast differentiation. American Journal of Respiratory Cell and Molecular Biology, 54(6), 843-852. https://doi.org/10.1165/rcmb.2015-0205OC
Derksen, V. F. A. M., Huizinga, T. W. J., & van der Woude, D. (2017). The role of autoantibodies in the pathophysiology of rheumatoid arthritis. Seminars in Immunopathology, 39(4), 437-446. https://doi.org/10.1007/s00281-017-0627-z
Espina, M. D. T., Gabarrini, G., Harmsen, H. J. M., Westra, J., van Winkelhoff, A. J., & van Dijl, J. M. (2019). Talk to your gut: The oral-gut microbiome axis and its immunomodulatory role in the etiology of rheumatoid arthritis. FEMS Microbiology Reviews, 43(1), 1-18. https://doi.org/10.1093/femsre/fuy035
Fan, D., Liu, B., Gu, X., Zhang, Q., Ye, Q., Xi, X., Xia, Y., Wang, Q., Wang, Z., Wang, B., Xu, Y., & Xiao, C. (2022). Potential target analysis of triptolide based on transcriptome-wide m6A methylome in rheumatoid arthritis. Frontiers in Pharmacology, 13, Article 843358. https://doi.org/10.3389/fphar.2022.843358
Fan, Y., Siklenka, K., Arora, S., Ribeiro, P., Kimmins, S., & Xia, J. (2016). miRNet - Dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Research, 44(W1), W135-W141. https://doi.org/10.1093/nar/gkw288
Firestein, G. S., & McInnes, I. B. (2017). Immunopathogenesis of rheumatoid arthritis. Immunity, 46(2), 183-196. https://doi.org/10.1016/j.immuni.2017.02.006
Gabriel, S. E., & Michaud, K. (2009). Epidemiological studies in incidence, prevalence, mortality, and comorbidity of the rheumatic diseases. Arthritis Research & Therapy, 11(5), Article 229. https://doi.org/10.1186/ar2669
Horani, A., & Ferkol, T. W. (2018). Advances in the genetics of primary ciliary dyskinesia: Clinical implications. Chest, 154(3), 645-652. https://doi.org/10.1016/j.chest.2018.05.007
Hu, G., Wu, Z., Uversky, V. N., & Kurgan, L. (2017). Functional analysis of human hub proteins and their interactors involved in the intrinsic disorder-enriched interactions. International Journal of Molecular Sciences, 18(12), 2761-2801. https://doi.org/10.3390/ijms18122761
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44-57. https://doi.org/10.1038/nprot.2008.211
Ivashkiv, L. B., & Donlin, L. T. (2014). Regulation of type I interferon responses. Nature Reviews Immunology, 14(1), 36-49. https://doi.org/10.1038/nri3581
Kłodziński, Ł., & Wisłowska, M. (2018). Comorbidities in rheumatic arthritis. Reumatologia, 56(4), 228-233. https://doi.org/10.5114/reum.2018.77974
Lee, H. S., Remmers, E. F., Le, J. M., Kastner, D. L., Bae, S. C., & Gregersen, P. K. (2007). Association of STAT4 with rheumatoid arthritis in the Korean population. Molecular Medicine, 13(9-10), 455-460.
Lee, Y. H., Bae, S. C., & Song, G. G. (2015). Association between the CTLA-4, CD226, FAS polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis. Human Immunology, 76(2-3), 83-89. https://doi.org/10.1016/j.humimm.2015.01.023
Liu, H., Wei, S.P., Zhi, L.Q. Liu, L. P., Cao, T.P., Wang, S. Z., Qing-Ping Chen, Q.P., Liu, D. (2018). Synovial GATA1 mediates rheumatoid arthritis progression via transcriptional activation of NOS2 signaling. Microbiology and Immunology, 62(9), 594-606. https://doi.org/10.1111/1348-0421.12637
Lubberts, E., & van den Berg, W. B. (2013). Cytokines in the pathogenesis of rheumatoid arthritis and collagen-induced arthritis. https://www.ncbi.nlm.nih.gov/books/NBK6288/
Ma, J., Hu, X., Dai, B., Wang, Q., & Wang, H. (2021). Bioinformatics analysis of laryngeal squamous cell carcinoma: Seeking key candidate genes and pathways. PeerJ, 9, Article e11259. https://doi.org/10.7717/peerj.11259
Massagué, J. (2012). TGFβ signalling in context. Nature Reviews Molecular Cell Biology, 13(10), 616-630. https://doi.org/10.1038/nrm3434
Osterman, T. J., Terry, M., & Miller, R. S. (2020). Improving cancer data interoperability: The promise of the minimal common oncology data elements (mCODE) initiative. JCO Clinical Cancer Informatics, 4, 993-1001. https://doi.org/10.1200/CCI.20.00059
Przybysz, M., Borysewicz, K., Szechinski, J., & Katnik-Prastowska, I. (2007). Synovial fibronectin fragmentation and domain expressions in relation to rheumatoid arthritis progression. Rheumatology (Oxford), 46(7), 1071-1075. https://doi.org/10.1093/rheumatology/kem067
Rees, D. C., Williams, T. N., & Gladwin, M. T. (2010). Sickle-cell disease. The Lancet, 376(9757), 2018-2031. https://doi.org/10.1016/S0140-6736(10)61029-X
Scherer, H. U., Häupl, T., & Burmester, G. R. (2020). The etiology of rheumatoid arthritis. Journal of Autoimmunity, 110, Article 102400. https://doi.org/10.1016/j.jaut.2019.102400
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B. & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498-2504. https://doi.org/10.1101/gr.1239303
Silva, G. L., Junta, C. M., Sakamoto-Hojo, E. T., Donadi, E. A., Louzada-Junior, P., & Passos, G. A. (2009). Genetic susceptibility loci in rheumatoid arthritis establish transcriptional regulatory networks with other genes. Annals of the New York Academy of Sciences, 1173, 521-537. https://doi.org/10.1111/j.1749-6632.2009.04629.x
Steinberg, M. H., Lu, Z. H., Barton, F. B., Terrin, M. L., Charache, S., & Dover, G. J. (1997). Fetal hemoglobin in sickle cell anemia: Determinants of response to hydroxyurea. Blood, 89(3), 1078-1088.
Sunkar, S., Namratha, K., & Neeharika, D. (2022). Identification of hub genes associated with human osteoarthritis cartilage: An in silico approach. Meta Gene, 31, Article 101015. https://doi.org/10.1016/j.mgene.2022.101015
Szklarczyk, D., Franceschini, A., Wyder, S., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., Kuhn, M., Bork, P., Jensen, L. J., & von Mering, C. (2015). STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Research, 43(D1), D447-D452. https://doi.org/10.1093/nar/gku1003
Tanaka, Y. (2020). Rheumatoid arthritis. Inflammation and Regeneration, 40, Article 20. https://doi.org/10.1186/s41232-020-00133-8
van Beers, J. J., Willemze, A., Stammen-Vogelzangs, J., Drijfhout, J. W., Toes, R. E., & Pruijn, G. J. (2012). Anti-citrullinated fibronectin antibodies in rheumatoid arthritis are associated with human leukocyte antigen-DRB1 shared epitope alleles. Arthritis Research & Therapy, 14(1), Article R35. https://doi.org/10.1186/ar3744
van der Pouw Kraan, T. C., van Gaalen, F. A., Kasperkovitz, P. V., Verbeet, N. L., Smeets, T. J. M., Kraan, M. C., Fero, M., Tak, P.-P., Huizinga, T. W. J., Pieterman, E., Breedveld, F. C., Alizadeh, A. A., & Verweij, C. L. (2003). Rheumatoid arthritis is a heterogeneous disease: Evidence for differences in the activation of the STAT-1 pathway between rheumatoid tissues. Arthritis and Rheumatism, 48(8), 2132-2145. https://doi.org/10.1002/art.11096
Wang, S., Wang, L., Wu, C., Sun, S., & Pan, J.-H. (2018). E2F2 directly regulates the STAT1 and PI3K/AKT/NF-κB pathways to exacerbate the inflammatory phenotype in rheumatoid arthritis synovial fibroblasts and mouse embryonic fibroblasts. Arthritis Research & Therapy, 20(1), Article 225. https://doi.org/10.1186/s13075-018-1713-x
Yan, X., Yin, L., Wang, Y., Zhao, Y., & Chang, X. (2013). The low binding affinity of ADAMTS4 for citrullinated fibronectin may contribute to the destruction of joint cartilage in rheumatoid arthritis. Clinical and Experimental Rheumatology, 31(2), 201-216.
Yap, H.-Y., Tee, S. Z., Wong, M. M., Chow, S. K., Peh, S. C., & Teow, S.-Y. (2018). Pathogenic role of immune cells in rheumatoid arthritis: Implications in clinical treatment and biomarker development. Cells, 7(10), 161-180. https://doi.org/10.3390/cells7100161
Yıldır, S., Sezgin, M., Barlas, İ. Ö., Türköz, G., Ankaralı, H. Ç., Şahin, G., & Erdal, M. E. (2013). Relation of the Fas and FasL gene polymorphisms with susceptibility to and severity of rheumatoid arthritis. Rheumatology International, 33(10), 2637-2645. https://doi.org/10.1007/s00296-013-2793-1
Yoshida, S., Arakawa, F., Higuchi, F., Ishibashi, Y., Goto, M., Sugita, Y., Nomura, Y., Niino, D., Shimizu, K., Aoki, R., Hashikawa, K., Kimura, Y., Yasuda, K., Tashiro, K., Kuhara, S., Nagata, K., & Ohshima, K. (2012). Gene expression analysis of rheumatoid arthritis synovial lining regions by cDNA microarray combined with laser microdissection: Up-regulation of inflammation-associated STAT1, IRF1, CXCL9, CXCL10, and CCL5. Scandinavian Journal of Rheumatology, 41(3), 170-189. https://doi.org/10.3109/03009742.2011.623137
Yu, C., Cantor, A. B., Yang, H., Browne, C., Wells, R. A., Fujiwara, Y. & Orkin, S. H. (2002). Targeted deletion of a high-affinity GATA-binding site in the GATA-1 promoter leads to selective loss of the eosinophil lineage in vivo. The Journal of Experimental Medicine, 195(11), 1387-1395. https://doi.org/10.1084/jem.20020656
Zhang, R., Yang, X., Wang, J., Han, L., Yang, A., Zhang, J., Zhang, D., Li, B., Li, Z., & Xiong, Y. (2019). Identification of potential biomarkers for differential diagnosis between rheumatoid arthritis and osteoarthritis via integrative genome wide gene expression profiling analysis. Molecular Medicine Reports, 19(1), 30-40. https://doi.org/10.3892/mmr.2018.9677
Zhu, M., Ding, Q., Lin, Z., Fu, R., Zhang, F., Li, Z., Zhang, M., & Zhu, Y. (2023). New targets and strategies for rheumatoid arthritis: From signal transduction to epigenetic aspect. Biomolecules, 13(5), 766-786. https://doi.org/10.3390/biom13050766