P<0.00001) and genotype . Therefore, the meta-analysis indicated the MIF-794 allele CATT7 and CATT8 may be a risk factor to increase the susceptibility of TB, which was confirmed by TSA.Tuberculosis (TB) is a chronic infectious disease that has been threatening public health for many years. Several studies have shown the relationship between the macrophage migration inhibitory factor (MIF)-794 CATT (MIF-794 CATT) microsatellite polymorphism and susceptibility to TB. However, the results remain inconclusive. Therefore, we aim to find out the impact of MIF-794 CATT microsatellite polymorphism on risk of TB by a comprehensive meta-analysis. We conducted a systematic study search in PubMed, Embase, the Cochrane Library, and the China National Knowledge Infrastructure (CNKI) up to October 2017. Five studies involving 836 cases and 678 controls were included in the current meta-analysis. We calculated the pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) to estimate the association between the MIF-794 CATT microsatellite polymorphism and risk of TB. The reliability of the results were evaluated with trial sequential analysis (TSA). The results suggested that the MIF-794 CATT microsatellite polymorphism was significantly associated with the susceptibility of TB in all comparisons for allele (7 + 8 compared with 5 + 6, OR = 1.56, 95% CI = 1.31–1.87,  Mycobacterium tuberculosis (Mtb) and threats to global human health seriously. According to a recent World Health Organization (WHO) report, there were an estimated 10.4 million new TB cases worldwide and nearly 1.4 million people died of TB in 2015. Although the number of TB deaths fell by 22% between 2000 and 2015, TB still remained one of the top ten causes of death worldwide in 2015 [Tuberculosis (TB) is a chronic infectious disease caused by the bacillus IRGM1  is the main autophagy gene that has been examined in TB patients, and different polymorphisms in the IRGM1 gene were found to be associated with susceptibility to TB in various regions such as China, West Africa, and U.S.A. [Many studies have shown that several factors were associated with the progression to infection of TB, which mainly include environmental factors, smoking, HIV infection, individual differences, and others . Moreoved U.S.A. .. However, there has not been a meta-analysis to evaluate the association between the MIF-794 CATT microsatellite polymorphism and risk of TB. Therefore, the present study aimed to perform a meta-analysis of all eligible studies to precisely investigate the association between the MIF-794 CATT microsatellite polymorphism and risk of TB.The gene of human migration inhibitory factor (MIF) is located on human chromosome 22q11.2. The MIF protein functions as a cytokine that promotes inflammatory processes in response to pathogenic infection. Studies have demonstrated that MIF can inhibit the migration and promote the aggregation of macrophages at sites of local inflammation or infection . The twoA comprehensive literature search of the PubMed, Embase, the Cochrane Library, and the China National Knowledge Infrastructure (CNKI) was conducted for articles published up to October 2017. The key search words were (‘macrophage migration inhibitory factor’ or ‘MIF’) and (‘polymorphism’ or ‘variant’ or ‘mutation’ or ‘genotype’) and TB. The language was restricted to English and Chinese.The inclusion criteria were as follows: (i) the design should be a case–control study; (ii) the study should refer to the association between the MIF-794 CATT microsatellite polymorphism and risk of TB; (iii) distribution of alleles, genotypes, or other necessary information should be provided to calculate the odds ratio (OR) and 95% confidence interval (CI); (iv) the object of study should be human; (v) the study was written in English or Chinese. The exclusion criteria were as follows: (i) not designed as a case–control study; (ii) review, abstract, comment, or case report; (iii) insufficient data and information in article; (iv) not refer to the relationship between the MIF-794 CATT microsatellite polymorphism and risk of TBWe evaluated the qualities of included studies according to the Newcastle–Ottawa scale (case–control study), which was based on three aspects including selection, comparability, and exposure in the study. The total scores ranged from 0 to 9. All the included studies were assessed by two of our authors independently. Any disagreement was resolved by discussion with a third author. Each item should be able to reach a final consensus. Studies with a score of 7 or greater were considered to be of high quality.Extraction of data was carried out by two independent authors (M.M. and L.T.) according to the inclusive criteria. If there was a disagreement, the third author (Y.P.) assessed those articles. The data extracted included the first author’s name, date of publication, country, ethnicity of participants, genotyping methods, age, Hardy–Weinberg equilibrium (HWE) test, sample size, and the quality score of each study.Q-test and 2Itest were used to evaluate the statistical heterogeneity amongst studies, P<0.05 or I2> 50% was considered to indicate statistically significant heterogeneity. If we observed the heterogeneity amongst studies, the pooled OR was calculated by random-effect models, otherwise, the fixed-effect model was applied.The present meta-analysis was performed with the RevMan 5.0 and software Stata 12.0. We measured the strength of association between the MIF-794 CATT microsatellite polymorphism and risk of TB by the OR and 95% CI. The Chi-square test was used to assess the HWE in order to analyze the genotype distribution in the control groups. www.ctu.dk/tsa). The required information size (RIS) was calculated by a priori diversity-adjusted information size (APDIS) defining 20% for relative risk reduction (RRR), 5% for type I error (two-sided), and 80% for the power. TSA boundaries were constructed to assess the risk of random error when the number of available participants was less than the RIS. If a Z-curve crossed the futility boundaries or TSA boundaries, there was sufficient information to support the conclusions and further trials were unlikely to change the findings.The small number of trials and patients would increase the random error and might cause spurious results. Therefore, we used the trial sequential analysis (TSA) to evaluate the reliability of the result for meta-analysis, and the TSA version 0.9.5.10 β was downloaded from the website  and genotype (5/X + 6/X compared with 7/X + 8/X). Therefore, we used the fixed-effect model to investigate the pooled OR. The results of both indicated that there are significant associations between the MIF-794 CATT polymorphism and risk of TB in allele  and genotype . So, the MIF-794 allele CATT7 and CATT8 may be the factors in increasing the risk of TB infection.In total, five studies involving 836 cases and 678 controls were included in our meta-analysis. As we can see from P=0.734/0.749 for allele, P=0.089/0.111 for genotype).We carried out Begg’s funnel plot and Egger’s test to evaluate the publication bias of the included studies. The results are presented in We performed sensitivity analysis to evaluate the reliability of the meta-analysis by deleting one single study each time. Although the sample size is a little less in our meta-analysis, however, the results both showed the stability of the study was not influenced by any individual study in allele and genotype. The results of sensitivity analysis were presented in TSA was performed as described by user manual to analyze whether the available data were powered enough to reach firm conclusions in the present study. For allele, TSA showed the RIS was 4924 patients and the Z-curve crossed both the conventional boundary and TSA boundary, which indicated that although the current trials did not reach the RIS, the result was conclusive and further trials were unlikely to change the conclusion A. For geMacrophage MIF was originally discovered at the process of study on delayed-type hypersensitivity by Boom and David in 1966, which was named for inhibiting the migration of macrophages. MIF was mainly expressed in macrophages, and their cell types can also produce products such as T/B lymphocytes, monocytes, and dendritic cells . MIF pla5–8) have been reported to affect the MIF level. The MIF-173C allele has been associated with a higher production of MIF protein [in vitro [5–8 microsatellite polymorphism is associated with the alteration of MIF gene transcription levels. The repeat number of the CATT can regulate the activity of MIF gene promoter. The higher the repeat number, the stronger the activity of the promoter.Human MIF gene is a single copy gene including three exons and two introns. Currently, four polymorphisms have been identified in MIF gene: a microsatellite polymorphism at position −794 (CATT5-8) and three SNPs at positions −173 rs755622), +254 (rs2096522), and +656 (rs2070766). However, because +254 (rs2096522) and +656 (rs2070766) are located in the intron, so only the MIF-173 (rs755622) and MIF (CATT protein , and thein vitro . Many stin vitro  revealed, +254 (r7 and CATT8 may be the factors in increasing the risk of TB infection.This is the first meta-analysis to investigate the association between the MIF-794 CATT microsatellite polymorphism and risk of TB, our meta-analysis indicated the MIF-794 microsatellite polymorphism may be associated with the susceptibility of TB. The MIF-794 allele CATTThere were several limitations in the present meta-analysis. First, due to lack of sufficient data and studies, we failed to perform further subgroup meta-analysis to evaluate the TB risk factors such as ethnicity, age, gender etc., which may play a profound role in the infection of TB. These factors had an effect on the heterogeneity in meta-analysis. Second, we only included studies published in English and Chinese, it is possible that some pertinent studies published in other languages may have been missed. At last, most of the included studies were conducted in Asian population, whereas other population should be included in the future analysis. Despite these limitations, we minimized the likelihood of bias through the whole process by creating a detailed protocol, by performing study identification, data selection, and statistical analysis, as well as in the control of publication bias.7 and CATT8 be the factors in increasing the risk of TB infection. For genotype CATT7/X + 8/X, there is a need to perform with well-designed case–control studies and larger sample size focussing on more ethnicities to confirm the results in the future.In summary, the present meta-analysis suggests that there are significant association between the MIF-794CATT polymorphism and risk of TB, and allele CATT


To verify the accuracy of serum dickkopf-1 protein (DKK-1) in the diagnosis of hepatocellular carcinoma (HCC) by an updated meta-analysis.We searched potential eligible studies in PubMed and Embase before July 8, 2018. Sensitivity (SN), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), summary receiver operating characteristics curve (sROC), and diagnostic odds ratio (DOR) were pooled with their 95% confidence intervals CIs) using a bivariate random-effects model.A total of 8 articles contained 10 studies on diagnosis of HCC with DKK-1 alone,7 articles contained 9 studies on diagnosis of HCC with a-fetoprotein (AFP) alone and 5 articles contained 7 studies on diagnosis of HCC with DKK-1 + AFP were identified. The pooled SN, SP, PLR, NLR, and DOR of DKK-1 alone, AFP alone and DKK-1 + AFP were 0.72 (95% CI: 0.70–0.75), 0.62 (95% CI:0.59–0.64) and 0.80 (95% CI:0.78–0.83), 0.86 (95% CI: 0.84–0.87), 0.82 (95% CI:0.80–0.84) and 0.87 (95% CI: 0.85–0.88), 4.91 (95% CI: 2.73–8.83), 3.60 (95% CI:2.01–6.44) and 6.18 (95% CI: 4.68–8.16), 0.32 (95% CI: 0.22–0.47), 0.49 (95% CI:0.40–0.60) and 0.20 (95% CI: 0.15–0.26), and 17.21 (95% CI: 9.10–32.57), 7.45 (95% CI:3.69–15.01) and 31.39 (95% CI: 23.59–43.20), respectively. The area under the sROC was 0.88, 0.70, and 0.92 for the 3 diagnostic methods.Serum DKK-1 + AFP showed a high accuracy for diagnosis of HCC, and serum DKK-1 alone had moderate accuracy as compared to a previous meta-analysis, while AFP alone owned an unsatisfied diagnostic behavior for HCC. Due to the limitations of the current analysis, further well-designed studies are needed to confirm the diagnostic value of DKK-1 and DKK-1 + AFP in HCC diagnosis. The 5-year survival rate differs by stages, with the rate of 50% to 75% in the early stage, which further decreases to 3% for distant metastasis HCC patients.,4 Hepatitis B/C virus infection, alcohol, nonalcoholic fatty liver disease, Budd-Chiari syndrome, aflatoxin, and so on, were identified as risk factors for HCC. In clinical practice, serum a-fetoprotein (AFP) and ultrasonography are widely utilized for early detection of HCC. However, with a sensitivity (SN) of 53% and specificity (SP) of 90% at a cut-off value of 20 ng/ml, western countries have excluded AFP for HCC diagnosis due to its lack of accuracy.–8 Furthermore, AFP-negative HCC could be missed if it is used as a marker for diagnosis of HCC.Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, with about 78,200 newly diagnosed cases per year and second highest mortality rate worldwide.,9 Current methods for early screening of HCC include imaging and tumor biomarkers.,11 Circulating cell-free nucleic acids could also contribute to the diagnosis of HCC. Among these methods, biomarkers seem to be more convenient and cost-effective.Surgery, local treatment, radiation therapy, systemic therapy, and so on, are currently used in the management of different stages of HCC, but there are limitations for clinical application of surgery and nonsurgical treatments are incapable of significantly improving overall survival and avoiding relapse of HCC. As a secreted glycoprotein, dysregulated expression of DKK-1 was found in many malignant tumors, such as HCC, pancreatic cancer, colorectal cancer, multiple myeloma, and chronic lymphocytic leukemia.–17 Subsequent studies showed that through competing with Wnt ligand, DKK-1 acts as an antagonist of the Wnt signaling pathway, and has been proverbially involved in tumorigenesis, metastasis, recurrence, and poor prognosis of HCC.–21 It was also significantly correlated with the tumor size, and concentrations of serum DKK-1 rapidly decreased after resection of HCC.,23 Meanwhile, elevated serum DKK-1 level was found in AFP-negative HCC.–25Dickkopf-1 protein (DKK-1) was first identified in 1998 and plays a key role in head-inducing/head embryogenesis of Xenopus.–31 A meta-analysis of 4 studies was conducted in 2014 to estimate the exact accuracy of serum DKK-1 for diagnosing HCC. Since several studies have been published in recent years, it is worthwhile to conduct an updated meta-analysis to better understand the diagnostic value of DKK-1 for detecting HCC.The diagnostic value of serum DKK-1 for HCC had been previously reported.2The present study was carried out based on the published studies. Thus, the approval from an ethics committee or institutional review board was not required.2.1 Relevant articles published in English were searched in PubMed and Embase before July 8, 2018. The search terms used were “hepatocarcinoma or hepatoma or liver cancer or hepatocellular carcinoma or HCC,” “dickkopf-1 or DKK-1.” The reference lists of all relevant articles were manually searched for additional eligible studies. The search procedure was conducted by 2 independent investigators.This systematic review was conducted based on the preferred reporting items for systematic reviews and meta-analyses guidelines.The inclusion criteria were:(1)the study used DKK-1 as a biomarker to diagnose HCC;(2)the sample type was serum DKK-1;(3)the diagnosis of HCC was established by pathological methods or in line with correlated accepted guidelines;(4)the study provided sufficient data to calculate the SN and SP of DKK-1.The exclusion criteria were:(1)review articles, meeting reports, comments, or abstracts;(2)nonhuman studies;(3)the papers with duplicate patient populations.2.2Data extraction was performed by 2 independent investigators (Xueyi Tang and Yi Zeng), and any disagreements were resolved by a third author (Yongqiang Zhan). The data extracted from each study included first author, date of publication, geographical region, study design, reference standards, measuring methods and cut-off values, gender and sex ratio of HCC patients, and the number of true positive, false positive, false negative, and true negative subjects.2.3,35 QUADAS-2 has 4 domains: patient selection, index test, reference standard, flow, and timing. Each domain of QUADAS-2 was assessed as “yes,” “no” or “unclear.” Signaling questions were used to judge risk of bias as “high” or “low.” A third author (Zuhui Pu) was consulted for any disagreements.The assessment tool quality assessment of diagnostic accuracy studies 2 (QUADAS-2), which was developed based on QUADAS, was used to assess the quality of each paper.2.4P < .05 represents statistical significance. SN, SP, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were pooled with their 95% confidence intervals (CIs). Substantial heterogeneity, a nonuniformity indicator, was demonstrated as I2 value > 50%, and a random-effects model was adopted. The DOR was also pooled since it is an independent factor calculated from PLR and NLR to indicate the performance of diagnosis test. The pooled diagnostic SN, SP, and heterogeneity were demonstrated by forest plots. Summary receiver operating characteristic curves (sROC) represented the total diagnostic efficacy of DKK-1. Threshold effect was evaluated by calculating the Spearman correlation coefficient and P < .05 indicated threshold effect. If heterogeneity was not found by threshold effect, subgroup analysis was used for further exploration. Deeks’ funnel plot asymmetry test was utilized for assessing publication bias, and a SN analysis was also performed.Two independent investigators (Xueyi Tang and Yi Zeng) performed the statistical analysis using MetaDisc version 1.4, Revman version 5.3 and STATA version 12.0 software programs, and 33.1,23,26–31 exactly met the criteria for the meta-analysis, with adequate data for calculating SN and SP. These 8 studies included 3256 participants (1399 HCC and 1857 controls). The search process is shown in Figure ,23,26–31 9 studies of AFP,26–29,31 and 7 studies of DKK-1 + AFP,28,29,31 in HCC diagnosis were included. Elevated serum DKK-1 was found in HCC in all the studies. Among the 8 articles, 5 were conducted in China,,26,29,30 while 3 in Korea, Turkey, and Egypt.,28,31 All these diagnostic studies were retrospective, and the composition of the control group was different. Seven studies in 6 articles included cirrhosis, chronic hepatitis, or non-HCC liver disease alone as high-risk population into the control group in the HCC diagnosis by DKK-1,,27–31 whereas 5 studies in 4 articles included high-risk population into the control group in the HCC diagnosis by DKK-1 + AFP.,28,29,31 Six studies in 4 articles provided data on diagnosis of early HCC with DKK-1 and DKK-1 + AFP.,23,28,29 Four studies in 3 articles provided data on distinguishing early HCC from high-risk control.,28,29 The clinical features of the eligible articles are shown in Tables A total of 241 articles aggregately related to the search terms were retrieved from Medline and EMBASE. After scanning the titles and abstracts, 74 articles were excluded as duplicates. There were 95 articles unrelated to HCC or DKK-1 or diagnosis, 28 reviews, 19 abstracts, meeting reports or comments and replies and 9 papers on nonhumans; which were all excluded based on the inclusion and exclusion criteria. Subsequently, we read the full-texts of 16 articles, of which 8 articles3.2,23,28 The risk of bias was noted as “high risk” in patient selection domain for 3 studies as they only included high-risk population in the control.,30 In the index test domain, the risk of bias was noted as “unclear” for the included articles without prespecified diagnostic thresholds, except 1. In the flow and timing domains, the risk of bias was noted as “low” for 4 articles as they used histopathology as reference standard for all included HCC,,23,26,29 whereas it was noted as “unclear” for the remaining articles because they did not use the same reference standard for all included HCC.The quality of 8 articles is demonstrated in Figure 3.33.3.1I2 values of 93.1% and 96.3%. A bivariate random-effect model was executed due to the existence of substantial heterogeneity. The pooled PLR, NLR, and DOR were 4.91 (95% CI: 2.73–8.83), 0.32 (95% CI: 0.22–0.47), and 17.21 (95% CI: 9.10–32.57) with I2 values of 96.1%, 94.6%, and 88.6%, respectively. The sROC curve was plotted, and the area under curve (AUC) was 0.88 (SE = 0.0255) , which indicated that the threshold effect did not result in heterogeneity among included studies. Consequently, subgroup analyses were performed to identify the potential sources of heterogeneity.The pooled SN and SP of DKK-1 in HCC diagnosis was 0.72 (95% CI: 0.70–0.75) and 0.86 (95% CI: 0.84–0.87), with 3.4Three subgroup analyses were conducted according to the stage of HCC and high-risk participants (patients with risk factors for HCC). The first analysis based on the composition of control group included those studies with high-risk population as control. Another analysis calculated diagnostic performance of DKK-1 for early HCC. The last subgroup analysis calculated diagnostic performance of DKK-1 for distinguishing early HCC from high-risk control Table .,27–31 and the results were pooled as follows: SN was 0.72 (95% CI: 0.69–0.74), SP was 0.82 (95% CI: 0.79–0.85), DOR was 16.79 (95% CI: 10.17–27.72), and AUC was 0.87. The I2 values of SN, SP, and DOR were 95.4%, 91.8%, and 61.1%, respectively , SP was 0.92 (95% CI: 0.90–0.93), DOR was 29.54 (95% CI: 18.18–48.00), and AUC was 0.91. The I2 values of SN, SP, and DOR were 0.0%, 78.6%, and 64.5%, respectively.A total of 2115 patients comprising of 611 early HCC patients and 1504 controls in 6 studies of 4 articles were identified,,28,29 and the results were pooled as follows: SN was 0.73 (95% CI: 0.68–0.77), SP was 0.86 (95% CI: 0.82–0.89), DOR was 18.64 (95% CI: 8.98–38.70), and AUC was 0.88. The I2 values of SN, SP, and DOR were 0.0%, 85.4%, and 67.1%, respectively.A total of 846 patients comprising of 427 early HCC patients and 419 high-risk participants in 4 studies of 3 articles were identified,3.5I2 values of 74.5% and 96.7%. A bivariate random-effect model was executed due to the existence of substantial heterogeneity. The pooled PLR, NLR, and DOR were 3.60 (95% CI: 2.01–6.44), 0.49 (95% CI: 0.40–0.60), and 7.45 (95% CI: 3.69–15.01) with I2 values of 96.3%, 84.7%, and 92.5%, respectively. The sROC curve was plotted, and the AUC was 0.70 (SE = 0.0484) (Table P = .898), which indicated that the threshold effect did not result in heterogeneity among included studies. Consequently, subgroup analyses were performed to identify the potential sources of heterogeneity.The pooled SN and SP of AFP in HCC diagnosis was 0.62 (95% CI: 0.59–0.64) and 0.82 (95% CI: 0.80–0.84), with 3.6Three subgroup analyses were conducted as previously described Table .,27–29,31 and the results were pooled as follows: SN was 0.64 (95% CI: 0.61–0.67), SP was 0.70 (95% CI: 0.66–0.74), DOR was 6.11 (95% CI:2.53–14.72), and AUC was 0.75. The I2 values of SN, SP, and DOR were 70.8%, 95.0%, and 91.0%, which meant that the type of control group was not the source of heterogeneity.A total of 1585 patients comprising of 1029 HCC patients and 556 high-risk patients in 6 studies of 5 articles were identified,,23,29 and the results were pooled as follows: SN was 0.56 (95% CI: 0.52–0.60), SP was 0.85 (95% CI: 0.83–0.87), DOR was 8.26 (95% CI: 5.96–11.45), and AUC was 0.77. The I2 values of SN, SP, and DOR were 71.7%, 94.7%, and 45.0%.A total of 2065 patients comprising of 590 early HCC patients and 1475 controls in 5 studies of 3 articles were identified,,28,29 and the results were pooled as follows: SN was 0.59 (95% CI: 0.54–0.64), SP was 0.59 (95% CI: 0.54–0.64), DOR was 2.87 (95% CI: 1.37–5.98), and AUC was 0.68. The I2 values of SN, SP, and DOR were 78.6%, 95.7%, and 78.1%.A total of 771 patients comprising of 406 early HCC patients and 365 high-risk participants in 3 studies of 2 articles were identified,3.7I2 values of 81.9% and 82.0%. A bivariate random-effect model was used due to the presence of substantial heterogeneity. The pooled PLR, NLR, and DOR were 6.18 (95% CI: 4.68–8.16), 0.20 (95% CI: 0.15–0.26), and 31.39 (95% CI: 23.59–43.20) with I2 values of 77.3%, 73.2%, and 37.1%, respectively , which meant that the threshold effect did not cause heterogeneity among the included studies. Consequently, subgroup analyses were performed to identify the potential sources of heterogeneity.The pooled SN and SP of DKK-1 + AFP in HCC diagnosis was 0.80 (95% CI: 0.78–0.83) and 0.87 (95% CI: 0.85–0.88) with ly Figs. . The sRO3.8Three subgroup analyses were conducted as previously described Table .,28,29,31 and the results were pooled as follows: SN was 0.81 (95% CI: 0.78–0.83), SP was 0.83 (95% CI: 0.80–0.86), DOR was 24.62 (95% CI:15.60–38.86), and AUC was 0.90. The I2 values of SN, SP, and DOR were 84.4%, 54.7%, and 54.2%, which meant that the type of control group was not the source of heterogeneity.A total of 1511 patients comprising of 979 HCC patients and 532 high-risk patients in 5 studies of 4 articles were identified,,23,28,29 and the results were pooled as follows: SN was 0.86 (95% CI: 0.83–0.88), SP was 0.86 (95% CI: 0.84–0.88), DOR was 35.39 (95% CI: 25.28–49.55), and AUC was 0.92. The I2 values of SN, SP, and DOR were 0.0%, 0.0%, and 25.3%.A total of 2125 patients comprising of 611 early HCC patients and 1504 controls in 6 studies of 4 articles were identified,,29 and the results were pooled as follows: SN was 0.86 (95% CI: 0.83–0.89), SP was 0.82 (95% CI: 0.78–0.86), DOR was 30.16 (95% CI: 14.29–63.63), and AUC was 0.91. The I2 values of SN, SP, and DOR were 0.0%, 63.1%, and 63.3%.A total of 846 patients comprising of 427 early HCC patients and 419 high-risk participants in 4 studies of 3 articles were identified,3.9P-value was .585  and .693 (DKK-1 + AFP), which indicated no potential publication bias among all the included studies  in the present meta-analysis, which indicated that serum DKK-1 alone may not be optimal in diagnosing HCC. For the combination of DKK-1 and AFP, the AUC was 0.92 versus 0.88 and the DOR was 31.93 versus 24.60 in the current and previous meta-analysis, which indicated that DKK-1 + AFP was more suitable for HCC diagnosis than DKK-1 alone.In this meta-analysis, we extracted adequate data for calculating SN, SP, PLR, NLR, DOR, and AUC of sROC from 8 articles that performed diagnostic tests for detecting HCC with serum DKK-1 alone, AFP alone, and DKK-1 + AFP. The pooled results were 0.72, 0.62, and 0.80; 0.86, 0.82, and 0.87; 4.91, 3.60, and 6.18; 0.32, 0.49, and 0.20; 17.21, 7.45, and 31.93; 0.88, 0.70, and 0.92, respectively. DKK-1 alone showed good accuracy in HCC diagnosis, while DKK-1 + AFP showed even better accuracy with DOR of 31.93, AUC of 0.92. However, AFP owned the worst diagnostic efficacy when compared to DKK-1 or the combination of DKK-1 and AFP, as revealed in Table –31 and majority of them concluded that DKK-1 could commendably detect HCC, except Mao et al to differentiate AFP (−) HCC from liver cirrhosis. In another study published in 2012, serum DKK-1 showed a moderate diagnostic value in distinguishing AFP (−) HCC from high-risk patients. However, it is difficult to predict whether DKK-1 could display a good diagnostic accuracy in AFP (−) HCC as there was insufficient data to analyze in the current meta-analysis. Thus, more studies on diagnosing AFP (−) HCC with serum DKK-1 are needed.Serum DKK-1 had shown diagnostic value in diagnosing HCC in many studies, with the pooled SN of 0.65 and 0.81, SP of 0.94 and 0.85, and AUC of 0.84 and 0.88. As compared to the above results by Zhang et al, others markers utilized for diagnosing HCC showed different diagnostic accuracy in the same year (2014). The meta-analysis of osteopontin (OPN), glypican-3 and des-γ-carboxy prothrombin (DCP) in 2014 demonstrated the following results–44: SN were 0.88, 0.53, and 0.71, respectively. SP were 0.87, 0.77, and 0.84, respectively. AUC were 0.91, 0.82, 0.89, respectively. The corresponding updated meta-analysis showed 0.71, 0.80, and 0.8786 for OPN, 0.68, 0.92, 0.87 for GCP3, 0.69, 0.89, and 0.88 for DCP.–47 As compared to the results pooled in meta-analysis of different markers, our results with SN of 0.72, SP of 0.86 and AUC of 0.8596 in HCC diagnosis with serum DKK-1 alone might seem moderate. However, with AUC of 0.92, the combination of DKK-1 and AFP showed an equivalent diagnostic performance as compared to OPN and DCP.,47The previous meta-analysis conducted by Zhang et al indicated that both DKK-1 and DKK-1 + AFP had satisfactory accuracy for diagnosing HCC,P = .226), −0.050 (P = .898), and 0.119 (P = .779), which indicated that threshold effect did not exist as all P-values were >.05. Then, we performed subgroup analyses according to the stage of HCC and high-risk control in DKK-1 alone, AFP alone, and DKK-1 + AFP, respectively. The I2 values of DOR in 3 subgroups of DKK-1 were 61.1%, 64.5%, and 67.1%, respectively. The I2 values of DOR in 3 subgroups of AFP were 91.0%, 45.0%, and 78.1%, respectively. The I2 values of DOR of DKK-1 + AFP were 54.2%, 25.3%, and 63.3%  of DOR of DKK-1 + AFP in diagnosing all HCC patients, we found that the stage of HCC was the source of heterogeneity, as the I2 value of DOR decreased >10% and the I2 values of both pooled SN and SP were 0.0%. Similar to the previous meta-analysis, I2 value of pooled SN in DKK-1 alone of early HCC subgroup was 0.0%, which indicated the stage of HCC was the source of heterogeneity in the current meta-analysis. However, the stage of HCC failed to appropriately explain the potential source of heterogeneity of SP in DKK-1 alone, even though the I2 values of DOR decreased >10%. Likewise, all of them were still >50%.Heterogeneity among the included studies was evaluated through different methods in this analysis since it is an indicator of the reliability of the results. Threshold effect was thought to be a primary cause for heterogeneity in diagnostic studies. In the current meta-analysis, the Spearman correlation coefficients of DKK-1 alone, AFP alone, and DKK-1 + AFP in diagnosing HCC were 0.378 ,23 which might lead to bias of the result. Hence, it is reasonable and necessary to further confirm the diagnostic performance of DKK-1 and DKK-1 + AFP.As compared to the previous meta-analysis, the overall participants in the diagnosis test did not significantly increase, although more studies were included in the current meta-analysis (2678 vs 1115). However, 2 large samples were predominantly included in the previous meta-analysis,(2)The study design of all included studies was retrospective, and poor results might be removed from raw data. Besides, the purpose of included studies was incongruous.(3),28,31 and only articles published in English were screened, which may have led to the limitations of geographical regions and languages.There were only 3 studies with non-Chinese blood samples,(4)Different cut-off values of serum DKK-1 were used among the included studies, which made it difficult to estimate the real diagnostic value. However, as a novel marker, DKK-1 should be tested for detecting HCC in future studies to explore the optimum cut-off value.(5)The standard references of HCC diagnosis differed among the included studies, including biochemistry, imaging characteristics, and pathology. However, it is difficult to have uniform methods for diagnosis of diseases in clinical practice.(6)Due to constraints of the small number of included studies, we did not perform meta-regression in the current meta-analysis to further search for the source of heterogeneity. Although subgroup analyses identified that the stage of HCC was the source of heterogeneity of DKK-1 + AFP in HCC diagnosis, it could not confirm whether the stage of HCC was the source of heterogeneity in DKK-1.5Serum DKK-1 + AFP showed high accuracy for diagnosing HCC, while serum DKK-1 alone, with a lower DOR, showed moderate accuracy as compared to the previous meta-analysis. However, more studies are needed to ascertain the diagnostic value of serum DKK-1 in AFP (−) HCC. Due to the limitations of the current meta-analysis, further well-designed studies are needed to confirm the diagnostic value of DKK-1 and DKK-1 + AFP in HCC diagnosis.Data curation: Yi Zeng, Xueyi Tang.Formal analysis: Zhenjie Li, Zuhui Pu.Investigation: Lisha Mou, Yi Zeng, Xueyi Tang.Methodology: Zhenjie Li, Yongqiang Zhan.Resources: Xueyi Tang.Software: Zhenjie Li, Lisha Mou, Haibin Gao, Yi Zeng.Supervision: Xuesong Deng, Yongqiang Zhan.Validation: Zhenjie Li, Haibin Gao, Xueyi Tang, Zuhui Pu, Yong Ni.Writing – original draft: Zhenjie Li.Writing – review and editing: Xuesong Deng.


Background: Several genetic association studies already investigated potential roles of cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) gene polymorphisms in diabetes mellitus (DM), with inconsistent results. Therefore, we performed this meta-analysis to better assess the relationship between CTLA-4 gene polymorphisms and DM in a larger pooled population.Methods: PubMed, Embase, Web of Science, and CNKI were systematically searched for eligible studies. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to estimate the strength of associations between CTLA-4 gene polymorphisms and DM in all possible genetic models.Results: A total of 76 studies were finally included in our analyses. Significant associations with susceptibility to type 1 diabetes mellitus (T1DM) were detected for rs231775  and rs5742909  polymorphisms in overall population. Further subgroup analyses revealed that rs231775 polymorphism was significantly associated with susceptibility to T1DM in Caucasians and South Asians, and rs5742909 polymorphism was significantly associated with susceptibility to T1DM in South Asians. Moreover, rs231775 polymorphism was also found to be significantly associated with susceptibility to type 2 diabetes mellitus (T2DM) in East Asians and South Asians.Conclusions: Our findings indicated that rs231775 and rs5742909 polymorphisms may serve as genetic biomarkers of T1DM, and rs231775 polymorphism may also serve as a genetic biomarker of T2DM. Diabetes mellitus (DM), characterized by chronic hyperglycemia caused by deficiency in insulin secretion or resistance against insulin, is the most prevalent metabolic disorder worldwide, and it currently affects over 350 million people globally ,2. So faCTLA-4 gene polymorphisms were intensively studied with regard to their associations with T1DM [CTLA-4 gene polymorphisms and the much more prevalent type 2 diabetes mellitus (T2DM) [CTLA-4 gene polymorphisms were associated with T1DM and T2DM or not remain controversial, especially when they were conducted in different populations. Therefore, we performed the present meta-analysis to pool the data of all relevant studies, and obtain more conclusive results on associations of CTLA-4 gene polymorphisms with T1DM and T2DM.Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) is mainly expressed on activated T cells, and it serves a negative regulator of T cell activation and proliferation . Previouith T1DM –12. Reces (T2DM) ,14. NeveThe current meta-analysis was complied with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement . PotentiCTLA-4 gene polymorphisms and individual susceptibility to DM; (2) provide adequate data to calculate odds ratios (ORs) and 95% confidence intervals (CIs); (3) full text in English or Chinese available. For duplicate reports, only the most complete one was included. Family-based association studies, case reports, case series, reviews, comments, letters, and conference presentations were excluded.Included studies should met all the following criteria: (1) case–control study on associations between CTLA-4 gene polymorphisms in cases and controls. The probability value  of Hardy–Weinberg equilibrium (HWE) test was also calculated.The following data were extracted from included studies: (1) name of first author; (2) year of publication; (3) country and ethnicity of participants; (4) type of disease; (5) the number of cases and controls; and (6) genotypic distributions of The Newcastle–Ottawa scale (NOS) was used to assess the quality of eligible studies from three aspects: (1) selection of cases and controls; (2) comparability between cases and controls; and (3) exposure in cases and controls . The NOSTwo reviewers conducted data extraction and quality assessment independently. When necessary, the reviewers wrote to the corresponding authors for extra information. Any disagreement between two reviewers was solved by discussion until a consensus was reached.CTLA-4 gene polymorphisms with the susceptibility to DM in dominant, recessive, over-dominant, and allele models, and a P value of 0.05 or less was considered to be statistically significant. Between-study heterogeneity was evaluated by I2 statistic. If I2 was greater than 50%, random-effect models (REMs) would be used for analyses due to the existence of significant heterogeneities. Otherwise, fixed-effect models (FEMs) would be employed for analyses. Subgroup analyses by ethnicity of participants were subsequently performed. Sensitivity analyses were carried out to test the stability of the results. Funnel plots were applied to evaluate possible publication biases.All statistical analyses in the present study were conducted with Review Manager Version 5.3.3 . ORs and 95% CIs were used to assess potential associations of Our systematic literature search yielded 842 results. After exclusion of irrelevant and duplicate articles by reading titles and abstracts, 135 potentially relevant articles were retrieved for further evaluation. Another 59 articles were subsequently excluded after reading the full text. Finally, a total of 76 studies that met the inclusion criteria of our meta-analysis were included see . CharactP=0.008, OR = 0.83, 95%CI 0.73–0.95; recessive model: P=0.003, OR = 1.27, 95%CI 1.09–1.50; allele model: P=0.004, OR = 0.85, 95%CI 0.77–0.95) and rs5742909  polymorphisms in overall population. Nevertheless, no any positive results were detected for T2DM in overall population.Significant associations with susceptibility to T1DM were detected for rs231775  and South Asians , but not in East Asians. Moreover, rs231775 polymorphism was also significantly associated with susceptibility to T2DM in East Asians (over-dominant model) and South Asians , but not in Caucasians. Additionally, we also found that rs5742909 polymorphism was significantly associated with susceptibility to T1DM in South Asians , but not in East Asians and Caucasians see .Table 2CTLA-4 gene polymorphisms in any comparisons, which indicated that our findings were quite statistically reliable.Sensitivity analyses were carried out to test the stability of meta-analysis results by eliminating studies that deviated from HWE. No changes of results were detected for investigated Potential publication biases in the present study were evaluated with funnel plots. No obvious asymmetry of funnel plots was observed in any comparisons, which suggested that our findings were unlikely to be impacted by severe publication biases.Despite enormous advancements in pharmacotherapy over the past few decades, DM and its associated vascular complications are still leading causes of death and disability all over the world ,18. To dCTLA-4 gene polymorphisms and DM, and our pooled analyses revealed that rs231775 and rs5742909 polymorphisms may serve as genetic biomarkers of T1DM, and rs231775 polymorphism may also serve as a genetic biomarker of T2DM. The stabilities of synthetic results were evaluated by sensitivity analyses, and no alterations of results were observed in any comparisons, which suggested that our findings were statistically stable. As for evaluation of heterogeneities, significant heterogeneities were detected for rs231775 polymorphism in every comparison of overall analyses for T1DM, and thus all analyses were performed with REMs. But in further subgroup analyses, a reduction tendency of heterogeneity was found in South Asians, which suggested that differences in ethnicity could partially explain observed heterogeneities between studies.Previous studies showed that interferon α and its associated pathways could induce autoantigen presentation, active autoreactive monocytes, cytotoxic T-lymphocytes and NK cells, elicit endoplasmic reticulum stress of human islet B cells, and impair insulin production ,21. ThesCTLA-4 gene may lead to alternations in gene expression or changes in CTLA-4 protein structure, which may subsequently affect biological functions of CTLA-4, result in immune dysfunction and ultimately impact individual susceptibility to DM, especially T1DM. Second, it is noteworthy that several analyses were still based on limited number of studies, and therefore, further replication studies, especially in T2DM are still warranted to confirm these findings. Third, the pathogenic mechanism of DM is extremely complex, and hence despite our positive findings, it is unlikely that a single genetic polymorphism could significantly contribute to its development [CTLA-4 gene polymorphisms. But to better illustrate the potential associations of CTLA-4 gene polymorphisms with DM, we strongly recommend further studies to perform haplotype analyses and explore potential gene–gene interactions.There are several points that need to be addressed about the present study. First, our findings indicated that rs231775 and rs5742909 polymorphisms could be used to identify individuals at higher risk of developing T1DM, and rs231775 polymorphism could also be used to identify individuals at higher risk of developing T2DM. There are two possible explanations for our positive findings. First, rs231775 and rs5742909 polymorphisms of the elopment ,23. FourCTLA-4 gene polymorphisms and DM may also be influenced by gene–environmental interactions. However, the majority of studies did not consider these potential interactions, which impeded us to perform relevant analyses accordingly [Our meta-analysis certainly has some limitations. First, although the general methodology qualities of included studies were good, it should be noted that we did not have access to genotypic distributions of investigated polymorphisms according to base characteristics of study subjects. Therefore, our results were derived from unadjusted estimations, and failure to conduct further adjusted analyses for baseline characteristics of participants such as age, gender, and co-morbidity conditions may influence the authenticity of our findings . Second,ordingly . Fourth,CTLA-4 gene polymorphisms in DM.In conclusion, our findings indicated that rs231775 and rs5742909 polymorphisms may serve as genetic biomarkers of T1DM, and rs231775 polymorphism may also serve as a genetic biomarker of T2DM. Further well-designed studies, especially in T2DM are still warranted to confirm our findings, and future investigations also need to explore possible roles of other


The mammalian homologs of Lin-28, Lin28  and Lin28B, are promising cancer biomarkers. This meta-analysis was performed to evaluate the prognostic values of Lin28A and Lin28B in multiple human malignancies.Systematic searches of the PubMed, Web of Science and Embase were used to identify relevant studies. Pooled hazard ratios (HRs) with 95% confidence intervals (CI) for overall survival (OS), recurrence-free survival (RFS), disease-free survival (DFS), or progression-free survival (PFS) were respectively calculated.3772 Lin28A-associated patients and 1730 Lin28B-related cases were ultimately enrolled in this meta-analysis. The elevated expression level of Lin28A was significantly associated with poor OS  and poor RFS/DFS/PFS  in patients with malignancies. Lin28B overexpression significantly correlated with unfavorable OS  and RFS/DFS/PFS  of human malignancies.Lin28A and Lin28B possess significant prognostic values in various human malignancies. Overexpression of Lin28A or Lin28B suggests poor prognosis for cancer patients.The online version of this article (10.1186/s12935-019-0788-z) contains supplementary material, which is available to authorized users. In recent years, cancer has become the primary cause of mortality in most countries and regions, and the incidence of human malignancies has increased substantially . Given tLin28A and Lin28B are highly conserved RNA-binding and microRNA-regulated proteins. In general, they selectively block the expression of let-7 microRNA family members, which act as tumor suppressors by inhibiting the expression of oncogenes and key regulators of mitogenic pathways, including RAS, MYC, and HMGA2 . Many stOverexpression of Lin28A and Lin28B is associated with poor prognosis in various cancers, such as oral squamous cell carcinoma (OSCC) , 7, coloWe carefully performed a comprehensive search on PubMed, Web of Science, and Embase until June 2017 to identify relevant potential literatures. The following set of keywords was simultaneously applied for the online study search: “Lin28” or “Lin28” or “Lin28A” or “Lin28B” and “tumor” or “tumour” or “cancer” or “carcinoma” or “neoplasm” or “malignancies.”This meta-analysis was strictly conducted in accordance with the guidelines of the preferred reporting items of systematic reviews and meta-analyses (PRISMA) statement . StudiesThe extracted data included the following elements: (i) first author and publication year; (ii) characteristics of the studied population, including patients’ nationality, region, mean or median age, study size, disease type, and examined type of sample; (iii) cut-off definition and method of sample analysis; (iv) source of HR, mean, or median follow-up duration; and (v) HRs of elevated Lin28A or Lin28B expression for OS, recurrence-free survival (RFS), disease-free survival (DFS), or progression-free survival (PFS). If HRs were not directly reported, then the survival data were extracted from Kaplan–Meier plots by using Engauge Digitizer V.5.1 . Furthermore, HRs with 95% confidence intervals (CIs) were calculated using practical methods by Stata V.12.0  [P value) and Higgins I2 statistic. Sensitivity analyses, including influence analysis and Galbraith plot, were implemented for individual studies to identify the source of heterogeneity. If statistically significant heterogeneity was observed (P < 0.10 or I2 > 50%), the random-effects model was applied to estimate the pooled HR; otherwise, the fixed-effects model was used. If necessary, we also classified the included studies into subgroups on the basis of similar characteristics to minimize the influence of significant heterogeneity. In addition, publication bias was estimated using Egger’s test with funnel plot. Stata version 12.0  was used to calculate all statistical analyses. All P values were two sided, and a P value of less than 0.05 was considered statistically significant.In this meta-analysis, HRs and corresponding 95% CIs were combined to estimate the prognostic values of high Lin28A or Lin28B expression for cancer prognosis. An individual or pooled HR of more than 1.0 indicated poor prognosis for patients with Lin28A or Lin28B overexpression, and an HR of less than 1.0 represented better prognosis. Moreover, a fixed-effects model or a random-effects model was applied for meta-analysis on the basis of the heterogeneity among the pooled studies . HeterogAs shown in Fig. The main characteristics and data of the included studies are summarized in Tables I2= 93.7%, P < 0.001) . The pooled HR value of Lin28A overexpression for OS was 1.60  (P < 0.001), as detected by the fixed-effects model  . The fixed-effects model was applied, and the pooled HR value of Lin28A overexpression for RFS/DFS/PFS was 1.62  (P < 0.001)  , and the pooled HR detected by the fixed-effects model demonstrated that high Lin28B expression significantly correlated with poor OS of human malignancies  (P < 0.001)    and EOC   and North America   were first identified through mutagenesis screening as heterochronic genes in  elegans , 34, and elegans . Recent  elegans . Althoug elegans –40. As o elegans , 7, colo elegans , EOC [4, elegans , 11, gas elegans –14, HCC  elegans –17, and  elegans , 19. Alt elegans . Many of elegans , and IGF elegans . TherefoIn this meta-analysis, we investigated the prognostic values of Lin28A and Lin28B for multiple human malignancies. Combining the outcomes of studies regarding the association of lin28 expression and tumor prognosis, we have successfully drawn many valuable results. First, increased expression level of Lin28A was considered to predict poor OS and RFS/DFS/PFS for cancer patients, with combined HR values of 1.60  and 1.62 , respectively. Second, considering the pooled outcomes of studies on the relation between Lin28B expression and cancer prognosis, we found that elevated Lin28B level was significantly associated with poor OS and RFS/DFS/PFS of malignant diseases, with pooled HR values of 1.72  and 2.35 , correspondingly, which also exerted statistical significance. Our findings suggested that Lin28A and Lin28B are promising biomarkers, and the detection of Lin28A and Lin28B expression in cancer patients is of potential value for monitoring patients’ survival.I2 = 69.2%, P = 0.006). In subgroup analyses, no heterogeneity was observed in the subtotals of Chinese Taiwan  and EOC . Also, no heterogeneity was shown in the subgroup analyses by different assay methods for Lin28A overexpression and OS, and RFS/DFS/PFS   and North America  showed significant heterogeneity  and cancer prognosis.Additional file 2: Table S2. Main characteristics of studies on Lin28B and cancer prognosis.Additional file 3: Figure S1. In initial meta-analysis, forest plots summarizing the association of Lin28A overexpression and OS (A), and RFS/DFS/PFS (B) in patients with various cancers.Additional file 4: Figure S2. Influence analysis and Galbraith plot of individual studies on Lin28A expression and OS. A, B Influence analysis and Galbraith plot for initial meta-analysis; C, D influence analysis and Galbraith plot after study exclusion.Additional file 5: Figure S3. Influence analysis and Galbraith plot of individual studies on Lin28A expression and RFS/DFS/PFS. A, B Influence analysis and Galbraith plot for initial meta-analysis; C, D influence analysis and galbraith plot after study exclusion.Additional file 6: Figure S4. In initial meta-analysis, forest plots summarizing the association of Lin28B overexpression and OS (A), and RFS/DFS/PFS (B) in patients with various cancers.Additional file 7: Figure S5. Influence analysis and Galbraith plot of individual studies on Lin28B expression and OS. A, B Influence analysis and Galbraith plot for initial meta-analysis; C, D influence analysis and Galbraith plot after study exclusion.Additional file 8: Figure S6. Influence analysis and Galbraith plot of individual studies on Lin28B expression and RFS/DFS/PFS. A, B Influence analysis and Galbraith plot for initial meta-analysis; C, D influence analysis and Galbraith plot after study exclusion.Additional file 9: Figure S7. Forest plots evaluating the association of Lin28B overexpression and RFS/DFS/PFS in subgroups of Chinese Taiwan and EOC. HR, hazard ratio; CI, confidence interval; EOC, epithelial ovarian carcinoma.Additional file 10: Figure S8. Forest plots evaluating the association of Lin28B overexpression and RFS/DFS/PFS in subgroups of Asian and North America.Additional file 11: Figure S9. Subgroup analyses by different assay methods evaluating the association of (A) Lin28A overexpression and OS, (B) Lin28A overexpression and RFS/DFS/PFS, (C) Lin28B overexpression and OS, (D) Lin28B overexpression and RFS/DFS/PFS.


In addition, there was a 14%  and a 28%  increase in the RR of AAA for every 20 mmHg and 10 mmHg increase in systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The analysis of DBP showed evidence of a strong and highly significant nonlinear dose–response relationship (p < 0.001) with a steeper association from 80 mmHg and above. This meta-analysis suggests that hypertension increases the risk of developing AAA by 66%. Further studies are needed to clarify the underlying mechanism explaining the much stronger association between DBP and AAA than for SBP.Abdominal aortic aneurysms (AAA) are fatal in 80% of the cases when ruptured. Hypertension has been considered a potential risk factor for AAA; but the findings from prospective cohort studies have not been entirely consistent, nor have they been summarised in a comprehensive meta-analysis. Our aim was to conduct a systematic review and meta-analysis of cohort studies of the association between blood pressure, hypertension and AAA to clarify the strength and shape of these associations. We searched PubMed and Embase databases for relevant cohort studies up to April 30th, 2018. Random-effects models were used to calculate summary relative risks (RRs) and 95% confidence intervals (CIs). The meta-analysis included 21 cohort studies (20 publications) with data on 28,162 cases and 5,440,588 participants. The findings indicate that the RR of AAA in hypertensive patients is 1.66 times  contains supplementary material, which is available to authorized users. Abdominal aortic aneurysms (AAA) are expansions of the aorta at the level of the abdomen caused by the weakening of its walls . CurrentRisk factors for the development of AAA include older age, male gender, Caucasian race, family history of the disorder, atherosclerotic disease and smoking , 10, theHypertension has been suggested as a risk factor for AAA . It is dSeveral cross-sectional and case–control studies have examined the association between blood pressure (BP) and AAA; however, the results have not been entirely consistent with some reporting a statistically significant positive association –20, and This analysis was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. This study was registered at PROSPERO, number CRD42018098490.The PubMed and Embase databases were searched from inception up to April 30th, 2018. Identified records were first screened based on titles and abstracts then based on full texts. A manual search of secondary sources was also performed to identify articles that were missed by electronic search.Eligibility criteria for inclusion were: (1) retrospective or prospective cohort studies, nested-case–control studies within cohort studies, or studies with follow-up periods in adult populations; (2) human-based studies; (3) available measures of association  adjusted for at least one confounding factor to be included in the meta-analysis of hypertension; and (4) at least three categories of SBP and DBP or a risk estimate on continuous scale had to be available for the study to be included in the dose–response analyses of SBP or DBP.Study characteristics and results were extracted into tables by one author (EK) and checked for accuracy by a second author (HP), and included the following data: first author, publication year, study location, study name or description, follow-up period, sample size with sex and age of participants, number of cases and type of outcome, exposure and subgroups, exposure categories or comparison, measure of association with a 95% confidence interval (CI) and variables adjusted for in the analysis.2 statistic was reported to express the percentage of total variation across studies [2 values of approximately 25, 50 and 75% were considered to indicate low, moderate and high heterogeneity, respectively.Random effects models that take into account heterogeneity between studies were used to calculate summary RR and 95% CI for the association between hypertension or BP and risk of AAA . Cochran studies . I2 valuFor the linear dose–response analysis, all increments for SBP and DBP were converted to 20 mmHg and 10 mmHg respectively before inclusion in the meta-analysis. When a study reported estimates for three or more categories the Greenland and Longnecker  method wEgger’s  and BeggA total of 3723 articles were identified, out of which 3380 articles were excluded based on their titles and abstracts. A total of 18 eligible cohort studies from 17 publications and three further articles from other sources were included in the main analyses , 23–42 , which was partly explained by a large  and outlying British study   and reduced to zero when excluding both studies by Howard et al. [2 = 0.0%). The summary RR ranged from 1.61 (95% CI: 1.47–1.76) when the study by Howard et al. [p = 0.69) or Begg’s test (p = 0.50) and there was no evidence of asymmetry in the funnel plot .Fourteen cohort studies (13 publications) –38, 42 wi et al. ). The lii et al.  from thei et al. –38, 42   and Begg’s test (p = 0.04) . Hence, the “Trim and Fill” method was used resulting in two “missing” studies being added to the analysis and an adjusted summary RR of 1.11 (95% CI: 1.02–1.22) which was not materially different from the original RR . There was a weak to moderate dose–response relationship between SBP and the risk of developing AAA .Six cohort studies , 33, 41 2 = 80.1%, pheterogeneity < 0.001) , Begg’s test (p = 0.85) or by inspection of the funnel plot . There was evidence of a nonlinear and strong dose–response relationship between DBP and AAA, pnonlinearity < 0.001, with a steeper increase in risk at higher levels of DBP than at lower levels  with a summary RR of 1.46  among men and 2.06  among women. There was also indication of heterogeneity between subgroups when the analyses were stratified by adjustment for dyslipidaemia (p for heterogeneity between subgroups = 0.02), with weaker associations among studies with such adjustment (Supplementary Table 4). There was no evidence of heterogeneity in any of the remaining subgroup analyses.Hypertension was positively associated with the risk of developing AAA in all subgroup analyses (Supplementary Table 4). There was evidence of heterogeneity when the analyses were stratified by sex  suggests that DBP has a larger impact on the risk of AAA than SBP. The current findings are consistent with a pooled analysis, by the Prospective Studies Collaboration , in findOur findings are partly consistent with a previous meta-analysis of nine cross-sectional studies  in findiAlthough the prevalence of AAA is five-fold higher in men than in women , our finSeveral potential limitations of this meta-analysis must be considered. There was high heterogeneity between the studies in the analyses of hypertension and DBP and the risk of AAA. However, the heterogeneity appeared to be driven more by differences in the size of the association rather than differences in the direction of the association. Subgroup analyses were conducted to investigate potential sources of heterogeneity and when studies were stratified by sex, heterogeneity substantially decreased. In addition, in the analysis of hypertension and DBP and AAA a few outlying studies , 33 appeThe association between hypertension and AAA could potentially be confounded by other risk factors because hypertension is more common among persons with overweight and obesity, less physical activity, who smoke and who have unhealthy diets. However, the association between hypertension and AAA persisted in a number of subgroup analyses when stratified by adjustment for a range of confounding factors including race, education, height, BMI/weight, physical activity, smoking, alcohol, dyslipidaemia, HRT, PAD, CVD, stroke, DM, COPD, GFR/renal disease and there was little evidence of heterogeneity between most of these subgroups with meta-regression analyses.Although there was some evidence of small study bias in the analyses of hypertension and SBP, the results were not substantially altered when using the “Trim and Fill” method, suggesting that small study bias is not likely to have had a large impact on the results.p = 0.13), thus any such misclassification would most likely be non-differential and bias the results toward the null. The studies did not take into account changes in BP or BP lowering treatment during follow-up and it is possible that a single baseline measurement may not have adequately represented long-term BP. Because of the prospective design of the included studies, any such regression dilution bias would most likely have lead to bias toward the null. However, antihypertensive medication use might have attenuated the results as it decreases BP and subsequently the effect the latter has on the risk of AAA. With regard to AAA, many cases are asymptomatic and the outcome was not always assessed by an ultrasound, but rather through medical records. Therefore, the number of cases might have been higher if diagnostic tests were performed on all participants during follow-up. However, again because of the prospective design of the included studies any such misclassification would most likely have been non-differential and led to bias towards the null.Misclassification of the exposure because of self-report of hypertension diagnosis could have influenced the results; however, 10 of the 13 studies used measured blood pressure and/or medical records to assess hypertension and only 3 studies were based on self-report, thus any misclassification would most likely have had a limited impact on the summary estimates. If anything, there was a somewhat stronger association between hypertension and AAA among studies where hypertension was assessed using measured blood pressure or medical records  than among studies with self-reported hypertension , although the test for heterogeneity between subgroups was not significant  provided sufficient statistical power to detect even moderate associations with relatively high precision. Detailed dose–response analyses clarified the strength and shape of the dose–response relationship between increasing SBP, DBP and AAA; and detailed subgroup and sensitivity analyses allowed for in-depth investigation and identification of sources of heterogeneity across studies, potential confounding as well as assessment of the robustness of the results.The NHS exclusively offers screening to men aged 65 years due to previous evidence indicating very low risk of AAA in women , 60. HowThe current meta-analysis provides the first meta-analytic evidence based only on prospective studies that hypertension and elevated BP (in particular DBP) increase the risk of AAAs. Because of the high mortality in ruptured AAAs, primary prevention may be a promising way to reduce the public health burden of this disease. The public health importance of the current analysis is illustrated by the observation that risk of AAA increased even below the cut-off point for hypertension (DBP < 90 mmHg) and there was a 75% increase in RR at around 90 mmHg compared to the reference category of 67 mmHg Fig. d, suggesIn summary, this meta-analysis found a 66% higher risk of AAA in hypertensive patients compared to non-hypertensive patients and a 14% and 28% increase in the risk of the disease for every 20 and 10 mmHg rise in SBP and DBP, respectively. Risk of AAA increased dose-dependently even within the normal BP range and there was a five to six-fold increase in the RR of AAA at the highest level of DBP compared to a 1.6 to 1.7-fold increase in the RR at the highest level of SBP based on the results from the nonlinear dose–response analysis. The mechanism underlying the much stronger association between DBP and AAA than for SBP needs further study.Supplementary material 1 (DOCX 338 kb)Below is the link to the electronic supplementary material.


Until now, headache disorders have not been established as a risk factor for dementia. The aim of this study was to determine whether headache was associated with an increased risk of dementia.We systematically searched electronic databases, including PubMed, Embase, and Web of Science, for studies investigating the association between headache and dementia. We then conducted a meta-analysis to determine a pooled-effect estimate of the association.P = 0.001), and that any headache was not statistically significantly associated with an increased risk of AD .We identified 6 studies  to investigate the association between headache and the risk of all-cause dementia or Alzheimer’s disease (AD). Pooled analyses showed that any headache was associated with a 24% greater risk of all-cause dementia  contains supplementary material, which is available to authorized users. Dementia is the most common neurological disease in the elderly, with devastating impact on the quality-of-life of both the patients and their family members, apart from placing a huge economic burden on the society. Over the past 20 years, researchers have been working on finding a treatment for dementia, especially that associated with Alzheimer’s disease (AD); however, results have been disappointing. Currently, there are no effective drugs that can significantly delay the progression of dementia . In addiGlobally, about 45% of adults in the general population suffer from headache disorders . These dWe conducted our systematic review and meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We systematically searched the PubMed, Embase and Web of Science databases from their inceptions to June 1, 2018 for relevant studies. Our complete search strategy is presented in Additional file Articles were included if they met the following criteria:  cohort sTwo investigators independently assessed the eligibility of the literature. First, they identified eligible articles by title and abstract; next, each of them independently read the full text of each eligible article. Discrepancies between investigators were rechecked and, if necessary, discussed with a third investigator until consensus was achieved.Two investigators independently extracted and summarized the relevant data of the included studies. The following information was extracted from each included study: author name, year of publication, country of study origin, population source, study design, sample size, years of follow-up, gender distribution, mean age and age range of study participants, headache type and dementia type.We assessed the quality of the included articles using the Newcastle–Ottawa Quality Assessment Scale (NOS) . Scores 2) statistic. Statistical heterogeneity was considered significant when P < 0.10 for the χ2 test or when I2 > 50% [P-value < 0.05 was considered statistically significant. We performed all analyses using Stata software version 12.0 .We extracted the adjusted RR and 95% CI from each study and used them to assess the association between headache and risk of dementia. We used random-effects models, which included assumptions about potential differences between studies, for our pooled analysis . HeterogI2 > 50% . We perfI2 > 50%  and BeggI2 > 50%  were useWe retrieved a total of 2871 studies from our database search. Out of those, we included 3 articles and 3 abstracts corresponding to 6 cohort studies in this meta-analysis. The study selection process is shown in Fig. Overall, we included 6 studies covering 291,549 individuals in our meta-analysis. Two studies , 24 wereWe were able to assess the quality of the 3 full-length articles only. Specific assessments with NOS scores for these 3 studies are shown in Additional file P = 0.001; Fig. I2 = 63.5%; Phetero = 0.018). Neither subgroup analysis by sample size  nor study design (prospective or retrospective) could explain the origin of this heterogeneity. Sensitivity analysis  showed that none of the individual studies had an evident influence on the pooled-effect size  and Egger’s (P = 0.089) regression tests likewise indicated no publication bias in this meta-analysis.The 6 included studies all assessed the association between any headache and the risk of all-cause dementia. Overall, the history of any headache was associated with an increased risk of all-cause dementia  for the association between history of migraine and risk of all-cause dementia. Only 1 study reported the association of migraine with the risk of AD, and its results showed that the history of migraine increased the risk of AD


Subgroup analyses, meta-regressions and Galbraith plots were conducted to conduct an exploration on the possible sources of heterogeneity. The genome-wide DNA methylation array studies were extracted from Gene Expression Omnibus (GEO) databases for validation of these outcomes.Literature selection were conducted through searching PubMed, Web of science, EMBASE, China National Knowledge Infrastructure and Wanfang databases (up to November 2018). An assessment of associations between MGMT hypermethylation gradually elevated the rates among control group (12.16%), LSIL (20.92%), HSIL (36.33%) and CC (41.50%) specimens. MGMT promoter methylation was significant associated with the increased risk of LSIL by 1.74-fold (P<0.001), HSIL by 3.71-fold (P<0.001) and CC by 7.08-fold (P<0.001) compared with control. A significant association between MGMT promoter methylation with FIGO stage was also found . The results of GEO datasets showed that 5 CpG sites in MGMT with a great diagnostic value for the screening of cervical cancer.In this meta-analysis of 25 published articles, MGMT promoter hypermethylation and squamous intraepithelial lesion and cervical cancer. MGMT methylation detection might have a potential value to be an epigenetic marker for the clinical diagnosis of cervical cancer.The meta-analysis indicated the association between  Human pworldwide, indicat6-methyguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme removing mutagenic and cytotoxic adducts out of O6-guanine in DNA. Methylation of discrete regions of the CpG island of MGMT becomes the main cause of gene silencing and decreased expression of MGMT in tumor tissues and cell lines[MGMT is related to elevated carcinogenic risk and sensitivity to therapeutic methylating agents. Thus, the promoter methylation of MGMT was widely regarded as a promising biomarker for the detection of early carcinoma. Previous researches concentrated upon this in other cancers like esophageal cancer[Virmani et al.[MGMT with the risk and histological type of cervical carcinoma. Thereafter, an increasing number of researches were conducted to investigate the association between MGMT hypermethylation with the process of cervical carcinogenesis. Nevertheless, the sample sizes of the studies are still small, resulting in inconsistent outcomes and a wide range of MGMT methylation rates in cervical carcinoma tissues. Even the opposite conclusions have also been reported in a few studies, suggesting that MGMT gene is rarely methylated in cervical cancer. Moreover, a meta-analysis pooled the data of 28 studies to investigate the association between MGMT methylation and the risk of breast and gynecologic cancers, among which 11 studies focused on cervical cancer, indicating that MGMT methylation and cervical cancer were positively correlated[MGMT promoter hypermethylation upon diverse phases of cervical carcinogenesis that from LSIL to HSIL, and finally to invasive carcinoma, as well as their clinicopathological features.DNA methylation, an epigenetic modification in genes that primarily causes transcriptional silencing of genes, which played a key role in regulating transcription, embryonic development, genomic imprinting, genome stability and chromatin structure. Aberrant DNA methylation of CpG islands is comparatively seldom seen in normal cells, based on which the detection of promoter hypermethylation of tumor suppressor genes (TSGs) in bodily fluids could serve as good biomarkers for screening and prognosis of tumorigenesis development. O6-methell lines. It has al cancer, 9, lungal cancer–12, glioal cancer, 14, colal cancer–18, gastal cancer, neck sqal cancer, 21, ovaal cancer and breaal cancer. In 2001ni et al. first reorrelated. HoweverMGMT promoter hypermethylation with squamous intraepithelial lesion, cervical cancer and their clinicopathological characteristics.Therefore, a meta-analysis was performed for comprehensive assessment of the association of MGMT) and (methylation or hypermethylation or epigene*) and ”. Reference list in retrieved articles and relevant reviews were retrieved in a manual manner.The review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2009 guidelines  the study should offer enough data for calculation of odds ratios (ORs) and 95% confidence intervals (CIs); (4) the study should be written in English or Chinese.Eligible studies were included when satisfying the following criteria: (1) the study should be an observation designing including cohort, case-control, case-only or cross-sectional study; (2) the study should evaluated the association between vitro or ex vivo experiments of cell lines or animals.Exclusion criteria were as follows: (1) they were meeting abstracts, reviews, letters or case reports; (2) they regarding in The data of eligible studies were extracted by two independent authors. The following information of each eligible study were collected: the first author’s name, publication year, ethnicity, country, study design, sample size, methylation detection methods, materials, source of controls, involved diseases , their clinicopathological characteristics  and quality of studies.vitro or ex vivo experiments of cell lines or animals; (2) datasets without CpG number.The genome-wide DNA methylation array studies were extracted from Gene Expression Omnibus (GEO) databases by using following items “Cervical cancer”, “Methylation” and “Homo sapiens”. Eligible criteria were as follows: (1) the quantitative methylation levels of datasets were detected by the Illumina HumanMethylation 27 or 450 k Beadchip; (2) datasets using cohort or case-control designs. Exclusion criteria were as follows: (1) datasets regarding in The following information of each eligible datasets were collected: submission and last update date, ethnicity, country, sample size, methylation detection methods, source of controls, involved diseases .The quality of eligible studies and datasets was assessed by two independent authors (JH and JYL) in line with a preset system derived from the REMARK  and BRISMGMT promoter hypermathylation rates in LSIL, HSIL and CC specimens were calculated by the inverse variance approach [MGMT promoter methylation possessing LSIL, HSIL, CC and their clinicopathological characteristics. Heterogeneity across the included studies were assessed by the Cochran’s Q test and I2 statistic. I2 value of greater than 25%, 50% and 75% meant mild, moderate and high heterogeneity, respectively[I2 value larger than 50% or PQ-test smaller than 0.1) was observed, the random-effect model was utilized to pool the results, otherwise, a fixed-effect was applied[MGMT and cervical cancer were calculated by Mann-Whitney U test. Trial sequential analysis (TSA) was performed by TSA 0.9 software  with type I errors of 5%, type II errors of 20% and a statistical test power of 80%. Publication bias was evaluated by funnel plots and Egger’s test[PEgger≤0.05 indicated the presence of publication bias. All the statistical analysis above were undertaken by RevMan 5.3  and Stata 15.0 .The approach . Cochranpectively. When sis applied. To furter’s test. Funnel MGMT. A total of 2933 patients with SIL or CC from 25 studies , the detection of MGMT promoter methylation was performed by methylation-specific PCR (MSP). Besides, two studies performed by HRM, only one study performed by pyrosequencing[MGMT methylation status, while other 10 studies involved cervical tissues, 3 studies involved tissue and plasma and one study only involved plasma. Regarding the type of ethnicity, eighteen studies were carries out on Asian, seven studies on Caucasians. The flow diagram for the process of included articles in this meta-analysis was revealed in Based on the definitions of the 2001 Bethesda System, the caty studies, 49, 51 equencing, one stuequencing, two stuequencing, 53, as equencing. Among tMGMT hypermethylation demonstrated a progressively increased trend (p<0.001) from control group  to LSIL , to HSIL  and eventually to CC  specimens.Altogether 1227 controls, 738 LSIL, 827 HSIL and 791 CC specimens were included in this meta-analysis. As shown in MGMT promoter hypermethylation on LSIL risk (MGMT promoter hypermethylation conferred a 1.75-fold (95% CI: 1.30–2.32) elevated risk of LSIL and a p value of <0.001 (I2: 0–39%).Twelve published studies including 738 patients with LSIL risk and 925 controls were included to estimate the effect of SIL risk . MGMT prf <0.001 . In ethnMGMT promoter methylation status with HSIL risk (MGMT promoter hypermethylation was associated with a 3.37-fold (95% CI: 1.86–6.14) increased risk of HSIL and a p value of <0.001 , subgroup, meta-regression and Galbraith plot analyses were performed to explore the potential sources of heterogeneity. In ethnicity based subgroup analysis, there were no significant differences in methylation rates between Asians  and Caucasians . Moderate heterogeneity remained in most of the subgroups, except for the “tissues” subgroup (I2 = 0%). The outcomes of meta-regression analyses illustrated that ethnicity (p = 0.978), source of controls (p = 0.999), materials (p = 0.513), publication year (p = 0.340) and quality of studies (p = 0.752) were all not main sources of heterogeneity .Due to observation of moderate heterogeneity in the overall comparison (MGMT promoter hypermethylation upon CC risk (MGMT promoter hypermethylation conferred a 7.08-fold (95% CI: 2.81–17.87) increased risk of CC and a p value of <0.001 (16 published studies including 791 CC patients and 597 controls were included to assess the effect of  CC risk . MGMT prf <0.001 .I2 = 78%), subgroup, meta-regression and Galbraith plot analyses were conducted for seeking the possible sources of heterogeneity. In subgroup analyses, such association was till significant in nearly every subgroups in addition to the low-quality studies. However, moderate or extensive heterogeneity was still in most of the subgroups, except for the subgroups involving high-quality studies (I2 = 0%). The outcomes of meta-regression analyses illustrated that ethnicity (p = 0.248), source of controls (p = 0.880), materials (p = 0.654) and publication year (p = 0.570) were not main sources of heterogeneity (Table D in p<0.001). Moreover, a Galbraith plot was further depicted, spotting four outliers [I2 value from 78 to 0%, followed by a significant association between the methylation of MGMT with increased CC risk , which further providing support to the outcomes of meta- regression.Due to observation of extensive heterogeneity was observed in the overall comparison  stage, histological grade, HPV infection, therapeutic response, age at diagnoses and lymph node metastasis. As shown in MGMT promoter methylation was significantly associated with FIGO stage , but not with histological types   .MGMT expression was related to overall survival (OS) in 292 patients with cervical cancer (p<0.05), as shown in The further analysis from GEPIA databases illustrated that MGMT were included. 5 of 7 CpG sites  showed significance results with p-values<0.001 when methylation level of cervical cancer compared with that of controls  involved genome-wide DNA methylation array of 67 controls, 63 patients with cervical cancer, as shown in In sensitivity analyses as shown in Figure E in In all comparisons, the shapes of funnel plots  was conducted to estimate the required information size. Based on the a priori anticipated information size method, when LSIL  than in low stage (FIGO stage I+II). Thus, the results further implicated that MGMT promoter methylation is likely to have a critical function in the progression of cervical cancer. This conclusion were consistent with previous studies in other malignant carcinoma such as follows. In Hengstler et al.’s research, it has been reported that MGMT expression is significantly associated with FIGO stages in ovarian tumors [Fu et al. reported that MGMT methylation status exert a possible prognostic value in patients with duodenal adenocarcinoma in stage III [Chen et al., focused on the association between MGMT hypermethylation and non-small-cell-lung carcinoma (NSCLC), reported that MGMT methylation was observed to be specifically associated with NSCLC clinical stage.Furthermore, whether n tumors . Fu et atage III . BesidesMGMT could be a biomarker for the prognosis of cervical cancer. Analysis from GEPIA databases showed that MGMT expression were associated with OS in cervical cancer, the survival percentage of patients with high MGMT expression is much higher than that of patients with low MGMT expression. We speculated that MGMT promoter hypermethylation could down-regulates MGMT mRNA expression, and progressively influenced the overall survival. This conclusion were consistent with previous studies in colorectal carcinoma, the results showed that hypermethylation of MGMT was an unfavourable prognostic markers in colorectal cancer[MGMT methylation and OS in cervical cancer. There is only one research[MGMT does not seem to be implicated in OS of cervical cancer, at least not by promoter methylation-dependent mechanisms. Therefore, prospective studies could be focus on the impact of MGMT promoter hypermethylation on the prognosis of cervical cancer.Moreover, this meta-analysis was conducted to assess whether al cancer. However research reportedMGMT methylation with HSIL and CC risk, respectively. Thus, such outcomes were firstly pooled by using a random-effect model, which conservatively estimates the study weights after adjusting for the inter-study variances[MGMT promoter hypermethylation and HSIL risk, the results of subgroup analysis showed that the specimen material not used by tumor tissues was probably the major origin of heterogeneity. And Galbraith plots spotted four outliers [MGMT promoter hypermethylation and HSIL risk. Notably, these four studies collected exfoliated cells as biospecimen. In addition, the hypermethylation rates of control group in these four studies  were much higher than that of control group in overall (12.16%), suggesting the existence of inter-study differences. Besides, in the comparison between MGMT promoter hypermethylation and CC risk, the results of subgroup analysis and meta-regression both showed that the low quality of studies was probably the major origin of heterogeneity. Galbraith plots spotted four outliers [MGMT promoter hypermethylation and HSIL risk. Notably, these four studies were all classified into low-quality studies, which is consistent with the conclusions of subgroup analysis and meta-regression. By appraising these four studies according to our quality scorning system, we found that the commonalities of these studies were as follows: lack of information of biospecimen characteristics, lack of blinding of laboratory staff, lack of clinical and pathological data. Besides, MGMT promoter hypermethylation was detected by MS-HRM in one56 of the two studies that regarded as both major sources of heterogeneity for the association of MGMT promoter hypermethylation with HSIL risk and cervical, which was the main difference in their study design from other studies.Moderate and extensive heterogeneity were observed in our meta-analysis for the association of variances. Then, toutliers , 56, 57 outliers , 53, 56 outliers , 56 of tMGMT with squamous intraepithelial lesions and cervical cancer remained significant in both Caucasian and Asian subgroups. Since no African study was available, more experiments should be performed to verify our observation in Africans together with other ethnicities in the future. Besides, in studies that regarded non-healthy as negative controls including autologous controls and controls with benign gynecological diseases, MGMT promoter methylation is significantly correlated with HSIL and CC risk, but not with LSIL risk, while MGMT methylation is significantly correlated with all SILs and cervical cancer in studies regarded healthy as controls. It could be due to the reason that the cytology of cervical cells in patients with benign gynecological disease and LSIL is similar [In subgroup meta-analysis, the association between the promoter hypermethylation of  similar .In sensitivity analysis, sequential removal of each study had no significant impact on the pooled results except one study. The metMGMT methylation were performed by MSP, a qualitative method relied on primer designs to guarantee the accuracy. However, different primers were designed to detect MGMT promoter methylation in the included studies, which may lead to the potential bias. Second, only full-text articles written in English or Chinese were included in this meta-analysis, while articles in other languages were excluded because of unreadable contents or insufficient data, thus resulting to a selection bias. Third, it is unfortunate that the pooled analyses of several clinicopathological features in this meta-analysis based on fewer than three studies, leading to an error or inaccurate conclusion in pooled results for the association between MGMT hypermethylation and these clinicopathological features including HPV infection, therapeutic response, age at diagnose and lymph node metastasis. Thus, more prospective clinical studies with detailed information are needed to confirm the association of MGMT hypermethylation with clinicopathological features in the future.However, several limitations were still needed to be mentioned in this meta-analysis. First, in most of included studies of this meta-analysis, detection of MGMT promoter hypermethylation was associated with squamous intra-epithelial lesion and cervical cancer. Moreover, MGMT promoter hypermethylation was also correlated with FIGO stage in patients with cervical cancer. Therefore, MGMT methylation detection might have a potential value to be an epigenetic marker for the clinical diagnosis of cervical cancer. And, it will help clinicians to decide whether to give complementary cytostatic drugs or not after the primary surgery. Besides, it also could make a contribution to explore the pathogenic mechanism of cervical cancer, as well as the preventive measures. However, prospective studies should be focus on the impact of MGMT methylation on the prognosis of cervical cancer.In this meta-analysis, we found that S1 FileTable A. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2009 Checklist. Table B. Definitions of 18 items in our quality scoring system. Table C. The univariate meta-regression results of the association of MGMT promoter methylation and HSIL risk. Table D. The univariate meta-regression results of the association of MGMT promoter methylation and CC risk. Figure A. Galbraith plot for the association of MGMT methylation and HSIL risk. Each number is the number of the respective study included in this meta-analysis Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Antidiabetic medications (ADMs) can alter the risk of different types of cancer, but the relationship between lung cancer incidence and metformin remains controversial. Our aim was to quantitatively estimate the relationship between incidences of lung cancer and metformin in patients with diabetes in this meta-analysis.  We performed a search in PubMed, Embase, ISI Web of Science, and Cochrane Library until September 20, 2017. The odds ratio (OR), relative risk (RR) or hazard ratio (HR), and 95% confidence interval (95% CI) were estimated using the random-effect model. The Newcastle-Ottawa Scale (NOS) was used to assess the study quality. P = 0.002) with significant heterogeneity . Subgroup analysis showed that cohort studies , location in Europe , the control drug of the sulfonylurea group , and adjusting for smoking  may be related to lower lung cancer risk. No significant publication bias was detected using a funnel plot.  A total of 13 studies (10 cohort studies and 3 case-control studies) were included in the meta-analysis. Compared to nonmetformin users, metformin probably decreased lung cancer incidence in diabetic patients (RR = 0.89; 95% CI, 0.83-0.96;  Metformin use was related to a lower lung cancer risk in diabetic patients compared to nonusers, but this result was retrieved from observational studies and our findings need more well-designed RCTs to confirm the association. Lung cancer is one of the leading causes of cancer-related deaths in the world , and desDiabetes mellitus (DM) is a worldwide metabolic disease, which is reported to increase the risk of a variety of cancers, such as lung, liver, kidney, and breast cancer –9. DM isStudies evaluating the link between lung cancer risk and metformin use among patients with diabetes have produced inconsistent results. Lai et al.  found a We conducted a study to investigate the association between the incidence of lung cancer and metformin or biguanides in diabetic patients. Databases including PubMed, Embase, ISI Web of Science, and Cochrane Library were searched until September 20, 2017. We used the following keywords or MeSH terms: (Metformin or biguanides) and (cancer or carcinoma or neoplasm or tumor) and lung. The reference lists of relevant reviews and meta-analyses were manually checked for other potential articles that may have been missed in the initial search.The inclusion criteria were as follows: (a) cohort or case-control studies, (b) assesses the relationship between incidence of lung cancer and metformin in patients with diabetes, and (c) odds ratio (OR), relative risk (RR) or hazard ratio (HR), and 95% confidence interval (95% CI) were reported . When several publications came from the same population, the most recent or comprehensive one was included. The comparators were defined as any treatment not including metformin.Data were extracted by two reviewers independently from all included publications on a standardized form. The following data were extracted from the selected studies: first author, publication year, country, study design, study time or follow-up time, case source, comparison, and unadjusted and/or adjusted ratio ), which were defined as a tool to measure associations. Any disagreements in the extraction were resolved by discussion when we refer to the original articles. The quality of the included studies was evaluated by the Newcastle-Ottawa Scale (NOS), which is a recommendation by the Cochrane Non-Randomized Studies Methods Working Group .P < 0.05 and the value one were not included in the 95% CI, indicating a statistically significant difference among all studies. Cochran's Q test and I2 statistics were used to evaluate heterogeneity among the studies. For the Q statistic, a P value less than 0.10 was considered as having statistically significant heterogeneity, and for I2, a value more than 50% was considered to be having severe heterogeneity. Then, subgroup analyses were conducted by study design and geographic area and were adjusted for smoking, BMI, HbA1C, alcohol use, and other glucose-lowering drugs. A sensitivity analysis was conducted using the following methodology: exclude each study and analyze the rest. Finally, the funnel plot for asymmetry was carried out to detect publication bias. All analyses were performed with RevMan version 5.3.The RRs from the cohort studies or ORs from the case-control studies were adopted to assess the estimated risk between metformin and lung cancer in the present study. Based on inverse variance, studies were pooled and measured using the DerSimonian and Laird statistic in the random-effect model, which considered both within-study and between-study variations. A total of 892 studies were obtained using the above search strategy, and 17 studies were identified through initial research. Of these studies, Hall et al. , Bodmer The main characteristics of the 13 studies, including year, first author, country, study design, time period, case source, comparison, and unadjusted and/or unadjusted estimated effect with 95% CI are shown in P = 0.002). Significant heterogeneity was found among these studies .The pooled RR from 13 studies is shown in P = 0.01) but still with significant heterogeneity .To confirm the stability and validity of the overall results, we performed a sensitivity analysis by sequentially excluding each study and analyzing the rest. These analyses verified the stability of the result of the significant relationship between the incidence of lung cancer and metformin in diabetic patients. For instance, when we excluded the study with the highest weight, the pooled RR remained significant  in case-control studies, while in cohort studies, a significant association was found . By study location, Asia  and North America  showed no significant association between the incidence of lung cancer and metformin use in diabetic patients, with high heterogeneity . In contrast, a significant association was found in European  studies with little heterogeneity . In the analysis stratified by control group drugs, a no-drug or insulin study had a null relationship between the incidence of lung cancer and metformin in diabetic patients . In contrast, the sulfonylurea group was considered to have a significant association . In addition, the sulfonylurea and insulin groups had little heterogeneity .Then, subgroup analysis was conducted by different characteristics  in orderP = 0.05), with high heterogeneity . With other glucose-lowering drugs, a crucial association was found between lung cancer incidence and metformin use in patients with diabetes , with little heterogeneity . The publication bias was assessed using a funnel plot, and no significant publication bias was detected as there is no visually apparent asymmetry . We also found that in 2 RCTs, namely, ADOPT and RECORD [In this meta-analysis assessing the relationship between the risk of lung cancer and metformin use among diabetic patients, we found that metformin was strongly related to decreased lung cancer risk , which has been reported to have a potential anticancer role in some solid tumors, such as lung, hepatocellular, and melanoma –37. In Nell line , and comell line , 43. TheThere are several strengths in the present meta-analysis. First, a large number of studies were included and all of the studies were observational, which may make the results more reliable. Second, the studies were selected by rigorous criteria. Hall et al. , Bodmer Nevertheless, our meta-analysis has some limitations. First, based on the inclusion criteria, all of the included studies were observational studies; therefore, the association observed between lung cancer incidence and metformin is affected by confounding factors. Inadequate vital-confounder adjustments might lead to spurious relationships between metformin and lung cancer risk. Smoking and previous lung diseases were strongly related to the incidence of lung cancer, and obesity is reported to decrease lung cancer risk, while no studies adjusted for these factors simultaneously. Other confounders, such as vitamin intake, drinking, and physical activity may affect the results as well. Second, the hyperglycemia severity or levels of glycated hemoglobin were not reported in these included studies, so we could not further assess the relationship between hyperglycemic severity and the risk of lung cancer. Third, the included studies did not differentiate between type 1 and type 2 diabetes, and the two types of diabetes may have different associations. Likewise, the studies did not provide lung cancer types, so we were unable to perform subgroup analyses based on each type of lung cancer. Last but not the least, most of observational studies were based on health care databases, resulting in an important amount of immortal time that was either misclassified or excluded. This immortal time bias led to an overestimation of the benefits of medical therapy. Therefore, possible overestimation of the effect requires more well-designed observational studies or randomized controlled trials on association between metformin and lung cancer.To conclude, metformin use appeared to decrease the incidence of lung cancer in diabetic patients according to the present meta-analysis. Due to significant heterogeneity and possible bias, more investigations, especially well-designed randomized controlled trials, are needed to validate the relationship.