Abstract
The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein–protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.
Similar content being viewed by others
References
Ahn T., Lee E., Huh N. and Park T. 2014 Personalized identification of altered pathways in cancer using accumulated normal tissue data. Bioinformatics 30, i422–i429.
Alamanos Y. and Drosos A. A. 2005 Epidemiology of adult rheumatoid arthritis. Autoimmun. Rev. 4, 130–136.
Andersen M., Meyer M. K., Nagaev I., Nagaeva O., Wikberg J. E. S., Minchevanilsson L. et al. 2016 AB0020 the melanocortin system is responsive in disease driving immune cells in rheumatoid arthritis and may offer a pathway to curative treatment.Ann. Rheum. Dis. 75, 903–904.
Avinazubieta J. A., Thomas J., Sadatsafavi M., Lehman A. J. and Lacaille D. 2012 Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann. Rheum. Dis. 71, 1524–1529.
Barabasi A.-L. and Oltvai Z. N. 2004 Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–113.
Begovich A. B., Carlton V. E., Honigberg L. A., Schrodi S. J., Chokkalingam A. P., Alexander H. C. et al. 2004 A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am. J. Hum. Genet. 75, 330–337.
Braga-Neto U. M. and Dougherty E. R. 2004 Is cross-validation valid for small-sample microarray classification? Bioinformatics 20, 374–380.
Choy E. 2012 Understanding the dynamics: pathways involved in the pathogenesis of rheumatoid arthritis. Rheumatology (Oxford). 51, suppl. 5, v3–v11.
Diogo D., Kurreeman F., Stahl E., Liao K., Gupta N., Greenberg J. et al. 2013 Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid a Arthritis. Am. J. Hum. Genet. 92, 15–27.
Firestein G. S. 2003 Evolving concepts of rheumatoid arthritis. Nature 423, 356–361.
Franceschini A., Szklarczyk D., Frankild S., Kuhn M., Simonovic M., Roth A. et al. 2013 STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, 808–815.
Glazier A. M., Nadeau J. H. and Aitman T. J. 2002 Finding genes that underlie complex traits. Science 298, 2345–2349.
Hair M. J. H., Sande M. G. H., Ramwadhdoebe T. H., Hanson M., Landewe R. and Leij C. 2014 Features of the synovium of individuals at risk of developing rheumatoid arthritis: implications for understanding preclinical rheumatoid arthritis. Arthritis Rheumatol. 66, 513–522.
Hendrich B. D. and Willard H. F. 1995 Epigenetic regulation of gene expression: the effect of altered chromatin structure from yeast to mammals. Hum. Mol. Genet. 4 spec no, 1765–1777.
Herrero J., Díazuriarte R. and Dopazo J. 2003 Gene expression data preprocessing. Bioinformatics 19, 655–656.
Hotelling H. 2010 Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441.
Hu R., Qiu X., Glazko G., Klebanov L. and Yakovlev A. 2009 Detecting intergene correlation changes in microarray analysis: a new approach to gene selection. BMC Bioinformatics 10, 20–28.
Karouzakis E., Gay R. E., Gay S. and Neidhart M. 2009 Epigenetic control in rheumatoid arthritis synovial fibroblasts. Nat. Rev. Rheumatol. 5, 266–272.
Khatri P., Sirota M. and Butte A. J. 2012 Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8, 1454–1459.
Kyburz D., Rethage J., Seibl R., Lauener R., Gay R. E., Carson D. A. et al. 2003 Bacterial peptidoglycans but not CpG oligodeoxynucleotides activate synovial fibroblasts by toll-like receptor signaling. Arthritis Rheumatol. 48, 642.
Li Y. and Agarwal P. 2009 A pathway-based view of human diseases and disease relationships. PLoS ONE 4, e4346–e4346.
Liu K. Q., Liu Z. P., Hao J. K., Chen L. and Zhao X. M. 2012 Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics 13, 1–11.
Matthews L., Gopinath G., Gillespie M., Caudy M., Croft D., De B. B. et al. 2009 Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37, 49–61.
Merikangas K. R., Low N. C. and Hardy J. 2006 Commentary: understanding sources of complexity in chronic diseases–the importance of integration of genetics and epidemiology. Int. J. Epidemiol. 35, 593–596.
Miao C. G., Yang Y. Y., He X., Li X. F., Huang C., Huang Y. et al. 2013 Wnt signaling pathway in rheumatoid arthritis, with special emphasis on the different roles in synovial inflammation and bone remodeling. Cell. Signal. 25, 2069–2078.
Michaud K. and Wolfe F. 2007 Comorbidities in rheumatoid arthritis. Best Pract. Res. Clin. Rheumatol. 21, 885–906.
Okada Y., Wu D., Trynka G., Raj T., Terao C., Ikari K. et al. 2014 Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381.
Pierer M., Rethage J., Seibl R., Lauener R., Brentano F., Wagner U. et al. 2004 Chemokine secretion of rheumatoid arthritis synovial fibroblasts stimulated by toll-like receptor 2 ligands. J. Immunol. 172, 1256–1265.
Ringn and Eacute M. 2008 What is principal component analysis? Nature Biotechnol. 26, 303–304.
Rosenberg A., Fan H., Chiu Y. G., Bolce R., Tabechian D., Barrett R. et al. 2014 Divergent gene activation in peripheral blood and tissues of patients with rheumatoid arthritis, psoriatic arthritis and psoriasis following infliximab therapy. PLOS One 9, e110657–e110657.
Roubille C., Richer V., Starnino T., Mccourt C., Mcfarlane A., Fleming P. et al. 2015 The effects of tumour necrosis factor inhibitors, methotrexate, non-steroidal anti-inflammatory drugs and corticosteroids on cardiovascular events in rheumatoid arthritis, psoriasis and psoriatic arthritis: a systematic review and meta-analysis. Ann. Rheum. Dis. 74, 480–489.
Sedgwick P. 1996 Pearson’s correlation coefficient. N. Z. Med. J. 109, 377.
Seibl R., Birchler T., Loeliger S., Hossle J. P., Gay R. E., Saurenmann T. et al. 2003 Expression and regulation of toll-like receptor 2 in rheumatoid arthritis synovium. Am. J. Pathol. 162, 1221–1227.
Song M., Yan Y. and Jiang Z. 2014 Drug-pathway interaction prediction via multiple feature fusion. Mol. Biosyst. 10, 2907–2913.
Stelzl U., Worm U., Lalowski M., Haenig C., Brembeck F. H., Goehler H. et al. 2005 A human protein-protein interaction metwork: a resource for annotating the proteome. Cell 122, 957–968.
Takeuchi O., Hoshino K. and Akira S. 2000 Cutting edge: TLR2-deficient and MyD88-deficient mice are highly susceptible to Staphylococcus aureus infection. J. Immunol. 165, 5392–5396.
Wang J., Warris A., Ellingsen E., Jørgensen P., Flo T., Espevik T. et al. 2001 Involvement of CD14 and toll-like receptors in activation of human monocytes by Aspergillus fumigatus hyphae. Infect. Immun. 69, 2402–2406.
Wilson A. G. 2008 Epigenetic regulation of gene expression in the inflammatory response and relevance to common diseases. J. Periodontol. 79, 1514–1519.
Xia Y., Yu H., Jansen R., Seringhaus M., Baxter S., Greenbaum D. et al. 2004 Analyzing cellular biochemistry in terms of molecular networks. Annu. Rev. Biochem. 73, 1051–1087.
Yamada H., Nakashima Y., Okazaki K., Mawatari T., Fukushi J. I., Kaibara N. et al. 2016 Comparative risk of hospitalized infection associated with biologic agents in rheumatoid arthritis patients enrolled in medicare. Arthritis Rheumatol. 68, 56–66.
Author information
Authors and Affiliations
Corresponding author
Additional information
Corresponding editor: Rajiva Raman
Rights and permissions
About this article
Cite this article
Song, XD., Song, XX., Liu, GB. et al. Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network. J Genet 97, 173–178 (2018). https://doi.org/10.1007/s12041-018-0897-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12041-018-0897-9