| 基于长链非编码RNA的生物信息学分析构建膀胱癌预后模型并确定预后生物标志物 |
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| 引用本文： | 杨飞龙,洪锴,赵国江,刘承,宋一萌,马潞林. 基于长链非编码RNA的生物信息学分析构建膀胱癌预后模型并确定预后生物标志物[J]. 北京大学学报(医学版), 2019, 51(4): 615-622. DOI: 10.19723/j.issn.1671-167X.2019.04.003 |
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| 作者姓名： | 杨飞龙 洪锴 赵国江 刘承 宋一萌 马潞林 |
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| 作者单位： | 北京大学第三医院泌尿外科,北京,100191;北京大学第三医院泌尿外科,北京,100191;北京大学第三医院泌尿外科,北京,100191;北京大学第三医院泌尿外科,北京,100191;北京大学第三医院泌尿外科,北京,100191;北京大学第三医院泌尿外科,北京,100191 |
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| 基金项目： | 国家自然科学基金(81711530048);国家自然科学基金(81572515);首都市民健康项目(Z151100003915105) |
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| 摘 要： | 目的:构建基于长链非编码RNA(long non-coding RNA,lncRNA)的膀胱癌预后模型,并寻找预后生物标志物。方法:从癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库下载膀胱癌转录组及临床数据,Perl软件和R软件用于数据处理和分析。首先筛选差异表达lncRNA,继而对筛选结果进行单因素Cox回归分析以初步筛选与预后相关的lncRNA,再进一步用Lasso回归分析筛选影响预后的关键lncRNA,并运用多因素Cox回归分析构建预后模型。根据风险评分的中位数将患者分为高风险组和低风险组,运用Kaplan-Meier(K-M)生存分析、受试者接受特征(receiver operating characteristic,ROC)曲线和C指数对模型进行评价。此外,运用多因素Cox回归分析计算预后模型中各lncRNA的危险比和95%置信区间,并对差异有统计学意义的lncRNA进行K-M生存分析以确定预后生物标志物。结果:单因素Cox回归分析显示,在691个差异表达的lncRNA中, 35个可能与预后相关,其中23个经Lasso回归分析确认为影响预后的关键lncRNA。此外,K-M生存分析结果显示低风险组的总生存时间较高风险组长[(2.85±2.72)年vs. (1.58±1.51)年, P<0.001], ROC曲线显示3年生存率和5年生存率的曲线下面积分别为0.813和0.778,C指数为0.73。多因素Cox回归表明,23个关键lncRNA中有11个lncRNA差异有统计学意义,进一步的K-M生存分析表明,其中有3个lncRNA可能具有独立的预后价值,包括lncRNA AL589765.1(P = 0.004), AC023824.1(P = 0.022)和PKN2-AS1(P = 0.016)。结论:通过生物信息学分析,成功构建了基于23个lncRNA表达水平的膀胱癌预后模型,预测准确性中等,并确定了一个保护性预后生物标志物AL589765.1,以及两个不利的预后生物标志物AC023824.1和PKN2-AS1。
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| 关 键 词： | 长链非编码RNA 预后模型 预后生物标志物 膀胱癌 生物信息学 |
| 收稿时间： | 2019-03-13 |
Construction of prognostic model and identification of prognostic biomarkers based on the expression of long non-coding RNA in bladder cancer via bioinformatics |
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| Fei-long YANG,Kai HONG,Guo-jiang ZHAO,Cheng LIU,Yi-meng SONG,Lu-lin MA. Construction of prognostic model and identification of prognostic biomarkers based on the expression of long non-coding RNA in bladder cancer via bioinformatics[J]. Journal of Peking University. Health sciences, 2019, 51(4): 615-622. DOI: 10.19723/j.issn.1671-167X.2019.04.003 |
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| Authors: | Fei-long YANG Kai HONG Guo-jiang ZHAO Cheng LIU Yi-meng SONG Lu-lin MA |
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| Affiliation: | Department of Urology, Peking University Third Hospital, Beijing 100191, China |
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| Abstract: | Objective: To construct the prognostic model and identify the prognostic biomarkers based on long non-coding RNA (lncRNA) in bladder cancer.Methods: The lncRNA expression data and corresponding clinical data of bladder cancer were collected from The Cancer Genome Atlas (TCGA) database. The software Perl and R, and R packages were used for data integration, extraction, analysis and visualization. Detailly, R package “edgeR” was utilized to screen differentially expressed lncRNA in bladder cancer tissues compared with the normal bladder samples. The univariate Cox regression and the least absolute shrinkage and selection operator (Lasso) regression were performed to identify key lncRNA that were utilized to construct the prognostic model by the multivariate Cox regression. According to the median value of the risk score, all patients were divided into the high-risk group and low-risk group to perform the Kaplan-Meier (K-M) survival curves, receiver operating characteristic (ROC) curve and C-index, estimating the prognostic power of the prognostic model. In addition, the hazard ratio (HR) and 95% confidence interval (CI) of each key lncRNA were also calculated by the multivariate Cox regression. Moreover, we performed the K-M survival analysis for each significant key lncRNA from the result of the multivariate Cox regression.Results: A total of 691 lncRNA were identified as differentially expressed lncRNA, and 35 lncRNA signatures were initially considered associated with the prognosis of bladder cancer, where in 23 lncRNA were identified as key lncRNA associated with the prognosis. The overall survival time in years of the low-risk group was obviously longer than that of the high-risk group [(2.85±2.72) years vs. (1.58±1.51) years, P<0.001]. The area under the ROC curve (AUC) was 0.813 (3-year survival) and 0.778 (5-year survival) respectively, and the C-index was 0.73. In addition, HR and 95%CI of each key lncRNA were calculated by the multivariate Cox regression and 11 lncRNA were significant. Furthermore, K-M survival analysis revealed the independent prognostic value of 3 lncRNA, including AL589765.1(P = 0.004), AC023824.1(P = 0.022)and PKN2-AS1(P = 0.016).Conclusion: The present study successfully constructed the prognostic model based on the expression level of 23 lncRNA and finally identified one protective prognostic biomarker AL589765.1, and two adverse prognostic biomarkers including AC023824.1 and PKN2-AS1 in bladder cancer. |
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| Keywords: | Long non-coding RNA Prognostic model Prognostic biomarker Bladder cancer Bioinformatics |
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