High Expression Levels of miR-21 and miR-210 Predict Unfavorable Survival in Breast Cancer: A Systemic Review and Meta-Analysis

Abstract

Background

MicroRNAs (miRNAs) have been emerging as valuable prognostic biomarkers of breast cancer. We therefore summarized recent research into miRNAs involved in human breast cancer and, further, completed a meta-analysis to predict the role of specific miRNAs in the survival of breast cancer patients.

Methods

Studies were identified by searching PubMed, Embase and Web of Science. Descriptive characteristics for studies were described, and an additional meta-analysis for specific miRNAs was performed. Pooled hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated.

Results

A total of 41 articles including 27 types of miRNAs were found regarding prognostic biomarkers for breast cancer survival, of which, micRNA-21 (miR-21) was the most-studied specific miRNA that appeared repeatedly among the selected classifiers. For the studies evaluating miR-21's association with clinical outcomes, the median HR in the studies was 2.32 (interquartile range [IQR] = 1.04-3.40), and the pooled HR suggested that high expression of miR-21 has a negative impact on overall survival (OS; HR = 1.46, 95% CI, 1.25-1.70; p<0.05) and disease/recurrence-free survival in breast cancer (HR = 1.49, 95% CI, 1.17-1.90; p<0.01). We also found that higher expression levels of miR-210 significantly predicted poorer outcome, with median HR in the reported studies of 4.07 (IQR = 1.54-4.43) and a pooled HR of 2.94 (95% CI, 2.08-4.17; p<0.05).

Conclusions

These results indicate that miRNAs show promising associations with prognosis in breast cancer. Moreover, specific miRNAs such as miR-21 and miR-210 can predict poor survival rates in breast cancer patients.

Introduction

Breast cancer is a significant health problem and ranks as the second most common cause of cancer death in female patients (1). There has been an increasing incidence of breast cancer in developing countries, resulting from urbanization and adoption of Western lifestyles, and reduced life expectancy (2). The heterogeneity of breast cancer poses serious clinical challenges. Thus, different approaches to stratifying patients with respect to their prognosis have been developed. In clinical practice, the management of breast cancer is based on established criteria such as tumor size, the presence of distant metastasis, histologic type and grade, the expression of hormone (i.e., estrogen and progesterone) receptors (ER and PR) and the human epidermal growth factor 2 (ERBB2/HER2) receptor (3). Ongoing advances in breast cancer management have significantly changed outcomes for patients (4). To further that advance, detectable and stable biomarkers are required for clinical practice.

Recently, the field of transcriptome has been further expanded through analyses of noncoding RNAs (ncRNAs). MicroRNAs (miRNAs) are small, ncRNAs of 19-24 nucleotides in length that were first discovered in Caenorhabditis elegans by Victor Ambros et al in 1993 (5). Since then, our knowledge of miRNA biology has continued to grow. More and more cancer-relevant miRNAs have been well characterized (6), and they have been critically implicated in carcinogenesis as either oncogenes or tumor suppressors (7). Due to their unique expression profile in human cancers, the fact that they are more stable than mRNAs and their levels and presence are easily assessed, miRNAs hold great promise as potential biomarkers for cancer prognosis.

Several studies have reported promising results for miRNA expression classifiers for prognosis of breast cancer (8, 9). The data from studies providing independent assessments of miRNAs in breast cancer were systematically reviewed in our study. Furthermore, we performed a meta-analysis of studies regarding specific miRNAs as important prognostic factors for breast cancer.

Materials and Methods

Search Studies and data Extract

We followed the guidelines of the critical checklist of the Dutch Cochrane Centre as proposed by the Meta-analysis of Observational Studies in Epidemiology (MOOSE) proposal (10) in our study. A comprehensive literature search of PubMed was done for original articles analyzing the prognostic value of miRNAs in breast cancer. Studies were selected using the following keywords variously combined: breast cancer, microRNA, miRNA, prognosis and prognostic. Original and review articles published until 20 December 2014 were sought, and no lower date limit was used. Additional records were found through Embase and Web of Science. Furthermore, the bibliographies reported in the selected studies were reviewed to deepen this search. Authors were contacted whenever useful data were not reported or to exclude overlapping data that may have been published elsewhere.

All studies published as a full paper in English, dealing with breast cancer only (any stage or histology) and analyzing prognosis in patients stratified by dichotomous miRNA expression levels in tissues or blood were included. Review articles, letters, conference abstracts or laboratory articles were excluded. Subtypes of of miRNAs, for example, miR-34a, miR-34b and miR-34c were defined as 1 type (miR-34). The extracted data elements included the following: (i) publication details including the first author's name and publication year; (ii) characteristics of the studied population including country, source, stage and histological type; (iii) miRNA assessments and cutoff values; (iv) hazard ratios (HRs) of miRNAs for overall survival (OS) and disease-free survival (DFS) or recurrence-free survival (RFS), along with the 95% confidence intervals (95% CIs) and p values. If the HRs and their 95% CIs were not directly provided, the numbers of deaths and total samples in each study were extracted to calculate this. If only Kaplan-Meier curves were available, data were extracted from the survival plots, and the HR was calculated using methods established by Parmar et al, Williamson et al and Tierney (11). When the authors reported results obviously obtained from the same patient population in multiple publications, only the most recent studies were included. When the authors reported the validation set results obviously inconsistent with the discovery set, the data was abandoned. Two reviewers (Y. T. and X. Z.) independently evaluated the titles and abstracts of the identified articles in duplicate. A flow diagram of the study selection process is presented in Figure 1.

Fig. 1

Flow diagram of the study selection process.

Statistical Analysis

Heterogeneity was assessed using the Q statistic (a p value <0.10 was considered heterogeneous). A random-effects model was used if prominent heterogeneity was observed (p<0.05 or I²>50%), otherwise a fixed-effects model was applied. Some studies did not list the HRs or 95% CI directly, instead giving Kaplan-Meier survival curves alone. The necessary statistics were performed using software designed by Tierney et al, Parmar et al, and Williamson et al (11-13). The effects of miR-21 and miR-210 expression on survival were estimated using forest plots. Pooled HR was calculated using a fixed-effects model or random-effects model as appropriate. A pooled HR >1 indicated poor prognosis for the groups with elevated miRNA expression and was considered statistically significant if the 95% CI did not overlap 1. Publication bias was evaluated using a funnel plot and Egger's test. A p value >0.05 was considered indicative of a lack of publication bias (14). All analyses were performed using STATA version 12.0 (Stata Corp., College Station, TX, USA). A p value <0.05 was considered to be statistically significant.

Results

Eligible Studies

A total of 403 studies of miRNAs and breast cancer prognosis were identified from a primary literature search in PubMed. Sixteen additional records were found in Embase and Web of Science. Of these, 356 studies were excluded based on manual screening of the title or abstract, and the remaining 47 were evaluated as full texts. Of the 47 candidate studies, 6 studies were not directly related to specific outcomes. Therefore, in the present study, we reviewed and listed a final 41 articles (9, 15-54) and identified unique miRNA classifiers associated with survival outcomes, as shown in Table I.

Table I

Main characteristics of the eligible studies

Author, date (ref.)	Country	Sample	Patients	Stage	miRNA assay	Hazard ratio	Cutoff	Results	Control	ER(+)	PR(+)	Prognosis miRNA(s)	p Value
Camps, 2008 (43)	Belgium	Tissues	219		qRT-PCR	R	Median	OS/DFS	RNU43			miR-210	<0.01
Yan, 2008 (37)	China	Tissues	113	I-III	qRT-PCR	R	Mean	OS	RNU6	57/113	65/113	miR-21	<0.01
Qian, 2009 (36)	USA	Tissues	307	I-IV	qRT-PCR	R	Median	OS	RNU6	218/338	175/337	miR-21	>0.05
Wu, 2010 (54)	China	Tissues	90	I-IV	ISH	R	Risk scores	OS	RNU6	33/90	34/90	miR-339-5p	0.03
Foekens, 2010 (53)	Netherlands	Tissues	114		qRT-PCR	R	Thirds	MFS	miR-132/miR-374	HER-negative	miR-210	<0.01
Ota, 2011 (50)	Japan	Bone marrow	291	I-III	qRT-PCR	R	5.84	OS/DFS	RNU6B	194/291	166/286	miR-21
		Bone marrow			qRT-PCR	R		DFS	RNU6B			miR-181a	<0.05
Peurala, 2011 (21)	Finland	Tissues	183	I-IV	ISH	R	Risk scores	BCSS/MFS	RNU6			miR-34a	>0.05
Rothe, 2011 (41)	USA	Tissues	73		qRT-PCR	R	Median	RFS	RNU44/RNU48	40/73	NR	miR-210	<0.01
Radojicic, 2011 (49)	Greece	Tissues	49		qRT-PCR	SC	Median	OS/DFS	RNU5A/RNU6B	TNBC	miR-21	>0.05
Lee, 2011 (34)	Korea	Tissues	109	I-III	qRT-PCR	R	Mean	OS/DFS	RNU6	41/109	65/109	miR-21	0.03
Walter, 2011 (35)	USA	Tissues	35		qRT-PCR	R	Median	OS	RNU6B	19%	38%	miR-21	0.99
Svoboda, 2012 (22)	Czech	Tissues	39	I-IV	qRT-PCR	SC	Zero	DFS/OS	RNU6B	0/39	0/39	miR-34b	<0.01
Wu, 2012 (23)	China	Tissues	90		ISH	SC	Risk scores	RFS	RNU6			miR-133a	0.04
Chen, 2012 (24)	China	Tissues	92	I-IV	qRT-PCR	R	Median	OS/DFS	RNU6	57/92	71/92	miR-155	0.03
Mulrane, 2012 (25)	Sweden	Tissues	117	I-III	ISH	R	Risk scores	BCSS		102/117	77/117	miR-187	0.06
			470	I-III	ISH	R	Risk scores	BCSS		378/470	212/470	miR-187	0.02
Madhavan, 2012 (26)	Germany	Plasma	133	I-IV	qRT-PCR	SC	Lower quartile	OS	miR-39	90/133	79/133	miR-200b	<0.05
												miR-210
Quesne, 2012 (27)	UK	Tissues		I-IV	ISH	R	Risk scores	OS	Cy3	Ductal tumor	miR-205	0.02
										Luminal tumor	let-7b	< 0.01
Toyama, 2012 (42)	Japan	Tissues	58		qRT-PCR	SC	Median	OS/RFS	RNU6B	0/58	0/58	miR-210	<0.05
Tang, 2012 (32)	China	Tissues	105	I-III	qRT-PCR	R	Risk scores	OS/RFS		63/105	48/105	miR-1258	<0.05
Volinia, 2012 (40)	Italy	Tissues	147			R	Median	OS				miR-21	<0.01
		Tissues	147		qRT-PCR	R	1.5-fold change	OS				miR-210	0.01
												miR-221	0.02
												miR-652	0.01
Cheng, 2012 (20)	Taiwan	Tissues	216	I-IV	qRT-PCR	R	Two-fold	OS/RFS	RNU6B	99/216	98/216	miR-30a	<0.05
Tang, 2012 (19)	China	Tissues	102	I-IV	ISH	R	Risk scores	OS/DFS		32/102	17/102	miR-27a	<0.01
						R						miR-107
						R						miR-103a-3p
Lei, 2013 (46)	China	Tissues	38		qRT-PCR	SC	Mean	RFS/MFS	RNU6			miR-182	<0.05
Li, 2013 (28)	China	Tissues	128	I-III	qRT-PCR	R	Median	OS	RNU6	74/128	85/128	miR-206	0.02
Hoppe, 2013 (17)	Germany	Tissues	81	I-IV	qRT-PCR	R	Median	RFS	miR-16/23a/24	81/81	71/81	miR-10a	0.01
						R	Median	RFS				miR-126	0.04
Falkenberg, 2013 (29)	Germany	Tissues	86		qRT-PCR	R	Multivariate	MFS	RNU43/RNU44	22/86	NR	miR-221/222	0.03
Wang, 2013 (31)	China	Tissues	128	I-III	qRT-PCR	R	Median	OS/DFS	RNU6	77/128	99/128	miR-497	<0.01
Markou, 2013 (39)	Greece	Tissues	112		qRT-PCR	R	Median	OS/RFS	miR-191	53/99	52/99	miR-21	0.01
					qRT-PCR	R	Mean	OS/RFS				miR-205	<0.05
Zhou, 2014 (48)	China	Tissues	144	I-III	qRT-PCR	R	Mean	DSS	RNU6	35/144	85/144	miR-215	0.01
Gasparini, 2014(51)	USA	Tissues	160	I-III	qRT-PCR	R	Median	OS	RNU6B	TNBC	miR-155 et al	<0.01
Nygren, 2014 (44)	Norway	Tissues	142		qRT-PCR	R		OS/MFS	RNU6B			miR-29c	<0.01
Luis, 2014 (52)	England	Tissues	75		qRT-PCR	R		RFS	RNU6B/miR-16	37/71	NR	miR-149 et al	0.02
Kong, 2014 (9)	USA	Tissues	231		qRT-PCR/ISH	R	Median/risk scores	OS	RNU6			miR-155	<0.01
Ma, 2014 (15)	China	Tissues	80	I-IV	ISH	DE	Median/risk scores	OS and RFS	/	33/80	32/80	let-7b	0.03
Gwak, 2014 (16)	Korea	Tissues	129	I-IV	qRT-PCR	R	Median	DFS	RNU6	73/129	61/129	miR-9	0.04
			166	I-IV	qRT-PCR	R	Median	DFS	RNU6			miR-9	0.02
Parrella, 2014 (18)	Italy	Tissues	101	I-IV	qRT-PCR	R	Median	OS/RFS/MFS	RNU48	63/101	51/101	miR-10b	<0.01
Shen, 2014 (47)	China	Tissues	99		qRT-PCR	R	ROC curve	OS/MFS	RNU6	0/99	0/99	miR-27b-3p	0.04
												miR-107	0.18
												miR-103a-3p	0.52
Yang, 2014 (30)	China	Tissues	130	I-IV	ISH	R	Risk scores	OS		69/130	55/130	miR 320a	<0.05
Dong, 2014 (33)	China	Tissues	72		qRT-PCR	R	1.5 fold	OS/DFS	RNU6	TNBC	miR-21	0.03
Muller, 2014 (38)	Germany	Serum	127	I-IV	qRT-PCR	SC	Median	OS	miR-16/miR-484	HER-positive	miR-21	0.01
Muller, 2014 (38)	Germany	Serum	126	I-IV	qRT-PCR	SC	Median	OS	miR-16/miR-484	HER-positive	miR-21	0.04

BCSS = breast cancer specific survival; DE = data extrapolated; DFS = disease-free survival; ER = estrogen receptor; HER = human epidermal growth factor; ISH = in situ hybridization; MFS = metastasis-free survival; NR = not report; OS = overall survival; PR = progesterone receptor; qRT-PCR = quantitative real-time polymerase chain reaction; R = report; RFS = recurrence-free survival; ROC = receiver-operating characteristic; SC = survival curve; TNBC = triple-negative breast cancer.

Characteristics of the Reviewed Studies

Among the 41 selected articles, there were 27 types of miRNAs within 55 study sets including a total of 5,507 patients, which were found to be prognostic biomarkers for breast cancer survival. The median study size was 113 patients (interquartile range [IQR] = 92-147 patients). Most studies (49/55) showed significant associations (p<0.05) between miRNA expression and survival of patients with breast cancer. Two studies used classifier information from more than 1 miRNA (51, 52), and another 39 studies used only a single microRNA for outcome analysis. Most studies analyzed breast cancer with frozen or formalin-fixed paraffin-embedded (FFPE) tumor tissues (49 studies), while 4 studies used blood samples and 2 studies used bone marrow. The quantitative real-time polymerase chain reaction (qRT-PCR) assay, which is widely used as the gold standard for validating microarray expression, was used in 45 studies, whereas in situ hybridization (ISH) assay was used in only 10 studies. Normalization was performed with endogenous controls in almost all studies. The most commonly used endogenous controls were U6 and U6B.

Most of the protective miRNAs (e.g., the let-7 family) were down-regulated (15), while the risky miRNAs (e.g., miR-21, miR-210 and miR-155) (24, 38, 39) were up-regulated in breast cancer and indicated poorer clinical outcomes in breast cancer patients. Moreover, up-regulated miRNAs were more common than down-regulated ones. We deduced that there may be a greater statistical ability to detect the up-regulated miRNAs as poorer prognostic biomarkers in breast cancer survival. All of the adjusted HRs (unless only an unadjusted value was available) revealed that the dysregulation of miRNAs in 6 studies was not statistically significantly associated with survival or recurrence (Tab. I). For all of the reported HRs, regardless of statistical significance in each study, the median HR was 1.31 (IQR = 0.51-2.80) and the overlapping of 1 indicated the variety role of miRNA classifiers in breast cancer. The ratio of combinations for high expression of miRNAs yielded a pooled HR of 1.07 (95% CI, 1.02-1.12; p<0.05; Fig. 2), with a prominent heterogeneity observed (p<0.01 or I² = 90.1%). Because high expression levels of miRNAs were shown to be only a weak prognostic factor in breast cancer, subgroup analysis was completed to validate which type of miRNA underlying the studies had a better prognostic role than the others.

Fig. 2

Forest plot for the association between high miRNA expression (specific miRNA indicated in parentheses next to author names and dates) and survival (overall survival [OS] and disease-free survival, recurrence-free survival or metastasis-free survival [DFS/RFS/MFS]) of patients with breast cancer. CI = confidence interval; HR = hazard ratio.

Meta-analysis Results of Specific miRNAs Associated with Prognosis

We extracted the data for the miRNA profiles from the 41 studies available and counted how many times each miRNA had been selected for survival analysis (Tab. II). A total of 27 prognostic miRNAs were identified in these classifiers, among which 6 miRNAs appeared more frequently, including miR-21 (11 times, 20.0%), miR-210 (6 times, 10.9%) and miR-10, miR-155, miR-27 and miR-30 (each 3 times, 5.5%). We performed a meta-analysis to identify whether miR-21 and miR-210 could be used as reliable prognostic biomarkers. Here we pooled the HR of miR-21 and miR-210 to look at the prognosis for breast cancer patients.

Table II

Statistical analysis of the defined miRNAs in this review, regarding how many times each miRNA was selected for survival analysis

Types	Counts
let-7b	2 (3.6%)
miR-103	2 (3.6%)
miR-107	2(3.6%)
miR-10	3 (5.5%)
miR-1258	1 (1.8%)
miR-126	1 (1.8%)
miR-133	1 (1.8%)
miR-155	3 (5.5%)
miR-181	1 (1.8%)
miR-182	1 (1.8%)
miR-187	2 (3.6%)
miR200	1 (1.8%)
miR-205	2 (3.6%)
miR-206	1 (1.8%)
miR-21	11 (20.0%)
miR-210	6 (10.9%)
miR-215	1 (1.8%)
miR-221	2 (3.6%)
miR-27	3 (5.5%)
miR-29	1 (1.8%)
miR-30	3 (5.5%)
miR 320	1 (1.8%)
miR-339	1 (1.8%)
miR-34	2 (3.6%)
miR-497	1 (1.8%)
miR-652	1 (1.8%)
miR-9	2 (3.6%)

For miR-21, as shown in Table I, most studies analyzed breast cancer tissues samples (8 studies), and 3 studied serum sample. qRT-PCR was the only assay used in the 11 studies of miR-21. For each study, the p value varied from 0.99 to less than 0.01, and 3 studies had p values of more than 0.05, which were thus not statistically significant. We found that higher expression levels of miR-21 significantly predicted poorer outcomes, with the median HR in the reported studies of 2.32 (IQR = 1.04-3.40). There was heterogeneity among the studies for miR-21 evaluating OS (p<0.05, I² = 78.9%) (Fig. 3A). Hence, a random-effects model was applied to calculate a pooled HR and its 95% CI. We found that higher expression levels of miR-21 significantly predicted poorer OS, with a pooled HR of 1.46 (95% CI, 1.25-1.70; p<0.05).

Fig. 3

Forest plot for the association between high miR-21 expression and overall survival (OS) (A) and disease-free survival or recurrence-free survival (DFS/RFS) (B) for patients with breast cancer. CI = confidence interval; HR = hazard ratio.

To reduce the influence of heterogeneity, we reorganized the subgroup analysis according to ethnicity, internal controls, cutoff value, pathological types and material (Tab. III). In the ethnicity subgroup analysis, heterogeneity was considerably reduced between the Asian (p = 0.08, I² = 55.7%) and Western groups (p = 0.17, I² = 38.0%). Meanwhile, the pooled HR of elevated miR-21 of Asian cancer patients (HR = 4.08, 95% CI, 2.66-6.24; p<0.01) was higher than that of Western patients (HR = 1.33, 95% CI, 1.11-1.59; p<0.01). In the internal controls subgroup analysis, heterogeneity was not reduced between U6 and other controls (Tab. III). In the cutoff subgroup analysis, mean (HR = 7.38, 95% CI, 3.75-14.53; p<0.01) and fixed (HR = 2.76, 95% CI, 1.60-4.78; p<0.01) cutoff values were significantly associated with OS under a fixed-effects model because the heterogeneity in both cases was under 50%. OS was not significantly reduced in the median cutoff group. In the type subgroup analysis, overexpression of miR-21 was predictive of worse OS (HR = 4.23, 95% CI, 1.81-9.90; p<0.01) in Her2+ group patients by fixed-effects model with no heterogeneity among studies (p = 0.62, I² = 0.0%). A similar result was found in the triple-negative group but not in the mixed type of breast cancer patients. In the material subgroup analysis, heterogeneity was eliminated in the blood/bone marrow group (p = 0.87, I² = 0.0%) but not in the tumor tissue group (p<0.01, I² = 82.1%).

Table III

Subgroup analyses of overall survival with elevated miR-21 expression

Subgroup analysis	Data sets, no.	Number of patients	Model	HR (95% CI)	p Value	Heterogeneity (I², p value)
All	11	1,488	Random	1.46 (1.25-1.70)	<0.01	78.9%, <0.01
Ethnicity
Asian	4	585	Fixed	4.08 (2.66-6.24)	<0.01	55.7%, 0.08
Western	7	903	Fixed	1.33 (1.11-1.59)	<0.01	38.0%, 0.17
Internal controls
Reported U6	8	1,088	Random	1.52 (1.29,1.78)	<0.01	77.6%, <0.01
Others	3	400	Random	0.99 (0.59,1.67)	0.98	85.5%, <0.01
Cut value
Mean	2	222	Fixed	7.38 (3.75-14.53)	<0.01	38.8%, 0.20
Median	7	903	Random	1.25 (1.06-1.48)	0.01	60.0%, 0.02
Fixed value	2	363	Fixed	2.76 (1.60-4.78)	<0.01	0.0%, 0.62
Pathological type
Mixed	7	1,114	Random	1.37 (1.17-1.61)	<0.01	84.6%, <0.01
Her2+	2	253	Fixed	4.23 (1.81-9.90)	<0.01	0.0%, 0.62
Triple-negative	2	121	Fixed	2.23 (1.31-3.76)	<0.01	0.0%, 0.69
Material
Tissue	8	944	Random	1.38 (1.18-1.62)	<0.01	82.1%, <0.01
Blood/bone marrow	3	544	Fixed	3.76 (1.92-7.37)	<0.01	0.0%, 0.87

CI = confidence interval; HR = hazard ratio.

For the study evaluating miR-21 in DFS/RFS of breast cancer (Fig. 3B), a fixed-effects model was applied, because there was no heterogeneity among the cohorts (p = 0.11, I² = 44.5%). The expression of miR-21 was also significantly correlated with DFS/RFS of breast cancer, with a combined HR of 1.49 (95% CI: 1.17-1.90, p<0.01). Finally, publication bias of the included studies for OS and DFS/RFS was evaluated by funnel plots and Egger's test, respectively. The p value of Egger's test for OS was 0.10 and DFS/RFS was 0.38, suggesting that no significant publication bias existed (Fig. 4A, B).

Fig. 4

Funnel plot of high miR-21 expression on the overall survival (OS) (A) and disease-free survival or recurrence-free survival (DFS/RFS) (B) of patients with breast cancer.

We also found that higher expression levels of miR-210 significantly predicted poorer outcomes, with the median HR in the studies that reported statistically significant results being 4.07 (IQR = 1.54-4.43) and a pooled HR of 2.94 (95% CI, 2.08-4.18; p<0.01), though a significant degree of heterogeneity was observed (p<0.01, I² = 77.4%) (Fig. 5A). This heterogeneity was eliminated when the study by Madhavan et al (26) was excluded. The pooled HR was 3.69 (95% CI, 2.57-5.30; p<0.05) after eliminating the heterogeneity (Fig. 5B). Finally, publication bias of the included studies for OS/RFS/DFS was evaluated by funnel plots (Fig. 6A, B) and Egger's tests. The p value of Egger's test was 0.69 (before elimination of heterogeneity) and 0.48 (after elimination of heterogeneity), suggesting no significant publication bias existed.

Fig. 5

Forest plot of miR-210 expression and survival among breast cancer patients before (A) and after (B) elimination of heterogeneity.

Fig. 6

Funnel plot of miR-210 expression and survival among breast cancer patients before (A) and after (B) elimination of heterogeneity.

Discussion

Previous reviews have shed light on the importance of associations between miRNAs and various cancers, and some studies have discussed the role of miRNAs in head and neck squamous cell carcinoma, carcinomas of the digestion system, non-small cell lung cancer and colorectal cancer (55, 56). An increasing number of studies have also demonstrated that aberrant expression of miRNAs is associated with prognosis for breast cancer, especially for invasive breast cancer (25). MiRNAs are key players in a wide array of pathological processes, which may partially explain their prognostic associations in breast cancer (42). However, the prognostic role of miRNAs in breast cancer is still a puzzle. Here we gathered more complete literature information and pooled the prognostic outcomes as compared with miRNA expression.

Both miR-21 and miR-210 were found to be highly expressed in multiple kinds of cancerous tissues including lung, pancreas, prostate, stomach, liver, colorectal and ovary cancers (57-59). Recent studies have shown that overexpression of miR-21 increases cell proliferation, migration, invasion and survival in a variety of cancer cell lines (60, 61). The oncogenic role of miR-21 has been shown by targeting several tumor suppressor genes including programmed cell death 4 (62), epithelial-mesenchymal transition breast cancer stem cell phenotypes (16) and extracellular matrix metalloproteinase inducer (15). The biological function described above may affect the relationship between miR-21 expression and cancer outcome. Meanwhile, up-regulation of miR-21 has also been observed in breast cancer tissues compared with nontumor tissues (63), and serum miR-21 was found to play a role in the pathogenesis of tumor (64), providing a clinical utility for miR-21 as an important potential diagnostic biomarker for breast cancer. MiR-210, which is up-regulated in both normal and transformed hypoxic cells has a functional role during tumor initiation (65). Tumors with high miR-210 expression may have a growth advantage via reduced mitochondrial respiration in a hypoxic microenvironment (65). Targets of miR-210 are involved in many processes, including cell proliferation, DNA repair, chromatin remodeling, metabolism and cell migration (8). But the potential diagnostic value of miR-210 in breast cancer has rarely been well elucidated.

In this study, we identified specific miRNAs that appeared repeatedly among the selected classifiers. Most of the protective miRNAs (e.g., the let-7 family) were down-regulated, while the risky miRNAs (e.g., miR-21, miR-210 and miR-155) were up-regulated in breast cancer. Two miRNAs (miR-21 and miR-210) appeared more frequently and accounted for 20.0% and 10.9% of the total miRNAs. By using a meta-analytical approach, we demonstrated that increased expression of miR-21 significantly predicted poorer OS and DFS/RFS, although a significant degree of heterogeneity was observed in the OS group. The results of the ethnicity subgroup analysis indicated that increased miR-21 expression significantly predicted low OS in Asians, and the heterogeneity was considerably reduced. Positive results (HR >2) were obtained in Her2+/ triple-negative type of breast cancer patients, the mean/fixed cutoff value and in the blood/bone marrow group, while subgroup analysis stratified by cutoff value, pathological type and source of material of breast cancer. However, due to limited numbers (n<3) of triple-negative/Her2+ studies, mean cutoff value studies and blood source studies, the results must be interpreted with caution. We also concluded that the pooled HR of miR-210 was higher than that of miR-21 in breast cancer survival. The pooled HR of miR-210 was 2.95 and increased to 3.69 when the study by Madhavan et al (26) was eliminated, suggesting that miR-210 levels have a higher statistical power to detect differences and are likely to be a stronger prognostic biomarker in breast cancer survival.

Nevertheless, the present meta-analysis had a number of limitations and needs to be refined for several reasons. Above all, empirically, HR of less than 1.5 is considered to be a weak prognostic factor (66). The pooled risks of miR-21 for survival outcomes, although statistically significant, were not strong, with an HR value for OS of 1.46 and for DFS/RFS of 1.49. Secondly, a significant degree of heterogeneity was observed among these studies. The heterogeneity was not eliminated after reorganizing subgroup analysis according internal controls, cutoff value, pathological types and source of material from breast cancer patients. Therefore, other factors such as age, sex, pathology and potentially, still other features, might have affected the reliability of the results. Meanwhile, some studies only reported unadjusted HR, which may have led to an overestimation for survival outcomes. The elevated expression of miRNA in serum may provide additional information regarding the host response and prognosis. Few studies detected miRNA expression in serum, and a positive result was obtained in the blood/bone marrow group (HR = 3.76, 95% CI, 1.92-7.37; p<0.01). Therefore, researchers should simultaneously investigate the prognostic value of circulating miRNA levels in breast cancer patients.

Conclusions

In conclusion, this study reviewed the complete literature regarding miRNAs and survival rates for breast cancer and found that high expression of miR-21 and miR-210 was significantly associated with poor survival in patients with breast cancer. However, it is necessary to collect large samples of data, perform stratified analyses and gather data from additional ethnicities to clarify the prognostic role of miR-21 and other miRNAs in breast cancer.

Footnotes

Financial support: This work is funded by National Natural Science Foundation of China (81472920; 81402518), Jiangsu Provincial Special Program of Medical Science (BL2012046) and Changzhou Scientific Project (ZD201315, CE20135050, Cy20130017 and CJ20140050).

Conflict of interest: None declared.

References

Lee

Liedke

Barrios

Simon

Finkelstein

Goss

Breast cancer in Brazil: present status and future goals. Lancet Oncol 2012;13(3):e95–e102

Reedijk

Notch signaling and breast cancer. Adv Exp Med Biol 2012;727:241–257

Bleckmann

Leha

Artmann

et al. Integrated miRNA and mRNA profiling of tumor-educated macrophages identifies prognostic subgroups in estrogen receptor-positive breast cancer. Mol Oncol 2015;9(1):155–166

VanderWalde

Hurria

Early breast cancer in the older woman. Clin Geriatr Med 2012;28(1):73–91

Lee

Feinbaum

Ambros

The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993;75(5):843–854

Calin

Croce

MicroRNA-cancer connection: the beginning of a new tale. Cancer Res 2006;66(15):7390–7394

Chen

MicroRNAs as oncogenes and tumor suppressors. N Engl J Med 2005;353(17):1768–1771

Hong

Yang

Han

Cao

Syed

High expression of miR-210 predicts poor survival in patients with breast cancer: a meta-analysis. Gene 2012;507(2):135–138

Kong

Richards

et al. Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer. Oncogene 2014;33(6):679–689

10.

Stroup

Berlin

Morton

et al. Meta-analysis of Observational Studies in Epidemiology: a proposal for reporting. JAMA 2000;283(15):2008–2012

11.

Tierney

Stewart

Ghersi

Burdett

Sydes

Practical methods for incorporating summary time-to-event data into meta analysis. Trials 2007;816

12.

Parmar

Torri

Stewart

Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med 1998;17(24):2815–2834

13.

Williamson

Smith

Hutton

Marson

Aggregate data meta-analysis with time-to-event outcomes. Stat Med 2002;21(22):3337–3351

14.

Begg

Mazumdar

Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50(4):1088–1101

15.

Meng

Prognostic significance of let-7b expression in breast cancer and correlation to its target gene of BSG expression. Med Oncol 2014;31(1):773

16.

Gwak

Kim

et al. MicroRNA-9 is associated with epithelial-mesenchymal transition, breast cancer stem cell phenotype, and tumor progression in breast cancer. Breast Cancer Res Treat 2014;147(1):39–49

17.

Hoppe

Achinger-Kawecka

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. Eur J Cancer 2013;49(17):3598–3608

18.

Parrella

Barbano

Pasculli

et al. Evaluation of microRNA-10b prognostic significance in a prospective cohort of breast cancer patients. Mol Cancer 2014;13(1):142

19.

Tang

Zhu

et al. MiR-27 as a prognostic marker for breast cancer progression and patient survival. PLoS ONE 2012; 7(12):e51702

20.

Cheng

Wang

Chang

et al. MicroRNA-30a inhibits cell migration and invasion by downregulating vimentin expression and is a potential prognostic marker in breast cancer. Breast Cancer Res Treat 2012;134(3):1081–1093

21.

Peurala

Greco

Heikkinen

et al. MiR-34a expression has an effect for lower risk of metastasis and associates with expression patterns predicting clinical outcome in breast cancer. PLoS ONE 2011;6(11):e26122

22.

Svoboda

Sana

Redova

et al. MiR-34b is associated with clinical outcome in triple-negative breast cancer patients. Diagn Pathol 2012;7(1):31

23.

Wang

Xiang

et al. Loss of miR-133a expression associated with poor survival of breast cancer and restoration of miR-133a expression inhibited breast cancer cell growth and invasion. BMC Cancer 2012;12(1):51

24.

Chen

Wang

Tang

Clinical significance of microRNA-155 expression in human breast cancer. J Surg Oncol 2012;106(3):260–266

25.

Mulrane

Madden

Brennan

et al. MiR-187 is an independent prognostic factor in breast cancer and confers increased invasive potential in vitro. Clin Cancer Res 2012;18(24):6702–6713

26.

Madhavan

Zucknick

Wallwiener

et al. Circulating miRNAs as surrogate markers for circulating tumor cells and prognostic markers in metastatic breast cancer. Clin Cancer Res 2012; 18(21):5972–5982

27.

Quesne

Jones

Warren

et al. Biological and prognostic associations of miR-205 and let-7b in breast cancer revealed by in situ hybridization analysis of micro-RNA expression in arrays of archival tumour tissue. J Pathol 2012;227(3):306–314

28.

Hong

Decreased expression of microRNA-206 in breast cancer and its association with disease characteristics and patient survival. J Int Med Res 2013;41(3):596–602

29.

Falkenberg

Anastasov

Rappl

et al. MiR-221/-222 differentiate prognostic groups in advanced breast cancers and influence cell invasion. Br J Cancer 2013;109(10):2714–2723

30.

Yang

Wang

et al. MiR-320a is an independent prognostic biomarker for invasive breast cancer. Oncol Lett 2014;8(3) 1043–1050

31.

Wang

Expression of microRNA-497 and its prognostic significance in human breast cancer. Diagn Pathol 2013;8(1):172

32.

Tang

Zhang

Zhao

et al. The expression and clinical significance of microRNA-1258 and heparanase in human breast cancer. Clin Biochem 2013;4610-11:926–932

33.

Dong

Liang

Wang

et al. High expression of miR-21 in triple-negative breast cancers was correlated with a poor prognosis and promoted tumor cell in vitro proliferation. Med Oncol 2014;31(7):57

34.

Lee

Kim

Bae

Prognostic implications of microRNA-21 overexpression in invasive ductal carcinomas of the breast. J Breast Cancer 2011;14(4):269–275

35.

Walter

Gómez-Macias

Valera

Sobel

Merino

MiR-21 Expression in pregnancy-associated breast cancer: a possible marker of poor prognosis. J Cancer 2011;2:67–75

36.

Qian

Katsaros

et al. High miR-21 expression in breast cancer associated with poor disease-free survival in early stage disease and high TGF-beta1. Breast Cancer Res Treat 2009; 117(1):131–140

37.

Yan

Huang

Shao

et al. MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis. RNA 2008;14(11):2348–2360

38.

Müller

Gade

Steinbach

et al. Changes in serum levels of miR-21, miR-210, and miR-373 in HER2-positive breast cancer patients undergoing neoadjuvant therapy: a translational research project within the Geparquinto trial. Breast Cancer Res Treat 2014;147(1):61–68

39.

Markou

Yousef

Stathopoulos

Georgoulias

Lianidou

Prognostic significance of metastasis-related microRNAs in early breast cancer patients with a long follow-up. Clin Chem 2014;60(1):197–205

40.

Volinia

Galasso

Sana

et al. Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microRNA. Proc Natl Acad Sci USA 2012;109(8):3024–3029

41.

Rothé

Ignatiadis

Chaboteaux

et al. Global microRNA expression profiling identifies MiR-210 associated with tumor proliferation, invasion and poor clinical outcome in breast cancer. PLoS ONE 2011;6(6):e20980

42.

Toyama

Kondo

Endo

et al. High expression of microRNA-210 is an independent factor indicating a poor prognosis in Japanese triple-negative breast cancer patients. Jpn J Clin Oncol 2012;42(4):256–263

43.

Camps

Buffa

Colella

et al. hsa-miR-210 is induced by hypoxia and is an independent prognostic factor in breast cancer. Clin Cancer Res 2008;14(5):1340–1348

44.

Nygren

Tekle

Ingebrigtsen

et al. Identifying microRNAs regulating B7-H3 in breast cancer: the clinical impact of microRNA-29c. Br J Cancer 2014;110(8):2072–2080

45.

Marino

Evangelista

Vieira

et al. MicroRNA expression as risk biomarker of breast cancer metastasis: a pilot retrospective case-cohort study. BMC Cancer 2014;14:739

46.

Lei

Tang

Zhuang

et al. Suppression of MIM by microRNA-182 activates RhoA and promotes breast cancer metastasis. Oncogene 2014;33(10):1287–1296

47.

Shen

Sun

Liang

et al. A prognostic model of triple-negative breast cancer based on miR-27b-3p and node status. PLoS ONE 2014;9(6):e100664

48.

Zhou

et al. Aberrant miR-215 expression is associated with clinical outcome in breast cancer patients. Med Oncol 2014;31(11):259

49.

Radojicic

Zaravinos

Vrekoussis

Kafousi

Spandidos

Stathopoulos

MicroRNA expression analysis in triple-negative (ER, PR and Her2/neu) breast cancer. Cell Cycle 2011; 10(3):507–517

50.

Ota

Mimori

Yokobori

et al. Identification of recurrence-related microRNAs in the bone marrow of breast cancer patients. Int J Oncol 2011;38(4):955–962

51.

Gasparini

Cascione

Fassan

et al. MicroRNA expression profiling identifies a four microRNA signature as a novel diagnostic and prognostic biomarker in triple negative breast cancers. Oncotarget 2014;5(5):1174–1184

52.

Pérez-Rivas

Jerez

Carmona

et al. A microRNA signature associated with early recurrence in breast cancer. PLoS ONE 2014;9(3):e91884

53.

Foekens

Sieuwerts

Smid

et al. Four miRNAs associated with aggressiveness of lymph node-negative, estrogen receptor-positive human breast cancer. Proc Natl Acad Sci USA 2008;105(35):13021–13026

54.

Wang

et al. MiR-339-5p inhibits breast cancer cell migration and invasion in vitro and may be a potential biomarker for breast cancer prognosis. BMC Cancer 2010; 10(1):542

55.

Han

et al. Prognostic role of microRNA-21 in various carcinomas: a systematic review and meta-analysis. Eur J Clin Invest 2011;41(11):1245–1253

56.

Xia

Yang

Zhai

et al. Prognostic role of microRNA-21 in colorectal cancer: a meta-analysis. PLoS ONE 2013;8(11):e80426

57.

Krichevsky

Gabriely

MiR-21: a small multi-faceted RNA. J Cell Mol Med 2009;13(1):39–53

58.

Volinia

Calin

Liu

et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 2006;103(7):2257–2261

59.

Jung

Santarpia

Kim

et al. Plasma microRNA 210 levels correlate with sensitivity to trastuzumab and tumor presence in breast cancer patients. Cancer 2012;118(10):2603–2614

60.

Liu

Stribinskis

et al. MicroRNA-21 promotes cell transformation by targeting the programmed cell death 4 gene. Oncogene 2008;27(31):4373–4379

61.

Yang

Yue

Pfeffer

Handorf

Pfeffer

MicroRNA miR-21 regulates the metastatic behavior of B16 melanoma cells. J Biol Chem 2011;286(45):39172–39178

62.

Frankel

Christoffersen

Jacobsen

Lindow

Krogh

Lund

Programmed cell death 4 (PDCD4) is an important functional target of the microRNA miR-21 in breast cancer cells. J Biol Chem 2008;283(2):1026–1033

63.

Zhu

Chen

Zhao

Tang

Searching for candidate microRNA biomarkers in detection of breast cancer: a meta-analysis. Cancer Biomark 2013;13(5):395–401

64.

Yang

Zhen

Zhang

Serum microRNA-21 as a potential diagnostic biomarker for breast cancer: a systematic review and meta-analysis. Clin Exp Med 2014;

65.

Quero

Dubois

Lieuwes

Hennequin

Lambin

MiR-210 as a marker of chronic hypoxia, but not a therapeutic target in prostate cancer. Radiother Oncol 2011;101(1):203–208

66.

Hayes

Isaacs

Stearns

Prognostic factors in breast cancer: current and new predictors of metastasis. J Mammary Gland Biol Neoplasia 2001;6(4):375–392