title: Long noncoding RNAs as novel predictors of survival in human cancer: a systematic review and meta-analysis.
abstract: Expression of various long noncoding RNAs (lncRNAs) may affect cancer prognosis. Here, we aim to gather and examine all evidence on the potential role of lncRNAs as novel predictors of survival in human cancer.
  
  
  
  We systematically searched through PubMed, to identify all published studies reporting on the association between any individual lncRNA or group of lncRNAs with prognosis in human cancer (death or other clinical outcomes). Where appropriate, we then performed quantitative synthesis of those results using meta-analytic methods to identify the true effect size of lncRNAs on cancer prognosis. The reliability of those results was then examined using measures of heterogeneity and testing for selective reporting biases.
  
  
  
  Three hundred ninety-two studies were screened to eventually identify 111 eligible studies on 127 datasets. In total, these represented 16,754 independent participants pertaining to 53 individual and 6 grouped lncRNAs within a total of 19 cancer sites. Overall, 83 % of the studies we identified addressed overall survival and 32 % of the studies addressed recurrence-free survival. For overall survival, 96 % (88/92) of studies identified a statistically significant association of lncRNA expression to prognosis. Meta-analysis of 6 out of 7 lncRNAs for which three or more studies were available, identified statistically significant associations with overall survival. The lncRNA HOTAIR was by far the most broadly studied lncRNA (n = 29; of 111 studies) and featured a summary hazard ratio (HR) of 2.22 (95 % confidence interval (CI), 1.86-2.65) with modest heterogeneity (I(2) = 49 %; 95 % CI, 14-79 %). Prominent excess significance was demonstrated across all meta-analyses (p-value = 0.0003), raising the possibility of substantial selective reporting biases.
  
  
  
  Multiple lncRNAs have been shown to be strongly associated with prognosis in diverse cancers, but substantial bias cannot be excluded in this field and larger studies are needed to understand whether these prognostic information may eventually be useful.
authors.affiliation: St. John's Hospital, Livingston, EH54 6PP, UK.|College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
authors.forename: Stylianos
authors.initials: S
authors.lastname: Serghiou
authors.affiliation: University Hospital of North Durham, North Rd, Durham, DH1 5TW, UK.
authors.forename: Aikaterini
authors.initials: A
authors.lastname: Kyriakopoulou
authors.affiliation: Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine Stanford, Stanford, CA, 94305, USA. jioannid@stanford.edu.|Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, 94305, USA. jioannid@stanford.edu.|Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, 94305, USA. jioannid@stanford.edu.|Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 1265 Welch Rd, MSOB X306, Stanford, CA, 94305, USA. jioannid@stanford.edu.
authors.forename: John P A
authors.initials: JP
authors.lastname: Ioannidis
mesh_terms: D014408:Biomarkers, Tumor; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015999:Multivariate Analysis; D009369:Neoplasms / Q000235:genetics* / Q000401:mortality*; D011379:Prognosis; D062085:RNA, Long Noncoding / Q000235:genetics*; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor; D062085:RNA, Long Noncoding
keywords: Cancer; Cancer biomarkers; Excess significance; LncRNA; Prognosis; Selective reporting biases; Small-study effects; Survival analysis