Printer Friendly

Correlation between miR-200 Family Overexpression and Cancer Prognosis.

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, HR (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 [greater than or equal to] 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). HR 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 Q and [I.sup.2] test across studies (no heterogeneity [I.sup.2] < 25%, moderate heterogeneity [I.sup.2] = 25%-50%, extreme heterogeneity [I.sup.2] > 50%) [12]. In case of extreme heterogeneity ([I.sup.2] > 50% or P < 0.01 for Q 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 HR. In Begg's funnel plots, logHR was plotted against SE. P 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).

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

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).

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 (HR = 1.32, 95% CI: 1.14-1.54, P < 0.001) (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).

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 (HR = 1.32, 95% CI: 1.16-1.49, P < 0.001) (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: HR = 1.02, 95% CI: 0.96-1.09, P = 0.47; multivariate: HR = 1.07, 95% CI: 1.00-1.14, P = 0.07) (Figure 3), PFS (univariate: HR = 0.96, 95% CI: 0.54-1.70, P = 0.88; multivariate: HR = 1.17, 95% CI: 0.86-1.61, P = 0.32) (Figure 4), and DFS (univariate: HR = 0.90, 95% CI: 0.74-1.09, P = 0.29; multivariate: HR = 0.98, 95% CI: 0.68-1.41, P = 0.90) (Figure 5).

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 Antolin et al. [22], heterogeneity was slightly reduced between miR-200 family overexpression and OS under multivariate analysis ([I.sup.2] from 75.1% to 73.3%), while the pooled results remained unchanged (multivariate: HR = 1.40, 95% CI: 1.21-1.63, P < 0.001). Likewise, as shown in Figure 8(a), the similar result was found between miR-200 family overexpression and PFS under univariate analysis ([I.sup.2] from 85.1% to 80.4%), and the pooled results remained unchanged (univariate: HR = 0.85, 95% CI: 0.38-1.88, P = 0.684) after excluding the aforementioned study [22].

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 (P = 0.028) and multivariate analysis (P < 0.001). However, no publication bias was found in meta-analysis of RFS (univariate: P = 0.760; multivariate: P = 0.855), PFS (univariate: P = 1000; multivariate: P = 0.087), and DFS (univariate: P = 0.296; multivariate: P = 0.308).

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.

https://doi.org/10.1155/2018/6071826

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).

References

[1] V. Ambros, "The functions of animal microRNAs," Nature, vol. 431, no. 7006, pp. 350-355, 2004.

[2] G. A. Calin, C. Sevignani, C. D. Dumitru et al., "Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 9, pp. 2999-3004, 2004.

[3] J. Niu, Y. Sun, Q. Guo, D. Niu, and B. Liu, "miR-1 inhibits cell growth, migration, and invasion by targeting VEGFA in osteosarcoma cells," Disease Markers, vol. 2016, Article ID 7068986, 8 pages, 2016.

[4] H. Grosshans and W. Filipowicz, "Molecular biology: the expanding world of small RNAs," Nature, vol. 451, no. 7177, pp. 414-416, 2008.

[5] Y. Wu, Z. Jia, D. Cao et al., "Predictive value of miR-219-1, miR-938, miR-34b/c, and miR-218 polymorphisms for gastric cancer susceptibility and prognosis," Disease Markers, vol. 2017, Article ID 4731891, 9 pages, 2017.

[6] A. R. Halvorsen, G. Kristensen, A. Embleton et al., "Evaluation of prognostic and predictive significance of circulating microRNAs in ovarian cancer patients," Disease Markers, vol. 2017, Article ID 3098542, 9 pages, 2017.

[7] P.-W. Choi and S.-W. Ng, "The functions of microRNA-200 family in ovarian cancer: beyond epithelial-mesenchymal transition," International Journal of Molecular Sciences, vol. 18, no. 6, p. 1207, 2017.

[8] M. Koutsaki, M. Libra, D. A. Spandidos, and A. Zaravinos, "The miR-200 family in ovarian cancer," Oncotarget, vol. 8, no. 39, pp. 66629-66640, 2017.

[9] N. Zidar, E. Bostjancic, M. Jerala et al., "Down-regulation of microRNAs of the miR-200 family and up-regulation of snail and slug in inflammatory bowel diseases-hallmark of epithelial-mesenchymal transition," Journal of Cellular and Molecular Medicine, vol. 20, no. 10, pp. 1813-1820, 2016.

[10] M. R. Munafo and J. Flint, "Meta-analysis of genetic association studies," Trends in Genetics, vol. 20, no. 9, pp. 439-444, 2004.

[11] A. Liberati, D. G. Altman, J. Tetzlaff et al., "The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration," PLoS Medicine, vol. 6, no. 7, article e1000100, 2009.

[12] J. P. T. Higgins and S. G. Thompson, "Quantifying heterogeneity in a meta-analysis," Statistics in Medicine, vol. 21, no. 11, pp. 1539-1558, 2002.

[13] R. DerSimonian and N. Laird, "Meta-analysis in clinical trials," Controlled Clinical Trials, vol. 7, no. 3, pp. 177-188, 1986.

[14] N. Mantel and W. Haenszel, "Statistical aspects of the analysis of data from retrospective studies of disease," Journal of the National Cancer Institute, vol. 22, no. 4, pp. 719-748, 1959.

[15] C. B. Begg and J. A. Berlin, "Publication bias and dissemination of clinical research," Journal of the National Cancer Institute, vol. 81, no. 2, pp. 107-115, 1989.

[16] J. Zou, L. Liu, Q. Wang et al., "Downregulation of miR-429 contributes to the development of drug resistance in epithelial ovarian cancer by targeting ZEB1," American Journal of Translational Research, vol. 9, no. 3, pp. 1357-1368, 2017.

[17] Y. Han, Q. Zhao, J. Zhou, and R. Shi, "miR-429 mediates tumor growth and metastasis in colorectal cancer," American Journal of Cancer Research, vol. 7, no. 2, pp. 218-233, 2017.

[18] M. Maierthaler, A. Benner, M. Hoffmeister et al., "Plasma miR-122 and miR-200 family are prognostic markers in colorectal cancer," International Journal of Cancer, vol. 140, no. 1, pp. 176-187, 2017.

[19] L. Si, H. Tian, W. Yue et al., "Potential use of microRNA-200c as a prognostic marker in non-small cell lung cancer," Oncology Letters, vol. 14, no. 4, pp. 4325-4330, 2017.

[20] X. Meng, V. Muller, K. Milde-Langosch, F. Trillsch, K. Pantel, and H. Schwarzenbach, "Diagnostic and prognostic relevance of circulating exosomal miR-373, miR-200a, miR-200b and miR-200c in patients with epithelial ovarian cancer," Oncotarget, vol. 7, no. 13, pp. 16923-16935, 2016.

[21] S. J. Dong, X. J. Cai, and S. J. Li, "The clinical significance of miR-429 as a predictive biomarker in colorectal cancer patients receiving 5-fluorouracil treatment," Medical Science Monitor, vol. 22, pp. 3352-3361, 2016.

[22] S. Antolin, L. Calvo, M. Blanco-Calvo et al., "Circulating miR-200c and miR-141 and outcomes in patients with breast cancer," BMC Cancer, vol. 15, no. 1, p. 297, 2015.

[23] Y. C. Gao and J. Wu, "MicroRNA-200c and microRNA-141 as potential diagnostic and prognostic biomarkers for ovarian cancer," Tumour Biology, vol. 36, no. 6, pp. 4843-4850, 2015.

[24] Y. B. Lu, J. J. Hu, W. J. Sun, X. H. Duan, and X. Chen, "Prognostic value of miR-141 down-regulation in gastric cancer," Genetics and Molecular Research, vol. 14, no. 4, article 17305, 17311 pages, 2015.

[25] J. Y. Liu, Y. R. Zhao, L. N. Zhang, Y. Yan, and H. Zheng, "Expression and clinical significance of four miRNAs in epithelial ovarian cancer," Tianjin Medical Journal, vol. 43, no. 9, pp. 996-999, 2015.

[26] Q. Cao, K. Lu, S. Dai, Y. Hu, and W. Fan, "Clinicopathological and prognostic implications of the miR-200 family in patients with epithelial ovarian cancer," International Journal of Clinical and Experimental Pathology, vol. 7, no. 5, pp. 2392-2401, 2014.

[27] M. K. Kim, S. B. Jung, J.-S. Kim et al., "Expression of microRNA miR-126 and miR-200c is associated with prognosis in patients with non-small cell lung cancer," Virchows Archiv, vol. 465, no. 4, pp. 463-471, 2014.

[28] W. Zhu, J. He, D. Chen et al., "Expression of miR-29c, miR-93, and miR-429 as potential biomarkers for detection of early stage non-small lung cancer," PLoS One, vol. 9, no. 2, article e87780, 2014.

[29] F. Song, D. Yang, B. Liu et al., "Integrated microRNA network analyses identify a poor-prognosis subtype of gastric cancer characterized by the miR-200 family," Clinical Cancer Research, vol. 20, no. 4, pp. 878-889, 2014.

[30] R. Tejero, A. Navarro, M. Campayo et al., "miR-141 and miR-200c as markers of overall survival in early stage non-small cell lung cancer adenocarcinoma," PLoS One, vol. 9, no. 7, article e101899, 2014.

[31] Y. Toiyama, K. Hur, K. Tanaka et al., "Serum miR-200c is a novel prognostic and metastasis-predictive biomarker in patients with colorectal cancer," Annals of Surgery, vol. 259, no. 4, pp. 735-743, 2014.

[32] Q. Sun, X. Zou, T. Zhang, J. Shen, Y. Yin, and J. Xiang, "The role of miR-200a in vasculogenic mimicry and its clinical significance in ovarian cancer," Gynecologic Oncology, vol. 132, no. 3, pp. 730-738, 2014.

[33] 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.

[34] A. Chao, C. Y. Lin, Y. S. Lee et al., "Regulation of ovarian cancer progression by microRNA-187 through targeting disabled homolog-2," Oncogene, vol. 31, no. 6, pp. 764-775, 2012.

[35] S. Marchini, D. Cavalieri, R. Fruscio et al., "Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: a retrospective study of two independent tumour tissue collections," Lancet Oncology, vol. 12, no. 3, pp. 273-285, 2011.

[36] H. Cheng, L. Zhang, D. E. Cogdell et al., "Circulating plasma miR-141 is a novel biomarker for metastatic colon cancer and predicts poor prognosis," PLoS One, vol. 6, no. 3, article e17745, 2011.

[37] S. Leskela, L. J. Leandro-Garcfa, M. Mendiola et al., "The miR-200 family controls [beta]-tubulin III expression and is associated with paclitaxel-based treatment response and progression-free survival in ovarian cancer patients," Endocrine Related Cancer, vol. 18, no. 1, pp. 85-95, 2010.

[38] X. Hu, D. M. Macdonald, P. C. Huettner et al., "A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer," Gynecologic Oncology, vol. 114, no. 3, pp. 457-464, 2009.

[39] F. Montani and F. Bianchi, "Circulating cancer biomarkers: the macro-revolution of the micro-RNA," eBioMedicine, vol. 5, pp. 4-6, 2016.

[40] A. Prat, M. J. Ellis, and C. M. Perou, "Practical implications of gene-expression-based assays for breast oncologists," Nature Reviews Clinical Oncology, vol. 9, no. 1, pp. 48-57, 2011.

[41] Z. Liu, Y. Guo, X. Pu, and M. Li, "Dissecting the regulation rules of cancer-related miRNAs based on network analysis," Scientific Reports, vol. 6, no. 1, p. 34172, 2016.

[42] C. Jay, J. Nemunaitis, P. Chen, P. Fulgham, and A. W. Tong, "miRNA profiling for diagnosis and prognosis of human cancer," DNA and Cell Biology, vol. 26, no. 5, pp. 293-300, 2007.

[43] A. Kozomara and S. Griffiths-Jones, "miRBase: annotating high confidence microRNAs using deep sequencing data," Nucleic Acids Research, vol. 42, no. D1, pp. D68-D73, 2014.

[44] L. Zhang, P. Wei, X. Shen et al., "MicroRNA expression profile in penile cancer revealed by next-generation small RNA sequencing," PLoS One, vol. 10, no. 7, article e0131336, 2015.

[45] S. Uhlmann, J. D. Zhang, A. Schwager et al., "miR-200bc/429 cluster targets PLCy1 and differentially regulates proliferation and EGF-driven invasion than miR-200a/141 in breast cancer," Oncogene, vol. 29, no. 30, pp. 4297-4306, 2010.

[46] M. Yu, A. Bardia, B. S. Wittner et al., "Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition," Science, vol. 339, no. 6119, pp. 580-584, 2013.

[47] P. A. Gregory, A. G. Bert, E. L. Paterson et al., "The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1," Nature Cell Biology, vol. 10, no. 5, pp. 593-601, 2008.

[48] V. Davalos, C. Moutinho, A. Villanueva et al., "Dynamic epigenetic regulation of the microRNA-200 family mediates epithelial and mesenchymal transitions in human tumorigenesis," Oncogene, vol. 31, no. 16, pp. 2062-2074, 2012.

[49] D. Chen, B. L. Dang, J. Z. Huang et al., "MiR-373 drives the epithelial-to-mesenchymal transition and metastasis via the miR-373-TXNIP-HIF1[alpha]-TWIST signaling axis in breast cancer," Oncotarget, vol. 6, no. 32, pp. 32701-32712, 2015.

[50] U. Wellner, J. Schubert, U. C. Burk et al., "The EMT-activator ZEB1 promotes tumorigenicity by repressing stemness-inhibiting microRNAs," Nature Cell Biology, vol. 11, no. 12, pp. 1487-1495, 2009.

[51] Y. A. Park, J. W. Lee, J. J. Choi et al., "The interactions between microRNA-200c and BRD7 in endometrial carcinoma," Gynecologic Oncology, vol. 124, no. 1, pp. 125-133, 2012.

[52] M. V. Iorio, R. Visone, G. di Leva et al., "MicroRNA signatures in human ovarian cancer," Cancer Research, vol. 67, no. 18, pp. 8699-8707, 2007.

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

(1) Prenatal 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

(2) Department of Urology, Anhui Provincial Children's Hospital and Children's Hospital of Anhui Medical University, Hefei, Anhui, China

(3) Hefei 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

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

Caption: 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.

Caption: 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.

Caption: 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.
Table 1: Main characteristics of the eligible studies.

First author          Year   Country       Age        Cancer
                                                       type

Zou J. [16]           2017    China         NA         EOC
Han Y. [17]           2017    China         NA         CRC
Maierthaler M. [18]   2017   Germany    70 (33-92)     CRC
                                       68.0 (36-92)
Si L. [19]            2017    China    60.5 (41-78)   NSCLC
Meng X. [20]          2016   Germany    60 (23-91)     EOC
Dong S. J. [21]       2016    China     56 (31-79)     CRC
Antolin S. [22]       2015    Spain    54.8 (29-73)     BC
Gao Y. C. [23]        2015    China         NA         EOC
Lu Y. B. [24]         2015    China         NA          GC
Liu J. Y. [25]        2015    China       57.48        EOC
Cao Q. [26]           2014    China     58 (26-88)     EOC
Kim M. K. [27]        2014    Korea     64 (26-77)    NSCLC
Zhu W. [28]           2014    China         59        NSCLC
Song F. [29]          2014    China        60.5         GC
Tejero R. [30]        2014    Spain     65 (35-85)    NSCLC
Toiyama Y. [31]       2014    Japan         67         CRC
Sun Q. [32]           2014    China         NA         EOC
Liu X. G. [33]        2012    China         NA        NSCLC
Chao A. [34]          2012    China         NA         EOC
Marchini S. [35]      2011    Italy     52 (21-82)     EOC
Cheng H. [36]         2011     USA          NA         CRC
Leskela S. [37]       2010    Spain     57 (35-85)     EOC

Hu X. [38]            2009     USA         58.3        EOC

First author                     MicroRNA              Sample
                                                        size

Zou J. [16]                      miR-429                 72
Han Y. [17]                      miR-429                 71
Maierthaler M. [18]   miR-200a, miR-200b, miR-200c,     527
                             miR-141, miR-429
Si L. [19]                       miR-200c               110
Meng X. [20]           miR-200a, miR-200b, miR-200c     163
Dong S. J. [21]                  miR-429                116
Antolin S. [22]             miR-200c, miR-141            57
Gao Y. C. [23]              miR-200c, miR-141            93
Lu Y. B. [24]                    miR-141                 95
Liu J. Y. [25]                   miR-200a                44
Cao Q. [26]            miR-200a, miR-200b, miR-200c     100
Kim M. K. [27]                   miR-200c                72
Zhu W. [28]                      miR-429                 70
Song F. [29]           miR-200a, miR-200b, miR-200c     385
Tejero R. [30]                 miR-200c/141             155
Toiyama Y. [31]                  miR-200c               182
Sun Q. [32]                      miR-200a                53
Liu X. G. [33]              miR-200c, miR-141            70
Chao A. [34]                     miR-200a               176
Marchini S. [35]            miR-200b, miR-200c          144
Cheng H. [36]                    miR-141                156
Leskela S. [37]       miR-200a, miR-200b, miR-200c,      72
                             miR-141, miR-429
Hu X. [38]                       miR-200a                55

First author             Follow-up,         Outcome
                        median (range)

Zou J. [16]                   NA             OS/PFS
Han Y. [17]                  34.2              OS
Maierthaler M. [18]           NA             OS/RFS

Si L. [19]                    NA             OS/DFS
Meng X. [20]              20 (1-136)         OS/RFS
Dong S. J. [21]               NA               OS
Antolin S. [22]        74.6 (74.2-77.7)      OS/PFS
Gao Y. C. [23]                NA               OS
Lu Y. B. [24]                 NA               OS
Liu J. Y. [25]            26 (5-49)          OS/PFS
Cao Q. [26]              36.8 (6-56)           OS
Kim M. K. [27]            31 (1-135)           OS
Zhu W. [28]                   NA               OS
Song F. [29]              35 (1-112)         OS/PFS
Tejero R. [30]            43 (2-160)           OS
Toiyama Y. [31]               NA               OS
Sun Q. [32]             56.79 (11-98)          OS
Liu X. G. [33]                24               OS
Chao A. [34]              40 (3-109)         OS/RFS
Marchini S. [35]      110.4 (82.8-139.2)     OS/PFS
Cheng H. [36]                 NA               OS
Leskela S. [37]               NA           OS/PFS/RFS

Hu X. [38]                    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;
HR: hazard ratio; CI: confidence interval.

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

First author       Year   Country    Test    Cancer   MicroRNA
                                    method    type

Zou J.             2017    China    RT-PCR    EOC      miR-429

Zou J.             2017    China    RT-PCR    EOC      miR-429

Han Y.             2017    China    RT-PCR    CRC      miR-429

Maierthaler M.-1   2017   Germany   TaqMan    CRC     miR-200a

Maierthaler M.-1   2017   Germany   TaqMan    CRC     miR-200b

Maierthaler M.-1   2017   Germany   TaqMan    CRC     miR-200c

Maierthaler M.-1   2017   Germany   TaqMan    CRC      miR-141

Maierthaler M.-1   2017   Germany   TaqMan    CRC      miR-429

Maierthaler M.-2   2017   Germany   TaqMan    CRC     miR-200a

Maierthaler M.-2   2017   Germany   TaqMan    CRC     miR-200b

Maierthaler M.-2   2017   Germany   TaqMan    CRC     miR-200c

Maierthaler M.-2   2017   Germany   TaqMan    CRC      miR-141

Maierthaler M.-2   2017   Germany   TaqMan    CRC      miR-429

Maierthaler M.-1   2017   Germany   TaqMan    CRC     miR-200a

Maierthaler M.-1   2017   Germany   TaqMan    CRC     miR-200b

Maierthaler M.-1   2017   Germany   TaqMan    CRC     miR-200c

Maierthaler M.-1   2017   Germany   TaqMan    CRC      miR-141

Maierthaler M.-1   2017   Germany   TaqMan    CRC      miR-429

Maierthaler M.-2   2017   Germany   TaqMan    CRC     miR-200a

Maierthaler M.-2   2017   Germany   TaqMan    CRC     miR-200b

Maierthaler M.-2   2017   Germany   TaqMan    CRC     miR-200c

Maierthaler M.-2   2017   Germany   TaqMan    CRC      miR-141

Maierthaler M.-2   2017   Germany   TaqMan    CRC      miR-429

Si L.              2017    China    RT-PCR   NSCLC    miR-200c
Si L.              2017    China    RT-PCR   NSCLC    miR-200c

Meng X.            2016   Germany   RT-PCR    EOC     miR-200a
Meng X.            2016   Germany   RT-PCR    EOC     miR-200b

Meng X.            2016   Germany   RT-PCR    EOC     miR-200c

Meng X.            2016   Germany   RT-PCR    EOC     miR-200a
Meng X.            2016   Germany   RT-PCR    EOC     miR-200b
Meng X             2016   Germany   RT-PCR    EOC     miR-200c

Dong S. J.         2016    China    RT-PCR    CRC      miR-429

Antolin S.         2015    Spain    RT-PCR     BC     miR-200c

Antolin S.         2015    Spain    RT-PCR     BC     miR-200c

Antolin S.         2015    Spain    RT-PCR     BC      miR-141
Antolin S.         2015    Spain    RT-PCR     BC      miR-141

Gao Y. C.          2015    China    RT-PCR    EOC     miR-200c
Gao Y. C.          2015    China    RT-PCR    EOC      miR-141

Lu Y. B.           2015    China    RT-PCR     GC      miR-141

Liu J. Y.          2015    China    RT-PCR    EOC     miR-200a
Liu J. Y.          2015    China    RT-PCR    EOC     miR-200a

Cao Q              2014    China     ISH      EOC     miR-200a

Cao Q.             2014    China     ISH      EOC     miR-200b

Cao Q.             2014    China     ISH      EOC     miR-200c

Kim M. K.          2014    Korea    RT-PCR   NSCLC    miR-200c

Zhu W.-1           2014    China    RT-PCR   NSCLC     miR-429

Zhu W.-2           2014    China    RT-PCR   NSCLC     miR-429

Song F.            2014    China    RT-PCR     GC     miR-200a

Song F.            2014    China    RT-PCR     GC     miR-200b

Song F.            2014    China    RT-PCR     GC     miR-200c

Song F.            2014    China    RT-PCR     GC     miR-200a

Song F.            2014    China    RT-PCR     GC     miR-200b

Song F.            2014    China    RT-PCR     GC     miR-200c

Tejero R.-1        2014    Spain    TaqMan   NSCLC    miR-200c/
                                                         141
Tejero R.-2        2014    Spain    TaqMan   NSCLC    miR-200c/
                                                         141

Toiyama Y.-1       2014    Japan    RT-PCR    CRC     miR-200c

Toiyama Y.-2       2014    Japan    RT-PCR    CRC     miR-200c

Sun Q.             2014    China    RT-PCR    EOC     miR-200a

Liu X. G.          2012    China    RT-PCR   NSCLC    miR-200c
Liu X. G.          2012    China    RT-PCR   NSCLC     miR-141

Chao A.            2012    China    RT-PCR    EOC     miR-200a
Chao A.            2012    China    RT-PCR    EOC     miR-200a

Marchini S.        2011    Italy    RT-PCR    EOC     miR-200b

Marchini S.        2011    Italy    RT-PCR    EOC     miR-200b

Marchini S.        2011    Italy    RT-PCR    EOC     miR-200c

Marchini S.        2011    Italy    RT-PCR    EOC     miR-200c

Cheng H.-1         2011     USA     RT-PCR    CRC      miR-141

Cheng H.-2         2011     USA     RT-PCR    CRC      miR-141

Cheng H.-3         2011     USA     RT-PCR    CRC      miR-141

Leskela S.         2010    Spain    RT-PCR    EOC     miR-200a
Leskela S.         2010    Spain    RT-PCR    EOC     miR-200b
Leskela S.         2010    Spain    RT-PCR    EOC     miR-200c
Leskela S.         2010    Spain    RT-PCR    EOC      miR-141

Leskela S.         2010    Spain    RT-PCR    EOC      miR-429
Leskela S.         2010    Spain    RT-PCR    EOC      miR-429
Leskela S.         2010    Spain    RT-PCR    EOC      miR-429

Hu X.              2009     USA     RT-PCR    EOC     miR-200a
Hu X.              2009     USA     RT-PCR    EOC     miR-200a

First author       Sample   Outcome          HR (95% CI)
                   source

Zou J.             Tissue     OS      (U) 0.641 (0.412-0.996)/
                                       (M) 0.763 (0.458-1.270)
Zou J.             Tissue     PFS     (U) 0.661 (0.478-0.915)/
                                       (M) 0.710 (0.504-1.001)

Han Y.             Tissue     OS       (M) 1.852 (1.019-3.326)

Maierthaler M.-1   Blood      OS      (U) 0.929 (0.707-1.211)/
                                       (M) 1.053 (0.791-1.401)
Maierthaler M.-1   Blood      OS      (U) 0.704 (0.524-0.945)/
                                       (M) 0.772 (0.570-1.045)
Maierthaler M.-1   Blood      OS      (U) 0.808 (0.646-1.010)/
                                       (M) 0.840 (0.659-1.070)
Maierthaler M.-1   Blood      OS      (U) 0.925 (0.713-1.200)/
                                       (M) 1.038 (0.785-1.374)
Maierthaler M.-1   Blood      OS      (U) 0.951 (0.734-1.235)/
                                       (M) 0.968 (0.721-1.300)
Maierthaler M.-2   Blood      OS      (U) 1.198 (0.986-1.456)/
                                       (M) 1.227 (1.008-1.495)
Maierthaler M.-2   Blood      OS      (U) 1.172 (0.946-1.453)/
                                       (M) 1.208 (0.975-1.497)
Maierthaler M.-2   Blood      OS      (U) 1.117 (0.947-1.318)/
                                       (M) 1.152 (0.975-1.362)
Maierthaler M.-2   Blood      OS      (U) 1.071 (0.877-1.305)/
                                       (M) 1.105 (0.904-1.350)
Maierthaler M.-2   Blood      OS      (U) 1.010 (0.853-1.196)/
                                       (M) 1.006 (0.845-1.198)
Maierthaler M.-1   Blood      RFS     (U) 0.929 (0.718-1.203)/
                                       (M) 1.031 (0.786-1.353)
Maierthaler M.-1   Blood      RFS     (U) 0.714 (0.539-0.947)/
                                       (M) 0.750 (0.561-1.005)
Maierthaler M.-1   Blood      RFS     (U) 0.819 (0.657-1.019)/
                                       (M) 0.835 (0.658-1.060)
Maierthaler M.-1   Blood      RFS     (U) 0.910 (0.705-1.175)/
                                       (M) 0.999 (0.760-1.312)
Maierthaler M.-1   Blood      RFS     (U) 0.954 (0.743-1.227)/
                                       (M) 1.076 (0.716-1.618)
Maierthaler M.-2   Blood      RFS     (U) 1.175 (0.973-1.420)/
                                       (M) 1.200 (0.989-1.456)
Maierthaler M.-2   Blood      RFS     (U) 1.109 (0.893-1.377)/
                                       (M) 1.143 (0.919-1.422)
Maierthaler M.-2   Blood      RFS     (U) 1.076 (0.911-1.272)/
                                       (M) 1.100 (0.930-1.302)
Maierthaler M.-2   Blood      RFS     (U) 1.057 (0.871-1.284)/
                                       (M) 1.085 (0.890-1.321)
Maierthaler M.-2   Blood      RFS     (U) 1.080 (0.916-1.272)/
                                       (M) 1.078 (0.910-1.277)

Si L.              Tissue     OS       (M) 2.095 (1.241-3.536)
Si L.              Tissue     DFS      (M) 1.647 (1.049-2.585)

Meng X.            Blood      OS          (U) 1.7 (0.8-3.5)
Meng X.            Blood      OS         (U) 2.7 (1.3-5.7)/
                                          (M) 2.8 (1.1-6.8)
Meng X.            Blood      OS         (U) 2.4 (1.2-4.9)/
                                          (M) 2.5 (1.1-6.1)

Meng X.            Blood      RFS         (U) 1.1 (0.6-1.9)
Meng X.            Blood      RFS         (U) 1.6 (0.9-2.8)
Meng X             Blood      RFS        (U) 2.0 (1.1-3.6)/
                                          (M) 1.7 (0.8-3.6)

Dong S. J.         Tissue     OS       (M) 2.296 (1.105-4.528)

Antolin S.         Blood      OS        (U) 1.38 (1.11-1.71)/
                                         (M) 2.79 (1.01-7.7)
Antolin S.         Blood      PFS       (U) 1.37 (1.09-1.71)/
                                        (M) 3.33 (1.22-9.07)
Antolin S.         Blood      OS       (M) 0.986 (0.942-1.032)
Antolin S.         Blood      PFS      (M) 0.987 (0.95-1.025)

Gao Y. C.          Blood      OS        (U) 3.14 (1.67-5.93)
Gao Y. C.          Blood      OS        (U) 1.83 (1.00-3.33)

Lu Y. B.           Tissue     OS      (M) 2.972 (1.297-10.001)

Liu J. Y.          Tissue     OS       (M) 0.354 (0.149-0.840)
Liu J. Y.          Tissue     PFS      (M) 0.395 (0.210-0.742)

Cao Q              Tissue     OS       (U) 22.69 (1.32-50.53)/
                                       (M) 17.26 (1.36-36.98)
Cao Q.             Tissue     OS       (U) 20.28 (1.20-42.28)/
                                        (M)15.41 (1.13-31.36)
Cao Q.             Tissue     OS       (U) 21.42 (1.26-48.33)/
                                       (M) 16.22 (1.27-33.81)

Kim M. K.           FFPE      OS        (M) 3.67 (1.17-11.45)

Zhu W.-1           Tissue     OS      (U) 1.686 (0.570-4.984)/
                                      (m) 2.749 (0.706-10.707)
Zhu W.-2           Blood      OS      (U) 6.458 (1.409-29.593)/
                                      (M) 12.875 (2.295-72.23)

Song F.             TMA       OS        (U) 0.82 (0.57-1.20)/
                                        (M) 0.72 (0.47-1.13)
Song F.             TMA       OS        (U) 0.87 (0.60-1.26)/
                                         (M)0.93 (0.63-1.41)
Song F.             TMA       OS        (U) 1.19 (0.80-1.77)/
                                        (M) 1.32 (0.82-2.12)
Song F.             TMA       DFS       (U) 0.81 (0.58-1.14)/
                                        (M) 0.67 (0.45-0.99)
Song F.             TMA       DFS       (U) 0.84 (0.60-1.18)/
                                        (M) 0.82 (0.56-1.19)
Song F.             TMA       DFS       (U) 1.08 (0.76-1.54)/
                                        (M) 1.06 (0.70-1.60)

Tejero R.-1         FFPE      OS       (M) 2.787 (1.087-7.148)

Tejero R.-2         FFPE      OS      (M) 10.649 (2.433-46.608)

Toiyama Y.-1       Blood      OS        (U) 2.43 (1.26-4.68)/
                                         (M)2.67 (1.28-5.67)
Toiyama Y.-2        FFPE      OS        (U) 0.56 (0.28-1.10)

Sun Q.              TMA       OS        (U) 0.58 (0.08-4.05)

Liu X. G.          Tissue     OS      (U) 6.020 (1.344-26.971)
Liu X. G.          Tissue     OS      (U) 4.135 (0.467-36.597)

Chao A.             FFPE      OS       (M) 1.466 (0.786-2.734)
Chao A.             FFPE      RFS      (M) 1.213 (0.70-2.101)

Marchini S.        Tissue     OS      (U) 2.137 (0.801-5.701)/
                                       (M) 2.051 (0.640-6.570)
Marchini S.        Tissue     PFS     (U) 3.197 (1.417-7.213)/
                                       (M) 2.335 (0.857-6.363)
Marchini S.        Tissue     OS      (U) 0.309 (0.112-0.850)/
                                       (M) 0.244 (0.076-0.785)
Marchini S.        Tissue     PFS     (U) 0.392 (0.174-0.885)/
                                       (M) 0.419 (0.146-1.204)

Cheng H.-1         Blood      OS        (U) 3.80 (1.46-9.91)/
                                        (M) 1.36 (0.45-4.14)
Cheng H.-2         Blood      OS       (U) 4.83 (2.06-11.35)/
                                        (M) 3.41 (1.36-8.56)
Cheng H.-3         Blood      OS        (U) 3.61 (1.96-6.65)/
                                        (M) 2.40 (1.18-4.86)

Leskela S.          FFPE      PFS       (M) 1.22 (0.57-2.58)
Leskela S.          FFPE      PFS       (M) 1.35 (0.62-2.93)
Leskela S.          FFPE      PFS       (M) 2.24 (1.00-5.03)
Leskela S.          FFPE      PFS       (M) 2.35 (0.98-5.59)

Leskela S.          FFPE      PFS       (M) 2.10 (0.92-4.79)
Leskela S.          FFPE      RFS       (M) 2.01 (1.11-3.66)
Leskela S.          FFPE      OS        (M) 2.08 (1.03-4.20)

Hu X.               FFPE      OS        (U) 0.70 (0.03-14.29)
Hu X.               FFPE      PFS       (U) 0.64 (0.22-1.81)

First author            Cut-off value

Zou J.                      >0.532

Zou J.

Han Y.                      Median

Maierthaler M.-1            Median

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-1

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-2

Maierthaler M.-2

Si L.               The 2-[DELTA][DELTA]Cq
Si L.

Meng X.                     Median
Meng X.

Meng X.

Meng X.
Meng X.
Meng X

Dong S. J.                  Median

Antolin S.              >1.29 relative
                       expression value
Antolin S.

Antolin S.
Antolin S.

Gao Y. C.             -[DELTA]Ct method
Gao Y. C.                with 95% CI

Lu Y. B.                    Median

Liu J. Y.           Log 2-[DELTA][DELTA]Ct
Liu J. Y.

Cao Q                       Median

Cao Q.

Cao Q.

Kim M. K.                   Median

Zhu W.-1                     Mean

Zhu W.-2

Song F.                     Median

Song F.

Song F.

Song F.

Song F.

Song F.

Tejero R.-1                  Mean

Tejero R.-2

Toiyama Y.-1                Median

Toiyama Y.-2

Sun Q.                      Median
                      ([greater than or
                      equal to] 12.623)

Liu X. G.          2-[DELTA][DELTA]Ct > 2.0
Liu X. G.

Chao A.                Log ratio > 1.3
Chao A.

Marchini S.                  >25%

Marchini S.

Marchini S.

Marchini S.

Cheng H.-1            2-[DELTA][DELTA]Ct

Cheng H.-2

Cheng H.-3

Leskela S.             75% of positive
Leskela S.                  cells
Leskela S.
Leskela S.

Leskela S.
Leskela S.
Leskela S.

Hu X.                        >11
Hu X.

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; HR: 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.

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

Categories          Subgroups          Univariate analyses

                                    Number of      HR (95% CI)
                                    datasets

All                                    19        1.32 (1.14-1.54)

Patient source         Asia            10        1.91 (1.26-2.92)
                      Europe            5        1.07 (0.95-1.21)
                  North America         4        3.81 (2.46-5.90)

Cancer type            EOC              7        2.18 (1.23-3.86)
                       CRC              7        1.12 (0.96-1.31)
                      NSCLC             3        3.36 (1.64-6.89)
                        GC              1        0.94 (0.75-1.17)
                        BC              1        1.38 (1.11-1.71)

Test method           RT-PCR           16        1.64 (1.24-2.16)
                       ISH              1       21.42 (7.54-60.83)
                      TaqMan            2        1.01 (0.95-1.08)

Sample source          FFPE             2        0.57 (0.29-1.10)
                      Tissue            5        3.19 (1.19-8.52)
                      Blood            10        1.34 (1.15-1.57)
                       TMA              2        0.93 (0.75-1.16)

Sample size      [greater than or      11        1.25 (1.06-1.47)
                  equal to] 100
                       <100             8        1.74 (1.10-2.75)

miR-200              miR-200a           7        1.14 (0.81-1.61)
component            miR-200b           6        1.38 (0.88-2.16)
                     miR-200c          11        1.38 (1.01-1.89)
                     miR-141            7        2.01 (1.26-3.21)
                     miR-429            5        0.99 (0.73-1.34)

Categories          Subgroups             Univariate analyses

                                    P value   [I.sup.2]     Ph

All                                 <0.001     77.50%     <0.001

Patient source         Asia          0.003     80.10%     <0.001
                      Europe         0.286     66.80%     <0.001
                  North America     <0.001      0.00%     0.685

Cancer type            EOC           0.008     79.90%     <0.001
                       CRC           0.140     77.70%     <0.001
                      NSCLC          0.001      0.00%     0.411
                        GC           0.565      1.90%     0.361
                        BC           0.003        /         /

Test method           RT-PCR         0.001     75.30%     <0.001
                       ISH          <0.001      0.00%     0.996
                      TaqMan         0.686     47.70%     0.046

Sample source          FFPE          0.095      0.00%     0.890
                      Tissue         0.021     84.40%     <0.001
                      Blood         <0.001     79.00%     <0.001
                       TMA           0.527      0.00%     0.519

Sample size      [greater than or    0.007     78.90%     <0.001
                  equal to] 100
                       <100          0.018     68.50%     0.001

miR-200              miR-200a        0.438     64.80%     0.009
component            miR-200b        0.166     82.10%     <0.001
                     miR-200c        0.040     82.40%     <0.001
                     miR-141         0.003     83.50%     <0.001
                     miR-429         0.953     62.20%     0.032

Categories          Subgroups          Multivariate analyses

                                    Number of      HR (95% CI)
                                    datasets

All                                    24        1.32 (1.16-1.49)

Patient source         Asia            13        1.98 (1.34-2.90)
                      Europe            8        1.11 (0.99-1.24)
                  North America         3        2.37 (1.44-3.91)

Cancer type            EOC              7        1.98 (1.03-3.80)
                       CRC              8        1.15 (1.02-1.30)
                      NSCLC             6        2.91 (1.99-4.26)
                        GC              2        1.10 (0.72-1.68)
                        BC              1        1.46 (0.54-3.91)

Test method           RT-PCR           19        1.57 (1.23-1.99)
                       ISH              1       16.28 (6.28-42.24)
                      TaqMan            4        1.07 (0.95-1.20)

Sample source          FFPE             5        2.27 (1.56-3.32)
                      Tissue            9        2.04 (1.13-3.68)
                      Blood             9        1.14 (1.02-1.28)
                       TMA              1        0.94 (0.73-1.21)

Sample size      [greater than or      14        1.29 (1.11-1.49)
                  equal to] 100
                       <100            10        1.84 (1.17-2.90)

miR-200              miR-200a           6        1.07 (0.72-1.59)
component            miR-200b           6        1.36 (0.89-2.08)
                     miR-200c          10        1.62 (1.12-2.33)
                     miR-141            7        1.24 (0.99-1.56)
                     miR-429            8        1.41 (1.01-1.98)

Categories          Subgroups            Multivariate analyses

                                    P value   [I.sup.2]     Ph

All                                 <0.001     75.10%     <0.001

Patient source         Asia          0.001     78.20%     <0.001
                      Europe         0.071     67.10%     <0.001
                  North America      0.001      0.00%     0.457

Cancer type            EOC           0.039     81.80%     <0.001
                       CRC           0.026     60.10%     0.001
                      NSCLC         <0.001     33.40%     0.185
                        GC           0.669     62.30%     0.047
                        BC           0.454     75.10%     0.045

Test method           RT-PCR        <0.001     75.10%     <0.001
                       ISH          <0.001      0.00%     0.995
                      TaqMan         0.249     58.80%     0.005

Sample source          FFPE         <0.001     43.10%     0.135
                      Tissue         0.017     80.70%     <0.001
                      Blood          0.019     68.30%     <0.001
                       TMA           0.649     40.90%     0.184

Sample size      [greater than or    0.001     71.60%     <0.001
                  equal to] 100
                       <100          0.008     79.60%     <0.001

miR-200              miR-200a        0.723     78.30%     <0.001
component            miR-200b        0.158     76.70%     0.001
                     miR-200c        0.010     79.30%     <0.001
                     miR-141         0.060     68.00%     0.005
                     miR-429         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; HR: hazard ratio; CI: confidence interval; Ph: P value of
the heterogeneity test.

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 HR and
95% CI. The area of the squares reflects the weight. The diamond
represents the summary HR and 95% CI. CI = confidence interval, HR =
hazard ratio.

(a)

Study or subgroup             Log (HR)         SE       Weight

Antolin S (miR-200c)          0.3220835    0.1102381     4.7%
Cao Q (miR-200a)              3.1219243    0.9283114     0.6%
Cao Q (miR-200b)              3.0096352    0.90867157    0.6%
Cao Q (miR-200c)              3.0643251    0.93034204    0.6%
Cheng H-1 (miR-141)           1.3350011    0.48854793    1.7%
Cheng H-2 (miR-141)           1.5748465    0.43533465    2.0%
Cheng H-3 (miR-141)           1.2837077    0.31165112    2.8%
Gao YC (miR-141)             0.60431599    0.30688068    2.8%
Gao YC (miR-200c)             1.1442228    0.32326545    2.7%
Hu X (miR-200a)              -0.35667496   1.5729893     0.2%
Liu XG (miR-141)              1.4194874    1.1126011     0.4%
Liu XG (miR-200c)             1.7950873    0.76507959    0.9%
Maierthaler M-1 (miR-141)    -0.07364652   0.13728853    4.5%
Maierthaler M-1 (miR-200a)   -0.07796153   0.13280496    4.5%
Maierthaler M-1 (miR-200b)   -0.35097693   0.15043195    4.3%
Maierthaler M-1 (miR-200c)   -0.21319319   0.11321809    4.7%
Maierthaler M-1 (miR-429)    -0.25024124   0.13273398    4.5%
Maierthaler M-2 (miR-200a)   0.18065346    0.0994367     4.8%
Maierthaler M-2 (miR-200b)   0.15871173    0.10947528    4.7%
Maierthaler M-2 (miR-141)    0.06859277    0.10139064    4.8%
Maierthaler M-2 (miR-200c)   0.11064651    0.08432948    4.9%
Maierthaler M-2 (miR-429)    0.00995032    0.08621898    4.9%
Marchini S (miR-200b)        0.75940302    0.50064693    1.6%
Marchini S (miR-200c)         -1.174414    0.51702487    1.6%
Meng X (miR-200a)            0.53062828    0.37650676    2.3%
Meng X (miR-200b)            0.99325179    0.37706681    2.3%
Meng X (miR-200c)            0.87546878    0.35890654    2.4%
Song F (miR-200a)            -0.19845095   0.1899083     3.9%
Song F (miR-200b)            -0.13926206   0.18926972    4.0%
Song F (miR-200c)            0.17395336    0.20258242    3.8%
Sun Q (miR-200a)             -0.5447272    1.0011341     0.5%
Toiyama Y-1 (miR-200c)       0.88789128    0.33474142    2.6%
Toiyama Y-2 (miR-200c)       -0.57981849   0.34904997    2.5%
Zhu W-1 (miR-429)            0.52235885    0.55315096    1.4%
Zhu W-2 (miR-429)             1.8653197    0.77669837    0.8%
Zou J (miR-429)              -0.44472586   0.22518467    3.6%

Total (95% CI)                               100.0%

Heterogeneity: [[tau].sup.2] = 0.12; [chi square] = 155.28, df = 35
(P < 0.0001); [I.sup.2] = 77%

Test for overall effect: Z = 3.60 (P = 0.0003)

(b)

Study or subgroup             Log [HR]         SE       Weight

Antolin S (miR-141)          -0.01409892   0.02327772    5.7%
Antolin S (miR-200c)          1.0260416    0.51818112    1.2%
Cao Q (miR-200a)              2.8483917    0.84257462    0.5%
Cao Q (miR-200b)              2.7350166    0.84778459    0.5%
Cao Q (miR-200c)              2.786245     0.83717851    0.5%
Chao A (miR-200a)            0.38253758    0.31800107    2.4%
Cheng H-1 (miR-141)          0.30748471    0.56612334    1.1%
Cheng H-2 (miR-141)           1.2267123    0.46928968    1.4%
Cheng H-3 (miR-141)          0.87546878    0.36110308    2.0%
Dong SJ (miR-429)            0.83116848    0.35980484    2.1%
Han Y (miR-429)              0.61626614    0.30177259    2.5%
Kim MK (miR-200c)             1.3001917    0.58188418    1.0%
Leskela S (miR-429)          0.73236786    0.35855248    2.1%
Liu JY (miR-200a)            -1.0384584    0.44118763    1.5%
Lu YB (miR-141)               1.0892351    0.52107938    1.2%
Maierthaler M-1 (miR-141)    0.03729577    0.14280553    4.5%
Maierthaler M-1 (miR-200a)   0.05164321    0.14582744    4.5%
Maierthaler M-1 (miR-200b)   -0.25877071   0.15462647    4.3%
Maierthaler M-1 (miR-200c)   -0.17435342   0.12364553    4.8%
Maierthaler M-1 (miR-429)    -0.0325232    0.15037764    4.4%
Maierthaler M-2 (miR-200a)   0.20457216    0.09433242    5.1%
Maierthaler M-2 (miR-200b)   0.18896605    0.10938288    5.0%
Maierthaler M-2 (miR-141)    0.09984535    0.10230371    5.0%
Maierthaler M-2 (miR-200c)   0.14149952    0.08527347    5.2%
Maierthaler M-2 (miR-429)    0.00598211     0.089049     5.2%
Marchini S (miR-200b)        0.71832754    0.59408188    1.0%
Marchini S (miR-200c)         -1.410587    0.59565062    1.0%
Meng X (miR-200b)             1.0296194    0.46469705    1.4%
Meng X (miR-200c)            0.91629073    0.43698433    1.6%
Si L (miR-200c)              0.73955357    0.26711189    2.9%
Song F (miR-200a)            -0.32850403   0.22378577    3.4%
Song F (miR-200b)            -0.07257069   0.20551662    3.6%
Song F (miR-200c)            0.27763178    0.24231301    3.2%
Tejero R-1 (miR-200c/141)     1.0249657    0.48046198    1.4%
Tejero R-2 (miR-200c/141)     2.365466     0.75322631    0.6%
Toiyama Y-1 (miR-200c)        0.9820785    0.37967578    1.9%
Zhu W-1 (miR-429)             1.0112372    0.69363209    0.7%
Zhu W-2 (miR-429)             2.5552874    0.87987822    0.5%
Zou J (miR-429)              -0.27049723   0.26017933    3.0%

Total (95% CI)                                          100.0%

Heterogeneity: [[tau].sup.2] = 0.07; [chi square] = 152.90, df = 38 (P
< 0.0001); [I.sup.2] = 75%

Test for overall effect: Z = 4.30 (P < 0.0001)

(a)

Study or subgroup                     HR
                              IV, random, 95% CI

Antolin S (miR-200c)           1.38 (1.11, 1.71)
Cao Q (miR-200a)             22.69 (3.68, 139.96)
Cao Q (miR-200b)             20.28 (3.42, 120.37)
Cao Q (miR-200c)             21.42 (3.46, 132.66)
Cheng H-1 (miR-141)            3.80 (1.46, 9.90)
Cheng H-2 (miR-141)           4.83 (2.06, 11.34)
Cheng H-3 (miR-141)            3.61 (1.96, 6.65)
Gao YC (miR-141)               1.83 (1.00, 3.34)
Gao YC (miR-200c)              3.14 (1.67, 5.92)
Hu X (miR-200a)               0.70 (0.03, 15.28)
Liu XG (miR-141)              4.14 (0.47, 36.60)
Liu XG (miR-200c)             6.02 (1.34, 26.97)
Maierthaler M-1 (miR-141)      0.93 (0.71, 1.22)
Maierthaler M-1 (miR-200a)     0.93 (0.71, 1.20)
Maierthaler M-1 (miR-200b)     0.70 (0.52, 0.95)
Maierthaler M-1 (miR-200c)     0.81 (0.65, 1.01)
Maierthaler M-1 (miR-429)      0.95 (0.73, 1.23)
Maierthaler M-2 (miR-200a)     1.20 (0.99, 1.46)
Maierthaler M-2 (miR-200b)     1.17 (0.95, 1.45)
Maierthaler M-2 (miR-141)      1.07 (0.88, 1.31)
Maierthaler M-2 (miR-200c)     1.12 (095, 1.32)
Maierthaler M-2 (miR-429)      1.01 (0.85, 1.20)
Marchini S (miR-200b)          2.14 (0.80, 5.70)
Marchini S (miR-200c)          0.31 (0.11, 0.85)
Meng X (miR-200a)              1.70 (0.81, 3.56)
Meng X (miR-200b)              2.70 (1.29, 5.65)
Meng X (miR-200c)              2.40 (1.19, 4.85)
Song F (miR-200a)              0.82 (0.57, 1.19)
Song F (miR-200b)              0.87 (0.60, 1.26)
Song F (miR-200c)              1.19 (0.80, 1.77)
Sun Q (miR-200a)               0.58 (0.08, 4.13)
Toiyama Y-1 (miR-200c)         2.43 (1.26, 4.68)
Toiyama Y-2 (miR-200c)         0.56 (0.28, 1.11)
Zhu W-1 (miR-429)              1.69 (0.57, 4.99)
Zhu W-2 (miR-429)             6.46 (1.41, 29.60)
Zou J (miR-429)                0.64 (0.41, 1.00)

Total (95% CI)                 1.32 (1.14, 1.54)

Heterogeneity: [[tau].sup.2] = 0.12; [chi square] = 155.28, df = 35
(P < 0.0001); [I.sup.2] = 77%

Test for overall effect: Z = 3.60 (P = 0.0003)

(b)

Study or subgroup                     HR
                              IV, random, 95% CI

Antolin S (miR-141)            0.99 (0.94, 1.03)
Antolin S (miR-200c)           2.79 (1.01, 7.70)
Cao Q (miR-200a)              17.26 (3.31, 90.00)
Cao Q (miR-200b)              15.41 (2.93, 81.18)
Cao Q (miR-200c)              16.22 (3.14, 83.69)
Chao A (miR-200a)              1.47 (0.79, 2.73)
Cheng H-1 (miR-141)            1.36 (0.45, 4.13)
Cheng H-2 (miR-141)            3.41 (1.36, 8.55)
Cheng H-3 (miR-141)            2.40 (1.18, 4.87)
Dong SJ (miR-429)              2.30 (1.13, 4.65)
Han Y (miR-429)                1.85 (1.03, 3.35)
Kim MK (miR-200c)             3.67 (1.17, 11.48)
Leskela S (miR-429)            2.08 (1.03, 4.20)
Liu JY (miR-200a)              0.35 (0.15, 0.84)
Lu YB (miR-141)                2.97 (1.07, 8.25)
Maierthaler M-1 (miR-141)      1.04 (0.78, 1.37)
Maierthaler M-1 (miR-200a)     1.05 (0.79, 1.40)
Maierthaler M-1 (miR-200b)     0.77 (0.57, 1.05)
Maierthaler M-1 (miR-200c)     0.84 (0.66, 1.07)
Maierthaler M-1 (miR-429)      0.97 (0.72, 1.30)
Maierthaler M-2 (miR-200a)     1.23 (1.02, 1.48)
Maierthaler M-2 (miR-200b)     1.21 (0.97, 1.50)
Maierthaler M-2 (miR-141)      1.11 (0.90, 1.35)
Maierthaler M-2 (miR-200c)     1.15 (0.97, 1.36)
Maierthaler M-2 (miR-429)      1.01 (0.84, 1.20)
Marchini S (miR-200b)          2.05 (0.64, 6.57)
Marchini S (miR-200c)          0.24 (0.08, 0.78)
Meng X (miR-200b)              2.80 (1.13, 6.96)
Meng X (miR-200c)              2.50 (1.06, 5.89)
Si L (miR-200c)                2.10 (1.24, 3.54)
Song F (miR-200a)              0.72 (0.46, 1.12)
Song F (miR-200b)              0.93 (0.62, 1.39)
Song F (miR-200c)              1.32 (0.82, 2.12)
Tejero R-1 (miR-200c/141)      2.79 (1.09, 7.15)
Tejero R-2 (miR-200c/141)     10.65 (2.43, 46.61)
Toiyama Y-1 (miR-200c)         2.67 (1.27, 5.62)
Zhu W-1 (miR-429)             2.75 (0.71, 10.71)
Zhu W-2 (miR-429)             12.87 (2.30, 72.23)
Zou J (miR-429)                0.76 (0.46, 1.27)

Total (95% CI)                 1.32 (1.16, 1.49)

Heterogeneity: [[tau].sup.2] = 0.07; [chi square] = 152.90, df = 38 (P
< 0.0001); [I.sup.2] = 75%

Test for overall effect: Z = 4.30 (P < 0.0001)

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 HR and
95% CI. The area of the squares reflects the weight. The diamond
represents the summary HR and 95% CI. CI = confidence interval,
HR = hazard ratio.

(a)

Study or subgroup             Log [HR]         SE       Weight

Maierthaler M-1 (miR-141)    -0.09431065   0.13031265    6.5%
Maierthaler M-1 (miR-200a)   -0.07364652   0.13165921    6.3%
Maierthaler M-1 (miR-200b)   -0.33687234   0.14377133    5.3%
Maierthaler M-1 (miR-200c)   -0.19967119   0.11196252    8.7%
Maierthaler M-1 (miR-429)    -0.04709161   0.1279672     6.7%
Maierthaler M-2 (miR-200a)   0.16126811    0.09643573   11.8%
Maierthaler M-2 (miR-200b)   0.10345868    0.11047854    9.0%
Maierthaler M-2 (miR-141)    0.05543475    0.09900346   11.2%
Maierthaler M-2 (miR-200c)   0.07325044    0.08515377   15.1%
Maierthaler M-2 (miR-429)    0.07696108    0.08375748   15.6%
Meng X (miR-200a)             0.0953102    0.29405088    1.3%
Meng X (miR-200b)            0.47000364    0.2895357     1.3%
Meng X (miR-200c)            0.69314718     0.302455     1.2%

Total (95% CI)                                          100.0%

Heterogeneity: [chi square] = 22.68, df = 12 (P = 0.03); [I.sup.2] =
47%

Test for overall effect: Z = 0.73 (P = 0.47)

(b)

Study or subgroup             log [HR]         SE       Weight

Chao A (miR-200a)            0.19309668    0.2803797     1.5%
Leskela S (miR-429)          0.69813472    0.30436305    1.3%
Maierthaler M-1 (miR-141)    -0.00100049   0.13928306    6.3%
Maierthaler M-1 (miR-200a)   0.03052922    0.13855175    6.3%
Maierthaler M-1 (miR-200b)   -0.28768207   0.14873008    5.5%
Maierthaler M-1 (miR-200c)   -0.18032358   0.12163755    8.2%
Maierthaler M-1 (miR-429)    0.07325044     0.207976     2.8%
Maierthaler M-2 (miR-200a)    0.1823216    0.0986617    12.5%
Maierthaler M-2 (miR-200b)   0.13365639    0.11136058    9.8%
Maierthaler M-2 (miR-141)    0.08158002    0.10074562   12.0%
Maierthaler M-2 (miR-200c)    0.0953102    0.08583476   16.5%
Maierthaler M-2 (miR-429)    0.07510743    0.08643474   16.3%
Meng X (miR-200c)            0.53062828    0.3836932     0.8%

Total (95% CI)                                          100.0%

Heterogeneity: [chi square] = 17.95, df = 12 (P = 0.12); [I.sup.2] =
33%

Test for overall effect: Z = 1.82 (P = 0.07)

(a)

Study or subgroup                   HR
                             IV, fixed, 95% CI

Maierthaler M-1 (miR-141)    0.91 (0.70, 1.17)
Maierthaler M-1 (miR-200a)   0.93 (072, 1.20)
Maierthaler M-1 (miR-200b)   0.71 (0.54, 0.95)
Maierthaler M-1 (miR-200c)   0.82 (0.66, 1.02)
Maierthaler M-1 (miR-429)    0.95 (0.74, 1.23)
Maierthaler M-2 (miR-200a)   1.17 (0.97, 1.42)
Maierthaler M-2 (miR-200b)   1.11 (0.89, 1.38)
Maierthaler M-2 (miR-141)    1.06 (0.87, 1.28)
Maierthaler M-2 (miR-200c)   1.08 (0.91, 1.27)
Maierthaler M-2 (miR-429)    1.08 (0.92, 1.27)
Meng X (miR-200a)            1.10 (0.62, 1.96)
Meng X (miR-200b)            1.60 (0.91, 2.82)
Meng X (miR-200c)            2.00 (1.11, 3.62)

Total (95% CI)               1.32 (1.16, 1.49)

Heterogeneity: [chi square] = 22.68, df = 12 (P = 0.03); [I.sup.2] =
47%

Test for overall effect: Z = 0.73 (P = 0.47)

(b)

Study or subgroup                   HR
                             IV, fixed, 95% CI

Chao A (miR-200a)            1.21 (0.70, 2.10)
Leskela S (miR-429)          2.01 (1.11, 3.65)
Maierthaler M-1 (miR-141)    1.00 (0.76, 1.31)
Maierthaler M-1 (miR-200a)   1.03 (0.79, 1.35)
Maierthaler M-1 (miR-200b)   0.75 (0.56, 1.00)
Maierthaler M-1 (miR-200c)   0.83 (0.66, 1.06)
Maierthaler M-1 (miR-429)    1.08 (0.72, 1.62)
Maierthaler M-2 (miR-200a)   1.20 (0.99, 1.46)
Maierthaler M-2 (miR-200b)   1.14 (0.92, 1.42)
Maierthaler M-2 (miR-141)    1.09 (0.89, 1.32)
Maierthaler M-2 (miR-200c)   1.10 (0.93, 1.30)
Maierthaler M-2 (miR-429)    1.08 (0.91, 1.28)
Meng X (miR-200c)            1.70 (0.80, 3.61)

Total (95% CI)               1.07 (1.00, 1.14)

Heterogeneity: [chi square] = 17.95, df = 12 (P = 0.12); [I.sup.2] =
33%

Test for overall effect: Z = 1.82 (P = 0.07)

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 HR and
95% CI. The area of the squares reflects the weight. The diamond
represents the summary HR and 95% CI. CI = confidence interval, HR =
hazard ratio.

(a)

Study or subgroup        Log [HR]         SE       Weight

Antolin S (miR-200c)    0.31481074    0.11487645   25.9%
Hu X (miR-200a)         -0.44628712   0.53761596   14.2%
Marchini S (miR-200b)    1.1622129    0.4151385    17.5%
Marchini S (miR-200c)   -0.93649347   0.41493173   17.5%
Zou J (miR-429)         -0.41400142   0.16564117   24.8%

Total (95% CI)                                     100.0%

Heterogeneity: [[tau].sup.2] = 0.32; [chi square] = 26.81, df = 4 (P <
0.0001); [I.sup.2] = 85%

Test for overall effect: Z = 0.15 (P = 0.88)

(b)

Study or subgroup        Log [HR]         SE       Weight

Antolin S (miR-141)     -0.01308525   0.01938416   17.1%
Antolin S (miR-200c)     1.2029723    0.51176565    6.2%
Leskela S (miR-141)     0.85441529    0.44417908    7.4%
Leskela S (miR-200a)    0.19885088    0.38518069    8.6%
Leskela S (miR-200b)    0.30010461    0.39618322    8.4%
Leskela S (miR-200c)    0.80647587    0.41209695    8.0%
Leskela S (miR-429)      0.7419373    0.42089592    7.9%
Liu JY (miR-200a)       -0.92886949   0.32200044   10.1%
Marchini S (miR-200b)   0.84801191    0.51143299    6.2%
Marchini S (miR-200c)   -0.86988436   0.53821378    5.8%
Zou J (miR-429)         -0.34249034   0.17504555   14.2%

Total (95% CI)                                     100.0%

Heterogeneity: [[tau].sup.2] = 0.15; [chi square] = 34.56, df = 10 (P
< 0.0001); [I.sup.2] = 71%

Test for overall effect: Z = 1.00 (P = 0.32)

(a)

Study or subgroup               HR
                        IV, random, 95% CI

Antolin S (miR-200c)    1.37 (1.09, 1.72)
Hu X (miR-200a)         0.64 (0.22, 1.84)
Marchini S (miR-200b)   3.20 (1.42, 7.21)
Marchini S (miR-200c)   0.39 (0.17, 0.88)
Zou J (miR-429)         0.66 (0.48, 0.91)

Total (95% CI)          0.96 (0.54, 1.70)

Heterogeneity: [[tau].sup.2] = 0.32; [chi square] = 26.81, df = 4 (P <
0.0001); [I.sup.2] = 85%

Test for overall effect: Z = 0.15 (P = 0.88)

(b)

Study or subgroup               HR
                        IV, random, 95% CI

Antolin S (miR-141)     0.99 (0.95, 1.03)
Antolin S (miR-200c)    3.33 (1.22, 9.08)
Leskela S (miR-141)     2.35 (0.98, 5.61)
Leskela S (miR-200a)    1.22 (0.57, 2.60)
Leskela S (miR-200b)    1.35 (0.62, 2.93)
Leskela S (miR-200c)    2.24 (1.00, 5.02)
Leskela S (miR-429)     2.10 (0.92, 4.79)
Liu JY (miR-200a)       0.40 (0.21, 0.74)
Marchini S (miR-200b)   2.34 (0.86, 6.36)
Marchini S (miR-200c)   0.42 (0.15, 1.20)
Zou J (miR-429)         0.71 (0.50, 1.00)

Total (95% CI)          1.17 (0.86, 1.61)

Heterogeneity: [[tau].sup.2] = 0.15; [chi square] = 34.56, df = 10 (P
< 0.0001); [I.sup.2] = 71%

Test for overall effect: Z = 1.00 (P = 0.32)

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 HR and
95% CI. The area of the squares reflects the weight. The diamond
represents the summary HR and 95% CI. CI = confidence interval, HR =
hazard ratio.

(a)

Study or subgroup    Log [HR]         SE       Weight

Song F (miR-200a)   -0.21072103   0.1723866    34.3%
Song F (miR-200b)   -0.17435342   0.17253571   34.3%
Song F (miR-200c)   0.07696108    0.18015797   31.4%

Total (95% CI)                                 100.0%

Heterogeneity: [chi square] = 1.56, df = 2 (P = 0.46); [I.sup.2] = 0%

Test for overall effect: Z = 1.07 (P = 0.29)

(b)

Study or subgroup    Log [HR]         SE       Weight

Si L (miR-200c)     0.49895542    0.23007351   23.4%
Song F (miR-200a)   -0.40047754   0.20113709   25.6%
Song F (miR-200b)   -0.19845095   0.19228874   26.2%
Song F (miR-200c)   0.05826885    0.2108874    24.8%

Total (95% CI)                                 100.0%

Heterogeneity: [[tau].sup.2] = 0.09, [chi square] = 9.59, df = 3 (P =
0.02); [I.sup.2] = 69%

Test for overall effect: Z = 0.12 (P = 0.90)

(a)

Study or subgroup           HR
                    IV, random, 95% CI

Song F (miR-200a)   0.81 (0.58, 1.14)
Song F (miR-200b)   0.84 (0.60, 1.18)
Song F (miR-200c)   1.08 (0.76, 1.54)

Total (95% CI)      0.90 (0.74, 1.09)

Heterogeneity: [chi square] = 1.56, df = 2 (P = 0.46); [I.sup.2] = 0%

Test for overall effect: Z = 1.07 (P = 0.29)

(b)

Study or subgroup           HR
                    IV, random, 95% CI

Si L (miR-200c)     1.65 (1.05, 2.59)
Song F (miR-200a)   0.67 (0.45, 0.99)
Song F (miR-200b)   0.82 (0.56, 1.20)
Song F (miR-200c)   1.06 (0.70, 1.60)

Total (95% CI)      0.98 (0.68, 1.41)

Heterogeneity: [[tau].sup.2] = 0.09, [chi square] = 9.59, df = 3 (P =
0.02); [I.sup.2] = 69%

Test for overall effect: Z = 0.12 (P = 0.90)
COPYRIGHT 2018 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Liu, Wen; Zhang, Kaiping; Wei, Pengfei; Hu, Yue; Peng, Yaqin; Fang, Xiang; He, Guoping; Wu, Limin; C
Publication:Disease Markers
Date:Jan 1, 2018
Words:11326
Previous Article:Identification of Core Gene Biomarkers in Patients with Diabetic Cardiomyopathy.
Next Article:Sickle Cell Anemia Patients in Use of Hydroxyurea: Association between Polymorphisms in Genes Encoding Metabolizing Drug Enzymes and Laboratory...
Topics:

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters