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

doi:10.19723/j.issn.1671-167X.2019.04.003

摘要/Abstract

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。

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

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.

Key words: Long non-coding RNA, Prognostic model, Prognostic biomarker, Bladder cancer, Bioinformatics

中图分类号:

R737.11

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.

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.

图/表 7

图1

图2

表1

TCGA数据库膀胱移形细胞癌临床基线资料表"

Clinical characteristics	Value
Gender, n
Male	303
Female	106
Age/years, $x ? ± s$	68.1±5.6
Grade
High	385
Low	21
Unknown	3
TNM stage
Ⅰ	2
Ⅱ	130
Ⅲ	139
Ⅳ	136
Unknown	2
T
T0	1
T1	3
T2	120
T3	194
T4	59
Unknown	32
N
N0	237
N1	47
N2	76
N3	8
Unknown	41
M
M0	194
M1	11
Unknown	204

表1

图3

表3

图4

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lncRNA name	Ensemble id	HR	P
AC005008.2	ENSG00000237896	1.54	0.001
AL159153.1	ENSG00000275611	1.17	<0.001
AC025437.2	ENSG00000253424	1.21	0.001
AC073316.2	ENSG00000231892	0.89	0.002
AC104793.1	ENSG00000249568	1.22	0.002
AC087071.1	ENSG00000229196	1.10	0.003
KRT73-AS1	ENSG00000257495	1.11	0.005
ADAMTS9-AS1	ENSG00000241158	1.09	0.006
AC023824.1	ENSG00000260073	1.10	0.007
AL589765.1	ENSG00000227045	0.89	0.008
AL139130.1	ENSG00000237390	0.89	0.008
AC092725.1	ENSG00000261482	1.20	0.009
MYO16-AS1	ENSG00000236242	1.08	0.009
AL391704.1	ENSG00000224750	1.10	0.011
AP002812.5	ENSG00000255449	0.90	0.011
LINC02474	ENSG00000228437	1.05	0.015
AL137804.1	ENSG00000255525	1.14	0.018
AC105053.1	ENSG00000229498	0.87	0.018
PKN2-AS1	ENSG00000237505	1.07	0.019
LINC00536	ENSG00000249917	1.09	0.019
AC104071.1	ENSG00000251434	1.17	0.023
RGMB-AS1	ENSG00000246763	1.13	0.024
LINC00608	ENSG00000236445	0.85	0.025
AL023584.1	ENSG00000233138	1.15	0.027
AL138885.3	ENSG00000231056	1.10	0.031
LINC01468	ENSG00000231131	1.06	0.033
AF279873.3	ENSG00000253642	1.06	0.034
AC020558.1	ENSG00000264666	1.07	0.035
AC026469.1	ENSG00000275088	0.91	0.038
AL138789.1	ENSG00000233589	1.07	0.039
AL356489.2	ENSG00000260947	1.08	0.039
AL353804.1	ENSG00000228906	1.08	0.041
AC104472.1	ENSG00000214919	0.90	0.044
AC004973.1	ENSG00000226661	0.90	0.046
LARGE-IT1	ENSG00000232081	1.13	0.047

lncRNA name	n	HR (95%CI)	P
AC004973.1	406	0.87 (0.77 - 0.99)	0.029
AC005008.2	406	1.73 (1.40 - 2.13)	<0.001
AC023824.1	406	1.10 (1.02 - 1.19)	0.019
AC025437.2	406	1.28 (1.09 - 1.50)	0.003
AC026469.1	406	0.84 (0.75 - 0.93)	0.001
AC073316.2	406	0.94 (0.87 - 1.02)	0.124
AC087071.1	406	1.03 (0.95 - 1.12)	0.501
AC092725.1	406	1.34 (1.12 - 1.61)	0.002
AC104071.1	406	1.13 (0.97 - 1.31)	0.113
AC104793.1	406	1.12 (0.99 - 1.28)	0.076
AC105053.1	406	0.85(0.75 - 0.96)	0.009
ADAMTS9-AS1	406	1.04 (0.97 - 1.12)	0.266
AL023584.1	406	1.15 (0.99 - 1.33)	0.070
AL137804.1	406	1.01 (0.89 - 1.14)	0.885
AL139130.1	406	0.93 (0.86 - 1.02)	0.137
AL159153.1	406	1.07 (0.96 - 1.19)	0.196
AL356489.2	406	1.12 (1.02 - 1.23)	0.020
AL589765.1	406	0.91 (0.84 - 0.99)	0.028
AP002812.5	406	0.94 (0.86 - 1.03)	0.189
LINC00608	406	0.74 (0.63 - 0.87)	<0.001
LINC02474	406	1.05 (0.99 - 1.11)	0.105
MYO16-AS1	406	0.98 (0.90 - 1.07)	0.679
PKN2-AS1	406	1.11 (1.03 - 1.19)	0.007