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
  • [41] Identification of key microRNAs and genes in preeclampsia by bioinformatics analysis
    Luo, Shouling
    Cao, Nannan
    Tang, Yao
    Gu, Weirong
    PLOS ONE, 2017, 12 (06):
  • [42] Identification of key genes and pathways in seminoma by bioinformatics analysis
    Chen, Ye-Hui
    Lin, Ting-Ting
    Wu, Yu-Peng
    Li, Xiao-Dong
    Chen, Shao-Hao
    Xue, Xue-Yi
    Wei, Yong
    Zheng, Qing-Shui
    Huang, Jin-Bei
    Xu, Ning
    ONCOTARGETS AND THERAPY, 2019, 12 : 3683 - 3693
  • [43] Identification of Key Genes and Pathways for Enchondromas by Bioinformatics Analysis
    Wu, Tianlong
    Cao, Honghai
    Liu, Lei
    Peng, Kan
    DOSE-RESPONSE, 2020, 18 (01):
  • [44] Identification of key genes and molecular mechanisms associated with temperature stress in lentil
    Sohrabi, Seyed Sajad
    Ismaili, Ahmad
    Nazarian-Firouzabadi, Farhad
    Fallahi, Hossein
    Hosseini, Seyedeh Zahra
    GENE, 2022, 807
  • [45] Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis
    Liu, Fei
    Wu, Yunyan
    Mi, Yunzhe
    Gu, Lina
    Sang, Meixiang
    Geng, Cuizhi
    PATHOLOGY RESEARCH AND PRACTICE, 2019, 215 (07)
  • [46] Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis
    Cao, Ling
    Chen, Yan
    Zhang, Miao
    Xu, De-quan
    Liu, Yan
    Liu, Tonglin
    Liu, Shi-xin
    Wang, Ping
    PEERJ, 2018, 6
  • [47] Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis
    Fei, L.
    Meixiang, S.
    Baoen, S.
    EUROPEAN JOURNAL OF IMMUNOLOGY, 2019, 49 : 2015 - 2015
  • [48] Bioinformatics and machine learning approaches reveal key genes and underlying molecular mechanisms of atherosclerosis: A review
    Su, Xiaoxue
    Zhang, Meng
    Yang, Guinan
    Cui, Xuebin
    Yuan, Xiaoqing
    Du, Liunianbo
    Pei, Yuanmin
    MEDICINE, 2024, 103 (31)
  • [49] Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis
    Guan, Qingzhou
    Tian, Yange
    Zhang, Zhenzhen
    Zhang, Lanxi
    Zhao, Peng
    Li, Jiansheng
    FRONTIERS IN GENETICS, 2021, 12
  • [50] Explore Key Genes and Mechanisms Involved in Colon Cancer Progression Based on Bioinformatics Analysis
    Lan, Yongting
    Yang, Xiuzhen
    Wei, Yulian
    Tian, Zhaobing
    Zhang, Lina
    Zhou, Jian
    APPLIED BIOCHEMISTRY AND BIOTECHNOLOGY, 2024, 196 (09) : 6253 - 6268