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 [
MicroRNA-126 (miR-126), located within the 7th intron of
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 [
This meta-analysis was performed following the guidelines of the Systematic Reviews and Meta-Analyses (PRISMA) and the Observational Studies in Epidemiology group (MOOSE) [
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.
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.
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 [
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 [
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
A flowchart of detailed searching process is illustrated in Figure
Flow diagram of the study selection process.
The main characteristics and basic information of eligible studies were listed in Table
Main characteristics of enrolled studies in the systematic review.
Author | Year | Country | Cancer | Number | Specimen | Assay | Cut-off value | Source of HR | Endpoint | Median follow-up (months) |
---|---|---|---|---|---|---|---|---|---|---|
Shibayama et al. [ |
2015 | Japan | AML | 108 | Bone marrow | qRT-PCR | Median | R | OS | NR |
Ishihara et al. [ |
2012 | Japan | ATL | 35 | Plasma | qRT-PCR | Median | SC | OS | NR |
de Leeuw et al. [ |
2014 | Netherlands | AML | 92 | Blood | qRT-PCR | Median | R | OS, EFS, RFS | NR |
Sanfiorenzo et al. [ |
2013 | France | NSCLC | 52 | Plasma | qRT-PCR | Median | R | DFS | 46 |
Donnem et al. [ |
2011 | Norway | NSCLC | 332 | Tissue | ISH | Expression score ≥ 2 | R | DSSa | 86 |
Kim et al. [ |
2014 | South Korea | NSCLC | 72 | Tissue | qRT-PCR | Median | R | OS | 31 |
Jusufović et al. [ |
2012 | Serbia | NSCLC | 50 | Tissue | qRT-PCR | Median | R | OS, PFS | 5.13 |
Yang et al. [ |
2012 | China | NSCLC | 442 | Tissue | qRT-PCR | Median | R | OS | 24.39–29.28 |
Li et al. [ |
2014 | China | NSCLC | 49 | Tissue | qRT-PCR | Median | SC | OS, DFS | 39 |
Han et al. [ |
2012 | China | HCC | 105 | Tissue | qRT-PCR | Fold change = 2 | R | OS | 42.89 |
Chen et al. [ |
2013 | China | HCC | 68 | Tissue | qRT-PCR | 0.70 (ROC curve) | SC | OS | 49 |
Yang et al. [ |
2014 | China | Cervical cancer | 133 | Tissue | qRT-PCR | Median | R | OS | 60 (max) |
Sun et al. [ |
2014 | China | LSCC | 38 | Plasma | qRT-PCR | Median | SC | OS | NR |
Hansen et al. [ |
2012 | Denmark | CRC | 89 | Tissue | ISH | Median | SC | PFS | 16.8–26.2 |
Hansen et al. [ |
2014 | Denmark | CRC | 63 | Plasma | qRT-PCR | Median | R | PFS | 8.8–9.2 |
Li et al. [ |
2013 | China | Colon cancer | 53 | Tissue | ISH | 0/1–3+ | SC | OS | 45.66–55.04 |
Liu et al. [ |
2014 | China | CRC | 92 | Tissue | qRT-PCR | Median | SC | OS | 65 |
Díaz et al. [ |
2008 | Spain | Colon cancer | 110 | Tissue | qRT-PCR | Median | R | OS, DFS | 68 |
Hansen et al. [ |
2011 | Denmark | CRC | 81 | Tissue | ISH | Median | R | OS, PFS | NR |
Hansen et al. [ |
2013 | Denmark/Sweden | CRC | 89 | Tissue | qRT-PCR | Median | R | PFS | NR |
Hansen et al. [ |
2015 | Denmark | CRC | 560 | Tissue | qRT-PCR | Median | R | OS, DSS | 7 years (max) |
Sasahira et al. [ |
2012 | Japan | Oral cancer | 94 | Tissue | qRT-PCR | Means | R | DFS | 3.4 years |
Sun et al. [ |
2013 | China | Prostate cancer | 128 | Tissue | qRT-PCR | Median | SC | RFS | 3–10 years |
Hoppe et al. [ |
2013 | Germany | Breast cancer | 80 | Tissue | qRT-PCR | 6.20 (ROC curve) | R | RFS | 8.84 years |
Vergho et al. [ |
2014 | Germany | cRCC | 37 | Tissue | qRT-PCR | 3.57 (ROC curve) | R | DSS | 41.4 |
Khella et al. [ |
2015 | Canada | cRCC | 257,481b | Tissue | qRT-PCR | 20th percentile | R | OS, DFS, OSb | 48.6 |
Liu et al. [ |
2015 | China | ESCC | 185 | Tissue | ISH | Fold change > 3 | R | DSS | 32 |
Hu et al. [ |
2011 | USA | ESCC | 158 | Tissue | ISH | 1–3+/0–0.5 | R | OS, DFS | 16.25 |
Wang et al. [ |
2013 | China | ESCC | 116 | Tissue | qRT-PCR |
|
SC | DFS | 21–32 |
Feng et al. [ |
2012 | USA | GBM | 248 | Tissue | qRT-PCR | Median | R | PFS/RFSb, OSb | NR |
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.
The main results of this meta-analysis were displayed in Table
Meta-analysis results.
Outcome | Variables | Number of studies | Number of patients | Model | HR (95% CI) | Heterogeneity | Publication bias | ||
---|---|---|---|---|---|---|---|---|---|
|
|
Begg’s |
Egger’s | ||||||
OS | All | 20 | 3232 | Random | 0.77 (0.64, 0.93) | 56.8 | 0.001 | 0.381 | 0.358 |
|
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NSCLC | 4 | 613 | Random | 0.42 (0.17, 1.08) | 82.2 | 0.001 | 1.000 | 0.340 | |
HCC | 2 | 173 | Fixed | 0.65 (0.49, 0.86) | 2.60 | 0.311 | |||
CRC | 5 | 896 | Fixed | 0.85 (0.69, 1.04) | 0 | 0.584 | 0.806 | 0.679 | |
RCC | 2 | 738 | Fixed | 0.65 (0.38, 1.12) | 0 | 0.624 | |||
AML | 2 | 200 | Fixed | 1.77 (1.15, 2.72) | 0 | 0.666 | |||
|
|||||||||
Asian | 12 | 1353 | Fixed | 0.76 (0.66, 0.88) | 37.0 | 0.129 | 0.837 | 0.668 | |
Caucasian | 8 | 1879 | Random | 0.77 (0.57, 1.05) | 73.8 | <0.001 | 0.536 | 0.479 | |
|
|||||||||
Circulation | 4 | 273 | Fixed | 1.65 (1.09, 2.51) | 0 | 0.647 | 0.734 | 0.162 | |
Tissue | 16 | 2959 | Random | 0.71 (0.60, 0.85) | 51.1 | 0.01 | 0.137 | 0.068 | |
|
|||||||||
qRT-PCR | 17 | 2940 | Random | 0.72 (0.58, 0.90) | 61.2 | <0.001 | 0.303 | 0.250 | |
ISH | 3 | 292 | Fixed | 1.00 (0.75, 1.34) | 0 | 0.804 | 1.000 | 0.646 | |
|
|||||||||
Multivariate | 7 | 1870 | Fixed | 0.81 (0.72, 0.90) | 11.0 | 0.344 | 0.072 | 0.095 | |
Univariate | 7 | 1530 | Random | 0.89 (0.79, 1.00) | 66.4 | 0.007 | 1.000 | 0.990 | |
|
|||||||||
HRs reported | 14 | 2897 | Random | 0.78 (0.64, 0.96) | 67.8 | <0.001 | 0.274 | 0.461 | |
K-M curve | 6 | 335 | Fixed | 0.79 (0.53, 1.18) | 0 | 0.666 | 1.000 | 0.705 | |
|
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DFS | All | 7 | 755 | Fixed | 0.64 (0.48, 0.85) | 0 | 0.780 | 0.133 | 0.203 |
|
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NSCLC | 2 | 101 | Fixed | 0.49 (0.26, 0.93) | 0 | 0.983 | |||
ESCC | 2 | 274 | Fixed | 0.77 (0.48, 1.24) | 0 | 0.629 | |||
|
|||||||||
Asian | 4 | 417 | Fixed | 0.64 (0.44, 0.94) | 0 | 0.532 | 0.308 | 0.081 | |
Caucasian | 3 | 419 | Fixed | 0.63 (0.41, 0.97) | 0 | 0.599 | 1.000 | 0.874 | |
|
|||||||||
Multivariate | 3 | 509 | Fixed | 0.65 (0.45, 0.94) | 0 | 0.384 | 0.296 | 0.360 | |
Univariate | 4 | 619 | Random | 0.67 (0.50, 0.90) | 88.0 | <0.001 | 0.734 | 0.586 | |
|
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RFS/PFS/DSS | All | 13 | 2014 | Random | 0.70 (0.50, 0.98) | 84.8 | <0.001 | 0.360 | 0.288 |
|
|||||||||
CRC | 5 | 882 | Fixed | 0.74 (0.59, 0.94) | 47.3 | 0.108 | 1.000 | 0.514 | |
NSCLC | 2 | 382 | Random | 0.43 (0.03, 7.25) | 97.2 | <0.001 | |||
|
|||||||||
Asian | 2 | 313 | Fixed | 0.69 (0.48, 0.99) | 0 | 0.417 | |||
Caucasian | 11 | 1701 | Random | 0.69 (0.46, 1.02) | 87.1 | <0.001 | 0.213 | 0.267 | |
|
|||||||||
Multivariate | 7 | 1531 | Random | 0.71 (0.50, 1.02) | 83.2 | <0.001 | 0.230 | 0.281 | |
Univariate | 5 | 651 | Random | 0.89 (0.77, 1.02) | 81.4 | <0.001 | 0.462 | 0.872 |
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.
Forest plots of studies evaluating the pooled HR of elevated miR-126 levels for overall survival (OS) (a), disease-free survival (DFS) (b), and recurrence-free survival/progression-free survival/disease-specific survival (PFS/RFS/DSS) (c). Fixed-effects (b) and random-effects (a, c) models were used as the pooling method, respectively.
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
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 (
Similar to OS analyses, we also performed subtotal investigation for DFS analyses (Table
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 (
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,
Obvious heterogeneity of subjects was observed among 13 of the 30 analysis groups, as shown in Table
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
Begg’s funnel plots of publication bias test for overall survival (OS).
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
Sensitivity analyses of studies concerning miR-126 and overall survival (OS).
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 [
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,
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.
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.
The authors declare that there is no conflict of interests regarding the publication of this paper.
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).