Conflicting results identifying the association between tooth loss and cardiovascular disease and stroke have been reported. Therefore, a dose-response meta-analysis was performed to clarify and quantitatively assess the correlation between tooth loss and cardiovascular disease and stroke risk. Up to March 2017, seventeen cohort studies were included in current meta-analysis, involving a total of 879084 participants with 43750 incident cases. Our results showed statistically significant increment association between tooth loss and cardiovascular disease and stroke risk. Subgroups analysis indicated that tooth loss was associated with a significant risk of cardiovascular disease and stroke in Asia and Caucasian. Furthermore, tooth loss was associated with a significant risk of cardiovascular disease and stroke in fatal cases and nonfatal cases. Additionally, a significant dose-response relationship was observed between tooth loss and cardiovascular disease and stroke risk. Increasing per 2 of tooth loss was associated with a 3% increment of coronary heart disease risk; increasing per 2 of tooth loss was associated with a 3% increment of stroke risk. Subgroup meta-analyses in study design, study quality, number of participants and number of cases showed consistent findings. No publication bias was observed in this meta-analysis. Considering these promising results, tooth loss might provide harmful health benefits. Cardiovascular disease affects millions of people in developed and developing countries that is now a public health crisis. Despite the decline in the mortality rate of developed countries, cardiovascular disease is still the main cause of death and has caused serious social and economic distress on a global scale over the past few decades. In low and middle-income countries, the incidence of cardiovascular disease has risen sharply –3. By 20Oral cavity is an important part of the body, and is starts in digestive system, mainly by the lip and cheek, tongue and palate, salivary glands, teeth and jaw, with mastication, swallowing, speech and feeling, and other functions, which maintain the normal shape of maxillofacial. Oral health is an important part of human health. The World Health Organization (WHO) identifies dental health as one of the top ten criteria for human health. Poor oral health may increase systemic inflammation, resulting in a local overly aggressive immune response, and thus could have important implications for cardiovascular disease. Periodontal disease and tooth loss are two common oral health measures. Tooth lPrevious studies have examined the correlation between tooth loss and cardiovascular disease and stroke risk–24. HoweOur meta-analysis was designed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA Compliant) statement25]..25].PubMed and EMBASE were searched for studies that contained risk estimates for the outcomes of coronary heart disease and were published update to April 2017, with keywords including “Coronary heart disease” [MeSH] OR”stroke” [MeSH] OR “Cardiovascular Diseases” [MeSH] OR “Coronary Disease” [MeSH] OR “myocardial infarction” [MeSH] AND “dentition” [MeSH] OR”tooth loss” [MeSH] OR”edentulous” [MeSH]. The search strategy is shown in detail in Two independent researchers investigate information the correlation between tooth loss and cardiovascular disease and stroke risk: outcome was coronary heart disease and stroke. To ensure the correct identification of qualified research, the two researchers read the reports independently, and the disagreements were resolved through consensus by all of the authors.Use standardized data collection tables to extract data. Each eligible article information was extracted by two independent researchers. We extracted the following information: first author; publication year; mean value of age; country; study name; sex; cases and participants; the categories of tooth loss; relative risk or odds ratio (OR). We collect the risk estimates with multivariable-adjusted. AccordiWe pooled relative risk estimates to measure the association between tooth loss and cardiovascular disease and stroke risk; the hazard ratio were considered equivalent to the relative risk. ResultsDue to different definitions cut-off points in the included studies for categories, we performed a relative risk estimates by using the method recommended by Greenland, Longnecker and Orsini and colleagues. A flexi2 statistic to assess heterogeneity among studies. Random-effect model was chosen if PQ< 0.10 or I2>50%, otherwise, fixed-effect mode was applied. Begg’s and Egger’s tests were to assess the publication bias of each study. P< 0.05 was considered signifcant for all tests.We use STATA software 12.0  to evaluate the relationships between tooth loss and risk of coronary heart disease and stroke. By using Q test and IA total of 2810 studies from Medline, 3246 studies from Embase. After removing duplicates study, 3028 studies were identifed. reviewing their titles and abstracts, 2978 citations were excluded. The remaining 50 citations were assessed in more detail for eligibility by reading the full text. Among them, 11 studies were excluded due to no relevant outcome measure; 15 studies were excluded due to no human studies; 3 study was excluded due to lack of detailed information; 4 study was excluded due to review; 1 study was excluded due to conference abstract. After review reference of studies, one article was identified. Finally, 28 studies were used for the final data synthesis. The flow chart of literature searching was presented in The characteristics of the included studies of tooth loss and risk of coronary heart disease and stroke are shown in the Tables P < .001)(P < .001)(P < .001)(P < .001) (P < .001) (P < .001) (P < .001) (P < .001) . SubgrouP < .001) and CaucP < .001). Further < .001)  and nonf < .001) . Also, t < .001)  and fema < .001) . Additio < .001) .P < .001)(P < .001) (P < .001) (P < .001) . Subgrou < .001)  and Cauc < .001) . Additio < .001) .Subgroup analysis was performed to check the stability of the primary outcome. Subgroup meta-analyses in study design, study quality, number of participants and number of cases showed consistent findings .Each studies in this meta-analysis were performed to evaluate the publication bias by both Begg’s funnel plot and Egger’s test. P>0.05 was considered no publication bias. The results show no obvious evidence of publication bias was found in the relationship between tooth loss and cardiovascular disease and stroke risk . A funneRecently, tooth loss has been found to be associated with decreased risks of lung cancer, colorectal cancer, prostate cancer, breast cancer and pancreatic cancer. HoweverIn the current meta-analysis was based on seventeen cohort study, with a total of 879084 participants with 43750 incident cases. Thus, this meta analysis provides the most up-to-date epidemiological evidence supporting tooth loss is harmful for cardiovascular disease and stroke. A dose-response analysis revealed that increasing tooth loss (per 2 increment) was associated with a 3% increment of coronary heart disease risk, increasing tooth loss (per 2 increment) was associated with a 3% increment of stroke risk. Furthermore, tooth loss was associated with a significantly cardiovascular disease and stroke risk in Asia and Caucasian. Also, tooth loss was associated with a significantly risk of cardiovascular disease in male and female. Additionally, tooth loss was associated with a significantly cardiovascular disease and stroke risk in fatal cases and nonfatal cases. Subgroup meta-analyses by various factors also showed consistent findings.Several plausible pathways may reasonable for the relationship between tooth loss and cancer. The influence of chronic inflammation on cancer development is one possible pathwa. Chronic systemic inflammation linked to periodontal disease. People with few or no teeth would this have an increased risk of systemic diseases such as cardiovascular disease and stroke. SecondlTo our knowledge, this is the first study to identify and quantify the potential dose-response association between tooth loss and cardiovascular disease and stroke risk in a large cohort of both men and women. Although, we performed this meta-analysis very carefully, however, some limitations must be considered in the current meta-analysis. First, we only select literature that written by English, which may have resulted in a language or cultural bias, other language should be chosen in the further. Second, in the subgroup analysis in different ethnic population, there might be insufficient statistical power to check an association.In conclusion, our dose–response meta-analysis suggests tooth loss was independently associated with deleterious coronary heart disease and stroke risk increment. In the future, large-scale and population based association studies must be performed to help identify the putative causal role that tooth loss plays in increasing the incidence of these diseases.S1 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 List(DOCX)Click here for additional data file.S1 Checklist(DOC)Click here for additional data file.


P < 0.001). When stratified by tumor type, the impact of HK2 overexpression on poor prognosis was observed in gastric cancer , hepatocellular carcinoma , and colorectal cancer , but not in pancreatic ductal adencarcinoma . Furthermore, high HK2 expression was significantly associated with some phenotypes of tumor aggressiveness, such as large tumor size , positive lymph node metastasis , advanced clinical stage  and high alpha fetoprotein level (OR = 1.47 [1.09-2.02] P = 0.013). In summary, HK2 might act as a prognostic indicator and a potential therapeutic target of these digestive system cancers.Several previous studies have reported the prognostic value of hexokinase 2 (HK2) in digestive system tumors. However, these studies were limited by the small sample sizes and the results were inconsistent among them. Therefore, we conducted a meta-analysis based on 15 studies with 1932 patients to assess the relationship between HK2 overexpression and overall survival (OS) of digestive system malignancies. The relationship of HK2 and clinicopathological features was also evaluated. Hazard ratio (HR) or odds ratio (OR) with its 95% confidence intervals (CI) were calculated to estimate the effect size. Positive HK2 expression showed poor OS in all tumor types (HR = 1.75 [1.41-2.18],  The digestive system is composed of many ducts and glands, and because of its complicated physiology and anatomy, numerous diseases are prone to occur, especially malignancies, which have become one of the most terrible threats for human health . AccordiReprogramming energy metabolism is one of the key hallmarks of many rapidly growing cancers . With reHexokinase (HK) catalyzes the conversion of glucose to glucose-6-phoshate (G6P), and is the first and rate-limiting step of both anaerobic glycolysis and ultimately oxidative phosphrylation. The four members of the HK family (HK1-4) in mammals are structurally similar but expressed in a tissue-specific manner . Among tThe concise process of literature selection was shown in Figure P < 0.001; random effects) , HCC , and CRC , but not in patients with PDAC  , in reverse transcription-polymerase chain reaction (RT-PCR) detection , and in immunofluorescence (IF) detection . Furthermore, this association did not only exist in the Eastern Asian population , but also in the North American population  . Thus, we used a random-effects model to estimate the overall HR for OS. When the subgroup analysis was conducted to assess the source of heterogeneity based on cancer type, detection method, analysis method, study region, blinding status, and sample size, the heterogeneity was obvious to be still significantly evident , positive lymph node metastasis , advanced clinical stage  and high alpha fetoprotein (AFP) level . This finding indicated that HK2 may promote tumor invasion and aggressiveness. However, no association existed between HK2 expression and certain factors, such as gender , depth of invasion , differentiation , distant metastasis , HBV infection , liver cirrhosis , and portal vein involvement .The relationship of HK2 expression with clinicopathological features are illustrated in Table A cumulative meta-analysis of 15 cohorts was performed to evaluate the cumulative HR estimate over time. The results of cumulative meta-analysis are shown in Figure P = 0.708), detection method (P = 0.144), study region (P = 0.358), blinding status (P = 0.320), sample size (P = 0.963), and analysis method (P = 0.068) did not contribute to the source of heterogeneity for OS.We also conducted a meta-regression analysis to investigate the potential source of heterogeneity among studies. However, the results showed that cancer type  and Egger’ test (P = 0.079) suggested that there was no statistical evidence of publication bias was found for the meta-analysis for OS. The shape of the funnel plot was symmetrical , glycogenesis and the hexosamine pathways . Thus, iOur meta-analysis included 15 studies with 1932 patients, and the combined outcomes showed that high density of HK2 expression was significantly associated with worse OS in solid tumors of digestive system; hence, HK2 overexpression could be an independent unfavorable predictor of prognosis in patients with digestive system malignancies. Given that traditional meta-analysis only reflects the outcome at a certain point of time, we conducted a cumulative meta-analysis to explore the variation trend of the overall effect with the passage of time . The cumDespite the robustness of the pooled results, the findings should be interpreted in caution. First, the heterogeneity among the included studies was extreme in our meta-analysis, even when we conducted subgroup analyses. The significant heterogeneity could be probably caused by the differences in the patient features, cancer types, ethnicity, study protocol, and literature quality. A meta-regression analysis was performed to find out the source of heterogeneity. However, none of these confounding factors could completely explain the heterogeneity. Second, according to the results of subgroup analyses, the overall outcomes did not changed significantly based on the grouping of detection method, analysis method, sample size, blinding status, and study region. However, when grouping by tumor type, the pooled results were also statistically significant in GC, HCC, and CRC, but not in PDAC, which was similar to our previous report about the association of pyruvate kinase M2 (PKM2) expression and prognosis of digestive system cancers . Since bc [To further investigate the prognostic impact of HK2 on digestive system cancers, we analyzed the correlation between HK2 expression and clinicopathological factors that may affected the survival outcomes. According to the pooled results, the abnormal expression of HK2 was significantly associated with some clinical parameters, such as large tumor size, positive lymph node metastasis, advanced clinical stage, and high AFP level. All of these factors have been documented to be the powerful variables related to tumor progression and compromise long-term survival , 36. Herc –42. Alsoc –46. Therc . The aboc –50. AlsoSome limitations of our meta-analysis should be acknowledged. First, due to the unified cut-off values and follow-up times among the included studies, heterogeneity may be virtually brought in. Second, several individual HRs were calculated from survival curves or univariate analysis, which may be less reliable than the actual HRs directly obtained from published statistics . Third, In conclusion, our meta-analysis provides evidence that HK2 may be a potential marker to predict the risk of all-caused mortality and cancer progression in patients with solid tumors of digestive system. High expression of HK2 may not only predict poor prognosis but might also be a promising therapeutic approach for developing strategies against this protein. Due to the limitations, further data are required to validate the clinical importance of HK2 by large multicenter prospective studies with larger sample sizes.A systematic computer-aided literature search of the PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Infrastructure databases was conducted (last updated in January 2017) by using the following terms: “HK2 or hexokinase 2 or hexokinase II or type 2 hexokinase , cancer or tumor or malignancy or neoplasm or carcinoma , and digestive system or alimentary system , and prognosis or prognostic or survival or outcome ”. We also screened the citation lists of the relevant studies for comprehensive search. This meta-analysis was conducted according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) .Publications were recruited in this meta-analysis when they fit all of the following criteria: (1) assessing the relationship between HK2 expression and OS in patients with digestive system tumors using a cohort design; (2) detecting HK2 protein or mRNA in tumor tissue; (3) dividing the patients into two groups, namely, HK2 positive and HK2 negative, regardless of the cutoff value; (4) providing sufficient information for estimating the HRs and 95% CIs for survival outcomes in the original data; (4) being written as full papers; (5) being with the largest patient cohort among duplicated publications by the same authors or institutes. We excluded the following studies: reviews, conference abstracts, editorials, letters, basic research, or animal experiments.Two authors (JYW and LRH) independently reviewed and collected information from each eligible study according to the selection criteria. Any disagreement between the reviewers was resolved by consensus. Data extracted from the studies included the name of the first authors, year of publication, study region, cancer type, duration period, follow-up time, sample size, detection method, blinding status, cutoff value, number of HK2 positive, analysis method, survival outcomes, HR estimation, and quality scores. Blinding status represented that the evaluation of HK2 was blinded to the clinical outcomes. In studies where the HRs and the corresponding 95% CIs of univariate and multivariate analyses were provided, only the latter was applied to the data synthesis because it is more precise and it considers the confounding factors. In the absence of results from multivariate analysis, HR was extracted from the univariate analysis or calculated using the Kaplan–Meier survival curves .The quality of included studies was assessed by NOS according to the following categories: selection, comparability, and outcome of interest. The total score of NOS ranged from 0 to 9, and we considered studies as high quality if they met at least six scores.P < 0.05. Subgroup analyses were conducted according to cancer type , detection method , study region (“Eastern Asia” and “America”), blinding status (“yes” and “none reported”), and sample size (“≥ 100” and “< 100”). Meta-regression analysis was also performed to determine the potential sources of heterogeneity. For the pooled analysis of the correlation between HK2 expression and clinicopathological features , the ORs and their corresponding 95% CI were combined to estimate the effect. All statistical tests were two sided.STATA 11.0 software  was used for all statistical analysis. The combined HR and 95% CI were used to assess the strength of HK2 expression with survival endpoints (OS) based on the data extracted from the eligible studies. HR > 1 indicated an increased risk of poor prognosis for patients with HK2 overexpression when the 95% CI exceeding 1. The statistical significance of the pooled HR was determined through Z–test. The results were considered statistically significant if Q statistic, and was considered statistically significant when P < 0.05. Heterogeneity was also quantitatively estimated using the I2 metric  [P < 0.05 or I2 > 50%), the pooled HR estimation of each study was calculated using a random-effects model (DerSimonian and Laird method). Otherwise, a fixed-effects model was applied (Mantel–Haenszel method) [P < 0.05 was defined as statistically significance) [Heterogeneity assumption was qualitatively examined through the chi-squared test based on the geneity) . When si method) . Sensitificance) , and was


Cataracts are the main cause of poor vision and blindness worldwide. The effects of statin administration on cataracts remain debated. Therefore, we conducted a systematic review and meta‐analysis to determine whether statin use affects the risk of cataracts.EMBASE, and the Cochrane Library through January 2016. Weighted averages were reported as relative risk values with 95% CIs. Statistical heterogeneity scores were assessed with the standard Cochran's Q test and the I2 statistic. A total of 6 cohort studies, 6 case–control studies, and 5 randomized controlled trials, together involving more than 313 200 patients, were included in our study. The pooled estimates of cohort studies indicated that the use of statins moderately increases the risk of cataracts . The pooled estimates of case–control studies  and randomized controlled trials  indicated that the use of statins does not increase the risk of cataracts. The sensitivity analysis confirmed the stability of the results. Heterogeneity was found among the cohort and case–control studies.We performed a systematic search of the electronic databases PubMed, Based on the present meta‐analysis of these studies, we could only conclude that there is no clear evidence showing that statin use increases the risk of cataracts. The most likely case is that there is no association between statin use and cataracts. Because of the considerable benefits of statins in cardiovascular patients, this issue should not deter their use. Cataracts are the main cause of low vision and blindness worldwide.Concern about the cataractogenic effect of statins arose from animal studies in which dogs were administered high doses of statins, such as simvastatin, fluvastatin, and lovastatin.We conducted this meta‐analysis following the guidance provided by the Cochrane HandbookThe Cochrane Library, PubMed, and EMBASE databases were searched from January 1980 to January 2016 for English language publications, including abstracts. The search was performed using the following terms: “statins OR HMG‐CoA reductase inhibitors OR Simvastatin OR Lovastatin OR Fluvastatin OR Pravastatin OR Rosuvastatin OR Atorvastatin” AND “cataract.” We also manually searched for relevant articles from the reference lists of the retrieved articles. When the available information was incomplete, we attempted to contact the study investigators for additional information.Studies were included in this meta‐analysis if they fulfilled the following criteria: (1) the study was a case–control, cohort study, or randomized controlled trials (RCTs); (2) non–statin users were included in the comparison group; (3) cataracts and/or cataract surgery was an outcome; (4) the association between statin use and the risk of cataracts/cataract surgery was investigated; (5) risk estimates of morbidity and 95% CIs were reported or the information required to calculate them was available. Basic science studies, reviews, editorials/letters, case reports, and studies without comparison groups were excluded.Data extraction was performed independently by 2 of the authors (Yu and Chu). The following information was extracted from each study: the last name of the first author, year of publication, study design, country of origin of the population studied, patient characteristics, statin use, information source for exposure ascertainment, risk estimates and corresponding 95% CIs, and covariates adjusted for in the multivariable analysis. For studies that provided more than 1 risk estimate, we extracted the estimate that was adjusted for the greatest number of confounding factors. We assessed the methodological quality of the included studies based on the Newcastle‐Ottawa Scale (NOS) for observational studies,The primary analysis focused on assessing the risk of cataracts and cataract surgery among users of statins. We also performed subgroup analyses based on study design , type of statin, the methodological quality of the study (high or low), study location , age, sex, follow‐up duration, outcome and outcome assessment, and whether potential confounders were included in the adjusting model .2 test and the I2 statistic.2 value of >50% or a P value of <0.05 for the Q‐statistic indicated significant heterogeneity.STATA 12.0 software  was used for statistical analysis. Heterogeneity was assessed using the Cochran Q χBy searching the 3 databases, 615 potentially eligible articles were identified. In total, 489 articles were excluded after reading the title and abstract, and the full texts of the remaining 120 articles were evaluated in detail. Of these 120 articles, 16 met our inclusion criteria. One of these articles included 2 studies.The main characteristics of the cohort and case–control studies are shown in Tables 2 value indicated significant heterogeneity across the studies . However, the pooled RRs of the case–control studies and RCTs were 1.10  , which indicated that the use of statins was associated with a 13% increase in cataract incidence or cataract surgery  Figure ,23, 24, s Figure , case–cos Figure , and RCTs Figure , and theThe results of the subgroup analyses of the cohort and case–control studies are presented in Tables The results of the subgroup analysis of RCTs are presented in Table When the studies were grouped according to patient age, no association was observed in the older than 60 years subgroup  and the no older than 60 years subgroup  Figure . AlthougWhen we grouped the studies by follow‐up duration, no significant association was observed in the no more than 5 years group  and in the more than 5 years group  Figure .In this comprehensive meta‐analysis of 6 cohort, 6 case–control studies, and 5 RCTs, we analyzed the effect of statin use on the risk of cataracts in more than 313 200 patients. Analysis of the cohort studies showed that statin use was associated with a 13% increased risk of cataracts. However, analysis of the case–control studies and RCTs revealed no association between statin use and the risk of cataract. The effect size of the case–control studies was marginal, namely, RR=1.10 . Based on the differing characteristics of observational (case–control and cohort) studies and RCTs, such discordant results are not unexpected. Because of the rigorous criteria of RCTs, individuals at greatest risk for adverse events may be excluded. Furthermore, the subjects of RCTs may be healthier than the subjects of observational studies.2 values decreased significantly when subgrouped by sex, outcome assessment, age, follow‐up duration, or consultation rate included/missing model . In the consultation rate included model subgroup . Consequently, the heterogeneity may be partly attributed to the outcome assessment. The evaluation criterion of various assessment methods may have varied among the studies, and patients diagnosed with cataracts by 1 method may not be so diagnosed when another method is used. Furthermore, even when the same method for diagnosis is used, different physicians may make different decisions, especially regarding cataract surgery. In the subgroup analysis of case–control studies, the I2 values were significantly decreased when subgrouped by quality assessment, study location, type of statin, CVD included/missing model, smoking included/missing model, consultation rate included/missing model, or hypertension included/missing model .Figure S6. Subgroup analysis by sex.Figure S7. Subgroup analysis by outcome assessment.Figure S8. Subgroup analysis by outcome (Cataract).Figure S9. Subgroup analysis by age.Figure S10. Subgroup analysis by follow‐up duration.Figure S11. Subgroup analysis by whether LDL included.Figure S12. Subgroup analysis by CVD.Figure S13. Subgroup analysis by smoking (smoking included).Figure S14. Subgroup analysis by consultation rate.Figure S15. Subgroup analysis by hypertension.Figure S16. Subgroup analysis by quality assessment.Figure S17. Subgroup analysis by location.Figure S18. Subgroup analysis by outcome assessment.Figure S19. Subgroup analysis by type of statins.Figure S20. Subgroup analysis by outcome.Figure S21. Subgroup analysis by CVD.Figure S22. Subgroup analysis by smoking.Figure S23. Subgroup analysis by consultation rate.Figure S24. Subgroup analysis by diabetes mellitus (diabetes mellitus included).Figure S25. Subgroup analysis by hypertension.Figure S26. Subgroup analysis by age (older than 60).Figure S27. Subgroup analysis by age (including Spence's).Figure S28. Subgroup analysis by follow‐up duration.Click here for additional data file.


The prognostic role of serum cancer antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) in breast cancer remains controversial. In this study, we conducted a meta-analysis to investigate the prognostic value of these two markers in breast cancer patients.  After electronic databases were searched, 36 studies (31 including information regarding CA15-3 and 23 including information regarding CEA) with 12,993 subjects were included. Based on the data directly or indirectly from the available studies, the hazard ratios (HRs) and odds ratios (ORs) and their 95% confidence intervals (CIs) were pooled according to higher or lower marker levels.  Elevated CA15-3 or CEA was statistically significant with poorer DFS and OS in breast cancer . Subgroup analysis showed that CA15-3 or CEA had significant predictive values in primary or metastasis types and different cut-offs and included sample sizes and even the study publication year. Furthermore, elevated CA15-3 was associated with advanced histological grade and younger age, while elevated CEA was related to the non-triple-negative tumor type and older age. These two elevated markers were all associated with a higher tumor burden.  This meta-analysis showed that elevated serum CA15-3 or CEA was associated with poor DFS and OS in patients with breast cancer, and they should be tested anytime if possible. Breast cancer has the highest incidence rate and second highest mortality rate among female cancers ; thus, iSerum markers can be easily achieved, and they play an important role in many malignant tumors –11, but We performed a systematic literature search using PubMed, Embase, and Web of Science. The search strategy terms are as follows: “CA15-3” , “CEA” , “prognosis” , and “breast cancer” . The search was updated to July 15, 2017. The relevant articles were also manually checked.The inclusion criteria for this meta-analysis were as follows: (i) the diagnosis of breast cancer was defined based on pathological results, (ii) CEA and CA15-3 were derived from a serum test, (iii) the correlation of serum CEA and/or CA15-3 with overall survival (OS) and/or disease-free survival (DSS) and/or disease-free survival (DFS) and/or progression-free survival (PFS) was reported, and (iv) hazard ratios (HRs) and 95% confidence intervals (CIs) can be directly or indirectly received. The exclusion criteria were as follows: (i) abstracts, letters, reviews, case reports, proficient opinions; (ii) literature not written in English; (iii) studies without hazard ratios (HRs) and 95% CIs; (iv) studies with duplicate data with few cases; and (v) nonhuman studies.We extracted data by two independent authors and through discussion or consensus with a third author regarding controversial data. The following items were recorded from each study: name of the first author, publication year, country, data inclusion criteria of each study, overall sample size, mean age and follow-up time of patients, tumor data of the TNM stage, time of serum sample collection, cut-off values of CEA and/or CA15-3 and the associated clinicopathological factors, and survival data, including HRs with 95% CIs. We used the Newcastle–Ottawa Scale (NOS) to assess the quality of each study by two authors. The scores included three parts: the selectivity of patients (0–4), comparability of groups (0–2), and assessment of outcome (0–3). Studies with scores > 5 were considered high quality.We directly obtained all multivariate data and some univariate data of HRs and 95% CIs from the literature. Few univariate data were estimated according to the methods illustrated by Parmar et al. and Tierney et al. using Engauge 4.0 and an HR calculation spreadsheet , 28. HR Q test was undertaken to assess the heterogeneity. All the studies first used fixed-effect (Mantel–Haenszel method) models to calculate the pooled results, but if  Ph < 0.1 or I2 > 50%, which suggested significant heterogeneity, we changed to a more conservative random-effect (DerSimonian–Laird method) model to confirm the results. Subgroup analysis was conducted to explore and explain the heterogeneity. Publication bias was first assessed by Begg's funnel plot, and the result of pr > |z| > 0.05 was regarded as no publication bias. If bias was not certain, Egger's bias indicator test was used to reconfirm. Results with p < 0.05 were considered statistically significant, and all the results were two sided.In this meta-analysis, Cochran's The initial search information retrieved 751 articles. After careful inspection, 36 studies published between 1986 and 2017 that comprised 12,993 breast cancer patients met our inclusion criteria and were finally included in this meta-analysis , 29–52. I2 = 50%, Ph = 0.02, random effects model). Ten studies provided multivariate HR and 95% CI information of DFS concerning CA15-3 . Among the fourteen studies, two studies reported the results of PFS that was the result of group 2, which showed that HR was 2.00 (95% CI: 1.46–2.74) for PFS without significant heterogeneity . In multivariate subgroup analysis, the results in European countries and the United States, compared with those in Asian and African countries, showed significant subgroup heterogeneity and lower cut-off values in pretreatment patients had higher prognostic values  with obvious heterogeneity . Heterogeneity was significantly different among the three groups as the pooled HR was the lowest in group 2 . Stratification by ethnicity found that CA15-3 was a negative predictor for OS in both European countries and the United States  and in Asian and African countries . Fifteen studies reported multivariate data of OS (p < 0.001), and heterogeneity was not significant . Among these three groups, no significant heterogeneity was seen. The subgroup analysis results are shown in Twenty-one studies had extractable univariate OS HR data concerning CA15-3 . Elevateta of OS . ElevateI2 = 0%, Ph = 0.91, fixed-effect model; I2 = 0%, Ph = 0.82, fixed-effect model; The univariate HR of ten studies and multivariate HR of nine studies of CEA and DFS could be obtained. Pooled univariate data showed that high CEA was associated with worse DFS with HR = 2.60 . Pooled multivariate HR results also provided similar results as higher CEA revealed an HR of 1.72 (95% CI: 1.49–1.99) in OS without significant heterogeneity , while younger ages had a lower rate of CEA rise . Higher rate of CA15-3 and CEA rise were correlated with tumor burden as larger tumor size, lymph node metastasis, and advanced TNM stages. Higher CA15-3 rates were also related to an advanced tumor histological grade . However, non-triple-negative-type breast cancer was more likely to associated with a CEA level increase .We extracted twelve factor data according to the cut-off of the high and low groups of CA15-3 or CEA . The resz| > 0.05. The multivariate pooled results of OS in CA15-3 had pr > |z| = 0.01 for Begg's test. Next, we carefully examined Begg's funnel plot and performed Egger's test for this result. Egger's test showed p > |t| = 0.104, and Begg's funnel plot was approximately symmetrical; thus, we believe that publication bias did not show a significant difference (Supplement Begg's funnel plot was used to evaluate publication bias. All the results except one showed no significant publication bias as the values of pr > |The prognostic factors of breast cancer include tumor biopathological factors such as tumor burden, hormone receptors, HER-2, and Ki-67 levels , 54. AllThe meta-analysis indicated that an elevated CA15-3 level significantly corresponded with poor DFS and OS of breast cancer. In our analysis, serum CA15-3 had prognostic ability in the pretreatment of primary early-stage and primary all-stage breast cancer patients. It also showed prognostic values in metastasis in OS but not in multivariate analysis of DFS because of only one study reported this result, and it may show great bias. The reason why CA15-3 can predict the prognosis in breast cancer is not fully clear, but as CA15-3 is the soluble form of MUC1, this may be related with the function of MUC1. MUC1 was reported to not only allow the cancer cells to escape the immune system but also promote cancer cell migration by activating some membrane receptors –59. SeruThe CEA levels are less likely to be elevated than CA15-3; however, in symptomatic breast cancer patients, CEA sensitivity increased, and some studies found that CEA are able to correlate with the stage of disease as well as prognosis though these results are also in dispute. De Jong-Bakker et al. reported the pretreatment level of CEA had no relationship with all groups of patients regarding prognosis as the same results of other small-sample studies report , 44, 66.CA15-3 and CEA have been reported to be associated with clinicopathological parameters . Both thSeveral limitations need to be carefully considered in this analysis. First, almost all the included studies in this analysis were retrospective, making them more susceptible to some biases. Though we could not exclude all potential residual confounding, the asymmetry in the funnel plots at least showed the absence of publication bias, thus maintaining the substantial consistency of the results. Second, the publication year of the included studies in this analysis ranged from 1986 to 2017. This long period led to great differences; thus, we performed subgroup analysis and used the cut-off year of 2010 to limit the heterogeneity. Third, some studies have found that CA15-3 and/or CEA only had prognostic value according to molecular tumor types, but we could not identify sufficient studies to pool the results according to different molecular types of breast cancer. Fourth, although we performed multivariate analysis of CA15-3 and CEA as prognostic factors, the multivariate analyses were adjusted to different factors in different studies; thus, the pooled results using CA15-3 and/or CEA as an independent predictor should be carefully considered.In conclusion, we found that both CA15-3 and CEA were good prognostic factors for poor DFS and OS when abnormal levels were found in breast cancer patients. Furthermore, higher CA15-3 or CEA had more significant prognostic significance for pretreatment primary breast cancer, a cancer occurring in Asian and African countries. Elevated CA15-3 and CEA were closely associated with age and tumor burden. In the future, more studies with uniform cut-off values, large-scale data, and good designs are needed to validate our conclusion.


P = 2.19E-4; GG vs. GC+CC: OR = 0.687, 95%CI = 0.564–0.837, P = 1.95E-4), which indicates that rs12220909 in miR-4293 may play a significant role in the development of non-small cell lung cancer.Non-small cell lung cancer is one of the most common cancers and the leading cause of cancer death worldwide. Genetic variants in regulatory regions of some miRNAs might be involved in non-small cell lung cancer susceptibility and survival. rs12220909 (G/C) genetic polymorphism in miR-4293 has been shown to be associated with decreased risk of esophageal squamous cell carcinoma. However, the influence of rs12220909 genetic variation on non-small cell lung cancer susceptibility has not been reported. In order to evaluate the potential association between miR-4293 rs12220909 and non-small cell lung cancer risk in a Chinese population, we performed a case-control study among 998 non-small cell lung cancer cases and 1471 controls. The data shows that miR-4293 rs12220909 was significantly associated with decreased susceptibility to non-small cell lung cancer (GC vs.GG: OR = 0.681, 95%CI = 0.555–0.835,  Lung cancer is the leading cause of cancer death in both men and women in western society . In receMicroRNAs(miRNAs) are small non-coding RNAs, which are evolutionarily conserved endogenous non-coding RNAs of about 22 nucleotides that regulate gene expression post-transcriptionally through binding to the 3'-untranslated region (3'-UTR) of target mRNAs in a sequence-specific manner, leading to mRNA cleavage , 7. miRNThe role of miR-4293 in cancer is not yet known. Gong et al. reported In our study, a total of 998 NSCLC patients and 1471 cancer-free controls were enrolled. NSCLC patients were diagnosed, and the disease histologically confirmed by doctors and recruited from two hospitals: Taizhou People’s Hospital and Shanghai Cancer Hospital, without restriction regarding age and gender between January, 2007 and April, 2012. Healthy controls were recruited from Taizhou City over the same period of time. The controls were free from NSCLC and other diseases. All enrolled subjects were unrelated Han ethnic Chinese who had signed a written informed consent. The case-control study is supported by the Institutional Review Board of the School of Life Sciences of Fudan University.http://www.mirbase.org), we performed a bioinformatics analysis to search SNPs in miRNAs' mature sequences, and the rs12220909 located in miR-4293 came to our attention. It was first reported by Gong et al.[On the basis of the public database miRBase  was employed to detect the DNA concentration. Genotyping of miR-4293 rs12220909 genetic polymorphism was completed with MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization-Time of Flight).Blood samples were extracted from enrolled subjects and genomic DNA was isolated using the Lai FengThe specific primers for PCR and iPLEX reaction were designed by Genotyping Tools & Mass ARRAY Assay Design software. Primers were synthesized by Shanghai Invitrogen Biotechnology. PCR amplification was performed in 384-well plates and shrimp alkaline phosphatase was used to dephosphorylate unincorporated dNTPs in the PCR mixture. After the mass ARRAY iPLEX reaction was performed, iPLEX products with different molecular masses were created by each base of SNP sites. Finally, distinct iPLEX products were identified by matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) and genotyping information of each SNP site was obtained for every subject by MALDI-TOF with TYPER 4.0 software.http://www.bioguo.org/miRNASNP2/). The ToppGene Suite [https://toppgene.cchmc.org/) was subsequently used to identify enriched functional annotation categories for genes lost or gained by the presence of rs12220909 in the miR-4293 seed region. Gene Ontology (GO)[To determine the molecular mechanism behind the decreased susceptibility to NSCLC mediated by miR-4293, in silico analysis was conducted to search for potential loss or gained of genes using the website (ne Suite  bioinforlogy (GO) terms anP-value<0.05 was considered statistically significant. All statistical analysis was performed on SPSS version 18.0 statistical software. A P-value < 0.05 was considered statistically significant for the Gene Ontology (GO) enrichment and pathway analysis. The default parameter of the hypergeometric test was selected as the statistical method and Bonferroni—Hochberg was used as the FDR correction method.We used chi-square and Student’s t test to evaluate differences in demographic variables between cases and controls. In the analysis procedure, we used the following genetic models: the recessive model (AA+AB vs. BB), the dominant model (AA vs. AB+BB), the allelic model (A vs. B), the homozygous model (AA vs. BB), and the heterozygous model (AA vs. AB); all models assume A is the wild-type allele and B is the mutant allele. The odds ratio (OR) and 95% confidence interval (95%CI) were calculated by logistic regression analysis and were used to estimate the association of SNPs with NSCLC susceptibility. Stratified analyses were conducted by selected demographic variables such as age , sex , smoking status  and histologic types. P<0.01) because of the random collection of the community.A total of 998 NSCLC cases  and 1471 controls  were enrolled. The relevant characteristics of subjects were listed in P>0.05, data not shown). Genotype and allele distributions of miR-4293 rs12220909 polymorphism showed significant difference between cases and controls. The GC genotype is associated with a decrease NSCLC susceptibility . The frequency of the C allele was significantly higher than the G allele in the cancer patients vs the controls . A protective role in NSCLC was found for the miR-4293 rs12220909 heterozygous mutation under the dominant model  and the additive model .The genotype, allele frequencies distributions and additive model of rs12220909 are showed in P = 1.46×10−4), lower age , higher age , non- smoker , smoker  and squamous cell carcinoma group , adenocarcinoma group . The results indicated GC/CC genotypes only have a significantly different influence on male and female groups.We further performed stratification analysis with selected demographic variables. The characteristics of gender, age, smoking status and histologic types were included in the following stratified analyses Tables  and 4. FTo further determine the molecular mechanism behind miR-4293-mediated increased risk of NSCLC in our population, in silico studies were performed to search for target genes that were lost and gained. The miR-4293 gained 288 and lost 1875 genes due to rs12220909 G>C. We then used the Toppgene online tool for GO enrichment and pathway analysis, which showed that the genes regulated by miR-4293 rs12220909 were significantly enriched in amino acid transmembrane transporter activity and neutral amino acid transmembrane transporter activity  Tables.In the present study, we explored the potential association of the miR-4293 rs12220909 genetic polymorphism with NSCLC risk in a Chinese population. We found that the miR-4293 rs12220909 is significantly associated with decreased susceptibility to NSCLC.P = 2.19×10−4), which indicates that the mutant allele C of rs12220909 can influence the function of miR-4293 . Similar results were also observed in the follow-up stratified analysis. However, we did not observe a significant association of miR-4293 rs12220909 with NSCLC risk among the female population in our study. This result indicates that SNP rs12220909 makes NSCLC a different susceptibility in gender population.Gong et al. have repIn silico studies showed that miR-4293 would potentially lose 1, 875 target genes and gain only 288 target genes after the G was replaced by the C allele in rs12220909. These results suggest that a SNP located in the miRNA seed region may change the quantity of target genes, which would lead to miRNA functional change. Based on our results, we speculate that the mutant allele C could increase the binding between miR-4293 and some cancer-related oncogenes, and may reveal the functional mechanism of the protective role of miR-4293 rs12220909G>C in NSCLC development. In addition, GO enrichment and pathway analysis results suggest that miR-4293 rs12220909-mediated genes are significantly enriched in the GO category; these genes are involved in molecular functions such as amino acid transmembrane transporter activity and neutral amino acid transmembrane transporter activity. SLC43A2 and SLC7A5, members of the L-type amino acid transporter (LAT) family, are responsible for the majority of cellular leucine uptake. It has been reported that SLC43A2 and SLC7A5 show increased expression in many types of cancer, and play a critical role in controlling protein translation and cell growth through the mTORC1 pathway . We specIn conclusion, our results provide the first evidence that rs12220909G>C polymorphism in the miR-4293 seed region is associated with decreased NSCLC risk in a Chinese population, and may play significant role in the process of NSCLC.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


MGMT) gene, a DNA repair gene, plays a critical role in the repair of alkylated DNA adducts that form following exposure to genotoxic agents. MGMT is generally expressed in various tumors, and its function is frequently lost because of hypermethylation in the promoter. The promoter methylation of MGMT has been extensively investigated in head and neck squamous cell carcinoma (HNSCC). However, the association between the promoter methylation of MGMT and HNSCC risk remains inconclusive and inconsistent. Therefore, we performed a meta-analysis to better clarify the association between the promoter methylation of MGMT and HNSCC risk.O6-methylguanine-DNA methyl-transferase (MGMT promoter methylation and HNSCC. Odds ratio (ORs) and 95% confidence intervals (CI) were calculated to estimate association between MGMT promoter methylation and risk of HNSCC. The meta-regression and subgroup analysis were undertaken to explore the potential sources of heterogeneity.A systematical search was conducted in PubMed, Web of Science, EMBASE, and Ovid for studies on the association between MGMT promoter methylation was 46.70% in HNSCC group and 23.23% in the control group. The frequency of MGMT promoter methylation in HNSCC group was significantly higher than the control group .Twenty studies with 1,030 cases and 775 controls were finally included in this study. The frequency of MGMT promoter was significantly associated with the risk of HNSCC, and it may be a potential molecular marker for monitoring the disease and may provide new insights to the treatment of HNSCC.This meta-analysis indicates that aberrant methylation of  Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy cancer worldwide and about 600,000 new cases each year . Among tMGMT) is a DNA repair gene that plays a crucial role in the mechanism of repair of DNA damage caused by alkylating agents [MGMT is widely expressed in various tumors, and its function is frequently lost due to hypermethylation in the promoter. Some studies had found that methylation of MGMT gene promoter was closely related to poor prognosis, metastasis, and recurrence in HNSCC [Hypermethylation of CpG islands in the promoter region of human genes often resulted in epigenetic inactivation, one of the most frequent events in human tumors. Gene-specific promoter methylation has been increasingly identified as a contributing factor to the development of HNSCC ,6. O6-meg agents . MGMT isin HNSCC –10.MGMT promoter and HNSCC risk. And most studies aimed to investigate the relationship by comparing the differences in the methylation frequencies of MGMT promoter between cancer and non-cancerous. However, the results remain inconclusive and inconsistent. Furthermore, some studies always have a small sample size and have different types of control. Therefore, we conducted a meta-analysis to better clarify the association between MGMT promoter methylation and risk of HNSCC.Up to now, many studies have explored the association between aberrant methylation of As described in detail previously ,12, the MGMT methylation). The search results were updated until May 20, 2016 with restricting to English language.We searched the relevant studies in various online electronic databases . The following search strategy was employed:  and (squamous cell carcinoma or cancer) and  case-control study and reporting the MGMT promoter methylation frequency in case and control groups, (3) specimens of HNSCC were limited to tissues. Firstly, we read the title and abstract of initial searching articles to assess whether it met the inclusion criteria. Then the potentially relevant articles were evaluated in full-text paper. Finally, a total of 20 articles which contain 1030 cases and 775 controls were included in the meta-analysis. The selection procedure of studies was illustrated in The inclusion criteria of the meta-analysis were: (1) articles studying with the association between MGMT methylation and the total people in the case and control groups). All the detailed information extracted from the eligible studies was checked by the third reviewer (Xiyue Xiao).Two reviewers (Fucheng Cai and Yi Zhong) independently reviewed the eligible studies. The following information was extracted from the eligible studies: first author’s name, publication year, study population, method for detecting the methylation status, sample type in case and control group, sample sizes (the number of people with MGMT promoter methylation and HNSCC risk. The between-study heterogeneity was tested by the x2-based Cochran Q statistic test and I2 statistics [P<0.05 for the Q statistic or I2≥50%) and a random-effects model was used to calculate the pooled ORs. Otherwise, a fixed-effects model was applied to calculate the pooled ORs. Moreover, we performed the meta-regression and subgroup analysis to explore the source of heterogeneity. A sensitivity analysis was executed to investigate the influence of each individual study to the final results of the meta-analysis. The Begg’s funnel plot [P<0.05 was considered statistically significant. In the Meta package, the default is to add 0.5 to all zero counts when the individual studies have cells with zero counts. All statistical analyses were performed with the Meta package in R .The combined odds ratios (ORs) and its 95% confidence intervals (CIs) were calculated to evaluate the association between atistics . The hetnel plot  and Eggenel plot  were utiA total of 373 studies were identified by searching the electronic databases.MGMT methylation frequency). The search and selection procedures of articles were shown in After eliminating duplicate articles and irrelevant studies  by reviewing the titles and abstracts, 53 articles were identified. Finally, 20 studies were included in the meta-analysis by reviewing full-text removing some studies . MGMT promoter methylation frequency was significantly associated with an increased HNSCC risk based on the fixed effects model  (The combining result of the association of 25–3.56) .I2 = 37.1%, P = 0.05), we also conducted the meta-regression to find the potential sources of heterogeneity. The results of meta-regression showed that the potential source of the heterogeneity was only found in the control source (P = 0.02) (Although the heterogeneity among the studies was found no significant ( = 0.02) . We alsoMGMT promoter methylation and we have put the study classified as QMSP group in the methylation detection method. The OR for was 2.62 (95%CI = 2.07–3.33) in the MSP group under, and 6.48 (95%CI = 2.76–15.18) in the QMSP group under the fixed-effects model. With the control source, the OR in autologous group was 1.78 (95%CI = 1.12–2.81) under the random-effects model, and 5.18 (95%CI = 3.56–7.53) in the heterogeneous group under the fixed-effects model. In the control sample type group, we classified the studies into non-tissue group  and tissue group (control sample type: tissue).The OR for was 2.61 (95%CI = 0.81–8.41) in the non-tissue group, and 3.14 (95%CI = 2.03–4.86) in the tissue group under the random-effects model. The OR for was 2.62 (95%CI = 1.72–3.99) in case sample size < = 50 group under the fixed-effects model, and 3.05 (95%CI = 1.89–4.92) in case sample size >50 under the random-effects model. The results of subgroup analysis were summarized in In the race subgroup analysis, the OR in Asians group was 3.82 (95%CI = 2.75–5.30) under the fixed-effects model, and 2.28 (95%CI = 1.40–3.71) in Caucasians group under the random-effects model. Onerci Celebi  used pyrMGMT promoter methylation and risk of HNSCC was reliable and stable  to 3.27  under the fixed effects model by omitting each single study, which demonstrates that the pooled OR between the d stable .P = 0.31).The Begg’s funnel plot and Egger’s test were employed to estimate the publication bias of the included studies. The Begg’s funnel plot of the pooled analysis in MGMT is a DNA repair gene, and the promoter methylation of MGMT plays an important role in the carcinogenic process and progression of cancer [MGMT also known as AGT , is well known to play a crucial role in repairing O6-alkylguanine in DNA, a major premutagenic lesion produced by environmental and therapeutic alkylating agents [MGMT gene promoter can diminish MGMT protein expression in tumor tissues of various types of cancers, including lung cancer, gastric cancer, colorectal cancer, and breast cancer [Epigenetic inactivation of the genes is common in human tumors. Hypermethylation of gene promoter is one of the important mechanisms for inactivation of tumor-suppressor genes involving apoptosis, DNA-repair, and cell cycle control . The MGMf cancer . The prog agents . Methylat cancer –43.MGMT promoter methylation and the risk of HNSCC. In this study, we found that 20 studies including 1030 cases and 775 controls were competent for inclusion criteria. The frequency of MGMT promoter methylation in tumor was 46.70% and 23.23% in control group. The result of the meta-analysis displayed that the methylation of MGMT promoter had an increased risk of HNSCC .To our knowledge, this is the first meta-analysis on evaluating the association between Subgroup analysis by the methylation detection method showed the OR was 2.62 (95%CI = 2.07–3.33) in the MSP group and 6.48 (95%CI = 2.76–15.18) in the QMSP group under the fixed-effects model. In fact, QMSP is reported to be more specific and more sensitive, and able to detect much smaller magnitude  ,45. In cMGMT methylation in autologous control was higher than that in heterogeneous control. The result of the subgroup analysis is consistent with most previous studies [With the control style, the OR was 5.18 (95%CI = 3.56–7.53) in the heterogeneous control subgroup under the fixed-effects model and 1.78 (95%CI = 1.12–2.81) in autologous tissues subgroup under the random-effects model. The result suggested that the incidence of  studies ,30,32. IMGMT promoter methylation and HNSCC in this study. We did not further investigate the association between MGMT promoter methylation and demographic (age and gender) and disease characteristics  of HNSCC.However, the present study had also several potential limitations. First, the search strategy was restricted to articles published in English language in this study. And then, some studies potentially suitable for inclusion that were published in other languages may be not included. Thus, some publication bias may exist. Second, we only study the association between MGMT promoter was associated with an increased risk of HNSCC. The findings suggested that the promoter methylation of MGMT gene may play an important role in the carcinogenic process of HNSCC, and it may be a promising molecular marker for monitoring the disease and may provide new insights to the treatment of HNSCC. However, more studies with larger sample size must be performed to acquire a more precise and representative result.In conclusion, we found that hypermethylation of S1 Checklist(DOC)Click here for additional data file.S2 Checklist(DOCX)Click here for additional data file.S3 Checklist(DOCX)Click here for additional data file.


Recent studies explored XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and ovarian cancer (OC) risk. However, the association between these two single nucleotide polymorphisms and OC risk remains conflicting. Thus, we conducted a comprehensive systematic review and meta-analysis to investigate the association. We searched the databases of PubMed, and Embase. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by using fixed-effect or random-effect models. 15 case-control studies published in 11 papers including 4,757 cases and 8,431 controls were included in this meta-analysis. No associations were obtained between XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and OC risk. Stratification analyses of Hardy–Weinberg equilibrium status indicated that rs3218536 polymorphism was associated with the decreased risk of OC when in analysis of combined HWE positive studies. In conclusion, this meta-analysis indicates that XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms may not be associated with the risk of OC. Ovarian cancer (OC), leading cause of gynaecologic cancer death, is the second most common gynaecologic cancer . OC is mDNA repair system takes part in maintaining the genomic integrity. The repair process usually contains two stages: the excision of lesion and the repair synthesis . The repA number of studies , 11–18 iWe yielded 97 citations after initial searching. 34 citations were removed after removing duplicates. After screening the titles and abstracts, 36 citations were excluded. 27 citations were selected for further full text review. 16 citations were excluded: 6 were meta-analyses –24; 6 noP = 0.058, Figure As presented in Table P = 0.764, dominant: P = 1.000, and recessive: P = 0.532; rs13181, allele: P = 0.573, dominant: P = 0.348, and recessive: P = 0.851). Our data revealed that there was no obvious publication bias for the two SNPs. Due to significant between-study heterogeneity among some genetic models, we conductedmeta-regression to explore whether ethnicity, HWE status and SOCwere the resources of heterogeneity. However, our data suggested that ethnicity, HWE status and SOC did not seem to be responsible for the heterogeneity (data not shown).We assessed sensitivity by omitting each study once at a time in every genetic model for XRCC2 rs3218536 or ERCC2 rs13181 polymorphisms. The pooled ORs for the effects about these two polymorphisms indicated that our data about the two SNPs were stable and trustworthy ; Recessive, I2 = 19.6%) when we excluded this Polish study. The reasons of high heterogeneity may due to different ethnic populations, clinical heterogeneity, and small sample sizes.To seek the sources of high heterogeneity in this meta-analysis, we conducted meta-regression analysis, stratification analyses, and sensitivity analysis. Meta-regression analysis of ethnicity, HWE status and SOC was conducted. Our data confirmed that ethnicity, HWE status and SOC were not the sources of heterogeneity. Sensitivity analysis about XRCC2 rs3218536 polymorphism indicated the Polish study [However, potential limitations should be addressed in this meta-analysis. First, due to limited data, we could not perform further stratification analyses of other populations, such as Asians. Second, our results were based on unadjusted estimates for confounding factors, which might influence the final findings. Third, we could not assess potential gene-gene and gene-environment interactions. Fourth, the sample sizes of subgroup analysis are limited. These stratification analyses were based on small numbers and any association is likely to be due to chance. Fifth, high heterogeneity existed in some genetic models of this meta-analysis.In summary, this meta-analysis suggests that XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms may not be associated with OC susceptibility. Stratification analysis indicates that XRCC2 rs3218536 polymorphism was significantly associated with a decreased risk of OC when in analysis of HWE positive studies. Further studies are necessary to validate whether these two SNPs is associated with OC susceptibility in other ethnic groups.We systematically searched the PubMed, and Embase to identify studies through September 13, 2016. The following search terms were used: “ovarian cancer,” “ovarian neoplasm,” “ovarian carcinoma,” “ERCC2,” “XPD,” “XRCC2” and “polymorphism”. Other potential omitted studies were identified by hand screening. The inclusion criteria of studies were as following: (1) studies that evaluated the association between XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and OC risk, (2) study provided sufficient data to calculate the odds ratios (ORs) and 95% confidence intervals (CIs), and (3) case-control study.Data was extracted from all eligible studies by two authors. The extracted information from all eligible studies including: name of first author, publication year, country, ethnicity, source of control, and genotype numbers of cases and controls. Two authors independently conducted the extraction of data. We assessed the study quality according to the NOS . All disP < 0.1 or I2 > 50% indicated heterogeneity across studies, a random-effect model was used. Otherwise, the fixed-effects model was applied [The crude ORs and 95%CIs were used to assess the strength of associations between XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and OC risk. Stratification analysis was carried out by HWE status, ethnicity and SOC. When a Q test indicated  applied . Pooled  applied . Meta-re


Other individual carotenoids did not show significant associations with hip fracture risk. Circulating carotene levels had no significant association with any fracture risk, the pooled OR (95% CI) was 0.83 . Based on the evidence from observational studies, our meta-analysis supported the hypothesis that higher dietary total carotenoids or β-carotene intake might be potentially associated with a low risk of hip fracture, however, future well-designed prospective cohort studies and randomized controlled trials are warranted to specify the associations between carotenoids and fracture.To quantify the association between dietary and circulating carotenoids and fracture risk, a meta-analysis was conducted by searching MEDLINE and EMBASE databases for eligible articles published before May 2016. Five prospective and 2 case-control studies with 140,265 participants and 4,324 cases were identified in our meta-analysis. Among which 5 studies assessed the association between dietary carotenoids levels and hip fracture risk, 2 studies focused on the association between circulating carotenoids levels and any fracture risk. A random-effects model was employed to summarize the risk estimations and their 95% confidence intervals (CIs). Hip fracture risk among participants with high dietary total carotenoids intake was 28% lower than that in participants with low dietary total carotenoids . A similar risk of hip fracture was found for β-carotene based on 5 studies, the summarized OR for high vs. low dietary β-carotene was 0.72 . However, a significant between-study heterogeneity was found (total carotene: I Osteoporosis, characterized by obvious bone loss and micro-architectural disruption, results in bone fragility and an increased susceptibility to fractures . ElderlyExperimental research has highlighted the involvement of reactive oxygen species (ROS) and free radicals in inducing bone loss by regulating osteoclastogenesis  and osteDietary antioxidant carotenoids, enriched in vegetables and fruits, have been testified to protect the human body's defense against the reactive oxygen species . CarotenThus, our present work aimed to conduct a quantitative meta-analysis by summarizing the evidence from current observational studies to comprehensively clarify the associations between carotenoids and fractures risk.We identified 374 articles from the MEDLINE database and 473 from EMBASE database. After excluding the review papers, papers without reporting risk estimates, papers with irrelevant exposure factors , papers with irrelevant endpoints (mainly about BMD or bone turnover), or basic experimental research by title/abstract screening, a total of 64 articles were selected for full-text review. We finally identified 7 studies by using full-text screening, including 5 prospective , 25, 26 2. Nearly 80%  of studies participants were women and all women were post-menopausal. Two studies were conducted among Asian population [The characteristics of all 7 selected studies are displayed in Table pulation , 27, 3 spulation , 16, 26,pulation  and Europulation , respectpulation , in whic2 = 59.4%, P = 0.06)  and β-carotene , respectively.Five studies explored the relationship between dietary individual carotenoids, such as α-, β-carotene, β-cryptoxanthin, lycopene, and Lutein/zeaxanthin, and hip fracture risk. As shown in Figure 2 , or Asians , while further meta-regression analysis showed that the study characteristics of gender, age, BMI and location did not affect our results.  or subjects aged over 60 years , or subjects with BMI ≤ 25 kg/mThere was no study concerning on the association between total circulating carotenoids and fracture risk. Only 2 studies prospectively assessed the relationship between circulating carotene levels and risk of any fracture; the pooled OR for high versus low carotene levels was 0.83 .Results of sensitivity analyses conducted by omitting one study at each time using a random-effects model showed that the combined results for dietary intake of total carotenoids and α-carotene were sensitive to individual study. Dai et al's study focused on female subjects affected the association between dietary total carotenoids, α-carotene and hip fracture risk, and the summary OR was 0.60  for total carotenoids and 0.65  for α-carotene respectively when deleted this data set.P = 0.16; P = 0.36; P = 0.10; P = 0. 49; P = 0.14; P = 0.60).The Egger's tests did not indicate significant publication bias in the meta-analyses for dietary total carotenoids, α-carotene, β-carotene, β-cryptoxanthin, lycopene, or Lutein/zeaxanthin  prospective, case-control, or cross-sectional study; (2) the exposure of interest was dietary or circulating carotenoids; (3) the outcome was prevalence or first incidence of fracture; (4) providing the estimation of odds ratio (OR) or relative risk (RR) and its corresponding 95% confidence interval (CI) or standard error of estimations, or reporting the data to calculate these values; (5) adult participants.The following information was extracted by two authors (Jiuhong XU and Chunli SONG) independently. Extracted study characteristics were as follows: last name of the first author, publication year, study location, number of cases/participants, age, sex, BMI, range of the carotenoids, method to determine the levels of the circulating/ diet carotenoids, OR or RR estimates and 95% CIs for each category of carotenoids, and variables adjusted for in the analysis models. If a study provided multiple risk estimates for several subtypes of carotenoids or for both sexes, the risk estimates were regarded as different reports only when they were divided into different subgroups. If the study calculated RR/OR according to the percentile categories of the carotenoids levels, the risk estimate compared highest with the lowest category was extracted. If two or more risk estimates adjusted for different confounders were reported in one study, the RRs or ORs based on the full model were selected. We also tried to contact with corresponding authors of several studies for additional data.The quality of the selected studies was assessed by using the Newcastle-Ottawa scale (NOS) . The stuQ-test and calculated I2 statistic to examine the between-study heterogeneity. A P-value for Q-test of < 0.10 or a I2 of > 50% indicated significant between-study heterogeneity [To assess the association between carotenoids and fracture risk, we calculated the pooled risk estimates and their 95% CIs using a random-effects model. OR was an approximate estimation for RR, because hip fracture is relatively rare. Therefore, we combined OR from case-control studies and RR from prospective studies in this meta-analysis. We performed ogeneity . Meta reP < 0.05 indicated a significant publication bias [P < 0.05 indicated statistical significance.Sensitivity analyses were performed by deleting one study at each analysis to explore the source of heterogeneity and to testify the robustness of the pooled risk estimate. Potential publication bias was tested by using funnel plots, Begg's test, and Egger's test; and a ion bias . All ana


The small number of studies included in the meta-analysis suggests that further investigation is necessary to support these findings.Dietary patterns were recently applied to examine the relationship between eating habits and prostate cancer (PC) risk. While the associations between PC risk with the glycemic index and Mediterranean score have been reviewed, no meta-analysis is currently available on dietary patterns defined by “a posteriori” methods. A literature search was carried out  to identify studies reporting the relationship between dietary patterns and PC risk. Relevant dietary patterns were selected and the risks estimated were calculated by a random-effect model. Multivariable-adjusted odds ratios (ORs), for a first-percentile increase in dietary pattern score, were combined by a dose-response meta-analysis. Twelve observational studies were included in the meta-analysis which identified a “Healthy pattern” and a “Western pattern”. The Healthy pattern was not related to PC risk : 0.88–1.04) while the Western pattern significantly increased it . In addition, the “Carbohydrate pattern”, which was analyzed in four articles, was positively associated with a higher PC risk . A significant linear trend between the Western ( Prostate cancer (PC) is the second most common cancer in men after lung cancer, with more than 1.1 million new cases and over 307,000 deaths estimated worldwide in 2012 [Several epidemiological studies have explored the association of dietary habits on PC risk, and meta-analysis has recently summarized the association of individual foods and nutrients with PC risk. Indeed, significant preventive effects have been found for the intake of allium vegetables , carrotsRather than studying the individual foods and nutrients, recently, dietary patterns have been applied in nutritional epidemiology to examine the relationship between diet and chronic diseases ,17. ThisIn this systematic review and meta-analysis, we selected studies addressing the correlation between the different dietary patterns defined using “a posteriori” methods and PC risk, and we used them to provide a quantitative estimation of the association.http://www.ncbi.nlm.nih.gov/pubmed/) and Web of Science (http://wokinfo.com/) databases. The original articles on the association between dietary patterns and PC risk were identified using the following search key words: (“neoplasm” OR “cancer” OR “neoplastic disease”) AND (“prostate” OR “prostatic”) AND (“dietary pattern” OR “eating pattern” OR “food pattern” OR “dietary habit” OR “diet” OR “dietary”) AND . Furthermore, the reference lists of included articles and recent significant reviews were manually examined to identify additional relevant publications. The standard procedure for conducting and reporting meta-analysis, according to the guidelines from the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group, were followed [We carried out a comprehensive literature search, without restrictions, up to December 2015 through PubMed , age, duration of follow-up for cohort studies, dietary assessment and dietary pattern identification methods , characteristics of the dietary assessment method, name of the dietary pattern type and its characteristics, cutoff points of the different categories of adherence to the dietary pattern , risk estimates with 95% confidence intervals for the different categories of adherence, and e method . Therefop < 0.05 was considered statistically significant. We restricted the analysis to the “a posteriori” dietary patterns. In addition, we carried out a dose-response meta-analysis to compute the trend across categories. The linear increase in PC risk, per percentile increase in dietary pattern, was estimated using the method proposed by Greenland and Longnecker [Q statistic and the I2 statistic were used to evaluate heterogeneity in results across studies [Q statistic, a p-value of < 0.1 was considered to be representative of statistically significant heterogeneity. The I2 statistic yields results ranged from 0% to 100%  [p-value of <0.05. Subgroup analyses were conducted for the case-control and cohort studies. The ProMeta Version 2.0 statistical program  and packages dosresmeta 1.3.2. for R 3.1.2.  were used for the analyses [p values are from two-sided statistical tests, and differences with p ≤ 0.05 were considered significant.The overall effect-size statistic estimated was the average of the logarithm of the observed odds ratio  associated with the highest versus the lowest level of consumption. We used the random effects model to calculate the summary OR and 95% confidence intervals. A two-tailed ngnecker , which angnecker . We estingnecker ,31,32,33ngnecker ,35,36. Tngnecker ,28,32, “ngnecker ,30,34,36ngnecker ,31,33,35ngnecker , “Westerngnecker ,29,32,35 studies . For thegeneity) . Resultsgeneity) ,40. Bothanalyses . All repn = 571) and Web of Science (n = 1160) databases, and after removing duplicates (n = 382), we identified 1349 records for title and abstract revision . Similar results were obtained when the analysis was carried out separately for case-control and cohort studies (p = 0.007), but the heterogeneity was rather high  , where an increase in the risk of PC was shown . Otherwise, there was no evidence of heterogeneity , and there was no evidence of a difference in the risk of PC  (p = 0.0001), in the absence of heterogeneity   . The Sub= 0.352)  in the c= 0.393) . I2 = 0%, p = 0.82). Furthermore, the linear dose-response curves showed a slightly inverse, but not significant (p = 0.345), association between Healthy diet consumption and PC risk. In contrast, for the Western diet, PC risk increased with the percentile of dietary adherence. The summary OR was 1.004  for each percentile increment in the intake of Western diet, with high heterogeneity . A linear trend was evident (p = 0.011) as shown in p = 0.021) with always high evidence of heterogeneity . Further, considering studies without missing data, we conducted a subgroup analysis for several case-control studies. We did not repeat the analysis for the selected cohort studies because only two of them were available for the dose-response analysis. A linear trend for the Western pattern was still evident in the case-control studies (p = 0.002). The summary OR was 1.007 . The heterogeneity was still quite high , but less than in the dose-response analysis—which considered the whole sample. In a sensitivity analysis, excluding one study at a time, no particular differences in the results arose. Even if only three studies identified a “Carbohydrate pattern” and showed complete data [p = 0.005). The estimated OR was 1.007 . There was quite a low heterogeneity .We started analyzing papers which reported complete observations. Eight studies were considered, six case control studies ,30,31,33nt p = 0.5, associp = 0.003) and for the Western pattern in pooled analysis (p = 0.045)  .p = 0.399). In addition, the PC risk estimates associated with the Western pattern ranged from 1.26 , omitting the study of Niclis et al. [p = 0.003), omitting the study of Muller et al. [p = 0.089) and Begg’s rank correlation (p = 0.586) tests.Sensitivity analyses, investigating the influence of a single study on the PC risk estimate, suggested that results were not substantially modified by removal any single study. In particular, no evident changes were found in the risk estimates after removal of the outlier study of Askari et al.  on the Hs et al. , to 1.42r et al. . Of noter et al. , in the To the best of our knowledge, this is the first systematic review and meta-analysis which considers the effect of different dietary patterns identified by “a posteriori” methods on PC risk. From the selected articles, we identified two very common dietary patterns—“Western” and “Healthy” patterns. These two dietary patterns were present in all of the selected studies. Additionally, four studies reported a further “Carbohydrate” pattern. The results indicated that both of the Western and Carbohydrate dietary patterns were significantly associated to an increase of PC risk, while our analyses showed no association between the Healthy dietary pattern and PC risk.Several systematic reviews and meta-analyses have recently reported the association between dietary patterns and the risk of cancer in different sites; such as breast , colon aRegarding the Healthy pattern, previous meta-analyses showed an evident and significant inverse correlation with cancer risk in all anatomic sites considered, with the exclusion of the rectum ,45,46,47In our study, we also found a Carbohydrate dietary pattern which was associated with a statistically significant (64%) increment of PC risk. This result is of particular interest, considering that the Carbohydrate pattern had high-factor loading for bread, pasta, and rice. However, this conclusion should be interpreted with caution, since it was obtained from only four studies. Further, epidemiological evidences are necessary to confirm this trend considering that a recent meta-analysis also showed no correlation between consumption of dietary fiber, whole grains, and carbohydrates with PC risk .One of the great limitations of meta-analysis is that the results are combined from studies conducted with different methods in different populations, resulting in heterogeneity. In our analysis, heterogeneity was more evident in the results regarding the “Western pattern”. This could be due to the difficulty in characterizing this pattern. Moreover, a possible misclassification within the considered dietary patterns may be present. Factor analysis and/or principal component analysis are subjective techniques with opportunities for variation at almost every step . Other lIn conclusion, we pooled information from twelve studies which identified different “a posteriori” dietary patterns in terms of single food or nutrient items mainly correlated to them. We selected two main dietary patterns, which were analyzed in all of the studies, and a dietary pattern was reported in just four of them. From the dietary pattern named as “Healthy”, mainly based on a high consumption of vegetables and fruits, poultry, fish, and whole grains, a statistical significance association with PC risk was not highlighted. Different results emerged from the dietary patterns named as “Western” and “Carbohydrate”, characterized by a high loading of red meat, processed meat, eggs, sweets and bread, pasta and rice, respectively. An increase in PC risk was pointed out in the highest, compared to the lowest, categories of dietary patterns in all pooled studies and in the dose-response meta-analysis, even if the heterogeneity was quite high. The high heterogeneity may be correlated to the wide variability between studies regarding different aspects of dietary data collection and analysis; such as food items and food sub-types considered, the different dietary patter identified, and the various and not uniformly adjusted confounding factors used to calculate the risk. As a consequence of this, and considering also the small number of cohort studies so far published, further investigations are necessary to support these findings.


I2 = 83%). In subgroup-analysis, both vitamin E intake and blood levels of vitamin E had a significant inverse association with the risk of cervical neoplasm. Additionally, we found the same relationship between vitamin E and cervical neoplasia among different populations and types of cervical neoplasia. Meta-regression showed that none of the including covariates were significantly related to the outcomes. No evidence of publication bias was observed. In conclusion, vitamin E intake and blood vitamin E levels were inversely associated with the risk of cervical neoplasia.Several epidemiological studies have suggested that vitamin E could reduce the risk of uterine cervical neoplasm. However, controversial data were presented by different reports. Hence, we conducted a meta-analysis to assess the relationship between vitamin E and the risk of cervical neoplasia. We performed a comprehensive search of the PubMed, Embase and Cochrane databases through December 31, 2016. Based on a fixed-effects or random-effects model, the odds ratio (OR) and 95% confidence intervals (CIs) were calculated to assess the combined risk. Subgroup analyses and meta-regression were done to assess the source of heterogeneity. Subgroup analyses were performed according to survey ways, types of cervical neoplasia, study populations. A protocol was registered with PROSPERO (No. CRD42016036672). In total, 15 case-control studies were included, involving 3741 cases and 6328 controls. Our study suggested that higher category of vitamin E could reduce the cervical neoplasia risk (OR = 0.58, 95% CIs = 0.47–0.72,  Cervical cancer remains the third most common gynecological malignancy in the world and the leading cause of mortality among women in developing countries , and resVitamin E, which contains putative anticancer and antimutagenic substances, had long been thought to protect against cancer, including cervical cancer. Vitamin E is a well-known inhibitor of lipid peroxidation and a powerful antioxidant, which has been reported to protect cells from oxidative DNA damage and mutagenesis, thereby preventing the occurrence of some tumors –9.In past few years, a number of epidemiology studies have been conducted to assess the relationship between dietary vitamin E and cervical neoplasia which included both cervical cancer and cervical intraepithelial neoplasia (CIN), but the results were not consistent ,10–23. TThe review met requirements of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. A protocol of this meta-analysis was registered in PROSPERO International prospective register of systematic reviews (registration number: CRD42016036672). We searched the PubMed, Embase and Cochrane library databases from their inception. The first search was conducted on July 28, 2016 and the latest search on December 31, 2016. The following keywords were used in searching:  and  with no restrictions. Relevant studies were selected by two independent reviewers , and any disagreement was resolved by discussion. The studies which met the following criteria could be included: (1) the study had a case-control or prospective cohort designed; (2) reported the association between vitamin E intake or blood levels of vitamin E and the risk of cervical neoplasia (CN), including invasive cervical carcinoma, cervical dysplasia, in situ cervical cancer, cervical intraepithelial neoplasia (CIN) and total cervical cancer; (3) papers were restricted to human studies published as full-length articles in English; (4) the odds ratio (OR) estimates with the corresponding 95% confidence intervals (CIs)  for the highest vs. the lowest level of vitamin E intake or blood levels of vitamin E were reported. Accordingly, studies were excluded if (1) they were reviews, letters, animal experiments, or comments; (2) they were duplicate publications; (3) they were not published as a full text; (4) the OR with 95% CIs were not usable.The following information were extracted from the included studies: the first author’ last name, year of publication, study population, number of cases and controls, age range of study participants, study quality, OR (95% CIs) from the most fully adjusted model for the highest compared with the lowest dietary vitamin E intake or blood levels of vitamin E, adjusted covariates. If there were disagreements between the two authors about eligibility of the data, they were resolved by consensus. If one study included several kinds of vitamin E or cervical neoplasia, we defined the research as -1, -2 and so on. The study quality was assessed by using the 9-star Newcastle-Ottawa Scale (NOS) . This scI2 statistics [I2 values of 0, 25, 50 and 75% represented no, low, moderate and high heterogeneity, respectively. A fixed-effects model was used to calculate the pooled OR when the heterogeneity of each study was low or P value of heterogeneity > 0.1. Otherwise, a random-effects model was used for outcomes when strong evidence of heterogeneity was found . OR and 95% CIs were calculated to assess the association between vitamin E intake or blood levels of vitamin E and the risk of cervical neoplasia.Statistical analyses were performed by RevMan5.3 software (The Nordic Cochrane Center). Besides, STATA software  was used to perform the Egger’s test and Begg’s test which could detect publication bias. Heterogeneity test was conducted by using Q and atistics . AdditioMeanwhile, subgroup analysis was used to investigate the possible source of heterogeneity among these included studies. Subgroup analyses were performed to according to survey ways, types of cervical neoplasia, geographical regions. Also, possible sources of heterogeneity were indicated by meta-regression. In order to assess the statistical outcome validity, we detected overall outcome by sensitivity analysis.In addition, a p-value < 0.05 in the Egger’s test or Begg’s test was considered as statistically significant for publication bias. We used the funnel plot to detect the publication bias .By the search strategy, we identified a total of 1253 studies  after deleting the duplicate researches. After excluding researches that studied obvious irrelevant exposure/outcome, or failed to report OR or 95% CIs, or didn’t meet the inclusion criteria, we identified a total of 15 observational studies involving 3741 cases and 6328 controls . These sThe main characteristics of the 15 studies are given in The quality assessment of studies based on NOS system was also presented in P < 0.00001) and considerable (I2 = 83%). In addition, both vitamin E intake and blood levels of vitamin E were negatively correlated with cervical neoplasia risk.The pooled effect estimate was significant shown in Pheterogeneity = 0.005, I2 = 70%) for vitamin E intake and 0.52  for blood levels of vitamin E. When stratified by geographical area as shown in Pheterogeneity < 0.00001, I2 = 84%), and Asia  indicated that vitamin E have a significant inverse association with the risk of cervical neoplasia. As shown in Pheterogeneity < 0.00001, I2 = 77%), and CIN . Meanwhile, we used sensitivity analysis to assess the influence of each single study on the pooled ORs by omitting a research in each turn. Overall, the combined ORs were not substantially different, indicating that the results of this meta-analysis were stable and reliable.Results of subgroup analysis were reported in I2 value was > 50%. The related p-value of heterogeneity were listed in Heterogeneity between the included researches was indicated when the p-value for Cochran's Q test was ≤ 0.1 and the P value for Egger = 0.53; p-value for Begg = 0.322).As shown in Our meta-analysis indicated that there was a significant negative relationship between vitamin E and the risk of cervical neoplasia. That is, a high level of vitamin E was significantly associated with a decreased risk of cervical neoplasia.The incidence of several human cancers has been reported to be decreased in patients with increased dietary intake of vitamin E –31. MoreNevertheless, the mechanisms of vitamin E impacted on cancer risk were still not well understood. The main mechanism of vitamin E might be as an antioxidant and anti-carcinogen, which can prevent DNA damage by scavenging lipid hydrogen peroxide radicals and finishing the lipid peroxidation chain reaction . In addiWhile Myung et al.  has suggI2 = 83%) in the research. Therefore, the random-effects model and sensitivity analysis were used in the meta analysis. Influence analysis indicated that no individual research had excessive influence on the association of vitamin E and cervical neoplasia risk. Meanwhile, we further conducted subgroup analyses based on survey ways, geographical area and different types of cervical neoplasia to explore the source of heterogeneity. As a result, the negative relationship between vitamin E and cervical neoplasia also existed in different measuring methods, ethnicities and types of cervical neoplasia. However, moderate or high heterogeneity was present in these subgroups, indicating that other unknown confounding factors may be subsistent. Additionally, adjustments for covariates were different among these researches.Meta-analysis is an important tool for indicating trends that might not be evident in a single study. There were several strengths of this meta-analysis. Sensitivity analysis suggested that the combined ORs were not altered by removing a single study each time, indicating that the results were stable. To evaluate the publication bias, the funnel plot was performed. The results of the funnel plot revealed that no publication bias existed. Our research including 15 observational studies with 3741 cases and 6328 controls obviously increased the statistical power and found a more credible relationship between vitamin E and the risk of cervical neoplasia. Between-study heterogeneity was often a concern in meta-analysis , and ourHowever, our study also had few limitations. First, the vitamin E formulation and dosage varied across the articles. In addition, the included studies were almost based on case-control studies. Although a case-control research can result in a recall or selection bias, such study was an important method in the study of etiology. So it was possible that our results had recall or selection bias. Thirdly, there was a possibility that the evaluation of dietary intake was mainly based on intake amounts during the past weeks which may not have precisely reflected nutrient intakes. Finally, misclassification and imprecise measurement of vitamin E intake should be of concern in observational researches.In addition, there was also considerable heterogeneity among included researches. In the presence of heterogeneity, the application of fixed effects was inappropriate. Therefore, fixed-effects models were replaced by random-effects models in statistical analysis. Meanwhile, we didn’t find the source of heterogeneity in meta-analysis by subgroup analyses and sensitivity analysis. Furthermore, some possible sources of heterogeneity were investigated by meta-regression analysis. However, the meta-regression showed that none of the considered covariates were significantly related to the pooled effect-estimate. Therefore, despite the usage of subgroup analysis and meta-regression, the heterogeneity between studies could not be investigated completely, which might result in bias of the outcome. The possible reasons for heterogeneity were that included studies differed in various study variables, such as different regions, ethnicities, testing methodologies used, and time periods to assess the level of vitamin E. In all, heterogeneity was an important aspect in meta-analysis, and should not be ignored.In summary, this meta-analysis suggested that both vitamin E intake and circulating vitamin E levels could reduce cervical neoplasia risk, including cervical cancer and cervical intraepithelial neoplasia. In other words, sufficient supplementation of vitamin E might reduce the risk of cervical neoplasia. However, more randomized controlled trials and cohort studies with high quality were required to further validate this inverse relationship.S1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.