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      The roles of ncRNAs in the diagnosis, prognosis and clinicopathological features of breast cancer: a systematic review and meta-analysis

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          Abstract

          Background

          A number of studies have shown that noncoding RNAs (ncRNAs) are abnormally expressed in breast cancers. However, the roles of ncRNAs remain unclear in breast cancer. Here, we aim to investigate the potential diagnostic and prognostic roles of ncRNAs in breast cancer.

          Methods

          Comprehensive literature search in Medline and Web of Science and a meta-analysis were performed to identify the association between ncRNAs and diagnosis, prognosis, and clinicopathological features of breast cancer.

          Results

          A total of 103 eligible studies, involving16, 828 independent participants, were included in the meta-analysis. In total, there were 98 individual and 11 grouped ncRNAs. 51 studies were eligible for survival analysis, 27 studies were eligible for diagnostic analysis, and 46 studies were eligible for clinicopathological features analysis. The abnormal expression of ncRNAs is associated with OS, RFS and PFS in breast cancer patients. For the diagnosis value of ncRNAs, the pooled OR and 95% CI for sensitivity, specificity, DOR and AUC on all ncRNAs were 0.83 [95% CI: 0.82- 0.84], 0.80 [95% CI: 0.79- 0.82], 24.77 [95% CI: 17.44- 35.16] and 0.9037, respectively. The analysis showed that downregulation of ncRNAs in breast cancer was associated with decreased risk of LNM, increased tumor size and PR expression, whereas, upregulation of ncRNAs was associated with increased HER2 expression.

          Conclusions

          High expression of ncRNAs was associated with poor OS, RFS, and PFS, while low expression of ncRNAs was related to favorable OS and RFS. Meanwhile, ncRNAs have potential diagnostic value for breast cancer.

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          Most cited references30

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          Meta-DiSc: a software for meta-analysis of test accuracy data

          Background Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis. Results Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities. Conclusion Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at .
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            Systematic reviews of evaluations of prognostic variables.

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              Association of large noncoding RNA HOTAIR expression and its downstream intergenic CpG island methylation with survival in breast cancer.

              Large noncoding RNA HOTAIR, transcribed from the antisense strand of HOXC12, interacts with Polycomb Repressive Complex 2 (PRC2) in the regulation of gene activities. Recent work suggests that it may have effects on breast cancer progression and survival. We evaluated HOTAIR expression and the methylation status of its downstream intergenic CpG island in primary breast cancers, and examined associations of these factors with clinical and pathologic features and patient survival. HOTAIR expression and DNA methylation were analyzed in tissue from 348 primary breast cancers with quantitative RT-PCR and methylation-specific PCR, respectively. HOTAIR expression and methylation varied widely in the tissues. A positive correlation was found between DNA methylation and HOTAIR expression. Methylation was associated with unfavorable disease characteristics, whereas no significant associations were found between HOTAIR expression and clinical or pathologic features. In multivariate, but not in univariate, Cox proportional hazard regression models, patients with high HOTAIR expression had lower risks of relapse and mortality than those with low HOTAIR expression. These findings suggest that the intergenic DNA methylation may have important biologic relevance in regulating HOTAIR expression, and that HOTAIR expression may not be an independent prognostic marker in breast cancer, but needs further validation in independent studies.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                6 October 2017
                10 August 2017
                : 8
                : 46
                : 81215-81225
                Affiliations
                1 Department of Clinical Laboratory & Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China
                2 Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, 430071, China
                3 Department of Otorhinolaryngology and Head & Neck Surgery, Hunan Children's Hospital, University of South China Hengyang, Hunan, 421000, China
                Author notes
                Correspondence to: Mingxia Yu, dewrosy520@ 123456163.com
                Article
                20149
                10.18632/oncotarget.20149
                5655276
                ce8c08fd-9b36-4507-8584-9f0aefd616e7
                Copyright: © 2017 Tang et al.

                This article is distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 7 November 2016
                : 12 April 2017
                Categories
                Meta-Analysis

                Oncology & Radiotherapy
                noncoding rna,breast cancer,meta-analysis,prognosis,diagnosis
                Oncology & Radiotherapy
                noncoding rna, breast cancer, meta-analysis, prognosis, diagnosis

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