Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics

被引:2
|
作者
Guo, Haichao [1 ,2 ]
Guo, Lifang [2 ]
Li, Li [2 ]
Li, Na [3 ]
Lin, Xiaoyun [1 ]
Wang, Yanjun [1 ,4 ]
机构
[1] Hebei Univ Chinese Med, Dept Acupuncture & Moxibust, Affiliated Hosp 1, Shijiazhuang, Hebei, Peoples R China
[2] Xingtai Hosp Tradit Chinese Med, Dept Dermatol, Xingtai, Hebei, Peoples R China
[3] Hebei Univ Chinese Med, Dept Psychiat, Affiliated Hosp 1, Shijiazhuang, Hebei, Peoples R China
[4] Hebei Univ Chinese Med, Dept Acupuncture & Moxibust, Affiliated Hosp 1, 389 Zhongshan East Rd, Shijiazhuang 050011, Hebei, Peoples R China
关键词
bioinformatics; chronic urticaria; differentially expressed genes; inflammation; molecular mechanisms; PATHOGENESIS;
D O I
10.1111/srt.13624
中图分类号
R75 [皮肤病学与性病学];
学科分类号
100206 ;
摘要
Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log(2) fold change >= 1 and p < .05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] Identification of potential crucial genes and molecular mechanisms in glioblastoma multiforme by bioinformatics analysis
    Chen, Xiaojie
    Pan, Yuanbo
    Yan, Mengxia
    Bao, Guanshui
    Sun, Xuhong
    MOLECULAR MEDICINE REPORTS, 2020, 22 (02) : 859 - 869
  • [22] Identification of candidate genes and molecular mechanisms related to asthma progression using bioinformatics
    Zou, Songbing
    Meng, Fangchan
    Xu, Guien
    Yu, Rongchang
    Yang, Chaomian
    Wei, Qiu
    Xue, Yanlong
    SLEEP AND BREATHING, 2024, 28 (05) : 2237 - 2246
  • [23] Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps using bioinformatics analysis
    Yao, Yao
    Xie, Shaobing
    Wang, Fengjun
    AMERICAN JOURNAL OF OTOLARYNGOLOGY, 2019, 40 (02) : 191 - 196
  • [24] Identification of Key Genes and Molecular Pathways in Keratoconus: Integrating Text Mining and Bioinformatics Analysis
    Hu, Di
    Lin, Zenan
    Jiang, Junhong
    Li, Pan
    Zhang, Zhehuan
    Yang, Chenhao
    BIOMED RESEARCH INTERNATIONAL, 2022, 2022
  • [25] Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies
    Joshi, Harish
    Vastrad, Basavaraj
    Joshi, Nidhi
    Vastrad, Chanabasayya
    Tengli, Anandkumar
    Kotturshetti, Iranna
    FRONTIERS IN ENDOCRINOLOGY, 2021, 12
  • [26] Identification of Sequential Molecular Mechanisms and Key Biomarkers in Early Glaucoma by Integrated Bioinformatics Analysis
    Huang, Jingqiu
    Chang, Zhaohui
    Deng, Xizhi
    Cai, Shuncheng
    Jiang, Bin
    Zeng, Wen
    Ke, Min
    MOLECULAR NEUROBIOLOGY, 2025, 62 (04) : 4952 - 4970
  • [27] Identification of Key Biomarkers and Potential Molecular Mechanisms in Renal Cell Carcinoma by Bioinformatics Analysis
    Li, Feng
    Guo, Peiyuan
    Dong, Keqin
    Guo, Peng
    Wang, Haoyuan
    Lv, Xianqiang
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2019, 26 (11) : 1278 - 1295
  • [28] Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
    Cai, Kaier
    Xie, Zhilong
    Liu, Yingao
    Wu, Junfeng
    Song, Hao
    Liu, Wang
    Wang, Xinyi
    Xiong, Yinghuan
    Gan, Siyuan
    Sun, Yanqin
    BIOMED RESEARCH INTERNATIONAL, 2023, 2023
  • [29] Identification of Key Genes Involved in Carcinogenesis and Progression of Colon Cancer Based on Bioinformatics
    Huang, Zhiqiang
    Huang, Lu
    Li, Lili
    Xiang, Chunming
    Xiong, Xin
    Lu, Yongxiu
    JOURNAL OF BIOMEDICAL NANOTECHNOLOGY, 2023, 19 (07) : 1279 - 1285
  • [30] IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS
    Sun, X.
    Zhang, S. X.
    Song, S.
    Kong, T.
    Zheng, C.
    Cheng, L.
    Feng, S.
    Shi, G.
    Li, X.
    He, P. F.
    Yu, Q.
    ANNALS OF THE RHEUMATIC DISEASES, 2021, 80 : 1037 - 1038