Wang Zizhuo, Yuan Huangbo, Huang Jia, Hu Dianxing, Qin Xu, Sun Chaoyang, Chen Gang, Wang Beibei
Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Department of Gynecology and Obstetrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Head Neck. 2021 Jan;43(1):182-197. doi: 10.1002/hed.26474. Epub 2020 Oct 3.
Head and neck squamous cell carcinoma (HNSCC) is one of the few malignant tumors that respond well to immunotherapy. We aimed to investigate the immune-related genes and immune cell infiltration of HNSCC and construct a predictive model for its prognosis.
We calculated the stromal/immune scores of patients with HNSCC from The Cancer Genome Atlas using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm and investigated the relationship between the scores and patients' prognosis. Three machine learning algorithms (LASSO, Random Forest, and Rbsurv) were performed to screen key immune-related genes and constructed a predictive model. The immune cell infiltrating was calculated by the Tumor Immune Estimation Resource algorithm.
The stromal and immune scores significantly correlated with prognosis. A 6-gene signature was selected and displayed a robust predictive effect. The expressions of key genes were associated with immune infiltrating. GSE65858 validated the results.
Our study comprehensively analyzed the tumor microenvironment of HNSCC and constructed a robust predictive model, providing a basis for further investigation of therapy.
头颈部鳞状细胞癌（HNSCC）是少数对免疫疗法反应良好的恶性肿瘤之一。我们旨在研究HNSCC的免疫相关基因和免疫细胞浸润情况，并构建其预后预测模型。
我们使用利用表达数据算法在恶性肿瘤组织中估计基质和免疫细胞（ESTIMATE）算法计算了来自癌症基因组图谱（The Cancer Genome Atlas）的HNSCC患者的基质/免疫评分，并研究了这些评分与患者预后之间的关系。运用三种机器学习算法（LASSO、随机森林和Rbsurv）筛选关键免疫相关基因并构建预测模型。通过肿瘤免疫估计资源（Tumor Immune Estimation Resource，TIMER）算法计算免疫细胞浸润情况。
基质和免疫评分与预后显著相关。选择了一个6基因特征并显示出强大的预测效果。关键基因的表达与免疫浸润相关。GSE65858验证了结果。
我们的研究全面分析了HNSCC的肿瘤微环境并构建了一个强大的预测模型，为进一步的治疗研究提供了依据。
