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Prognostic Role of the MicroRNA-200 Family in Various Carcinomas: A Systematic Review and Meta-Analysis.

1. Introduction

MicroRNAs (miRNAs) are a class of small (19-22 nucleotides), endogenous, noncoding, highly conserved, and single-stranded RNAs. miRNAs negatively regulate numerous genes by forming base-pairs with target mRNAs, thereby facilitating translational silencing or mRNA degradation of targeted genes [1]. The miRNA binding sites, complementary sequences within the 3'-untranslated regions of target genes, are critical for the regulatory effects of miRNAs on gene expression [1]. MiRNAs are implicated in regulating many fundamental and biological processes such as cellular differentiation, proliferation, metabolism, cell-cycle control, and apoptosis [2]. MiRNAs frequently reside in fragile sites and genomic regions involved in various cancers, suggesting that they play a potentially critical and complex role in cancer [3]. Unique miRNA expression profiles have been observed in various cancer types. In addition, miRNAs may act as tumor suppressors or oncogenes in cancer and can influence the response to treatment [4].

The miR-200 family includes five members (miR-200a, miR-200b, miR-200c, miR-141, and miR-429) and can be divided into two clusters based on chromosomal location. The miR-200b/a/429 cluster is comprised of miR-200a, miR-200b, and miR-429 and is located on chromosome 1p36. The miR-200c/141 cluster is comprised of miR-200c and miR-141 and is located on chromosome 12p13 [5]. MiR-200b, miR-200c, and miR-429 have the same seed region (nucleotides 2-7), and miR-200a and miR-141 share a seed region with a difference in only the fourth nucleotide (U to C) among these regions [6]. The miR-200 family was first reported to play a role in olfactory neurogenesis [7]. A number of studies showed that miR-200 family members are aberrantly expressed in multiple human malignancies, suggesting that these miRNAs play a role in tumor pathogenesis during all stages of carcinogenesis. The miR-200 family acts as key inhibitors of epithelial-to-mesenchymal transition (EMT) by directly targeting transcriptional repressors of E-cadherin, ZEB1, and ZEB2 [5]. MiR-200 family members are also likely downregulated during tumor progression. In addition, these miRNAs suppress cell proliferation by inhibiting self-renewal and differentiation of cancer stem cells and modulating cell division and apoptosis. These finding suggest that the miR-200 family members function as tumor suppressor genes. The tumor-suppressive roles of the miR-200 family have also been reportedingastric [8], breast [9], endometrial, [10] pancreatic cancers [11, 12], hepatocellular carcinoma [13], gliomas [14], and lung cancer [15,16].

EMT, thought to play a fundamental role during tumorigenesis, is associated with poor histological differentiation, local invasiveness, and distant metastasis in various cancers. Thus, expression of miR-200 family members could influence the cancer phenotype and prognosis of cancer patients [5]. However, due to small sample sizes and different detection methods used in previous studies, the prognostic role of miR-200 has not been clearly elucidated. The discovery of molecular prognostic factors could contribute to classifying patients by prognosis and identifying high-risk cases requiring aggressive approaches. Meta-analyses offer increasing statistical power and resolve any inconsistencies or discrepancies among different studies. Therefore, we performed a literature-based meta-analysis of eligible studies to obtain evidence-based results on the prognostic role of miR-200 family members in various types of malignancies.

2. Materials and Methods

2.1. Search Strategy and Selection Criteria. We searched the CINAHL, Embase, and Google scholar using the defined keywords and PubMed using medical subject headings (MeSH) vocabulary to identify relevant articles up to December 2015. The articles were searched using the following keywords and MeSH vocabulary (Supplementary Table 1 in Supplementary Material available online at https://doi.org/10.1155/2017/ 1928021): miR-141, miR-200, or miR-429 combined with prognostic, prognosis, survival, tumor, cancer, neoplasm, or carcinoma. We also conducted a manual search. Articles meeting the following criteria were included: (1) human patient versus animal study on any type of malignant cancer or neoplasm and (2) assessment data on patient survival (overall survival [OS] and progression-free survival [PFS]) and the miR-200 family with multivariate hazard ratios (HRs) included. Exclusion was based on the following criteria: (1) review articles, letters, or abstracts, (2) no appropriate data, and (3) non-English or unpublished articles. The statistical data were reviewed before inclusion in the final selection, and the study data were extracted based on a predefined standardized form.

2.2. Data Extraction, Quality Assessment, and Statistical Methods. For the meta-analysis, the effect size was evaluated using multivariate HRs with 95% confidence intervals (95% CIs) for OS or PFS according to high miRNA expression. OS was measured from the time at which the baseline blood or tissuesamplewas obtained to thedateofdeath from anycause or the date of last follow-up. PFS was measured as the time between the baseline blood and tissue sampling for miRNA analysis and documentation of the first tumor progression, based on clinical and radiological findings or death (events).

Two reviewers systematically evaluated the assessment of all selected studies according to the Newcastle-Ottowa Scale for the quality assessment of articles [42]. The study information was collected using a predefined form. The meta-analysis statistics were obtained using Revman (version 5.3.5). Heterogeneityofthe combined HRs was assessed using Cochran's Q test and Higgins /-squared statistic. A P value less than 0.1 was considered statistically significant. A random effect model (DerSimonian and Laird method) was applied if heterogeneity was observed among studies (P < 0.1), while the fixed-effects model was used if no heterogeneity was observed (P > 0.1). Publication bias was evaluated using the funnel plot with Egger's bias indicator test [43].

3. Results

3.1. Literature Selection. After removal of duplicates, 895 studies were identified from the searches in the PubMed, CINAHL, Embase, and Google scholar databases. 750 studies were excluded using these criteria; unpublished, non-English, letters or abstracts, withdrawn articles, review articles, nonhuman studies, or irrelevant to the current analysis. Of the remaining 145 studies, 74 were excluded because they did not have the survival data associated with miR-200 family. Of the remaining 71 studies, 26 did not include the data of hazard ratio associated with OS or PFS data, and 11 included odds ratio or univariate Cox regression HRs for survival data. Finally, 34 eligible studies were selected for the final analysis. A flow chart depicting the article selection process is shown in Figure 1.

3.2. Literature Characteristics. The main features of the 34 enrolled studies are systematically summarized in Tables 1 and 2. Briefly, these studies were published between 2011 and 2015, and the study sample sizes ranged from 30 to 373 (median 105.5) patients. A total of 4497 patient samples were included. Patient OS data were reported in 33 studies, PFS data in 11, and both OS and PFS data in 10. All studies were nonrandomized and retrospective except for one prospective study. The malignant neoplasms assessed in these studies included brain, breast, colorectal, endometrial, esophageal, gastric, hepatocellular, non-small-cell lung, ovarian, pancreatic, and prostate cancers. Nineteen cohorts staged with IIV cancers were included. Quantitative real-time PCR was performed in 22 studies, in situ hybridization in 2 studies, and two separate techniques in 2 studies to assess miR-200 family expression. Tissue (in 26 studies), serum (in 9 studies), and both tissue and serum samples (in 1 study) were used to determine miR-200 expression.

3.3. Quality Assessment and Meta-Analysis. We systematically assessed the quality of all non-randomized studies included in the meta-analysis based on the Newcastle-Ottawa Scale criteria. The following aspects of each study were evaluated based on the (1) selection of the study groups, (2) comparability of the groups, and (3) ascertainment of either the exposure or outcome of interest. These criteria were assessed on a star scoring system, with higher scores given to higher-quality studies. The quality assessment is summarized in Tables 1 and 2.

3.4. Overall Effects of miR-200 Expression in Cancer Tissues on OS and PFS. Because a growing body of evidence suggests that miRNA function differs between cancer tissue and blood [44, 45], the prognostic role of miR-200 family members in both tumor tissue and serum was evaluated. Twenty-five studies on miR-200 expression in tissue samples were evaluated for OS analysis (Figure 2(a)) using a random-effects model due to high heterogeneity (OS, P < 0.00001, [I.sup.2] = 85%). Pooled HRs and 95% CIs were calculated. The pooled results showed that high miR-200 expression was a favorable prognostic factor in patients with various types of cancer (pooled HR = 0.70, 95% CI 0.54-0.91). In addition, the PFS analysis of seven studies revealed a protective role for increased miR-200 tissue expression (pooled HR = 0.63, 95% CI 0.52-0.76), as determined using a random-effects model (P = 0.03, [I.sup.2] = 44%; Figure 2(b)).

3.5. Overall Effects of Circulating miR-200 Expression on OS and PFS. The prognostic role of circulating miR-200 family members on OS was evaluated in eight studies, and heterogeneity was apparent among studies (P = 0.0004, [I.sup.2] = 70%). We found that higher expression of circulating miR-200 significantly predicted poor OS (pooled HR = 1.68, 95% CI 1.15-2.46; Figure 3(a)). PFS analysis of three studies (Figure 3(b)) demonstrated a significant association between circulating miR-200 levels and PFS (pooled HR = 2.62, 95% CI 1.68-4.07).

3.6. Subgroup Analyses of OS and PFS. To evaluate intrastudy inconsistencies and heterogeneity, the studies were stratified by the variables shown in Table 1. The heterogeneity decreased in meta-analyses of OS and PFS when the studies were stratified by the primary tumor site and individual miRNA. Pooled analyses of the brain tumor and pancreatic cancer subgroups indicated that tissue miR-200 family expression was positively correlated with OS (pooled HR = 0.51, 95% CI 0.32-0.82 in brain tumor subgroup; pooled HR = 0.35, 95% CI 0.21-0.60 in pancreatic cancer subgroup), with low heterogeneity among the studies analyzed (P = 0.71, [I.sup.2] = 0% in brain tumor subgroup; P = 0.26, [I.sup.2] = 26% in pancreatic cancer subgroup; Supplementary Figure 1A). In the stratified analyses of PFS, increased tissue miR-200 expression was significantly associated with increased PFS in the ovarian cancer subgroup (pooled HR = 0.50,95% CI 0.35-0.72) with low heterogeneity (P = 0.26, [I.sup.2] = 21%; Supplementary Figure 1B). In contrast, a pooled analysis of the colorectal cancer subgroup showed that serum miR-200 expression was negatively correlated with OS (pooled HR = 2.50, 95% CI 1.50-4.18) with low heterogeneity (P = 0.44, [I.sup.2] = 0%; Supplementary Figure 2A). In the breast cancer subgroup, circulating miR-200 expression showed a significantly negative correlation with PFS (pooled HR = 2.87, 95% CI 1.43-5.73) with low heterogeneity (P = 0.69, [I.sup.2] = 0%, Supplementary Figure 2B).

Among the subgroup analyses stratified by individual miRNAs, a pooled analysis of the miR-141 subgroup indicated that increased tissue expression was significantly correlated with enhanced OS (pooled HR = 0.38, 95% CI 0.23-0.64), which was determined using a random-effects model given the moderate heterogeneity among the studies (P = 0.09, [I.sup.2] = 53%; Supplementary Figure 3A). In addition, the high miR-200b subgroup showed a longer PFS than that of the low miR-200b subgroup (pooled HR = 0.71, 95% CI 0.54-0.94), which was determined using a fixed-effects model given the low heterogeneity among the studies (P = 0.68, [I.sup.2] = 0%; Supplementary Figure 3B). In contrast, the analysis stratified by circulating miRNA levels showed that circulating miR-200c expression was negatively correlated with OS (pooled HR = 1.97, 95% CI 1.47-2.65; Supplementary Figure 4A) and PFS (pooled HR = 2.65, 95% CI 1.61-4.35) which was determined using a fixed-effects model given the low heterogeneity among the studies (P = 0.83, [I.sup.2] = 0%; Supplementary Figure 4B).

4. Discussion

MiRNAs have numerous advantages over mRNAs for predicating clinical outcomes in cancer patients, because miRNAs are posttranscriptional regulators of multiple target genes and are involved in various cellular pathways [1]. Thus, miRNAs potentially regulate complex biological processes and biomarkers involved in cancer prognosis [4]. Although the miR-200 family is a determinant of epithelial cell phenotypes, its prognostic role has not yet been elucidated. In addition, increasing evidence suggests that miRNAs have different roles in tumor tissues and blood [44, 45], and thus the prognostic roles of miR-200 family members in both tumor and serum samples were analyzed in this study. This systemic review and meta-analysis showed that elevated cancer tissue expression of miR-200 was associated with longer survival in patients with multiple carcinoma types. In contrast, high levels of miR-200 in serum were associated with poor prognosis.

Recently, two meta-analyses on the prognostic value of miR-200 were published. Shi and Zhang [46] evaluated seven ovarian cancer studies and showed that high expression of miR-200c may predict improved survival (OS: HR = 0.34, 95% CI 0.20-0.58; PFS: HR = 0.64, 95% CI 0.50-0.82). However, this study focused on ovarian cancer and cannot be applied to other cancer types due to population heterogeneity and a small sample size. Wu et al. [47] found that miR-200c was not significantly correlated with either OS (HR = 1.41, 95% CI 0.95-2.10; P = 0.09) or PFS (HR = 1.12, 95% CI 0.68-1.84; P = 0.67) in various types of cancer. However, considering that some miRNAs have similar functions as their target genes, evaluating a set of miRNAs is preferable compared with a single miRNA to increase the prediction power. For example, Song et al. identified a signature of 17 miRNAs, which included the miR-200 family, in patients with gastric cancer [24]. This miRNA risk signature remained a strong predictor of survival (P = 0.015 and P = 0.006 for OS and PFS, resp.) in a multivariate analysis, compared with analysis of an individual miR-200 family member. This suggests that a panel of miRNAs is a better predictor of survival than is an individual miRNA. Therefore, we evaluated all five miR-200 family members instead of a single miRNA in this meta-analysis.

The results of this meta-analysis showed a pooled HR of 0.70 (95% CI 0.54-0.91), demonstrating that increased miR-200 family expression in cancer tissues is associated with a favorable outcome (P = 0.007). Furthermore, in a subgroup analysis based on tumor type, a statistically significant difference in OS was observed between brain and pancreatic cancer subgroups, with pooled HRs of 0.51 and 0.35, respectively. Subgroup analyses also showed that miR-141 and miR-200b were associated with favorable OS, with pooled HRs of 0.40 and 0.58, respectively. The miR-200 family has regulatory functions in diverse biological processes. Zhu et al. described miR-141 as a significant tumor suppressor in pancreatic cancer, as it interferes with the proliferative pathway mediated by Yes-associated protein-1 [11]. In addition, the miR-200 family inhibits EMT by regulating a number of target genes such as ZEB1andZEB2 [5]. MiR-200c strongly suppressed mammary duct formation from normal mammary stem cells and tumor formation from breast cancer stem cells in vivo by targeting B lymphoma Mo-MLV insertion region 1 homolog, a regulator of stem cell self-renewal [48]. In addition, downregulation of miR-200 family members has been associated with resistance to cytotoxic chemotherapeutic agents and EGFR inhibitors [16, 22, 31, 49, 50]. In addition, this may be mediated by two antiapoptotic factors, B-cell lymphoma 2 and X-linked inhibitor of apoptosis protein [51]. Taken together, the miR-200 family can affect cancer progression by regulating various cell signaling and genetic pathways.

Interestingly, the miR-200 levels in plasma and tumor tissues had opposing associations with survival in this study. The pooled outcome from the OS and PFS analyses revealed HRs of 1.68 (P = 0.007) and 2.62 (P < 0.001), respectively, showing that increased circulating miR-200 family expression is associated with unfavorable survival. Similarly, Wu et al.'s meta-analysis indicated that higher blood levels of miR-200c were significantly associated with poor OS (HR = 2.10, 95% CI 1.52-2.90, P < 0.00001), but there was no significant association in tumor tissue (HR = 1.41, 95% CI 0.95-2.10; P = 0.09) [47]. MiR-200 family members are increased in the blood of patients with breast [34], prostate [35], esophageal [37], gastric [40], ovarian [52], and metastatic colorectal cancers [41]. MiR-200 expression is correlated with metastasis and relapse in breast cancer [34]. Moreover, expression of miRNA, including miR-200, may be an early predictor of chemotherapy outcomes in prostate and esophageal cancers [35, 37]. In 258 cases of colorectal cancer [41], high levels of plasma miR-141 were associated with unfavorable OS (HR = 2.40, 95% CI 1.182-4.86). The reason for the discrepancies between cancer tissue and circulating levels is likely explained by the different functions of miRNAs in extracellular vesicles compared with tissue miRNAs. Le et al. reported that miR-200 family members are secreted by highly metastatic epithelial breast cancer cells and that the secretion of these miRNAs results in increased metastatic potential in xenograft models [45]. The authors proposed that the miR-200 family is potentially involved in promoting the last step of the metastatic cascade in the development of macroscopic metastatic masses at distant sites.

It is unknown whether miRNA expression in the systemic circulation reflects their expression in cancer tissues. Some studies have shown no correlation between miR-200 levels in serum and tumor tissues [53]. However, Tsujiura et al. found that the levels of plasma oncomiRNAs, including miR-21 and miR-106b, may reflect tumor miRNA levels [54]. Furthermore, a previous meta-analysis of miR-21 demonstrated that high miR-21 expression in both tissues and the circulation predicted poor outcomes [55]. Clinically, circulating biomarkers have numerous advantages, including easy access for monitoring, and their evaluation is therefore preferred for predicting early diagnosis, prognosis, and individualized treatments. However, there are still many barriers to overcome before utilizing circulating miRNAs as diagnostic or prognostic biomarkers in the clinic. These barriers include clarifying miRNA correlations between tumor tissues and circulation, normalizing data from different studies using reference genes [56] or internal controls [57], and developing sensitive, specific, reliable, reproducible, and inexpensive detection methods. In addition, circulating miRNA expression can be significantly altered by physiological or pathological conditions, such as pregnancy, heart failure, or sepsis [57]. Therefore, further clarification on the clinical roles of circulating miR-200 family members in well-designed prospective studies is needed.

Our meta-analysis has several limitations. Marked heterogeneity among the subjects was present in the OS and PFS groups. The heterogeneity of the population was likely due to differences in sample size, baseline patient characteristics (e.g., age, cancer type, tumor stage, and treatment type), follow-up duration, detection methods, and cut-off values. Thus, we only selected high-quality studies using a quality assessment based on the Newcastle-Ottawa Scale. When the studies were stratified by tumor type, heterogeneity was no longer detected in the brain tumor and pancreatic cancer subgroups (P = 0.71 and P = 0.26, resp.).

In conclusion, our meta-analysis suggests that the miR-200 family members are potential biomarkers and accurate prognostic predictors in patients with various carcinomas. The decreased tumor expression of the miR-200 family was significantly associated with poor survival in patients with brain, pancreas, and ovarian cancers. In contrast, low circulating miR-200 levels were associated with a positive prognosis in patients with colon and breast cancers. For future clinical application, large prospective studies are needed to validate the prognostic values of circulating miR-200 in individual cancer types.

http://dx.doi.org/10.1155/2017/1928021

Competing Interests

The authors declare that they have no competing interests.

Authors' Contributions

Jung Soo Lee and Yoon Ho Ko performed acquisition, analysis, and interpretation of data and drafted the article; YoungHo Ahn, Der Sheng Sun, Yeo Hyung Kim, and Hye Sung Won revised the article for important intellectual content; all the authors performed final approval of the version to be published.

Acknowledgments

This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A01054591) (Yoon Ho Ko)

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[52] C. W. S. Kan, M. A. Hahn, G. B. Gard et al., "Elevated levels of circulating microRNA-200 family members correlate with serous epithelial ovarian cancer," BMC Cancer, vol. 12, article 627, 2012.

[53] X.-G. Liu, W.-Y. Zhu, Y.-Y. Huang et al., "High expression of serum miR-21 and tumor miR-200c associated with poor prognosis in patients with lung cancer," Medical Oncology, vol. 29, no. 2, pp. 618-626, 2012.

[54] M. Tsujiura, D. Ichikawa, S. Komatsu et al., "Circulating microRNAs in plasma of patients with gastric cancers," British Journal of Cancer, vol. 102, no. 7, pp. 1174-1179, 2010.

[55] X. Zhou, X. Wang, Z. Huang et al., "Prognostic value of miR-21 in various cancers: an updating meta-analysis," PLoS ONE, vol. 9, no. 7, Article ID e102413, 2014.

[56] G.-H. Liu, Z.-G. Zhou, R. Chen et al., "Serum miR-21 and miR92a as biomarkers in the diagnosis and prognosis of colorectal cancer," Tumor Biology, vol. 34, no. 4, pp. 2175-2181, 2013.

[57] F. Wang, G. Long, C. Zhao et al., "Atherosclerosis-related circulating miRNAs as novel and sensitive predictors for acute myocardial infarction," PLOS ONE, vol. 9, no. 9, Article ID e105734, 2014.

Jung Soo Lee, (1) Young-Ho Ahn, (2) Hye Sung Won, (3) Der Sheng Sun, (3) Yeo Hyung Kim, (1) and Yoon Ho Ko (3,4)

(1) Department of Rehabilitation Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

(2) Department of Molecular Medicine and Tissue Injury Defense Research Center, Ewha Womans University School of Medicine, Seoul, Republic of Korea

(3) Division of Oncology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

(4) Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Correspondence should be addressed to Yoon Ho Ko; koyoonho@catholic.ac.kr

Received 5 September 2016; Accepted 26 December 2016; Published 22 February 2017

Academic Editor: Lei Yao

Caption: Figure 1: Flow chart of the selection process of the eligible articles.
Table 1: Characteristics of the eligible studies evaluating high miRNA
expression levels in tissue samples and patient survival data.

Study (year) (ref)                    Country    Cancer       Stage

Feng et al. (2015) [13]                China       HCC          NA
Li et al. (2015) [17]                  China       HCC         I-IV
Lu et al. (2015) [8]                   China       GC          I-IV
Wang et al. (2015) [14]                China     Glioma        I-IV
Yao et al. (2015) [9]                  China       BC         I III

Zhao et al. (2015) [18] ([dagger])     China      NSCLC    [II.sub.B]-
                                                           [III.sub.B]
Cao et al. (2014) [19]                 China       OC          I-IV
Diaz et al. (2014) [20]                Spain       CRC          II
Kim et al. (2014) [21]                 Korea      NSCLC        I-IV
Li et al. (2014) [22] ([dagger])       China      NSCLC      IIIB-IV
Liu et al. (2014) [23]                 China       HCC         I-IV
Song et al. (2014) [24] *              China       GC          I-IV

Tejero et al. (2014) [25]              Spain      NSCLC       I III
Zhang et al. (2014) [26] *             China       AST        III-IV
Zhu et al. (2014) [11]                 China       PC          I-IV
Zhu et al. (2014) [15]                 China      NSCLC        I-IV
Berghmans et al. (2013) [16]           Europe     NSCLC      IV (79%)
Huang et al. (2013) [27]               China       HCC         III
Li et al. (2013) [28]                  China       CRC        I III
Tang et al. (2013) [29]                China       GC          I-IV
Torres et al. (2013) [10] ([dagger])   Europe      EEC         I-IV
Xiao et al. (2013) [30]                China       HCC        I III
Zhao at al. (2013) [12]                China       PC          I-IV
Leskela et al. (2011) [31] *           Spain       OC          I-IV

Marchini et al. (2011) [32] *          Italy       OC           I

Yu et al. (2010) [33]                  Japan       PC          I-IV

Study (year) (ref)                        Test         Cut-off value

Feng et al. (2015) [13]               qRT-PCR/ISH           ROC
Li et al. (2015) [17]                 qRT-PCR/ISH           NA
Lu et al. (2015) [8]                    qRT-PCR           Median
Wang et al. (2015) [14]                 qRT-PCR             NA
Yao et al. (2015) [9]                   qRT-PCR         Comparison
                                                      w/normal group
Zhao et al. (2015) [18] ([dagger])      qRT-PCR           Median

Cao et al. (2014) [19]                  qRT-PCR           Median
Diaz et al. (2014) [20]                 qRT-PCR      Maxstat R package
Kim et al. (2014) [21]                  qRT-PCR           Median
Li et al. (2014) [22] ([dagger])        qRT-PCR       Minimum P value
Liu et al. (2014) [23]                    ISH             Median
Song et al. (2014) [24] *               qRT-PCR       Median/Lowest
                                                      quintile values
Tejero et al. (2014) [25]               qRT-PCR      Maxstat R package
Zhang et al. (2014) [26] *              qRT-PCR           Median
Zhu et al. (2014) [11]                  qRT-PCR            Mean
Zhu et al. (2014) [15]                  qRT-PCR             ROC
Berghmans et al. (2013) [16]            qRT-PCR       predicted score
Huang et al. (2013) [27]                qRT-PCR            Mean
Li et al. (2013) [28]                   qRT-PCR           Median
Tang et al. (2013) [29]                   ISH             Median
Torres et al. (2013) [10] ([dagger])    qRT-PCR         Median/ROC
Xiao et al. (2013) [30]                 qRT-PCR            Mean
Zhao at al. (2013) [12]                 qRT-PCR           Median
Leskela et al. (2011) [31] *            qRT-PCR           Median

Marchini et al. (2011) [32] *           qRT-PCR          Contal &
                                                     O'Quigley method
Yu et al. (2010) [33]                   qRT-PCR           Median

Study (year) (ref)                            miRNA            Sample
                                                                size

Feng et al. (2015) [13]                        200a              115
Li et al. (2015) [17]                          429               161
Lu et al. (2015) [8]                           141               141
Wang et al. (2015) [14]                        200b              123
Yao et al. (2015) [9]                          200b              278

Zhao et al. (2015) [18] ([dagger])             200c              78

Cao et al. (2014) [19]                       200a/b/c            100
Diaz et al. (2014) [20]                        429               127
Kim et al. (2014) [21]                         200c              72
Li et al. (2014) [22] ([dagger])               200c              150
Liu et al. (2014) [23]                         141               212
Song et al. (2014) [24] *                    200a/b/c            373

Tejero et al. (2014) [25]                    141/200C            155
Zhang et al. (2014) [26] *                     200b              122
Zhu et al. (2014) [11]                         141               94
Zhu et al. (2014) [15]                         429               70
Berghmans et al. (2013) [16]                   200c              38
Huang et al. (2013) [27]                       429               138
Li et al. (2013) [28]                          429               107
Tang et al. (2013) [29]                       200b/c             126
Torres et al. (2013) [10] ([dagger])      200c/429 [429]         30
Xiao et al. (2013) [30]                        200a              120
Zhao at al. (2013) [12]                        141               40
Leskela et al. (2011) [31] *           429 [141, 200a/b/c,       72
                                               429]
Marchini et al. (2011) [32] *               200b/c (A)           89
                                             200c(B)             55
Yu et al. (2010) [33]                          200c              99

Study (year) (ref)                         MFD          Newcastle-
                                                          Ottawa
                                                         Quality
                                                        Assessment
                                                           Scale

                                                         Selection

Feng et al. (2015) [13]                   120 m             **
Li et al. (2015) [17]                      96 m            ****
Lu et al. (2015) [8]                       60 m             ***
Wang et al. (2015) [14]                     5y              ***
Yao et al. (2015) [9]                      10 y            ****

Zhao et al. (2015) [18] ([dagger])         40 m             ***

Cao et al. (2014) [19]                     56 m            ****
Diaz et al. (2014) [20]                   120 m             ***
Kim et al. (2014) [21]                    125 m             ***
Li et al. (2014) [22] ([dagger])       18.5 [9.6] m         ***
Liu et al. (2014) [23]                    100 m             ***
Song et al. (2014) [24] *                 112 m             ***

Tejero et al. (2014) [25]                 160 m             ***
Zhang et al. (2014) [26] *                120 m            ****
Zhu et al. (2014) [11]                    200 m            ****
Zhu et al. (2014) [15]                     30 m             **
Berghmans et al. (2013) [16]               60 m             ***
Huang et al. (2013) [27]                  140 m             ***
Li et al. (2013) [28]                      82 m            ****
Tang et al. (2013) [29]                     NA              ***
Torres et al. (2013) [10] ([dagger])      150 m            ****
Xiao et al. (2013) [30]                    60 m             ***
Zhao at al. (2013) [12]                    50 m             **
Leskela et al. (2011) [31] *              128 m             **

Marchini et al. (2011) [32] *             240 m

Yu et al. (2010) [33]                      101m             ***

Study (year) (ref)                      Newcastle-Ottawa Quality
                                            Assessment Scale

                                      Compatibility     Outcome

Feng et al. (2015) [13]                     *              **
Li et al. (2015) [17]                       *             ***
Lu et al. (2015) [8]                        *              **
Wang et al. (2015) [14]                     **             **
Yao et al. (2015) [9]                       *              **

Zhao et al. (2015) [18] ([dagger])          *              **

Cao et al. (2014) [19]                      *             ***
Diaz et al. (2014) [20]                     **             **
Kim et al. (2014) [21]                      **            ***
Li et al. (2014) [22] ([dagger])            **            ***
Liu et al. (2014) [23]                      *              **
Song et al. (2014) [24] *                   *              **

Tejero et al. (2014) [25]                   **            ***
Zhang et al. (2014) [26] *                  **            ***
Zhu et al. (2014) [11]                      *              **
Zhu et al. (2014) [15]                      *              **
Berghmans et al. (2013) [16]                **             **
Huang et al. (2013) [27]                    *             ***
Li et al. (2013) [28]                       *             ***
Tang et al. (2013) [29]                     *              **
Torres et al. (2013) [10] ([dagger])        *              **
Xiao et al. (2013) [30]                     *             ***
Zhao at al. (2013) [12]                     *              **
Leskela et al. (2011) [31] *                *              **

Marchini et al. (2011) [32] *

Yu et al. (2010) [33]                       *             ***

[Value] indicates the microRNA type or maximum follow-up duration for
progression-free survival.

MFD: maximal follow-up duration, AST: astrocytoma, BC: breast cancer,
CRC: colorectal cancer, EEC: endometrioid endometrial carcinoma, EOC:
epithelial ovarian cancer, ESC: esophageal squamous cancer, GC:
gastric cancer, HCC: hepatocellular carcinoma, OC: ovarian cancer, PC:
pancreatic cancer, PrC: castration-resistant prostate cancer, NSCLC:
non-small-cell lung cancer, ROC, receiver operating characteristic
analysis, NA: not available, mo: months, wk: weeks, and y: years.

* Study reporting both overall survival and progression-free survival
data.

([dagger]) Study reporting only progression-free survival data.

Table 2: Characteristics of the eligible studies evaluating high miRNA
expression levels in serum samples and patient survival data.

Study (year)                     Country     Cancer   Stage      Test

Antoh'n et al. (2015) [34] *      Spain        BC      I-IV    qRT-PCR
Lin et al. (2014) [35]          Australia     PrC       IV     qRT-PCR
Liu et al. (2014) [36]            China       HCC      I-IV    qRT-PCR
Zhu et al. (2014) [15]            China      NSCLC     I-IV    qRT-PCR
Yu et al. (2013) [37]             China       ESC     III-IV   qRT-PCR
Tanaka et al. (2013) [38] *       Japan       ESC      I-IV    qRT-PCR

Toiyama et al. (2014) [39]        Japan       CRC      I-IV    qRT-PCR
Valladares-Ayerbes et al.         Spain        GC      I-IV    qRT-PCR
   (2012) [40] *
Cheng et al. (2011) [41]        China USA     CRC      I-IV    qRT-PCR

Study (year)                       Cut-off value           miRNA

Antoh'n et al. (2015) [34] *            ROC              200c/141
Lin et al. (2014) [35]                 Median              200b
Liu et al. (2014) [36]                 Median              200a
Zhu et al. (2014) [15]                  ROC                 429
Yu et al. (2013) [37]                  Median              200c
Tanaka et al. (2013) [38] *     Comparison w/normal        200c
                                       group
Toiyama et al. (2014) [39]              ROC                200c
Valladares-Ayerbes et al.        Mean Comparison w/        200c
   (2012) [40] *                    normal group
Cheng et al. (2011) [41]                ROC            141 (Tianjin)
                                                       141 (TexGen)

Study (year)                    Sample         MFD         Newcastle-
                                 size                       Ottawa
                                                            Quality
                                                          Assessment
                                                             Scale

                                                           Selection
Antoh'n et al. (2015) [34] *      57       265 [235] w        ***
Lin et al. (2014) [35]            97          62 m            ***
Liu et al. (2014) [36]            136         50 m             **
Zhu et al. (2014) [15]            70          30 m             **
Yu et al. (2013) [37]             157         50 m             **
Tanaka et al. (2013) [38] *       64           2y             ***

Toiyama et al. (2014) [39]        321         60 m            ***
Valladares-Ayerbes et al.         52          60 m            ***
   (2012) [40] *
Cheng et al. (2011) [41]        156/102     50/100 m          ***

Study (year)                      Newcastle-Ottawa Quality
                                      Assessment Scale

                                Compatibility     Outcome
Antoh'n et al. (2015) [34] *          **            ***
Lin et al. (2014) [35]                *             ***
Liu et al. (2014) [36]                *              **
Zhu et al. (2014) [15]                *              **
Yu et al. (2013) [37]                 **             **
Tanaka et al. (2013) [38] *           *              **

Toiyama et al. (2014) [39]            *              **
Valladares-Ayerbes et al.             **            ***
   (2012) [40] *
Cheng et al. (2011) [41]              *             ***

[Value] indicates microRNA type or maximum follow-up duration for
progression-free survival.

MFD: maximal follow-up duration, AST: astrocytoma, BC: breast cancer,
CRC: colorectal cancer, EEC: endometrioid endometrial carcinoma, EOC:
epithelial ovarian cancer, ESC: esophageal squamous cancer, GC:
gastric cancer, HCC: hepatocellular carcinoma, OC: ovarian cancer, PC:
pancreatic cancer, PrC: castration-resistant prostate cancer, NSCLC:
non-small-cell lung cancer, ROC, receiver operating characteristic
analysis, NA: not available, mo: months, wk: weeks, and y: years.

* Study reporting both overall survival and progression-free survival
data.

Figure 2: Forest plot of hazard ratios for the prediction of overall
(a) and progression-free survival (b) by high-expressing miR-200
family members in tissue samples. Funnel plot showing publication bias
of the overall (c) and progression-free survival (d) prediction by
high-expressing miR-200 family members in tissue samples. * Sample
from grade IV astrocytoma. ([dagger]) Sample from grade III
astrocytoma. ([double dagger],[section]) Samples from different tissue
collection.

(a)

Study or subgroup                         log          SE       Weight
                                        [hazard
                                         ratio]

Wang et al. (miRNA-200b)                -0.3754      0.4436      2.9%
Feng et al. (miRNA-200a)                -1.0989      0.3509      3.3%
Yao et al. (miRNA-200b)                 -0.7715      0.3463      3.4%
Lu et al. (miRNA-141)                   -1.2814      0.5211      2.6%
Li et al. (miRNA-429)                    0.7275      0.2106      3.9%
Li et al. (miRNA-200c)                  -0.5684      0.2242      3.9%
Zhang et al. (miRNA-200b) *             -0.6831      0.4051      3.1%
Diaz et al. (miRNA-429)                 -1.2641      0.8584      1.5%
Tejero et al. (miRNA-141 & 200c)         1.025       0.4804      2.8%
Song et al. (miRNA-200a)                -0.3285      0.2176      3.9%
Cao et al. (miRNA-200a)                  2.8484      1.2964      0.8%
Cao et al. (miRNA-200c)                  2.7862      1.2996      0.8%
Zhu et al. (miRNA-429)                  -1.0114      1.8684      0.5%
Zhang et al. (miRNA-200b) ([dagger])    -0.8629      0.3819      3.2%
Cao et al. (miRNA-200b)                  2.735       1.3331      0.8%
Song et al. (miRNA-200b)                -0.0726      0.1987      4.0%
Kim et al. (miRNA-200c)                  1.3002      0.5833      2.4%
Song et al. (miRNA-200c)                -0.2784      0.2421      3.8%
Liu et al. (miRNA-141)                  -0.5447      0.177       4.1%
Zhu et al. (miRNA-141)                  -0.9361      0.3131      3.5%
Zhao et al. (miRNA-141)                 -1.9951      0.6506      2.1%
Huang et al. (miRNA-429)                 1.5347      0.3034      3.6%
Torres et al. (miRNA-200c)              -1.0017      0.3144      3.5%
Li et al. (miRNA-429)                    0.7724      0.3865      3.2%
Xiao et al. (miRNA-200a)                -0.9636      0.2916      3.6%
Torres et al. (miRNA-429)               -0.3271      0.0997      4.3%
Tang et al. (miRNA-200c)                -0.9163      0.2005      4.0%
Tang et al. (miRNA-200b)                -0.9163      0.2005      4.0%
Berghmans et al. (miRNA-200c)            0.4101      0.1028      4.3%
Marchini et al. (miRNA-200c)            -2.3645      1.0502      1.2%
   ([section])
Marchini et al. (miRNA-200b)            -0.7195      0.5941      2.3%
Leskela et al. (miRNA-429)              -0.7324      0.3585      3.3%
Marchini et al. (miRNA-200c)            -1.4106      0.5951      2.3%
   ([double dagger])
Yu et al. (miRNA-200c)                  -0.7885      0.3763      3.2%
Total (95% CI)                                                  100.0%

Study or subgroup                           Hazard ratio         Year
                                         IV, random, 95% CI

Wang et al. (miRNA-200b)                 0.69 [0.29, 1.64]       2015
Feng et al. (miRNA-200a)                 0.33 [0.17, 0.66]       2015
Yao et al. (miRNA-200b)                  0.46 [0.23, 0.91]       2015
Lu et al. (miRNA-141)                    0.28 [0.10, 0.77]       2015
Li et al. (miRNA-429)                    2.07 [1.37, 3.13]       2015
Li et al. (miRNA-200c)                   0.57 [0.37, 0.88]       2014
Zhang et al. (miRNA-200b) *              0.51 [0.23, 1.12]       2014
Diaz et al. (miRNA-429)                  0.28 [0.05, 1.52]       2014
Tejero et al. (miRNA-141 & 200c)         2.79 [1.09, 7.15]       2014
Song et al. (miRNA-200a)                 0.72 [0.47, 1.10]       2014
Cao et al. (miRNA-200a)                 17.26 [1.36, 219.05]     2014
Cao et al. (miRNA-200c)                 16.22 [1.27, 207.13]     2014
Zhu et al. (miRNA-429)                   0.36 [0.01, 14.16]      2014
Zhang et al. (miRNA-200b) ([dagger])     0.42 [0.20, 0.89]       2014
Cao et al. (miRNA-200b)                 15.41 [1.13, 210.15]     2014
Song et al. (miRNA-200b)                 0.93 [0.63, 1.37]       2014
Kim et al. (miRNA-200c)                  3.67 [1.17, 11.51]      2014
Song et al. (miRNA-200c)                 0.76 [0.47, 1.22]       2014
Liu et al. (miRNA-141)                   0.58 [0.41, 0.82]       2014
Zhu et al. (miRNA-141)                   0.39 [0.21, 0.72]       2014
Zhao et al. (miRNA-141)                  0.14 [0.04, 0.49]       2013
Huang et al. (miRNA-429)                 4.64 [2.56, 8.41]       2013
Torres et al. (miRNA-200c)               0.37 [0.20, 0.68]       2013
Li et al. (miRNA-429)                    2.16 [1.01, 4.62]       2013
Xiao et al. (miRNA-200a)                 0.38 [0.22, 0.68]       2013
Torres et al. (miRNA-429)                0.72 [0.59, 0.88]       2013
Tang et al. (miRNA-200c)                 0.40 [0.27, 0.59]       2013
Tang et al. (miRNA-200b)                 0.40 [0.27, 0.59]       2013
Berghmans et al. (miRNA-200c)            1.51 [1.23, 1.84]       2013
Marchini et al. (miRNA-200c)             0.09 [0.01, 0.74]       2011
   ([section])
Marchini et al. (miRNA-200b)             0.49 [0.15, 1.56]       2011
Leskela et al. (miRNA-429)               0.48 [0.24, 0.97]       2011
Marchini et al. (miRNA-200c)             0.24 [0.08, 0.78]       2011
   ([double dagger])
Yu et al. (miRNA-200c)                   0.45 [0.22, 0.95]       2010
Total (95% CI)                           0.70 [0.54, 0.91]

Heterogeneity: [[tau].sup.2] = 0.40; [chi square] = 216.44, df =33
(P < 0.00001); [I.sup.2] = 85%

Test for overall effect: Z = 2.68 (P = 0.007)

(b)

Study or subgroup                         log          SE       Weight
                                        [hazard
                                         ratio]

Zhang et al. (miRNA-200b) ([dagger])    -0.7372      0.444       3.8%
Zhang et al. (miRNA-200b) *             -0.3439      0.3732      4.9%
Song et al. (miRNA-200b)                -0.1985      0.1946     10.6%
Zhao et al. (miRNA-200c)                -1.0757      0.3066      6.5%
Song et al. (miRNA-200c)                -0.0584       0.21       9.9%
Li et al. (miRNA-200c)                  -0.5906      0.2129      9.8%
Song et al. (miRNA-200a)                -0.4005      0.2031     10.2%
Torres et al. (miRNA-429)               -0.1985      0.0785     16.4%
Marchini et al. (miRNA-200c)            -0.8699      0.5379      2.8%
   ([double dagger])
Marchini et al. (miRNA-200b)             -0.848      0.5117      3.0%
Leskela et al. (miRNA-429)              -0.7419      0.4209      4.1%
Marchini et al. (miRNA-200c)            -3.3524      1.1067      0.7%
   ([section])
Leskela et al. (miRNA-200c)             -0.8065      0.4128      4.2%
Leskela et al. (miRNA-141)              -0.8544      0.4421      3.8%
Leskela et al. (miRNA-200b)             -0.3001      0.3955      4.5%
Leskela et al. (miRNA-200a)             -0.1989      0.3822      4.8%
Total (95% CI)                                       100.0%

Study or subgroup                           Hazard ratio         Year
                                         IV, random, 95% CI

Zhang et al. (miRNA-200b) ([dagger])     0.48 [0.20, 1.14]       2015
Zhang et al. (miRNA-200b) *              0.71 [0.34, 1.47]       2015
Song et al. (miRNA-200b)                 0.82 [0.56, 1.20]       2014
Zhao et al. (miRNA-200c)                 0.34 [0.19, 0.62]       2014
Song et al. (miRNA-200c)                 0.94 [0.63, 1.42]       2014
Li et al. (miRNA-200c)                   0.55 [0.36, 0.84]       2014
Song et al. (miRNA-200a)                 0.67 [0.45, 1.00]       2014
Torres et al. (miRNA-429)                0.82 [0.70, 0.96]       2013
Marchini et al. (miRNA-200c)             0.42 [0.15, 1.20]       2011
   ([double dagger])
Marchini et al. (miRNA-200b)             0.43 [0.16, 1.17]       2011
Leskela et al. (miRNA-429)               0.48 [0.21, 1.09]       2011
Marchini et al. (miRNA-200c)             0.04 [0.00, 0.31]       2011
   ([section])
Leskela et al. (miRNA-200c)              0.45 [0.20, 1.00]       2011
Leskela et al. (miRNA-141)               0.43 [0.18, 1.01]       2011
Leskela et al. (miRNA-200b)              0.74 [0.34, 1.61]       2011
Leskela et al. (miRNA-200a)              0.82 [0.39, 1.73]       2011
Total (95% CI)                           0.63 [0.52, 0.76]

Heterogeneity: [[tau].sup.2] = 0.05; [chi square] = 26.62, df = 15
(P = 0.03); [I.sup.2] = 44%

Test for overall effect: Z = 4.82 (P < 0.00001)

Figure 3: Forest plot of hazard ratios for the prediction of overall
(a) and progression-free survival (b) by high-expressing miR-200
family members in serum samples. Funnel plot showing publication bias
of the overall (c) and progression-free survival (d) prediction by
high-expressing miR-200 family members in serum samples.

(a)

Study or subgroup                          log
                                         [hazard       SE      Weight
                                          ratio]

Antolin et al. (miRNA-141)                -1.017     0.4989     8.1%
Antolin et al. (miRNA-200c)               1.026      0.5184     7.8%
Zhu et al. (miRNA-429)                   -2.5553     0.8799     3.8%
Yu et al. (miRNA-200c)                    0.5098     0.1955     15.0%
Lin et al. (miRNA-200b)                   1.1314     0.3375     11.5%
Liu et al. (miRNA-200a)                   0.5621     0.0975     16.9%
Valladares-Ayerbes et al. (miRNA-200c)    0.8065     0.367      10.8%
Toiyama et al. (miRNA-200c)               0.991      0.3797     10.5%
Cheng et al. (miRNA-141 Tianjin)          1.2267     0.469      8.6%
Cheng et al. (miRNA-141 TextGen)          0.3075     0.5643     7.0%
Total (95% CI)                                       100.0%

Study or subgroup                           Hazard ratio        Year
                                         IV, random, 95% CI

Antolin et al. (miRNA-141)                0.36 [0.14, 0.96]     2015
Antolin et al. (miRNA-200c)               2.79 [1.01, 7.71]     2015
Zhu et al. (miRNA-429)                    0.08 [0.01, 0.44]     2014
Yu et al. (miRNA-200c)                    1.66 [1.13, 2.44]     2014
Lin et al. (miRNA-200b)                   3.10 [1.60, 6.01]     2014
Liu et al. (miRNA-200a)                   1.75 [1.45, 2.12]     2014
Valladares-Ayerbes et al. (miRNA-200c)    2.24 [1.09, 4.60]     2012
Toiyama et al. (miRNA-200c)               2.69 [1.28, 5.67]     2012
Cheng et al. (miRNA-141 Tianjin)          3.41 [1.36, 8.55]     2011
Cheng et al. (miRNA-141 TextGen)          1.36 [0.45, 4.11]     2011
Total (95% CI)                            1.68 [1.15, 2.46]

Heterogeneity: [[tau].sup.2] = 0.21; [chi square] = 30.20, df =9 (P =
0.0004); [I.sup.2] = 70%

Test for overall effect: Z = 2.70 (P = 0.007)

(b)

Study or subgroup                          log
                                         [hazard       SE      Weight
                                          ratio]

Antolin et al. (miRNA-141)                0.9163     0.4883     21.3%
Antolin et al. (miRNA-200c)               1.203      0.5123     19.3%
Tanaka et al. (miRNA-200c)                1.025      0.4706     22.9%
Valladares-Ayerbes et al. (miRNA-200c)    0.8198     0.3729     36.5%
Total (95% CI)                                       100.0%

Study or subgroup                           Hazard ratio        Year
                                          IV, fixed, 95% CI

Antolin et al. (miRNA-141)                2.50 [0.96, 6.51]     2015
Antolin et al. (miRNA-200c)               3.33 [1.22, 9.09]     2015
Tanaka et al. (miRNA-200c)                2.79 [1.11, 7.01]     2013
Valladares-Ayerbes et al. (miRNA-200c)    2.27 [1.09, 4.71]     2012
Total (95% CI)                            2.62 [1.68, 4.07]

Heterogeneity: [chi square] = 0.39, df = 3 (P = 0.94); [I.sup.2] = 0%

Test for overall effect: Z = 4.27 (P < 0.0001)
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Author:Lee, Jung Soo; Ahn, Young-Ho; Won, Hye Sung; Sun, Der Sheng; Kim, Yeo Hyung; Ko, Yoon Ho
Publication:BioMed Research International
Article Type:Report
Date:Jan 1, 2017
Words:8587
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