Department of otorhinolaryngology,the first affiliated hospital, Zhejiang University School of medicine, 310003, Qingchun Road 79, Hangzhou city, Zhejiang province, China.
Department of otorhinolaryngology, the second affiliated hospital, Zhejiang University school of medicine, 310003, Qingchun Road 79, Hangzhou city, Zhejiang province, China.
Sci Rep. 2017 Mar 22;7(1):309. doi: 10.1038/s41598-017-00252-2.
Dysregulation of mRNAs and long non-coding RNAs (lncRNAs) is one of the most important features of carcinogenesis and cancer development. However, studies integrating the expression of mRNAs and lncRNAs to predict the survival of head and neck squamous cell carcinoma (HNSC) are still limited, hitherto. In current work, we identified survival related mRNAs and lncRNAs in three datasets (TCGA dataset, E-TABM-302, GSE41613). By random forest, seven gene signatures (six mRNAs and lncRNA) were further selected to develop the risk score model. The risk score was significantly associated with survival in both training and testing datasets (E-TABM-302, GSE41613, and E-MTAB-1324). Furthermore, correlation analyses showed that the risk score is independent from clinicopathological features. According to Cox multivariable hazard model and nomogram, the risk score contributes the most to survival than the other clinical information, including gender, age, histologic grade, and alcohol taking. The Gene Set Enrichment Analysis (GSEA) indicates that the risk score is associated with cancer related pathways. In summary, the lncRNA-mRNA based risk score model we developed successfully predicts the survival of 755 HNSC samples in five datasets and two platforms. It is independent from clinical information and performs better than clinical information for prognosis.
mRNA 和长非编码 RNA(lncRNA)的失调是致癌和癌症发展的最重要特征之一。然而,迄今为止,将 mRNAs 和 lncRNAs 的表达整合起来预测头颈部鳞状细胞癌(HNSC)生存的研究仍然有限。在目前的工作中,我们在三个数据集(TCGA 数据集、E-TABM-302、GSE41613)中鉴定了与生存相关的 mRNAs 和 lncRNAs。通过随机森林,进一步选择了七个基因特征(六个 mRNAs 和 lncRNA)来开发风险评分模型。风险评分在训练和测试数据集(E-TABM-302、GSE41613 和 E-MTAB-1324)中均与生存显著相关。此外,相关性分析表明风险评分与临床病理特征无关。根据 Cox 多变量风险模型和列线图,风险评分比其他临床信息(包括性别、年龄、组织学分级和饮酒)对生存的贡献最大。基因集富集分析(GSEA)表明风险评分与癌症相关途径有关。总之,我们开发的基于 lncRNA-mRNA 的风险评分模型成功预测了五个数据集和两个平台的 755 个 HNSC 样本的生存情况。它独立于临床信息,在预后方面优于临床信息。