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Journal of Peking University(Health Sciences) ›› 2019, Vol. 51 ›› Issue (4): 615-622. doi: 10.19723/j.issn.1671-167X.2019.04.003

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Construction of prognostic model and identification of prognostic biomarkers based on the expression of long non-coding RNA in bladder cancer via bioinformatics

Fei-long YANG,Kai HONG,Guo-jiang ZHAO,Cheng LIU,Yi-meng SONG(),Lu-lin MA()   

  1. Department of Urology, Peking University Third Hospital, Beijing 100191, China
  • Received:2019-03-13 Online:2019-08-18 Published:2019-09-03
  • Contact: Yi-meng SONG,Lu-lin MA E-mail:song_yimeng@126.com;malulin@medmail.com.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(81711530048);Supported by the National Natural Science Foundation of China(81572515);Beijing Municipal Science & Technology Commission(Z151100003915105)

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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.

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

CLC Number: 

  • R737.11

Figure 1

Identification of the differentially expressed lncRNA and Lasso regression A, volcano plot of the differentially expressed lncRNA; B, tuning parameter (lambda) selection in the Lasso regression used 10 fold cross-validation via minimum criteria; C, Lasso coefficient profiles of the features against the log2(lambda)."

Figure 2

Heatmap of the lncRNA screened by the univariate Cox regression, the first 19 genes were highly expressed,while the last 16 genes were poorly expressed"

Table 1

Clinical baseline data for bladder transitional cell carcinoma of TCGA database"

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

Table 2

lncRNA related to bladder cancer prognosis identified preliminary by univariate Cox regression analysis"

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

Figure 3

Evaluation of the prognostic model of bladder cancer by K-M survival curve and ROC curves A, K-M survival curve of the risk level; B, ROC curves of the 3- and 5-year survival."

Table 3

Result of the multivariate Cox regression analysis based on the 23 key lncRNA"

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

Figure 4

K-M survival curves of the 11 significant key lncRNA identified by the multivariate Cox regression A, AC023824.1; B, PKN2-AS1; C, AL589765.1."

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