Liu Jie, Han Mengmeng, Yue Zhenyu, Dong Chao, Wen Pengbo, Zhao Guoping, Wu Lijun, Xia Junfeng, Bin Yannan
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China.
Anhui Provincial Engineering Laboratory of Beidou Precision Agricultural Information, Anhui Agricultural University, Hefei, China.
Front Genet. 2020 Aug 18;11:960. doi: 10.3389/fgene.2020.00960. eCollection 2020.
Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT ( = 287) and without RT ( = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set ( = 149) and a test set ( = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.
头颈部鳞状细胞癌（HNSCC）是一种恶性肿瘤。放射治疗（RT）是HNSCC的重要治疗方法，但由于放射敏感性存在个体差异，并非所有患者都能从RT中获得生存益处。基于多组学数据的放射敏感性预测模型可能会解决这个问题。与单一组学数据相比，多组学数据可以揭示复杂分子特征与癌症表型之间更系统的关联。在本研究中，我们通过分析从癌症基因组图谱下载的接受RT（n = 287）和未接受RT（n = 189）的HNSCC患者的基因表达数据，获得了122个差异表达基因。然后，将接受RT的HNSCC患者随机分为训练集（n = 149）和测试集（n = 138）。最后，我们将122个差异基因的多组学数据与训练集上的临床结果相结合，通过两阶段正则化和多变量Cox回归模型建立了一个12基因特征。以训练集上12基因特征的中位数分数作为临界值，将患者分为高分和低分两组。分析显示，低分患者组具有更高的放射敏感性，并且将从RT中获益。此外，我们开发了一种列线图来预测接受RT的HNSCC患者的总生存期。我们将12基因特征的预后价值与基于单一组学数据的基因特征的预后价值进行了比较。结果表明，基于多组学数据的12基因特征在预测放射敏感性方面具有最佳能力。总之，所提出的12基因特征是一种有前景的生物标志物，可用于评估HNSCC患者的RT选择。
