Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis

被引:1
|
作者
Zhang, Zexin [1 ]
He, Yan [1 ]
Lin, Rongjie [3 ]
Lan, Junhong [1 ]
Fan, Yueying [1 ]
Wang, Peng [1 ,2 ]
Jia, Chiyu [1 ]
机构
[1] Xiamen Univ, Xiangan Hosp, Sch Med, Dept Burns & Plast & Wound Repair Surg, Xiamen, Peoples R China
[2] Xi An Jiao Tong Univ, Dept Burns & Plast & Cosmet Surg, Affiliated Hosp 9, Xi'an, Peoples R China
[3] 900th Hosp Joint Logist Support Force, Dept Orthoped, Fuzhou, Peoples R China
关键词
immunosuppression; burns; WGCNA; LASSO; GSVA; CIBERSORT; prognostic biomarker; KEY GENES; TRAUMA; INJURY; DYSFUNCTION; SEPSIS; SHOCK;
D O I
10.3389/fgene.2022.908510
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: Immunosuppression is an important trigger for infection and a significant cause of death in patients with severe burns. Nevertheless, the prognostic value of immune-related genes remains unclear. This study aimed to identify the biomarkers related to immunosuppression in severe burns.Methods: The gene expression profile and clinical data of 185 burn and 75 healthy samples were obtained from the GEO database. Immune infiltration analysis and gene set variation analysis were utilized to identify the disorder of circulating immune cells. A weighted gene co-expression network analysis (WGCNA) was carried out to select immune-related gene modules. Enrichment analysis and protein-protein interaction (PPI) network were performed to select hub genes. Next, LASSO and logistic regression were utilized to construct the hazard regression model with a survival state. Finally, we investigated the correlation between high- and low-risk patients in total burn surface area (TBSA), age, and inhalation injury.Results: Gene set variation analysis (GSVA) and immune infiltration analysis showed that neutrophils increased and T cells decreased in severe burns. In WGCNA, four modular differently expressed in burns and controls were related to immune cells. Based on PPI and enrichment analysis, 210 immune-related genes were identified, mainly involved in T-cell inhibition and neutrophil activation. In LASSO and logistic regression, we screened out key genes, including LCK, SKAP1 and GZMB, and LY9. In the ROC analysis, the area under the curve (AUC) of key genes was 0.945, indicating that the key genes had excellent diagnostic value. Finally, we discovered that the key genes were related to T cells, and the regression model performed well when accompanied by TBSA and age.Conclusion: We identified LCK, SKAP1, GZMB, and LY9 as good prognostic biomarkers that may play a role in post-burn immunosuppression against T-cell dysfunction and as potential immunotherapeutic targets for transformed T-cell dysfunction.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Weighted gene co-expression network analysis reveals specific modules and hub genes related to immune infiltration of osteoarthritis
    Cao, Jiangang
    Ding, Han
    Shang, Jun
    Ma, Lei
    Wang, Qi
    Feng, Shiqing
    ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (20)
  • [2] Identification of Co-Expression Modules of Cotton Plant Height-Related Genes Based on Weighted Gene Co-Expression Network Analysis
    Huang, Qian
    Liu, Li
    Li, Hang
    Wang, Xuwen
    Si, Aijun
    He, Liangrong
    Yu, Yu
    AGRONOMY-BASEL, 2025, 15 (01):
  • [3] Identification of glioblastomagene prognosis modules based on weighted gene co-expression network analysis
    Xu, Pengfei
    Yang, Jian
    Liu, Junhui
    Yang, Xue
    Liao, Jianming
    Yuan, Fanen
    Xu, Yang
    Liu, Baohui
    Chen, Qianxue
    BMC MEDICAL GENOMICS, 2018, 11
  • [4] Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis
    Pengfei Xu
    Jian Yang
    Junhui Liu
    Xue Yang
    Jianming Liao
    Fanen Yuan
    Yang Xu
    Baohui Liu
    Qianxue Chen
    BMC Medical Genomics, 11
  • [5] Identification of key genes and immune infiltration based on weighted gene co-expression network analysis in vestibular schwannoma
    Fu, Yanpeng
    Zhu, Yaqiong
    Guo, Liqing
    Liu, Yuehui
    MEDICINE, 2023, 102 (14) : E33470
  • [6] Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network
    Zhou, Wanbang
    Chen, Yiyang
    Luo, Ruixing
    Li, Zifan
    Jiang, Guanwei
    Ou, Xi
    PATHOLOGY & ONCOLOGY RESEARCH, 2021, 27
  • [7] Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis
    Cao, Fang
    Fan, Yinchun
    Yu, Yunhu
    Yang, Guohua
    Zhong, Hua
    CANCER MANAGEMENT AND RESEARCH, 2021, 13 : 5477 - 5489
  • [8] 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
  • [9] 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
  • [10] Identifying Key Biomarkers and Immune Infiltration in Female Patients with Ischemic Stroke Based on Weighted Gene Co-Expression Network Analysis
    Xu, Haipeng
    He, Kelin
    Hu, Rong
    Ge, YanZhi
    Li, Xinyun
    Ni, Fengjia
    Que, Bei
    Chen, Yi
    Ma, Ruijie
    NEURAL PLASTICITY, 2022, 2022