The correlation between miR-200 family overexpression and cancer prognosis remains controversial. Therefore, we conducted a systematic review and meta-analysis by searching PubMed, Embase, Cochrane Library, China Biology Medicine disc (CBM), and China National Knowledge Infrastructure (CNKI) to identify eligible studies. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to evaluate the strength of the correlations. Additionally, different subgroup analyses and publication bias test were performed. Eventually, we analyzed 23 articles that included five tumor types and 3038 patients. Consequently, high expression of miR-200 family in various tumors was associated with unfavorable overall survival (OS) in both univariate (
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 [
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 [
The PRISMA statement was used to conduct the current meta-analysis [
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.
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.
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,
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).
Consequently, 23 studies consisted of 3038 samples satisfied the eligible criteria [
Flow diagram of the study selection process in the meta-analysis.
The principal characteristics of the eligible studies were summarized in Table
Main characteristics of the eligible studies.
First author | Year | Country | Age | Cancer type | MicroRNA | Sample size | Follow-up, median (range) | Outcome |
---|---|---|---|---|---|---|---|---|
Zou J. [ |
2017 | China | NA | EOC | miR-429 | 72 | NA | OS/PFS |
Han Y. [ |
2017 | China | NA | CRC | miR-429 | 71 | 34.2 | OS |
Maierthaler M. [ |
2017 | Germany | 70 (33–92) |
CRC | miR-200a, miR-200b, miR-200c, miR-141, miR-429 | 527 | NA | OS/RFS |
Si L. [ |
2017 | China | 60.5 (41–78) | NSCLC | miR-200c | 110 | NA | OS/DFS |
Meng X. [ |
2016 | Germany | 60 (23–91) | EOC | miR-200a, miR-200b, miR-200c | 163 | 20 (1–136) | OS/RFS |
Dong S. J. [ |
2016 | China | 56 (31–79) | CRC | miR-429 | 116 | NA | OS |
Antolín S. [ |
2015 | Spain | 54.8 (29–73) | BC | miR-200c, miR-141 | 57 | 74.6 (74.2–77.7) | OS/PFS |
Gao Y. C. [ |
2015 | China | NA | EOC | miR-200c, miR-141 | 93 | NA | OS |
Lu Y. B. [ |
2015 | China | NA | GC | miR-141 | 95 | NA | OS |
Liu J. Y. [ |
2015 | China | 57.48 | EOC | miR-200a | 44 | 26 (5–49) | OS/PFS |
Cao Q. [ |
2014 | China | 58 (26–88) | EOC | miR-200a, miR-200b, miR-200c | 100 | 36.8 (6–56) | OS |
Kim M. K. [ |
2014 | Korea | 64 (26–77) | NSCLC | miR-200c | 72 | 31 (1–135) | OS |
Zhu W. [ |
2014 | China | 59 | NSCLC | miR-429 | 70 | NA | OS |
Song F. [ |
2014 | China | 60.5 | GC | miR-200a, miR-200b, miR-200c | 385 | 35 (1–112) | OS/PFS |
Tejero R. [ |
2014 | Spain | 65 (35–85) | NSCLC | miR-200c/141 | 155 | 43 (2–160) | OS |
Toiyama Y. [ |
2014 | Japan | 67 | CRC | miR-200c | 182 | NA | OS |
Sun Q. [ |
2014 | China | NA | EOC | miR-200a | 53 | 56.79 (11–98) | OS |
Liu X. G. [ |
2012 | China | NA | NSCLC | miR-200c, miR-141 | 70 | 24 | OS |
Chao A. [ |
2012 | China | NA | EOC | miR-200a | 176 | 40 (3–109) | OS/RFS |
Marchini S. [ |
2011 | Italy | 52 (21–82) | EOC | miR-200b, miR-200c | 144 | 110.4 (82.8–139.2) | OS/PFS |
Cheng H. [ |
2011 | USA | NA | CRC | miR-141 | 156 | NA | OS |
Leskelä S. [ |
2010 | Spain | 57 (35–85) | EOC | miR-200a, miR-200b, miR-200c, miR-141, miR-429 | 72 | NA | OS/PFS/RFS |
Hu X. [ |
2009 | USA | 58.3 | EOC | miR-200a | 55 | NA | OS/PFS |
NA: not available; EOC: epithelial ovarian cancer; BC: breast cancer; NSCLC: nonsmall cell lung cancer; GC: gastric cancer; CRC: colorectal cancer; OS: overall survival; DFS: disease-free survival; PFS: progression-free survival; RFS: recurrence- or relapse-free survival;
Among these studies, Cheng’s study was involved with three different cohorts of Tianjin cohort, TexGen cohort, and all cohort [
MicroRNA evaluation and survival data of the selected studies.
First author | Year | Country | Test method | Cancer type | MicroRNA | Sample source | Outcome | Cut-off value | |
---|---|---|---|---|---|---|---|---|---|
Zou J. | 2017 | China | RT-PCR | EOC | miR-429 | Tissue | OS | (U) 0.641 (0.412–0.996)/(M) 0.763 (0.458–1.270) | >0.532 |
Zou J. | 2017 | China | RT-PCR | EOC | miR-429 | Tissue | PFS | (U) 0.661 (0.478–0.915)/(M) 0.710 (0.504–1.001) | |
Han Y. | 2017 | China | RT-PCR | CRC | miR-429 | Tissue | OS | (M) 1.852 (1.019–3.326) | Median |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-200a | Blood | OS | (U) 0.929 (0.707–1.211)/(M) 1.053 (0.791–1.401) | Median |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-200b | Blood | OS | (U) 0.704 (0.524–0.945)/(M) 0.772 (0.570–1.045) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-200c | Blood | OS | (U) 0.808 (0.646–1.010)/(M) 0.840 (0.659–1.070) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-141 | Blood | OS | (U) 0.925 (0.713–1.200)/(M) 1.038 (0.785–1.374) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-429 | Blood | OS | (U) 0.951 (0.734–1.235)/(M) 0.968 (0.721–1.300) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-200a | Blood | OS | (U) 1.198 (0.986–1.456)/(M) 1.227 (1.008–1.495) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-200b | Blood | OS | (U) 1.172 (0.946–1.453)/(M) 1.208 (0.975–1.497) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-200c | Blood | OS | (U) 1.117 (0.947–1.318)/(M) 1.152 (0.975–1.362) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-141 | Blood | OS | (U) 1.071 (0.877–1.305)/(M) 1.105 (0.904–1.350) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-429 | Blood | OS | (U) 1.010 (0.853–1.196)/(M) 1.006 (0.845–1.198) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-200a | Blood | RFS | (U) 0.929 (0.718–1.203)/(M) 1.031 (0.786–1.353) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-200b | Blood | RFS | (U) 0.714 (0.539–0.947)/(M) 0.750 (0.561–1.005) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-200c | Blood | RFS | (U) 0.819 (0.657–1.019)/(M) 0.835 (0.658–1.060) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-141 | Blood | RFS | (U) 0.910 (0.705–1.175)/(M) 0.999 (0.760–1.312) | |
Maierthaler M.-1 | 2017 | Germany | TaqMan | CRC | miR-429 | Blood | RFS | (U) 0.954 (0.743–1.227)/(M) 1.076 (0.716–1.618) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-200a | Blood | RFS | (U) 1.175 (0.973–1.420)/(M) 1.200 (0.989–1.456) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-200b | Blood | RFS | (U) 1.109 (0.893–1.377)/(M) 1.143 (0.919–1.422) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-200c | Blood | RFS | (U) 1.076 (0.911–1.272)/(M) 1.100 (0.930–1.302) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-141 | Blood | RFS | (U) 1.057 (0.871–1.284)/(M) 1.085 (0.890–1.321) | |
Maierthaler M.-2 | 2017 | Germany | TaqMan | CRC | miR-429 | Blood | RFS | (U) 1.080 (0.916–1.272)/(M) 1.078 (0.910–1.277) | |
Si L. | 2017 | China | RT-PCR | NSCLC | miR-200c | Tissue | OS | (M) 2.095 (1.241–3.536) | The 2−ΔΔCq |
Si L. | 2017 | China | RT-PCR | NSCLC | miR-200c | Tissue | DFS | (M) 1.647 (1.049–2.585) | |
Meng X. | 2016 | Germany | RT-PCR | EOC | miR-200a | Blood | OS | (U) 1.7 (0.8–3.5) | Median |
Meng X. | 2016 | Germany | RT-PCR | EOC | miR-200b | Blood | OS | (U) 2.7 (1.3–5.7)/(M) 2.8 (1.1–6.8) | |
Meng X. | 2016 | Germany | RT-PCR | EOC | miR-200c | Blood | OS | (U) 2.4 (1.2–4.9)/(M) 2.5 (1.1–6.1) | |
Meng X. | 2016 | Germany | RT-PCR | EOC | miR-200a | Blood | RFS | (U) 1.1 (0.6–1.9) | |
Meng X. | 2016 | Germany | RT-PCR | EOC | miR-200b | Blood | RFS | (U) 1.6 (0.9–2.8) | |
Meng X | 2016 | Germany | RT-PCR | EOC | miR-200c | Blood | RFS | (U) 2.0 (1.1–3.6)/(M) 1.7 (0.8–3.6) | |
Dong S. J. | 2016 | China | RT-PCR | CRC | miR-429 | Tissue | OS | (M) 2.296 (1.105–4.528) | Median |
Antolín S. | 2015 | Spain | RT-PCR | BC | miR-200c | Blood | OS | (U) 1.38 (1.11–1.71)/(M) 2.79 (1.01–7.7) | >1.29 relative expression value |
Antolín S. | 2015 | Spain | RT-PCR | BC | miR-200c | Blood | PFS | (U) 1.37 (1.09–1.71)/(M) 3.33 (1.22–9.07) | |
Antolín S. | 2015 | Spain | RT-PCR | BC | miR-141 | Blood | OS | (M) 0.986 (0.942–1.032) | |
Antolín S. | 2015 | Spain | RT-PCR | BC | miR-141 | Blood | PFS | (M) 0.987 (0.95–1.025) | |
Gao Y. C. | 2015 | China | RT-PCR | EOC | miR-200c | Blood | OS | (U) 3.14 (1.67–5.93) | −ΔCt method with 95% CI |
Gao Y. C. | 2015 | China | RT-PCR | EOC | miR-141 | Blood | OS | (U) 1.83 (1.00–3.33) | |
Lu Y. B. | 2015 | China | RT-PCR | GC | miR-141 | Tissue | OS | (M) 2.972 (1.297–10.001) | Median |
Liu J. Y. | 2015 | China | RT-PCR | EOC | miR-200a | Tissue | OS | (M) 0.354 (0.149–0.840) | Log 2−ΔΔCt |
Liu J. Y. | 2015 | China | RT-PCR | EOC | miR-200a | Tissue | PFS | (M) 0.395 (0.210–0.742) | |
Cao Q | 2014 | China | ISH | EOC | miR-200a | Tissue | OS | (U) 22.69 (1.32–50.53)/(M) 17.26 (1.36–36.98) | Median |
Cao Q. | 2014 | China | ISH | EOC | miR-200b | Tissue | OS | (U) 20.28 (1.20–42.28)/(M)15.41 (1.13–31.36) | |
Cao Q. | 2014 | China | ISH | EOC | miR-200c | Tissue | OS | (U) 21.42 (1.26–48.33)/(M) 16.22 (1.27–33.81) | |
Kim M. K. | 2014 | Korea | RT-PCR | NSCLC | miR-200c | FFPE | OS | (M) 3.67 (1.17–11.45) | Median |
Zhu W.-1 | 2014 | China | RT-PCR | NSCLC | miR-429 | Tissue | OS | (U) 1.686 (0.570–4.984)/(M) 2.749 (0.706–10.707) | Mean |
Zhu W.-2 | 2014 | China | RT-PCR | NSCLC | miR-429 | Blood | OS | (U) 6.458 (1.409–29.593)/(M) 12.875 (2.295–72.23) | |
Song F. | 2014 | China | RT-PCR | GC | miR-200a | TMA | OS | (U) 0.82 (0.57–1.20)/(M) 0.72 (0.47–1.13) | Median |
Song F. | 2014 | China | RT-PCR | GC | miR-200b | TMA | OS | (U) 0.87 (0.60–1.26)/(M)0.93 (0.63–1.41) | |
Song F. | 2014 | China | RT-PCR | GC | miR-200c | TMA | OS | (U) 1.19 (0.80–1.77)/(M) 1.32 (0.82–2.12) | |
Song F. | 2014 | China | RT-PCR | GC | miR-200a | TMA | DFS | (U) 0.81 (0.58–1.14)/(M) 0.67 (0.45–0.99) | |
Song F. | 2014 | China | RT-PCR | GC | miR-200b | TMA | DFS | (U) 0.84 (0.60–1.18)/(M) 0.82 (0.56–1.19) | |
Song F. | 2014 | China | RT-PCR | GC | miR-200c | TMA | DFS | (U) 1.08 (0.76–1.54)/(M) 1.06 (0.70–1.60) | |
Tejero R.-1 | 2014 | Spain | TaqMan | NSCLC | miR-200c/141 | FFPE | OS | (M) 2.787 (1.087–7.148) | Mean |
Tejero R.-2 | 2014 | Spain | TaqMan | NSCLC | miR-200c/141 | FFPE | OS | (M) 10.649 (2.433–46.608) | |
Toiyama Y.-1 | 2014 | Japan | RT-PCR | CRC | miR-200c | Blood | OS | (U) 2.43 (1.26–4.68)/(M)2.67 (1.28–5.67) | Median |
Toiyama Y.-2 | 2014 | Japan | RT-PCR | CRC | miR-200c | FFPE | OS | (U) 0.56 (0.28–1.10) | |
Sun Q. | 2014 | China | RT-PCR | EOC | miR-200a | TMA | OS | (U) 0.58 (0.08–4.05) | Median (≥12.623) |
Liu X. G. | 2012 | China | RT-PCR | NSCLC | miR-200c | Tissue | OS | (U) 6.020 (1.344–26.971) | 2−ΔΔCt > 2.0 |
Liu X. G. | 2012 | China | RT-PCR | NSCLC | miR-141 | Tissue | OS | (U) 4.135 (0.467–36.597) | |
Chao A. | 2012 | China | RT-PCR | EOC | miR-200a | FFPE | OS | (M) 1.466 (0.786–2.734) | Log ratio > 1.3 |
Chao A. | 2012 | China | RT-PCR | EOC | miR-200a | FFPE | RFS | (M) 1.213 (0.70–2.101) | |
Marchini S. | 2011 | Italy | RT-PCR | EOC | miR-200b | Tissue | OS | (U) 2.137 (0.801–5.701)/(M) 2.051 (0.640–6.570) | >25% |
Marchini S. | 2011 | Italy | RT-PCR | EOC | miR-200b | Tissue | PFS | (U) 3.197 (1.417–7.213)/(M) 2.335 (0.857–6.363) | |
Marchini S. | 2011 | Italy | RT-PCR | EOC | miR-200c | Tissue | OS | (U) 0.309 (0.112–0.850)/(M) 0.244 (0.076–0.785) | |
Marchini S. | 2011 | Italy | RT-PCR | EOC | miR-200c | Tissue | PFS | (U) 0.392 (0.174–0.885)/(M) 0.419 (0.146–1.204) | |
Cheng H.-1 | 2011 | USA | RT-PCR | CRC | miR-141 | Blood | OS | (U) 3.80 (1.46–9.91)/(M) 1.36 (0.45–4.14) | 2−ΔΔCt |
Cheng H.-2 | 2011 | USA | RT-PCR | CRC | miR-141 | Blood | OS | (U) 4.83 (2.06–11.35)/(M) 3.41 (1.36–8.56) | |
Cheng H.-3 | 2011 | USA | RT-PCR | CRC | miR-141 | Blood | OS | (U) 3.61 (1.96–6.65)/(M) 2.40 (1.18–4.86) | |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-200a | FFPE | PFS | (M) 1.22 (0.57–2.58) | 75% of positive cells |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-200b | FFPE | PFS | (M) 1.35 (0.62–2.93) | |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-200c | FFPE | PFS | (M) 2.24 (1.00–5.03) | |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-141 | FFPE | PFS | (M) 2.35 (0.98–5.59) | |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-429 | FFPE | PFS | (M) 2.10 (0.92–4.79) | |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-429 | FFPE | RFS | (M) 2.01 (1.11–3.66) | |
Leskelä S. | 2010 | Spain | RT-PCR | EOC | miR-429 | FFPE | OS | (M) 2.08 (1.03–4.20) | |
Hu X. | 2009 | USA | RT-PCR | EOC | miR-200a | FFPE | OS | (U) 0.70 (0.03–14.29) | >11 |
Hu X. | 2009 | USA | RT-PCR | EOC | miR-200a | FFPE | PFS | (U) 0.64 (0.22–1.81) |
EOC: epithelial ovarian cancer; BC: breast cancer; NSCLC: nonsmall cell lung cancer; NMIBC: nonmuscle-invasive bladder cancer; GC: gastric cancer; CRC: colorectal cancer; OS: overall survival; DFS: disease-free survival; PFS: progression-free survival; RFS: recurrence- or relapse-free survival;
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 (
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
Stratified analysis of the high expression of the miR-200 family and overall survival.
Categories | Subgroups | Univariate analyses | Multivariate analyses | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Number of datasets | Ph | Number of datasets | Ph | ||||||||
All | 19 | <0.001 | 77.50% | <0.001 | 24 | <0.001 | 75.10% | <0.001 | |||
Patient source | Asia | 10 | 0.003 | 80.10% | <0.001 | 13 | 0.001 | 78.20% | <0.001 | ||
Europe | 5 | 1.07 (0.95–1.21) | 0.286 | 66.80% | <0.001 | 8 | 1.11 (0.99–1.24) | 0.071 | 67.10% | <0.001 | |
North America | 4 | <0.001 | 0.00% | 0.685 | 3 | 0.001 | 0.00% | 0.457 | |||
Cancer type | EOC | 7 | 0.008 | 79.90% | <0.001 | 7 | 0.039 | 81.80% | <0.001 | ||
CRC | 7 | 1.12 (0.96–1.31) | 0.140 | 77.70% | <0.001 | 8 | 0.026 | 60.10% | 0.001 | ||
NSCLC | 3 | 0.001 | 0.00% | 0.411 | 6 | <0.001 | 33.40% | 0.185 | |||
GC | 1 | 0.94 (0.75–1.17) | 0.565 | 1.90% | 0.361 | 2 | 1.10 (0.72–1.68) | 0.669 | 62.30% | 0.047 | |
BC | 1 | 0.003 | / | / | 1 | 1.46 (0.54–3.91) | 0.454 | 75.10% | 0.045 | ||
Test method | RT-PCR | 16 | 1.64 (1.24–2.16) | 0.001 | 75.30% | <0.001 | 19 | <0.001 | 75.10% | <0.001 | |
ISH | 1 | <0.001 | 0.00% | 0.996 | 1 | <0.001 | 0.00% | 0.995 | |||
TaqMan | 2 | 1.01 (0.95–1.08) | 0.686 | 47.70% | 0.046 | 4 | 1.07 (0.95–1.20) | 0.249 | 58.80% | 0.005 | |
Sample source | FFPE | 2 | 0.57 (0.29–1.10) | 0.095 | 0.00% | 0.890 | 5 | <0.001 | 43.10% | 0.135 | |
Tissue | 5 | 0.021 | 84.40% | <0.001 | 9 | 0.017 | 80.70% | <0.001 | |||
Blood | 10 | <0.001 | 79.00% | <0.001 | 9 | 0.019 | 68.30% | <0.001 | |||
TMA | 2 | 0.93 (0.75–1.16) | 0.527 | 0.00% | 0.519 | 1 | 0.94 (0.73–1.21) | 0.649 | 40.90% | 0.184 | |
Sample size | ≧100 | 11 | 0.007 | 78.90% | <0.001 | 14 | 0.001 | 71.60% | <0.001 | ||
<100 | 8 | 0.018 | 68.50% | 0.001 | 10 | 0.008 | 79.60% | <0.001 | |||
miR-200 component | miR-200a | 7 | 1.14 (0.81–1.61) | 0.438 | 64.80% | 0.009 | 6 | 1.07 (0.72–1.59) | 0.723 | 78.30% | <0.001 |
miR-200b | 6 | 1.38 (0.88–2.16) | 0.166 | 82.10% | <0.001 | 6 | 1.36 (0.89–2.08) | 0.158 | 76.70% | 0.001 | |
miR-200c | 11 | 0.040 | 82.40% | <0.001 | 10 | 0.010 | 79.30% | <0.001 | |||
miR-141 | 7 | 0.003 | 83.50% | <0.001 | 7 | 1.24 (0.99–1.56) | 0.060 | 68.00% | 0.005 | ||
miR-429 | 5 | 0.99 (0.73–1.34) | 0.953 | 62.20% | 0.032 | 8 | 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;
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 (
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:
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
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
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
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
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.
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.
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.
Begg’s funnel plot indicated that there was a significant publication bias in meta-analysis of OS under both univariate analysis (
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 [
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 [
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.
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.
All data have been shared in the figures and tables.
The authors have no conflict of interests to declare.
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.
This work was supported by grants from the National Natural Science Foundation of China (81601600) and the China Postdoctoral Science Foundation (2016M590576, 2017T100455).