{'fraction_non_alphanumeric': 0.042200350546042875, 'fraction_numerical': 0.020088984764729676, 'mean_word_length': 5.329351535836177, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Prognostic role of dysregulated circRNAs in patients with non-small cell lung cancer: a meta-analysis

Background Lung cancer is the leading cause of cancer incidence and mortality. Non-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancer, which lacks comprehensive prognostic biomarkers to predict the prognosis of patients. This research was performed to assess the potential prognostic role of circular RNAs (circRNAs) in patients with NSCLC. Methods We searched the following databases: PubMed, Web of Science, Embase, and Ovid MEDLINE(R) up to May 20, 2019 to identify studies which explored the association between circRNAs and NSCLC. Newcastle-Ottawa Scale (NOS) was applied to assess the quality of the included studies. Pooled hazard ratios (HRs) and the corresponding 95% confidence interval (CI) were calculated to assess the prognostic value of circRNAs in patients with NSCLC. Subgroup analyses were performed to explain heterogeneity among the included studies. Publication bias was estimated using Begg’s funnel plot. Sensitivity analysis was performed to test the stability of pooled results. Results A total of 19 eligible studies including 1,650 NSCLC patients were included in this research. Pooled results indicated that the up-regulated expression of circRNAs was significantly associated with worse prognosis of patients with NSCLC (HR =2.08, 95% CI: 1.81–2.40). Conclusions Our finding indicated that circRNAs could serve as prognostic biomarkers in patients with NSCLC. However, further large-scale prospective studies about the clinical significance of circRNAs are of great need in order to obtain conclusive results.

CircRNAs were defined as the by-products of splicing mistakes without biological functions in the past several decades (8). More and more circRNAs have been detected in different cancer tissues with the widely application of high-throughput sequencing technology and bioinformatic approaches (9). Recent studies demonstrated that circRNAs could play a role in a series of biological processes such as microRNA sponging, RBP sponging and mRNA regulation which make effect to carcinogenesis (7,(10)(11)(12). However, these studies determining the prognostic roles of circRNAs in NSCLC patients were limited by their small sample size.
This meta-analysis aimed to evaluate the prognostic value of circRNAs in NSCLC patients.

Search strategy
This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (13). Two authors independently searched in PubMed, Web of Science, Embase, and Ovid MEDLINE(R) up to May 20, 2019 using the following search term: ("lung squamous cell carcinoma" OR "lung adenocarcinoma" OR "large cell lung cancer" OR "non-small cell lung cancer" OR NSCLC) AND ("circular RNAs" OR "circular RNA" OR circRNAs OR circRNA) AND (prognosis OR prognostic OR survival).

Inclusion and exclusion criteria
The inclusion criteria were as follows: (I) studies detected circRNAs expression in NSCLC patients; (II) studies investigated the association between circRNAs expression and NSCLC patients' prognosis; and (III) studies presented hazard ratio (HR) and corresponding 95% confidence interval (CI) or Kaplan-Meier (K-M) curve.
The exclusion criteria were as follows: (I) duplicate publications; (II) reviews, case reports, or letters; (III) studies no published in English; (IV) studies which sample size <35; and (V) studies without available data.

Data extraction
Two authors independently extracted the data from all eligible studies. The following information were extracted from the included studies: first author, publication year, country, sample size, sample types, circRNAs studied, detection methods, circRNAs expression level, cut-off value, number of patients in low expression groups, number of patients in high expression groups, outcome, followup period, multivariate analysis, and HR and 95% CI for overall survival (OS). Engauge Digitizer 4.1 software and Tierney's protocol (14) were applied to extract the HR and 95% CI indirectly from K-M curves if the studies did not present complete survival data.

Quality assessment
Two authors assessed the quality of the included studies independently using Newcastle-Ottawa Scale (NOS) (15). Total scores of NOS were ranged from 0 to 9, and studies with scores greater than 6 were considered as high quality studies.

Statistical analysis
Statistical analyses were analyzed using STATA 12.0 software. Heterogeneity test was performed by I-squared statistics. If I-squared value >50% and P value <0.05, there was significant heterogeneity among the included studies, and a random effects model was applied to estimate the pooled results; otherwise, a fixed effects model was performed. Pooled HR and corresponding 95% CI were calculated to assess the prognostic value of circRNAs in patients with NSCLC. Sensitivity analysis was performed to test the stability of pooled results. Publication bias was estimated using Begg's funnel plot. Statistical significance was defined as P value <0.05.
Furthermore, we assessed the quality of each included studies and the scores ranged from 6-8, which suggests the included studies are in a high quality.

Publication bias
Publication bias was assessed by Begg's test. As shown in Figure 5, there was no obvious asymmetry in the Begg's funnel plot; and the P value for Begg's test was 0.837, which was greater than 0.05. Thus, there was no significant publication bias among the included studies.

Discussion
Research on the functional roles of circRNAs in carcinogenesis is still in its infancy. There is some evidence to indicate that circRNAs could function as valuable biomarkers for cancer diagnosis and prognosis (12). The use of circRNAs as prognostic biomarkers has the following advantages: (I) circRNAs are more stable than linear RNAs; (II) circRNAs are superior to traditional biomarker in terms of organ specificity (35); (III) circRNAs can be easily NSCLC patients were included in this meta-analysis. After a fixed-effects model was performed to estimate pooled HR we became more convinced that up-regulated expression of circRNAs was significantly associated with poor prognosis of patients with NSCLC. However, there were several limitations in this metaanalysis. Firstly, as HR and 95% CI values from 8 studies (16,17,22,27,(31)(32)(33)(34) were retrieved from the K-M curves, there could be some marginal error as numerical values were not reported explicitly in the full-text of those studies. Secondly, since we combined multiple types of circRNAs to assess their prognostic role in patients with NSCLC, the heterogeneity might be inevitable. Thirdly, all the included studies of this meta-analysis just tested the expression of circRNAs among Chinese population, thus it is unclear whether our results might be applicable to other populations. Last but not least, we only enrolled 1,650 NSCLC patients from 19 studies (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) in this meta-analysis making it hard to widespread the clinical applicability of this meta-analysis because of the small sample size and the small number of studies.

Conclusions
The current meta-analysis might provide evidence that upregulated expression of circRNAs is significantly associated with poor prognosis of NSCLC patients in Chinese