基于芯片重注释技术构建口腔鳞状细胞癌预后相关的lncRNA风险评分模型

Construction of a lncRNA-risk score signature associated with the prognosis of oral squamous cell carcinoma using microarray re-annotation

目的:利用芯片重注释技术,构建预测口腔鳞状细胞癌(oral squamous cell carcinoma,OSCC)的lncRNA风险评分(lncRNA-RS)模型。方法:下载GEO数据库中的GSE42743数据集作为训练集(n=74),TCGA中的头颈部肿瘤队列作为测试集(n=78)。利用生物信息学方法对训练集中的基因探针进行重注释,并通过单因素和多因素Cox回归分析建立OSCC预后相关的lncRNA-RS模型。然后,采用生存曲线和多因素Cox回归分析评价lncRNA-RS模型对预后的预测作用。最后,运用KEGG通路分析探究模型中lncRNA可能的作用机制。结果:单因素Cox回归分析共发现5个与OSCC预后显著相关的lncRNA(P<0. 001)。基于这5个lncRNA进一步构建OSCC预后相关的lncRNA-RS模型,并根据lncRNA-RS将患者分为高风险组(n=37)和低风险组(n=37)。生存曲线分析表明,低风险组患者的总生存期在训练集(P<0. 001)和测试集(P=0. 022)中均优于高风险组。多因素Cox回归分析表明,lncRNA-RS在训练集(HR:9. 860,95%CI:4. 289～22. 667,P <0. 001)和测试集(HR:2. 259,95%CI:1. 171～4. 357,P=0. 015)中均为影响OSCC预后的独立因素。KEGG富集分析表明,这些lncRNA主要参与调节代谢相关通路、钙信号通路、m TOR信号通路和AMPK信号通路等。结论:本研究基于5个lncRNA构建了lncRNA-RS模型,可作为一种潜在的OSCC预后标志物组合。

Objective:To construct alncRNA risk score(lncRNA-RS)model for predicting the prognosis of oral squamous cell carcinoma(OSCC)by using microarray re-annotation pipeline.Methods:Datasets of GSE42743 from GEO and Head and Neck Cancer cohort from TCGA were obtained as training dataset and test dataset,respectively.Gene probes in the training dataset were re-annotated using bioinformatics method,and univariate and multivariate Cox regression analysis were used to establish the lncRNA-RS model asscoiated with OSCC prognosis.Survival curve analysis and multivariate Cox regression were then performed to evaluate the prognostic power of lncRNA-RS for OSCC.Finally,KEGG pathway analysis was conducted to explore the potentially functional mechanisms of lncRNAs in the model.Results:Univariate Cox regression analysis found that 5 lncRNAs were significantly correlated with the prognosis of OSCC(P<0.001).The lncRNA-RS model was then constructed based on these lncRNAs,and patients were divided into high-risk group(n=37)and low-risk group(n=37)according to the median value of lncRNA-RS.Survival curve analysis indicated that compared with high-risk group,the overall survival in low-risk group was significantly longer in both training dataset(P<0.001)and test dataset(P=0.022).Multivariate Cox regression showed that lncRNA-RS could independently affect the prognosis of OSCC in either training(HR:9.860,95%CI:4.289~22.667,P<0.001)or test datasets(HR:2.259,95%CI:1.171~4.357,P=0.015).The results of KEGG enrichment analysis suggested that these lncRNAs were mainly involved in metabolic pathways,calcium signaling pathway,m TOR signaling pathway and AMPK signaling pathway.Conclusion:Our study has constructed alncRNA-RS model based on 5 lncRNAs,which may be used as a panel biomarker to predict OSCC prognosis and deserves further investigation.