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. 2019 Aug 20;11(16):6237-6251.
doi: 10.18632/aging.102185. Epub 2019 Aug 20.

Prognostic value of long non-coding RNA signatures in bladder cancer

Anbang He  1   2   3   4 , Shiming He  1   2   3   4 , Ding Peng  1   2   3   4 , Yonghao Zhan  1   2   3   4 , Yifan Li  1   2   3   4 , Zhicong Chen  1   2   3   4 , Yanqing Gong  1   2   3   4 , Xuesong Li  1   2   3   4 , Liqun Zhou  1   2   3   4
Affiliations

Prognostic value of long non-coding RNA signatures in bladder cancer

Anbang He et al. Aging (Albany NY). .

Abstract

Bladder cancer (BLCA) is a devastating cancer whose early diagnosis can ensure better prognosis. Aim of this study was to evaluate the potential utility of lncRNAs in constructing lncRNA-based classifiers of BLCA prognosis and recurrence. Based on the data concerning BLCA retrieved from TCGA, lncRNA-based classifiers for OS and RFS were built using the least absolute shrinkage and selection operation (LASSO) Cox regression model in the training cohorts. More specifically, a 14-lncRNA-based classifier for OS and a 12-lncRNA-based classifier for RFS were constructed using the LASSO Cox regression. According to the prediction value, patients were divided into high/low-risk groups based on the cut-off of the median risk-score. The log-rank test showed significant differences in OS and RFS between low- and high-risk groups in the training, validation and whole cohorts. In the time-dependent ROC curve analysis, the AUCs for OS in the first, third, and fifth year were 0.734, 0.78, and 0.78 respectively, whereas the prediction capability of the 14-lncRNA classifier was superior to a previously published lncRNA classifier. As for the RFS, the AUCs in the first, third, and fifth year were 0.755, 0.715, and 0.740 respectively. In summary, the two-lncRNA-based classifiers could serve as novel and independent prognostic factors for OS and RFS individually.

Keywords: OS; RFS; bladder cancer; lasso; lncRNA.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare that there are no conflicts of interest concerning this article.

Figures

Figure 1
Figure 1
Study flowchart showing steps involved in construction of lncRNA-based prognostic signatures.
Figure 2
Figure 2
(A) Volcano plot of differentially expressed lncRNAs in TCGA-BLCA cohort. (B and C) Venn diagram of prognostic DElncRNAs in prognostic lncRNAs (OS/RFS univariate cox p < 0.05) and DElncRNAs(|logFC| >1 and padj < 0.05). (D) 20-time cross-validation for tuning parameter selection in the LASSO model for OS. (E) LASSO coefficient profiles of 463 prognostic DElncRNAs for OS. (F) 20-time cross-validation for tuning parameter selection in the LASSO model for RFS. (G) LASSO coefficient profiles of 201 prognostic DElncRNAs for RFS.
Figure 3
Figure 3
(A, C and E) Overall survival curves of BLCA patients in training, validation and all cohorts with a low or high risk of death, according to 14-lncRNA-based classifier risk score level. (B, D and F): Relapse-free survival curves of BLCA patients in training, validation and all cohorts with a low or high risk of death, according to 12-lncRNA-based classifier risk score level.
Figure 4
Figure 4
Boxplot of risk score in patients with pT (A, OS), pN (B, OS), grade (C, OS) and pT (D, RFS).
Figure 5
Figure 5
(A and B) Time dependent ROC curves at 1, 3 and 5 years, separately for OS and RFS. (C and D) The ROC for the lncRNA-score, stage, and lncRNA-score combined with stage for OS and RFS in whole BLCA cohorts. (E and F) Survival curves of BLCA patients with combinations of lncRNA-score risk and stage in the whole cohorts for OS and RFS.
See this image and copyright information in PMC

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