Disease Markers

Disease Markers / 2015 / Article

Research Article | Open Access

Volume 2015 |Article ID 739469 | 11 pages | https://doi.org/10.1155/2015/739469

Prognostic Role of MicroRNA-126 for Survival in Malignant Tumors: A Systematic Review and Meta-Analysis

Academic Editor: Sheng Pan
Received16 Jun 2015
Accepted28 Jul 2015
Published17 Aug 2015

Abstract

Background. Increasing studies found that miR-126 expression may be associated with the prognosis of cancers. Here, we performed a meta-analysis to assess the prognostic role of miR-126 in different cancers. Methods. Eligible studies were identified by searching in PubMed, Embase, the Cochrane Library, CNKI, and Wan Fang databases up to March 2015. Pooled hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated to investigate the correlation between miR-126 and survival of cancers. Results. Thirty studies including a total of 4497 participants were enrolled in this meta-analysis. The pooled results showed that high level of miR-126 was a predictor for favorable survival of carcinomas, with pooled HR of 0.77 (95% CI 0.64–0.93) for OS, 0.64 (95%CI 0.48–0.85) for DFS, and 0.70 (95% CI 0.50–0.98) for PFS/RFS/DSS. However, high level of circulating miR-126 predicted a significantly worse OS in patients with cancer (HR = 1.65, 95% CI 1.09–2.51). Conclusions. Our results indicated that miR-126 could act as a significant biomarker in the prognosis of various cancers.

1. Introduction

MicroRNAs (miRNAs), which are a new class of small noncoding RNAs (21–23 nucleotides), have emerged as crucial players regulating the magnitude of gene expression in a variety of organisms [1, 2]. Regulation of microRNAs is achieved via binding to the 3′ untranslated regions (3′ UTR) of target mRNAs, which leads to their inhibition of the expression of target genes in the translation level [3]. Mounting evidence suggests that microRNAs play crucial and complex roles in the initiation and progression of cancer [4], including cell proliferation, differentiation, apoptosis, and metabolism [5, 6]. Obviously, microRNAs may be exploited as new promising molecular biomarkers for early diagnosis and efficient treatment in human cancers [7].

MicroRNA-126 (miR-126), located within the 7th intron of EGFL7 (epidermal growth factor-like domain 7), plays an important role in cellular biology, including cancer biology [8, 9]. Many studies have demonstrated that miR-126 contributes to progression of angiogenesis, proliferation, migration, invasion, and cell survival in some cancers [8, 1012]. As a tumor suppressor, miR-126 was shown to downregulate expression in lung, breast, gastric, colon, pancreatic, oral, and some other cancers in previous studies [1318]. Cancer patients with lower expression of miR-126 always had a worse prognostic outcome; however, the results from different studies indicated that miR-126 functioned as an oncogene and its expression was upregulated [1922].

The majority of cancers at the time of initial diagnosis are often at an advanced stage and have poor prognosis, and therefore there is an urgent need for the identification of novel prognostic and predictive biomarkers to improve treatment of patients with various cancers [23]. In spite of some contradictory results, miR-126 is still a significant tumor biomarker and a potential therapeutic target [24]. Moreover, the result from individual study is inadequate to evaluate whether miR-126 can be considered as a promising biomarker. So we performed this meta-analysis to assess the prognostic value of tissue and blood-based miR-126 levels in various cancers.

2. Materials and Methods

This meta-analysis was performed following the guidelines of the Systematic Reviews and Meta-Analyses (PRISMA) and the Observational Studies in Epidemiology group (MOOSE) [25].

2.1. Search Strategy

Literatures were systematically searched through PubMed, Embase, the Cochrane Library, CNKI (China National Knowledge Infrastructure), and Wan Fang databases up to March 2015 without any language restrictions by two independent reviewers (Jie Bu and Hui Li). The search strategy of key words and their combination was the following terms: “microRNA-126 OR miR-126 OR miR-126-3p” AND “tumor OR tumour OR neoplasm OR cancer OR carcinoma” AND “prognosis OR survival OR outcome OR prognostic.” We also carefully performed a manual search in order to identify other potentially eligible studies.

2.2. Inclusion and Exclusion Criteria

The eligible studies in this systematic review must meet all the following criteria: (1) patients are included with any type of cancers, (2) the association between miR-126 expression and survival outcome was measured in cancerous tissues or circulatory system, and (3) sufficient data was provided to calculate the hazard ratio (HR) and 95% confidence intervals (CIs). Articles were excluded according to the following criteria: (1) letters, case reports, reviews, conference abstracts, and animal or laboratory studies, (2) studies analyzing a set of miRNAs altogether and nondichotomous miR-126 expression levels, and (3) studies with fewer than 30 patients. When the same patient cohort was reported from multiple published data, only the most recent or complete study was selected.

2.3. Quality Assessment and Data Extraction

Quality assessment of included studies was assessed by two researchers independently (Jie Bu and Hui Li) following a critical review checklist of the Dutch Cochrane Centre proposed by MOOSE [25]. The following items were included: first author’s name, publication year, country or area of origin, cancer type, sample type, TNM stage, method, total number of patients, cut-off value, follow-ups and HRs of miR-126 for overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS), progression-free survival (PFS), and disease-specific survival (DSS), with their 95% confidence intervals (CIs). Disagreements were resolved by discussion between these reviewers (Jie Bu, Hui Li, and Xiao-yang Li) or consultation with senior reviewer (Li-hong Liu). If both univariate and multivariate analysis results were reported for survival, the latter ones would be selected [26, 27].

We extracted the statistical variables according to the following methods. If HRs and 95% CIs were described in publications, we extracted them directly. Otherwise, survivals and deaths at specified times in each group were extracted to calculate HRs. If only Kaplan-Meier curves are available, they were extracted from the graphical survival plots to estimate the HRs following the previously described method [28, 29]. We used Engauge Digitizer version 4.1 to extract the data from Kaplan-Meier survival curves, and three independent researchers (Jie Bu, Hui Li, and Xiao-yang Li) read the curves to reduce reading variability. We also contacted the authors of eligible articles by email for additional information and the essential data needed for the meta-analytic calculations.

2.4. Statistical Analysis

HRs with their 95% CIs were combined to evaluated the effect of miR-126 expression on the survival outcome of cancer. Patients with overexpression of miR-126 indicated a better prognosis if HR < 1 and its 95% CI did not overlap with 1. Heterogeneity of pooled HRs was carried out using Cochran’s -test and Higgins -square () statistic [30, 31]. If there was significant heterogeneity ( or .), the random-effects model (Der Simonian and Laird method) was used [32]. Otherwise, a fixed-effects model (Mantel-Haenszel test) was applied [33]. Subgroup analysis and metaregression were further performed to explore possible explanations for heterogeneity. Begg’s funnel plot and Egger’s bias were used to evaluate the potential publication bias [34, 35]. Analysis of sensitivity was performed to evaluate the stability of the results. All statistical tests were two-sided, and was regarded as statistically significant. All analyses were conducted using the Cochrane Collaboration RevMan 5.2 or STATA package version 12.0 (Stata Corporation, College Station, Texas, USA).

3. Results

3.1. Eligible Studies and Characteristics

A flowchart of detailed searching process is illustrated in Figure 1. Using the described searching strategy above, a total of 549 articles were initially retrieved out of PubMed, Embase, the Cochrane Library, CNKI, and Wan Fang databases. After manually screening the titles, publication types, and abstracts and then checking the full texts by two investigators (Jie Bu and Hui Li), 30 articles were selected for the present meta-analysis [3665]. Among these eligible studies, 20 studies evaluated the prognostic effect of miR-126 for OS, 8 studies for DFS, and 6/4/3 studies for PFS/RFS/DSS.

The main characteristics and basic information of eligible studies were listed in Table 1 and Table S1 (in Supplementary Material available online at http://dx.doi.org/10.1155/2015/739469). A total of 4497 patients from the United States [63, 65], Spain [53], Japan [36, 37, 57], China [4348, 51, 52, 58, 62, 64], South Korea [41], Netherlands [38], Norway [40], France [39], Bosnia and Herzegovina [42], Serbia [42], Denmark [49, 50, 5456], Sweden [55], Canada [61], and Germany [59, 60] were diagnosed with a wide range of carcinomas, including acute myeloid leukemia [36, 38], adult T-cell leukemia [37], non-small cell lung cancer [3944], colorectal cancer [49, 50, 52, 5456], laryngeal squamous cell carcinoma [48], esophageal squamous cell cancer [63], hepatocellular carcinoma [45, 46], colon cancer [51, 53], cervical cancer [47], prostate cancer [58], oral cancer [57], breast cancer [59], clear cell renal cell carcinoma [60, 61], esophageal squamous cell carcinoma [6264], and glioblastoma multiforme [65]. The sample size ranged from 35 to 560. The expression of miR-126 was most often examined in cancerous tissue, while 5 studies examined it in serum/plasma and 1 study tested it in bone marrow. The majority of these studies assessed miR-126 expression by quantitative real-time PCR (qRT-PCR), and in situ hybridization (ISH) was applied in six studies. The most frequently used cut-off value was the median which was applied in 19 studies and the other values were different.


AuthorYearCountryCancerNumberSpecimenAssayCut-off valueSource of HREndpointMedian follow-up (months)

Shibayama et al. [36]2015JapanAML108Bone marrowqRT-PCRMedianROSNR
Ishihara et al. [37]2012JapanATL35PlasmaqRT-PCRMedianSCOSNR
de Leeuw et al. [38]2014NetherlandsAML92BloodqRT-PCRMedianROS, EFS, RFSNR
Sanfiorenzo et al. [39]2013FranceNSCLC52PlasmaqRT-PCRMedianRDFS46
Donnem et al. [40]2011NorwayNSCLC332TissueISHExpression score ≥ 2RDSSa86
Kim et al. [41]2014South KoreaNSCLC72TissueqRT-PCRMedianROS31
Jusufović et al. [42]2012SerbiaNSCLC50TissueqRT-PCRMedianROS, PFS5.13
Yang et al. [43]2012ChinaNSCLC442TissueqRT-PCRMedianROS24.39–29.28
Li et al. [44]2014ChinaNSCLC49TissueqRT-PCRMedianSCOS, DFS39
Han et al. [45]2012ChinaHCC105TissueqRT-PCRFold change = 2ROS42.89
Chen et al. [46]2013ChinaHCC68TissueqRT-PCR0.70 (ROC curve)SCOS49
Yang et al. [47]2014ChinaCervical cancer133TissueqRT-PCRMedianROS60 (max)
Sun et al. [48]2014ChinaLSCC38PlasmaqRT-PCRMedianSCOSNR
Hansen et al. [49]2012DenmarkCRC89TissueISHMedianSCPFS16.8–26.2
Hansen et al. [50]2014DenmarkCRC63PlasmaqRT-PCRMedianRPFS8.8–9.2
Li et al. [51]2013ChinaColon cancer53TissueISH0/1–3+SCOS45.66–55.04
Liu et al. [52]2014ChinaCRC92TissueqRT-PCRMedianSCOS65
Díaz et al. [53]2008SpainColon cancer110TissueqRT-PCRMedianROS, DFS68
Hansen et al. [54]2011DenmarkCRC81TissueISHMedianROS, PFSNR
Hansen et al. [55]2013Denmark/SwedenCRC89TissueqRT-PCRMedianRPFSNR
Hansen et al. [56]2015DenmarkCRC560TissueqRT-PCRMedianROS, DSS7 years (max)
Sasahira et al. [57]2012JapanOral cancer94TissueqRT-PCRMeansRDFS3.4 years
Sun et al. [58]2013ChinaProstate cancer128TissueqRT-PCRMedianSCRFS3–10 years
Hoppe et al. [59]2013GermanyBreast cancer80TissueqRT-PCR6.20 (ROC curve)RRFS8.84 years
Vergho et al. [60]2014GermanycRCC37TissueqRT-PCR3.57 (ROC curve)RDSS41.4
Khella et al. [61]2015CanadacRCC257,481bTissueqRT-PCR20th percentileROS, DFS, OSb48.6
Liu et al. [62]2015ChinaESCC185TissueISHFold change > 3RDSS32
Hu et al. [63]2011USAESCC158TissueISH1–3+/0–0.5ROS, DFS16.25
Wang et al. [64]2013ChinaESCC116TissueqRT-PCRCT < −1SCDFS21–32
Feng et al. [65]2012USAGBM248TissueqRT-PCRMedianRPFS/RFSb, OSbNR

CRC: colorectal cancer; HCC: hepatocellular carcinoma; NSCLC: non-small cell lung cancer; cRCC: clear renal cell carcinoma; ESCC: esophageal squamous cell carcinoma; AML: acute myeloid leukemia; ATL: adult T-cell leukemia; LSCC: laryngeal squamous cell carcinoma; GBM: glioblastoma multiforme; qRT-PCR: quantitative real-time PCR; ISH: in situ hybridization; OS: overall survival; DFS: disease-free survival; RFS: recurrence-free survival; PFS: progression-free survival; DSS: disease-specific survival; HR: hazard ratio; SC: survival curve; NR: not reported; R: reported.
aDSS included any of the following: DSS, CSS (cancer-specific survival). bData extracted from TCGA (The Cancer Genome Atlas) in the paper.
3.2. OS Associated with miR-126 Expression

The main results of this meta-analysis were displayed in Table 2. 20 studies including 3232 cancer patients investigated the relationship between miR-126 expression and the prognosis. For these studies evaluating OS for miR-126, a random-effects model was utilized to calculate the pooled HR and its 95% CI due to the high heterogeneity among these studies ( = 57.0%, ). The result showed that high miR-126 level may predict a favorable OS with the combined HR of 0.77 (95% CI: 0.64–0.93; = 0.001) (Table 2, Figure 2(a)).


OutcomeVariablesNumber of studiesNumber of patientsModelHR (95% CI)HeterogeneityPublication bias
(%)Begg’s Egger’s

OSAll203232Random0.77 (0.64, 0.93)56.80.0010.3810.358
Tumor type
NSCLC4613Random0.42 (0.17, 1.08)82.20.0011.0000.340
HCC2173Fixed0.65 (0.49, 0.86)2.600.311
CRC5896Fixed0.85 (0.69, 1.04)00.5840.8060.679
RCC2738Fixed0.65 (0.38, 1.12)00.624
AML2200Fixed1.77 (1.15, 2.72)00.666
Ethnicity
Asian121353Fixed0.76 (0.66, 0.88)37.00.1290.8370.668
Caucasian81879Random0.77 (0.57, 1.05)73.8<0.0010.5360.479
Sample
Circulation4273Fixed1.65 (1.09, 2.51)00.6470.7340.162
Tissue162959Random0.71 (0.60, 0.85)51.10.010.1370.068
Assay method
qRT-PCR172940Random0.72 (0.58, 0.90)61.2<0.0010.3030.250
ISH3292Fixed1.00 (0.75, 1.34)00.8041.0000.646
Analysis type
Multivariate71870Fixed0.81 (0.72, 0.90)11.00.3440.0720.095
Univariate71530Random0.89 (0.79, 1.00)66.40.0071.0000.990
HR estimated
HRs reported142897Random0.78 (0.64, 0.96)67.8<0.0010.2740.461
K-M curve6335Fixed0.79 (0.53, 1.18)00.6661.0000.705

DFSAll7755Fixed0.64 (0.48, 0.85)00.7800.1330.203
Tumor type
NSCLC2101Fixed0.49 (0.26, 0.93)00.983
ESCC2274Fixed0.77 (0.48, 1.24)00.629
Ethnicity
Asian4417Fixed0.64 (0.44, 0.94)00.5320.3080.081
Caucasian3419Fixed0.63 (0.41, 0.97)00.5991.0000.874
Analysis type
Multivariate3509Fixed0.65 (0.45, 0.94)00.3840.2960.360
Univariate4619Random0.67 (0.50, 0.90)88.0<0.0010.7340.586

RFS/PFS/DSSAll132014Random0.70 (0.50, 0.98)84.8<0.0010.3600.288
Tumor type
CRC5882Fixed0.74 (0.59, 0.94)47.30.1081.0000.514
NSCLC2382Random0.43 (0.03, 7.25)97.2<0.001
Ethnicity
Asian2313Fixed0.69 (0.48, 0.99)00.417
Caucasian111701Random0.69 (0.46, 1.02)87.1<0.0010.2130.267
Analysis type
Multivariate71531Random0.71 (0.50, 1.02)83.2<0.0010.2300.281
Univariate5651Random0.89 (0.77, 1.02)81.4<0.0010.4620.872

CRC: colorectal cancer; HCC: hepatocellular carcinoma, NSCLC: non-small cell lung cancer; cRCC: clear renal cell carcinoma; ESCC: esophageal squamous cell carcinoma; AML: acute myeloid leukemia; K-M curve: Kaplan-Meier curve; fixed: fixed-effects model; random: random-effects model.

Furthermore, six subgroup analyses of overall survival were performed which stratified patients by tumor type, ethnicity, sample, assay method, analysis type, and HR estimated (Table 2). Subgroup analyses by tumor type showed that high miR-126 levels were significantly associated with a favorable OS in HCC (HR = 0.65, 95% CI 0.49–0.86, = 0.311). However, AML indicated the opposite result (HR = 1.77, 95% CI 1.15–2.72, = 0.666). In the subgroup analyses by sample type, high miR-126 levels were predictive of better outcome OS in tissue sample (HR = 0.71, 95% CI 0.60–0.85, = 0.01). While elevated miR-126 yielded a worse OS in circulation sample (HR = 1.65, 95% CI 1.09–2.51, = 0.647). With further analyses of studies evaluating OS by ethnicity, we found that the high expression of miR-126 was a significantly favorable predictor for OS in Asians (HR = 0.76, 95% CI 0.66–0.88, = 0.129). Similarly, this conclusion was also found in other subgroups of qRT-PCR assay (HR = 0.72, 95% CI 0.58–0.90, < 0.001), multivariate analysis (HR = 0.81, 95% CI 0.72–0.90, = 0.344), and HRs reported (HR = 0.78, 95% CI 0.64–0.96, ≤ 0.001) (Table 2).

3.3. DFS Associated with miR-126 Expression

7 studies included 755 cancer patients evaluated DFS for miR-126, a fixed-effects model was used to assess the pooled effect size due to no heterogeneity among the studies ( = 0%, ) (Table 2), and we found that high expression of miR-126 was demonstrated to predict favorable DFS in various cancer (HR = 0.64, 95% CI 0.48–0.85, = 0.780) (Table 2, Figure 2(b)).

Similar to OS analyses, we also performed subtotal investigation for DFS analyses (Table 2). In the subgroup analyses by tumor type, high miR-126 levels were significantly associated with a favorable DFS in NSCLC (HR = 0.49, 95% CI 0.26–0.93, = 0.983). And for ethnicity and analysis type, the high expression of miR-126 was still a significantly better prognosis for DFS (Asian: HR = 0.64, 95% CI 0.44–0.94; = 0.532; Caucasian: HR = 0.63, 95% CI 0.41–0.97, = 0.599; multivariate: HR = 0.65, 95% CI 0.45–0.94; = 0.384; univariate: HR = 0.67, 95% CI 0.50–0.90; < 0.001).

3.4. PFS/RFS/DSS Associated with miR-126 Expression

We combined the results for PFS, RFS, and DSS together as PFS/RFS/DSS. A total of 13 studies including 2014 tumor patients focused on PFS/RFS/DSS analysis with significant heterogeneity among them (, ). A random-effects model was applied, and elevated expression of miR-126 was a significant predictor of favorable PFS/RFS/DSS (HR = 0.70, 95% CI 0.50–0.98, = 0.161) (Table 2, Figure 2(c)).

In the subgroup analysis of patients with tumor type, the pooled HR indicated that the high expression of miR-126 was a favorable prognostic marker in CRC (HR = 0.74, 95% CI 0.59–0.94, = 0.108) (Table 2). The same trend was found in subgroup of Asians (HR = 0.69, 95% CI 0.48–0.99, = 0.417) (Table 2).

3.5. Heterogeneity Analysis

Obvious heterogeneity of subjects was observed among 13 of the 30 analysis groups, as shown in Table 2. We performed a meta-regression analysis to investigate the sources of this heterogeneity in the OS analysis group (, ). The obvious heterogeneity was induced by tumor sample () rather than tumor type (), miR-126 assay method (), patients origin (), cut-off values (), publication year (), and HRs estimate ().

3.6. Publication Bias and Sensitivity Analysis

Begg’s funnel plot and Egger’s test were used to assess the potential publication bias of the included studies. The funnel plots of the OS, DFS, and PFS/RFS/DSS analysis based on tissue and blood miR-126 did not reveal any evidence of obvious asymmetry. Moreover, the values of Egger’s and Begg’s tests were all greater than 0.05 in the 30 analysis groups (Table 2, Figure 3, and Figures S1 and S3). Hence, there was no obvious risk of publication bias in our meta-analysis.

Furthermore, we performed sensitivity analysis to investigate the influence of each individual study on the overall meta-analysis estimate, which computes the pooled HRs by omitting one study in each turn. And there was no obvious influence of individual study on the pooled HRs (Figure 4 and Figures S2 and S4).

4. Discussion

Cancer is considered one of the leading causes of death worldwide. The occurrence of cancer is increasing because of the growth and aging of the population, as well as increasing prevalence of established risk factors [66]. Despite the advances in technology and its access, to date, there are few defined prognostic and diagnostic biomarkers available in cancers. Essentially, high cancer mortality rates have remained high, mainly due to the late diagnosis and lack of prognostic markers for various cancers [67]. Hence, many research groups are carrying out studies to develop biomarkers, which can be applied to early detection and correlation of treatment efficacy and prognosis [68].

MiR-126, which is highly expressed in vascular endothelial cells, is one of the most commonly observed cancer-related microRNAs and is dysregulated in most cancers. As one of the major targets of miR-126, EGFL7 is known to be involved in cell migration and the process of angiogenesis. The conclusion suggests that one of the main functions of miR-126 is to inhibit angiogenesis to reduce blood vessels, which is facilitated by cell migration [69, 70]. Additionally, previous studies have demonstrated that miR-126 may play a role in tumorigenesis and growth by regulating the vascular endothelial growth factor (VEGF)/phosphoinositol 3-kinase (PI3K)/AKT signaling pathways [43, 71]. miR-126 also maintains its role as a suppressor of metastasis that could reduce metastatic rate and size of carcinoma [14, 72]. Furthermore, interactions of miR-126 and ADAM9 are related to epithelial-mesenchymal transition and the invasive growth of pancreatic cancer cells [73]. In most of the cancers studied, miR-126 functioned as a tumor suppressor and its expression was suppressed; however, several reports using different types of samples have described an oncogenic role for miR-126. Notably, several studies have shown that miR-126 is upregulated in some malignancies due to high tissue specificity, such as gastric cancer, liver cancer, ovarian cancer, and acute myeloid leukaemia [19, 20, 74, 75]. In addition, miR-126 acting as an oncogene, which was found to downregulate HOXA9/PLK, was often upregulated in myeloid leukaemia and associated with poor prognosis [22, 76]. Moreover, higher expression of miR-126 was shown to be a poor prognostic factor in NSCLC and promote metastasis in prostate cancer [77, 78]. Obviously, it is controversial that miR-126 expression can be used as a prognostic biomarker in different cancers. Hence, in order to evaluate the prognostic role of miR-126 expression in various cancers, we systematically reviewed the published studies and performed a meta-analysis for the first time.

In terms of this, a total of 4497 participants from 30 studies finally were included into the meta-analysis. This result showed that high expression of miR-126 was a significant marker for predicting better outcomes of various cancers (HR was 0.77, 0.64, and 0.70 for OS, DFS, and RFS/PFS/DSS, resp.). For OS, stratified analyses displayed that high expression of miR-126 was a better prognostic marker in HCC, Asians, tissue sample, qRT-PCR assay, multivariate analysis, and HRs reported. However, AML and circulation sample indicated the opposite result. For DFS, subgroup analyses revealed that high expression of miR-126 could predict a favorable DFS in NSCLC, Asian, Caucasian, multivariate, and univariate subgroups. Furthermore, we found that high expression of miR-126 significantly relates to a favorable RFS/PFS/DSS in CRC and Asian subgroup, but no statistical significance is shown in NSCLC, Caucasian, multivariate, and univariate analysis. Additionally, there was no obvious risk of publication bias in our meta-analysis. From the above results, we found that high expression of tissue miR-126 was a positive prognostic factor in cancer patients. But high circulating miR-126 levels predicted a significantly worse OS in patients with cancer. As we know, circulating samples are more convenient to collect and keep monitored, which can effectively evaluate prognosis during or after clinical therapy. Therefore, circulating miR-126 may be an efficacious method for dynamically monitoring the prognosis and therapeutic effects in cancer patients. In this study, only four studies investigated circulating samples, and more studies on these cancers are needed in the future.

Although the present meta-analysis revealed that the expression of miR-126 in cancer patients could be a valuable prognostic biomarker for patients, some limitations should be noticed. Firstly, there was significant heterogeneity existing in our meta-analysis, which was probably attributed to the differences in baseline demographic characters of population, characteristics of patients, the types of cancer, the samples of cancer, the disease stages, the cut-off criteria, the duration of follow-up, and so on. Secondly, several HRs were calculated based on the data extracted from the survival curve; some minor differences exist between the exact HRs and the extrapolated data. Thirdly, due to the lack of a unified cut-off value in miR-126 expression, cut-off values were not consistent among included studies. The different cut-off values may influence the availability of miR-126 as a prognostic biomarker in human cancer. Fourth, in subgroup analyses by sample type and subtype analyses, the number of studies was relatively small. More studies on these cancers are needed in the future. Finally, treatments may influence the expression of miR-126 in cancer samples; however, few researches referred to the treatment effect on HRs or miR-126 expression.

5. Conclusion

In sum, in this meta-analysis, we concluded that overexpression of miR-126 was effectively predictive of better prognosis in various carcinomas. Increased miR-126 level in cancerous tissues was associated with favorable OS, DFS, and PFS/RFS/DSS, while elevated circulating miR-126 was indicative of poor OS. However, our results should be regarded cautiously due to the limitations of the present analysis listed above. Further prospective multicenter studies with larger sample size are needed to focus on the relationship between miR-126 and cancer prognosis as well as to explore effective therapies.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported by the Natural Science Foundation of China (no. 81372871), the Natural Science Foundation of Hunan (no. 13JJ3022), and Hunan Health and Family Planning Commission Research Fund (no. B2013-015).

Supplementary Materials

S1 PRISMA Checklist. PRISMA 2009 Checklist.

Table S1. HRs and corresponding 95% CIs of eligible studies in the meta-analysis

Figure S1. Begg funnel plots of publication bias test for disease-free survival (DFS).

Figure S2. Sensitivity analyses of studies concerning mir-126 and disease-free survival (DFS).

Figure S3. Begg funnel plots of publication bias test for recurrence free survival/ progression-free survival /disease-specific survival (PFS/RFS/DSS).

Figure S4. Sensitivity analyses of studies concerning mir-126 and recurrence free survival /progression-free survival/disease-specific survival (PFS/RFS/DSS).

  1. Supplementary Material

References

  1. J. Lu, G. Getz, E. A. Miska et al., “MicroRNA expression profiles classify human cancers,” Nature, vol. 435, no. 7043, pp. 834–838, 2005. View at: Publisher Site | Google Scholar
  2. Y. Suarez and W. C. Sessa, “MicroRNAs as novel regulators of angiogenesis,” Circulation Research, vol. 104, no. 4, pp. 442–454, 2009. View at: Publisher Site | Google Scholar
  3. R. Garzon, M. Fabbri, A. Cimmino, G. A. Calin, and C. M. Croce, “MicroRNA expression and function in cancer,” Trends in Molecular Medicine, vol. 12, no. 12, pp. 580–587, 2006. View at: Publisher Site | Google Scholar
  4. Z. Li and T. M. Rana, “Therapeutic targeting of microRNAs: current status and future challenges,” Nature Reviews Drug Discovery, vol. 13, no. 8, pp. 622–638, 2014. View at: Publisher Site | Google Scholar
  5. L. P. Lim, N. C. Lau, P. Garrett-Engele et al., “Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs,” Nature, vol. 433, no. 7027, pp. 769–773, 2005. View at: Publisher Site | Google Scholar
  6. V. Ambros, “The functions of animal microRNAs,” Nature, vol. 431, no. 7006, pp. 350–355, 2004. View at: Publisher Site | Google Scholar
  7. M. Ferracin, A. Veronese, and M. Negrini, “Micromarkers: miRNAs in cancer diagnosis and prognosis,” Expert Review of Molecular Diagnostics, vol. 10, no. 3, pp. 297–308, 2010. View at: Publisher Site | Google Scholar
  8. J. E. Fish, M. M. Santoro, S. U. Morton et al., “miR-126 regulates angiogenic signaling and vascular integrity,” Developmental Cell, vol. 15, no. 2, pp. 272–284, 2008. View at: Publisher Site | Google Scholar
  9. J. Meister and M. H. H. Schmidt, “miR-126 and miR-126*: new players in cancer,” TheScientificWorldJOURNAL, vol. 10, pp. 2090–2100, 2010. View at: Publisher Site | Google Scholar
  10. S. Wang, A. B. Aurora, B. A. Johnson et al., “The endothelial-specific microRNA miR-126 governs vascular integrity and angiogenesis,” Developmental Cell, vol. 15, no. 2, pp. 261–271, 2008. View at: Publisher Site | Google Scholar
  11. T. A. Harris, M. Yamakuchi, M. Ferlito, J. T. Mendell, and C. J. Lowenstein, “MicroRNA-126 regulates endothelial expression of vascular cell adhesion molecule 1,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 5, pp. 1516–1521, 2008. View at: Publisher Site | Google Scholar
  12. Y. Zhou, X. Feng, Y.-L. Liu et al., “Down-regulation of miR-126 is associated with colorectal cancer cells proliferation, migration and invasion by targeting IRS-1 via the AKT and ERK1/2 signaling pathways,” PLoS ONE, vol. 8, no. 11, Article ID e81203, 2013. View at: Publisher Site | Google Scholar
  13. B. Liu, X.-C. Peng, X.-L. Zheng, J. Wang, and Y.-W. Qin, “MiR-126 restoration down-regulate VEGF and inhibit the growth of lung cancer cell lines in vitro and in vivo,” Lung Cancer, vol. 66, no. 2, pp. 169–175, 2009. View at: Publisher Site | Google Scholar
  14. S. F. Tavazoie, C. Alarcón, T. Oskarsson et al., “Endogenous human microRNAs that suppress breast cancer metastasis,” Nature, vol. 451, no. 7175, pp. 147–152, 2008. View at: Publisher Site | Google Scholar
  15. R. Feng, X. Chen, Y. Yu et al., “miR-126 functions as a tumour suppressor in human gastric cancer,” Cancer Letters, vol. 298, no. 1, pp. 50–63, 2010. View at: Publisher Site | Google Scholar
  16. Z. Li, N. Li, M. Wu, X. Li, Z. Luo, and X. Wang, “Expression of miR-126 suppresses migration and invasion of colon cancer cells by targeting CXCR4,” Molecular and Cellular Biochemistry, vol. 381, no. 1-2, pp. 233–242, 2013. View at: Publisher Site | Google Scholar
  17. L. R. Jiao, A. E. Frampton, J. Jacob et al., “Micrornas targeting oncogenes are down-regulated in pancreatic malignant transformation from benign tumors,” PLoS ONE, vol. 7, no. 2, Article ID e32068, 2012. View at: Publisher Site | Google Scholar
  18. X. Yang, H. Wu, and T. Ling, “Suppressive effect of microRNA-126 on oral squamous cell carcinoma in vitro,” Molecular Medicine Reports, vol. 10, no. 1, pp. 125–130, 2014. View at: Publisher Site | Google Scholar
  19. T. Otsubo, Y. Akiyama, Y. Hashimoto, S. Shimada, K. Goto, and Y. Yuasa, “Microrna-126 inhibits SOX2 expression and contributes to gastric carcinogenesis,” PLoS ONE, vol. 6, no. 1, Article ID e16617, 2011. View at: Publisher Site | Google Scholar
  20. I. Barshack, E. Meiri, S. Rosenwald et al., “Differential diagnosis of hepatocellular carcinoma from metastatic tumors in the liver using microRNA expression,” International Journal of Biochemistry and Cell Biology, vol. 42, no. 8, pp. 1355–1362, 2010. View at: Publisher Site | Google Scholar
  21. Z. Li and J. Chen, “In vitro functional study of miR-126 in leukemia,” Methods in Molecular Biology, vol. 676, pp. 185–195, 2011. View at: Publisher Site | Google Scholar
  22. W.-F. Shen, Y.-L. Hu, L. Uttarwar, E. Passegue, and C. Largman, “microRNA-126 regulates HOXA9 by binding to the homeobox,” Molecular and Cellular Biology, vol. 28, no. 14, pp. 4609–4619, 2008. View at: Publisher Site | Google Scholar
  23. M. V. Iorio and C. M. Croce, “MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review,” EMBO Molecular Medicine, vol. 4, no. 3, pp. 143–159, 2012. View at: Publisher Site | Google Scholar
  24. F. Ebrahimi, V. Gopalan, R. A. Smith, and A. K. Lam, “miR-126 in human cancers: clinical roles and current perspectives,” Experimental and Molecular Pathology, vol. 96, no. 1, pp. 98–107, 2014. View at: Publisher Site | Google Scholar
  25. D. F. Stroup, J. A. Berlin, S. C. Morton et al., “Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group,” The Journal of the American Medical Association, vol. 283, no. 15, pp. 2008–2012, 2000. View at: Publisher Site | Google Scholar
  26. Y. Shao, Y. Geng, W. Gu, J. Huang, H. Pei, and J. Jiang, “Prognostic role of tissue and circulating microRNA-200c in malignant tumors: a systematic review and meta-analysis,” Cellular Physiology and Biochemistry, vol. 35, no. 3, pp. 1188–1200, 2015. View at: Publisher Site | Google Scholar
  27. Z. Zhang, T. Wang, J. Zhang et al., “Prognostic value of epidermal growth factor receptor mutations in resected non-small cell lung cancer: a systematic review with meta-analysis,” PLoS ONE, vol. 9, no. 8, Article ID e106053, 2014. View at: Publisher Site | Google Scholar
  28. J. F. Tierney, L. A. Stewart, D. Ghersi, S. Burdett, and M. R. Sydes, “Practical methods for incorporating summary time-to-event data into meta-analysis,” Trials, vol. 8, article 16, 2007. View at: Publisher Site | Google Scholar
  29. M. K. B. Parmar, V. Torri, and L. Stewart, “Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints,” Statistics in Medicine, vol. 17, no. 24, pp. 2815–2834, 1998. View at: Publisher Site | Google Scholar
  30. J. Lau, J. P. A. Ioannidis, and C. H. Schmid, “Quantitative synthesis in systematic reviews,” Annals of Internal Medicine, vol. 127, no. 9, pp. 820–826, 1997. View at: Publisher Site | Google Scholar
  31. J. P. Higgins and S. G. Thompson, “Quantifying heterogeneity in a meta-analysis,” Statistics in Medicine, vol. 21, no. 11, pp. 1539–1558, 2002. View at: Publisher Site | Google Scholar
  32. R. DerSimonian and N. Laird, “Meta-analysis in clinical trials,” Controlled Clinical Trials, vol. 7, no. 3, pp. 177–188, 1986. View at: Publisher Site | Google Scholar
  33. N. Mantel and W. Haenszel, “Statistical aspects of the analysis of data from retrospective studies of disease,” Journal of the National Cancer Institute, vol. 22, no. 4, pp. 719–748, 1959. View at: Google Scholar
  34. C. B. Begg and M. Mazumdar, “Operating characteristics of a rank correlation test for publication bias,” Biometrics, vol. 50, no. 4, pp. 1088–1101, 1994. View at: Publisher Site | Google Scholar
  35. M. Egger, G. D. Smith, M. Schneider, and C. Minder, “Bias in meta-analysis detected by a simple, graphical test,” British Medical Journal, vol. 315, no. 7109, pp. 629–634, 1997. View at: Publisher Site | Google Scholar
  36. Y. Shibayama, T. Kondo, H. Ohya, S. Fujisawa, T. Teshima, and K. Iseki, “Upregulation of microRNA-126-5p is associated with drug resistance to cytarabine and poor prognosis in AML patients,” Oncology Reports, vol. 33, no. 5, pp. 2176–2182, 2015. View at: Publisher Site | Google Scholar
  37. K. Ishihara, D. Sasaki, K. Tsuruda et al., “Impact of miR-155 and miR-126 as novel biomarkers on the assessment of disease progression and prognosis in adult T-cell leukemia,” Cancer Epidemiology, vol. 36, no. 6, pp. 560–565, 2012. View at: Publisher Site | Google Scholar
  38. D. C. de Leeuw, F. Denkers, M. C. Olthof et al., “Attenuation of microRNA-126 expression that drives CD34+38 stem/progenitor cells in acute myeloid leukemia leads to tumor eradication,” Cancer Research, vol. 74, no. 7, pp. 2094–2105, 2014. View at: Publisher Site | Google Scholar
  39. C. Sanfiorenzo, M. I. Ilie, A. Belaid et al., “Two panels of plasma microRNAs as non-invasive biomarkers for prediction of recurrence in resectable NSCLC,” PLoS ONE, vol. 8, no. 1, Article ID e54596, 2013. View at: Publisher Site | Google Scholar
  40. T. Donnem, K. Lonvik, K. Eklo et al., “Independent and tissue-specific prognostic impact of miR-126 in nonsmall cell lung cancer: coexpression with vascular endothelial growth factor-a predicts poor surviva,” Cancer, vol. 117, no. 14, pp. 3193–3200, 2011. View at: Publisher Site | Google Scholar
  41. M. K. Kim, S. B. Jung, J.-S. Kim et al., “Expression of microRNA miR-126 and miR-200c is associated with prognosis in patients with non-small cell lung cancer,” Virchows Archiv, vol. 465, no. 4, pp. 463–471, 2014. View at: Publisher Site | Google Scholar
  42. E. Jusufović, M. Rijavec, D. Keser et al., “Let-7b and miR-126 are down-regulated in tumor tissue and correlate with microvessel density and survival outcomes in non-small-cell lung cancer,” PLoS ONE, vol. 7, no. 9, Article ID e45577, 2012. View at: Publisher Site | Google Scholar
  43. J. Yang, H. Lan, X. Huang, B. Liu, and Y. Tong, “MicroRNA-126 inhibits tumor cell growth and its expression level correlates with poor survival in non-small cell lung cancer patients,” PLoS ONE, vol. 7, no. 8, Article ID e42978, 2012. View at: Publisher Site | Google Scholar
  44. X. Li, G. Wan, Y. Liang, C. Sun, and H. Dong, “Effect of miR-126 on cell cycle regulation and prognosis of lung cancer patients,” Journal of Practical Oncology, vol. 29, no. 5, pp. 440–445, 2014. View at: Google Scholar
  45. Z. B. Han, L. Zhong, M. J. Teng et al., “Identification of recurrence-related microRNAs in hepatocellular carcinoma following liver transplantation,” Molecular Oncology, vol. 6, no. 4, pp. 445–457, 2012. View at: Publisher Site | Google Scholar
  46. H. Chen, R. Miao, J. Fan et al., “Decreased expression of miR-126 correlates with metastatic recurrence of hepatocellular carcinoma,” Clinical & Experimental Metastasis, vol. 30, no. 5, pp. 651–658, 2013. View at: Publisher Site | Google Scholar
  47. Y. Yang, K. L. Song, H. Chang, and L. Chen, “Decreased expression of microRNA-126 is associated with poor prognosis in patients with cervical cancer,” Diagnostic Pathology, vol. 9, no. 1, article 220, 2014. View at: Publisher Site | Google Scholar
  48. X. Sun, Z.-M. Wang, Y. Song, X. U.-H. Tai, W.-Y. Ji, and H. Gu, “MicroRNA-126 modulates the tumor microenvironment by targeting calmodulin-regulated spectrin-associated protein 1 (Camsap1),” International Journal of Oncology, vol. 44, no. 5, pp. 1678–1684, 2014. View at: Publisher Site | Google Scholar
  49. T. F. Hansen, F. B. Sørensen, J. Lindebjerg, and A. Jakobsen, “The predictive value of microRNA-126 in relation to first line treatment with capecitabine and oxaliplatin in patients with metastatic colorectal cancer,” BMC Cancer, vol. 12, article 83, 2012. View at: Publisher Site | Google Scholar
  50. T. F. Hansen, S. Kjær-Frifeldt, S. Morgenthaler et al., “The prognostic value of microRNA-126 and microvessel density in patients with stage II colon cancer: results from a population cohort,” Journal of Translational Medicine, vol. 12, no. 1, article 254, 2014. View at: Publisher Site | Google Scholar
  51. N. Li, X. Li, S. Huang, S. Shen, and X. Wang, “miR-126 inhibits colon cancer proliferation and invasion through targeting IRS1, SLC7A5 and TOM1 gene,” Zhong Nan Da Xue Xue Bao Yi Xue Ban, vol. 38, no. 8, pp. 809–817, 2013. View at: Publisher Site | Google Scholar
  52. Y. Liu, Y. Zhou, X. Feng et al., “Low expression of MicroRNA-126 is associated with poor prognosis in colorectal cancer,” Genes Chromosomes and Cancer, vol. 53, no. 4, pp. 358–365, 2014. View at: Publisher Site | Google Scholar
  53. R. Díaz, J. Silva, J. M. García et al., “Deregulated expression of miR-106a predicts survival in human colon cancer patients,” Genes, Chromosomes and Cancer, vol. 47, no. 9, pp. 794–802, 2008. View at: Publisher Site | Google Scholar
  54. T. F. Hansen, C. L. Andersen, B. S. Nielsen et al., “Elevated microRNA-126 is associated with high vascular endothelial growth factor receptor 2 expression levels and high microvessel density in colorectal cancer,” Oncology Letters, vol. 2, no. 6, pp. 1101–1106, 2011. View at: Publisher Site | Google Scholar
  55. T. F. Hansen, R. D. P. Christensen, R. F. Andersen, F. B. Sørensen, A. Johnsson, and A. Jakobsen, “MicroRNA-126 and epidermal growth factor-like domain 7-an angiogenic couple of importance in metastatic colorectal cancer. Results from the Nordic ACT trial,” British Journal of Cancer, vol. 109, no. 5, pp. 1243–1251, 2013. View at: Publisher Site | Google Scholar
  56. T. F. Hansen, A. L. Carlsen, N. H. H. Heegaard, F. B. Sørensen, and A. Jakobsen, “Changes in circulating microRNA-126 during treatment with chemotherapy and bevacizumab predicts treatment response in patients with metastatic colorectal cancer,” British Journal of Cancer, vol. 112, no. 4, pp. 624–629, 2015. View at: Publisher Site | Google Scholar
  57. T. Sasahira, M. Kurihara, U. K. Bhawal et al., “Downregulation of miR-126 induces angiogenesis and lymphangiogenesis by activation of VEGF-A in oral cancer,” British Journal of Cancer, vol. 107, no. 4, pp. 700–706, 2012. View at: Publisher Site | Google Scholar
  58. X. Sun, Z. Liu, Z. Yang et al., “Association of microRNA-126 expression with clinicopathological features and the risk of biochemical recurrence in prostate cancer patients undergoing radical prostatectomy,” Diagnostic Pathology, vol. 8, article 208, 2013. View at: Publisher Site | Google Scholar
  59. R. Hoppe, J. Achinger-Kawecka, S. Winter et al., “Increased expression of miR-126 and miR-10a predict prolonged relapse-free time of primary oestrogen receptor-positive breast cancer following tamoxifen treatment,” European Journal of Cancer, vol. 49, no. 17, pp. 3598–3608, 2013. View at: Publisher Site | Google Scholar
  60. D. C. Vergho, S. Kneitz, C. Kalogirou et al., “Impact of miR-21, miR-126 and miR-221 as prognostic factors of clear cell renal cell carcinoma with tumor thrombus of the inferior vena cava,” PLoS ONE, vol. 9, no. 10, Article ID e109877, 2014. View at: Publisher Site | Google Scholar
  61. H. W. Khella, A. Scorilas, R. Mozes et al., “Low expression of miR-126 is a prognostic marker for metastatic clear cell renal cell carcinoma,” The American Journal of Pathology, vol. 185, no. 3, pp. 693–703, 2015. View at: Publisher Site | Google Scholar
  62. R. Liu, J. Gu, P. Jiang et al., “DNMT1-microrna126 epigenetic circuit contributes to esophageal squamous cell carcinoma growth via ADAM9-EGFR-akt signaling,” Clinical Cancer Research, vol. 21, no. 4, pp. 854–863, 2015. View at: Publisher Site | Google Scholar
  63. Y. Hu, A. M. Correa, A. Hoque et al., “Prognostic significance of differentially expressed miRNAs in esophageal cancer,” International Journal of Cancer, vol. 128, no. 1, pp. 132–143, 2011. View at: Publisher Site | Google Scholar
  64. J. Wang, Z. Ling, and W. Mao, “Expressions and clinical significances of microRNA-126 and microRNA-7 in esophageal squmous cell carcinoma,” Journal of International Oncology, vol. 40, no. 12, pp. 936–940, 2013. View at: Publisher Site | Google Scholar
  65. J. Feng, S.-T. Kim, W. Liu et al., “An integrated analysis of germline and somatic, genetic and epigenetic alterations at 9p21.3 in glioblastoma,” Cancer, vol. 118, no. 1, pp. 232–240, 2012. View at: Publisher Site | Google Scholar
  66. L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, J. Lortet-Tieulent, and A. Jemal, “Global cancer statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 65, no. 2, pp. 87–108, 2015. View at: Publisher Site | Google Scholar
  67. D. Paul, A. Kumar, A. Gajbhiye, M. K. Santra, and R. Srikanth, “Mass spectrometry-based proteomics in molecular diagnostics: discovery of cancer biomarkers using tissue culture,” BioMed Research International, vol. 2013, Article ID 783131, 16 pages, 2013. View at: Publisher Site | Google Scholar
  68. C. A. González and A. Agudo, “Carcinogenesis, prevention and early detection of gastric cancer: where we are and where we should go,” International Journal of Cancer, vol. 130, no. 4, pp. 745–753, 2012. View at: Publisher Site | Google Scholar
  69. Y.-Q. Sun, F. Zhang, Y.-F. Bai, and L.-L. Guo, “miR-126 modulates the expression of epidermal growth factor-like domain 7 in human umbilical vein endothelial cells in vitro,” Nan Fang Yi Ke Da Xue Xue Bao, vol. 30, no. 4, pp. 767–770, 2010. View at: Google Scholar
  70. K. J. Png, N. Halberg, M. Yoshida, and S. F. Tavazoie, “A microRNA regulon that mediates endothelial recruitment and metastasis by cancer cells,” Nature, vol. 481, no. 7380, pp. 190–194, 2012. View at: Publisher Site | Google Scholar
  71. N. Zhu, D. Zhang, H. Xie et al., “Endothelial-specific intron-derived miR-126 is down-regulated in human breast cancer and targets both VEGFA and PIK3R2,” Molecular and Cellular Biochemistry, vol. 351, no. 1-2, pp. 157–164, 2011. View at: Publisher Site | Google Scholar
  72. C. Du, Z. Lv, L. Cao et al., “MiR-126-3p suppresses tumor metastasis and angiogenesis of hepatocellular carcinoma by targeting LRP6 and PIK3R2,” Journal of Translational Medicine, vol. 12, article 259, 2014. View at: Publisher Site | Google Scholar
  73. S. Hamada, K. Satoh, W. Fujibuchi et al., “MiR-126 acts as a tumor suppressor in pancreatic cancer cells via the regulation of ADAM9,” Molecular Cancer Research, vol. 10, no. 1, pp. 3–10, 2012. View at: Publisher Site | Google Scholar
  74. K. E. Resnick, H. Alder, J. P. Hagan, D. L. Richardson, C. M. Croce, and D. E. Cohn, “The detection of differentially expressed microRNAs from the serum of ovarian cancer patients using a novel real-time PCR platform,” Gynecologic Oncology, vol. 112, no. 1, pp. 55–59, 2009. View at: Publisher Site | Google Scholar
  75. G. Cammarata, L. Augugliaro, D. Salemi et al., “Differential expression of specific microRNA and their targets in acute myeloid leukemia,” American Journal of Hematology, vol. 85, no. 5, pp. 331–339, 2010. View at: Publisher Site | Google Scholar
  76. Z. Li, J. Lu, M. Sun et al., “Distinct microRNA expression profiles in acute myeloid leukemia with common translocations,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 40, pp. 15535–15540, 2008. View at: Publisher Site | Google Scholar
  77. T. Donnem, C. G. Fenton, K. Lonvik et al., “MicroRNA signatures in tumor tissue related to angiogenesis in non-small cell lung cancer,” PLoS ONE, vol. 7, no. 1, Article ID e29671, 2012. View at: Publisher Site | Google Scholar
  78. A. Watahiki, Y. Wang, J. Morris et al., “MicroRNAs associated with metastatic prostate cancer,” PLoS ONE, vol. 6, no. 9, Article ID e24950, 2011. View at: Publisher Site | Google Scholar

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