Zhao Ge, Li Chang-Xue, Guo Chao, Zhu Hui
Dept. of Stomatology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi 832000, China.
Hua Xi Kou Qiang Yi Xue Za Zhi. 2020 Dec 1;38(6):622-627. doi: 10.7518/hxkq.2020.06.003.
The microRNA (miRNA) prognostic model can predict the prognosis of patients with oral squamous cell carcinoma (OSCC) on the basis of bioinformatics. Moreover, it can accurately group OSCC patients to improve targeted treatment.
We downloaded the miRNA and mRNA expression profile and clinical data of OSCC from The Cancer Genome Atlas (TCGA). The risk score model of miRNA was screened and established by univariate and multivariate Cox regression models. The performance of this prognostic model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). The target genes of six miRNAs were predicted and intersected with differential mRNA for enrichment analysis by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and gene ontology (GO) enrichment analysis. A protein protein interaction network (PPI) was constructed to screen hub genes.
By using univariate and multivariate Cox regression analyses, the prognostic risk model was obtained. The AUC of the ROC curve for predicting 5-year survival in the training group, test group, and whole cohort were 0.757, 0.673, and 0.724, respectively. Furthermore, univariate Cox regression and multivariate Cox regression considering other clinical factors showed that the six-miRNAs signature could serve as an independent prognostic factor (P<0.001). The top 10 hub genes in the PPI network screened by intersecting target genes include CCNB1, EGF, KIF23, MCM10, ITGAV, MELK, PLK4, ADCY2, CENPF, and TRIP13. EGF and ADCY2 were associated with survival prognosis (P<0.05).
The six-miRNAs signature could efficiently function as a novel and independent prognostic model for OSCC patients, which may be a new method to guide the accurate targeting treatment of OSCC.
微小RNA（miRNA）预后模型可基于生物信息学预测口腔鳞状细胞癌（OSCC）患者的预后。此外，它还能准确地对OSCC患者进行分组，以改善靶向治疗。
我们从癌症基因组图谱（TCGA）下载了OSCC的miRNA和mRNA表达谱以及临床数据。通过单变量和多变量Cox回归模型筛选并建立了miRNA风险评分模型。通过受试者工作特征（ROC）曲线和曲线下面积（AUC）对该预后模型的性能进行测试。预测了6种miRNA的靶基因，并与差异mRNA进行交集，通过京都基因与基因组百科全书（KEGG）信号通路和基因本体（GO）富集分析进行富集分析。构建了蛋白质-蛋白质相互作用网络（PPI）以筛选枢纽基因。
通过单变量和多变量Cox回归分析，获得了预后风险模型。训练组、测试组和整个队列预测5年生存率的ROC曲线AUC分别为0.757、0.673和0.724。此外，考虑其他临床因素的单变量Cox回归和多变量Cox回归表明，6-miRNAs特征可作为独立的预后因素（P<0.001）。通过交集靶基因筛选出的PPI网络中前10个枢纽基因包括CCNB1、EGF、KIF23、MCM10、ITGAV、MELK、PLK4、ADCY2、CENPF和TRIP13。EGF和ADCY2与生存预后相关（P<0.05）。
6-miRNAs特征可有效地作为OSCC患者一种新的独立预后模型，这可能是指导OSCC精准靶向治疗的一种新方法。
