DataSheet4_Immune Infiltration Characteristics and a Gene Prognostic Signature Associated With the Immune Infiltration in Head and Neck Squamous Cell Carcinoma.ZIP

Background: Immunotherapy has become the new standard of care for recurrent and metastatic head and neck squamous cell carcinoma (HNSCC), and PD-L1 is a widely used biomarker for immunotherapeutic response. However, PD-L1 expression in most cancer patients is low, and alternative biomarkers used to screen the population benefiting from immunotherapy are still being explored. Tumor microenvironment (TME), especially tumor immune-infiltrating cells, regulates the body’s immunity, affects the tumor growth, and is expected to be a promising biomarker for immunotherapy.
Purpose: This article mainly discussed how the immune-infiltrating cell patterns impacted immunity, thereby affecting HNSCC patients’ prognosis.
Method: The immune-infiltrating cell profile was generated by the CIBERSORT algorithm based on the transcriptomic data of HNSCC. Consensus clustering was used to divide groups with different immune cell infiltration patterns. Differentially expressed genes (DEGs) obtained from the high and low immune cell infiltration (ICI) groups were subjected to Kaplan–Meier and univariate Cox analysis. Significant prognosis-related DEGs were involved in the construction of a prognostic signature using multivariate Cox analysis.
Results: In our study, 408 DEGs were obtained from high- and low-ICI groups, and 59 of them were significantly associated with overall survival (OS). Stepwise multivariate Cox analysis developed a 16-gene prognostic signature, which could distinguish favorable and poor prognosis of HNSCC patients. An ROC curve and nomogram verified the sensitivity and accuracy of the prognostic signature. The AUC values for 1 year, 2 years, and 3 years were 0.712, 0.703, and 0.700, respectively. TCGA-HNSCC cohort, GSE65858 cohort, and an independent GSE41613 cohort proved a similar prognostic significance. Notably, the prognostic signature distinguished the expression of promising immune inhibitory receptors (IRs) well and could predict the response to immunotherapy.
Conclusion: We established a tumor immune cell infiltration (TICI)-based 16-gene signature, which could distinguish patients with different prognosis and help predict the response to immunotherapy.

研究背景：免疫治疗已成为复发转移性头颈部鳞状细胞癌（head and neck squamous cell carcinoma, HNSCC）的标准治疗方案，程序性死亡配体1（PD-L1）是目前应用广泛的免疫治疗应答生物标志物。然而，多数癌症患者的PD-L1表达水平较低，用于筛选免疫治疗获益人群的替代生物标志物仍有待探索。肿瘤微环境（Tumor microenvironment, TME），尤其是肿瘤免疫浸润细胞，可调控机体免疫状态、影响肿瘤生长，有望成为极具潜力的免疫治疗生物标志物。
研究目的：本文旨在探讨免疫浸润细胞特征如何调控机体免疫，进而影响头颈部鳞状细胞癌患者的预后。
研究方法：基于头颈部鳞状细胞癌的转录组数据，通过CIBERSORT算法生成免疫浸润细胞谱；采用一致性聚类将样本划分为具有不同免疫细胞浸润模式的亚组。从高、低免疫细胞浸润（immune cell infiltration, ICI）组中获取的差异表达基因（DEGs），经Kaplan-Meier生存分析与单因素Cox回归分析筛选；将与预后显著相关的差异表达基因通过多因素Cox回归分析构建预后特征模型。
研究结果：本研究从高、低免疫细胞浸润组中筛选得到408个差异表达基因，其中59个与总生存期（overall survival, OS）显著相关。通过逐步多因素Cox回归分析构建了包含16个基因的预后特征模型，该模型可有效区分头颈部鳞状细胞癌患者的良好与不良预后。受试者工作特征曲线（ROC曲线）与列线图（nomogram）验证了该预后特征模型的灵敏度与准确性：1年、2年、3年的AUC值分别为0.712、0.703与0.700。TCGA-HNSCC队列、GSE65858队列以及独立队列GSE41613均证实该预后特征模型具有相似的预后预测价值。值得注意的是，该预后特征模型可有效区分潜在免疫抑制性受体（immune inhibitory receptors, IRs）的表达水平，并能预测患者的免疫治疗应答情况。
研究结论：本研究构建了基于肿瘤免疫细胞浸润（Tumor Immune Cell Infiltration, TICI）的16基因预后特征模型，该模型可有效区分不同预后的头颈部鳞状细胞癌患者，有助于预测患者的免疫治疗应答效果。