Disease Markers
Volume 2018 (2018), Article ID 6071826, 16 pages
https://doi.org/10.1155/2018/6071826

Research Article

Correlation between miR-200 Family Overexpression and Cancer Prognosis

Wen Liu,¹ Kaiping Zhang,² Pengfei Wei,³ Yue Hu,¹ Yaqin Peng,¹ Xiang Fang,² Guoping He,¹ Limin Wu,¹ Min Chao,² and Jing Wang¹

¹Prenatal Diagnostic Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Hospital, Hefei, Anhui, China
²Department of Urology, Anhui Provincial Children’s Hospital and Children’s Hospital of Anhui Medical University, Hefei, Anhui, China
³Hefei National Laboratory for Physical Sciences at Microscale, CAS Key Laboratory of Innate Immunity and Chronic Disease, Innovation Center for Cell Signaling Network, School of Life Sciences and Medical Center, University of Science and Technology of China, Hefei, Anhui, China

Correspondence should be addressed to Min Chao; cm0654@sina.com and Jing Wang; ahwangjing1968@126.com

Received 5 February 2018; Accepted 11 April 2018; Published 4 July 2018

Academic Editor: Gad Rennert

Copyright © 2018 Wen Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The correlation between miR-200 family overexpression and cancer prognosis remains controversial. Therefore, we conducted a systematic review and meta-analysis by searching PubMed, Embase, Cochrane Library, China Biology Medicine disc (CBM), and China National Knowledge Infrastructure (CNKI) to identify eligible studies. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to evaluate the strength of the correlations. Additionally, different subgroup analyses and publication bias test were performed. Eventually, we analyzed 23 articles that included five tumor types and 3038 patients. Consequently, high expression of miR-200 family in various tumors was associated with unfavorable overall survival (OS) in both univariate (, 95% CI: 1.14–1.54, ) and multivariate (, 95% CI: 1.16–1.49, ) analyses. Likewise, a similar result was found in different subgroups of the patient source, cancer type, test method, sample source, miR-200 component, and sample size. However, no association of miR-200 family was detected with recurrence- or relapse-free survival (RFS) (univariate: , 95% CI: 0.96–1.09, ; multivariate: , 95% CI: 1.00–1.14, ), progression-free survival (PFS) (univariate: , 95% CI: 0.54–1.70, ; multivariate: , 95% CI: 0.86–1.61, ), and disease-free survival (DFS) (univariate: , 95% CI: 0.74–1.09, ; multivariate: , 95% CI: 0.68–1.41, ). Our findings have provided convincing evidence that miR-200 family overexpression suggested poor prognosis of various cancer types, which efforts may raise the potential use of miR-200 family for cancer prognosis in clinical practice.

1. Introduction

MicroRNAs (miRNAs) are evolutionarily conserved, endogenous small noncoding, and single-stranded RNAs of 18–22 nucleotides in length. They often negatively regulate gene targets by translational inhibition or mRNA degradation [1, 2]. It has been revealed that the posttranscriptional regulation could influence various biological processes including apoptosis, differentiation, proliferation, stress response, and metabolism [3, 4]. miRNAs could also be able to predict cancer prognosis due to their crucial roles in cancer progression and metastasis. Previous studies have explored that deregulated miRNAs with aberrant expression levels were closely correlated with cancer prognosis and even could be a novel kind of biomarkers for various cancer types [5, 6].

Interestingly, the miR-200 family is a typical and most extensively studied example in functional miRNAs. The miR-200 family, composed of five miRNA sequences (miR-141, miR-200a, miR-200b, miR-200c, and miR-429) and located in two clusters in the genome, is involved in the epithelial to mesenchymal transition (EMT) through regulation of E-cadherin expression via suppression of ZEB1 and ZEB2 [7, 8]. Recent studies have reported that miR-200 cluster is overexpressed in different tumors and played a critical role in mRNA degradation or inhibition through targeted binding to the relevant 3-untranslated region (UTR) [9]. miR-200 family has been shown to offer a great potential in both cancer diagnosis and prognosis. Despite the potential roles of miR-200 family high expression in prognosis for cancer patients that have been attempted, no definite conclusions have been drawn so far. Meta-analysis can explore the authentic and comprehensive results through incorporating all available evidences to get a relatively precise and accurate estimation by using statistical analyses [10]. Thus, we have performed the current meta-analysis to explore the potential associations between miR-200 family and cancer prognosis, which efforts should hold great promise in verifying the potential of miRNAs as biomarkers for evaluating therapeutic efficacy and prognosis of various cancers.

2. Methods

2.1. Ethics Statement

The PRISMA statement was used to conduct the current meta-analysis [11]. No patient’s privacy or clinical samples were involved in this study; hence, the ethical approval was not required.

2.2. Search Strategy

Literature resources including PubMed, Cochrane Library, Embase, CBM, and CNK were introduced to search eligible studies, by using the terms “microRNA OR miRNA OR miR-200 OR miR-141 OR miR-429 OR miR-200 family OR miR-200 cluster,” “survival OR prognosis OR prognostic,” and “cancer OR tumor OR tumour OR neoplasm OR neoplasma OR neoplasia OR carcinoma OR cancers OR tumors OR tumours OR neoplasms OR neoplasmas OR neoplasias OR carcinomas.” Last search of current investigation was updated on November 25th, 2017. Additionally, the publication language was only limited to English and Chinese. In case of omission, we identified the reference lists of the relevant articles and reviewed articles to seek for the potentially relevant studies. Conventionally, we have not contacted the corresponding authors even if the relevant data were unavailable.

2.3. Inclusion and Exclusion Criteria

Studies complied with the following criteria could be identified: (1) clinical study about the association of miR-200 family with cancer prognosis and (2) relevant data of the hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) to evaluate its associations were available. Studies which met the following four criteria were excluded: (1) the available data regarding associations was absent; (2) similar or duplicate study (when the same or similar cohort was applied, after careful examination, the most complete information was included); (3) other types of articles such as reviews or abstracts; and (4) studies involved with cell lines or animal models.

2.4. Data Extraction

In the light of inclusion and exclusion criteria, we extracted the relevant data from each eligible study. If disagreements were noticed, we are clearly open to discussion by each other (Wen Liu and Kaiping Zhang) or reviewed by a third author (Pengfei Wei). The data on first author, publication year, study country, age, cancer type, miRNA category, sample source, sample size, follow-up time, test method, survival outcome, analysis method, (95% CI), and the cut-off value were extracted. We have not contacted any author of the original researches even if the essential information could not be available. Besides, patient sources came from Asia, Europe, and North America. Sample sources were stratified into tissue, blood, formalin-fixed and paraffin-embedded (FFPE), and tissue microarray (TMA). Test methods included TaqMan, in situ hybridization (ISH), and reverse transcription polymerase chain reaction (RT-PCR). Sample sizes were separated into ≥100 and <100. Cancer types included epithelial ovarian cancer (EOC), breast cancer (BC), nonsmall cell lung cancer (NSCLC), gastric cancer (GC), and colorectal cancer (CRC). Analyses methods were divided into univariate analysis and multivariate analysis. Patients’ prognostic outcomes included overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-free survival (DFS).

3. Statistical Analysis

We have explored the association of miR-200 family with cancer prognosis by applying Review Manager software (RevMan 5, The Cochrane Collaboration, Oxford, UK) and Stata software (Version 12.0, Stata Corporation, College Station, TX). and 95% CI were collected for assessing the prognostic value of high expression of miR-200 family in various cancers. Meanwhile, the heterogeneity has been assessed via chi-square-based and ² test across studies (no heterogeneity ² < 25%, moderate heterogeneity ² = 25%–50%, extreme heterogeneity ² > 50%) [12]. In case of extreme heterogeneity (² > 50% or for test), we used random-effects (DerSimonian and Laird method) model [13]. Otherwise, fixed-effects (Mantel-Haenszel method) model was introduced [14]. One-way sensitivity analyses which individually removed publications in meta-analysis were conducted to assess results’ stability. It mainly explores the impact of specific study upon mixed . In Begg’s funnel plots, logHR was plotted against SE. value less than 0.05 indicated that there was a bias of the study [15]. Additionally, different subgroups consisted of patient source, cancer type, test method, sample source, sample size, and miR-200 component were conducted.

4. Results

4.1. Characteristics of the Studies

Consequently, 23 studies consisted of 3038 samples satisfied the eligible criteria [16–38] (Figure 1).

Figure 1: Flow diagram of the study selection process in the meta-analysis.

The principal characteristics of the eligible studies were summarized in Table 1.

Table 1: Main characteristics of the eligible studies.

First author
Year
Country
Age
Cancer type
MicroRNA
Sample size
Follow-up, median (range)
Outcome

Zou J. [16]
2017
China
NA
EOC
miR-429
72
NA
OS/PFS

Han Y. [17]
2017
China
NA
CRC
miR-429
71
34.2
OS

Maierthaler M. [18]
2017
Germany
70 (33–92)
68.0 (36–92)
CRC
miR-200a, miR-200b, miR-200c, miR-141, miR-429
527
NA
OS/RFS

Si L. [19]
2017
China
60.5 (41–78)
NSCLC
miR-200c
110
NA
OS/DFS

Meng X. [20]
2016
Germany
60 (23–91)
EOC
miR-200a, miR-200b, miR-200c
163
20 (1–136)
OS/RFS

Dong S. J. [21]
2016
China
56 (31–79)
CRC
miR-429
116
NA
OS

Antolín S. [22]
2015
Spain
54.8 (29–73)
BC
miR-200c, miR-141
57
74.6 (74.2–77.7)
OS/PFS

Gao Y. C. [23]
2015
China
NA
EOC
miR-200c, miR-141
93
NA
OS

Lu Y. B. [24]
2015
China
NA
GC
miR-141
95
NA
OS

Liu J. Y. [25]
2015
China
57.48
EOC
miR-200a
44
26 (5–49)
OS/PFS

Cao Q. [26]
2014
China
58 (26–88)
EOC
miR-200a, miR-200b, miR-200c
100
36.8 (6–56)
OS

Kim M. K. [27]
2014
Korea
64 (26–77)
NSCLC
miR-200c
72
31 (1–135)
OS

Zhu W. [28]
2014
China
59
NSCLC
miR-429
70
NA
OS

Song F. [29]
2014
China
60.5
GC
miR-200a, miR-200b, miR-200c
385
35 (1–112)
OS/PFS

Tejero R. [30]
2014
Spain
65 (35–85)
NSCLC
miR-200c/141
155
43 (2–160)
OS

Toiyama Y. [31]
2014
Japan
67
CRC
miR-200c
182
NA
OS

Sun Q. [32]
2014
China
NA
EOC
miR-200a
53
56.79 (11–98)
OS

Liu X. G. [33]
2012
China
NA
NSCLC
miR-200c, miR-141
70
24
OS

Chao A. [34]
2012
China
NA
EOC
miR-200a
176
40 (3–109)
OS/RFS

Marchini S. [35]
2011
Italy
52 (21–82)
EOC
miR-200b, miR-200c
144
110.4 (82.8–139.2)
OS/PFS

Cheng H. [36]
2011
USA
NA
CRC
miR-141
156
NA
OS

Leskelä S. [37]
2010
Spain
57 (35–85)
EOC
miR-200a, miR-200b, miR-200c, miR-141, miR-429
72
NA
OS/PFS/RFS

Hu X. [38]
2009
USA
58.3
EOC
miR-200a
55
NA
OS/PFS

NA: not available; EOC: epithelial ovarian cancer; BC: breast cancer; NSCLC: nonsmall cell lung cancer; GC: gastric cancer; CRC: colorectal cancer; OS: overall survival; DFS: disease-free survival; PFS: progression-free survival; RFS: recurrence- or relapse-free survival; : hazard ratio; CI: confidence interval.

Among these studies, Cheng’s study was involved with three different cohorts of Tianjin cohort, TexGen cohort, and all cohort [36]. Zhu et al. designed a study to detect tissue and serum miRNA expression [28]. Tejero et al. analyzed the role of members of the miR-200 family from NSCLC patients after surgery both in the entire cohort and adenocarcinoma cohort [30]. Maierthaler et al. explore miRNA expression in two different cohorts of nonmetastatic and metastatic CRC [18]. Toiyama et al. conducted a study to detect the prognostic value of the miR-200 family in CRC from blood and FFPE samples. As mentioned above, we treated them independently into meta-analysis [31]. Eventually, this meta-analysis was established based on 29 studies (Table 2). Among these 29 studies, 28 were written in English while one was published in Chinese. The sample sizes ranged from 44 to 527. The cancer types contained ten EOC, one BC, seven NSCLC, two GC, and nine CRC. Meanwhile, one ISH, 24 RT-PCR, and four TaqMan in test methods were applied. According to the sample sources, there were seven FFPE, ten tissue, ten blood, and two TMA. For the survival outcomes, 29 eligible studies were divided into 42 datasets: 29 for OS, six for PFS, five for RFS, and two for DFS. However, the cut-off value for the miR-200 family was inconsistent among these included studies (Table 2).

Table 2: MicroRNA evaluation and survival data of the selected studies.

First author
Year
Country
Test method
Cancer type
MicroRNA
Sample source
Outcome
(95% CI)
Cut-off value

Zou J.
2017
China
RT-PCR
EOC
miR-429
Tissue
OS
(U) 0.641 (0.412–0.996)/(M) 0.763 (0.458–1.270)
>0.532

Zou J.
2017
China
RT-PCR
EOC
miR-429
Tissue
PFS
(U) 0.661 (0.478–0.915)/(M) 0.710 (0.504–1.001)

Han Y.
2017
China
RT-PCR
CRC
miR-429
Tissue
OS
(M) 1.852 (1.019–3.326)
Median

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-200a
Blood
OS
(U) 0.929 (0.707–1.211)/(M) 1.053 (0.791–1.401)
Median

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-200b
Blood
OS
(U) 0.704 (0.524–0.945)/(M) 0.772 (0.570–1.045)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-200c
Blood
OS
(U) 0.808 (0.646–1.010)/(M) 0.840 (0.659–1.070)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-141
Blood
OS
(U) 0.925 (0.713–1.200)/(M) 1.038 (0.785–1.374)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-429
Blood
OS
(U) 0.951 (0.734–1.235)/(M) 0.968 (0.721–1.300)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-200a
Blood
OS
(U) 1.198 (0.986–1.456)/(M) 1.227 (1.008–1.495)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-200b
Blood
OS
(U) 1.172 (0.946–1.453)/(M) 1.208 (0.975–1.497)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-200c
Blood
OS
(U) 1.117 (0.947–1.318)/(M) 1.152 (0.975–1.362)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-141
Blood
OS
(U) 1.071 (0.877–1.305)/(M) 1.105 (0.904–1.350)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-429
Blood
OS
(U) 1.010 (0.853–1.196)/(M) 1.006 (0.845–1.198)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-200a
Blood
RFS
(U) 0.929 (0.718–1.203)/(M) 1.031 (0.786–1.353)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-200b
Blood
RFS
(U) 0.714 (0.539–0.947)/(M) 0.750 (0.561–1.005)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-200c
Blood
RFS
(U) 0.819 (0.657–1.019)/(M) 0.835 (0.658–1.060)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-141
Blood
RFS
(U) 0.910 (0.705–1.175)/(M) 0.999 (0.760–1.312)

Maierthaler M.-1
2017
Germany
TaqMan
CRC
miR-429
Blood
RFS
(U) 0.954 (0.743–1.227)/(M) 1.076 (0.716–1.618)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-200a
Blood
RFS
(U) 1.175 (0.973–1.420)/(M) 1.200 (0.989–1.456)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-200b
Blood
RFS
(U) 1.109 (0.893–1.377)/(M) 1.143 (0.919–1.422)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-200c
Blood
RFS
(U) 1.076 (0.911–1.272)/(M) 1.100 (0.930–1.302)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-141
Blood
RFS
(U) 1.057 (0.871–1.284)/(M) 1.085 (0.890–1.321)

Maierthaler M.-2
2017
Germany
TaqMan
CRC
miR-429
Blood
RFS
(U) 1.080 (0.916–1.272)/(M) 1.078 (0.910–1.277)

Si L.
2017
China
RT-PCR
NSCLC
miR-200c
Tissue
OS
(M) 2.095 (1.241–3.536)
The 2−ΔΔCq

Si L.
2017
China
RT-PCR
NSCLC
miR-200c
Tissue
DFS
(M) 1.647 (1.049–2.585)

Meng X.
2016
Germany
RT-PCR
EOC
miR-200a
Blood
OS
(U) 1.7 (0.8–3.5)
Median

Meng X.
2016
Germany
RT-PCR
EOC
miR-200b
Blood
OS
(U) 2.7 (1.3–5.7)/(M) 2.8 (1.1–6.8)

Meng X.
2016
Germany
RT-PCR
EOC
miR-200c
Blood
OS
(U) 2.4 (1.2–4.9)/(M) 2.5 (1.1–6.1)

Meng X.
2016
Germany
RT-PCR
EOC
miR-200a
Blood
RFS
(U) 1.1 (0.6–1.9)

Meng X.
2016
Germany
RT-PCR
EOC
miR-200b
Blood
RFS
(U) 1.6 (0.9–2.8)

Meng X
2016
Germany
RT-PCR
EOC
miR-200c
Blood
RFS
(U) 2.0 (1.1–3.6)/(M) 1.7 (0.8–3.6)

Dong S. J.
2016
China
RT-PCR
CRC
miR-429
Tissue
OS
(M) 2.296 (1.105–4.528)
Median

Antolín S.
2015
Spain
RT-PCR
BC
miR-200c
Blood
OS
(U) 1.38 (1.11–1.71)/(M) 2.79 (1.01–7.7)
>1.29 relative expression value

Antolín S.
2015
Spain
RT-PCR
BC
miR-200c
Blood
PFS
(U) 1.37 (1.09–1.71)/(M) 3.33 (1.22–9.07)

Antolín S.
2015
Spain
RT-PCR
BC
miR-141
Blood
OS
(M) 0.986 (0.942–1.032)

Antolín S.
2015
Spain
RT-PCR
BC
miR-141
Blood
PFS
(M) 0.987 (0.95–1.025)

Gao Y. C.
2015
China
RT-PCR
EOC
miR-200c
Blood
OS
(U) 3.14 (1.67–5.93)
−ΔCt method with 95% CI

Gao Y. C.
2015
China
RT-PCR
EOC
miR-141
Blood
OS
(U) 1.83 (1.00–3.33)

Lu Y. B.
2015
China
RT-PCR
GC
miR-141
Tissue
OS
(M) 2.972 (1.297–10.001)
Median

Liu J. Y.
2015
China
RT-PCR
EOC
miR-200a
Tissue
OS
(M) 0.354 (0.149–0.840)
Log 2−ΔΔCt

Liu J. Y.
2015
China
RT-PCR
EOC
miR-200a
Tissue
PFS
(M) 0.395 (0.210–0.742)

Cao Q
2014
China
ISH
EOC
miR-200a
Tissue
OS
(U) 22.69 (1.32–50.53)/(M) 17.26 (1.36–36.98)
Median

Cao Q.
2014
China
ISH
EOC
miR-200b
Tissue
OS
(U) 20.28 (1.20–42.28)/(M)15.41 (1.13–31.36)

Cao Q.
2014
China
ISH
EOC
miR-200c
Tissue
OS
(U) 21.42 (1.26–48.33)/(M) 16.22 (1.27–33.81)

Kim M. K.
2014
Korea
RT-PCR
NSCLC
miR-200c
FFPE
OS
(M) 3.67 (1.17–11.45)
Median

Zhu W.-1
2014
China
RT-PCR
NSCLC
miR-429
Tissue
OS
(U) 1.686 (0.570–4.984)/(M) 2.749 (0.706–10.707)
Mean

Zhu W.-2
2014
China
RT-PCR
NSCLC
miR-429
Blood
OS
(U) 6.458 (1.409–29.593)/(M) 12.875 (2.295–72.23)

Song F.
2014
China
RT-PCR
GC
miR-200a
TMA
OS
(U) 0.82 (0.57–1.20)/(M) 0.72 (0.47–1.13)
Median

Song F.
2014
China
RT-PCR
GC
miR-200b
TMA
OS
(U) 0.87 (0.60–1.26)/(M)0.93 (0.63–1.41)

Song F.
2014
China
RT-PCR
GC
miR-200c
TMA
OS
(U) 1.19 (0.80–1.77)/(M) 1.32 (0.82–2.12)

Song F.
2014
China
RT-PCR
GC
miR-200a
TMA
DFS
(U) 0.81 (0.58–1.14)/(M) 0.67 (0.45–0.99)

Song F.
2014
China
RT-PCR
GC
miR-200b
TMA
DFS
(U) 0.84 (0.60–1.18)/(M) 0.82 (0.56–1.19)

Song F.
2014
China
RT-PCR
GC
miR-200c
TMA
DFS
(U) 1.08 (0.76–1.54)/(M) 1.06 (0.70–1.60)

Tejero R.-1
2014
Spain
TaqMan
NSCLC
miR-200c/141
FFPE
OS
(M) 2.787 (1.087–7.148)
Mean

Tejero R.-2
2014
Spain
TaqMan
NSCLC
miR-200c/141
FFPE
OS
(M) 10.649 (2.433–46.608)

Toiyama Y.-1
2014
Japan
RT-PCR
CRC
miR-200c
Blood
OS
(U) 2.43 (1.26–4.68)/(M)2.67 (1.28–5.67)
Median

Toiyama Y.-2
2014
Japan
RT-PCR
CRC
miR-200c
FFPE
OS
(U) 0.56 (0.28–1.10)

Sun Q.
2014
China
RT-PCR
EOC
miR-200a
TMA
OS
(U) 0.58 (0.08–4.05)
Median (≥12.623)

Liu X. G.
2012
China
RT-PCR
NSCLC
miR-200c
Tissue
OS
(U) 6.020 (1.344–26.971)
2−ΔΔCt > 2.0

Liu X. G.
2012
China
RT-PCR
NSCLC
miR-141
Tissue
OS
(U) 4.135 (0.467–36.597)

Chao A.
2012
China
RT-PCR
EOC
miR-200a
FFPE
OS
(M) 1.466 (0.786–2.734)
Log ratio > 1.3

Chao A.
2012
China
RT-PCR
EOC
miR-200a
FFPE
RFS
(M) 1.213 (0.70–2.101)

Marchini S.
2011
Italy
RT-PCR
EOC
miR-200b
Tissue
OS
(U) 2.137 (0.801–5.701)/(M) 2.051 (0.640–6.570)
>25%

Marchini S.
2011
Italy
RT-PCR
EOC
miR-200b
Tissue
PFS
(U) 3.197 (1.417–7.213)/(M) 2.335 (0.857–6.363)

Marchini S.
2011
Italy
RT-PCR
EOC
miR-200c
Tissue
OS
(U) 0.309 (0.112–0.850)/(M) 0.244 (0.076–0.785)

Marchini S.
2011
Italy
RT-PCR
EOC
miR-200c
Tissue
PFS
(U) 0.392 (0.174–0.885)/(M) 0.419 (0.146–1.204)

Cheng H.-1
2011
USA
RT-PCR
CRC
miR-141
Blood
OS
(U) 3.80 (1.46–9.91)/(M) 1.36 (0.45–4.14)
2−ΔΔCt

Cheng H.-2
2011
USA
RT-PCR
CRC
miR-141
Blood
OS
(U) 4.83 (2.06–11.35)/(M) 3.41 (1.36–8.56)

Cheng H.-3
2011
USA
RT-PCR
CRC
miR-141
Blood
OS
(U) 3.61 (1.96–6.65)/(M) 2.40 (1.18–4.86)

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-200a
FFPE
PFS
(M) 1.22 (0.57–2.58)
75% of positive cells

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-200b
FFPE
PFS
(M) 1.35 (0.62–2.93)

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-200c
FFPE
PFS
(M) 2.24 (1.00–5.03)

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-141
FFPE
PFS
(M) 2.35 (0.98–5.59)

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-429
FFPE
PFS
(M) 2.10 (0.92–4.79)

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-429
FFPE
RFS
(M) 2.01 (1.11–3.66)

Leskelä S.
2010
Spain
RT-PCR
EOC
miR-429
FFPE
OS
(M) 2.08 (1.03–4.20)

Hu X.
2009
USA
RT-PCR
EOC
miR-200a
FFPE
OS
(U) 0.70 (0.03–14.29)
>11

Hu X.
2009
USA
RT-PCR
EOC
miR-200a
FFPE
PFS
(U) 0.64 (0.22–1.81)

EOC: epithelial ovarian cancer; BC: breast cancer; NSCLC: nonsmall cell lung cancer; NMIBC: nonmuscle-invasive bladder cancer; GC: gastric cancer; CRC: colorectal cancer; OS: overall survival; DFS: disease-free survival; PFS: progression-free survival; RFS: recurrence- or relapse-free survival; : hazard ratio; CI: confidence interval; U: univariate analysis; M: multivariate analysis; ISH: in situ hybridization; RT-PCR: reverse transcription-polymerase chain reaction; FFPE: formalin-fixed and paraffin-embedded; TMA: tissue microarray; OS: overall survival; DFS: disease-free survival; PFS, progression-free survival; RFS: recurrence- or relapse-free survival.

4.2. Meta-Analysis of OS

In univariate analysis, 19 studies were involved in current meta-analysis to assess the prognosis of miR-200 family overexpression in various cancers. High expression of miR-200 family was found to be associated with unfavorable OS (, 95% CI: 1.14–1.54, ) (Figure 2(a)). Besides, it indicated that there were certain associations via subanalyses regarding patient source, cancer type, test method, sample source, sample size, and miR-200 component (Table 3).

(a)

(b)

Figure 2: Forest plot of the association between high expression of the miR-200 family in various tumors and OS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. The squares and horizontal lines correspond to the study-specific and 95% CI. The area of the squares reflects the weight. The diamond represents the summary and 95% CI. CI = confidence interval,  = hazard ratio.

Table 3: Stratified analysis of the high expression of the miR-200 family and overall survival.

Categories
Subgroups
Univariate analyses
Multivariate analyses

Number of datasets
(95% CI)
value
²
Ph
Number of datasets
(95% CI)
value
²
Ph

All

19
1.32 (1.14–1.54)
<0.001
77.50%
<0.001
24
1.32 (1.16–1.49)
<0.001
75.10%
<0.001

Patient source
Asia
10
1.91 (1.26–2.92)
0.003
80.10%
<0.001
13
1.98 (1.34–2.90)
0.001
78.20%
<0.001

Europe
5
1.07 (0.95–1.21)
0.286
66.80%
<0.001
8
1.11 (0.99–1.24)
0.071
67.10%
<0.001

North America
4
3.81 (2.46–5.90)
<0.001
0.00%
0.685
3
2.37 (1.44–3.91)
0.001
0.00%
0.457

Cancer type
EOC
7
2.18 (1.23–3.86)
0.008
79.90%
<0.001
7
1.98 (1.03–3.80)
0.039
81.80%
<0.001

CRC
7
1.12 (0.96–1.31)
0.140
77.70%
<0.001
8
1.15 (1.02–1.30)
0.026
60.10%
0.001

NSCLC
3
3.36 (1.64–6.89)
0.001
0.00%
0.411
6
2.91 (1.99–4.26)
<0.001
33.40%
0.185

GC
1
0.94 (0.75–1.17)
0.565
1.90%
0.361
2
1.10 (0.72–1.68)
0.669
62.30%
0.047

BC
1
1.38 (1.11–1.71)
0.003
/
/
1
1.46 (0.54–3.91)
0.454
75.10%
0.045

Test method
RT-PCR
16
1.64 (1.24–2.16)
0.001
75.30%
<0.001
19
1.57 (1.23–1.99)
<0.001
75.10%
<0.001

ISH
1
21.42 (7.54–60.83)
<0.001
0.00%
0.996
1
16.28 (6.28–42.24)
<0.001
0.00%
0.995

TaqMan
2
1.01 (0.95–1.08)
0.686
47.70%
0.046
4
1.07 (0.95–1.20)
0.249
58.80%
0.005

Sample source
FFPE
2
0.57 (0.29–1.10)
0.095
0.00%
0.890
5
2.27 (1.56–3.32)
<0.001
43.10%
0.135

Tissue
5
3.19 (1.19–8.52)
0.021
84.40%
<0.001
9
2.04 (1.13–3.68)
0.017
80.70%
<0.001

Blood
10
1.34 (1.15–1.57)
<0.001
79.00%
<0.001
9
1.14 (1.02–1.28)
0.019
68.30%
<0.001

TMA
2
0.93 (0.75–1.16)
0.527
0.00%
0.519
1
0.94 (0.73–1.21)
0.649
40.90%
0.184

Sample size
≧100
11
1.25 (1.06–1.47)
0.007
78.90%
<0.001
14
1.29 (1.11–1.49)
0.001
71.60%
<0.001

<100
8
1.74 (1.10–2.75)
0.018
68.50%
0.001
10
1.84 (1.17–2.90)
0.008
79.60%
<0.001

miR-200 component
miR-200a
7
1.14 (0.81–1.61)
0.438
64.80%
0.009
6
1.07 (0.72–1.59)
0.723
78.30%
<0.001

miR-200b
6
1.38 (0.88–2.16)
0.166
82.10%
<0.001
6
1.36 (0.89–2.08)
0.158
76.70%
0.001

miR-200c
11
1.38 (1.01–1.89)
0.040
82.40%
<0.001
10
1.62 (1.12–2.33)
0.010
79.30%
<0.001

miR-141
7
2.01 (1.26–3.21)
0.003
83.50%
<0.001
7
1.24 (0.99–1.56)
0.060
68.00%
0.005

miR-429
5
0.99 (0.73–1.34)
0.953
62.20%
0.032
8
1.41 (1.01–1.98)
0.043
70.30%
0.001

EOC: epithelial ovarian cancer; BC: breast cancer; NSCLC: nonsmall cell lung cancer; GC: gastric cancer; CRC: colorectal cancer; RT-PCR: reverse transcription-polymerase chain reaction; ISH: in situ hybridization; FFPE: formalin-fixed and paraffin-embedded; TMA: tissue microarray; : hazard ratio; CI: confidence interval; Ph: value of the heterogeneity test.

In multivariate analysis, 24 studies were included in meta-analysis to explore the prognostic value of the miR-200 family. As a result, high expression of the miR-200 family in various cancers was associated with unfavorable overall survival (, 95% CI: 1.16–1.49, ) (Figure 2(b)). Likewise, a similar result was found in different subgroups (Table 3).

4.3. Meta-Analysis of RFS/PFS/DFS

In univariate analysis, there were three studies, four studies, and one study involved with RFS, PFS, and DFS, respectively. Correspondingly, five studies, five studies, and two studies were collected in multivariate analysis, respectively. Ultimately, we found that no association of high expression of the miR-200 family was detected with RFS (univariate: , 95% CI: 0.96–1.09, ; multivariate: , 95% CI: 1.00–1.14, ) (Figure 3), PFS (univariate: , 95% CI: 0.54–1.70, ; multivariate: , 95% CI: 0.86–1.61, ) (Figure 4), and DFS (univariate: , 95% CI: 0.74–1.09, ; multivariate: , 95% CI: 0.68–1.41, ) (Figure 5).

(a)

(b)

Figure 3: Forest plot of the association between high expression of the miR-200 family in various tumors and RFS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. The squares and horizontal lines correspond to the study-specific and 95% CI. The area of the squares reflects the weight. The diamond represents the summary and 95% CI. CI = confidence interval,  = hazard ratio.

(a)

(b)

Figure 4: Forest plot of the association between high expression of the miR-200 family in various tumors and PFS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. The squares and horizontal lines correspond to the study-specific and 95% CI. The area of the squares reflects the weight. The diamond represents the summary and 95% CI. CI = confidence interval,  = hazard ratio.

(a)

(b)

Figure 5: Forest plot of the association between high expression of the miR-200 family in various tumors and DFS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. The squares and horizontal lines correspond to the study-specific and 95% CI. The area of the squares reflects the weight. The diamond represents the summary and 95% CI. CI = confidence interval,  = hazard ratio.

4.4. Sensitivity Analysis

Each single included study was deleted at a time to assess the specific effect of the individual data on the pooled HRs, and one-way sensitivity analysis suggested that most pooled results were relatively stable. Among them, the pooled results of OS, RFS, and PFS in both univariate analysis and multivariate analysis were shown in Figures 6(a), 6(b), Figures 7(a), 7(b), and Figures 8(a), 8(b), respectively. As shown in Figure 6(b), after excluding the study conducted by Antolín et al. [22], heterogeneity was slightly reduced between miR-200 family overexpression and OS under multivariate analysis (² from 75.1% to 73.3%), while the pooled results remained unchanged (multivariate: , 95% CI: 1.21–1.63, ). Likewise, as shown in Figure 8(a), the similar result was found between miR-200 family overexpression and PFS under univariate analysis (² from 85.1% to 80.4%), and the pooled results remained unchanged (univariate: , 95% CI: 0.38–1.88, ) after excluding the aforementioned study [22].

(a)

(b)

Figure 6: One-way sensitivity analysis of high expression of the miR-200 family in various tumors with OS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. Individually removed the studies and suggested that the results of this meta-analysis were relatively stable.

(a)

(b)

Figure 7: One-way sensitivity analysis of high expression of the miR-200 family in various tumors with RFS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. Individually removed the studies and suggested that the results of this meta-analysis were stable.

(a)

(b)

Figure 8: One-way sensitivity analysis of high expression of the miR-200 family in various tumors with PFS under different types of analysis. (a) Univariate analysis; (b) multivariate analysis. Individually removed the studies and suggested that the results of this meta-analysis were relatively stable.

4.5. Publication Bias Evaluation

Begg’s funnel plot indicated that there was a significant publication bias in meta-analysis of OS under both univariate analysis () and multivariate analysis (). However, no publication bias was found in meta-analysis of RFS (univariate: ; multivariate: ), PFS (univariate: ; multivariate: ), and DFS (univariate: ; multivariate: ).

5. Discussion

Generally, cancer progression and blood-borne metastasis are the primary factors contributed to the great majority of cancer deaths. The specific biomarkers of metastatic phenotype hold great promise in individualized therapy and improved prognosis prediction in several neoplastic diseases [39]. In recent decades, to explore the clinically useful cancer signatures remains to be research hotpot due to the complexity of cancer. Gene expression signatures of carcinomas have led to new classifications of cancer subgroups and also carried prognostic and predictive information [40]. miRNAs are small noncoding RNAs that regulate human protein-coding gene expression of specific mRNAs by either translational repression or degradation. miRNA expression signatures have distinct functions in controlling the cell cycle, proliferation, invasion, and metastasis [41], which could thus be developed into a potential prognostic signature [42]. The latest miRBase release contains 24,521 miRNA loci from 206 species, further processed to produce 30,424 mature miRNA products [43]. To date, significant miRNA expression changes have been observed in multiple cancers analyzed by profiling and next generation sequencing technologies [44].

The miR-200 family of miRNAs consists of five members grouped into two independent transcriptional clusters: miR-200a, miR-200b, and miR-429 on chromosome 1 (1p36.33), and miR-141 and miR-200c on chromosome 12 (12p13.31). Deregulation of the miR-200 family of microRNAs has been involved in cell plasticity, apoptosis, molecular subtype, oestrogen regulation, control of the growth and function of stem cells, and regulation of the downstream transcriptional program that mediate distant metastasis [45]. Cancer progression is associated with a dynamic process of epithelial-to-mesenchymal transition (EMT), during which epithelial cells lose their cell polarity and cell-cell adhesion and gain migratory as well as invasive properties by downregulating E-cadherin and upregulating vimentin expression [46, 47]. The miR-200 family members may play a major role in the suppression of EMT and metastasis [48]. Deregulation of miR-200 in cancer cell lines caused upregulation of E-cadherin and reduced motility of cancer cells. Conversely, inhibition of miR-200 reduced E-cadherin expression, increased expression of vimentin, and induced EMT [49]. In addition, the miR-200 family is known as a key transcriptional regulator of EMT and the maintenance of a less invasive and aggressive epithelial phenotype by targeting ZEB1 and ZEB2, two important transcriptional repressors of the E-cadherin gene [48]. ZEB was inhibited by miR-200 members at the posttranscriptional level by binding to highly conserved target sites in their 3-UTR; the functional link of ZEB factors with the miR-200 family in a double negative feedback loop is known as the ZEB/miR-200 feedback loop [50]. It also has been reported that several tumor suppressor genes, including BRD7, BAP1, GATA, CLOCK, and PTPN12, might be potential targets of the miR-200 family [51, 52].

To date, studies focused on the association of high expression of the miR-200 family with cancer prognosis have yielded conflicting results. Notably, small sample-sized studies lacking statistical power often have resulted in apparently contradicting conclusions. Meta-analysis is a useful tool for providing convincing evidence as it could present inconsistent results from different studies to get a relatively precise result. As far as we know, the current meta-analysis is the first try to comprehensively assess the correlation of miR-200 cluster high expression with cancer prognosis. We have explored the potential associations in overall population and the corresponding subgroups. Consequently, of particular interest is the finding of significant correlation between high expression of miR-200 cluster and poor OS by two different statistical methods. Likewise, a similar result was found in different subgroups. However, no association of miR-200 family was detected with RFS/PFS/DFS.

In the current meta-analysis, significant heterogeneity was found, which required careful interpretation and searched for influencing factors by further subgroup analyses. Consequently, impact of ethnicity, detection methods, cancer types, sample size, and sample source on prognosis in patients was considerable, which should be taken into consideration when evaluating the prognosis of cancer for patients. Some potential or undiscovered factors including adjustment for surgery, radiation, chemotherapy, socioeconomic status, and tumor characteristics should not be ignored. Moreover, there was a significant publication bias in meta-analysis of OS under both univariate analysis and multivariate analysis, suggesting that only published studies in English and Chinese might not provide so sufficient evidences. As for RFS/PFS/DFS, we did not perform subgroup analyses due to relatively fewer eligible studies. Although the studies regarding various tumors without a consistent cut-off value may influence the ultimate results and the heterogeneity suggested that potential or undiscovered factors might be ignored, a certain relationship of high expression of the miR-200 family in cancer prognosis was found in the current study.

6. Conclusion

In summary, the current study is the first original meta-analysis to address the correlation between miR-200 family expression and prognosis for cancer patients. A significant correlation was explored in overall population as well as the corresponding subgroups. Concretely, it presented that miR-200 family overexpression might be associated with poor OS to some extent, while no association was detected between high miR-200 family expression and RFS/PFS/DFS. In the future, detailed investigations comprising large cohort size from multicenter are required to confirm our conclusions.

Data Availability

All data have been shared in the figures and tables.

Conflicts of Interest

The authors have no conflict of interests to declare.

Authors’ Contributions

Wen Liu, Kaiping Zhang, Min Chao, and Jing Wang conceived and designed the study. Wen Liu, Kaiping Zhang, and Yue Hu conducted the eligible study collection, quality assessment, and data extraction. Pengfei Wei and Yaqin Peng analyzed the data. Wen Liu, Xiang Fang, and Guoping He interpreted the results. Limin Wu and Min Chao prepared the tables and figures. Wen Liu and Kaiping Zhang wrote the manuscript; Pengfei Wei, Min Chao, and Jing Wang revised it. Wen Liu, Kaiping Zhang, and Pengfei Wei contributed equally to this work. All authors read and approved the final manuscript.

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China (81601600) and the China Postdoctoral Science Foundation (2016M590576, 2017T100455).

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