Identification of key genes and pathways associated with sex differences in rheumatoid arthritis based on bioinformatics analysis

被引:5
|
作者
Wang, Tingting [1 ]
Zeng, Fanxin [2 ]
Li, Xue [2 ]
Wei, Yuanli [1 ]
Wang, Dongmei [1 ]
Zhang, Weihua [1 ]
Xie, Huanhuan [1 ]
Wei, Lingli [1 ]
Xiong, Siying [1 ]
Liu, Caizhen [1 ]
Li, Shilin [2 ]
Wu, Jianhong [1 ]
机构
[1] Dazhou Cent Hosp, Dept Rheumatol, Dazhou 635000, Sichuan, Peoples R China
[2] Dazhou Cent Hosp, Dept Clin Res Ctr, Dazhou 635000, Sichuan, Peoples R China
关键词
Differentially expressed gene (DEG); MAPK pathway and autophagy; Rheumatoid arthritis (RA); Sex differences; STRESS;
D O I
10.1007/s10067-022-06387-6
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Introduction Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women. Methods First, the Gene Expression Omnibus database's gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses. Results According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy. Conclusion The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease.
引用
收藏
页码:399 / 406
页数:8
相关论文
共 50 条
  • [21] Identification of potential ferroptosis key genes and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
    Fan, Yihua
    Li, Yuan
    Fu, Xiaoyan
    Peng, Jing
    Chen, Yuchi
    Chen, Tao
    Zhang, Di
    HELIYON, 2023, 9 (11)
  • [22] 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
  • [23] Identification of key genes associated with esophageal adenocarcinoma based on bioinformatics analysis
    Qi, Weifeng
    Li, Rongyang
    Li, Lin
    Li, Shuhai
    Zhang, Huiying
    Tian, Hui
    ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (23)
  • [24] Identification of key genes associated with cervical cancer based on bioinformatics analysis
    Yang, Xinmeng
    Zhou, Mengsi
    Luan, Yingying
    Li, Kanghua
    Wang, Yafen
    Yang, Xiaofeng
    BMC CANCER, 2024, 24 (01)
  • [25] Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis
    Geng, Xiao-dong
    Wang, Wei-wei
    Feng, Zhe
    Liu, Ran
    Cheng, Xiao-long
    Shen, Wan-jun
    Dong, Zhe-yi
    Cai, Guang-yan
    Chen, Xiang-mei
    Hong, Quan
    Wu, Di
    JOURNAL OF DIABETES INVESTIGATION, 2019, 10 (04) : 972 - 984
  • [26] Identification of key candidate genes and pathways in rheumatoid arthritis and osteoarthritis by integrated bioinformatical analysis
    Huang, Huijing
    Dong, Xinyi
    Mao, Kaimin
    Pan, Wanwan
    Nie, Bin'en
    Jiang, Lindi
    FRONTIERS IN GENETICS, 2023, 14
  • [27] Identification of key pathways and candidate genes in gliomas by bioinformatics analysis
    Cao, Yuan
    Song, Yali
    Quan, Juan
    Zhang, Liyu
    Tian, Qianqian
    Wu, Shuang
    Zhao, Chuanmei
    Li, Qiao
    INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL MEDICINE, 2019, 12 (11): : 12679 - 12692
  • [28] Identification of Key Genes and Pathways in Cervical Cancer by Bioinformatics Analysis
    Wu, Xuan
    Peng, Li
    Zhang, Yaqin
    Chen, Shilian
    Lei, Qian
    Li, Guancheng
    Zhang, Chaoyang
    INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2019, 16 (06): : 800 - 812
  • [29] IDENTIFICATION OF KEY GENES AND PATHWAYS IN GBM THROUGH BIOINFORMATICS ANALYSIS
    Wang, Ziheng
    FRESENIUS ENVIRONMENTAL BULLETIN, 2019, 28 (07): : 5248 - 5252
  • [30] Drug screening and identification of key candidate genes and pathways of rheumatoid arthritis
    Shi, Yu-Quan
    Qi, Wu-Fang
    Kong, Chun-Yu
    MOLECULAR MEDICINE REPORTS, 2020, 22 (02) : 986 - 996