The definition of normal values of two-dimensional speckle-tracking echocardiography derived left ventricular (LV) deformation parameters, is of critical importance for the routine application of this modality in children. The objectives of this study were to perform a meta-analysis of normal ranges for longitudinal, circumferential and radial strain/strain rate values and to identify confounders that may contribute to differences in reported measures.A systematic search was conducted. Studies describing normal healthy subjects and observational studies that used control groups as a comparison were included. Data were combined using a random-effect model. Effects of demographic, clinical and equipment variables were assessed through meta-regression. The search identified 1,192 subjects form 28 articles. Longitudinal strain (LS) normal mean values varied from -12.9 to -26.5 . Normal mean values of circumferential strain (CS) varied from -10.5 to -27.0 . Radial strain (RS) normal mean values varied from 24.9 to 62.1 . Meta-regression showed LV end diastolic diameter as a significant determinant of variation for LS. Longitudinal systolic strain rate (LSRs) was significantly determined by the age and RS by the type of vendor used.Variations among different normal ranges were dependent on the vendor used, LV end-diastolic diameter and age. Vendor-independent software for analyzing myocardial deformation in children, using images from different vendors would be the ideal solution for strain measurements or else using the same system for patient’s follow up. As left ventricular (LV) function is an important predictor of clinical outcome in various systemic and congenital heart diseases, new echocardiographic modalities and techniques are developed to help accurate assessment of segmental and global myocardial deformation. The most recent development is the two-dimensional speckle-tracking echocardiography (2D STE), which is a relatively angle-independent method for assessing myocardial strain and is used to quantify deformation function –4. STE hThe objective of this study was to conduct a meta-analysis of normal ranges of LV myocardial deformation measurements (strain and strain rate) derived from 2D STE in longitudinal, circumferential and radial planes in children and to identify confounders that may contribute to differences and variability in the reported measures.MEDLINE, Embase, Scopus, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov were searched using the key terms “strain”, “speckle tracking”, “2DSTE”, “myocardial deformation”, “echocardiography”, “left ventricle”, “children”, “infant”, “newborn”,”neonate”, “toddler” and “adolescent”. The search was completed in May 2015. No language filter was applied. Two investigators  independently examined the resulted articles. To ensure accurate identification of all relevant articles, the reference lists of the articles were manually searched to further identify studies of interest that comply with the inclusion criteria.Only articles reporting LV strain using 2D speckle-tracking echocardiography in normal healthy children were included. Also, this meta-analysis incorporated studies that explicitly described normal healthy subjects as well as observational studies that used control groups as a comparison for study population. Since myocardial deformation parameters were reported to change between different age groups, classifications previously used by Marcus et al.  and KlitThe commonly used 2D STE measures included were LS  and LSRs . Nine studies measured global longitudinal LV strain in apical long-axis and 2- and 4-chamber views using automated function imaging, a novel 2-dimensional speckle tracking algorithm (bull’s eye). Other studies used only 4-chamber view to measure LS, which represents an average of three septal and three lateral segmental strains. Hence, in order to estimate LV longitudinal segmental strain, we averaged the values from the base, mid-cavity and apical segments of lateral and septal walls to obtain comparable patterns with the bull’s eye model.Since, global circumferential and radial strain was reported only in 8 and 3 datasets respectively, when also considering different age groups, it seemed not eligible for conducting a Meta analysis. However, circumferential and radial strain at the level of papillary muscles could be extracted from 24 and 18 datasets respectively, hence, Meta analysis of mid-level short axis deformation parameters was conducted.GE echocardiograph was used in 24 studies, MyLab, Siemens and Philips in one study each. One study used three different types of echocardiographs and analyzed them using Tomtec system, a vendor independent software  and verified by a second investigator (PI). Mean LS , LSRs  as well as deformation measurements at the papillary muscle level including; CS , CSRs , RS  and RSRs  were extracted from paper’s text or tables. When only segmental measures were available, the global longitudinal strain was calculated by pooling means and variances. A comparison between LV longitudinal segments degree of basal-to-apex gradient was conducted for all studies when available. For any unclear information, we requested additional information from the study investigators and allowed two weeks for them to respond. When they did not respond within two weeks, we used the information available.In the absence of corresponding author’s response, studies with incomplete data were not included –15 as weStudies that did not measure longitudinal strain of the whole LV were not included for LS/LSRs analysis , 19.Studies where only Global CS and/or RS were available and the strain at the level of papillary muscles could not be extracted were not included for that analysis , 21.One study was not included due to highly irrelevant data presented .After full text revision of preliminary selected studies further filtration was conducted.An adopted Downs and Black score suitable for meta-analysis of normal ranges was used , 8 Appe. Not all2 which is the percentage of total variance across studies attributable to heterogeneity rather than chance [The means and 95 % confidence intervals (CI) of LS, LSRs, CS, CSRs, RS, RSRs were calculated using random-effects models weighted by inverse variance . Betweenn chance . The infStatistical analysis was performed using standard software packages , with two-tailed P values <0.05 considered significant. Analysis is presented in forest plots, which is the standard way for illustrating the results of individual studies and meta-analyses. The forest plot was used as a graphical display of the relative strengths of the effect estimates and CIs for each of the individual studies, age groups and the entire meta-analysis , 8. PublOur search identified 282 articles. After excluding duplicates and triplicates (n = 97), 185 studies were screened for relevance, from which only 28 fulfilled the inclusion criteria  and inconsistency by I2 was 94.47 %  and probe frequency  were the only significant determinants of variations between reported ranges of LS measurements (Table p = 0.88) as shown by the Egger’s test for the LS . Overall study heterogeneity (within and between age group studies) as shown by Cochran’s Q test was 596 (p < 0.0001) and inconsistency was 96.9 %. Meta-regression of variables available in more than 10 studies, showed age as the only significant determinant of LSRs variations . In addition, Egger’s test for LSRs suggested significant publication bias (p = 0.011). However, even after using Duval and Tweedie’s trim and fill, and adjusting the effect size for funnel plot asymmetry the adjusted effect was the same to the original effect.Normal values for LSRs for all 13 data sets combined varied from -0.9 to -2.7   and inconsistency was 96.1 %. Meta-regression analysis did not find any significant determinant of variation among variables present in more than ten studies. There was no evidence for publication bias (p = 0.85).Normal mean values of CS for all 24 age group studies combined varied from -10.5 to -27.0   and I2 showed inconsistency of 96.25 %. Meta-regression analysis was not conducted due to small number of available studies (less than ten). There was no evidence for publication bias (p = 0.52).CSRs meta-analysis from six datasets of different age groups varied from -0.84 to -1.84  Fig. . Cochranp < 0.0001) and inconsistency was 99.03 %. Vendor was the only significant determinant of inter-study variability (p = 0.005). There was no evidence for publication bias (p = 0.122). Three studies of different age groups were not enough for meta-analysis of RSRs.RS meta-analysis from 18 datasets of different age groups varied from 24.9 to 62.1  . In addition, only RS resulted to be significantly influenced by the change of vendor (p = 0.005).Even though all myocardial deformation parameters seemed to differ between age groups, only LSRs was significantly affected by the age of children with β = 0.04 . However, it turned insignificant in a bivariate model together with age (p = 0.07). Furthermore, examination of the LS forest plot also suggested higher variability in the neonates and infants. Low number of studies in these age groups might also play role for such variability.Of the LV volumes and dimensions, the LVEDD effect on LS was the only variable tested, because others were scarcely reported. LS decreased as the LVEDD increased. Higher probe frequencies were significantly related to higher LS values (p = 0.1). Moreover, variables such as BSA, HR, SBP, DBP, LV mass, tissue tracking and FR did not significantly affected tested parameters.There was no difference between longitudinal strain values generated by two different models, global longitudinal strain (bull’s eye model) and six-segment longitudinal strain model . Also, in the meta-regression analysis we found that age did not significantly affect the base-to-apex gradient (p = 0.53). However, when analyzing LS in individual age groups, significant base-to-apex gradient was evident in 5-9 and 10-14 years age groups .Segmental strain values were available in 18 datasets. Of these, four used bull’s eye model and 14 used six-segment model. Even though, an increasing longitudinal strain from base towards apex is noticed, it was not significant for all group ages together . However, since all vendors other than GE reporting on base-to-apex gradient used endocardial in spite of endomyocardial tissue tracking, such influence could not be determined if it is vendor or tissue tracking dependent. In addition, BSA, HR, SBP, DBP, LV mass, probe frequency, model used to generate LV strain and FR did not significantly affected base-to-apex gradient.The type of vendor and tissue tracking used were just about to significantly affect the base-to-apex gradient , mean LSRs is -1.3 , mean CS is -22.06 , mean CSRs is -1.33  and mean RS is 45.47 . Variations among different normal ranges were dependent on the vendor used, LV end-diastolic dimension and age. Vendor-independent software for analyzing myocardial deformation in children, using images from different vendors would be the ideal solution for strain measurements or else using the same system for patient’s follow up.


We further performed a subgroup analysis, and found significant association between rs10757278 polymorphism and MI in Asian and Caucasian populations. We identified that the association between rs10757278 polymorphism and MI did not vary substantially by excluding any one study. However, the heterogeneity among the selected studies varies substantially by excluding the study from the Pakistan population. We found even more significant association between rs10757278 polymorphism and MI in pooled population, p = 3.55 × 10−53, after excluding the study from the Pakistan population. In summary, previous studies reported weak or no significant association between rs10757278 polymorphism and MI. Interestingly, our analysis suggests that rs10757278 polymorphism is significantly associated with MI susceptibility by analyzing large-scale samples.Large-scale genome-wide association studies (GWAS) have revealed that rs10757278 polymorphism (or its proxy rs1333049) on chromosome 9p21 is associated with myocardial infarction (MI) susceptibility in individuals of Caucasian ancestry. Following studies in other populations investigated this association. However, some of these studies reported weak or no significant association. Here, we reevaluated this association using large-scale samples by searching PubMed and Google Scholar databases. Our results showed significant association between rs10757278 polymorphism and MI with  Myocardial infarction (MI) is a complex human disease with a strong genetic component . MI is het al. investigated a total of 4587 MI cases and 12,767 controls [p = 1.00 × 10−20, odds ratio (OR) = 1.28, 95% confidence interval (CI) 1.22–1.35) [Helgadottir controls . They idhttp://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar databases (http://scholar.google.com/).GWAS and candidate gene studies also investigated the association between rs10757278 polymorphism and MI in other populations. Some studies reported significant association between rs10757278 polymorphism and MI ,5,6,7,8.A total of 114 articles were identified through PubMed database and 17 independent studies were finally included for following analysis. More detailed information about the inclusion or exclusion of selected studies was described in p = 0.0021 and I2 = 56.8%. We calculated the overall OR by the random-effect model. Our results showed significant association between rs10757278 polymorphism and MI with p = 6.09 × 10−22, OR = 1.29, 95% CI 1.22–1.36  and Caucasian population (p = 0.1354 and I2 = 46%).However, we observed moderate heterogeneity (I2 = 25%–50%). We found significant association between rs10757278 polymorphism and MI in Asian population with p = 1.82× 10−17, OR = 1.21, 95% CI 1.16–1.27 and Caucasian population with p = 8.51× 10−39, OR = 1.34, 95% CI 1.28–1.40.We further performed a subgroup analysis in Asian and Caucasian populations. We did not identify significant heterogeneity in Asian (p = 0.0733 and I2 = 36.3%) and Asian population (p = 0.4283 and I2 = 0%). We found significant association between rs10757278 polymorphism and MI in Asian population with p = 6.22 × 10−17, OR = 1.26, 95% CI 1.20–1.34 and pooled population with p = 3.55 × 10−53, OR = 1.31, 95% CI 1.26–1.35.By excluding any one study, we identified that the association between rs10757278 polymorphism and MI did not vary substantially. By excluding the study from the Pakistan population, we observed no heterogeneity in pooled population  and MI [Large-scale GWAS reported the association between rs10757278 variant and its proxy rs1333049  ) and MI ,12,13,14n = 42,963). By careful quality evaluation, data extraction, heterogeneity test, meta-analysis, sensitivity analysis, and publication bias analysis, we observed significant association between rs10757278 polymorphism and MI with p = 6.09 × 10−22, OR = 1.29, 95% CI 1.22–1.36.The novelty and significance of this study can be described as the following. First, we conducted a careful literature search in PubMed and Google Scholar databases. We reevaluated this association using the relatively large-scale samples (I2 = 25%–50%). We found significant association between rs10757278 polymorphism and MI in Asian population and Caucasian population. We identified that the association between rs10757278 polymorphism and MI did not vary substantially by excluding any one study. However, the heterogeneity among the selected studies varies substantially by excluding the study from the Pakistan population. We found even more significant association between rs10757278 polymorphism and MI in pooled population with p = 3.55 × 10−53 after excluding the study from Pakistan population.Second, we further performed a subgroup analysis in Asian and Caucasian populations. We observed moderate heterogeneity , we accessed the PubMed database. We did not find any study investigating the association between the rs10757278 polymorphism and MI by a meta-analysis method. To our knowledge, this is the first meta-analysis that further supports the association between rs10757278 polymorphism and MI susceptibility.et al. performed a retrospective analysis of data collected prospectively in two independent registries of consecutive patients to investigate the association of the 9p21.3 locus  with five-year overall mortality in patients with ST-elevation myocardial infarction [et al. investigated whether rs10757278 was associated with acute coronary syndrome (ACS) in a Chinese Han population [Szpakowicz farction . They fofarction . Zeng etet al. performed a systematic review and meta-analysis of the association between genetic variants at chromosome 9p21 and risk of first versus subsequent CHD events [versus subsequent CHD events [It is recognized that the human chromosome 9p21 is a risk factor for a first coronary heart disease (CHD) event. Until now, it is unclear about the association of 9p21 with risk of subsequent events in patients with established CHD. Patel D events . They caD events . Their rD events . The 9p2D events . In estaD events .Despite these interesting results, we also realized a limitation in this study. Here, we investigated the association between rs10757278 and MI with additive model. It is reported that most meta-analyses used an additive genetic model . In geneWe searched PubMed and Google Scholar databases to select all possible studies with key words “Myocardial infarction”, “rs10757278”or “rs1333049” or “9p21”. The literature search was updated on 17 December 2014.The selected studies must (1) use a case-control design; (2) evaluate the association between rs10757278 (or its proxy rs1333049) polymorphism and MI; (3) provide an OR with 95% CI for allele model; or (4) provide sufficient data to calculate the OR and 95% CI for allele model; and (5) rs10757278 (or its proxy rs1333049) polymorphism must be in Hardy-Weinberg equilibrium (HWE).et al. were selected to evaluate the quality of selected studies [The quality evaluation criteria proposed by Clark  studies . This sc studies . These s studies .For all the selected studies, we extracted (1) the name of the first author; (2) the year of publication; (3) the population and ethnicity; (4) the numbers of MI cases and controls; (5) the genotyping platform; (6) the OR with 95% CI or to calculate the OR and 95% CI; and (7) the quality score.r2) = 0.98, LD (D’) = 0.99 in EUR (European) population, LD (r2) = 0.94, LD (D’) = 0.98 in ASN (East Asian) population, and LD (r2) = 0.97, LD (D’) = 0.99 in AMR (Ad Mixed American) population. The rs10757278 polymorphism includes A and G alleles, among which A is the reference allele and G is the variant allele. The rs1333049 polymorphism includes G and C alleles, among which G is the reference allele and C is the variant allele. The frequencies of the rs10757278 (A) and rs1333049 (C) are also almost equivalent  , we idenuivalent . We seles1333049 .Q test andQ test approximately follows a χ2 distribution with k−1 degrees of freedom . I2 is a measure of heterogeneity and a statistic that indicates the percentage of variance in a meta-analysis that is attributable to study heterogeneity [p < 0.01 from Cochran’s Q test and I2 > 50% were considered to be statistically significant heterogeneity.Genetic heterogeneity among the selected studies is evaluated using Cochran’s ogeneity . Low, moogeneity . A p < 0Z test is used to determine the significance of OR. All statistical tests for heterogeneity and meta-analysis were computed using R Package .If there is no significant heterogeneity among the included studies, the pooled OR is calculated by the fixed effect model (Mantel-Haenszel), otherwise the OR is calculated by random-effect model (Der Simonian-Laird). We omit each study, one at a time, to assess the influence of each individual study on the pooled OR and 95% CI as well as the association between rs10757278 and MI.et al. is used to investigate potential publication bias [et al., is used to test for publication bias, which evaluate the asymmetry of the funnel plot to provide statistical evidence, with a p < 0.01 indicating that there was a significant publication bias [A funnel plot from Egger ion bias ,30. Meanion bias .Previous studies reported weak or no significant association between rs10757278 polymorphism and MI. Our analysis suggests that rs10757278 polymorphism is significantly associated with MI susceptibility by analyzing large-scale samples.


DAPK1) is an important tumor suppressor gene. DNA methylation can inactivate genes, which has often been observed in the carcinogenesis of cervical cancer. During the past several decades, many studies have explored the association between DAPK1 promoter methylation and cervical cancer. However, many studies were limited by the small samples size and the findings were inconsistent among them. Thus, we conducted a meta-analysis to assess the association between DAPK1 promoter methylation and cervical cancer.Death-associated protein kinase1  and 95% confidence interval (95% CI) were calculated by Meta-Analysis in R.DAPK1 promoter methylation ranged from 30.0% to 78.6%  in cervical cancer tissue and 0.0% to 46.7%  in normal cervical tissue. The pooled OR was 19.66 (95%CI = 8.72–44.31) with the random effects model, and heterogeneity was found through the sensitivity analysis. The 2I = 60% (P = 0.002) decreased to 2I = 29.2% (P = 0.144) when one heterogeneous study was excluded, and the pooled OR increased to 21.80 (95%CI = 13.44–35.36) with the fixed effects model.A total of 15 studies from 2001 to 2012, comprising 818 tumor tissues samples and 671 normal tissues samples, were analyzed in this meta-analysis. The frequencies of DAPK1 promoter methylation and cervical cancer. This study also indicated that DAPK1 promoter methylation may be a biomarker during cervical carcinogenesis that might serve as an early indication of cervical cancer.The results suggested a strong association between  Cervical cancer is the third most common cancer, after breast and colorectal cancer, among women worldwide, with 529,500 estimated new cases and 275,000 deaths in 2008 according to Ferlay et al. DAPK1 is an important tumor suppressor gene that has been studied extensively, we performed a meta-analysis to assess the association between DAPK1 promoter methylation and cervical cancer.Hypermethylation of the promoter regions of tumor suppressor genes can cause gene inactivation, which is important in the pathogenesis of cancers, and usually occurs in the early stages of cancer development in various types of cancer, including cervical cancer DAPK1 promoter methylation in cervical cancer, and attempted to find the eligible studies within PubMed, EMBASE, Web of Science and CNKI, using various combinations of Medical Subject Headings (MeSH) and non-MeSH terms. The keywords were “cervical cancer”, “DAPK1”, and “methylation”, while the search strategy was performed in PubMed with “uterine cervical neoplasms” (MeSH), “DAPK1”and“methylation”. The study was conducted till November 1, 2013 without any language limitation.We systematically reviewed the studies of DAPK1 methylation and cervical cancer, (ii) the studies provided detailed information about the frequency of DAPK1 methylation for both the cancer group and the normal control group, (iii) methods for the detection of DAPK1 methylation were limited to the methylation-specific polymerase chain reaction (MSP) and real-time quantitative polymerase chain reaction (QMSP).The studies for inclusion in this meta-analysis had to meet the following standards: (i) the studies assessed the association of DAPK1 methylation.Studies were excluded based on the following criteria: (i) the studies did not have a normal group (control group), (ii) the raw data could not be isolated from the studies in which the cancer group (case group) also contained individuals with various types of precancerous lesions such as Atypical Squamous Cells of Undetermined Significance (ASCUS), Low-grade Squamous Intraepithelial Lesions (LSIL), and High-grade Squamous Intraepithelial Lesions(HSIL), (iii) a case-control study did not feature the frequency of DAPK1 methylation, the tissue source of the control group, the mean age of the case group, and the number of participants in the case and control groups. All the information was verified by three reviewers.Two authors independently conducted the extraction of data from the selected studies. The extracted information contained the following: first author's name, publication year, the patients' ethnicities, the methods used in the measurement of http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp) was implemented for quality assessment of observational studies. The NOS is a quality assessment tool which is often used for nonrandomized studies, specifically case-control and cohort studies, included in systematic reviews. It has also been widely used in systematic reviews of nonrandomized studies by The Cochrane Collaboration.To assess the quality of the studies, The Newcastle–Ottawa scale (NOS) .All statistical analyses were calculated with the Meta package (version 2.5–1) in R . A strong association was observed between DAPK1 promoter methylation and cervical cancer with a pooled OR of  = 19.66 (95%CI = 8.72–44.31) based on the random effects model . With this method, the ORs of the studies that used MSP was 19.10  and 15.30 . Similarly, the OR in group A was 25.80  while the OR in group B was 13.55   and 17.88  in Non-Caucasians, the s model) .2I = 60.0%, P = 0.002) decreased to 2I = 29.2% (P = 0.144) with a pooled OR of  = 21.80  when the heterogeneous study was removed   to 21.80 (95%CI = 13.44–35.36) with a pooled OR of  = 15.32 (95%CI = 9.97–23.66) with the fixed effects model . After t., 2010) . When, w., 2010) .P = 0.551, which indicates that publication bias was very low; no significant bias was found among the included studies. The fail-safe number  also indicated that the degree of publication bias was very small.Funnel plots and Egger's test were performed to assess the publication bias of the literature. The shape of the funnel plot in DAPK1) could mediate cell death via IFN-gamma and could lead to tumor pathogenesis and metastasis when inactivated DAPK1 expression, mainly by hypermethylation of its promoter region, enhances the metastatic potential of cancer cells and has been proven to occur in a variety of cancers, including cancer of the uterine cervix Death-associated protein kinase1  was reduced to 2I = 29.2% (P = 0.144) when this study was omitted (the result of the pooled OR was 19.66 (95%CI = 8.72–44.31) with the random effects model , which s omitted . In addi omitted .DAPK1 promoter methylation and cervical cancer. The heterogeneity presented in QMSP was decreased from 2I = 78.8% to 2I = 0.0%, which indicated that the method of QMSP is better than that of MSP. This conclusion was consistent with the study by Eads et al. 2I = 75.9% to 2I = 0.0%, which might have been caused by two major reasons. First, the detection of DAPK1 promoter methylation in Caucasians by the QMSP method excluded the study by Flatley et al.. The other reason may be that the heterogeneity in Caucasians is relatively small. Publication bias was evaluated through Funnel plots and Egger's test, and the Egger's test showed P = 0.551, which indicated that there was no significant publication bias. The fail-safe number further confirmed that the trend for publication bias was very small.When the heterogeneous study was omitted, the pooled OR value was increased from 15.32 to 21.80 (the fixed effects model), which suggested a stronger association between This meta-analysis had some limitations. The first limitation was that some studies did not provide detailed information regarding the age of individuals in the case groups and control groups. The second limitation in this meta-analysis was that some studies did not reveal the stage of the cervical cancers or the subtype, which might also be sources of the heterogeneity. Considering the small number of articles that described the stage and type of cervical cancer, the power was too small to make a subgroup for them. Other confounding variables such as method, ethnicity, sample size, and the source of control may also exist. Publication bias was the third limitation. Some unpublished and negative studies may contribute to some bias though no significant publication bias was detected according to Egger's test.DAPK1 promoter methylation and cervical cancer, and therefore, DAPK1 promoter methylation may be valuable as a biomarker. Considering that the quality and quantity of the reviewed articles were limited, larger and well-designed studies should be employed in the future for further confirmation of the association between DAPK1 promoter methylation and cervical cancer.In conclusion, a strong association was observed between Checklist S1PRISMA Checklist.(DOC)Click here for additional data file.


Statistics in Medicine Published by John Wiley & Sons Ltd.A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta‐analysis is difficult because primary studies often use different methods of measurement and/or different cut‐points to dichotomise continuous factors into ‘high’ and ‘low’ groups; selective reporting is also common. We illustrate how multivariate random effects meta‐analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut‐points and/or methods of measurement. The models account for within‐study and between‐study correlations, which utilises more information and reduces the impact of unreported cut‐points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut‐point to reduce the number of unknown parameters. The models provide important inferential results for each cut‐point and method of measurement, including the summary prognostic effect, the between‐study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta‐analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut‐points than previously thought. In the second, a multivariate meta‐analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut‐points). © 2015 The Authors.  However, there is often inconsistency in their findings, with some suggesting a particular factor is prognostic and others suggesting the opposite et al.Two common problems for meta‐analysis of a particular factor are between‐study differences in its method of measurement and, for continuous factors, the cut‐point value used to define ‘high’ and ‘low’  groups. For example, de Azambuja multiple prognostic results for each factor, relating to different cut‐points and/or methods of measurement. We show how multivariate meta‐analysis models can accommodate the correlation between such results In this article, we suggest approaches to meta‐analysis of prognostic factor studies when faced with multiple cut‐points and/or methods of measurement and missing results in some studies. Firstly, in 2i = 1 to k studies available for meta‐analysis, and let each study provide just one prognostic effect estimate, yi, and its variance, si2, for a particular continuous factor of interest when dichotomised at some cut‐point and measured using a chosen method of measurement. The yi will typically be either a log hazard ratio or a log odds ratio estimate. When studies use different cut‐points and methods of measurement, a sensible option is to perform a separate meta‐analysis for each subset of studies that used the same measurement and cut‐point. However, in practice, we rarely see this approach, probably because most subsets contain only a few studies. It is more common to see researchers meta‐analyse all studies together and account for potential between‐study heterogeneity in prognostic effects using a random‐effects model: si2 is assumed known, which is a common assumption in the meta‐analysis field θi, is assumed normally distributed about a summary (mean) prognostic effect, β, with between‐study variance, τ2. The model can be estimated using, for example, restricted maximum likelihood (REML) or methods of moments β, is difficult to interpret clinically as it does not relate to a particular cut‐point or method of measurement. If one adopts this model, we argue that it is better to focus on the range of prognostic effects across studies by calculating a prediction interval for the potential prognostic effect of the factor in a new study β^) is the variance of β^, and tN − 2 is the 100(1 − α/2) percentile of the t‐distribution with N − 2 degrees of freedom, with α usually chosen as 0.05 to give a 95% prediction interval. A t‐distribution, rather than a normal distribution, is used to help account for the uncertainty in τ^2Let there be xi) and the prognostic effect can be examined in a meta‐regression by γ1 gives the expected change in the summary prognostic effect for a 1‐unit increase in the cut‐point value. After the estimation of model α^+γ^1xi. Similarly, covariates could be included for the method of measurement.If the entire prediction interval does not include the value of no effect (e.g. a log odds ratio or log hazard ratio of 0), this suggests that the factor is likely to have prognostic value in new populations that use similar cut‐points and methods of measurement to those in the included studies. If the interval contains the value of no effect, this indicates the factor may not be prognostic in at least some situations, and the reasons for this could then be explored. For example, if the number of studies is sufficient (e.g. > 10), then the association of the cut‐point value . De Azambuja I2=52.3%;τ^=0.28). A 95% prediction interval for the prognostic effect of Ki‐67 in a new population which, using equation γ^1=0.008, 95% CI: ‐0.012 to 0.028, p = 0.43), or the method of measurement  were associated with the prognostic effect of Ki‐67.Unsurprisingly, there is a large heterogeneity in the meta‐analysis , and let these cut‐points be ordered in an increasing value. Further, assume that the within‐study covariance between each pair of cut‐points  is the within‐study covariance between yi1 and yiT; the estimates for cut‐points 1 and T, respectively) is known. Section Ω is the between‐study variance–covariance matrix for the true log hazard ratios and, if unstructured, is a T by T matrix containing T between‐study variances  in the diagonal and (2T‐1)between‐study covariances in the off‐diagonals  : yij), their variances sij2, and covariances ) are required to fit model Without loss of generalisability, assume that there is a prognostic effect estimate yij is missing for a cut‐point depends solely on the observed yij for other cut‐points and not on the actual value of the missing yij itself. Interestingly, even when data are not missing at random, this multivariate meta‐analysis model has been shown to obtain summary estimates with improved statistical properties compared with univariate meta‐analysis yij , then the missing data are missing not at random. The multivariate results are less biased than univariate results in this situation; although, the bias is not removed in full Crucially, the model can accommodate missing results for some cut‐points in a study, assuming they are missing at random β^j terms give the summary (mean) prognostic effect (e.g. log hazard ratio or log odds ratio) at cut‐point j.The model can be fitted using, for example, methods of moments 3.2T summary means, T between‐study variances and (T2−T)/2 between‐study covariances (correlations). One could impose a structure to Ω to reduce the number of parameters to be estimated. For example, one could assume a common between‐study variance at each cut‐point and the same between‐study correlation for all pairs of cut‐point. This is potentially over‐simplistic, as the between‐correlation is likely to be higher for two neighbouring cut‐points than for two cut‐points far apart. Adopting an auto‐regressive structure for Ω may help address this, as done in linear mixed effects models with repeated measurements ordered over time.Model xj, leads to a constant change of γ in the prognostic effect xj is the jth cut‐point value . In model α is the average intercept (the summary prognostic effect when the cut‐point = 0) and τα2 is the between‐study variance in the intercept. The slope, γ, gives the summary change in log hazard ratio (odds ratio) for a 1‐unit increase in the cut‐point value. Extensions to model Alternatively, one could assume a particular functional form for the relationship between the true prognostic effect and the cut‐point value. A similar idea has been proposed in meta‐analysis of test accuracy studies reporting multiple thresholds t, can be obtained by α^+γ^xj. Confidence and prediction intervals can be obtained as before. SAS code to fit model (5) is given in Supplementary Material 1.Following estimation of model Nonlinear extensionsModel 3.3covi), or equivalently the within‐study correlations =si1siTcovi) between pairs of prognostic effect estimates. These are unlikely to be available from publications et al.Models are available for unadjusted but not adjusted results, then one might assume the former is a close approximation for the latter et al.et al.βjs, although it may fail to converge if the between‐study heterogeneity is small relative to the within‐study variances.For situations where the effect estimates are adjusted odds ratios, unadjusted hazard ratios, or adjusted hazard ratios, it is unlikely that IPD can be recreated from published information to allow within‐study correlations (covariances) to be derived via bootstrapping. One could then impute plausible values for the missing within‐study correlation. For example, if correlations 3.4et al. The Apgar score is measured in babies immediately after birth 3.4.1First, consider those 10 studies reporting prognostic results for the two most frequently used cut‐points, 3 and 6, where values less than or equal to the cut‐point are defined as ‘poor’. Five studies presented prognostic results for both cut‐points, four studies considered just cut‐point 3 and one study considered just cut‐point 6. The unadjusted odds ratio estimates for each cut‐point are shown in Table et al.Table Using the model 3.4.2In the previous example, just two cut‐points were considered for illustration. However, there were actually 10 different cut‐points considered by a total of 11 studies identified by the review . One study examined all 10 cut‐points, but the other studies just examined one or two cut‐points. Only cut‐points 3 and 6 were evaluated by more than two studies. As two by two tables were available for all reported cut‐points, IPD were recreated and bootstrapping used to obtain the within‐study covariance estimates (available on request).α^) was 2.43 , which suggests that the odds of death for babies with an Apgar score of zero are 11.36 (= exp(2.43)) times those for babies with an Apgar value greater than zero. The estimate of the slope (γ^) was −0.068 , indicating that the log odds ratio (comparing those below with those above the cut‐point) decreases as the cut‐point increases. There was substantial between‐study heterogeneity in the intercept, with τ^α=0.67, a similar value to that observed for cut‐points 3 and 6 (the most commonly reported cut‐points) in the previous multivariate analyses of Model 2), rather than linear . This nonlinear function provides noticeably higher summary odds ratios at the first and last cut‐point values than the linear function . Thus, model β^j now giving the summary prognostic effect for the jth method of measurement.Assume that, for each method of measurement considered in the studies for meta‐analysis, there is a consistent specification of the candidate prognostic factor; that is, the same cut‐point is used in all studies, or it is always modelled as a linear trend. In this situation, multiple effect estimates arise only because of the multiple methods of measurement, which could be written as As before, model 4.2both multiple methods of measurement and multiple cut‐points, then the multivariate models in yijs for the meta‐analysis, thereby enabling a summary mean prognostic effect to be obtained for each method at each cut‐point. To keep the number of parameters to a minimum, this is best achieved by extending model If there are 4.3et al.et al., but the patient‐level correlations were given as 0.55, 0.74 and 0.27, respectively. Here, we take these as an approximation for the missing within‐study correlations  to be adjusted for There are a number of clinical applications where a cut‐point may be useful for implementing the prognostic factor. For example, in complex health economic models or disease outcome simulation models Linear and nonlinear prognostic effectset al.were able to examine the prognostic effect of a 1‐unit increase in the factor, for each of two different methods of measurement, as the original meta‐analysts used the IPD to analyse the factor on its continuous scale in each study. Analysing prognostic factors on their continuous scale is especially important in risk prediction research, where prognostic models are required to predict absolute outcome risk for individuals. Maintaining a continuous scale improves the range of possible predictions from the model, and is more likely to lead to a generalisable model than when including factors dichotomised. This is a major reason why IPD is increasingly sought when developing such models from multiple studies Although cut‐point specific results can be useful, it is well known that dichotomising continuous factors loses statistical power to detect their true prognostic effect However, without IPD, meta‐analysts will predominately have to use reported prognostic results, which are most typically given for two groups defined by a cut‐point. In this situation, if researchers still want to examine the effect of a 1‐unit increase in a prognostic factor on outcome risk, then meta‐analysis approaches for examining dose‐response relationship are potentially applicable, as proposed by Greenland and Longnecker Modelling issues and solutionset al. Researchers wishing to implement our multivariate approaches should recognise potential modelling issues. We showed how to model the functional relationship between prognostic effect size and cut‐point value, and this will often be the most useful approach as it substantially reduces the number of parameters to estimate. Another practical issue is the derivation of within‐study correlations. These are most easily derived using bootstrapping when IPD are available Limitations of our workp‐value Our multivariate models with a functional relationship should not be extrapolated outside the range of cut‐points available in the studies for meta‐analysis. Furthermore, the multivariate models are unlikely to be reliable where most studies just report one cut‐point, especially if that cut‐point was selected on the basis of optimising the We note also that, in some situations, there may be a known transformation that maps values from one method of measurement to another method of measurement. Clearly, if one can reliably transform findings from one scale to the other, then this is preferable to our approach and can be used to obtain missing method of measurement in studies that only report a subset of those of interest. However, in many situations, the relationship between competing methods is not known with high precision, or only applicable if the IPD are available. In this situation, our suggested joint multivariate meta‐analysis of all methods is then useful to account for their correlation.Our models dealing with multiple cut‐points provide prognostic effect sizes such as odds ratios or hazard ratios. However, they do not provide absolute risks for those above and below a particular cut‐point. To derive absolute risks following our model, additional information would be required such as the absolute risk in at least one of the groups defined by a cut‐point and the distribution of the factors values, such that the proportion of the population that fall in between each cut‐point can be derived. However, absolute risks are typically more important from a prognostic model (which contains multiple prognostic factors in combination) Is meta‐analysis sensible?Other researchers have dealt with multiple cut‐points and methods of measurement by transforming to a standardised scale 6In conclusion, we have proposed approaches for handling different cut‐points and methods of measurement in a meta‐analysis of prognostic factor studies. These are especially important when synthesising published prognostic results but are also potentially useful when IPD are available for some or all studies. Many issues remain in this field, and ultimately, a move toward prospectively planned pooled analyses would be preferred Supporting info itemClick here for additional data file.


MTHFR) is a key enzyme of folate metabolic pathway which catalyzes the irreversible conversion of 5, 10-methylenetetrahydrofolate to 5-methyltetrahydrofolate. 5-methyltetrahydrofolate donates methyl group for the methylation of homocysteine to methionine. Several studies have investigated maternal MTHFR C677T polymorphism as a risk factor for DS, but the results were controversial and inconclusive. To come into a conclusive estimate, authors performed a meta-analysis.Methylenetetrahydrofolate reductase  with 95%confidence interval were calculated for risk assessment.MTHFR 677C>T polymorphism and Down syndrome under four genetic models except recessive model .Thirty four studies with 3,098 DS case mothers and 4,852 control mothers were included in the present meta-analysis. The pooled OR was estimated under five genetic models and significant association was found between maternal MTHFR C677T polymorphism is a risk factor for DS- affected pregnancy.The results of the present meta-analysis support that maternal  MTHFR) C677T polymorphism might be a risk factor for maternal meiotic non-disjunction. Since then several studies have investigated the risk of DS to variants of folate pathway genes like MTHFR, Methionine synthase (MTR) and Methionine synthase reductase (MTRR) in Asian Down syndrome (DS) is the most common chromosomal disorder with the prevalence of 1/700–1000 live birth. It is characterized by the trisomy 21, which results from maternal meiotic nondisjunction in majority (90%) of cases. The established risk factor for DS is advanced (>35 years) maternal age at the time of conception. However, a fairly high number of DS children born to younger mothers suggest that risk factors other than advanced maternal age might be involved in predisposing younger mothers to DS-affected pregnancy MTHFR is a key enzyme in folate metabolism, which catalyzes the reduction of 5, 10-methylenetetrahydrofolate to the predominant circulating form of folate i.e. 5-methyltetrahydrofolate (5-THF). 5-THF donates methyl group for the conversion of homocysteine to methionine, which is further converted into S-adenosylmethionine (SAM). SAM is the main methyl group donor for all cellular methylation reactions. Folate deficiency and/or dysfunctional MTHFR reduces the conversion of 5, 10-methylene THF to 5-methyl THF, and elevates plasma homocysteine concentration. Both folate and MTHFR are involved in many complex biochemical reactions like DNA synthesis, repair and methylation.MTHFR gene and among them C677T variant is the most studied and clinically important. The C677T variant  has been associated with a decreased activity of MTHFR, and increased homocysteine level MTHFR C677T polymorphism with Down syndrome. However, the results were conflicting and inconclusive. In light of the above facts, we conducted a meta-analysis of published case control studies relating the C677T polymorphism of the maternal MTHFR gene to the risk of having DS offspring.There are more than 40 polymorphisms reported in MTHFR’ ‘Methylenetetrahydrofolate reductase’, and ‘C677T polymorphism’, ‘maternal risk’ and ‘Down syndrome’. In addition, bibliographies of all articles and reviews were hand searched for additional suitable studies.Electronic searches were conducted using PubMed, Google Scholar, Elsevier and Springer link and all published manuscripts up to January, 2014 were considered in present meta-analysis. The following index terms were used for search ‘Included studies had to meet the following criteria: (1) article should be published; (2) article should have sufficient data to calculate the odds ratio with 95% CI; (3) article should be case control association study; and (4) author should describe the genotyping protocols.MTHFR genotype numbers.The following data were extracted from each study: first author’s name, publication year, journal name, country name, genotyping method, and different MTHFR C677T polymorphism and DS risk was estimated by Odds ratio (ORs) with 95% confidence intervals (CIs). The heterogeneity was tested by the Q-statistics with p-values <0.05. Subgroup analysis was done to know the source of heterogeneity. If higher heterogeneity (I2>50%) would be observed, the random effect model Statistical analysis of maternal Funnel plots of precision by log (OR) and standard error by log (OR) were plotted to determine publication bias and asymmetrical funnel plots represent publication bias. Begg and Mazumdar rank correlation With our original search criterion, 85 articles were found. After reviewing each original article, 50 publications were excluded including reviews, case studies, editorials etc. . FollowiThese studies were published between 1999 and 2013. All these thirty four studies were performed in different countries- Brazil In thirty four studies included in the present meta-analysis, the smallest case sample size was 26 In all thirty four studies, total cases were 3,098 with CC , CT  and TT (376), and controls were 4,852 with CC , CT , and TT (508) genotypes. In controls genotypes, percentage of CC, CT and TT were 48.00%, 41.53%, and 10.47% respectively. In total cases, genotype percentage of CC, CT, and TT was 45.06%, 42.8% and 12.14% respectively. Frequencies of CC and CT genotypes were highest in both cases and controls . In caseTvsC = 1.22; 95% CI = 1.13–1.31; p = <0.0001) and random effect models     and random  effect models with moderate statistical heterogeneity between-study  and random  effect models. Similarly combined mutant genotypes (TT+CT vs. CC) showed significant association with DS using both fixed  and random  effect models     ; Table 3 = 0.11)  no signi= 0.084) ; Table 3heterogeneity = <0.0001, Q = 107.92, df = 33, I2 = 69.42%, t2 = 0.12) and mutant genotypes  comparisons. The ‘I2’ value of more than 50% for between studies comparison in both allele and genotype analysis shows high level of true heterogeneity. In Asian  and American  allele contrast meta-analysis significant high heterogeneity was observed, in European sub-group meta-analysis low heterogeneity was observed  in allele contrast model.A true heterogeneity existed between studies for allele (P2 = 70.00%). Exclusion of seven studies with small sample size, less than 50 . Similarly exclusion of eleven studies with very high p value  but publication bias was observed in co-dominant model  of overall by using Begg’s and Egger’s test . Funnel In 1999, James et al MTHFR as risk factor to various diseases/disorders like- neural tube defects MTHFR C677T as DS risk, but no consistent conclusion was achieved. Zintzaras MTHFR polymorphisms and DS risk. Medica et al. Meta-analysis is a powerful tool for analyzing cumulative data with small and low power studies. Several meta-analyses were published accessing MTHFR C677T) of folate pathway. Present meta-analysis had several advantages/strength to the previous published meta-analyses like- (i) the publication bias was not detected in present meta-analysis, (ii) pooled number of cases and controls from different studies significantly increased the statistical power of the analysis, (iii) largest number of studies (34 studies) with largest sample size  was included in the present meta-analysis, (iv) controls included in the present meta-analysis was mothers of healthy child, (v) distribution of genotypes in control mothers except two studies was in Hardy-Weinberg equilibrium, (vi) significant association was found between maternal MTHFR C677T polymorphism and DS risk in allelic contrast, homozygote, co-dominant and dominant genetic models and (vii) in addition we did sub-group analysis according to geographical regions.There are few limitations of the present meta-analysis like- i) we used crude ORs in the pooled analysis without adjustment; ii) the relatively small sample size in some of the included studies, especially those from Asia; iii) we considered only one gene polymorphism Click here for additional data file.


GSTM1 and/or GSTT1 polymorphisms and endometriosis susceptibility. However, these associations remain inconclusive. To derive a more precise estimation, we conducted a comprehensive search to identify all existing studies and then performed a meta-analysis. Electronic literature searches of the PubMed, Chinese Biomedical, and China National Knowledge Infrastructure databases were performed up to December 2013. GSTM1-, GSTT1-, and dual-null genotypes were analyzed independently, and pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated by comparing the null genotype with other genotypes using the random-effects or fixed-effects model. Twenty-five and 16 independent studies on GSTM1 and GSTT1 polymorphisms, respectively, and five GSTM1-GSTT1 interaction analyses were identified and included in this meta-analysis. Both GSTM1- and GSTT1-null genotypes increased risk of endometriosis . Moreover, we found a significant positive association between the dual null genotype GSTM1-GSTT1 and endometriosis susceptibility . This meta-analysis provides evidence that null genotypes of GSTM1 and/or GSTT1 contribute to risk of endometriosis. Further investigations are required to confirm these findings.Endometriosis is one of the most frequent benign gynecological disorders. Numerous studies have shown an association between  Endometriosis, a benign gynecological disease, is characterized by the presence of endometrial glands and stroma at extrauterine sites. Approximately 6–10% of women of reproductive age suffer from this condition The etiology and pathogenesis of endometriosis remain unclear. Endometriosis is commonly considered as a complex trait caused by the interaction between genetic and environmental factors GSTM1  GSTT1  GSTM1 and GSTT1 polymorphisms have been suggested to be associated with endometriosis by many epidemiological studies Polymorphisms in the glutathione S-transferase (GST) system have long been recognized as a risk factor for endometriosis and have been extensively explored. Human GSTs are a multigene family of phase II metabolizing enzymes that are crucial in detoxification of xenobiotics such as carcinogens, environmental toxins and drugs GSTT1-null genotype and endometriosis, however no such association was found between the GSTM1- or GSTM1-GSTT1-null genotypes and endometriosis risk. Since then, 12 relevant studies A previous meta-analysis conducted by Sun-Wei Guo GSTM1”, “GSTT1”, “polymorphisms”, “endometriosis”, and combined phrases. Additional literature was collected from cross-references within both original and review articles. No restrictions on language, population, or sample size were set in this meta-analysis. Studies were required to comply with the following inclusion criteria: (1) original case-control or cohort studies; (2) studies investigated the association of GSTM1 or GSTT1 polymorphism with risk of endometriosis; (3) sufficient information to calculate odds ratios (ORs) with 95% confidence intervals (CIs); and (4) Chinese articles were published in Chinese core periodicals. Exclusion criteria were: (1) not case-control or cohort studies evaluating the association of the GSTM1 or GSTT1 polymorphism with endometriosis; (2) case reports, letters, reviews, editorials, or correspondence articles; (3) studies based on incomplete raw data; and (4) studies that contained overlapping data.Studies were identified by searching the PubMed, CBM , and CNKI  databases for relevant reports published prior to December 2013, using the key words “The data from eligible studies was independently checked and extracted according to the pre-specified selection criteria, and the discrepancies were resolved by discussion and agreement between all investigators. The following information was collected from each included study: name of the first author, publication year, study location, ethnicity, source of controls, sample-size of cases and controls, and genotype frequency in cases and controls. Different ethnicity descents were categorized as European and Asian. According to source of controls, all included studies were defined as population-based (PB) and hospital-based (HB).Two investigators independently assessed the quality of included studies using the Newcastle–Ottawa Scale (NOS) GSTM1 and GSTT1 polymorphisms and endometriosis risk were measured by ORs and respective 95% CIs, which were calculated by comparing the null genotype with other genotypes. The significance of the pooled ORs was determined by the Z-test (P<0.05 was considered significant). Subgroup analyses were performed by ethnicity and source of controls.Meta-analyses were performed for polymorphisms investigated in at least three studies 2 tests. The I2 statistic was used to evaluate variations due to heterogeneity rather than chance. P<0.10 or I2>50% indicated the presence of between-study heterogeneity, and the random-effects model (DerSimonian-Laird method) Heterogeneity among studies in terms of degree of association was evaluated using χP<0.05 was treated as significant publication bias). All statistical analyses were performed using Stata statistical software, version 12.0 .Sensitivity analyses were performed to evaluate the stability of the results of the meta-analysis. The influence of individual studies was evaluated by estimating the pooled ORs in the absence of each study GSTM1 and GSTT1 polymorphisms, respectively, and five were on GSTM1-GSTT1 interaction analysis. The characteristics of the included studies are described in This meta-analysis is guided by the PRISMA statement Figure 1The scores of included studies were 5 to 9 Table 1.GSTM1 polymorphism and endometriosis was investigated in 25 studies, which included a total of 3330 cases and 3959 controls. The heterogeneity was significant, so the random-effects model was selected. The result showed that the null genotype of GSTM1 was associated with an increased endometriosis risk . A forest plot is shown in P<0.001). Subgroup analysis by ethnicity was performed, and an increased risk of endometriosis was observed in Europeans and Asians . In stratified analysis by source of controls, the results showed that source of controls did not affect the pooled results and a significantly increased risk of endometriosis was detected both in PB and HB studies .The association between GSTT1 polymorphism. We found significant heterogeneity between studies, so the random-effects model was used to pool the results. The results indicated a positive association between the null genotype of GSTT1 with endometriosis risk . A forest plot is shown in P = 0.005), but not in Europeans .Sixteen independent studies, with a total of 2263 cases and 2380 controls, were included in the meta-analysis of GSTM1-GSTT1 interaction analysis, five independent studies, including 1008 cases and 1164 controls, were subjected to meta-analysis. The heterogeneity was not significant, so we selected the fixed-effects model. The results showed that the dual null genotype of GSTM1-GSTT1 was associated with an increased endometriosis risk . A forest plot is shown in For GSTM1 polymorphism, the pooled ORs were not qualitatively changed when any single study was omitted, suggesting that no single study exhibited excessive influence, and that the results are reliable  vs. GSTM1 (+): Begg, P = 0.129 and Egger, P = 0.079), and Begg’s funnel plots suggested no substantial asymmetry (GSTT1 (−) vs. GSTT1 (+): Begg, P = 0.344 and Egger, P = 0.207, and the shape of the funnel plot also did not reveal any evidence of obvious asymmetry (GSTM1-GSTT1 (−) vs. other genotypes: Begg, P = 0.462 and Egger, P = 0.613).We conducted Begg’s and Egger’s tests to evaluate potential publication bias. There was no statistical evidence of publication bias regarding analysis of the symmetry Figure 6GSTM1 and GSTT1, two of the most widely-studied polymorphisms, are characterized by homozygous deletions of the respective genes GSTM1 as risk factors was conducted by Baranov et al. GSTT1-null subjects among endometriosis patients was detected in 1999, although it was not statistically significant GSTM1 and GSTT1 and endometriosis susceptibility. However, the results are inconsistent and conflict, which compelled us to pay attention to the two polymorphisms at a meta-analytical level.As many GST genes are polymorphic, whether particular allelic variants in GST genes are correlated with altered risk of some kinds of diseases has provoked great interest GSTM1- and GSTT1-null genotypes with endometriosis risk, which allows us to expand the discussion of possible implications and interpretations of the findings. Our meta-analysis suggested that there were significant associations between the null genotypes of GSTM1 and GSTT1 and endometriosis. Moreover, the combined GSTM1-GSTT1-null genotype also showed a positive association with endometriosis susceptibility.The current study, including 25 case-control studies with 3330 cases and 3959 controls, is the most comprehensive meta-analysis of the association of GSTM1 and endometriosis risk. The large number of studies and subjects included in this meta-analysis were substantial enough to resolve the issue of conflicting results obtained in individual studies, which was primarily caused by their small sample size. Furthermore, the results in subgroup analysis by ethnicity also indicated an association in both Europeans and Asians suggesting this association is reliable.A novel finding of the present study was the significant positive association between the null genotype of GSTT1 and endometriosis risk, which is consistent with the result of a previous meta-analysis GSTT1-null genotype is nearly 50% in the Chinese population GSTT1 polymorphism may exert varying effects in different populations. Second, the different results could also be explained by study design or sample size. Other confounding factors, such as age and lifestyle may also be considered.This meta-analysis showed a moderate positive association between the null genotype of GSTM1-GSTT1 interaction analysis indicated that women with double-null genotype had significantly increased endometriosis risk compared with those with other genotypes. If genetic susceptibility to endometriosis is, at least in part, mediated through polymorphisms of genes that encode enzymes responsible for detoxification, it is possible that the combination of GSTM1- and GSTT1-null genotypes may be more discriminating as a risk factor for endometriosis than a single null genotype.The GSTM1, GSTT1, and GSTM1-GSTT1 increased the risk of developing endometriosis is biologically plausible. Environmental contaminants, such as polychlorinated dibenzo-p-dioxins, polychlorinated biphenyls, and polycyclic aromatic hydrocarbon, have been suggested to promote the occurrence and development of endometriosis by interfering with the estrogen signaling pathway and their immunosuppressive effects GSTM1 and GSTT1 cause a complete loss of the activity of their encoded enzymes GSTM1 and GSTT1 deletions are detected in 42–60% and 13–26% of Caucasians, respectively The observation that the null genotypes of Heterogeneity is an important issue in meta-analysis. Although we minimized the likelihood by performing a careful search for published studies, using the explicit criteria for study inclusion, statistically significant heterogeneity still existed in most comparisons. There are several explanations for the significant between-study heterogeneity, such as different study populations, genetic factors, and environmental factors. In particular, environmental contaminant exposure, a risk factor for endometriosis, is an important factor contributing to heterogeneity. The status of environmental contamination varies between countries, so the endometriosis incidence varies between populations. Publication bias is another important issue which should also be accounted for in meta-analyses. After evaluating the publication bias using Begg’s funnel plots we did not detect a publication bias, indicating the strength of the results.GSTM1 and GSTT1 polymorphisms and endometriosis risk by performing a meta-analysis including 14 studies with 1539 cases and 1805 controls. That study suggested that the endometriosis risk associated with the null genotype of GSTT1 was 29% higher than other genotypes, but it failed to find positive associations between the null genotype of GSTM1 or GSTM1-GSTT1 and the risk of endometriosis. There were some differences between that study and ours. First, our meta-analysis provided more comprehensive information on the relationships between the two polymorphisms and endometriosis by extracting data from more studies with more total cases and controls. Second, some issues that may affect the results of meta-analysis, such as publication bias, sensitivity analysis, and quality assessment of the included studies, were addressed in our study. Third, the current study also showed distinct findings, with the added statistical power.Sun-Wei Guo GSTM1- and GSTT1-null genotypes and endometriosis risk, additional studies with larger sample sizes and involving different ethnicities  are needed. Third, gene-environment interactions were not evaluated in this meta-analysis. Subgroup analyses based on environmental exposures were not performed because of insufficient data on such associations in all included studies.This meta-analysis had several limitations that should be taken into account when considering its contributions. First, heterogeneity among studies existed in some comparisons of polymorphisms. Second, the results of our meta-analysis were applicable to only two ethnic groups, Europeans and Asians, as there were no relevant studies including data from African ethnic groups. Hence, to conduct a more precise analysis of the association between GSTM1- and GSTT1-null genotypes are associated with an increased risk of endometriosis. Null genotypes of GSTM1 and GSTT1 could act as biomarkers of endometriosis susceptibility. Larger and well-designed studies are needed to confirm these findings. Moreover, future studies should further evaluate potential gene-to-gene and gene-to-environment interactions to clarify the role of the GSTM1 and GSTT1 genes in endometriosis.In conclusion, this meta-analysis suggests that Checklist S1PRISMA meta-analysis checklist.(DOC)Click here for additional data file.


CFTR variations and the congenital bilateral absence of the vas deferens (CBAVD).The aim of our study was to evaluate the relationship between four CFTR variations with the risk of CBAVD. A total of 29 studies among 1139 controls and 1562 CBAVD patients were gathered for the meta-analyses of three commonly tecsted variations  with CBAVD.A systematic search was performed in the literature databases for the case–control studies of Our meta-analyses observed significant associations between CBAVD and all the three variations, including 5T , M470V  and ΔF508 .CFTR variations and CBAVD. Our results showed that the 5T variation was a risk factor of CBAVD in French, Spanish, Japanese, Chinese, Iranian, Indian, Mexican and Egyptian populations. CFTR ΔF508 was another important risk factor in Caucasians, including Slovenians, Canadians, Iranians, and Egyptians. In addition, M470V was a protective factor among French, Chinese, Italian and Iranian populations.In the current study, we demonstrated a significant association between  Congenital bilateral absence of the vas deferens  is one of the abnormal forms in the male reproductive system, causing obstruction to sperm outflow into the urethra. CBAVD is responsible for 1-2% of male infertility , which wTR gene variations [CFTR gene encodes a glycosylated transmembrane protein that is a chloride channel conducts the regulation of other transport pathways. CFTR was widely expressed in epithelial cells of exocrine tissues, such as the sweat glands, lungs, and vas deferens [CFTR contributed to the dysfunction of body organs, including the sweat glands, respiratory system and reproductive systems. CBAVD is one of the generally acknowledged monosymptomatic CFTR-related disorders [CF is a severe genetic disease with a incidence of 1/2500 births every year in Caucasians . CF patiriations  that werriations ,9. The Cdeferens . Publishisorders .CFTR gene contains 27 exons and 1945 polymorphisms according to the Cystic Fibrosis Variations Database [Located on chromosome 7q31.2, the Database . There aCFTR transcript without exon 9, resulting in variable phenotypes which were observed in CBAVD or mild CF patients [The abnormal fold of the △F508-CFTR protein triggered the activity of proteasomes for degradation, which caused a lower open probability than normal . Anotherpatients . Experimpatients ,16. M470patients -19. The patients . There wCFTR variations, including F508del, IVS8-Tn, and M470V. The goal of our study was to evaluate the overall contribution of the three CFTR variations to the risk of CBAVD in different ethnic groups through systematic meta-analyses.CBAVD incidence is rare 1-2%) in the patients with male infertility [-2% in thCFTR gene were used in the literature search on the PubMed. We investigated all publications on the association between CFTR variants and CBAVD disease from 1993 to 2013. Reference lists in the harvested literatures were explored for additional relevant studies suitable for inclusion in our meta-analysis. The selection criteria of studies for our meta-analysis were as follows: (1) CBAVD patients with typical CF were excluded; (2) complete data with allele or genotype in the case–control studies was available; (3) only CBAVD diagnosis for studies with physical examination, semen analysis and ultrasonography were considered. The retrieved information consisted of author, year of publication, ethnicity, and the number of participants with different alleles.The search terms including CBAVD, SNP or variations and CFTR variations and CBAVD risk. Cochran’s Q statistic and I2 test were used to test heterogeneity in our study. The Fixed-effect model was used for meta-analysis with moderate heterogeneity (I2 < 50%). Otherwise, a random-effect model was adopted for the meta-analysis with significant heterogeneity (I2 > =50%) [All the eligible case–control studies were involved in the meta-analysis done by Stata software  . Total o > =50%) ,23. Z-te > =50%) . SubgrouCFTR and CBAVD from PubMed and two studies from CNKI. A total of 104 studies was harvested after being filtered by their titles and abstracts. Among those articles, we removed four family studies and three reviews or meta-analysis. Consequently, 26 studies without obtainable allele or genotype data were removed. Among them, there were 29 studies on three CFTR variations. Our meta-analyses of three CFTR variations were performed among 1139 cases and 1563 controls , M470V (I2 = 61.0%) and ΔF508 (I2 = 0.00%). Furthermore, subgroup meta-analyses of the 5T variations by ethnicity showed that the heterogeneity mainly came from Asians (I2 = 71.5%) instead of Caucasians (I2 = 30.0%). Our results indicated that 5T was a risk factor of CBAVD in Caucasians  the strong association with CBAVD onset with the risk of CBAVD for all the three variations, including 5T , power analysis showed a lack of power in the meta-analyses, including 5T (47.3%), ΔF508 (7.1%) of LPL, and M470V (52.1%). Thirdly, there was a lack of haplotype information among these loci in most of involved studies. Reports have shown there were interactions among these variations -30. We dCFTR variants and CBAVD risk. Our meta-analysis among 1069 cases and 1418 controls showed that 5T was a risk factor of CBAVD in the combined populations, including French, Spanish, Japanese, Chinese, Iranian, Indian, Mexican and Egyptian. Meta-analysis among 60 cases and 133 controls confirmed that CFTR ΔF508 was another important risk factor in Caucasian populations including Slovenian, Canadian, Iranian and Egyptian. In addition, M470V was shown to be a protective factor by our meta-analysis among 10 cases and 11 controls from French, Chinese, Italian and Iranian populations.In conclusion, the present study demonstrated an association between 2: Inconsistency index.SNP: Single nucleotide polymorphism; CBAVD: Congenital bilateral absence of the vas deferens; CF: Cystic fibrosis; OR: Odds ratio; 95% CI: 95% confidence interval; IAll authors declare no competing financial interests.XTX drafted the manuscript; JZ and QL were responsible for analysis; HZ and HX participated in literature search. SJ and SD carried out language editing. All authors read and approved the final manuscript.


Results of the association of folate metabolism and carcinogenesis are conflicting. We performed a meta-analysis to examine the effect of the interaction of serum concentration of homocysteine (Hcy), folate, and vitamin B12 and 5,10-methylenetetrahydrofolate reductase (MTHFR) polymorphism on risk of cancer overall.Two reviewers independently searched for all published studies of Hcy and cancer in PubMed, EMBASE-MEDLINE and Chinese databases. Pooled results were reported as odds ratios (ORs) and mean differences and presented with 95% confidence intervals (95% CIs) and 2-sided probability values.We identified 83 eligible studies of 15,046 cases and 20,712 controls. High level of Hcy but low level of folate was associated with risk of cancer overall, with little effect by type of cancer or ethnicity. Vitamin B12 level was inversely associated with only urinary-system and gastrointestinal carcinomas and for Asian and Middle Eastern patients. As well, MTHFR C677T, A1298C and G1793A polymorphisms were related to elevated serum level of Hcy, and folate and vitamin B12 deficiency. However, only MTHFR C677T homogeneity/wild-type (TT/CC) polymorphism was positively associated with overall risk of cancer.Elevated serum Hcy level and folate deficiency are associated with increased overall risk of cancer. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries . UnderstFolate may play a significant role in determining the risk of developing several human epithelial cell malignancies, including cancer of the breast, ovary, pancreas, brain, lung, prostate and cervix, although the evidence is not consistent –8. DespiSome genetic studies suggest that 5,10-methylenetetrahydrofolate reductase (MTHFR) genetic polymorphisms related to serum Hcy and folate status can further affect cancer risk . Of noteTherefore, we aimed to determine the possible association of serum concentrations of Hcy, folate, and vitamin B12 as well as MTHFR polymorphism and overall risk of cancer.We searched for all published studies evaluating the role of Hcy and its related metabolizing factors folate and vitamin B12 as well as MTHFR genotypes in cancer. Two investigators  independently searched MEDLINE via PubMed, EMBASE and Chinese databases (Wanfang Data and CNKI Database) with the key words (“homocysteine”) AND (“cancer” OR “tumor” OR “carcinoma”) for articles in any language describing the association of serum Hcy concentration and cancer (up to November 2013). We also manually searched the reference lists of review and research articles for articles. All disagreements were resolved by discussion and with a third reviewer, if necessary.Studies were included if they met the following criteria: (1) case—control studies; (2) diagnosis of cancers by immunohistochemical methods; (3) sufficient data for serum Hcy concentration  for both cases and controls; (4) subjects 18 years or older; and (5) containing an English abstract. If the same data were used in more than one publication, the latest or largest study was selected. The same 2 unmasked investigators independently assessed study validity. Quality was defined as (1) cancer patients without other complications such as diabetes, hypertension, myocardial infarction, stroke, renal failure, drug abuse or other serious diseases; (2) the control group age- and sex-matched to the case group; (3) cases not receiving any clinical or radiological anticancer treatment at the time of study; (4) cases and controls randomly selected; and (5) clear statement for the measurement of serum Hcy, folate and vitamin B12 levels as well as MTHFR genotyping, and the coefficient of variation for quality control repeats. Only studies that satisfied at least 3 criteria were defined as high-quality studies and were included. Agreement was analyzed by the kappa statistic, with Kappa >0.75 considered agreement between reviewers in study selection .The following data were extracted from each eligible study: study design and execution; type of cancers; publication date; country of study; number of cases and controls; ethnicity; mean (SD) serum concentrations of Hcy, folate and vitamin B12; and MTHFR genotyping in cancer and controls.The mean differences in serum concentrations of Hcy, folate and vitamin B12 in cases and controls were calculated by a random- or fixed-effects model to compute ORs with 95% CIs by the Z-test with use of RevMan 5.0. The heterogeneity of studies was evaluated by the Q-statistic at p<0.05. A random-effects model was used if heterogeneity was significant; otherwise, a fixed-effects model was applied. Subgroup analysis involved mainly cancer type, organ system and geographic location to explore potential sources of heterogeneity. The Z-test was used to calculate differences in serum concentrations of Hcy, folate and vitamin B12 by MTHFR genotype for cases and controls and for the association of the pooled OR for MTHFR polymorphism and risk of cancer. In our study, high Hcy concentration or deficiency of folate and B12 was evaluated relatively by comparing patients with cancer and controls.Publication bias was assessed by funnel plots and statistically by Egger’s linear regression test with use of STATA 12.0. To be more conservative, the statistical significance level for interpreting Egger’s test results was p = 0.10. For cells with 0 values for number of events of interest, continuity correction was implemented by the addition of 0.5, whenever possible, to assess the presence of publication bias. To assess the stability of the meta-analysis results, one-way sensitivity analysis was performed by omitting each study one at a time from the analysis. Two-sided p<0.05 was considered statistically significant.In total, from 978 records searched, 83 eligible studies met our inclusion criteria . The chaAs compared with healthy controls, cancer risk was associated with high serum Hcy level  but low folate level  –S3 Figs,We divided data for cancer patients into 5 groups by organ system: digestive-system carcinomas , genital neoplasms , respiratory tumors (including LC and LSCC), urinary-system tumors  and other  . Except We performed a further subgroup analysis based on type of cancer. Except for HNSCC, LSCC, RCC, PCa and BLC, risk of some cancers was associated with high Hcy level (OR 2.63 to 10.21) as compared with controls. Except for PC, BC, PCa, BLC and other cancers, risk of most cancers was inversely associated with folate level. Vitamin B12 level was inversely associated with risk of CRC , PC , HCC , CC , LSCC , RCC  and BLC .Given the typical diets of ethnic groups, we performed a subgroup analysis by location of the study. The cancer risk associated with high Hcy level was sustained in each geographic location except Latin America, but the protective effect of folate was found only in Europe , Asia , the Middle East  and Latin America  and that of vitamin B12 only in Asia  and the Middle East .MTHFR polymorphisms are associated with reduced enzyme activity and increased total Hcy (tHcy) level. We explored the association of MTHFR C677T, A1298C and G1793A polymorphisms and risk of cancer and found only MTHFR C677T homogeneity/wild-type (TT/CC) polymorphism positively associated with overall risk (OR 1.18 (95% 1.05–1.33), We found heterogeneity for studies of Hcy, folate and vitamin B12 levels in the whole meta-analysis and on sub-group analysis. However, no further definite source of heterogeneity was identified. Egger’s test results suggested the absence of publication bias for levels of folate (P = 0.06) but not Hcy and vitamin B12  in cancer patients and controls, and the overall shape of the funnel plots revealed some asymmetry . We founWe performed a meta-analysis of studies involving 15,046 cases and 20,712 controls to examine the association of serum levels of Hcy as well as its metabolizing factors on overall risk of cancer. High serum level of Hcy and folate deficiency was consistently associated with risk of cancer, with little effect by type of cancer or ethnicity. However, only MTHFR C677T homogeneity/wild-type (TT/CC) polymorphism was positively associated with overall risk of cancer. Our study highlights the role of Hcy metabolism in carcinogenesis.An association does not prove causality. Indeed, Hcy and folate have a critical role in maintaining DNA stability by donating one-carbon moieties ,103. MulIn our meta-analysis, we found an association of elevated serum Hcy level and deficent folate level and risk of cancer that differed by type of cancer, ethnicity and genetic polymorphisms. First, high serum Hcy level was associated with more than two-fold risk of each cancer type, and folate level showed an inverse association for each cancer type except PCa and BLC. Especially, the null association of levels of Hcy and folate and PCa risk was consistent with findings from Collin et al. . SimilarSecond, ethnicity is a significant contributor to the association of Hcy level and risk of cancer in each geographic location except Latin America, but the protective effect of folate was found only in Europe, Asia, the Middle East and Latin America and that of vitamin B12 only in Asia and the Middle East. Evidence of the ethnic effect of Hcy status mainly comes from South Africa. Black adults showed low Hcy concentrations and lacked the rightward skew of values seen for white adults . DetaileIn addition, we found elevated Hcy level but not folate and vitamin B12 deficiency related to MTHFR C677T, G1793A and A1298C polymorphisms in controls or cases (data not shown). However, we found no significant positive association of MTHFR polymorphism (except for C677T homogeneity) and cancer risk. Similarly, MTHFR-knockout mice did not show increased incidence of tumor . FurtherThe study’s main limitation is that blood samples were drawn after the occurrence of cancers. However, the status of Hcy, folate and vitamin B12 level over the long term is largely unknown, so causality cannot be directly inferred from our results. As well, we found only 83 relevant articles, for lack of sufficient data for an association of original or potential confounding factors such as diet and medication use, which further limited our evaluation of gene—environment interactions for cancer risk. Additionally, although some of the Egger tests and funnel plots showed some publication bias, a conservative p value was used and removal of each individual study did not significantly alter the results. Although about 44% of eligible studies were of Chinese people, we found no effect of Hcy, folate and vitamin B12 on risk of cancer in the total group or by subgroup in Asia when we deleted all Chinese. However, Chinese data highlighted the role of Hcy, folate and vitamin B12 in carcinogenesis. Finally, we evaluated heterogeneity in the whole and subgroup analyses. We explored several possible sources of heterogeneity, including ethnicity, sample size, quality score and control types but did not find a reason for this variation, so unknown confounding variables in single studies may have biased the findings. However, the quality of studies in our meta-analysis was satisfactory according our selection criteria, and we detected no publication bias, so pooled results were credible and stable.In conclusion, the present meta-analysis is so far the largest study of an association of the circulating levels of folate-pathway vitamin and metabolite concentrations and overall risk of cancer, contributing 5.06- and 2.68-fold (inverse variance) by weight to the meta-analytical results for tHcy and folate levels, respectively. However, the type of cancer, ethnicity and genetic polymorphisms contribute to the effect of vitamin B12 on carcinogenesis. Supplementation with folate to prevent cancer should be individualized, taking into account diet, habits, folate status and MTHFR polymorphism.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.


Numerous studies have investigated the risk of cancer associated with the polymorphism of p21 3′ UTR (rs1059234 C > T), but results have been inconsistent. We performed this meta-analysis to drive a more precise estimation of the association between this polymorphism and risk of cancer. A comprehensive search was conducted to identify all case-control studies of the rs1059234 C > T polymorphism of p21 3′ UTR and cancer susceptibility. A total of eleven eligible studies, including 3,099 cases and 4,354 controls, relating to the rs1059234 polymorphism of p21 3′ UTR to the risk of cancer were identified. Multivariate and univariate methods revealed no association between this polymorphism and cancer risk. However, subgroup analysis by cancer type suggested that rs1059234 C > T polymorphism was associated with increased risk of squamous cell carcinoma of the head and neck (SCCHN) . No significant association was found in other subgroup analyses. This meta-analysis suggested that rs1059234 polymorphism of p21 3′ UTR may be associated with increased SCCHN risk. And larger scale primary studies are required to further evaluate the interaction of p21 3′ UTR rs1059234 polymorphism and cancer risk in specific populations. Cancer is a major health problem in the most parts of the world. Approximately 12.7 million cancer cases and 7.6 million cancer deaths are estimated to occur each year worldwideThe gene for p21 (CDKN1A) is localized on chromosome 6p21.2. It consists of three exons and two introns and encodes a 21-kDa protein. The translation region lies mainly in exon 24Several studies have shown that p21 polymorphisms may affect protein expression and activity and play a role in susceptibility to cancer791012131471617To date, a number of molecular epidemiological studies have been done to evaluate the association between p21 3′ UTR rs1059234 polymorphism and different types of cancer risk in diverse populations7192021222324252627As shown in 7192021222324252627212 revealed significant heterogeneity among the constituent studies. To avoid the influence of heterogeneity among studies, we carried out subgroup analyses for ethnic group, each type of cancer, source of controls and score. We failed to detect significant association between the polymorphisms and cancer risk neither in Caucasian nor in Asian populations , not the recessive model (Phet = 0.895). The meta-regression analysis did not yield any significant difference between subgroup analysis (tau2 > 0.05). Thus, one-way sensitivity analyses of the pooled odds ratios and 95% confidence intervals for rs1059234 were performed. The pooled ORs were calculated by means of a random effects model. When omitting each dataset in the meta-analysis, the pooled ORs were always persistent. The analyses for carriers of the rs1059234 T-allele versus the CC genotype was shown at As shown in P = 1.00, Egger’s P = 0.372; CT vs. CC: Begg’s P = 0.64, Egger’s P = 0.409; dominant model: Begg’s P = 0.64, Egger’s P = 0.426; recessive model: Begg’s P = 0.917, Egger’s P = 0.474). The shapes of the funnel plots seemed symmetrical for all analyses (data not shown).The Begg’s test and Egger’s test were performed to quantitatively evaluate the publication bias of the studies. No significant publication bias was observed in this meta-analysis  and the CT vs CC, an OR: 0.93 (95% CI: 0.78–1.27) and p-value = 0.628. The multivariate methods produced wider confidence intervals compared to univariate ones. The estimate of λ , which was close to 1, suggested that a dominant model was more likely. Nevertheless, these findings were expected since the majority of the univariate tests were unable to show an association. Multivariate meta-analysis could help in avoiding an inflation of the Type I error rate , but it does not offer greater statistical power.Previous studies evaluating the association between p21 3′ UTR rs1059234 polymorphism and cancer risk have provided inconsistent results, and most of these studies involved no more than a few hundred cancer cases, which is too few to assess any genetic effects reliably. Meta-analysis has been recognized as an important way to detect the effect of selected genetic polymorphisms on disease risk precisely and to identify potential important sources of between-study heterogeneity. Hence, we performed this meta-analysis including 11 eligible studies involved 3099 cases and 4354 controls to investigate the association between p21 3′ UTR rs1059234 polymorphism and cancer risk. To the best of our knowledge, this is the first meta-analysis of the association between p21 3′ UTR rs1059234 polymorphism and cancer risk. Our results suggested that the p21 3′ UTR rs1059234 polymorphism  was not associated with cancer risk when all studies were pooled together. In subgroup analyses stratified by ethnicity, cancer type, source of control, and study quality, statistical significant association was observed just among SCCHN patients.Cell cycle progression is regulated by cyclin-dependent kinases, crucial for normal growth and differential. Disruption of cell cycle control is common in cancer cells and is believed to play an essential role in cancer initiation and development. The p21 protein binds to and inhibits the activity of cyclin-CDK2 or -CDK4 complexes, and disrupts cell cycle progression at G1 phase293132919et al.Furthermore, a significant association was found only in some of the subgroup analyses, such as cancer type. Our meta-analysis does reveal that rs1059234 CT + TT genotypes conferred increased risk to SCCHN but not other cancers. It is suggested that there may be the histological difference in risk, due to certain genotypes conferring a greater susceptibility to a particular histological type of cancer, which can be seen anywhereIn the present meta-analysis, between-studies heterogeneity was observed between p21 3′ UTR rs1059234 polymorphism and risk of cancer. Deviation of HWE may reflect methodological problem such as genotyping errors, population stratification or selection bias. When these studies were excluded, the results were not changed among overall cancer and some subgroup analyses, indicating that our meta-analysis was statistically robust. When the meta-analysis was performed excluding studies with small sample sizes and any subgroup analysis, indicating that small sample size did not influence statistically robust.Our meta-analysis has several strengths. First, a systematic review of the association of p21 3′ UTR rs1059234 polymorphisms with cancer risk is statistically more powerful than any single study. Second, the quality of eligible studies included in current meta-analysis was satisfactory and met our inclusion criterion. Third, we did not detect any publication bias indicating that the whole pooled results should be unbiased. However, although we have put considerable efforts and resources into testing possible association between p21 3′ UTR rs1059234 polymorphisms and cancer risk, there are still some limitations inherited from the published studies. First, our results were based on single-factor estimations without adjustment for other risk factors including environmental factor and other lifestyles. Second, in the subgroup analysis may have had insufficient statistical power to check an association. Third, the controls were not uniformly defined. Some studies used a healthy population as the reference group, whereas others selected hospital patients without organic cancer as the reference group. Therefore, non-differential misclassification bias is possible because these studies may have included the control groups who have different risks of developing cancer of various organs.In summary, this meta-analysis suggests that no significantly increased cancer risk associated with p21 3′ UTR rs1059234 polymorphism, however, marginally increased risks were observed for rs1059234 in SCCHN. It is necessary to conduct large sample studies using standardized unbiased genotyping methods, homogeneous cancer patients and well-matched controls. Moreover, further studies estimating the effect of gene-gene and gene-environment interactions may eventually lead to our better, comprehensive understanding of the association between p21 3′ UTR rs1059234 polymorphisms and cancer risk.We searched the literature databases, including Pubmed, Medline, ISI Web of Knowledge and Embase databases. The strategy to identify all eligible articles involved the combined use of the following search items: ‘p21’ or ‘CDKN1A’ or ‘rs1059234’, ‘polymorphism’ or ‘variant’ and ‘cancer’, ‘carcinoma’, ‘tumor’ or ‘neoplasms’ and including all alternative locations and combinations of the terms. Reviews and bibliographies of the relevant studies identified were hand searched to find additional eligible studies, including clinical trials, cohort studies, etc. There was no language and sample limitation, and the literature search was updated on Sep 20, 2014. We evaluated all associated publications to retrieve the most eligible literatures. Their reference lists were hand-searched to find other relevant publications. Of the studies with overlapping data published by the same investigators, only the most recent or complete study was included in this meta-analysis.The following inclusion criteria were used to select literatures for the meta-analysis: (1) be a case-control study, nested case-control or a cohort study; (2) evaluated the association of p21 3′ UTR rs1059234 genetic polymorphisms with cancer risk, and (3) provide sufficient data for calculating the odds ratio (OR) with 95% confidence interval (95% CI). The major reasons for exclusion of the studies were as follows: (1) not for cancer research, (2) only case population; case reports, conference abstract, reviews, meta-analyses and studies without detailed data, (3) duplicate of previous publication, and (4) control population including malignant tumor patients.Two investigators reviewed and extracted information from all eligible publications independently, according to the inclusion and exclusion criteria listed above. An agreement was reached by discussion between the two reviewers whenever there was a conflict. The following items were collected from each study: first author’s surname, year and country of the study, source of control groups , statistical data, and ethnicity, total number of cases and controls and genotyping method. Different ethnic descents were categorized as Caucasian and Asian.Two investigators assessed the quality of each investigation using the quality assessment , which w2 that represents the percentage of total variation across studies. As a guide, values of I2 less than 25% may be considered “low”, values about 50% may be considered “moderate”, and values more than 75% may be considered “high”2 goodness of fit tests in controls. In addition to the comparison among all subjects, we also performed stratification analyses by cancer type .Crude ORs and their corresponding 95% CIs were used to evaluate the strength of associations between p21 3′ UTR rs1059234 polymorphisms and cancer risk. The pooled ORs were estimated for p21 3′ UTR rs1059234 polymorphism under the recessive model (TT vs. CT + CC) and dominant model (TT + CT vs. CC). To evaluate whether results of the data sets were homogeneous, we used Q testThe multivariate random-effects method of meta-analysis was also applied as a more advanced method for testing gene-disease associations. In this framework, the two summary log-odds ratios related to the risk allele, e.g. the log-odds ratio of heterozygotes vs. homozygotes (AB vs. AA) and the log-odds ratio of homozygotes for the risk allele vs. homozygotes for the wild type allele (BB vs. AA), are modeled simultaneously as a bivariate response. This method has several important properties, since it can infer and quantify the genetic model of inheritance directly, by estimating the ratio λ of the two log-odds ratios4243p-value < 0.05 were considered statistical significant.Begg and Mazumdar adjusted rank correlation test and Egger’s linear regression test were used to provide diagnosis of the potential publication bias. Sensitivity analyses were also performed to assess the stability of the results, namely, a single study in the meta-analysis was deleted each time to manifest the influence of the individual dataset to the pooled odds radio (OR). Stata Version 12.0  was the statistical package that was used for all analyses. Results with How to cite this article: Li, J. et al. Association of p21 3' UTR gene polymorphism with cancer risk: Evidence from a meta-analysis. Sci. Rep.5, 13189; doi: 10.1038/srep13189 (2015).