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Meta-Analysis
. 2018 Sep 28;38(5):BSR20181095.
doi: 10.1042/BSR20181095. Print 2018 Oct 31.

Prognostic value of lncRNA ROR expression in various cancers: a meta-analysis

Renfu Lu  1 , Junjian Chen  1 , Lingwen Kong  2 , Hao Zhu  1
Affiliations
Meta-Analysis

Prognostic value of lncRNA ROR expression in various cancers: a meta-analysis

Renfu Lu et al. Biosci Rep. .

Abstract

Background: There is a dispute on the prognostic value of long non-coding RNA regulator of reprogramming (lncRNA ROR) in cancers. The purpose of the present study was to evaluate the prognostic significance of lncRNA ROR expression in human cancers. Methods: PubMed, Embase, and Cochrane Library were searched to look for relevant studies. The meta-analyses of prognostic and clinicopathological parameters (CPs) were conducted. Results: A total of ten studies were finally included into the meta-analysis. High lncRNA ROR expression was significantly associated with shorter overall survival (hazard ratio [HR] = 2.88, 95% confidence interval [CI] = 2.16-3.84, P<0.01) and disease-free survival (HR = 3.25, 95% CI = 2.30-4.60, P<0.01) compared with low lncRNA ROR expression. Besides, high lncRNA ROR expression was obviously related to more advanced clinical stage (P<0.01), earlier tumor metastasis (P=0.02), lymph node metastasis (P<0.01), and vascular invasion (P<0.01) compared with low lncRNA ROR expression. However, there was no significant correlation between lncRNA ROR expression and other CPs, including age (P=0.18), gender (P=0.33), tumor size (P=0.25), or tumor differentiation (P=0.13). Conclusion: High lncRNA ROR expression was associated with worse prognosis in cancers. LncRNA ROR expression could serve as an unfavorable prognostic factor in various cancers.

Keywords: cancer; lncRNA ROR; meta-analysis; prognosis.

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Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1
Figure 1. The flow chart of literature search and selection
Figure 2
Figure 2. The meta-analysis of OS
Figure 3
Figure 3. The meta-analysis of DFS
Figure 4
Figure 4. The detection of publication bias for meta-analysis of OS
Figure 5
Figure 5. The detection of publication bias for meta-analysis of DFS
Figure 6
Figure 6. The detection of publication bias for meta-analyses of CPs
(A) age; (B) gender; (C) clinical stage; (D) tumor size; (E) tumor metastasis; (F) tumor differentiation; (G) lymph node metastasis; (H) vascular invasion.
Figure 7
Figure 7. The sensitivity analysis for meta-analysis of OS
Figure 8
Figure 8. The sensitivity analysis for meta-analysis of DFS
See this image and copyright information in PMC

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