Due to the considerable heterogeneity of head and neck squamous cell carcinoma (HNSCC), individuals with comparable TNM stages who receive the same treatment strategy have varying prognostic outcomes. In HNSCC, immunotherapy is developing quickly and has shown effective. We want to develop an immune-related gene (IRG) prognostic model to forecast the prognosis and response to immunotherapy of patients. In order to analyze differential expression in normal and malignant tissues, we first identified IRGs that were differently expressed. Weighted gene coexpression network analysis (WGCNA) was used to identify modules that were highly related, and univariate and multivariate Cox regression analyses were also used to create a predictive model for IRGs that included nine IRGs. WGCNA identified the four most noteworthy related modules. Patients in the model’s low-risk category had a better chance of survival. The IRGs prognostic model was also proved to be an independent prognostic predictor, and the model was also substantially linked with a number of clinical characteristics. The low-risk group was associated with immune-related pathways, a low incidence of gene mutation, a high level of M1 macrophage infiltration, regulatory T cells, CD8 T cells, and B cells, active immunity, and larger benefits from immune checkpoint inhibitors (ICIs) therapy. The high-risk group, on the other hand, had suppressive immunity, high levels of NK and CD4 T-cell infiltration, high gene mutation rates, and decreased benefits from ICI therapy. As a result of our research, a predictive model for IRGs that can reliably predict a patient’s prognosis and their response to both conventional and immunotherapy has been created.

中文翻译：

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建立免疫相关基因模型来预测头颈鳞状细胞癌的治疗、细胞外基质和预后

由于头颈鳞状细胞癌 (HNSCC) 具有相当大的异质性，具有相似 TNM 分期的个体接受相同的治疗策略具有不同的预后结果。在头颈部鳞癌中，免疫疗法发展迅速，并已显示出有效性。我们希望开发一种免疫相关基因（IRG）预后模型来预测患者的预后和对免疫治疗的反应。为了分析正常组织和恶性组织中的差异表达，我们首先鉴定了不同表达的IRG。加权基因共表达网络分析 (WGCNA) 用于识别高度相关的模块，单变量和多变量 Cox 回归分析也用于创建包含 9 个 IRG 的 IRG 预测模型。WGCNA 确定了四个最值得注意的相关模块。该模型中低风险类别的患者有更好的生存机会。IRGs预后模型也被证明是一个独立的预后预测因子，并且该模型还与许多临床特征存在显着相关性。低风险组与免疫相关途径、基因突变发生率低、M1巨噬细胞浸润水平高、调节性T细胞、CD8T细胞和B细胞、主动免疫、免疫检查点抑制剂获益更大有关（ICIs）疗法。另一方面，高危人群具有抑制性免疫、高水平的 NK 和 CD4 T 细胞浸润、高基因突变率以及 ICI 治疗的获益减少。我们的研究结果是，创建了一种 IRG 预测模型，可以可靠地预测患者的预后及其对传统疗法和免疫疗法的反应。