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Long non coding RNA XIST as a prognostic cancer marker - A meta-analysis

期刊

CLINICA CHIMICA ACTA
卷 482, 期 -, 页码 1-7

出版社

ELSEVIER
DOI: 10.1016/j.cca.2018.03.016

关键词

Long non coding RNA; XIST; Cancer; Prognostic marker

资金

  1. Fundamental Research Funds for the Central Universities [2015305020202]
  2. China Postdoctoral Science Foundation [2017M620340]
  3. National Postdoctoral Program for Innovative Talents [BX201700178]
  4. Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, China [OHIC2017Y02]

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Background: The X inactivate-specific transcript (XIST), derived from XIST gene, is aberrantly expressed in various cancers. High-expression of XIST is related to poor clinical outcome. This meta-analysis evaluated the potential role of XIST as novel predictor of prognosis in human cancer. Materials and methods: This meta-analysis collected eligible studies about XIST and tumor prognosis through retrieving keywords in Web of Science, PubMed, Embase and the CNKI database, from 1993 to August 21, 2017. The quantitative meta-analysis was carried out with Stata SE12.0 and RevMan3.23 software. The aim was to determine whether XIST expression is associated with cancer prognosis and clinicopathology. Results: A total of 858 patients from 10 eligible studies were included in the final meta-analysis. Overall, a significant negative association between XIST and overall survival (OS) time (HR = 2.62, 95% CI: 2.18-3.14) was observed. Statistical significance was also showed in subgroup meta-analysis stratified by the country, sample size, follow-up and publication year. It was reported that increased XIST was positively related to advanced clinical TNM stage (OR = 4.03, 95% CI: 2.22-7.30), lymph node metastasis (LNM) (OR = 2.70, 95% CI: 1.73-4.21), distant metastasis (DM) (OR = 2.61, 95% CI: 1.57-4.33) and tumor size (OR = 3.10, 95% CI: 2.24-4.30). Conclusions: LncRNA XIST may serve as a potential biomarker to predict solid tumor prognosis. This molecule can be effectively used to predict the clinical and pathological features of cancers.

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