Identification of Key Biomarkers and Immune Infiltration in Human Gingival Tissue of Periodontitis by Bioinformatics Analysis

doi:10.13701/j.cnki.kqyxyj.2021.04.007

Abstract

Abstract: Objective: To identify the key biomarkers and immune infiltration in gingival tissue of periodontitis by bioinformatics analysis. Methods: The gene expression profiles of GSE10334, GSE16134, and GSE23586 were downloaded from the Gene Expression Omnibus datasets. The differentially expressed genes were identified by the LIMMA package. PPI network was constructed to determine hub genes by using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of periodontal soft tissue between periodontitis and healthy controls. Results: A total of 129 differentially expressed genes were identified. Based on the PPI network, 10 hub genes were identified, which were significantly enriched in various cellular physiological activities and signal pathways such as chemokine and cytokine activity, cytokine-cytokine receptor interaction signals pathway, chemokine signaling pathway, and IL-17 signaling pathway. Compared with the healthy control, the gingival tissue of periodontitis contained a higher proportion of naive B cells, plasma cells, naive CD4⁺T cells, activated memory CD4⁺T cells, monocytes, M1 macrophages, and neutrophils (P<0.05). Conclusion: The functional analysis of hub genes and the difference of immune infiltration in gingival tissue between periodontitis and healthy control could provide new insights for understanding the development and pathogenesis mechanism of periodontitis.

Key words: periodontitis, bioinformatics, molecular biology, immune infiltration, cytokine

WANG Zihui, GAO Hongyu, MO Feifei, TIAN Guangjie, WANG Yonglan. Identification of Key Biomarkers and Immune Infiltration in Human Gingival Tissue of Periodontitis by Bioinformatics Analysis[J]. Journal of Oral Science Research, 2021, 37(4): 304-309.

References

[1] 杨磊,郭留云,程志芬,等.Shh蛋白与慢性牙周炎炎症程度的相关性研究[J].口腔医学研究,2020,36(2):131-134.
[2] Demmer RT, Behle JH, Wolf DL, et al. Transcriptomes in healthy and diseased gingival tissues [J]. J Periodontol, 2008, 79(11):2112-2124.
[3] Papapanou PN, Behle JH, Kebschull M, et al. Subgingival bacterial colonization profiles correlate with gingival tissue gene expression [J]. BMC Microbiol, 2009, 9:221.
[4] Abe D, Kubota T, Morozumi T, et al. Altered gene expression in leukocyte transendothelial migration and cell communication pathways in periodontitis-affected gingival tissues [J]. J Periodontal Res, 2011, 46(3):345-353.
[5] Leek JT, Johnson WE, Parker HS, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments [J]. Bioinformatics, 2012, 28(6):882-883.
[6] Ritchie ME, Belinda P, Di W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies [J]. Nucleic Acids Res, 2015, 43(7):e47.
[7] Kolde R. pheatmap: Pretty Heatmaps [J]. 2015,
[8] Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters [J]. OMICS, 2012, 16(5):284-287.
[9] Song Y, Jin D, Ou N, et al. Gene expression profiles identified novel urine biomarkers for diagnosis and prognosis of high-grade bladder urothelial carcinoma [J]. Front Oncol, 2020,10:394.
[10] Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome [J]. BMC Syst Biol, 2014, 8 Suppl 4:S11.
[11] Wang W, Lou W, Ding B, et al. A novel mRNA-miRNA-lncRNA competing endogenous RNA triple sub-network associated with prognosis of pancreatic cancer [J]. Aging (Albany NY), 2019, 11(9):2610-2627.
[12] Li C, Xu J. Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma [J]. Sci Rep, 2019, 9(1):17283
[13] Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles [J]. Nat Methods, 2015,12(5):453-457.
[14] Cai W, Li H, Zhang Y, et al. Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis [J]. PeerJ, 2020, 8:e8390.
[15] 李冬雪,王国芳,崔晓宇,等.β-隐黄素对大鼠实验性牙周炎炎症因子的影响[J].口腔医学研究,2019,35(5):471-475.
[16] Ha H, Debnath B, Neamati N. Role of the CXCL8-CXCR1/2 axis in cancer and inflammatory diseases [J]. Theranostics, 2017,7(6):1543-1588.
[17] Krishnan S, Prise IE, Wemyss K, et al. Amphiregulin-producing γδ T cells are vital for safeguarding oral barrier immune homeostasis [J]. Proc Natl Acad Sci U S A, 2018, 115(42):10738-10743.
[18] Mahanonda R, Champaiboon C, Subbalekha K, et al. Human memory B cells in healthy gingiva, gingivitis, and periodontitis [J]. J Immunol, 2016, 197(3):715-725.
[19] Cardoso EM, Arosa FA. CD8⁺ T cells in chronic periodontitis: roles and rules [J]. Front Immunol, 2017,8:145-145.
[20] Carla A, Carolina R, Leticia R, et al. Regulatory T lymphocytes in periodontitis: A translational view [J]. Mediators Inflamm, 2018, 2018:7806912.
[21] Wilensky A, Chaushu S, Shapira L. The role of natural killer cells in periodontitis [J]. Periodontol 2000, 2015, 69(1):128-141.
[22] Marjanovic D, Andjelkovic Z, Brkic Z, et al. Quantification of mast cells in different stages of periodontal disease [J]. Vojnosanit Pregl, 2016, 73(5):458-462.
[23] Hajishengallis G, Kajikawa T, Hajishengallis E, et al. Complement-dependent mechanisms and interventions in periodontal disease [J]. Front Immunol, 2019,10:406.
[24] Cheng R, Wu Z, Li M, et al. Interleukin-1β is a potential therapeutic target for periodontitis: a narrative review [J]. Int J Oral Sci, 2020,12(1):2.