The detection of long non-coding RNA (lncRNA) is a novel method for lung cancer diagnosis. However, the diagnostic efficacy of lncRNA in different studies is inconsistent. Therefore, we conducted this meta-analysis to elucidate the diagnostic efficacy of lncRNA in identification of lung cancer including small cell lung cancer. The online PubMed, Medline, EMBASE, CNKI and Wanfang literature databases were searched to identify all related articles about the diagnostic efficacy of lncRNA for lung cancer. 28 articles including 3044 patients with lung cancer and 2598 controls were enrolled in our meta-analysis. lncRNA sustained a high diagnostic efficacy, pooled sensitivity of 0.82 (95% CI 0.79 to 0.84), specificity of 0.82 (95% CI 0.78 to 0.84) and area under the curve (AUC) of 0.88 (95% CI 0.85 to 0.91) in identification of patients with lung cancer from controls. Furthermore, the diagnostic efficacy of paralleled lncRNA was better than single lncRNA (sensitivity: 0.86 vs 0.80; specificity: 0.88 vs 0.78; AUC: 0.93 vs 0.86). MALAT1 had a better diagnostic efficacy than GAS5 (AUC: 0.90 vs 0.81; sensitivity: 0.83 vs 0.70; specificity: 0.83 vs 0.78). lncRNA in tissues was observed to achieve lower diagnostic efficacy than that in plasma or serum (AUC: 0.87 vs 0.90 vs 0.90) when stratified by sample types. In summary, our meta-analysis suggests that lncRNA might be a promising biomarker(s) for identifying lung cancer and the combination of lncRNA or with other biomarkers had a better diagnostic efficacy.
- long non-coding RNA
- lung cancer
Statistics from Altmetric.com
Contributors WML and SPD designed and revised the work. SPD and JJ drafted the work and independently assessed the included studies, and any scoring discrepancies were resolved through a third reviewer (WML). The data were extracted from the included studies by two reviewers (WML and JJ). All authors did the final approval of the version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding This work was supported by the foundation of the Research on precision medical platform and demonstration application in Chengdu (grant number 2016-HM02-00001-SF).
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.