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A TP53 mutation model for the prediction of prognosis and therapeutic responses in head and neck squamous cell carcinoma
BMC Cancer ( IF 3.8 ) Pub Date : 2021-09-16 , DOI: 10.1186/s12885-021-08765-w
Congyu Shi 1 , Shan Liu 1 , Xudong Tian 1 , Xiaoyi Wang 1 , Pan Gao 2
Affiliation  

Tumor protein p53 (TP53) is the most frequently mutated gene in head and neck squamous cell carcinoma (HNSC), and TP53 mutations are associated with inhibited immune signatures and poor prognosis. We established a TP53 mutation associated risk score model to evaluate the prognosis and therapeutic responses of patients with HNSC. Differentially expressed genes between patients with and without TP53 mutations were determined by using data from the HNSC cohort in The Cancer Genome Atlas database. Patients with HNSC were divided into high- and low-risk groups based on a prognostic risk score that was generated from ten TP53 mutation associated genes via the multivariate Cox regression model. TP53 was the most common mutant gene in HNSC, and TP53 mutations were associated with immunogenic signatures, including the infiltration of immune cells and expression of immune-associated genes. Patients in the high-risk group had significantly poorer overall survival than those in the low-risk group. The high-risk group showed less response to anti-programmed cell death protein 1 (PD-1) therapy but high sensitivity to some chemotherapies. The risk score based on our TP53 mutation model was associated with poorer survival and could act as a specific predictor for assessing prognosis and therapeutic response in patients with HNSC.

中文翻译:

用于预测头颈鳞状细胞癌预后和治疗反应的 TP53 突变模型

肿瘤蛋白 p53 (TP53) 是头颈鳞状细胞癌 (HNSC) 中最常见的突变基因,TP53 突变与免疫特征抑制和不良预后相关。我们建立了TP53突变相关风险评分模型来评估HNSC患者的预后和治疗反应。通过使用癌症基因组图谱数据库中 HNSC 队列的数据来确定有和没有 TP53 突变的患者之间的差异表达基因。根据通过多变量 Cox 回归模型从 10 个 TP53 突变相关基因生成的预后风险评分,将 HNSC 患者分为高风险组和低风险组。TP53是HNSC中最常见的突变基因,TP53突变与免疫原性特征相关,包括免疫细胞的浸润和免疫相关基因的表达。高风险组患者的总体生存率明显低于低风险组患者。高危人群对抗程序性细胞死亡蛋白 1 (PD-1) 疗法反应较差,但对某些化疗疗法敏感。基于我们的 TP53 突变模型的风险评分与较差的生存率相关,并且可以作为评估 HNSC 患者预后和治疗反应的特定预测因子。
更新日期:2021-09-16
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