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摘要
Objective: To construct an immune gene prognostic evaluation model and risk stratification system for thyroid cancer based on bioinformatics methods, and to analyze the influence of model score on immune cell infiltration and immune regulatory network. Methods: The clinical, transcritome and immune gene data of patients with thyroid cancer from The Cancer Genome Atlas (TCGA) database and ImmoPort Resources database were used to screen the differentially expressed immune genes, and transcription factor data from the Cistrome Project database were used to construct a regulatory network between the differentially expressed immune related genes and differentially expressed transcription factors. Univariate and multivariate Cox analysis were used to screen out the independent prognosis immune genes and construct a prognostic evaluation model. The correlation between the risk score of the prognostic evaluation model and the clinical characteristics and prognosis of patients was analyzed. The tumor-related immune cell infiltration data were downloaded from TIMER2.0 database to analyze the correlation between the risk score of the prognostic evaluation model and the abundance of tumor-related immune cells (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells). Results: A total of 272 differentially expressed immune-related genes and 36 transcription factors in thyroid cancer were screened out by R language[false discovery rate (FDR)< 0.05]. Univariate Cox analysis showed that 13 immune-related genes were related to prognosis (all P< 0.01). The results of correlation analysis with transcription factors showed that a total of 13 transcription factors were related to 11 prognostic immune genes (all |r|> 0.3, all P< 0.05), and their regulatory network was constructed. Multivariate Cox analysis showed that 6 immune genes (CXCL5, COLEC10, S100A9, MMP12, APOD, and FGF7) were independent prognostic factors to construct a prognostic evaluation model (all P< 0.05). The patients were divided into the high- and low-risk groups based on the model risk score. The 10-year overall survival (OS) rate was 95.6% in the high-risk group and 85.4% in the low-risk group. The prognostic evaluation model had high accuracy[area under curve (AUC)=0.992]. Univariate and multivariate Cox analysis showed that the risk score of the prognostic evaluation model was an independent predictor of OS (P< 0.05), and the high risk score was a risk factor for poor OS, which was related to the increase of neutrophils and the decrease of CD8+ T cells in tumor microenvironment. Conclusions: Based on bioinformatics methods, a thyroid cancer immune-related gene prognosis evaluation model and risk scoring system is constructed to provide a reference for predicting the prognosis of patients with thyroid cancer, and the interactive network relationship among immune genes may play a role by affecting the biological functions of immune cells in the tumor microenvironment. ? 2022, The Second Affiliated Hospital, College of Medicine, Zhejiang University.. All right reserved.
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