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. 2014 Jul 14;9(7):e102413. doi: 10.1371/journal.pone.0102413

Prognostic Value of miR-21 in Various Cancers: An Updating Meta-Analysis

Xin Zhou 1,#, Xiaping Wang 3,#, Zebo Huang 1, Jian Wang 1, Wei Zhu 1,*, Yongqian Shu 1,2, Ping Liu 1,2,*
Editor: Georgina L Hold4
PMCID: PMC4097394  PMID: 25019505

Abstract

Background

Recently, more and more studies investigated the value of microRNA (miRNA) as a diagnostic or prognostic biomarker in various cancers. MiR-21 was found dysregulated in almost all types of cancers. While the prognostic role of miR-21 in many cancers has been studied, the results were not consistent.

Methods

We performed a meta-analysis to investigate the correlation between miR-21 and survival of general cancers by calculating pooled hazard ratios (HR) and 95% confidence intervals (CI).

Results

The pooled results of 63 published studies showed that elevated miR-21 was a predictor for poor survival of general carcinomas, with pooled HR of 1.91 (95%CI: 1.66–2.19) for OS, 1.42 (95% CI: 1.16–1.74) for DFS and 2.2 (95% CI: 1.64–2.96) for RFS/CSS. MiR-21 was also a prognostic biomarker in the patients who received adjuvant therapy, with pooled HR of 2.4 (95%CI: 1.18–4.9) for OS.

Conclusions

Our results showed that miR-21 could act as a significant biomarker in the prognosis of various cancers. Further studies are warranted before the application of the useful biomarker in the clinical.

Introduction

Due to the aging and growth of population as well as an increasing adoption of cancer-related lifestyle such as smoking and “westernized” diets, cancer has been a major public health problem all around the world [1]. Almost one in four deaths in the United States is related with cancer in 2012 [2]. Lack of efficiently diagnostic and prognostic biomarkers is responsible for the high mortality rates caused by cancer [3].

MicroRNAs (miRNAs), approximately 22 nucleotides in length, are a class of highly conserved RNAs that negatively regulate gene expression at post-transcriptional level by base pairing with the 3′-untranslated region of target mRNAs, resulting in either mRNA degradation or translational inhibition [4], [5]. Many studies have demonstrated that miRNAs play important roles in various biological processes, such as cellular development, differentiation, proliferation, cell death, angiogenesis and metabolism [6]–[9]. The success of utilizing miRNAs as diagnostic or prognostic markers from expression profiling has been reported in many studies.

MiR-21 was one of the most frequently studied cancer-related miRNAs and dysregulated in most cancers by acting as oncogene [10]–[14]. Up-regulated miR-21 could increase tumor growth, metastasis and invasion and reduce sensitivity to chemotherapy by its various targets [15]–[18]. Cancer patients with higher expression of miR-21 always had a worse prognostic outcome. But some studies represented inconsistent or even opposite results, such as the study of Valladares-Ayerbes et al. [19]. So we performed this meta-analysis to reveal the prognostic value of miR-21 in various cancers.

Material and Methods

Publication search and inclusion criteria

Medical subheading (Mesh) terms relating to miR-21 (e.g. “miR-21” or “microRNA-21”) in combination with words related to cancer (e.g. “cancer”, “tumor”, “carcinoma” or “neoplasm”) and terms to prognosis (e.g. “prognosis”, “survival”, “outcome” or “prognostic”) were searched on PubMed, EMBASE and WEB of science to retrieve eligible studies till December, 2013 .

We also carefully examined the references of articles and reviews to explore potentially additional studies. Studies were eligible if they met the following criteria: (a) studied patients with any type of cancers; (b) expression of miR-21 was measured; (c) the association between expression of miR-21 and clinical outcome was investigated; (d) full text articles in English. Studies were excluded based on the following criteria: (a) reviews, letters or laboratory studies; (b) studies had overlapping or duplicate data; (c) absence of key information for further analysis [20].

Data extraction

Data were evaluated and extracted independently from the eligible studies by two investigators (Zhou and Wang) under the guidelines of a critical review checklist of the Dutch Cochrane Centre proposed by Meta-analysis of Observational Studies in Epidemiology (MOOSE) [21]. The following items were recorded: first author's name, year of publication, country or area of origin, ethnicity, cancer type, sample type, TNM stage, method, total number of patients, cut-off value, follow ups and HRs of miR-21 for overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS) or cancer specific survival (CSS) with their 95% confidence intervals (CIs) and P value. If not available, data were extracted by the method of Tierney et al. [20]. When discrepancies existed between the two investigators, another investigator (Huang) was invited to discuss until a consensus was reached.

Statistical analysis

All the HRs with their 95% CIs were used to calculate pooled HRs. Cochran's Q test and Higgins I-squared statistic were used to check the heterogeneity of pooled results. A P<0.10 for Q-test suggested significant heterogeneity among studies, and the random-effects model (DerSimonian-Laird method) was applied to calculate the pooled HRs [22]. Otherwise, the fixed-effects model (Mantel-Haenszel method) was used [23]. Begg's funnel plot and the Egger's linear regression test were conducted to evaluate publication bias of literatures and a p<0.05 was considered significant [24]. Trim and fill method was applied to assess potential asymmetry in the funnel plot. Statistical analyses were performed in STATA software version 12.0 (STATA Corporation, College Station, TX, USA). All P values were two-sided.

Results

Study characteristics

After careful read and selection, a total of 63 articles [19], were retrieved according to the inclusion and exclusion criteria. 55 of 63 articles investigated the prognostic role of miR-21 for OS, 17 for DFS, 8 for RFS and 3 for CSS. Schetter et al. [25], Hwang et al. [40] and Akagi et al. [79] presented separate HR by different ethnic background; Mathe et al. [33]_ENREF_34, Liu et al. [51], Toiyama et al. [80], Nielsen et al. [44] and Markou et al. [82] investigated the role of miR-21 in different type of samples; Voortman et al. [43] reported results from two centers. So the data from these studies were considered separately in our analysis. As there were only 3 studies for CSS, we combined the results for CSS and RFS together as RFS/CSS. Thus, a total of 63 studies including 6720 patients evaluating OS, 19 studies including 1965 cases for DFS and 11 studies including 1696 patients for RFS/CSS were considered in this analysis. The detailed screening process was shown in Figure 1.

Figure 1. Methodological flow diagram of the review.

Figure 1

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The main characteristics of eligible studies were listed in Table 1. Ethnicity background of patients were classified as Asian, Caucasian and mixed populations. Cancer types of cases were various, among which lung cancer, pancreatic cancer and gastrointestinal (GI) cancers were mostly investigated. Tissue samples including Frozen or formalin-fixed and paraffin-embedded (FFPE) tissues were used in 53 studies, while 11 studies used circulation samples (plasma, serum or blood) and one study by Ota et al. [47] applied bone marrow samples. Quantitative real-time PCR (qRT-PCR) was widely used in 57 studies and in situ hybridisation (ISH) assay was used in the other 6 studies. The most frequently used cut-off value was the median which was applied in 26 studies and the other values ranged from the mean to the highest quarter value.

Table 1. Main characteristics of eligible studies.

Author Year Country Ethnicity Type Sample Stage Number Method Endogenous control cut-off Results
Schetter 2008 USA/HK Caucasian/Asian Colon Frozen tissue I-IV 197 qRT-PCR U6 Highest tertile OS
Dillhoff 2008 USA Caucasian Pancreatic FFPE NR 80 In Situ Hybridization U6 Highest score OS
Markou 2008 Greece Caucasian NSCLC Frozen tissue I-IV 48 qRT-PCR U6 2-fold OS and DFS
Yan 2008 China Asian Breast FFPE I-III 113 qRT-PCR U6 Mean OS
Qian 2009 Italy Caucasian Breast Frozen tissue I-IV 301 qRT-PCR U6 NR OS and DFS
Busacca 2010 Italy Caucasian Malignant mesothelioma FFPE NR 24 qRT-PCR U6 Median OS
Li 2009 China Asian Tongue Frozen tissue I-IV 103 qRT-PCR U6 Median OS
Schetter 2009 HK/USA Caucasian/Asian Colon Frozen tissue I-IV 196 qRT-PCR U6 Highest tertile CSS
Mathe 2009 USA,Canada/Japan Caucasian/Asian Esophageal Tissue I-IV 170 qRT-PCR U66 Median OS
Avissar 2009 USA Caucasian HNSCC Frozen tissue I-IV 169 qRT-PCR U48 Highest quarter OS
Zhi 2010 China Asian Astrocytoma Frozen tissue I-IV 124 qRT-PCR miR-16 Median OS
Hu 2011 USA Caucasian Esophageal FFPE I-IV 158 In situ hybridization NR 1–3+/0–0.5 OS and DFS
Gao 2010 China Asian NSCLC Frozen tissue I-III 47 qRT-PCR U6 Median OS
Rossi 2010 USA Caucasian CLL Blood NR 99 qRT-PCR U6 Median OS
Giovannetti 2010 Netherlands Caucasian Pancreatic Tissue I-IV 59 qRT-PCR U43 Median OS and DFS
Hwang 2010 Korea/Italy Asian/Caucasian Pancreatic Frozen tissue II-IV 82/45 qRT-PCR U66/U43 Median OS,DFS and RFS
Gao 2011 China Asian SCLC Frozen tissue I-III 30 qRT-PCR U6 Median OS
Kulda 2010 Czech Republic Caucasian CRC Frozen tissue I-IV 44 qRT-PCR U6 NR DFS
Voortman 2010 14 countries Mixed NSCLC FFPE I-III 631 qRT-PCR/In situ hybridization U66/U6 Median OS
Nielsen 2011 Denmark Caucasian Colon/rectum FFPE II 129/67 In Situ Hybridization NR 2-fold DFS
Hamano 2011 Japan Asian Esophageal FFPE I-IV 98 qRT-PCR U48 Median OS
Radojicic 2011 Greece Caucasian Breast FFPE NR 49 qRT-PCR RNU5A/U6 Median OS and DFS
Ota 2011 Japan Asian Breast Bone marrow NR 207 qRT-PCR U6 5.84 OS and DFS
Walter 2011 USA Caucasian Breast FFPE NR 25 qRT-PCR U6 Median OS
Saito 2011 USA/Norway/Japan Caucasian/Asian NSCLC Frozen tissue I-II 126/191 qRT-PCR U66 Median CSS/RFS
Shibuya 2010 Japan Asian CRC Frozen tissue Dukes:A-D 156 qRT-PCR U6 Mean OS and DFS
Liu 2012 China Asian NSCLC Frozen tissue I-IV 70 qRT-PCR U6 2-fold OS
Wang 2011 China Asian NSCLC Serum I-III 88 qRT-PCR U6 5-fold OS
Ayerbes 2011 Spain Caucasian Colon or rectum/gastric/pancreas FFPE I-IV 32 qRT-PCR U6 Mean OS
Jiang 2011 China Asian Gastric FFPE III,IV 55 qRT-PCR U44 NR OS
Nagao 2012 Japan Asian Pancreatic FFPE I-IV 65 qRT-PCR U6 Mean OS
Jamieson 2012 UK Caucasian Pancreatic Frozen tissue II-III 72 qRT-PCR U6 Median OS
Jiang 2012 China Asian Melanoma Frozen tissue I-IV 86 qRT-PCR U6 Median OS and DFS
Liu 2012 China Asian Pancreatic Serum I-IV 38 qRT-PCR NR NR OS
Karakatsanis 2013 Greece Caucasian Hepatocellular FFPE I-IV 60 qRT-PCR U6 Mean OS
Gao 2012 China Asian NSCLC Frozen tissue I-III 58 qRT-PCR U6 Median DFS
Lee 2011 Korea Asian Breast FFPE I-III 109 qRT-PCR U6 Mean OS and DFS
Li 2012 China Asian Prostate FFPE II-III 168 in situ hybridization NR Score>1 RFS
Faltejs kova 2012 Czech Republic Caucasian CRC Frozen tissue I-IV 44 qRT-PCR U6 Median OS
Faragalla 2012 Canada Caucasian Renal FFPE I-III 89 qRT-PCR U44 NR OS and DFS
Zaravinos 2012 Greece Caucasian Bladder Tissue NR 77 qRT-PCR RNU1A1,5A and U6 Median OS and RFS
Jung 2012 USA Caucasian Oral Frozen tissue NR 17 qRT-PCR U6 Median OS
Le 2012 China Asian Lung Serum I-IV 82 qRT-PCR miR-16 NR OS
Xu 2012 China Asian Gastric Frozen tissue I-IV 86 qRT-PCR Let-7a ROC curve (AUC) OS
Osawa 2011 Japan Asian Gastric FFPE I-IV 37 qRT-PCR NR T/N ratio >1.40 OS
Papaconstantinou 2013 Greece Caucasian Pancreatic FFPE I-IV 88 qRT-PCR U6 Mean OS
Frifeldt 2012 Denmark Caucasian Colon FFPE II 520 in situ hybridization NR Tertiles OS and RFS
Hermansen 2013 Denmark Caucasian Gliomas FFPE NR 189 in situ hybridization NR NR OS
Caponi 2013 UK/Italy Caucasian Pancreatic FFPE II-III 81 qRT-PCR U6 Median OS and DFS
Wang 2013 China Asian Pancreatic Serum III-IV 177 qRT-PCR U6 Median OS
Komatsu 2013 Japan Asian Gastric Plasma I-IV 69 qRT-PCR NR Median CSS
Amankwah 2013 USA Caucasian Prostate FFPE I-IV 65 qRT-PCR U6 median RFS
Chusorn 2013 Thailand Asian Cholangiocarcinoma Frozen tissue NR 23 qRT-PCR U6 Mean OS
Huang 2013 China Asian Cholangiocarcinoma FFPE NR 41 qRT-PCR U6 NR OS and RFS
Liu 2013 China Asian CRC Serum I-IV 166 qRT-PCR MiR-16 0.0043 OS
Akagi 2013 USA,Norway/Japan Caucasian/Asian Lung Frozen tissue I-II 92/198 qRT-PCR NR Median OS and RFS
Toiyama 2013 Japan Asian CRC FFPE/serum I-IV 166/188 qRT-PCR miR-16/Cel-miR-39 Youden's index OS
Bovell 2013 USA Mixed CRC FFPE IV 55 qRT-PCR U6 NR OS
Markou 2013 Greece Caucasian NSCLC FFPE/plasma I-IV 40/37 qRT-PCR miR-191/miR-16 Median OS and DFS
Chen 2013 Taiwan Asian CRC Tissue I-IV 195 qRT-PCR U6 Mean OS
Ferrajoli 2013 USA Caucasian CLL Blood NR 93 qRT-PCR miR-16 44th percentile OS
Menendez 2013 Spain Caucasian CRC Serum I-IV 102 qRT-PCR miR-16 Relative expression>1 OS and DFS
Kadera 2013 USA Caucasian Pancreatic Tissue I-IV 147 qRT-PCR U6 NR OS
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NSCLC: non-small cell lung cancer; HNSCC: head and neck squamous cell carcinomas; CLL: chronic lymphocytic leukemia; SCLC: squamous cell lung carcinoma; CRC: colorectal carcinoma; ALL: acute lymphoblastic leukemia NR: not reported; FFPE: formalin-fixed and paraffin-embedded; OS: overall survival; DFS: disease-free survival; RFS: recurrence-free survival; CSS: cancer-specific survival.

Outcomes from eligible studies

The main results of this meta-analysis are shown in Table 2. For 63 studies evaluating OS for miR-21, we found high expression of miR-21 predicting a worse outcome with the combined HR of 1.91 (95%CI: 1.66–2.19; Pheterogeneity<0.001; Figure 2). Similarly predictive roles of miR-21 for DFS and RFS/CSS were also investigated with pooled HR of 1.42 (95% CI: 1.16–1.74; Pheterogeneity = 0.001) and 2.2 (95% CI: 1.64–2.96; Pheterogeneity = 0.022), respectively.

Table 2. Meta-analysis results.

Outcome Variables Number of studies Model HR (95% CI) Pheterogeneity
OS ALL 63 Random 1.91(1.66,2.19) <0.001
Cancer type
GI 15 Random 1.68(1.12,2.52) <0.001
Pancreas 11 Random 2.53(1.82,3.51) 0.003
Lung 13 Random 1.59(1.2,2.1) <0.001
Breast 6 Random 2.55(1.04,6.29) 0.002
Oral 2 Random 2.02(0.41,9.88) 0.016
Esophagus 4 Random 1.53(0.74,3.15) 0.018
Liver 3 Fixed 1.93(1.39,2.69) 0.688
Ethnicity
Asian 29 Random 2.19(1.76,2.73) <0.001
Caucasian 29 Random 1.86(1.46,2.37) <0.001
Sample
Tissue 51 Random 1.87(1.61,2.16) <0.001
FFPE 25 Random 1.68(1.29,2.18) <0.001
Frozen tissue 23 Random 1.99(1.59,2.49) <0.001
Circulation 11 Random 2.06(1.42,2.99) 0.008
Serum 8 Random 1.94(1.25,3.03) 0.003
Therapy
Adjuvant therapy 7 Random 2.4(1.18,4.9) <0.001
Mixed 56 Random 1.85(1.61,2.13) <0.001
DFS ALL 19 Random 1.42(1.16,1.74) 0.001
Cancer type
GI 5 Random 1.12(0.81,1.55) 0.01
Pancreas 3 Fixed 2.87(1.89,4.35) 0.524
Lung 4 Fixed 2.05(1.32,3.18) 0.839
Breast 4 Fixed 1.1(0.82,1.49) 0.919
Ethnicity
Asian 6 Random 1.62(1.06,2.47) 0.008
Caucasian 14 Random 1.37(1.07,1.76) 0.006
RFS/CSS ALL 11 Random 2.2(1.64,2.96) 0.022
Cancer type
GI 3 Random 2.5(1.1,5.71) 0.005
Lung 3 Fixed 2.25(1.57,3.23) 0.605
Prostate 2 Fixed 2.04(1.17,3.54) 0.957
Ethnicity
Asian 5 Fixed 2.17(1.52,3.09) 0.322
Caucasian 5 Random 2.1(1.34,3.27) 0.065
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OS: overall survival; DFS: disease-free survival; RFS: recurrence-free survival; CSS: cancer-specific survival; GI: gastrointestinal; FFPE: formalin-fixed and paraffin-embedded.

Figure 2. Forrest plots of studies evaluating hazard ratios (HRs) of miR-21 for overall survival.

Figure 2

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Subgroup analyses by cancer type showed that elevated miR-21 yielded a worse OS in GI cancers (HR = 1.68, 95%CI: 1.12–2.52; Pheterogeneity<0.001), lung cancer (HR = 1.59, 95%CI: 1.2–2.1; Pheterogeneity<0.001), breast cancer (HR = 2.55, 95%CI: 1.04–6.29; Pheterogeneity = 0.002), pancreatic cancer (HR = 2.53, 95%CI: 1.82–3.51; Pheterogeneity = 0.003) and liver cancer (HR = 1.93, 95%CI: 1.39–2.69; Pheterogeneity = 0.688); a worse DFS in lung cancer (HR = 2.05, 95%CI: 1.32–3.18; Pheterogeneity = 0.839) and pancreatic cancer (HR = 2.87, 95%CI: 1.89–4.35; Pheterogeneity = 0.524); a poorer RFS/CSS in GI cancers (HR = 2.5, 95%CI: 1.1–5.71; Pheterogeneity = 0.005), lung cancer (HR = 2.25, 95%CI: 1.57–3.23; Pheterogeneity = 0.605) and prostate cancer (HR = 2.04, 95%CI: 1.17–3.54; Pheterogeneity = 0.957).

In the subgroup analyses by ethnicity, we found that no matter the cases were Asian or Caucasian, the high expression of miR-21 was still a significantly poor predictor for OS (Asian: HR = 2.19, 95%CI: 1.76–2.73; Pheterogeneity<0.001; Caucasian: HR = 1.86, 95%CI: 1.46–2.37; Pheterogeneity<0.001), DFS (Asian: HR = 1.62, 95%CI: 1.06–2.47; Pheterogeneity = 0.008; Caucasian: HR = 1.37, 95%CI: 1.07–1.76; Pheterogeneity = 0.006) and RFS/CSS (Asian: HR = 2.17, 95%CI: 1.52–3.09; Pheterogeneity = 0.322; Caucasian: HR = 2.1, 95%CI: 1.34–3.27; Pheterogeneity = 0.065).

Further analyses of studies evaluating OS by sample type also revealed that high expression of miR-21 remained to be a worse prognostic marker regardless of sample source (tissue sample: HR = 1.87, 95%CI: 1.61–2.16; Pheterogeneity<0.001; circulation sample: HR = 2.06, 95%CI: 1.42–2.99; Pheterogeneity = 0.008). In addition, high miR-21 in FFPE (HR = 1.68, 95%CI: 1.29–2.18; Pheterogeneity<0.001) and frozen tissue (HR = 1.99, 95%CI: 1.59–2.49; Pheterogeneity<0.001) showed consistent results. Pooled results of 8 studies that explored serum miR-21 also revealed negative prognostic role of increased miR-21 (HR = 1.94, 95%CI: 1.25–3.03; Pheterogeneity = 0.003)

A total of seven studies [27], [39], [40], [43], [53], [82] investigated the prognostic role of miR-21 in the patients who received adjuvant therapy which yielded a significantly pooled HR of 2.4 (95%CI: 1.18–4.9; Pheterogeneity<0.001).

Publication bias

Begg's funnel plot and the Egger's linear regression test were used to assess publication bias. However, the funnel plots were asymmetric and the P values of Egger's test for OS, DFS and RFS/CSS were <0.001, 0.011 and 0.003, respectively. Thus, a trim and fill method was conducted and pooled HRs were recalculated with hypothetically non-published studies to evaluate the asymmetry in the funnel plots. The recalculated HRs did not change significantly for OS (HR = 1.61, 95%CI: 1.41–1.83; Pheterogeneity<0.001;Figure 3) and RFS/CSS (HR = 2.01, 95%CI: 1.54–2.77; Pheterogeneity = 0.018). But the prognostic role of high expression of miR-21 for DFS was weaken with a recalculated HR of 1.11 (95%CI: 0.9–1.38; Pheterogeneity<0.001).

Figure 3. Funnel plot adjusted with trim and fill method for overall survival.

Figure 3

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Circles: included studies. Diamonds: presumed missing studies.

Discussion

MiR-21, a well-known onco-miR, is up-regulated in most malignancies. Acting on various target genes such as PTEN [87] and PDCD4 [18], miR-21 plays an important role in the process of cell proliferation, migration, invasion, drug resistance [88] and so on. It has been reported that miR-21 could regulate Ras/MEK/ERK pathway so to influence the tumor formation. Moreover the incidence of lung tumors is higher in miR-21 overexpression mice, while lower in miR-21 knockout mice [89]. Additionally, miR-21 has been proposed as a marker of cancers for diagnosis in circulation [90], [91], stool [92] and sputum [93], prediction in therapy response [59] and prognosis of patients.

Nair et al. [94] systematically reviewed and synthesized that miRNAs showed promising associations with outcomes of various cancers. As the first meta-analysis [95] of miR-21 related to outcomes of various cancers, Fu et al. retrieved 17 studies and found higher level of miR-21 might be associated with poorer clinical outcome, especially in subgroup of head and neck squamous cell carcinoma and digestive carcinoma. Recently, Wang et al. [96] analyzed the value of circulating miR-21 and yielded a conclusion that circulating miR-21 might act as a significantly prognostic biomarker but not be suitable for a sensitive diagnostic biomarker. However, the number of studies included in these analyses was relatively small and the obtained results might not be powerful. In terms of this, we performed this updated meta-analysis including 63 articles and demonstrated that high expression of miR-21 was a significant marker for predicting worse outcomes of various cancers (HR was 1.91, 2.2 and 1.42 for OS, RFS/CSS and DFS, respectively). Subgroup analyses revealed that high expression of miR-21 could predict a worse OS in GI tumors, pancreatic cancer, lung cancer, breast cancer and liver cancer, a worse DFS in pancreatic cancer and lung cancer and poor RFS/CSS in GI tumors, lung cancer and prostate cancer. Regardless of the ethnicity background or sample source, high expression level of miR-21 was a significantly negative prognostic marker for various malignancies. As publication bias was observed, a trim and fill method was adopted to calculate the adjusted HRs. The results for OS and RFS/CSS did not change, but the results for DFS were altered.

Recently, many studies demonstrated that miRNAs including miR-21 had great potential as biomarkers for various cancers. However, several problems should be well solved before utilizing them as diagnostic or prognostic biomarkers in the clinical. As is known, non-invasive circulation sample (plasma/serum) or body fluid sample could be obtained more conveniently than tissue sample. However, studies using different types of samples may yield different results [51]. Tsujiura et al. [91] found that some individuals might even have opposite tendency of the expression levels of miRNAs in tumor tissue and plasma. Now, many studies have investigated the clinical impact of miRNAs from exosomes which were small membrane vesicles containing proteins and nucleotides [97]. In our study, it is pleasing that high expression of miR-21 in the tissue (FFPE/frozen tissue) or circulation both predicted poor outcomes. Thus, we might assume that patients with high expression of miR-21 from any type of sample might suffer worse clinical outcomes. Yet, normalization among different studies was not consistent. The internal controls used for tissue samples are relatively consistent ranging from U6 to U44, while there is no consensus on suitable small RNA reference genes for circulation or body fluid sample. MiR-16 was used as a reference gene in some studies [66], [78]. But the optimal way for miRNA normalization in circulation or body fluid sample is probably the spiked-in normalization method [98]. Therefore, future studies focusing on the consistent normalization are warranted. In addition, as biomarkers, a panel of miRNAs might be more sensitive and specific than a single miRNA [99], [100]. The combination of miR-21 and some specific miRNAs might elevate its predictive power. Finally, methods for detecting miRNAs were diverse, among which RT-PCR was one of the most widely used approaches. Nevertheless many new methodologies emerged, such as the next-generation sequencing approach [101] and the electrochemical approach [102]. In short, a proper method for clinical application should be less expensive, reproducible, stable and with high sensitivity and specificity. Accordingly, great efforts should be made in the future to apply miRNAs including miR-21 as reliable biomarkers in the clinical.

Several limitations of this study should be considered. First, the studies retrieved in our study were limited in English, which might partially contribute to the observed publication bias. By conducting the trim and fill method, we found that the pooled results did not change significantly except for DFS. Thus, attention should be paid to the prognostic role of miR-21 for DFS. Second, different countries, cancer types, methods and other variables might contribute to the relatively large heterogeneity in this study. Third, the number of studies investigating some special types of cancer was small. For instance, there was only one study focusing on mesothelioma [30]. More studies on these cancers are needed in the future.

In conclusion, the evidence from the meta-analysis revealed that high expression level of miR-21 was a negative predictor for survival in various cancers, especially for OS and RFS/CSS. However, our results should be considered with caution due to the limitations listed above. To better understand and use miRNAs as biomarkers in the clinical, more large-scale and standard investigations are worth conducting.

Supporting Information

Checklist S1

PRISMA Checklist.

(DOC)

Click here for additional data file. (60KB, doc)

Funding Statement

This work was supported by National Natural Science Foundation of China (Grant number: 81171908). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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