Identification of potential immunotherapeutic targets and prognostic biomarkers in Graves' disease using weighted gene co-expression network analysis

被引:0
|
作者
Mi, Nianrong [1 ]
Li, Zhe [2 ]
Zhang, Xueling [3 ]
Gao, Yingjing [4 ]
Wang, Yanan [4 ]
Liu, Siyan [4 ]
Wang, Shaolian [3 ]
机构
[1] Shandong First Med Univ, Dept Gen Practice, Cent Hosp, Jinan 250013, Shandong, Peoples R China
[2] Shandong First Med Univ, Dept Hlth Management Ctr, Cent Hosp, Jinan 250013, Shandong, Peoples R China
[3] Shandong First Med Univ, Dept Integrated Chinese & Western Med, Cent Hosp, Jinan 250013, Shandong, Peoples R China
[4] Shandong First Med Univ, Dept Endocrinol, Jinan 250013, Shandong, Peoples R China
关键词
Graves ' disease; Immunotherapeutic biomarkers; Immune infiltration; WGCNA; Gene co-expression modules; Molecular mechanisms; THYROID EYE DISEASE; MANAGEMENT; EPIDEMIOLOGY; ASSOCIATION; MECHANISMS;
D O I
10.1016/j.heliyon.2024.e27175
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Graves' disease (GD) is an autoimmune disorder characterized by hyperthyroidism resulting from autoantibody-induced stimulation of the thyroid gland. Despite recent advancements in understanding GD's pathogenesis, the molecular processes driving disease progression and treatment response remain poorly understood. In this study, we aimed to identify crucial immunogenic factors associated with GD prognosis and immunotherapeutic response. To achieve this, we implemented a comprehensive screening strategy that combined computational immunogenicity-potential scoring with multi-parametric cluster analysis to assess the immunomodulatory genes in GD-related subtypes involving stromal and immune cells. Utilizing weighted gene co-expression network analysis (WGCNA), we identified co-expressed gene modules linked to cellular senescence and immune infiltration in CD4(+) and CD8(+) GD samples. Additionally, gene set enrichment analysis enabled the identification of hallmark pathways distinguishing high- and low-immune subtypes. Our WGCNA analysis revealed 21 gene co-expression modules comprising 1,541 genes associated with immune infiltration components in various stages of GD, including T cells, M1 and M2 macrophages, NK cells, and Tregs. These genes primarily participated in T cell proliferation through purinergic signaling pathways, particularly neuroactive ligand-receptor interactions, and DNA binding transcription factor activity. Three genes, namely PRSS1, HCRTR1, and P2RY4, exhibited robustness in GD patients across multiple stages and were involved in immune cell infiltration during the late stage of GD (p < 0.05). Importantly, HCRTR1 and P2RY4 emerged as potential prognostic signatures for predicting overall survival in high-immunocore GD patients (p < 0.05). Overall, our study provides novel insights into the molecular mechanisms driving GD progression and highlights potential key immunogens for further investigation. These findings underscore the significance of immune infiltration-related cellular senescence in GD therapy and present promising targets for the development of new immunotherapeutic strategies.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Identification of candidate miRNA biomarkers for pancreatic ductal adenocarcinoma by weighted gene co-expression network analysis
    M. Giulietti
    G. Occhipinti
    G. Principato
    F. Piva
    Cellular Oncology, 2017, 40 : 181 - 192
  • [32] Identification of potential key mRNAs and LncRNAs for psoriasis by bioinformatic analysis using weighted gene co-expression network analysis
    Huotao Li
    Chao Yang
    Jiao Zhang
    Wei Zhong
    Lei Zhu
    Yongfeng Chen
    Molecular Genetics and Genomics, 2020, 295 : 741 - 749
  • [33] Application of weighted gene co-expression network analysis to explore the potential diagnostic biomarkers for colorectal cancer
    Qin, Liping
    Zeng, Jianping
    Shi, Nannan
    Chen, Liu
    Wang, Li
    MOLECULAR MEDICINE REPORTS, 2020, 21 (06) : 2533 - 2543
  • [34] Identification of key modules and prognostic markers in adrenocortical carcinoma by weighted gene co-expression network analysis
    Zou, Yong
    Jing, Luanlian
    ONCOLOGY LETTERS, 2019, 18 (04) : 3673 - 3681
  • [35] Identification of Key Gene Modules in Human Osteosarcoma by Co-Expression Analysis Weighted Gene Co-Expression Network Analysis (WGCNA)
    Liu, Xiangsheng
    Hu, Ai-Xin
    Zhao, Jia-Li
    Chen, Feng-Li
    JOURNAL OF CELLULAR BIOCHEMISTRY, 2017, 118 (11) : 3953 - 3959
  • [36] Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis
    Wang, Peng
    Zheng, Huaixin
    Zhang, Jiayu
    Wang, Yashu
    Liu, Pingping
    Xuan, Xiaoyan
    Li, Qianru
    Du, Ying
    BIOSCIENCE REPORTS, 2020, 40
  • [37] Weighted gene co-expression network analysis of circulating miRNAs as a tool to discover prognostic biomarkers for hepatocelluar carcinoma
    Pascut, D.
    Pratama, M. Y.
    Gilardi, F.
    Patti, R.
    Croce, L. S.
    Tiribelli, C.
    DIGESTIVE AND LIVER DISEASE, 2019, 51 : E33 - E33
  • [38] Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis
    Yang, Jun
    Li, Cuili
    Zhou, Jiaying
    Liu, Xiaoquan
    Wang, Shaohua
    FRONTIERS IN GENETICS, 2020, 10
  • [39] Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease
    Jin, Xiaojing
    Li, Jing
    Li, Wei
    Wang, Xiao
    Du, Chongbo
    Geng, Zhangyan
    Geng, Yuan
    Kang, Longfei
    Zhang, Xiaoman
    Wang, Mingwei
    Tian, Shujuan
    NEUROSCIENCE LETTERS, 2020, 728
  • [40] Identification potential epigenetic biomarkers of a human immunodeficiency virus/tuberculosis co-infection based on weighted gene co-expression network analysis
    Xu, Shaohua
    Yuan, Huicheng
    Li, Ling
    Bai, Feng
    Yang, Kai
    Zhao, Liangcun
    MICROBIOLOGY AND IMMUNOLOGY, 2021, 65 (10) : 422 - 431