Zhu Chunmei, Wu Qiuji, Yang Ningning, Zheng Zhewen, Zhou Fuxiang, Zhou Yunfeng
Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Genet. 2022 May 2;13:848841. doi: 10.3389/fgene.2022.848841. eCollection 2022.
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. This article mainly discussed how the immune-infiltrating cell patterns impacted immunity, thereby affecting HNSCC patients' prognosis. 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. 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. 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.
免疫疗法已成为复发和转移性头颈部鳞状细胞癌（HNSCC）的新治疗标准，而PD-L1是一种广泛用于评估免疫治疗反应的生物标志物。然而，大多数癌症患者的PD-L1表达水平较低，因此仍在探索用于筛选能从免疫治疗中获益人群的替代生物标志物。肿瘤微环境（TME），尤其是肿瘤免疫浸润细胞，调节机体免疫力，影响肿瘤生长，有望成为免疫治疗的一种有前景的生物标志物。本文主要探讨了免疫浸润细胞模式如何影响免疫力，进而影响HNSCC患者的预后。基于HNSCC的转录组数据，通过CIBERSORT算法生成免疫浸润细胞图谱。采用一致性聚类法对具有不同免疫细胞浸润模式的组进行划分。对从高、低免疫细胞浸润（ICI）组获得的差异表达基因（DEG）进行Kaplan-Meier分析和单变量Cox分析。使用多变量Cox分析，将与预后显著相关的DEG用于构建预后特征。在我们的研究中，从高、低ICI组获得了408个DEG，其中59个与总生存期（OS）显著相关。逐步多变量Cox分析建立了一个16基因的预后特征，可区分HNSCC患者的预后优劣。ROC曲线和列线图验证了预后特征的敏感性和准确性。1年、2年和3年的AUC值分别为0.712、0.703和0.700。TCGA-HNSCC队列、GSE65858队列和独立的GSE41613队列显示出相似的预后意义。值得注意的是，预后特征能很好地区分有前景的免疫抑制受体（IR）的表达，并可预测免疫治疗反应。我们建立了基于肿瘤免疫细胞浸润（TICI）的16基因特征，可区分不同预后的患者，并有助于预测免疫治疗反应。
