The screening of key genes and signaling pathways in rosacea by bioinformatics_West China Medical Journal

Authors：

HE Yang ¹ , YANG Honghao ² , KANG Yumeng ¹ , ZHANG Ziyan ¹ , LIU Shupeng ³ , XU Hong ² , YANG Fang ² ,  YANG Jie ¹

1. Department of Dermatology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P. R. China;
2. School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063000, P. R. China;
3. School of Basic Medicine, North China University of Science and Technology, Tangshan, Hebei 063000, P. R. China;

Corresponding author：

YANG Jie, Email: Yangjj1971@126.com

Keywords：

Gene Expression Omnibus; Rosacea; Bioinformatics; Differentially expressed genes

DOI：

10.7507/1002-0179.202107154

Video：

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Objective To screen the differentially expressed genes and pathways involved in rosacea using bioinformatics analysis. Methods The GSE65914 gene chipset was collected from the Gene Expression Omnibus (up to July 12th, 2021). It was searched according to the keyword “rosacea”. The data was analyzed by GEO2R platform. The common differential genes of three subtypes of rosacea were screened out. The online DAVID analysis tool was used to perform the gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction networks of differentially expressed genes were made by String and Cytoscape. The key modules and genes were screened by Mcode and Cytohubba. Results A total of 957 common differential genes were identified, including 533 up-regulated genes and 424 down-regulated genes. GO enrichment analysis showed that these genes were mainly involved in immune response, inflammatory response, intercellular signal transduction, positive regulation of T cell proliferation, chemokine signaling pathways, cell surface receptor signaling pathways, cellular response to interferon-γ, and other biological processes. KEGG pathway enrichment analysis mainly included cytokine-cytokine receptor interaction, rheumatoid arthritis, chemokine signaling pathway, PPAR signaling pathway, Toll-like receptor signaling pathway, nuclear transcription factor-κB signaling pathway, tumor necrosis factor signaling pathway and other signaling pathways. Cytohubba analysis revealed 10 key genes, including PTPRC, MMP9, CCR5, IL1B, TLR2, STAT1, CXCR4, CXCL10, CCL5 and VCAM1. Conclusion The key genes and related pathways may play an important role in the pathogenesis of rosacea.

Citation： HE Yang, YANG Honghao, KANG Yumeng, ZHANG Ziyan, LIU Shupeng, XU Hong, YANG Fang, YANG Jie. The screening of key genes and signaling pathways in rosacea by bioinformatics. West China Medical Journal, 2021, 36(9): 1232-1238. doi: 10.7507/1002-0179.202107154 Copy

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2.	Ahn CS, Huang WW. Rosacea pathogenesis. Dermatol Clin, 2018, 36(2): 81-86.
3.	Tan J, Blume-Peytavi U, Ortonne JP, et al. P. An observational cross-sectional survey of rosacea: clinical associations and progression between subtypes. Br J Dermatol, 2013, 169(3): 555-562.
4.	Deng Y, Peng Q, Yang S, et al. The Rosacea-specific Quality-of-Life instrument (RosQol): revision and validation among Chinese patients. PLoS One, 2018, 13(2): e0192487.
5.	Wilkin J, Dahl M, Detmar M, et al. Standard classification of rosacea: report of the National Rosacea Society Expert Committee on the Classification and Staging of Rosacea. J Am Acad Dermatol, 2002, 46(4): 584-587.
6.	Two AM, Wu W, Gallo RL, et al. Rosacea: partⅠ. Introduction, categorization, histology, pathogenesis, and risk factors. J Am Acad Dermatol, 2015, 72(5): 749-758.
7.	Thompson KG, Rainer BM, Antonescu C, et al. Comparison of the skin microbiota in acne and rosacea. Exp Dermatol, 2020.
8.	Rodrigues-Braz D, Zhao M, Yesilirmak N, et al. Cutaneous and ocular rosacea: common and specific physiopathogenic mechanisms and study models. Mol Vis, 2021, 27: 323-353.
9.	Wei CY, Zhu MX, Lu NH, et al. Bioinformatics-based analysis reveals elevated MFSD12 as a key promoter of cell proliferation and a potential therapeutic target in melanoma. Oncogene, 2019, 38(11): 1876-1891.
10.	刘萌, 段琪琪, 王敏, 等. 基于生物信息学分析探究银屑病关键基因和通路. 中国皮肤性病学杂志, 2019, 33(11): 1232-1238.
11.	Buhl T, Sulk M, Nowak P, et al. Molecular and morphological characterization of inflammatory infiltrate in rosacea reveals activation of Th1/Th17 pathways. J Invest Dermatol, 2015, 135(9): 2198-2208.
12.	Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res, 2013, 41(Databaseissue): D991-D995.
13.	Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009, 4(1): 44-57.
14.	Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
15.	Su G, Morris JH, Demchak B, et al. Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics, 2014, 47: 8.13.1-8.13.24.
16.	Li M, Li D, Tang Y, et al. CytoCluster: a cytoscape plugin for cluster analysis and visualization of biological networks. Int J Mol Sci, 2017, 18(9): 1880.
17.	Chin CH, Chen SH, Wu HH, et al. CytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol, 2014, 8(Suppl 4): S11.
18.	Mlecnik B, Galon J, Bindea G. Comprehensive functional analysis of large lists of genes and proteins. J Proteomics, 2018, 171(16): 2-10.
19.	Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 2009, 37(1): 1-13.
20.	Chen M, Xie H, Chen Z, et al. Thalidomide ameliorates rosacea-like skin inflammation and suppresses NF-κB activation in keratinocytes. Biomed Pharmacother, 2019, 116: 109011.
21.	Harden JL, Shih YH, Xu J, et al. Paired transcriptomic and proteomic analysis implicates IL-1β in the pathogenesis of papulopustular rosacea explants. Invest Dermatol, 2021, 141(4): 800-809.
22.	Barcellos LF, Caillier S, Dragone L, et al. PTPRC (CD45) is not associated with the development of multiple sclerosis in U.S. patients. Nat Genet, 2001, 29(1): 23-24.
23.	Jacobsen M, Schweer D, Ziegler A, et al. A point mutation in PTPRC is associated with the development of multiple sclerosis. Nat Genet, 2000, 26(4): 495-499.
24.	Cui J, Saevarsdottir S, Thomson B, et al. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factorαtherapy. Arthritis Rheum, 2010, 62(7): 1849-1861.
25.	Bikfalvi A, Billottet C. The CC and CXC chemokines: major regulators of tumor progression and the tumor microenvironment. Am J Physiol Cell Physiol, 2020, 318(3): C542-C554.
26.	Yuan X, Li J, Li Yf, et al. Artemisinin, a potential option to inhibit inflammation and angiogenesis in rosacea. Biomed Pharmacother, 2019, 117: 109181.
27.	Deng Z, Chen M, Liu Y, at al. A positive feedback loop between mTORC1 and cathelicidin promotes skin inflammation in rosacea. EMBO Mol Med, 2021, 13(5): e13560.
28.	Moura AKA, Guedes F, Rivitti-Machado MC, et al. Inate immunity in rosacea. Langerhans cells, plasmacytoid dentritic cells, Toll-like receptors and inducible oxide nitric synthase (iNOS) expression in skin specimens: case-control study. Arch Dermatol Res, 2018, 310(2): 139-146.
29.	Deng Z, Liu F, Chen M, et al. Keratinocyte-immune cell crosstalk in a STAT1-mediated pathway: novel insights into rosacea pathogenesis. Front Immunol, 2021, 12: 674871.

1. Gether L, Overgaard LK, Egeberg A, et al. Incidence and prevalence of rosacea: a systematic review and meta-analysis. Br J Dermatol, 2018, 179(2): 282-289.
2. Ahn CS, Huang WW. Rosacea pathogenesis. Dermatol Clin, 2018, 36(2): 81-86.
3. Tan J, Blume-Peytavi U, Ortonne JP, et al. P. An observational cross-sectional survey of rosacea: clinical associations and progression between subtypes. Br J Dermatol, 2013, 169(3): 555-562.
4. Deng Y, Peng Q, Yang S, et al. The Rosacea-specific Quality-of-Life instrument (RosQol): revision and validation among Chinese patients. PLoS One, 2018, 13(2): e0192487.
5. Wilkin J, Dahl M, Detmar M, et al. Standard classification of rosacea: report of the National Rosacea Society Expert Committee on the Classification and Staging of Rosacea. J Am Acad Dermatol, 2002, 46(4): 584-587.
6. Two AM, Wu W, Gallo RL, et al. Rosacea: partⅠ. Introduction, categorization, histology, pathogenesis, and risk factors. J Am Acad Dermatol, 2015, 72(5): 749-758.
7. Thompson KG, Rainer BM, Antonescu C, et al. Comparison of the skin microbiota in acne and rosacea. Exp Dermatol, 2020.
8. Rodrigues-Braz D, Zhao M, Yesilirmak N, et al. Cutaneous and ocular rosacea: common and specific physiopathogenic mechanisms and study models. Mol Vis, 2021, 27: 323-353.
9. Wei CY, Zhu MX, Lu NH, et al. Bioinformatics-based analysis reveals elevated MFSD12 as a key promoter of cell proliferation and a potential therapeutic target in melanoma. Oncogene, 2019, 38(11): 1876-1891.
10. 刘萌, 段琪琪, 王敏, 等. 基于生物信息学分析探究银屑病关键基因和通路. 中国皮肤性病学杂志, 2019, 33(11): 1232-1238.
11. Buhl T, Sulk M, Nowak P, et al. Molecular and morphological characterization of inflammatory infiltrate in rosacea reveals activation of Th1/Th17 pathways. J Invest Dermatol, 2015, 135(9): 2198-2208.
12. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res, 2013, 41(Databaseissue): D991-D995.
13. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009, 4(1): 44-57.
14. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
15. Su G, Morris JH, Demchak B, et al. Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics, 2014, 47: 8.13.1-8.13.24.
16. Li M, Li D, Tang Y, et al. CytoCluster: a cytoscape plugin for cluster analysis and visualization of biological networks. Int J Mol Sci, 2017, 18(9): 1880.
17. Chin CH, Chen SH, Wu HH, et al. CytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol, 2014, 8(Suppl 4): S11.
18. Mlecnik B, Galon J, Bindea G. Comprehensive functional analysis of large lists of genes and proteins. J Proteomics, 2018, 171(16): 2-10.
19. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 2009, 37(1): 1-13.
20. Chen M, Xie H, Chen Z, et al. Thalidomide ameliorates rosacea-like skin inflammation and suppresses NF-κB activation in keratinocytes. Biomed Pharmacother, 2019, 116: 109011.
21. Harden JL, Shih YH, Xu J, et al. Paired transcriptomic and proteomic analysis implicates IL-1β in the pathogenesis of papulopustular rosacea explants. Invest Dermatol, 2021, 141(4): 800-809.
22. Barcellos LF, Caillier S, Dragone L, et al. PTPRC (CD45) is not associated with the development of multiple sclerosis in U.S. patients. Nat Genet, 2001, 29(1): 23-24.
23. Jacobsen M, Schweer D, Ziegler A, et al. A point mutation in PTPRC is associated with the development of multiple sclerosis. Nat Genet, 2000, 26(4): 495-499.
24. Cui J, Saevarsdottir S, Thomson B, et al. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factorαtherapy. Arthritis Rheum, 2010, 62(7): 1849-1861.
25. Bikfalvi A, Billottet C. The CC and CXC chemokines: major regulators of tumor progression and the tumor microenvironment. Am J Physiol Cell Physiol, 2020, 318(3): C542-C554.
26. Yuan X, Li J, Li Yf, et al. Artemisinin, a potential option to inhibit inflammation and angiogenesis in rosacea. Biomed Pharmacother, 2019, 117: 109181.
27. Deng Z, Chen M, Liu Y, at al. A positive feedback loop between mTORC1 and cathelicidin promotes skin inflammation in rosacea. EMBO Mol Med, 2021, 13(5): e13560.
28. Moura AKA, Guedes F, Rivitti-Machado MC, et al. Inate immunity in rosacea. Langerhans cells, plasmacytoid dentritic cells, Toll-like receptors and inducible oxide nitric synthase (iNOS) expression in skin specimens: case-control study. Arch Dermatol Res, 2018, 310(2): 139-146.
29. Deng Z, Liu F, Chen M, et al. Keratinocyte-immune cell crosstalk in a STAT1-mediated pathway: novel insights into rosacea pathogenesis. Front Immunol, 2021, 12: 674871.

West China Medical Journal

The screening of key genes and signaling pathways in rosacea by bioinformatics

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