Jin Yi, Wang Zhanwang, Tang Weizhi, Liao Muxing, Wu Xiangwei, Wang Hui
Department of Radiation Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Front Oncol. 2022 Jul 13;12:891716. doi: 10.3389/fonc.2022.891716. eCollection 2022.
Tongue squamous cell carcinoma (TSCC) is a prevalent cancer of the oral cavity. Survival metrics are usually unsatisfactory, even using combined treatment with surgery, radiation, and chemotherapy. Immune checkpoint inhibitors can prolong survival, especially in patients with recurrent or metastatic disease. However, there are few effective biomarkers to provide prognosis and guide immunotherapy. Here, we utilized weighted gene co-expression network analysis to identify the co-expression module and selected the turquoise module for further scrutiny. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed the innate pathways. The findings indicated that cell junction organization, response to topologically incorrect protein, and regulation of cell adhesion pathways may be essential. Eleven crucial predictive genes (, , , , , , , , , , and ) were used to establish a risk model based on Cox and LASSO analyses of The Cancer Genome Atlas and GSE65858 databases (regarding overall survival). Kaplan-Meier analysis and receiver operating characteristic curve suggested that the risk model had better prognostic effectiveness than other clinical traits. Consensus clustering was used to classify TSCC samples into two groups with significantly different survival rates. ESTIMATE and CIBERSORT were used to display the immune landscape of TSCC and indicate the stromal score; specific types of immune cells, including naïve B cells, plasma cells, CD8 T cells, CD4 memory resting and memory activated T cells, follicular helper T cells, and T regulatory cells, may influence the heterogeneous immune microenvironment in TSCC. To further identify hub genes, we downloaded GEO datasets (GSE41613 and GSE31056) and successfully validated the risk model. Two hub genes ( and ) were strongly associated with CD4+ and CD8+ T cells and programmed cell death protein 1 (PD1) and PD-ligand 1.
舌鳞状细胞癌（TSCC）是口腔中一种常见的癌症。即使采用手术、放疗和化疗联合治疗，生存指标通常也不尽人意。免疫检查点抑制剂可以延长生存期，尤其是对于复发或转移性疾病患者。然而，很少有有效的生物标志物可用于提供预后信息和指导免疫治疗。在此，我们利用加权基因共表达网络分析来识别共表达模块，并选择绿松石模块进行进一步研究。基因本体论和京都基因与基因组百科全书分析揭示了固有途径。研究结果表明，细胞连接组织、对拓扑错误蛋白质的反应以及细胞粘附途径的调节可能至关重要。基于对癌症基因组图谱和GSE65858数据库（关于总生存期）的Cox和LASSO分析，使用11个关键预测基因（、、、、、、、、、和）建立了一个风险模型。Kaplan-Meier分析和受试者工作特征曲线表明，该风险模型比其他临床特征具有更好的预后效果。共识聚类用于将TSCC样本分为两组，其生存率有显著差异。使用ESTIMATE和CIBERSORT来展示TSCC的免疫格局并指示基质评分；特定类型的免疫细胞，包括幼稚B细胞、浆细胞、CD8 T细胞、CD4记忆静止和记忆激活T细胞、滤泡辅助性T细胞和调节性T细胞，可能会影响TSCC中异质性免疫微环境。为了进一步识别枢纽基因，我们下载了GEO数据集（GSE41613和GSE31056）并成功验证了该风险模型。两个枢纽基因（和）与CD4 +和CD8 + T细胞以及程序性细胞死亡蛋白1（PD1）和PD-配体1密切相关。
