华西口腔医学杂志 ›› 2020, Vol. 38 ›› Issue (6): 622-627.doi: 10.7518/hxkq.2020.06.003

• 基础研究 • 上一篇    下一篇

基于生物信息学分析的口腔鳞状细胞癌微小RNA预后模型

赵格(), 黎昌学(), 郭超, 朱慧   

  1. 石河子大学医学院第一附属医院口腔科,石河子 832000
  • 收稿日期:2020-03-05 修回日期:2020-09-10 出版日期:2020-12-01 发布日期:2020-12-07
  • 通讯作者: 黎昌学 E-mail:zhaoge_1994_y@163.com;lichangxue100@163.com
  • 作者简介:赵格,硕士,E-mail:zhaoge_1994_y@163.com
  • 基金资助:
    石河子大学科研项目(自然科学)(ZZZC201962A)

MicroRNA model that can predict the prognosis of oral squamous cell carcinoma based on bioinformatics analysis

Zhao Ge(), Li Changxue(), Guo Chao, Zhu Hui   

  1. Dept. of Stomatology, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi 832000, China
  • Received:2020-03-05 Revised:2020-09-10 Online:2020-12-01 Published:2020-12-07
  • Contact: Li Changxue E-mail:zhaoge_1994_y@163.com;lichangxue100@163.com
  • Supported by:
    Scientific Research Project of Shihezi University(Natural Science)(ZZZC201962A)

摘要:

目的 通过生物信息学筛选并建立与口腔鳞状细胞癌(OSCC)患者预后相关的微小RNA(miRNA)预后模型,以期对OSCC患者进行精准的分组,提高治疗的针对性。方法 通过癌症基因组图谱(TCGA)数据库下载OSCC患者的miRNA、mRNA表达谱和临床数据。采用单因素和多因素Cox风险回归分析筛选和建立miRNA预后模型。受试者工作特征曲线(ROC)和曲线下面积(AUC)检验预后模型的性能。预测6-miRNAs靶基因,与差异mRNA取交集后行基因本体论(GO)、京都基因与基因组百科全书(KEGG)信号通路富集分析。构建蛋白互作网络(PPI)筛选中枢基因。结果 通过单因素和多因素Cox回归分析得到基于6个miRNA的预后风险模型。train组、test组和所有样品组中预测5年生存率的ROC曲线下AUC值分别为0.757、0.673、0.724。单因素和多因素Cox回归分析显示,6-miRNAs预后模型可以作为一个独立的预后因素(P<0.001)。靶基因构建PPI网络中前10个中枢基因为CCNB1、EGF、KIF23、MCM10、ITGAV、MELK、PLK4、ADCY2、CENPF、TRIP13。其中EGF和ADCY2与生存预后相关(P<0.05)。结论 6-miRNAs可有效地作为OSCC患者一种新的独立的预后模型,或可成为指导OSCC精准治疗的新方法。

关键词: 口腔鳞状细胞癌, 微小RNA, 癌症基因组图谱, Cox回归模型, 预后模型

Abstract:

Objective 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. Methods 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. Results 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). Conclusion 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.

Key words: oral squamous cell carcinoma, microRNA, The Cancer Genome Atlas, Cox regression analysis, prognostic model

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