Purpose. Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). Methods. Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. Results. Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. Conclusion. Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.

中文翻译：

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基于免疫相关基因挖掘甲状腺癌患者的预后生物标志物并开发可靠的预后风险模型

目的。肿瘤免疫在甲状腺癌（THCA）的发生、发展中发挥着重要作用。本研究的目的是利用免疫相关基因（IRG）建立 THCA 患者的免疫相关预后模型。方法。采用Wilcox检验筛选THCA和正常组织中差异表达的免疫相关基因（DEIRGs），然后通过单变量Cox回归分析鉴定与预后相关的DEIRGs。根据癌症基因组图谱（TCGA）队列，我们​​开发了最小绝对收缩和选择算子（LASSO）回归预测模型，并对模型的内部（TCGA）和外部（国际癌症基因组联盟）的预测价值进行了验证分析）分别为队列。最后，我们分析了预后模型、临床变量和免疫细胞浸润之间的相关性。结果。2,498个IRG中有82个在THCA和正常组织之间差异表达，其中18个与预后相关。LASSO Cox 回归分析确定了七个具有最大预后价值的 DEIRG，以构建预后模型。该风险模型对两个独立队列中 THCA 的生存显示出较高的预测价值。根据风险模型得出的风险评分与较差的生存率和免疫细胞的浸润水平呈正相关，它可以独立于任何其他临床病理特征来评估THCA患者的预后。分别评估七个风险基因的预后价值和遗传变异。结论。我们的研究建立并验证了与THCA免疫相关的可靠预后模型，确定的IRG和免疫相关风险模型均具有临床意义，有利于推广针对THCA的个体化免疫治疗。