# Prognostic Significance of MicroRNAs in Glioma: A Systematic Review and Meta-Analysis

Authors: Yanming Zhang; Jigang Chen; Qiang Xue; Junyu Wang; Liang Zhao; Kaiwei Han; Danfeng Zhang; Lijun Hou
Journal: BioMed Research International
 (2019)

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## Abstract

Purpose. Different microRNAs (miRs) have been demonstrated to relate with the outcome of glioma patients, while the conclusions are inconsistent. We perform a meta-analysis to clarify the relationship between different miRs and prognosis of glioma.Methods. Related studies were retrieved from PubMed, Embase, and Cochrane Library. Pooled hazard ratios (HRs) of different miRs expression for survival and 95% confidence intervals (CIs) were calculated using random-effects model.Results. A total of 15 miRs with 4708 glioma patients were ultimately included. Increased expression of miR-15b (HR, 1.584; 95% CI, 1.199-2.092), 21 (HR, 1.591; 95% CI, 1.278-1.981), 148a (HR, 1.122; 95% CI, 1.023-1.231), 196 (HR, 1.877; 95% CI, 1.033-3.411), 210 (HR, 1.251; 95% CI, 1.010-1.550), and 221 (HR, 1.269; 95% CI, 1.054-1.527) or decreased expression of miR-106a (HR, 0.809; 95% CI, 0.655-0.998) and 124 (HR, 0.833; 95% CI, 0.729-0.952) was correlated with poor outcome of glioma patients.Conclusions. miR-15b, 21, 148a, 196, 210, 221, 106a, and 124 are valuable biomarkers for the prognosis of glioma which might be used in clinical settings.


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## Body

## 1. Introduction

Central nervous system cancer accounts for 2.3% of all cancer-related mortality worldwide and the annual incidence is reported to be 35 per million individuals [1]. As the most prevalent type of central nervous system cancer, glioma comprises nearly half of malignant brain cancers in adult population [2, 3]. Glioma can be categorized into grades I to IV pathologically according to the World Health Organization (WHO) grading system, and the majority belongs to grade IV, which is known as the most deadly type [4, 5]. In spite of currently available treatment strategies such as surgical resection, adjuvant radiotherapy, and combined radio-chemotherapy, the prognosis of glioma remains pessimistic with its 5-year survival rate being only 2% to 10% [6, 7]. Therefore, identification of prognostic factors is important to clinical decision for proper treatment modality and improvement of long-term outcome.Advances in studies of genetic biomarkers, such as microRNAs (miRs), have promoted the application of biomarkers in the prognosis of glioma. miRs are a group of short and noncoding RNA molecules and have been identified as the regulators of gene expression [8]. They can work as tumor-suppressing genes as well as oncogenes and thus mediate the progression of cancers [9–11]. Studies show that miRs may be related to the prognosis of different cancers such as lung cancer, gastric cancer, and breast cancer [12–14]. Moreover, the relationships between different kinds of miRs, such as miRNA-15b [15, 16], 21 [17, 18], and 222 [19, 20], and prognosis of glioma have been investigated, while their results are conflicting due to the variability in study design, size of sample, or specimens. Additionally, no systematic review has been performed to explore the role of all pertinent miRs in evaluating glioma prognosis as a whole. In this study, relevant literatures investigating the relationship between numerous kinds of miRs and glioma were systematically reviewed, and pooled results were quantitatively analyzed to evaluate the prognostic value of different miRs in glioma.


## 2. Materials and Methods

### 2.1. Search Strategy

The meta-analysis was conducted in line with the recommendations of Meta-Analysis of Observational Studies in Epidemiology group (MOOSE) [21] and Preferred Reporting Items for Systematic Reviews and Meta-Analysis: The PRISMA Statement [22]. Three databases including PubMed, Embase, and Cochrane Library were searched for studies examining the relationships between miRs and prognosis of glioma by two authors (Danfeng Zhang and Qiang Xue) independently on August 8th, 2017 without date limit. We restricted the language to English. The Mesh terms were defined as “microrna/micrornas/mirna/miRs” with “gliomas/glial cell tumor/glioblastoma” in the search process. The reference lists of retrieved articles were also checked for pertinent literatures. The complete search strategy for PubMed and Cochrane Library was presented in Supplementary Material.


### 2.2. Inclusion Criteria

Studies were included in this meta-analysis if they (1) recruited patients of glioma; (2) measured the expression of miRs in tumor tissue, serum, or plasma, as well as the survival prognosis of patients; (3) reported the survival curves for overall survival (OS) or disease-free survival (DFS) or cause-specific survival (CSS) or recurrence-free survival (RFS) with or without the hazard ratio (HR) and its 95% confidence intervals (CIs).


### 2.3. Exclusion Criteria

We excluded studies if (1) they were letters, reviews, or experimental studies; (2) the number of articles examining the relationship between miRs and glioma was less than three; (3) the original data could not be pooled. If one cohort was reported in two or more articles, we included the study most fully adjusted in order to prevent the disturbance of confounders.


### 2.4. Data Extraction and Quality Assessment

Study characteristics and original data were collected by three authors (Yanming Zhang, Qiang Xue, and Jigang Chen), including first author’s name, publication year, study design, study population, size of population, age and sex of participants, follow-up duration, type of sample, method of measuring miRs expression, and HRs and their 95% CIs. If HRs and 95% CIs were not reported in the included articles, we estimated them from Kaplan-Meier survival curves with methods described by Tierney et al. using Engauge Digitizer version 4.1 [23]. If only HRs and P values were reported, we estimated the 95% CIs using previously described method [24].Studies were included according to the following checklist on the basis of the criteria provided by MOOSE group [21]: clearly defined study design; clearly described study population (country); sufficiently large sample (N>30); clearly described outcome (OS, CSS, DFS, or RFS); clear defined miRs measurement, including quantitative real-time polymerase chain reaction (qRT-PCR) or in situ hybridization (ISH); clear definition of cut-off values; miRs measurement in tumor tissue, plasma, or serum; sufficiently long follow-up. Studies were excluded if they did not meet these criteria. Quality of included studies was systematically evaluated according to Newcastle-Ottawa Scale by two reviewers (Liang Zhao and Danfeng Zhang) independently [25]. Disagreement was solved by joint review.


### 2.5. Statistical Analysis

HRs and their 95% CIs extracted from studies were pooled using Stata version 12.0 (StataCorp, College Station, Texas, USA) and random-effects model. We used Chi-square test andI2 statistic in the assessment of heterogeneities among studies, andI2 values of <40%, 40%-75%, and >75% were defined as low, moderate, and high, respectively [26]. Subgroups analysis was conducted according to the type of survival prognosis (OS versus DFS) and data sources (direct extraction versus calculation from HR and P versus calculation from survival curve). In the pooled analysis, Egger’s test was employed in the analysis of publication bias. Sensitivity analysis was conducted by the removal of individual study by turns. P<0.1 was considered as significant in the analysis of publication bias and heterogeneity, while a significant level of 0.05 was used in other analyses.


## 2.1. Search Strategy

The meta-analysis was conducted in line with the recommendations of Meta-Analysis of Observational Studies in Epidemiology group (MOOSE) [21] and Preferred Reporting Items for Systematic Reviews and Meta-Analysis: The PRISMA Statement [22]. Three databases including PubMed, Embase, and Cochrane Library were searched for studies examining the relationships between miRs and prognosis of glioma by two authors (Danfeng Zhang and Qiang Xue) independently on August 8th, 2017 without date limit. We restricted the language to English. The Mesh terms were defined as “microrna/micrornas/mirna/miRs” with “gliomas/glial cell tumor/glioblastoma” in the search process. The reference lists of retrieved articles were also checked for pertinent literatures. The complete search strategy for PubMed and Cochrane Library was presented in Supplementary Material.


## 2.2. Inclusion Criteria

Studies were included in this meta-analysis if they (1) recruited patients of glioma; (2) measured the expression of miRs in tumor tissue, serum, or plasma, as well as the survival prognosis of patients; (3) reported the survival curves for overall survival (OS) or disease-free survival (DFS) or cause-specific survival (CSS) or recurrence-free survival (RFS) with or without the hazard ratio (HR) and its 95% confidence intervals (CIs).


## 2.3. Exclusion Criteria

We excluded studies if (1) they were letters, reviews, or experimental studies; (2) the number of articles examining the relationship between miRs and glioma was less than three; (3) the original data could not be pooled. If one cohort was reported in two or more articles, we included the study most fully adjusted in order to prevent the disturbance of confounders.


## 2.4. Data Extraction and Quality Assessment

Study characteristics and original data were collected by three authors (Yanming Zhang, Qiang Xue, and Jigang Chen), including first author’s name, publication year, study design, study population, size of population, age and sex of participants, follow-up duration, type of sample, method of measuring miRs expression, and HRs and their 95% CIs. If HRs and 95% CIs were not reported in the included articles, we estimated them from Kaplan-Meier survival curves with methods described by Tierney et al. using Engauge Digitizer version 4.1 [23]. If only HRs and P values were reported, we estimated the 95% CIs using previously described method [24].Studies were included according to the following checklist on the basis of the criteria provided by MOOSE group [21]: clearly defined study design; clearly described study population (country); sufficiently large sample (N>30); clearly described outcome (OS, CSS, DFS, or RFS); clear defined miRs measurement, including quantitative real-time polymerase chain reaction (qRT-PCR) or in situ hybridization (ISH); clear definition of cut-off values; miRs measurement in tumor tissue, plasma, or serum; sufficiently long follow-up. Studies were excluded if they did not meet these criteria. Quality of included studies was systematically evaluated according to Newcastle-Ottawa Scale by two reviewers (Liang Zhao and Danfeng Zhang) independently [25]. Disagreement was solved by joint review.


## 2.5. Statistical Analysis

HRs and their 95% CIs extracted from studies were pooled using Stata version 12.0 (StataCorp, College Station, Texas, USA) and random-effects model. We used Chi-square test andI2 statistic in the assessment of heterogeneities among studies, andI2 values of <40%, 40%-75%, and >75% were defined as low, moderate, and high, respectively [26]. Subgroups analysis was conducted according to the type of survival prognosis (OS versus DFS) and data sources (direct extraction versus calculation from HR and P versus calculation from survival curve). In the pooled analysis, Egger’s test was employed in the analysis of publication bias. Sensitivity analysis was conducted by the removal of individual study by turns. P<0.1 was considered as significant in the analysis of publication bias and heterogeneity, while a significant level of 0.05 was used in other analyses.


## 3. Results

### 3.1. Study Selection

The study selection process was shown in FigureI of Supplementary Material. A total of 2470 records were available in the initial search, including 1160 records from PubMed, 1294 from Embase, and 16 from Cochrane Library. 1837 studies remained for full texts review after removing the duplicates and reviewing the abstract. No eligible study was detected by screening the reference lists. Finally, 31 studies met the inclusion criteria and were included in our meta-analysis.


### 3.2. Study Characteristics

The quality assessment of each study was shown in TableI of Supplementary Material. The number of literatures evaluating the association between miRs and the prognosis of glioma were listed in Table II of Supplementary Material. The main characteristics of included articles were described in Table 1. All of them were retrospective and published between 2010 and 2017. A total of 4708 glioma patients were evaluated for the prognostic value of 15 different miRs, with a median sample size of 109 patients (range, 38–548 patients). Expression of miRs was mainly measured in tumor tissues, while four studies examined miRs in serum or plasma [36, 41, 47, 49]. Most studies used qRT-PCR to detect miRs, while three employed ISH and microarray [18, 31, 41]. HRs and 95% CIs were not reported in 14 studies [15, 16, 20, 27, 30, 32, 33, 37, 39, 42, 43, 45, 46, 48], and we estimated them by methods described above. The cutoff value was not reported in 11 articles [15, 18, 33, 36, 38, 40, 45–48, 50]. The reported HRs were adjusted for related variables such as pathological grade, Karnofsky performance score (KPS) and tumor size in nine studies [17–19, 28, 29, 34, 38, 41, 49] (Table 1).Table 1
Characteristics of articles with Kaplan-Meier survival curves in glioma.
microRNA Study Country Study design Sample Number Stage Cut-off Follow-up (months) Result HR(H/L) 95%CI p 10b Ji, Y 2015 China R Frozen 95 I-IV Median 60 OSm 4.71 1.45-8.32 <0.001 10b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.09 0.98-1.21 0.12 10b Zhang, X 2016 China R Frozen 128 I-IV None 80 OSu 3.42 2.08-5.62 <0.001 10b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.30 0.53-3.2 0.58 10b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.06 0.97-1.15 0.18 15b Guan, Y 2010 Japan R Frozen 39 I-IV Mean >60 OSm 1.87 0.68-5.16 0.227 15b Pang, C 2015 China R Frozen 76 II-IV None >60 OSu 5.68 2.81-11.50 <0.001 15b Sun, G 2015 China R Frozen 92 I-IV Median >60 OSu 2.21 1.36-3.6 0.001 15b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.67 0.51-0.87 0.003 15b Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 0.76 0.40-1.52 0.028 15b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.80 0.65-0.99 0.04 15b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.76 0.63-0.91 0.003 17 Lu, S 2012 China R Tissue 108 I-IV Median >100 OSm 2.14 1.06-4.30 0.034 17 Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6517 0.50-0.85 0.002 17-5b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.934 0.76-1.16 0.54 17-5b Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.7 1.05-4.01 0.043 17-5b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.16 0.95-1.42 0.15 17-5b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.01 0.80-1.28 0.94 17-5p Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 0.68 0.54-0.85 0.0008 20a Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 0.68 0.55-0.84 <0.001 20a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.95 0.79-1.14 0.59 20a Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6708 0.51-0.88 0.005 20a Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.69 1.06-3.79 0.04 20a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.05 0.87-1.27 0.63 20a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.93 0.80-1.08 0.35 21 Guan, Y 2010 Japan R Frozen 39 I-IV Mean >60 OSm 0.57 0.21-1.52 0.264 21 Hermansen, S 2012 Denmark R FFPE 189 I-IV None >60 OSm 1.545 1.002-2.381 0.049 21 Wu, L 2013 China R Frozen 152 I-IV Mean 60 OSm 3.17 2.39-4.179 <0.001 21 Barbano, R 2014 TCGA R Tissue 191 I-IV None >110 OSu 1.26 1.06-1.48 0.007 21 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.73 0.58-0.91 0.006 21 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.54 0.37-0.80 0.002 21 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.72 0.56-0.92 0.009 21 Zhi, F 2010 China R Tissue 124 I-IV Median 100 OSm 1.882 1.07-3.308 0.028 106a Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 0.66 0.52-0.83 <0.001 106a Zhao, S 2013 China R FFPE 114 I-IV Median 50 OSm 0.504 0.297–0.854 0 .011 106a Zhao, S 2013 China R FFPE 103 I-IV Median 50 OSm 0.452 0.255–0.800 0 .006 106a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.94 0.73-1.20 0.62 106a Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6341 0.47-0.85 0.003 106a Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.71 1.07-3.63 0.038 106a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.96 0.80-1.15 0.67 106a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.97 0.81-1.17 0.76 106a Zhi, F 2010 China R Tissue 124 I-IV Median 100 OSm 0.6139 0.34-1.11 0.108 124 Chen, T 2015 China R Frozen 137 I-IV None 60 OSm 2.37 1.24-4.528 0.009 124 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.19 1.06-1.33 0.003 124 Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 0.65 0.26-1.03 0.062 124 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.33 1.08-1.64 0.007 124 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.23 1.11-1.36 <0.001 148a Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.21 1.08-1.356 0.001 148a Kim, J 2014 TCGA R Tissue 482 I-IV None >60 OSu 1.19 1.10-1.29 <0.001 148a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.9 0.79-1.03 0.13 148a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 2.44 0.77-7.74 0.13 148a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.99 0.90-1.09 0.85 155 Qiu, S 2013 TCGA R Tissue 480 I-IV 50th percentile >100 OSm 0.796 0.646-0.982 0.033 155 Barbano, R 2014 TCGA R Tissue 191 I-IV None >110 OSu 1.23 1.06-1.44 0.008 155 Sun, J 2014 China R Tissue 131 I-IV Mean 80 OSu 2.05 1.35-3.12 <0.001 155 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.91 0.77-1.07 0.27 155 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.70 0.18-2.73 0.62 155 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.91 0.77-1.07 0.26 182 Jiang, L 2010 China R FFPE 119 I-IV Median 80 OSm 3.39 1.98-5.80 <0.001 182 Xiao, Y 2016 China R Blood 112 I-IV None 60 OSm 1.25 0.89-2.53 0.013 182 Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 0.6 0.29-0.92 0.037 182a Zhao, S 2013 China R FFPE 114 I-IV Median 50 OSm 0.974 0.611–1.554 0 .912 182a Zhao, S 2013 China R FFPE 103 I-IV Median 50 OSm 1.032 0.630–1.693 0 .900 196 Guan, Y 2010 Japan R Frozen 39 I-IV Mean >60 OSm 3.37 1.20-9.46 0.021 196 Lakomy, R 2011 Czech R FFPE 38 I-IV Median >60 OSu 0.547 0.2776-1.0776 0.049 196a Zhao, S 2013 China R FFPE 114 I-IV Median 50 OSm 2.252 1.321-3.841 0.003 196a Zhao, S 2013 China R FFPE 103 I-IV Median 50 OSm 1.906 1.108-3.281 0.021 196a Guan, Y 2015 China R Frozen 63 I-IV None >60 OSu 3.17 1.82-5.53 0.007 200b Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.21 1.067-1.372 0.003 200b Liu, Q 2014 China R Tissue 73 I-IV None 40 OSu 0.3 0.09-0.96 0.05 200b Men, D 2014 China R Frozen 266 I-IV Median 60 OSm 2.9 1.166-7.21 0.022 210 Qiu, S 2013 TCGA R Tissue 480 I-IV 50th percentile >100 OSm 0.749 0.591-0.949 0.017 210 Barbano, R 2014 TCGA R Tissue 191 I-IV None >110 OSu 1.16 1.01-1.33 0.038 210 Lai, N 2014 China R Frozen 125 I-IV Mean >100 OSu 2.3 1.47-3.61 0.0003 210 Lai, N 2015 China R Serum 126 I-IV Mean >80 OSm 3.84 2.09-7.08 <0.001 210 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.97 0.83-1.14 0.71 210 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.53 0.20-1.43 0.21 210 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.93 0.84-1.03 0.17 221 Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.27 1.097-1.471 0.001 221 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.92 0.79-1.06 0.26 221 Li, X 2016 China R Tissue 45 I-IV Mean 36 OSu 2.18 1.02-4.65 0.044 221 Zhang, R 2016 China R Blood 50 I-IV None 50 OSu 2.4 1.42-4.05 0.001 221 Chen, Y 2017 China R Tissue 114 I-IV None 72 OSm 2.039 1.06-3.91 0.032 221 Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6856 0.53-0.88 0.003 221 Xue, L 2017 China R Tissue 165 I-IV Median 60 OSu 1.656 1.135-2.486 0.009 221 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.80 0.19-3.30 0.77 221 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.88 0.74-1.04 0.14 222 Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.26 1.11-1.43 0.0004 222 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.04 0.92-1.18 0.53 222 Li, X 2016 China R Tissue 45 I-IV Mean 36 OSu 2.13 1.01-4.48 0.043 222 Zhang, R 2016 China R Blood 50 I-IV None 50 OSu 2.81 1.70-4.65 0.0004 222 Chen, Y 2017 China R Tissue 114 I-IV None 72 OSm 0.899 0.559-1.447 0.661 222 Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.5947 0.44-0.81 0.001 222 Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.71 1.07-3.63 0.038 222 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.41 0.91-2.17 0.12 222 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.90 0.80-1.01 0.07 CI: confidence interval; DFS: disease free survival; FFPE: formalin-fixed paraffin-embedded; HR (H/L): hazard ratio (High/Low); OS: overall survival; R: retrospective; TGGA: The Cancer Genome Atlas; m: multivariate analysis; u: univariate analysis.


### 3.3. Meta-Analysis

The pooled HRs together with the heterogeneity for all miRs were demonstrated in Table2.Table 2
Summary of the HR for microRNA expression in glioma.
microRNA Survival analysis Number of articles Included references HR 95%CI P value ∗Heterogeneity Total patients Figure Publication bias 10b OS/DFS 3 [27–29] 1.349 0.984-1.849 0.063 89.7%, p<0.001 550 4A 0.16 15b OS/DFS 5 [15, 16, 26, 28, 30] 1.584 1.199-2.092 0.001 74.9%, p=0.001 640 2A 0.263 17 OS/DFS 5 [20, 26, 28, 31, 32] 0.933 0.759-1.149 0.516 75.8%, p<0.001 1200 4B 0.368 20a OS/DFS 4 [20, 26, 28, 31] 0.919 0.755-1.119 0.399 77.9%, p<0.001 1092 4C 0.925 21 OS/DFS 6 [18, 28, 30, 33–35] 1.591 1.278-1.981 <0.001 82.6%, p<0.001 1022 2B 0.536 106a OS/DFS 6 [20, 26, 28, 31, 34, 36] 0.809 0.655-0.998 0.048 77.9%, p<0.001 1433 3A 0.177 124 OS/DFS 3 [26, 28, 37] 0.833 0.729-0.952 0.007 66.6%, p=0.018 570 3B 0.516 148a OS/DFS 3 [28, 31, 38] 1.122 1.023-1.231 0.015 60.5%, p=0.038 920 2C 0.254 155 OS/DFS 4 [28, 33, 39, 40] 1.143 0.942-1.387 0.175 74.9%, p=0.001 1129 4D 0.586 182 OS 4 [26, 36, 41, 42] 1.206 0.709-2.051 0.489 81%, p<0.001 554 4E 0.955 196 OS 4 [30, 36, 43, 44] 1.877 1.033-3.411 0.039 77.5%, p=0.001 357 2D 0.893 200b OS 3 [31, 45, 46] 1.113 0.451-2.744 0.816 77.5%, p=0.012 450 4F 0.923 210 OS/DFS 5 [28, 33, 40, 47, 48] 1.251 1.010-1.550 0.04 84.7%, p<0.001 1249 2E 0.181 221 OS/DFS 7 [19, 20, 28, 31, 49–51] 1.269 1.054-1.527 0.012 77.0%, p<0.001 1360 2F 0.194 222 OS/DFS 7 [19, 20, 26, 28, 31, 49, 50] 1.104 0.907-1.343 0.325 83.5%, p<0.001 1301 4G 0.765 DFS: disease free survival; HR: hazard ratio; OS: overall survival;∗Higgins I2 statistic.


### 3.4. High Expression of miR-15b, 21, 148a, 196, 210, and 221 Predicts Poor Prognosis in Glioma Patients

Five studies were included to investigate the relationship between high expression of miR-15b and DFS/OS [15, 16, 34, 37, 41]. The pooled results indicated that high miR-15b expression was significantly associated with the poor prognosis in glioma (HR, 1.584; 95% CI, 1.199-2.092, P=0.001, Figure 1(a)).Figure 1
Forest plots of miR-15b (a), 21 (b), 148a (c), 196 (d), 210 (e), and 221 (f) and glioma prognosis.
(a) (b) (c) (d) (e) (f)Six studies examined the prognostic value of miR-21 in glioma [17, 18, 29, 34, 37, 38], suggesting that miR-21 overexpression significantly predicted poor prognosis in glioma (HR, 1.591; 95% CI, 1.278-1.981, P<0.001, Figure 1(b)).Three literatures focused on the relationship between high expression of miR-148a and OS/DFS [30, 34, 50]. The summary results suggested that miR-148a was correlated with shorter DFS/OS (HR, 1.122; 95% CI, 1.023-1.231, P=0.015, Figure 1(c)).Pooled results also demonstrated significant relationship between miR-196, 210, and 221 and poor prognosis in glioma (HR, 1.877; 95% CI, 1.033-3.411, P=0.039 for miR-196; HR, 1.251; 95% CI, 1.010-1.550, P=0.04 for miR-210; HR, 1.269; 95% CI, 1.054-1.527, P=0.012 for miR-221; Figures1(d)–1(f)).


### 3.5. Low Expression of miR-106a and 124 Predicts Poor Prognosis in Glioma Patients

There were six [20, 29–31, 34, 41] and three [34, 41, 45] studies investigating the prognostic value of miR-106a and miR-124 in glioma, respectively. The summary HRs indicated these two miRs were negatively associated with poor prognosis in glioma (HR, 0.809; 95% CI, 0.655-0.998, P=0.048 for miR-106a; HR, 0.833; 95% CI, 0.729-0.952, P=0.007 for miR-124, Figures 2(a)-2(b)).Figure 2
Forest plots of miR-106a (a) and 124 (b) and glioma prognosis.
(a) (b)


### 3.6. No Significant Relationship between Overexpression of miR-10b, 17, 20a, 155, 182, 200b, and 222 and Poor Prognosis in Glioma Patients

Several different studies were included to examine the prognostic value of miR-10b, 17, 20a, 155, 182, 200b, and 222 in glioma. However, pooled HRs suggested no statistical relationships between these miRs and prognosis of glioma. The detailed results were illustrated in Table2 and Figure 3.Figure 3
Forest plots of miR-10b (a), 17 (b), 20a (c), 155 (d), 182 (e), 200b (f), and 222 (g) and glioma prognosis.


### 3.7. Subgroups Analysis

In the subgroup of OS outcomes, we found high expression of miR-10b predicted poor prognosis in glioma patients (HR, 3.70; 95% CI, 2.40-5.70, P<0.05) (TableIII in Supplementary Material). For data calculation from HR and P value, we detected that low expression of miR-17 and 20a was associated with poor prognosis in glioma patients (HR, 0.67, 95% CI, 0.56-0.79, P<0.05 for miR-17; HR, 0.68, 95% CI, 0.57-0.80, P<0.05 for miR-20a, Table III in Supplementary Material). For data calculation from survival curve, overexpression of miR-10b and 182 was detected to be related to poor prognosis after glioma (HR, 3.42, 95% CI, 2.08-5.62, P<0.05 for miR-10b; HR, 3.39, 95% CI, 1.98-5.80, P<0.05 for miR-182, Table III in Supplementary Material).


### 3.8. Publication Bias

Publication bias was assessed for the meta-analysis of all miRs and we found no publication bias by Egger’s test, which was shown in Table2.


### 3.9. Sensitivity Analysis

We have done the sensitivity analysis through removing studies one by one in the analysis of all miRs. Our results were roughly not altered suggesting that our pooled HRs and the 95%CIs were basically stable. However, when it went to miR-10b, the result turned to be significant, suggesting that high miR-10b expression was associated with the poor prognosis in glioma if we removed data from Chen et al.’s article (HR, 1.428; 95% CI, 1.022-1.995, P=0.037) [34]. For miR-155, high miR-155 expression was associated with the poor prognosis in glioma (HR,1.22; 95% CI, 1.044-1.425, P=0.012) after removing Qiu et al.’s study [28].


## 3.1. Study Selection

The study selection process was shown in FigureI of Supplementary Material. A total of 2470 records were available in the initial search, including 1160 records from PubMed, 1294 from Embase, and 16 from Cochrane Library. 1837 studies remained for full texts review after removing the duplicates and reviewing the abstract. No eligible study was detected by screening the reference lists. Finally, 31 studies met the inclusion criteria and were included in our meta-analysis.


## 3.2. Study Characteristics

The quality assessment of each study was shown in TableI of Supplementary Material. The number of literatures evaluating the association between miRs and the prognosis of glioma were listed in Table II of Supplementary Material. The main characteristics of included articles were described in Table 1. All of them were retrospective and published between 2010 and 2017. A total of 4708 glioma patients were evaluated for the prognostic value of 15 different miRs, with a median sample size of 109 patients (range, 38–548 patients). Expression of miRs was mainly measured in tumor tissues, while four studies examined miRs in serum or plasma [36, 41, 47, 49]. Most studies used qRT-PCR to detect miRs, while three employed ISH and microarray [18, 31, 41]. HRs and 95% CIs were not reported in 14 studies [15, 16, 20, 27, 30, 32, 33, 37, 39, 42, 43, 45, 46, 48], and we estimated them by methods described above. The cutoff value was not reported in 11 articles [15, 18, 33, 36, 38, 40, 45–48, 50]. The reported HRs were adjusted for related variables such as pathological grade, Karnofsky performance score (KPS) and tumor size in nine studies [17–19, 28, 29, 34, 38, 41, 49] (Table 1).Table 1
Characteristics of articles with Kaplan-Meier survival curves in glioma.
microRNA Study Country Study design Sample Number Stage Cut-off Follow-up (months) Result HR(H/L) 95%CI p 10b Ji, Y 2015 China R Frozen 95 I-IV Median 60 OSm 4.71 1.45-8.32 <0.001 10b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.09 0.98-1.21 0.12 10b Zhang, X 2016 China R Frozen 128 I-IV None 80 OSu 3.42 2.08-5.62 <0.001 10b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.30 0.53-3.2 0.58 10b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.06 0.97-1.15 0.18 15b Guan, Y 2010 Japan R Frozen 39 I-IV Mean >60 OSm 1.87 0.68-5.16 0.227 15b Pang, C 2015 China R Frozen 76 II-IV None >60 OSu 5.68 2.81-11.50 <0.001 15b Sun, G 2015 China R Frozen 92 I-IV Median >60 OSu 2.21 1.36-3.6 0.001 15b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.67 0.51-0.87 0.003 15b Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 0.76 0.40-1.52 0.028 15b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.80 0.65-0.99 0.04 15b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.76 0.63-0.91 0.003 17 Lu, S 2012 China R Tissue 108 I-IV Median >100 OSm 2.14 1.06-4.30 0.034 17 Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6517 0.50-0.85 0.002 17-5b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.934 0.76-1.16 0.54 17-5b Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.7 1.05-4.01 0.043 17-5b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.16 0.95-1.42 0.15 17-5b Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.01 0.80-1.28 0.94 17-5p Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 0.68 0.54-0.85 0.0008 20a Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 0.68 0.55-0.84 <0.001 20a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.95 0.79-1.14 0.59 20a Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6708 0.51-0.88 0.005 20a Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.69 1.06-3.79 0.04 20a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.05 0.87-1.27 0.63 20a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.93 0.80-1.08 0.35 21 Guan, Y 2010 Japan R Frozen 39 I-IV Mean >60 OSm 0.57 0.21-1.52 0.264 21 Hermansen, S 2012 Denmark R FFPE 189 I-IV None >60 OSm 1.545 1.002-2.381 0.049 21 Wu, L 2013 China R Frozen 152 I-IV Mean 60 OSm 3.17 2.39-4.179 <0.001 21 Barbano, R 2014 TCGA R Tissue 191 I-IV None >110 OSu 1.26 1.06-1.48 0.007 21 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.73 0.58-0.91 0.006 21 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.54 0.37-0.80 0.002 21 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.72 0.56-0.92 0.009 21 Zhi, F 2010 China R Tissue 124 I-IV Median 100 OSm 1.882 1.07-3.308 0.028 106a Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 0.66 0.52-0.83 <0.001 106a Zhao, S 2013 China R FFPE 114 I-IV Median 50 OSm 0.504 0.297–0.854 0 .011 106a Zhao, S 2013 China R FFPE 103 I-IV Median 50 OSm 0.452 0.255–0.800 0 .006 106a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.94 0.73-1.20 0.62 106a Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6341 0.47-0.85 0.003 106a Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.71 1.07-3.63 0.038 106a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.96 0.80-1.15 0.67 106a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.97 0.81-1.17 0.76 106a Zhi, F 2010 China R Tissue 124 I-IV Median 100 OSm 0.6139 0.34-1.11 0.108 124 Chen, T 2015 China R Frozen 137 I-IV None 60 OSm 2.37 1.24-4.528 0.009 124 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.19 1.06-1.33 0.003 124 Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 0.65 0.26-1.03 0.062 124 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.33 1.08-1.64 0.007 124 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.23 1.11-1.36 <0.001 148a Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.21 1.08-1.356 0.001 148a Kim, J 2014 TCGA R Tissue 482 I-IV None >60 OSu 1.19 1.10-1.29 <0.001 148a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.9 0.79-1.03 0.13 148a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 2.44 0.77-7.74 0.13 148a Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.99 0.90-1.09 0.85 155 Qiu, S 2013 TCGA R Tissue 480 I-IV 50th percentile >100 OSm 0.796 0.646-0.982 0.033 155 Barbano, R 2014 TCGA R Tissue 191 I-IV None >110 OSu 1.23 1.06-1.44 0.008 155 Sun, J 2014 China R Tissue 131 I-IV Mean 80 OSu 2.05 1.35-3.12 <0.001 155 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.91 0.77-1.07 0.27 155 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.70 0.18-2.73 0.62 155 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.91 0.77-1.07 0.26 182 Jiang, L 2010 China R FFPE 119 I-IV Median 80 OSm 3.39 1.98-5.80 <0.001 182 Xiao, Y 2016 China R Blood 112 I-IV None 60 OSm 1.25 0.89-2.53 0.013 182 Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 0.6 0.29-0.92 0.037 182a Zhao, S 2013 China R FFPE 114 I-IV Median 50 OSm 0.974 0.611–1.554 0 .912 182a Zhao, S 2013 China R FFPE 103 I-IV Median 50 OSm 1.032 0.630–1.693 0 .900 196 Guan, Y 2010 Japan R Frozen 39 I-IV Mean >60 OSm 3.37 1.20-9.46 0.021 196 Lakomy, R 2011 Czech R FFPE 38 I-IV Median >60 OSu 0.547 0.2776-1.0776 0.049 196a Zhao, S 2013 China R FFPE 114 I-IV Median 50 OSm 2.252 1.321-3.841 0.003 196a Zhao, S 2013 China R FFPE 103 I-IV Median 50 OSm 1.906 1.108-3.281 0.021 196a Guan, Y 2015 China R Frozen 63 I-IV None >60 OSu 3.17 1.82-5.53 0.007 200b Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.21 1.067-1.372 0.003 200b Liu, Q 2014 China R Tissue 73 I-IV None 40 OSu 0.3 0.09-0.96 0.05 200b Men, D 2014 China R Frozen 266 I-IV Median 60 OSm 2.9 1.166-7.21 0.022 210 Qiu, S 2013 TCGA R Tissue 480 I-IV 50th percentile >100 OSm 0.749 0.591-0.949 0.017 210 Barbano, R 2014 TCGA R Tissue 191 I-IV None >110 OSu 1.16 1.01-1.33 0.038 210 Lai, N 2014 China R Frozen 125 I-IV Mean >100 OSu 2.3 1.47-3.61 0.0003 210 Lai, N 2015 China R Serum 126 I-IV Mean >80 OSm 3.84 2.09-7.08 <0.001 210 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.97 0.83-1.14 0.71 210 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.53 0.20-1.43 0.21 210 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.93 0.84-1.03 0.17 221 Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.27 1.097-1.471 0.001 221 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.92 0.79-1.06 0.26 221 Li, X 2016 China R Tissue 45 I-IV Mean 36 OSu 2.18 1.02-4.65 0.044 221 Zhang, R 2016 China R Blood 50 I-IV None 50 OSu 2.4 1.42-4.05 0.001 221 Chen, Y 2017 China R Tissue 114 I-IV None 72 OSm 2.039 1.06-3.91 0.032 221 Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.6856 0.53-0.88 0.003 221 Xue, L 2017 China R Tissue 165 I-IV Median 60 OSu 1.656 1.135-2.486 0.009 221 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.80 0.19-3.30 0.77 221 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.88 0.74-1.04 0.14 222 Srinivasan, S 2011 TCGA R Tissue 111 I-IV 60th percentile 120 OSm 1.26 1.11-1.43 0.0004 222 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.04 0.92-1.18 0.53 222 Li, X 2016 China R Tissue 45 I-IV Mean 36 OSu 2.13 1.01-4.48 0.043 222 Zhang, R 2016 China R Blood 50 I-IV None 50 OSu 2.81 1.70-4.65 0.0004 222 Chen, Y 2017 China R Tissue 114 I-IV None 72 OSm 0.899 0.559-1.447 0.661 222 Sun, C 2017 TCGA R Tissue 548 I-IV Median 130 OSu 0.5947 0.44-0.81 0.001 222 Zhao, H 2017 America R Serum 106 I-IV Median 24 OSu 1.71 1.07-3.63 0.038 222 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 1.41 0.91-2.17 0.12 222 Chen, W 2016 TCGA R Tissue 109 I-IV Median >60 DFSu 0.90 0.80-1.01 0.07 CI: confidence interval; DFS: disease free survival; FFPE: formalin-fixed paraffin-embedded; HR (H/L): hazard ratio (High/Low); OS: overall survival; R: retrospective; TGGA: The Cancer Genome Atlas; m: multivariate analysis; u: univariate analysis.


## 3.3. Meta-Analysis

The pooled HRs together with the heterogeneity for all miRs were demonstrated in Table2.Table 2
Summary of the HR for microRNA expression in glioma.
microRNA Survival analysis Number of articles Included references HR 95%CI P value ∗Heterogeneity Total patients Figure Publication bias 10b OS/DFS 3 [27–29] 1.349 0.984-1.849 0.063 89.7%, p<0.001 550 4A 0.16 15b OS/DFS 5 [15, 16, 26, 28, 30] 1.584 1.199-2.092 0.001 74.9%, p=0.001 640 2A 0.263 17 OS/DFS 5 [20, 26, 28, 31, 32] 0.933 0.759-1.149 0.516 75.8%, p<0.001 1200 4B 0.368 20a OS/DFS 4 [20, 26, 28, 31] 0.919 0.755-1.119 0.399 77.9%, p<0.001 1092 4C 0.925 21 OS/DFS 6 [18, 28, 30, 33–35] 1.591 1.278-1.981 <0.001 82.6%, p<0.001 1022 2B 0.536 106a OS/DFS 6 [20, 26, 28, 31, 34, 36] 0.809 0.655-0.998 0.048 77.9%, p<0.001 1433 3A 0.177 124 OS/DFS 3 [26, 28, 37] 0.833 0.729-0.952 0.007 66.6%, p=0.018 570 3B 0.516 148a OS/DFS 3 [28, 31, 38] 1.122 1.023-1.231 0.015 60.5%, p=0.038 920 2C 0.254 155 OS/DFS 4 [28, 33, 39, 40] 1.143 0.942-1.387 0.175 74.9%, p=0.001 1129 4D 0.586 182 OS 4 [26, 36, 41, 42] 1.206 0.709-2.051 0.489 81%, p<0.001 554 4E 0.955 196 OS 4 [30, 36, 43, 44] 1.877 1.033-3.411 0.039 77.5%, p=0.001 357 2D 0.893 200b OS 3 [31, 45, 46] 1.113 0.451-2.744 0.816 77.5%, p=0.012 450 4F 0.923 210 OS/DFS 5 [28, 33, 40, 47, 48] 1.251 1.010-1.550 0.04 84.7%, p<0.001 1249 2E 0.181 221 OS/DFS 7 [19, 20, 28, 31, 49–51] 1.269 1.054-1.527 0.012 77.0%, p<0.001 1360 2F 0.194 222 OS/DFS 7 [19, 20, 26, 28, 31, 49, 50] 1.104 0.907-1.343 0.325 83.5%, p<0.001 1301 4G 0.765 DFS: disease free survival; HR: hazard ratio; OS: overall survival;∗Higgins I2 statistic.


## 3.4. High Expression of miR-15b, 21, 148a, 196, 210, and 221 Predicts Poor Prognosis in Glioma Patients

Five studies were included to investigate the relationship between high expression of miR-15b and DFS/OS [15, 16, 34, 37, 41]. The pooled results indicated that high miR-15b expression was significantly associated with the poor prognosis in glioma (HR, 1.584; 95% CI, 1.199-2.092, P=0.001, Figure 1(a)).Figure 1
Forest plots of miR-15b (a), 21 (b), 148a (c), 196 (d), 210 (e), and 221 (f) and glioma prognosis.
(a) (b) (c) (d) (e) (f)Six studies examined the prognostic value of miR-21 in glioma [17, 18, 29, 34, 37, 38], suggesting that miR-21 overexpression significantly predicted poor prognosis in glioma (HR, 1.591; 95% CI, 1.278-1.981, P<0.001, Figure 1(b)).Three literatures focused on the relationship between high expression of miR-148a and OS/DFS [30, 34, 50]. The summary results suggested that miR-148a was correlated with shorter DFS/OS (HR, 1.122; 95% CI, 1.023-1.231, P=0.015, Figure 1(c)).Pooled results also demonstrated significant relationship between miR-196, 210, and 221 and poor prognosis in glioma (HR, 1.877; 95% CI, 1.033-3.411, P=0.039 for miR-196; HR, 1.251; 95% CI, 1.010-1.550, P=0.04 for miR-210; HR, 1.269; 95% CI, 1.054-1.527, P=0.012 for miR-221; Figures1(d)–1(f)).


## 3.5. Low Expression of miR-106a and 124 Predicts Poor Prognosis in Glioma Patients

There were six [20, 29–31, 34, 41] and three [34, 41, 45] studies investigating the prognostic value of miR-106a and miR-124 in glioma, respectively. The summary HRs indicated these two miRs were negatively associated with poor prognosis in glioma (HR, 0.809; 95% CI, 0.655-0.998, P=0.048 for miR-106a; HR, 0.833; 95% CI, 0.729-0.952, P=0.007 for miR-124, Figures 2(a)-2(b)).Figure 2
Forest plots of miR-106a (a) and 124 (b) and glioma prognosis.
(a) (b)


## 3.6. No Significant Relationship between Overexpression of miR-10b, 17, 20a, 155, 182, 200b, and 222 and Poor Prognosis in Glioma Patients

Several different studies were included to examine the prognostic value of miR-10b, 17, 20a, 155, 182, 200b, and 222 in glioma. However, pooled HRs suggested no statistical relationships between these miRs and prognosis of glioma. The detailed results were illustrated in Table2 and Figure 3.Figure 3
Forest plots of miR-10b (a), 17 (b), 20a (c), 155 (d), 182 (e), 200b (f), and 222 (g) and glioma prognosis.


## 3.7. Subgroups Analysis

In the subgroup of OS outcomes, we found high expression of miR-10b predicted poor prognosis in glioma patients (HR, 3.70; 95% CI, 2.40-5.70, P<0.05) (TableIII in Supplementary Material). For data calculation from HR and P value, we detected that low expression of miR-17 and 20a was associated with poor prognosis in glioma patients (HR, 0.67, 95% CI, 0.56-0.79, P<0.05 for miR-17; HR, 0.68, 95% CI, 0.57-0.80, P<0.05 for miR-20a, Table III in Supplementary Material). For data calculation from survival curve, overexpression of miR-10b and 182 was detected to be related to poor prognosis after glioma (HR, 3.42, 95% CI, 2.08-5.62, P<0.05 for miR-10b; HR, 3.39, 95% CI, 1.98-5.80, P<0.05 for miR-182, Table III in Supplementary Material).


## 3.8. Publication Bias

Publication bias was assessed for the meta-analysis of all miRs and we found no publication bias by Egger’s test, which was shown in Table2.


## 3.9. Sensitivity Analysis

We have done the sensitivity analysis through removing studies one by one in the analysis of all miRs. Our results were roughly not altered suggesting that our pooled HRs and the 95%CIs were basically stable. However, when it went to miR-10b, the result turned to be significant, suggesting that high miR-10b expression was associated with the poor prognosis in glioma if we removed data from Chen et al.’s article (HR, 1.428; 95% CI, 1.022-1.995, P=0.037) [34]. For miR-155, high miR-155 expression was associated with the poor prognosis in glioma (HR,1.22; 95% CI, 1.044-1.425, P=0.012) after removing Qiu et al.’s study [28].


## 4. Discussion

Mounting evidences have shown that various miRs are related to the survival outcome in glioma patients. However, different studies present with inconsistent conclusions. For example, three studies investigate the association between miR-10b and glioma prognosis, and the results are significant in Zhang et al. and Ji et al. [46, 82] and insignificant in Chen et al. [34]. Similar conflicting results are also demonstrated in researches exploring other miRs [13, 36, 83–85]. Therefore, it is crucial to perform current meta-analysis to have an overall understanding of relationships between miRs expression and prognosis of glioma patients.A total of 15 miRs and their ability in predicting prognosis of glioma are investigated in this study. Patients with high levels of miR-15b, 21, 148a, 196, 210, and 221 expression have a statistically significant poorer DFS/OS than those with low expression levels. Contrastively, decreased expression of miR-106a and 124 is associated with poor prognosis in patients of glioma. There are some other miRs including miR-10b, 17, 20a, 155, 182, 200b, and 222 which are indicated to have no prognostic value in glioma. There is no publication bias after the assessment using Egger’s test and the pooled HRs remain the same when removing studies one by one.Among the miRs whose overexpression is indicated to be associated with poor DFS/OS, miR-21 is the first discovered microRNA and known to be widely expressed in human tissues. It is also the most studied tumor-related biomarkers and might play an essential role in many different cancers [86]. Increased expression level of miR-21 has been discovered to be related to dismal outcome in cancer patients [13]. miR-21 is indicated to be overly expressed in glioma in a WHO-grade specific manner [54]. Several literatures assure that miR-21 can induce the tumor growth, invasion, and migration and inhibit cell apoptosis [55, 87]. miR-21 has been identified to target at the tumor suppressing genes, such as the protein tyrosine phosphatase (PTEN), programmed cell death 4 (PDCD4), and B cell translocation gene 2 (BTG2). Inhibition of miR-21 would lead to the upregulation of these genes, which ultimately affect the cancer progression and prognosis [13, 66, 88–90].Only two miRs (miR-106a and miR-124) are proved in this study that their downregulations are connected with poor prognosis. As a protective microRNA, miR-106a is located at Xq26.2 and the length of mature miR-106a is 23 nucleotides. Previous study has shown that miR-106a has a cancer suppressing effect through antiproliferation and inducing apoptosis in glioma cells. This effect might arise from E2F1 inhibition via posttranscriptional regulation [91]. Similarly, miR-124 is reported to have effects of tumor suppression via the regulation of cell proliferation, apoptosis, migration, and invasion in certain cancer diseases [67, 92, 93]. Study also indicated that miR-124 works through the inhibition of STAT3 signal to enhance the T cell mediated clearance of glioma cells [70].The relationships between overexpression of seven miRs (miR-10b, 17, 20a, 155, 182, 200b, and 222) and DFS/OS for glioma patients were not proved in our study. This might attribute to the nature of miRs themselves. For example, miR-17 is extensively studied, and it proves to have both the tumor suppressing and oncogenic functions. Upregulation of miR-17 can promote cancer growth via aiming E2F1 and increase angiogenesis through thrombospondin-1 [71]. Contrastively, overexpression of miR-17 can also lead to the decreased cell migration and proliferation by the repressing of fibronectin expression [94]. Moreover, different sample size, type of specimens, and prognosis assessment might also produce the inconsistent conclusions which ultimately lead to the insignificant results in our meta-analysis. Summary of miRs along with altered expression and potential targets as well as pathways in this study is listed in Table 3.Table 3
Summary of miRs with altered expression, their potential targets, and pathways entered this study.
miRNA Expression Potential targets Pathways Reference 15b Up cyclin D1, MMP-3, NRP Angiogenesis, cell apoptosis, cell cycle progression, cell invasion [52, 53] 21 Up BTG2, PDCD4, PTEN Cell apoptosis, invasion, migration, tumor growth [54–56] 148a Up BIM, MIG6 Cell apoptosis [38] 196 Up HOXA7, HOXB8, HOXC8, HOXD8, IκBα Malignant transformation, tumorigenesis, [57–60] 210 Up FGFRL1, HIF-1a Angiogenesis, cell migration, cell proliferation, [61–63] 221 Up AKT, p27Kipl, Growth factor signaling pathways Cell proliferation, cell apoptosis, malignant phenotype [64, 65] 106a Down E2F1, TIMP-2 Cell apoptosis, cell invasion, cell proliferation, [66] 124 Down STAT3 T cell mediated clearance of glioma [67] 10b Up RhoC, uPA Cell invasion, cell migration [68, 69] 17 Up or down E2F1, TSP-1 Angiogenesis, cell growth, cell migration [70, 71] 20a Up E2F1, TIMP-2 Cell invasion, cell proliferation [72–75] 155 Up FOXO3a, p53 Cell invasion, cell migration [76–78] 182 Down FOXO3, MITF-M Cell migration, cell survival [79] 200b Up or down cyclin D1, EGFR, RND3 Cell migration, epithelial-to mesenchymal transition [80, 81] 222 Up p27Kip1 Cell cycle progression, cell invasion, cell proliferation [19, 65] AKT, AKT serine/threonine kinase; BTG2, B cell translocation gene 2; E2F1, E2F transcription factor 1; EGFR, epidermal growth factor receptor; FGFRL1, fibroblast growth factor receptor-like 1; FOXO3a, forkhead box O3; HIF-1a, hypoxia-inducible factor 1a; HOX, homeobox; MIG6, mitogen-inducible gene 6; MITF-M, microphthalmia-associated transcription factor-M; MMP-3, matrix metalloproteinase-3; NRP, nitrogen regulatory protein; PDCD4, programmed cell death 4; PTEN, protein tyrosine phosphatase; RHOC, ras homolog family member C; RND3, rho family GTPase 3; STA3, signal transducers and activators of transcription; TIMP-2, tissue inhibitor of metalloproteinases-2; TSP-1, thrombospondin-1; uPA, urokinase-type plasminogen activator.Though the measurement of miRs expression levels is a convenient way in predicting the glioma prognosis, difficulties still exist before applying miRs in the clinical settings. First, cell-free miRs would release from some normal human tissues as well and might interfere the final results to some degree [95]. Therefore, it is important to determine the source of tumor-specific miRs and create a method which could differentiate cancer population from healthy group. Second, no standard procedure for the measurement of miRs has been confirmed, which might be the source of contradictory results. Moreover, a single microRNA can be associated with different tumor tissues. For example, the prognostic value of miR-21 has been established among the patients of breast cancer [96], pancreatic ductal adenocarcinoma [97], and gastric cancer [98]. Therefore, a group of miRs specific to glioma is useful and might significantly improve the prognostic accuracy [35].To our knowledge, though some meta-analyses regarding the prognostic value of several miRs in glioma patients have been published [36, 83–85], these studies are incomplete and avoid assessing some other available miRs. We include all of the miRs which have been explored previously, and a total of 15 miRs are investigated ultimately. Among these 15 miRs, eight of them have established the prognostic significance with glioma. However, relationship between the remaining miRs and prognosis of glioma patients should be validated by further large-scale prospective studies in future. Our study also has advantages in including the newly published trials from different places and times, which are representative enough.Limitations of our meta-analysis should be noticed before interpreting the results. Firstly, as we mentioned above, there is no single microRNA which is specific to glioma exclusively, and the panel of miRs which can be used to distinguish glioma from other cancers and satisfactorily predict the prognosis has not been discovered yet. Therefore, the clinical application of miRs is restricted. Secondly, the heterogeneity among studies is generally significant. Thirdly, the prognosis is evaluated by different indicators, such as the overall survival and disease-free survival, which might be the source of heterogeneity. Fourthly, all the included literatures are retrospective and there lack relative high-quality trials. Lastly, the number of available studies is limited for some miRs and it might be insufficient to draw a definite conclusion.



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*Source: 4015969-2019-03-26.xml*