Li Yu, Pan Xiaozhou, Luo Wenwei, Gamalla Yaser, Ma Zhan, Zhou Pei, Dai Chunfu, Han Dingding
Department of Clinical Laboratory, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, China.
Institute of Pediatric Infection, Immunity, and Critical Care Medicine, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, China.
Heliyon. 2024 May 23;10(11):e31877. doi: 10.1016/j.heliyon.2024.e31877. eCollection 2024 Jun 15.
Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.
肿瘤微环境（TME）与头颈部鳞状细胞癌（HNSCC）的进展和预后密切相关。为了研究预测HNSCC治疗结果的潜在生物标志物，我们使用ESTIMATE算法基于与TME相关的基因分析了HNSCC的免疫和基质状态。通过差异基因表达和加权基因共表达网络分析（WGCNA）鉴定免疫和基质基因。从这些基因中，最初通过Cox单变量回归选择了118个基因，然后进一步输入到最小绝对收缩和选择算子（LASSO）回归分析中。结果，筛选出11个基因用于构建TME相关风险（TMErisk）评分模型，该模型具有良好的总生存预测潜力。TMErisk评分与免疫和基质评分呈负相关，但与肿瘤纯度呈正相关。TMErisk评分高的个体大多数免疫检查点和所有人白细胞抗原家族基因的表达降低，浸润免疫细胞的丰度减少。HNSCC中存在不同的基因突变。在高TMErisk评分组和低TMErisk评分组中，肿瘤蛋白P53的突变频率均最高。发现较高的TMErisk评分与总生存概率降低和免疫治疗效果较差相关。因此，TMErisk评分可作为临床上预测HNSCC预后的有价值模型。
