ADIPOQ) with hypertension risk and the changes of circulating adiponectin and blood pressure.This meta-analysis was prepared to synthesize published data on the association of two polymorphisms (T45G and G276T) in adiponectin-encoding gene (I2 = 53.8%). Overall changes in adiponectin and blood pressure were also nonsignificant for T45G, while contrastingly 276GT genotype was associated with significantly higher levels of adiponectin , systolic  and diastolic  blood pressure with evident heterogeneity , and these associations were more obvious in hypertensive patients. Publication bias was a low probability event for overall comparisons.Data were collected and corrected by two authors, and were managed with Stata software. In total, 12 articles were synthesized, including 12 studies (3358 cases and 5121 controls) for the association of two study polymorphisms with hypertension risk and 11 studies (3053 subjects) for the between-genotype changes of adiponectin and/or blood pressure. Based on all qualified studies, the risk prediction for hypertension was nonsignificant for both polymorphisms, with significant heterogeneity for G276T polymorphism (ADIPOQ T45G or G276T polymorphism and hypertension, the heterozygous mutation of G276T was observed to account for increased levels of circulating adiponectin and blood pressure, especially in hypertensive patients.Our findings suggested that in spite of the nonsignificant association between  The coding sequences of ADIPOQ contain 1273 validated single nucleotide polymorphisms (http://www.ncbi.nlm.nih.gov/gene/). In ADIPOQ, two polymorphisms, T45G (rs2241766) in the 2nd exon and G276T (rs1501299) in the 2nd intron, have been widely investigated in association with hypertension risk and circulating adiponectin changes [ADIPOQ with the risk of hypertension and the changes of circulating adiponectin and blood pressure. Meanwhile, we intended to track certain potential sources of heterogeneity between available studies.Adiponectin, secreted exclusively by adipocytes, is a collagen-like protein containing 247 amino acids in length . There iesponses . Circulaesponses –8. It isesponses . The gen changes , 11. How changes . In addi changes , yet a 4 changes  when com G276T rs01299 in After rigorous literature search, a total of 41 articles were found, wherein 12 articles were qualified for the current meta-analysis , 16–22. n = 9 studies), and 2 studies adopted sandwich enzyme immunoassays.Of 11 qualified studies for the changes of circulating adiponectin and/or blood pressure, 5 studies exhibited these changes in hypertensive patients, 3 studies in normotensive controls, 2 studies in combined subjects and 1 study in patients with primary aldosteronism. Circulating adiponectin concentration was measured mainly by enzyme linked immunosorbent assay (ELISA) kit , but marginally significant for G276T polymorphism (I2 = 53.8%). The probability of publication bias was low under all genetic models for both polymorphisms, as shown by the Egger's test . However, there were estimated 4 missing studies to make the filled funnel plot of T45G polymorphism symmetrical, and no missing studies were reported for G276T polymorphism , systolic  and diastolic  blood pressure than those with 276GG genotype, with evident heterogeneity  . As presented in Figure For the changes of adiponectin and blood pressure, only heterozygous genotypic model was summarized as the majority of enrolled studies provided their mean values only in carriers of wild homozygotes and heterozygotes Figure . OverallP = 0.037). Significance was also noted in studies with RFLP technique for the comparison of the 45TG genotype with the 45TT genotype , and the 45TG+45GG genotypes with the 45TT genotype , with null heterogeneity . No significance was detected in the other subgroups. For G276T polymorphism, besides genotyping method, metabolic syndrome complication and repeated blood pressure measurement were two significant sources of between-study heterogeneity. For instance, in subjects complicated with metabolic syndrome, the 276T allele and the 276TT genotype were associated with a 33%  and 58%  reduced risk of hypertension relatively to the 276G allele and 276GG genotype, respectively, with null heterogeneity (I2 = 0.0% for both comparisons).For genetic hypertension susceptibility, a set of subgroup analyses were conducted respectively under allelic Table , heterozP = 0.018), systolic  and diastolic  blood pressure were increased significantly in hypertensive patients, but with moderate or strong heterogeneity .For the phenotypic changes across genotypes, subgroup analyses were only conducted by the status of subjects under the heterozygous genotypic model Figure . No signP > 0.05 for testing the significance of regression coefficients).In order to look for additional sources of heterogeneity, meta-regression analyses were conducted by incorporating age, sex, body mass index, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and fasting blood glucose as covariates, while none of them exhibited an obvious confounding influence on the association between the two examined polymorphisms and hypertension risk  statement , as preshttp://www.endnote.com).Two public databases, MEDLINE and EMBASE, were searched from the date of creation to October 8, 2016 for all potential articles that were published in English language. The predefined subject headings included (“adiponectin” OR “ADIPOQ” OR “APM-1” OR “APM1”) AND (“hypertension” OR “blood pressure”) AND . Beyond this, the reference lists of some original articles or reviewers were also inspected for likely missing hits during the subject-heading search. The literature search was done in duplicate by two investigators (Jianmin Wu and Guoyan Xu), and the finally retrieved articles were managed by the EndNote X5 software  T45G and/or G276T polymorphisms were genotyped by a valid method; (ii) for hypertension risk, the genotype or allele counts of study polymorphism(s) were provided between hypertensive and normotensive subjects; (iii) for the genotype-phenotype relationship, the mean value and standard deviation or derivates  of circulating adiponectin or systolic/diastolic blood pressure were provided.An article was excluded if it was a conference abstract or poster, case report, editorial comment, narrative or systematic review. In addition, only primary hypertension was considered, and other types of hypertension such as gestational hypertension were excluded. If data or sub-data of the same study group were published more than once, only the complete or latest article was retained for meta-analysis.With abovementioned inclusion/exclusion criteria in mind, the two investigators (Jianmin Wu and Guoyan Xu) independently collected data from each qualified article. The data were typed into a pre-determined Excel format that covered information on the first author's surname, publication year, country, race, sample size, associated complication, matched status, repeated measure of blood pressure (yes or no), source of normotensive controls , genotyping method, diagnosis of hypertension, as well as anthropometric indices , clinical markers , genetic distributions  and phenotype changes (mean and standard deviation of circulating adiponectin and/or blood pressure across genotypes). Here, population-based controls referred to the normotensive subjects enrolled from either general populations, communities, volunteers or medical examination centers. The two independent Excel forms were compared for divergence by the self-compiled computer program, and a universe consensus was reached finally between the two investigators who were in charge of data collection.I2 statistic, which ranges from 0% to 100% with a higher value denoting a higher likelihood of heterogeneity. In fact, the I2 statistic is a transformation of Q statistic, as 100% × (Q- degree of freedom)/Q and it estimates the percentage of the variation in effect sizes that is due to heterogeneity [The risk prediction of study polymorphisms for hypertension was measured by OR with 95% CI. The changes of circulating adiponectin and blood pressure between genotypes were measured by WMD with 95% CI. Given the fact that risk estimates are almost equal in case of no heterogeneity between fixed-effects and random-effects models, individual ORs or WMDs were pooled unanimously under the random-effects model with the DerSimonian-Laird method. The magnitude of between-study heterogeneity was marked by the ogeneity . Usuallyogeneity .The contribution of individual studies to pooled estimate was assessed by a sensitivity analysis, which omitted each study one by one and calculated the differential estimate of the other studies. Potential sources of heterogeneity were determined by both subgroup analyses and meta-regression analyses.The possibility that some small studies with negative findings are not easily to be published is termed as publication bias, which was justified by the Egger's test and the filled funnel plots. For the Egger's test, the probability of 10% was chosen as a significant cutoff point. The filled funnel plots were generated by the trim-and-fill method, which can estimate the number of putative missing studies.Data analyses were done by the Stata software version 12.0 for Windows .


CyclinD1 (CCND1) is a key cell cycle regulatory protein. A large number of epidemiological studies have assessed the potential correlation between the CCND1 G870A polymorphism and the risk of colorectal cancer (CRC), but their findings have been inconsistent. To obtain a more precise understanding of the association between the G870A polymorphism in the CCND1 gene and the CRC risk, we conducted a more comprehensive meta-analysis.We searched PubMed, Ovid, Springer, Weipu, China National Knowledge Infrastructure (CNKI), and Wanfang databases, covering all publications . The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were derived from a fixed effect or random effect model. Statistical analyses were performed using Review Manager 5.3 and STATA 10.0 software.A total of 7276 CRC patients and 9667 controls from 27 publications were included in this meta-analysis. We found that compared with GG homozygote genetic model, AA, AG, AA + AG genetic models of the CCND1 G870A polymorphism were significantly associated with overall CRC risk . Subgroup analyses by ethnicity and cancer location showed that A carriers were consistently associated with a significantly increased risk of CRC in all subsets of participants . When stratified by study design, we found a significant association in hospital-based studies (HB), but no significant associations were found in either population-based studies (PB) or family-based studies (FB). According to subgroup analysis by cancer type, the risk of sporadic colorectal cancer (sCRC) and hereditary nonpolyposis colorectal cancer (HNPCC) were not correlated with the CCND1 G870A polymorphism, except AG .This meta-analysis suggests that the CCND1 G870A polymorphism is associated with an increased risk of CRC, especially that A carriers may be a major risk factor for CRC. Although effective therapeutic strategies have been developed over the past decades, the 5-year overall survival of CRC still remains unsatisfactory because of the presence of poor prognostic factors such as vascular and neural invasion, a low lymphocyte-to-monocyte ratio (LMR), and tumor stage III/IV. The economic burden of CRC is substantial. Particularly, the long-term cost of CRC causes huge social burden. Welch and Robertson provided evidence that population aged 50 years or older had a steady decline in colorectal cancer. However, according to the same data source, the incidence had steadily increased among people younger than 50 years. Therefore, it is extremely important to find the risk factors that can lead to CRC except for advanced age. Factors including environment, life style, diet habit, and others all contribute to the development of CRC.,7 Several environmental factors have attributed to the incidence of CRC is more than 85%, especially smoking, drinking, meat consumption, exposure to aryl amines, and heterocyclic amines. Approximately 20% of CRC patients have a family history of cancer, indicating that genetic factors may play a role in CRC susceptibility.–12 And the discovery is also evidence supporting that the disease has polygenic and multiple-factorial bases.Colorectal cancer (CRC) is a common malignant tumor of the digestive tract, which has become a serious threat to human health. Globally, there were an estimated 1.36 million new cases of CRC and 694,000 deaths in 2012. CyclinD1 (CCND1) is a key cell cycle regulatory protein, and its expression and cellular localization is often transformed in human tumor cells. CCND1 is the gate keeping protein that charges regulating the transition through the restriction point in the G1 phase to S phase of the cell cycle. So the mechanisms of CCND1 gene amplification, posttranscriptional or posttranslational modifications, rearrangements, and variant polymorphisms can lead to abnormal protein levels and result in risk of cancer.–18 The common guanine-to-adenine polymorphism at nucleotide position 870 of the CCND1 gene is known to modulate the frequency of alternate splicing and presumably reduce transcript levels.Cancer is a genetic and cell-cycle disease, its occurrence and development involve a multistep and polygenic process.,21 And there were many case-control studies that have evaluated the potential impact of CCND1 (G870A) gene polymorphism on the risk of CRC,,23 and meta-analyses have also been performed to investigate the association between the CCND1 G870A polymorphism and the CRC risk.–27 Notwithstanding, their findings remain inconclusive and controversial. Therefore, we conducted this current meta-analysis to provide more compelling evidence for the relationship between the CRC risk and the CCND1 G870A polymorphism.It has been demonstrated in recent studies that high levels of CCND1 protein expression are related to poorer outcomes in patients with CRC.22.1We searched 6 online databases including PubMeb, Ovid, Springer, Weipu, China National Knowledge Infrastructure (CNKI), and Wanfang databases . We used a search strategy of Me-SH terms and keywords: “Colorectal Neoplasms or Colon Neoplasms or Rectal Neoplasms or Colorectal cancer or Colon cancer or Rectal cancer” and “CyclinD1 or CCND1 or CyclinD1 G870A or CCND1 G870A” and “Polymorphism, genetic or Polymorphism.” The search was restricted to English and Chinese publications.2.2The inclusion criteria of studies in our meta-analysis were as follows: studies that evaluated the impact of the G870A polymorphism in the CCND1 gene on the risk of CRC; studies that used a case-control design; studies with sufficient data (genotype distributions for both patients and controls); and genotype distributions of the control population must be consistent with Hardy–Weinberg equilibrium (HWE). The major exclusion criteria were: no control group was included; genotype frequencies or number were not reported; or reviews, abstracts, and duplicate studies.2.3Two reviewers independently and carefully extracted the information from all selected publications. Also they reached a consensus on all the items. If the 2 authors had a debate about the selected studies, a third author would adjudicate disputes. For each selected study the following items of information were extracted and tabulated: first author, year of publication, original country, ethnicity , study design , type of CRC , sporadic colorectal cancer (sCRC), or mixed) and location of CRC , genotyping methods (polymerase chain reaction (PCR) single-stranded conformation polymorphism (PCR-SSCP), PCR restriction fragment length polymorphism (PCR-RFLP), high-performance liquid chromatography (HPLC), TaqMan PCR, or Multiplex PCR), as well as the number of patients and controls.2.4P value of less than.05 was considered statistically significant. Heterogeneity was checked by a χ2-based Q statistic among the included studies. When the consequence was P > .10 for the Q-test indicating a lack of heterogeneity among studies, the fixed–effects model was used to calculate the pooled OR; otherwise, the pooled OR was calculated by the random effect model. We performed stratified analyses by ethnicity, location of CRC, study design, and type of CRC, so as to evaluate their specific effects on the risk of CRC. The subgroup analysis by ethnicity was classified as Caucasian, Asian, or Mixed . The different classification according to location of CRC was: colon cancer, rectal cancer, or mixed (the specific location was not mentioned). The study design was described as HB, PB, or FB. To evaluate the effect of CRC type, the participants were stratified into sCRC, HNPCC, or mixed .For each study, the genotype distribution was tested in controls, which was based on HWE using an Internet-based program. The association between the CCND1 G870A polymorphism and the risk of CRC was evaluated by crude ORs with 95% CI. We assessed the CRC risk of individuals with genotype AA versus GG, AG versus GG, AA and AG versus GG, A versus G, respectively. We performed all statistical analyses of the meta-analysis by using Review Manager Version 5.3. A ,29 All statistical analyses were performed using Review Manager 5.3 and STATA 10.0 software.Moreover, sensitivity analysis was performed to assess the stability of the results, by orderly excluding individual studies. Publication bias was analyzed by Begg funnel plot and Egger test.33.1,23,30–53 which met our inclusion criteria, including 7276 cases and 9667controls. The main characteristics of each study identified are listed in Table ,50,52,53 12 in Caucasians,,46,47,51 5 in Mixed.,43,48,49 As for study design, there were 2 FB,,31 14 PB,,48,50,53 and 11 HB.,49,51,52 Eleven studies described the specific location of CRC.,50,52,53 Genotype and allele distributions for each case-control study are shown in Table As shown in Fig. 3.2P = .02) and the χ2 value was 42.59 with 26 degrees of freedom . The results suggested that the variant A allele carriers had a 19% increased risk of CRC. We also found that compared with GG homozygote, AA homozygote, or AG heterozygote of the CCND1 G870A polymorphism was significantly associated with a higher overall risk for CRC . Summary results of other genetic comparisons are listed in Table The heterogeneity between AA + AG versus GG was assessed for the 33 studies P = .0 and the 3.3We performed subgroup analyses by ethnicity (Asian or Caucasian), location of CRC , study design , and type of CRC (sCRC or HNPCC). Using GG genotype as a reference, A carriers were associated with a significantly increased risk of CRC in both Asians  and Caucasians . This indicated that A carriers might be a low-penetrant risk factor for CRC in both Asian and Caucasian populations. When stratified by cancer location, significant associations between A carriers and CRC risk were found in both subsets of patients with colon cancer  and rectal cancer . Subgroup analysis by study design indicated that significant association between the CCND1 G870A polymorphism and the risk of CRC was only observed in HB studies , rather than PB  or FB studies . According to analysis by cancer type, no significant association was noted between the CCND1 G870A polymorphism and an increased risk of CRC in patients with sCRC  and HNPCC , but a significantly increased CRC risk was found in sCRC patients with genotype AG  .We performed a sensitivity analysis through sequentially excluded individual studies. No individual study affected the overall OR dominantly, statistically similar results were obtained, suggesting the stability of this meta-analysis (data not shown). We used the Begg funnel plot and the Egger test. The shape of the funnel plots of the 27 publications appeared symmetrical for the AA + AG versus GG model Fig. , indicat4 To date, the pathogenesis of CRC has not yet been fully clarified. Several risk factors such as age, environment, high-fat diet, and heredity have been recognized. In recent years, numerous studies have revealed a direct relationship between the CCND1 gene and tumors, including lymphoma,,55 breast cancer,,57 lung cancer,,59 bladder cancer,,61 and colorectal cancer.,63 It has been demonstrated that the over-expression of CCND1 may collaboratively participate in cancer carcinogenesis.CRC is the third most common cancer in both men and women across the world. Variant polymorphisms can result in abnormal protein levels and lead to cancer. The CCND1 over expression has been reported to occur in 72% of colorectal tumors. However, results of case-control studies about this genetic polymorphism were inconsistent. A few meta-analyses–27 were also designed to confirm the influence of CCND1 G870A polymorphism on CRC susceptibility. These analyses found that A carriers of the CCND1 G870A polymorphism were significantly associated with an increased risk of CRC. However, their detailed descriptions on ethnicity, cancer location, study design, and family history varied significantly. Zou et al, the most recent study published in 2012 included 23 case-control studies, and concluded that the CCND1 870A allele might be a low-penetrant risk factor for CRC. The result was consistent with the findings reported by Yang et al and Zhang et al. But in further stratified analyses by ethnicity and study design, such a correlation was not found in any subsets of participants. This result was contradictory to those from the other 3 meta-analyses,,25,27 which observed an increased risk in the subgroups of sCRC and in Caucasians.As we know, CCND1 has been considered to be a cancer gene which could regulate progression from the G1 phase of the cell cycle to the S phase. Cells with the mutant allele accumulate mutations as a result of defective mismatch repair and bypass the G1-S checkpoint of the cell cycle more easily than in cells not carrying the polymorphism. When stratified by type of CRC, no relationship was identified between the CCND1 G870A polymorphism and the risk of CRC in subsets of patients with either sCRC or HNPCC. We speculated that this might be explained by the differences in case-control conditions, genetic classification method, living environment, genetic background, tumor stage, and/or living habits among the included studies.This meta-analysis of 33 case-control studies supported that the G870A polymorphism of CCND1 was a risk factor for CRC. A allele carriers had a 1.19-fold elevated risk of CRC. As previous studies reported inconsistent findings in their subgroup analyses, we performed more robust stratified analyses to comprehensively analyze these subset associations. Our findings revealed that the CCND1 G870A polymorphism was associated with an increased risk of CRC in both Asian and Caucasian. Meanwhile, such an association was also observed in subsets of either cancer location . As for the study design, we found a significant association in HB studies, but not in PB or FB studies, which was consistent with a previous meta-analysis.,65 In this study, we come to a similar conclusion that the CCND1 G870A polymorphism is a potential factor of CRC. However, a few meta-analyses,67 reported that the CCND1 G870A polymorphism may not be associated with an increased risk factor for cervical cancer and head and neck cancer. Perhaps this is due to similar CCND1 gene expressions in gastrointestinal carcinomas rather than tumors of other systems.It has been shown in prior studies that CCND1 870A allele carriers had been confirmed that may be an increase the risk of developing esophageal cancer and hepatocellular carcinoma.Compared with previous meta-analyses, we found a significant association between the CCND1 G870A polymorphism and the CRC risk in many different subgroups. We inferred that 3 reasons might explain the different results between our study and prior studies. First, a larger number of case-control studies were included in our meta-analysis than previous studies, so our conclusion seemed to be more powerful and reliable. Second, no conspicuous publication bias was detected in our study, which indicated that the entire pooled results might be unbiased. Third, comparisons of all genetic models were performed in our study, suggesting that this polymorphism analysis might be more comprehensive and credible.We also acknowledge several limitations of this meta-analysis. First, we only selected articles published electronically in 6 databases, so it is possible that some pertinent studies not included in these databases or unpublished studies with negative results may have been missed. Second, as participants in the control groups were selected from healthy persons or patients, there might be a lack of proper matching of controls in the included studies, which is likely to influence the consistency of our results. Third, only small numbers of participants were included in some subgroups such as subsets of FB studies and HNPCC patients. Therefore, these subgroup analyses may not have enough statistical power with the small sample size and the conclusions may be biased.In conclusion, this meta-analysis demonstrated that the CCND1 G870A polymorphism may be associated with an increased risk of developing CRC. Subgroup analyses by ethnicity, cancer location, and study design revealed significant associations between the CCND1 G870A polymorphism and CRC susceptibility in A carriers (AA or AG or AA + AG), especially among Asian and Caucasian populations, patients with colon cancer or rectal cancer, and in hospital-based studies. This may provide a vital theoretical basis to understand the effect of the CCND1 G870A polymorphism on the pathogenesis of CRC. As the CCND1 G870A may play an important role in predicting the occurrence and progression of CRC. Our findings may provide valuable insights into the development of novel diagnostic approaches, gene-targeted therapies, and prevention strategies to combat against CRC. Considering the above-mentioned limitations, larger-scale and well-designed studies are still required to further validate these findings and investigate an even wider range of associations in the future.


Uveitis is an eye inflammatory disease, which is sometimes associated with underlying systemic disease. Interleukin-23 plays an important role in autoimmune disease. The aim of this meta-analysis was to evaluate the association between the interleukin-23 receptor (IL-23R) and susceptibility to uveitis.Published literature from PUBMED and EMBASE were retrieved. Seven studies were included in this meta-analysis, covering a total of 1309 cases of uveitis and 2400 controls. Meta-analyses were conducted on the associations between uveitisand rs7517847, rs17375018, and rs11209032 polymorphisms in the IL-23R gene.P = 0.83; for A allele vs. G allele of rs17375018, OR 0.68, 95% CI 0.47–0.99, P = 0.05; rs11209032 OR 1.12, 95% CI 0.84–1.51, P = 0.43. In contrast, there were significant associations between the AA + AG gene versus GG gene of rs17375018 and the AA gene versus AG + GG gene of rs11209032 polymorphism with uveitis .There were no significant associations between IL-23R polymorphisms and uveitis with regard to the following alleles: for G allele vs. T allele of rs7517847, OR 1.01, 95% CI 0.92–1.12, This meta-analysis suggests that each allele of IL-23R, including rs7519847, rs17375018 and rs11209032 was negatively associated with uveitis. However, homozygote models, including the rs17375018 GG genotype and rs11209032 AA genotype, were significantly associated with uveitis. Uveitis, or inflammation of the uvea, is classified clinically into infectious and non-infectious groups . InfectiIn up to 50% of the cases, autoimmune uveitis precedes or follows the onset of an autoimmune disease. Some of the associated autoimmune diseases include the spondyloarthritides, Behçet’s disease, Vogt-Koyanagi-Harada (VKH) syndrome, systemic lupus erythematosus, sarcoidosis, autoimmune hepatitis, and multiple sclerosis . There iThe IL-23R gene is located on chromosome 1p31. It is highly expressed in dendritic cells, and is involved in several chronic inflammatory diseases . SeveralThree of several polymorphisms of IL-23R have been studied in some detail. These include rs7517847, rs17375018, and rs11209032. However, the results from different studies have been inconsistent. The aim of this meta-analysis was to investigate the genetic association between IL-23R polymorphisms and the susceptibility to non-infectious uveitis.P-value, and the allele and genotype frequencies for each of the polymorphisms . This meta-analysis was reported based on the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines [We performed a search for studies that examined the associations between IL-23R polymorphisms and uveitis. Genetic association studies that determined the distributions of rs7517847, rs17375018, and rs11209032 polymorphisms in uveitis and in normal controls were included. The literature was searched using the PUBMED and EMBASE databases to identify available articles in which IL-23R polymorphisms were analyzed in uveitis patients (up to June 2016). We listed combinations of key words and subject terms such as “interleukin-23 receptor,” “IL23R,” “polymorphism,” and “uveitis.” Only papers written in the English language were included. References from the identified studies were also investigated to identify additional studies that were not indexed by PUBMED and EMBASE. The following information was extracted from each study: author, year of publication, ethnicity of the study population, demographics, number of cases and controls, Hardy-Weinberg equilibrium (HWE) P-value < 0.10 was considered statistically significant heterogeneity) and I2 statistics  [The allele counting method was used to determine the allele frequencies of the genetic polymorphisms. The Chi-squared test was used to detect if the controls in each study conformed to HWE. The associations between rs7517847, rs17375018, rs11209032 and uveitis were estimated using the crude odds ratio (OR) and 95% confidence interval (CI). We performed meta-analyses using the 1) allelic contrast, 2) homozygote contrast, 3) recessive, and 4) dominant models. Inter-study heterogeneity was assessed with the Cochran Q test . There was also no significant association between the rs17375018 A allele and uveitis . The allele frequencies of rs11209032 in patients and in controls were not significantly different . A meta-analysis of the AA genotype of the rs11209032 polymorphism was significantly associated with uveitis .Table P values >0.1).The genotype distributions in the normal control groups were consistent with HWE in all studies. Some heterogeneity was found in the meta-analyses of the IL-23R polymorphisms. Given the small number of included studies, it was difficult to correlate the funnel plot, which is usually used to detect publication bias. However, Egger’s regression test showed no evidence of publication bias . In the present meta-analysis, combined evidence demonstrated that the IL23R gene rs11209032 polymorphisms were not associated with uveitis. However, the rs11209032 AA genotype was significantly associated with uveitis. We found that the homozygote models, including the rs17375018 GG genotype and rs11209032 AA genotype, were significantly associated with uveitis.There was no significantly association between the rs7517847 polymorphism and uveitis. The rs17375018 polymorphism was also not associated with uveitis, except the AA + AG vs. GG genotype. Although results of the rs17375018 A allele, AA vs. AG + GG genotype, and AA vs. GG genotype showed no significant association with uveitis, the A allele tended to decrease uveitis (OR 0.68, 95% CI 0.47–0.99, Many theories on the etiology of Fuchs’ syndrome have been suggested including viral infection, toxoplasmosis, vascular abnormality, and autoimmunity. Some studies have shown that infections such as rubella virus and toxoplasma could lead to the development of Fuchs’ syndrome. However, other studies found that patients with Fuchs’ syndrome had high levels of proinflammatory cytokines, the pathology of the iris was rich in CD8+ lymphocytes, and the aqueous humor had abundant oligoclonal IgG production. These findings indicate that Fuchs’ syndrome has immunologic characteristics. Thus, we performed this meta-analysis with and without Fuchs’ syndrome. The results showed that there was no association between IL23R polymorphisms and uveitis.Th17 cells may be relevant to human uveitis. Transfer of Th17 cells led to neutrophilic infiltrates, which is consistent with the cytokine and chemokine profile typical of the respective responses . Th17 ceIL-23 was found to be elevated in VKH syndrome and Behçet’s disease , 19. SomThe strength of our meta-analysis could be summarized as follows. We attempted to identify as many publications as possible using various search techniques. To the best of our knowledge, our study is the first meta-analysis regarding overall uveitis. It includes uveitis of ankylosing spondylitis, Behçet’s disease, VKH syndrome, sarcoidosis, Fuchs’ syndrome, and any other endogenous non anterior uveitis. All studies satisfied HWE. This implies that there was no potential bias regarding the control selection or genotyping errors.This study also has several limitations. First, there was significant heterogeneity in some comparisons. There was variability in both diseases of developing uveitis as well as ethnicity. Unfortunately, stratification for ethnicity was not possible due to the low number of published studies on this topic. In addition, because only English publications were included, there may have been a language bias. A third limitation is that publication bias may have affected the analysis, because studies that produced negative results may not have been published or may have been missed. Fourth, we did not conduct a meta-analysis of haplotype analysis; however, haplotype analysis may have provided more information and would have been more powerful than single polymorphism analysis. Fifth, we did not classify the cases into anterior uveitis, posterior uveitis, and panuveitis. Lastly, although IL-23R polymorphisms may be associated with disease severity and susceptibility, we did not perform a meta-analysis for this association.The rs7519847, rs17375018 and rs11209032 alleles of IL-23R were negatively associated with uveitis. However, our results also suggest that both rs17375018 GG and rs11209032 alleles of IL-23R are predisposing genotypes for uveitis. These results should be interpreted with caution, given the study’s limitations. Further studies with larger sample sizes, as well as polymorphisms of other proinflammatory cytokines, are needed.


In the last decade, multiple studies have been published that analyze the relationship between the risk of experiencing biological complications with implants and the presence of certain types of genetic polymorphisms. In the present report, we analyze the controversies that have arisen from this important area of investigation and synthesize the most prominent aspects of knowledge related to this possible etiopathogenic relationship.For this review, the biomedical databases PubMed-Medline, SciELO, and DOAJ were used. Different search strategies were employed, from which 298 articles initially emerged. After refinement of the search, 55 articles published between 2002 and 2018 were finally selected based on relevance.In certain population groups, there is evidence to support that about a dozen polymorphisms could in some way be related to biological complications in implantology. Indeed, the results may vary according to the ethnic origin of the population studied. Most of the published investigations are initial studies reporting small sample sizes and utilizing different study group homogenization methods. We are still at a preliminary stage of our understanding and development with regard to these types of biomarkers. The interesting results identified indicate that new investigations will be necessary to eliminate the biases observed in some studies and to homogenize the research groups. In order to clarify the controversies surrounding the current knowledge in this field, we believe that it will be necessary to employ larger study groups and search for possible synergistic effects between different polymorphisms. Key words:Polymorphism, genetic markers, peri-implantitis, biological complication, dental implant. Currently, implantology is a predictable and reproducible alternative for replacing missing teeth. Improvements in the design and physical-chemical properties of implants and related treatment protocols have led to a drastic reduction in problems with osseointegration, which were initially observed in a significant number of cases. Indeed, the most recent studies have shown that the rate of early failure does not exceed 1-2% of cases . DespiteSeveral factors are known to play a role in the complex etiology of peri-implant disease, such as tobacco consumption, oral hygiene, the presence of periodontal disease, oral biofilm composition, bone quality, certain systemic pathologies , implant type, the techniques employed for implant placement, prosthesis design or transmission of occlusal loads -8.In a straightforward manner, all of these factors act through three different pathogenic mechanisms, which are: disruption of the balance between the biofilm and the patient’s own immune system, overcoming the repair capacity of bone tissue and modifications in implant surfaces resulting from corrosion -8. Each Of course, these etiopathogenic factors could be directly and indirectly conditioned by genetic factors that determine their magnitude and relevance in each individual. They are part of what we call “individual susceptibility” and explain the different biological responses observed in individuals presenting the same risk factors -8.Epidemiological studies have shown the existence of patients having a “certain predisposition” or augmented risk of suffering from peri-implant disease. In these patients, failures tend to cluster or “clump together” . This obThese genetic “peculiarities” are caused by DNA mutations. Some of these mutations are significantly prevalent in the population (above 1%), constituting what are called “genetic polymorphisms”. For this reason, in recent years, several studies have been conducted to try to understand the relationship between certain polymorphisms and peri-implant disease. The majority of the polymorphisms studied affect a single nucleotide and are called SNPs (Single Nucleotide Polymorphism). Among these, in relation to peri-implant disease, those that are present in the genes involved in inflammatory responses and regulation of bone metabolism have been specifically studied .The role of genetic polymorphisms in different autoimmune, neurological and even oncological pathologies has been widely studied ,12. On tFor this literature review, the biomedical databases PubMed-Medline, SciELO and DOAJ were used. Different search strategies were employed, resulting from the combination of keywords from the MeSH terminology dictionary such as: “polymorphism”, “genetic markers”, “genetic polymorphism”, “peri-implantitis”, “implant failure” and “dental implant”.Using this initial search strategy, 298 articles were found. After the elimination of duplicate articles, followed by evaluation and filtering of the remaining articles, 55 relevant publications were finally selected, published between 2002 and 2018 Figure -Polymorphisms of IL-1 Cytokines are signaling molecules responsible for intercellular communication that induces the activation of specific membrane receptors. Some of their functions contribute to control of the inflammatory response -15. For These proteins are encoded by three different genes: IL1A, IL1B, and IL1RN, which make up the IL-1 gene cluster, located on chromosome 2 ,14,16. ASome functionally relevant polymorphisms in the genes that encode IL-1, under certain circumstances, can condition overproduction and even a basal increase in production of the respective cytokines -16. For et al. , IL-1B (511-) and IL-1B (+3954) -25. Repoet al.  analyzedet al.  / IL-1B (+3954) or IL-1A (889-) / IL-1B (511-) / IL-1B (+3954). In this way, the meta-analysis conducted by Liao et al.  reportedet al.  is another key proinflammatory cytokine in the early stages of the inflammatory response . Similaret al.  it induces the resorptive capacity of osteoclasts. These activated osteoclasts subsequently acidify their environment, destabilizing the inorganic bone component and releasing enzymes such as matrix metalloproteinases (MMPs) or Cathepsin K, which in turn destroy organic components .The MMPs are a family of 24 enzymes of similar structure but different genetic origin. They are divided into five different groups and are responsible for the degradation of the organic components of extracellular matrix, both in healthy homeostasis  and in disease . MMPs display diverse activities and functions, and are known to participate in tumor growth, the phenomena of metastasis and even cellular apoptosis. In addition to osteoclasts, various other cell types also produce MMPs, including fibroblasts, epithelial cells of the gingival sulcus, endothelial cells, plasma cells, monocytes/macrophages and neutrophils. Interestingly, even some oral microorganisms are capable of producing MMPs (MMP-1) . The phyDifferent authors ,34 have et al. (p= 0.0009) in the patient group with early failure. Therefore, they concluded that carriers of this genotype would have a higher risk of early failure.Costa-Junior et al.  analyzedet al. (p = 0.013). With respect to the second SNP in MMP-9, they did not observe statistically significant differences.In a group of 74 Brazilian patients (34 patients with early failure and 40 controls), Santos et al.  analyzedThe above researchers ,38, in aet al.  and not only analyze single polymorphisms. Although it is true that this would represent an enormous task, it could allow analysis of multivariable statistics and identify if there are summative or even synergistic effects between different types of polymorphisms.Likewise, new studies are needed to measure the synergistic effect already observed by some authors ,25 betweAn excellent option to discover new polymorphisms associated with peri-implant pathology, would be the realization of a Genome-Wide Association Study (GWAS) using large samples of multiethnic origin and “extreme genotypes”. Within these extreme genotypes it would be very interesting to include, among others, patients who have suffered what some authors have termed a “cluster failure”.There are few studies that have attempted to relate the presence of certain polymorphisms with a higher risk of early implant failure and/or peri-implantitis. The most studied polymorphisms are the SNPs. Among those that have shown a statistically significant relationship, there are some SNPs identified within the coding regions of genes, including mediators of inflammatory responses as well as molecules participating in physiological bone remodeling processes.Based on available research and the current level of understanding regarding the relationship between polymorphisms for early failure or peri-implantitis, a reliable and/or reproducible genetic biomarker of risk has not yet been identified that could be used in the general population or commercialized. However, many authors agree that this is a field that warrants deeper investigation. Given the positive results of some preliminary investigations, new studies are needed that employ larger sample sizes and stringent methodologies in order to minimize the lack of uniformity and bias.


MICA) single nucleotide polymorphism (SNP) rs2596542G>A and hepatocellular carcinoma (HCC) has been broadly studied, with inconsistent results. Therefore, we conducted the current meta-analysis to better elucidate the roles of SNP rs2596542G>A in HCC. Eligible articles were searched in PubMed, CNKI, Wanfang, Embase, VIP, Web of Science, and CBM databases up to November 2018. Odds ratios (ORs) and 95% CIs were applied. A total of 11 articles, including 4528 HCC patients and 16,625 control subjects, were analyzed. Results revealed that rs2596542G>A was significantly associated with HCC in the heterozygote ; and dominant  genetic models. Nevertheless, we also detected significant associations between rs2596542G>A and HCV-induced HCC. Additionally, according to our analyses, SNP rs2596542G>A was not correlated with HBV-induced HCC. In conclusion, our findings suggest that MICA SNP rs2596542G>A is associated with HCC susceptibility amongst the Asian, Caucasian, and African ethnicity in certain genetic models. Specifically, MICA SNP rs2396542G>A is associated with risk of HCV-induced HCC, not HBV-induced HCC.The association of major histocompatibility complex class I chain-related gene A ( Liver cancer is one of the leading causes of death worldwide responsible for nearly 746,000 deaths per year. It is reported to be the fifth most common cancer in men  and the ninth in women  occurred in 2012 [MICA) [HBV and HCV are discrepant viruses that target and persist in hepatocytes, leading to chronic liver diseases (CLD) and subsequent HCC ,5. CLD iMICA) .MICA, or Perth β block transcript 11 (PERB11), was first described by Bahram et al. in 1994 [MICA can be markedly up-regulated or expressed de novo when the organisms encounter stimulations from virus, bacteria, environment, and autoimmune conditions [MICA plays an important role in immune activation and surveillance against infection and tumorigenesis, recent studies have mainly focussed on the association between MICA polymorphism and susceptibility to virus-associated cancers, including human papillomavirus-induced cervical cancer [ in 1994 . It is e in 1994 . Functionditions ,17. Consl cancer ,19, HBV-l cancer , and Epsl cancer ,24.MICA single nucleotide polymorphism (SNP) rs2596542G>A is located in the promoter region of MICA, 4.7 kb upstream of MICA exon1, and 41.7 kb downstream of HLA-B. Therefore, SNP rs2596542G>A cannot change the MICA coding sequence [MICA polymorphism rs2596542G>A has been identified to be associated with HCC. Tong et al. demonstrated that rs2596542G>A A polymorphism was associated with increased progression from HBV-induced cirrhosis to HCC in a case-controlled study in a Vietnamese cohort [MICA rs2596542G>A and the risk of HCC.The sequence . Howevere cohort . Converse cohort . Howevere cohort . Moreovee cohort . AltogetMICA polymorphisms and liver cancer, with the last updated search being conducted on November 2018. We conducted literature searches in PubMed database with the following strategy: (‘major histocompatibility complex class I-related chain A’ or ‘major histocompatibility complex class I chain-related gene A’ or ‘MHC class I polypeptide-related sequence A’ or MICA or MIC-A or PERB11.1) and (‘polymorphism*’ or SNP) and (liver or hepatic or hepatocellular) and (cancer or carcinoma or neoplasm or tumor). Searching strategies adopted in Embase and Web of Science database were adjusted based on these. There was no restriction for language. The following inclusion criteria were applied: (1) conducted as a case-control study on the association of MICA SNP and liver cancer risks; (2) OR and 95% CI can be counted based on the genotype frequencies provide in the study; (3) the genotype distributions in control groups should conform to the Hardy–Weinberg equilibrium (HWE); and (4) the study must be administrated in human samples. Exclusion criteria were as follows: (1) not carried out as a case-control study; (2) reviews, abstracts with no original data or overlapping studies with replicate data; and (3) unavailable genotype frequencies or other essential information in the study.Two reviewers (Haichuan Wang and Hui Cao) performed searches in PubMed, CNKI, Wanfang, Embase, VIP, Web of Science, and CBM databases to ascertain studies which had presented the association between P value for HWE (if applicable). To evaluate the quality of non-randomized studies regarding to comparability, selection, and exposure, we applied the quality score (QS) to estimate the qualification of included studies [Two reviewers (Haichuan Wang and Hui Cao) sorted out the essential information from every study on the basis of the inclusion and exclusion criteria. For missing information, we contacted with the authors for original information or raw data. If the reviewers encountered with any disagreements, they would discussed until reaching a consensus. Senior reviewers (Yong Zeng) eventually reviewed final results before moving to next step. The following information was extracted from the included studies: first author’s name and country, publication year, study subjects’ ethnicity, genotyping methods, control group number and case group number, allocations of alleles and genotypes in control subjects and HCC patients, and the control group  studies ,29. Acco2 statistic test and Q test. Between-study heterogeneity was significant if I2 was greater than 50% or the P value of Q test was less than 0.1. In this situation, we conducted analyses in a random-effect model (REM). Otherwise, if I2 was equal to or less than 50% or the P value of Q test was no more than 0.1, the study would be considered homogeneous. Therefore, a fixed-effect model (FEM) would be applied for analyses. Five genetic models  were tested to explore any potential variations in the distribution of SNP rs2596542G>A amongst HCC cases and control subjects. Moreover, ORs and the corresponding 95% CIs were adopted to estimate associations of SNP rs2596542G>A with HCC, and statistically significance between HCC patients and control subjects was considered if the P value was equal to or less than 0.05. Furthermore, we conducted subgroup analyses based on distinct ethnicity and different virus types of study population to get more precise results. HWE in the control group was explored by using the χ2 test. Publication bias was assessed with Egger’s test and Beg’s funnel plots, P value of 0.05 or less was considered representative of statistically significant publication bias.Statistical analyses in the current study were performed with Stata 12.0 . To evaluate heterogeneity between studies, we adopted ITrail sequential analysis (TSA) was performed by using TSA-Trial Sequential Analysis Viewer  . A levelP value of χ2 test was greater than 0.1 in each study except for two GWAS. However, they were also included for further analysis because a HWE P<1.0 × 10−6 for controls was applied as standard SNP quality control in these two studies [The literature search generated 144 potential results for selection. After removing duplicated articles, 92 records were included for further evaluation. By reading titles and abstracts, 63 records were excluded for either irrelevant studies or duplicated studies. Altogether, 29 articles were screened for full-text assessment. Amongst these, two articles were excluded for duplication, nine articles were excluded for no usable data, six articles were excluded for not SNP and liver cancer studies, and one article was exclude for not case-control study. Therefore, a total of 11 studies containing 4528 HCC patients and 16,625 controls were finally included , 7/11 we studies . CharactP=0.006, OR = 0.854; 95% CI: 0.763–0.956, P=0.021; OR = 0.796; 95% CI: 0.655–0.967, A total of 11 independent studies consisting of 4528 HCC patients and 16,625 healthy controls were included for analyzing association of rs2596542G>A and susceptibility of HCC. All genetic models were tested to explore any potential differences in genotypic and allelic frequencies regarding to SNP rs2596542G>A amongst HCC cases and control. For G/A versus A/A, between-study heterogeneities were trivial, so FEM was applied for analyses. For G/G versus A/A, G versus A, G/G+G/A versus A/A, and G/G versus G/A+A/A, analyses were performed with REM because of significant between-study heterogeneities. Overall, significant association between rs2596542G>A and HCC was found in G/A versus A/A , G/A versus A/A , G versus A  and G/G+G/A versus A/A . However, the striking between-study heterogeneities remained unchanged in the Asian subgroup. Therefore, REM was applied to all genetic models. Significant association were found in G/G versus A/A , G versus A , G/G+G/A versus A/A , and G/G versus G/A+A/A . However, no significant association between SNP rs2596542G>A and HCC were found in G/A versus A/A model , G/G+G/A versus A/A . However, no significant association between rs2596542G>A and HCC was observed in high score group  and low score group (QS≤9). A significantly decreased risk of HCC was observed in low score group : G/A verre group . BesidesP value for Begg’s funnel plot and Eggers funnel plots is 0.938 and 0.709  to narrow down the random errors and strengthen the robustness of our conclusions . ResultsWe finally calculated the FPRP values for all observed significant findings. With the assumption of a prior probability of 0.2, the FPRP values and statistic power for significant findings at different prior probability levels are shown in MICA. Kumar et al. first found a previously unidentified locus in the 5′ flanking region of MICA on 6p21.33 (rs2596542G>A) to be strongly associated with HCV-induced HCC in a GWAS [MICA promoter region and may alter the binding of stress inducible transcription factors. Tong et al. [MICA or initiate pathways related with tumor development. Therefore, studies on the relationship of HCC risk and MICA SNP rs2596542G>A emerged with inconclusive results from different study cohort. Thus, to get a more conclusive and convincing results, we conducted this comprehensive meta-analysis.HCC has always been one of the most widespread primary tumors and accounts for almost 90% of primary liver cancers. Generally, risk factors of HCC include excessive consumption of alcohol, HBV, and HCV infection and aflatoxin B. Its heterogeneity and geographic variability has been tightly related to different HBV or HCV susceptible factors worldwide, leading to different epidemic features of HBV- or HCV-associated HCC. Although the mechanisms of HBV- or HCV-related HCC have been widely studied, the exact mechanisms remain to be further explored. With respect to genetic risk factors, there are many studies focus on the polymorphisms in the n a GWAS . The rs2g et al.  hypothesMICA in a study amongst Sicilian population. Moreover, they even demonstrated that rs2596542G>A G/G homozygote showed a higher HCC risk in association with age following multivariate adjustment. These are also consistent with Lange et al.’s study, which elucidated that minor allele A of rs2596542G>A, had a protective impact on HCC amongst a Swiss cohort study. However, study amongst African population is limited. Mobhanded et al. [This meta-analysis, including 11 case-control studies from 11 research articles, investigated SNP rs2596542G>A amongst Asian, Caucasian, and African people. We found that the rs2596542G>A polymorphism was associated with a decreased risk of HCC in G/A versus A/A and G/A+G/G versus A/A from a total of 4528 cases and 16,625 controls. These results indicate that GA heterozygotes or G allele carriers in MICA SNP rs2596542G>A are potentially protected amongst all the population. Consistent with our finding, Li et al.  reportedd et al.  reportedGenerally, while HBV is the major etiological factor in high incidence HCC areas, HCV is the main causative agent in low incidence HCC areas, such as western Europe and North America. Therefore, we also conducted subgroup analysis of HBV-induced HCC and HCV-induced HCC. No significant association between polymorphism rs2596542G>A and HBV-induced HCC was found. This result is contradictory to a GWAS conducted by Kumar et al.  also fouMICA SNP rs2596542G>A according to previous studies [MICA SNP rs2596542G>A can be used a genetic indicator for HCC progression to provide new thoughts into genetic therapy of malignant diseases.Clinically, several studies have indicated that sMICA can be used as a prognostic marker for various malignant diseases ,39. sMIC studies . TherefoMICA gene are not discussed in the current study because of limited reachable raw data. It might not completely elucidate the functional role of MICA in HCC development. Therefore, a new meta-analysis shall be administrated with more available high-quality studies in future.There are still some disadvantages in our meta-analysis. First, we currently only included the published studies in the selected electronic databases. Therefore, some relevant published data or unpublished studies with raw data might be missed. It might cause bias of our results. Second, we did not stratify data into other subgroups on the basis of other potentially factors including age, gender, and study location. Third, correlation between genetic factors and environmental factors relating to polymorphisms should be discussed more thoroughly. Fourth, some included studies in this meta-analysis have small sample size, which shall influence publication bias. Fifth, some other polymorphisms in Despite the weaknesses, we have improved our study identification and data selection process by double-checked policy and minimized potential bias of publication bias and sensitivity. Furthermore, we conducted FPRP analysis to investigate potential false positive report probability of the included studies. Most significant results were noteworthy at a FPRP level of 0.2 except for results from Asian subgroup, which indicates that more evidence need to be collected. Moreover, TSA results indicate that evidences for all individuals under dominant model are enough and no more evidence is needed to ensure the robustness of our results. Thus, we believe the current results are reliable.MICA rs2596542G>A polymorphism is associated with susceptibility to HCV-induced HCC, suggesting that MICA polymorphism plays an important role in HCV-associated liver cancer progression. Large scale, well-designed case-control studies are still warranted to validate our results.In conclusion, the current meta-analysis indicates that


P = 0.036) between per year older AAM and ovarian cancer risk in case–control studies, but no association was observed in cohort studies. In MR analyses, the OR of EOC risk per year increase in AAM was 0.81  in Chinese and 0.94  in Europeans, respectively. Our study supports a causal association between AAM and EOC risk.Age at menarche (AAM) was found to be associated with ovarian cancer risk in previous observational studies. However, the causality of this association remains unclear. Here, after systematic meta‐analyses, we performed two‐sample Mendelian randomization (MR) analyses to evaluate the causal effect of AAM in epithelial ovarian cancer (EOC) etiology. We performed meta‐analyses including 11 410 cases and 1 163 117 noncases to quantitatively evaluate the association between AAM and ovarian cancer risk. In MR analyses, we used 25 single nucleotide polymorphisms (SNPs) associated with AAM for Chinese and 390 SNPs for Europeans as instrumental variables. MR estimates were calculated using inverse‐variance weighted methods from 1044 cases and 1172 controls in a Chinese genome‐wide association study and validated by the Ovarian Cancer Association Consortium and Consortium of Investigators of Modifiers of BRCA1/2 studies with 29 396 cases and 68 502 controls of European ancestry. In meta‐analyses, we observed an inverse association (odds ratio [OR] = 0.96, 95% confidence interval [CI] = 0.93 to 1.00,  In order to test the relationship between AAM and EOC risk, we performed two‐sample MR analyses using our previous EOC GWAS data in Chinese and validated the results in women of European ancestry.22.1This meta‐analysis was conducted according to the Preferred Reporting Items for Systematic reviews and Meta‐Analyses guidelines.If a linear trend was estimated in each included study, it was used directly. For studies only reporting category results, we used the generalized least squares method, assuming linearity of the natural log‐scale estimates of association.http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). This system has been developed based on three perspectives : the selection of study groups (a maximum of four stars); the comparability of groups (a maximum of two stars); and the ascertainment of the exposure or outcome for case–control or cohort studies, respectively (a maximum of three stars). A study with quality scores greater than or equal to seven was defined as a high‐quality study.We used the nine‐star system of the Newcastle‐Ottawa Scale to conduct the quality assessments of studies  and 206 controls, who had the available information  from our previous GWAS.Q and I2 statistics were used to test for heterogeneity and to quantify heterogeneity across studies, respectively. We used the random‐effect model to calculate the summary estimate if heterogeneity existed across studies. Otherwise, the fix‐effect model was used. Egger’s linear regression, Begg’s rank correlation methods, and funnel plots were used to assess potential publication bias. Sensitivity analyses were carried out to evaluate the robustness of the results by excluding one study at a time. Analyses were performed using R v3.3.1 and STATA v13.0. All tests were two‐sided with P < 0.05 considered statistically significant, except for heterogeneity test (P < 0.1) and publication bias (P < 0.1) in meta‐analyses.We performed meta‐analyses based on study designs. The subgroup analyses were based on participants’ ancestry. Participants’ ancestry consisted of Asian, European, and other ancestries. Other ancestry meant participants in the study were non‐Asian and non‐European ancestry, or that participants’ ancestry was not indicated in the study. The Cochran's 2.22.2.1P < 0.05). Among them, three SNPs  were excluded because of high linkage disequilibrium (r2 > 0.6) with another SNP included. Then, we excluded two SNPs associated with ovarian cancer‐related risk factors in Asians  by searching the GWAS Catalog.Due to genetic disparity, instrumental variables for Chinese and Europeans were constructed separately. Figure P < 1 × 10−5); individuals were excluded if they had a low call rate (<95%), sex discrepancies between records and data genetically inferred, or if they were relatives or population outliers. We imputed the whole genome data into the 1000 Genomes phase 1 reference panel by IMPUTE2 and SHAPEIT.We calculated the association between AAM‐associated SNPs and EOC risk using our previous GWAS data including 1044 EOC (594 SOC) patients and 1172 age‐matched healthy controls.2.2.2P < 5 × 10−8) in two studies.r2 > 0.6) with another SNP included and nine SNPs associated with ovarian cancer‐related risk factors identified in the GWAS Catalog,http://ocac.ccge.medschl.cam.ac.uk).We searched for AAM GWAS performed in Europeans, and selected 432 AAM‐associated SNPs , or directly associated with ovarian cancer risk. We constructed a weighted genetic score (wGS) using the 25 SNPs to assess the pleiotropic effects in MR analyses in Chinese. The wGS was constructed with the formula F statistic. F statistic was calculated with the formula R2 is the phenotypic variation of AAM explained by variants, n is the sample size, and k is the number of variants. We calculated R2 using the individual‐level genetic data from previous GWAS.F statistic over 10 indicates strong instrumental variables.The strength of instrumental variables in MR analyses for the Chinese population was evaluated by P < 0.05 considered statistically significant.Analyses were performed using R v3.3.1. All tests were two sided with 33.1A total of 26 studies including 11 410 cases and 1 163 117 noncases were included in meta‐analyses.The results of meta‐analyses for EOC are as follows:P = 0.071) with evidence of heterogeneity across studies   to 0.98  in sensitivity analyses  or with Begg’s test  or in the funnel plot  for Asians and 0.96  for Europeans, respectively  without evidence of heterogeneity across studies   to 1.00  in sensitivity analyses  or with Begg’s test  or in the funnel plot  for Asians and 1.00  for Europeans, respectively  Figure . The RRss Figure C. No pubt Figure D. In theThe results of meta‐analyses for all subtypes’ ovarian cancer and SOC are as follows:P = 0.036) with evidence of heterogeneity across studies   without evidence of heterogeneity across studies   Figure E. For co) Figure E. For SO) Figure F.3.23.2.1 F statistic for combined SNPs was 14.1, which suggested our instrument was unlikely to suffer from weak instrument bias.Among 25 SNPs used as instrumental variables, none of them were associated with ovarian cancer‐related risk factors in Asians by searching the GWAS Catalog. Additionally, no association was observed between wGS and age, BMI, or family history of cancer Table . The insP = 0.026) by IVW methods. In sensitivity analyses, the results by MR‐Egger and median‐based methods were similar to those by IVW‐based methods. Although the median‐based method indicated no association, the direction of the association was consistent and the OR was similar. The MR‐Egger intercept suggested that pleiotropy might not influence the results (MR‐Egger intercept P > 0.05). AAM was also negatively associated with SOC risk. Table In two‐sample MR analyses, the OR of EOC risk per year increase in genetically predicted AAM was 0.81  by IVW methods. In sensitivity analyses, the results by MR‐Egger and median‐based methods were similar to those by IVW‐based methods. The MR‐Egger intercept suggested that pleiotropy might not influence the results (MR‐Egger intercept P >  0.05). AAM was also negatively associated with SOC risk. Table Among 390 SNPs used as instrumental variables, none of them were associated with ovarian cancer‐related risk factors in Europeans by searching the GWAS Catalog. In two‐sample MR analyses, the OR of EOC risk per year increase in genetically predicted AAM was 0.94  and a 24% lower risk of SOC per year increase in AAM  in Chinese, and a 6% lower risk of EOC per year increase in AAM  and a 5% lower risk of SOC per year increase in AAM  in Europeans.In this study, after systematic meta‐analyses, we performed two‐sample MR analyses to evaluate the causal effect of AAM in EOC (SOC) etiology. In meta‐analyses of case–control studies, we observed an inverse association between per year older AAM and ovarian cancer risk. However, no association was observed in cohort studies. In our MR study using the IVW method, we observed a 19% lower risk of EOC per year increase in AAM  and cohort (RR = 0.99) studies. The estimates of associations were weaker in meta‐analyses than in MR analyses. The estimate of MR studies reflects the effects of lifelong interference, whereas that of observational studies reflects more acute effects.KiSS1, GNRHR, and DUSP6 on the HPG axis are involved in the development of ovarian cancer.LIN28B in pituitary . LIN28B was reported to be related to the proliferation, migration, and apoptosis of ovarian cancer.AAM appears to have a significant genetic component.P < 0.05, and the F statistic was above 10 (assumption one); No association was observed between wGS constructed by instrumental variables and age, BMI, or family history of cancer, and none of instrumental variables were associated with other ovarian cancer‐related risk factors in Asians by searching the GWAS Catalog, and the MR‐Egger intercept indicated that pleiotropic effects might not be a problem (assumption two); none of instrumental variables were associated with ovarian cancer risk in Asians by searching the GWAS Catalog (assumption three). For the MR analyses in Europeans, the instrumental variables were selected from AAM GWAS in Europeans at P < 5 × 10−8 (assumption one); none of instrumental variables were associated with other ovarian cancer‐related risk factors in Europeans by searching the GWAS Catalog, and the MR‐Egger intercept indicated that pleiotropic effects might not be a problem (assumption two); none of instrumental variables were associated with ovarian cancer risk in Europeans by searching the GWAS Catalog (assumption three). There are several limitations in our study. Firstly, the threshold of P‐value of SNPs we selected as instrumental variables in Chinese was 0.05 rather than 5 × 10−8, because only rs7759938  reached the genome‐wide significant threshold. Notably, most of these selected SNPs have been validated in the European population (P < 5 × 10−8).Our study has several strengths and limitations. One of the strengths is that our study was performed in two independent populations, which makes the conclusion be generalized to a broader population. Additionally, three assumptions of MR studies were basically satisfied . For the MR analyses in Chinese, the instrumental variables were selected from AAM GWAS in Chinese at In summary, our study provides evidence that AAM may be a causal risk factor of EOC. The underlying mechanisms remain to be elucidated.None declared. Click here for additional data file.


Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas . One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion‐associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high‐risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Consensus molecular subtyping is an RNA expression‐based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction‐based methodology. DNA copy number was assessed by low‐coverage DNA sequencing ( BRAF mutation, promoter hypermethylation, and immune infiltration. Chromosomal instability (CIN), the most common type of genomic instability in CRC, is a feature characteristic of CMS2–CMS4. CMS2 is the most prevalent CRC subtype (37%) and shows the hallmarks of canonical CRC carcinogenesis, including activation of the Wnt and Myc pathways. Approximately 13% of CRCs are in CMS3, characterised by dysregulated metabolism and KRAS mutation. Finally, CMS4 (23%) is described as a mesenchymal, stroma‐rich group, associated with poor prognosis Colorectal cancer (CRC) is heterogeneous in its molecular characteristics and its treatment response. Stratifying CRC patients into biologically and clinically distinct subtypes, based on gene expression profiles, has been performed in many studies, with the common aim of improving clinical precision Most CRCs progress from normal epithelium, through a benign precursor adenoma, by accumulating genetic alterations in oncogenes and tumour suppressor genes AURKA and TPX2 promote 20q amplicon‐driven adenoma‐to‐carcinoma progression A further question is whether adenomas with a high risk of progressing to cancer would differ in their CMS pattern from adenomas with a low risk of progression. In general, the progression of dysplastic epithelial premalignant lesions such as colorectal adenomas is associated with the acquisition of genomic instability. Often, this concerns aneuploidy or CIN, which marks ∼85% of CRC cases The present study therefore aimed to investigate whether the differentiation of colorectal epithelial neoplasia into CMS classes can already be recognised at the adenoma stage, and whether specific CMS classes are associated with the absence or presence of specific DNA copy number aberrations in colorectal adenomas that reflect a high risk of progressing to cancer.A total of 62 snap‐frozen advanced adenomas and 59 CRCs were collected from two independent sample collections: Series 1 and Series 2 . Clinical information is shown in Table n = 2). For Series 2, DNA copy number data for 28 adenomas were obtained from the array‐comparative genomic hybridisation (arrayCGH) analysis in an earlier study n = 4) or only a minor part of the chromosomal arm was gained or lost (n = 2). For both series, adenomas with at least two of seven CAEs were labelled as high‐risk For Series 1, copy number analysis by low‐coverage whole genome sequencing was performed . Gains and losses of whole chromosomal arms were used for the identification of high‐risk adenomas. Samples were considered to have undetermined risk when the copy number aberrations were present but did not reach the probability cut‐off of 0.5  according to standard procedures, as described previously Both series were subjected to RNA‐seq and data preprocessing separately. Expression matrices were obtained for each series .TCGA data served as a reference for performance of the analysis in the present study 2‐transformed RPKMs of the top 30 and the top 1000 variable genes.The batch effect was removed with M‐Combat Ensembl IDs were translated to Entrez IDs with the biomaRt Bioconductor package To validate the results in an independent series of adenomas measured with a different platform, expression data from the Affymetrix Human Genome U133 Plus 2.0 Array of 45 colorectal adenomas and 36 CRCs (GSE20916) were downloaded from the Gene Expression Omnibus. This validation set will be referred as ‘Series 3’ P value was ≤0.05 (Fisher's exact test). Additionally, associations between CMS classes in CRCs and clinical features were analysed.The multinomial exact test was used to perform a goodness‐of‐fit test for the distributions of CMS classes in the adenomas in comparison with cancers from the study dataset, adenomas from the validation set, and cancers from the original CMS publication 2 fold change, whereby genes upregulated in CMS2 adenomas were at the top of the list. (Fold change is defined as the ratio of test to reference expression level.) The log2 fold change‐based ranked list was submitted to GSEA Prior to GSEA An overview of the data analysis is shown in Figure n = 59) and TCGA data (n = 556); see Figure On the basis of two tissue datasets, Series 1 and Series 2, we collected a cohort of 62 adenomas and 59 CRCs, referred to as the study dataset. To ensure proper classification of the adenomas, which constitute a different entity from CRCs, the CMS classification was applied to a merged dataset with carcinomas from the present study (p = 0.004) and with differentiation grade (p = 0.006) were observed, but no association with stage was identified .The CMS classification results of the study dataset were extracted. In total, 48 of 59 cancers were classified with a posterior probability of ≥0.5. Of these, seven were classified as CMS1, 15 as CMS2, two as CMS3, and 24 as CMS4  and CRCs from the original CMS publication (p < 2.2 × 10–16) CMS subtype signatures were indeed expressed in the adenomas, and 54 of 62 samples were successfully classified with a probability threshold of ≥0.5. The vast majority of the adenomas, i.e. 45 samples (73%), were assigned to CMS3. Additionally, eight adenomas (13%) were subtyped as CMS2, representing the canonical CRC carcinogenesis. Only a single adenoma was classified as CMS1, being one of the two MSI adenomas identified in the whole dataset. No adenomas were subtyped as CMS4  and CMS3 (n = 45) were the most prevalent; there were no CMS4 adenomas, and there was one adenoma classified as CMS1. Therefore, only differences between CMS2 and CMS3 adenomas were examined in terms of risk of progression, cancer‐specific DNA copy number aberrations, clinical characteristics, and biological processes specific for each group. Examination of associations between CMS class and risk of progression revealed that CMS2 was significantly associated with high‐risk adenomas and CMS3 with low‐risk adenomas . No statistically significant associations were observed between CMS class and histological type (p = 0.362) and grade of dysplasia (p = 0.389), or between high‐risk genotypic features and histological type (p = 0.77) and grade of dysplasia .Adenomas classified as CMS2 –AKT–mammalian target of rapamycin (mTOR) pathway, and the Wnt–β‐catenin signalling pathway, or genes regulated by MYC. These results are in line with the biological characterisation of CMS2 CRCs, which are known to be enriched with proliferation and cell cycle pathways To explore associations of CMS2 and CMS3 adenomas with well‐defined biological processes, we performed GSEA on the hallmark gene sets  and cancers (n = 36) measured on the Affymetrix array. To perform a similar analysis as that used for the study dataset, CRCs from the GSE39582 dataset (n = 566) were chosen as the reference dataset for batch effect removal, normalisation, and CMS classification n = 28, 62%), none as CMS4, and a small number as CMS1  or CMS2  (Table p < 2.2 × 10–16). No significant differences between the distribution of CMS classes among adenomas from the study dataset and those from the validation set were observed (p = 0.13).Validation of the CMS classification results in colorectal adenomas was performed in an independent series – Series 3 GSE20916) 0916 n = CMS classification constitutes an established consensus gene expression‐based subtyping of CRC. We set out to determine whether this molecular classification is already present at the adenoma stage. Classification of adenomas according to CMS was achieved for 54 of 62 adenomas, in a group‐wise analysis together with 59 CRCs from the study dataset and 556 TCGA CRC samples The distribution of CMS classes in adenomas differed significantly from that in CRCs, in both the study dataset and the validation dataset. The vast majority  of adenomas were classified as the ‘metabolic’ CMS3 type, which was the least frequent CMS class among CRCs from the study dataset (3%). Multiple gene expression profiling studies of colorectal adenomas and CRCs have shown upregulated metabolism in adenomas. In particular, pathway analysis of genes overexpressed in adenomas in comparison with cancers revealed the same pathways that were dysregulated in CMS3, including fatty acid, amino acid and sugar metabolism Conversely, none of the adenomas from either the study dataset or the validation dataset were classified as the stroma‐rich poor‐prognosis CMS4 class. A process inherent to invasion and thus colorectal adenoma‐to‐carcinoma progression is activation of tumour stroma AURKA and TPX2, which play a role in the G2/M phase of the cell cycle 2/M checkpoint and mitotic spindle assembly gene sets in CMS2 adenomas. Another characteristic specific for adenoma‐to‐carcinoma progression and the CMS2 adenoma class is upregulation of pathways such as the cell cycle and epithelial differentiation in vitro by the use of, for example, organoid models. Although this has been done by perturbing frequently mutated cancer genes with prominent roles in CRC pathogenesis Regarding the CMS1 and CMS2 classes, the CMS classifier subtyped one of the adenomas with MSI as CMS1 and the second one as CMS3. MSI is rare in colorectal adenomas, with a prevalence of 3% overall The CMS classification of cancers revealed a relatively large number of CMS4 cases in the present series. Taking into account the different sample sizes of the current study and the original CMS publication, and given the variation in distributions of CMS classes among the six datasets from which the CMS classification originated In the study dataset, we used large adenomas to sample fresh frozen material for research purposes, as well as routine tissue processing for diagnostics. Therefore, the majority (95%) of the adenomas were > 1 cm. Given the association of adenoma size with progression risk The present study focused on conventional adenomas, which are the most common precursors of CRC, especially in the context of CIN Technically, a combined analysis of the study dataset and the TCGA CRC series was performed to reduce the effect of the RNA‐seq data normalisation on the CMS classification. Additionally, because of a further normalisation step implemented in the random forest CMS algorithm, combined analysis reduced the impact of the potentially different distribution of CMS classes in the study dataset from that in the original CMS training set. The concept of batch effect adjustment to a ‘gold‐standard’ dataset, which the model was trained on, and classification by use of a merged dataset was previously introduced So far, classification of colorectal neoplasia has been morphology‐based. Adenomas are classified on the basis of histological type, size and grade of dysplasia, whereas cancers are subtyped on the basis of grade of differentiation and stage. The CMS classification is an approach for molecular classification of cancers based on RNA expression. The present study has extended this approach to colorectal adenomas, and has demonstrated that CMS classification can be effectively applied to these lesions. In conclusion, colorectal adenomas proved to be heterogeneous in terms of CMS class, but with a different distribution from that of cancers. CMS3 turned out to be the most prevalent among the conventional adenomas, and our results indicate that it may represent mostly adenomas at low risk of progressing to CRC as compared with CMS1 or CMS2 adenomas. The frequency of CMS classes observed in adenomas is consistent with what could be expected on the basis of differences between adenomas and carcinomas, and on the proportion of adenomas expected to progress to cancer.MAK, LJWB, GB, YH, APS, GJ, BC, LFAW, CR, RJAF and GAM conceived the study and the experiments. NCTvG and GAM performed the histopathological review. LJWB, ASB, PDvD and MdJ performed experiments. MAK, GB and LFAW contributed to the design of the data analysis. MAK and CR performed the bioinformatics analysis. All authors were involved in writing the paper and gave final approval to the submitted and published versions.Supplementary materials and methodsFigure S1. Multidimensional scaling of the Euclidian distance between the gene expression profiles of all the samples from the study dataset with TCGAFigure S2. Hierarchical clustering based on the gene expression profiles of the top 1000 most variable genesFigure S3. Multidimensional scaling of the Euclidian distance between the gene expression profiles of all the samples for the validation setFigure S4. Hierarchical clustering based on the gene expression profiles of the top 1000 most variable genes for the validation setFigure S5. ESTIMATE scores and ssGSEA enrichment scores among CMS classes in adenomas and cancerTable S1. Availability of the study dataTable S2. Comparison of the CMS classification of the TCGA data set in the current study to the original TCGA CMS labelsTable S3. CMS classification of colorectal cancers from the study datasetTable S4. MSI samples in the study dataset: association between CMS classes in CRCs and differentiation grade, stage and MSI statusTable S5. CMS classification of adenomas from the study datasetTable S6. Cancer‐associated events (CAEs): DNA copy number aberrations and the risk of progression for adenomas in the study datasetTable S7. Fisher exact test results for the association analysis in adenomas from the study datasetTable S8. CMS classification of adenomas and cancers from the validation setTable S9. CMS classification of colorectal adenomas from the study dataset performed with Single Sample PredictorTable S10. Comparison of the CMS classification of colorectal adenomas by the study approach (random forest CMS classifier) and single sample predictorAppendix S1. Supplementary Materials and MethodsClick here for additional data file.Figure S1. Multidimensional scaling of the Euclidian distance between the gene expression profiles of all the samples from the study dataset with TCGA. (A) Plot before batch effect removal. Three separate batches can be clearly distinguished, with white dots representing samples from the TCGA dataset, blue from Series 1 and red from Series 2. (B) Plot after batch effect removal. Samples originating from different datasets cannot be distinguished by their locations on the plot, indicating that the batch effect was removed.Figure S2. Hierarchical clustering based on the gene expression profiles of the top 1000 most variable genes. (A) Heatmap of all three datasets before batch effect removal. The batches corresponding to the TCGA dataset, Series 1 and Series 2 can be distinguished in the heatmap. (B) Heatmap before batch correction of the Series 1 and Series 2 study datasets only. Next to the two batches, one can distinguish clusters enriched with adenomas and clusters enriched with cancers. (C) Heatmap of all three datasets after batch effect removal. Samples from the three experiments do not cluster together. (D) Heatmap of the Series 1 and Series 2 study datasets after batch effect removal. Clusters enriched with adenomas or cancers can still be distinguished, meaning that batch effect correction did not remove the variability between different lesions.Figure S3. Multidimensional scaling of the Euclidian distance between the gene expression profiles of all the samples for the validation set. Series 3 is the validation set with colorectal adenomas and cancers. Reference is the reference series with only colorectal cancers. (A) Plot before batch effect removal. Two separate batches can be clearly distinguished, with white dots representing samples from the reference dataset and blue dots from the Series 3. (B) Plot after batch effect removal. The samples originating from different datasets cannot be distinguished by their location on the plot, indicating that the batch effect was removed.Figure S4. Hierarchical clustering based on the gene expression profiles of the top 1000 most variable genes. Reference is the reference dataset used for normalisation, Series 3 is the validation set. (A) Heatmap of the two datasets before batch effect removal. The batches corresponding to the Reference and Series 3 can be distinguished in the heatmap. (B) Heatmap before batch correction of the Series 3 only. Clusters enriched with adenomas and clusters enriched with cancers can be distinguished. (C) Heatmap of the two datasets after batch effect removal. Samples from the two experiments do not cluster together. (D) Heatmap of the Series 3 after batch effect removal. Clusters enriched with adenomas or cancers can still be distinguished, meaning that batch effect correction did not remove the variability between different lesions.Figure S5. ESTIMATE scores and ssGSEA enrichment scores among CMS classes in adenomas and cancer. “Stromal” (A) and “Immune” (B) scores were calculated using the ESTIMATE algorithm and plotted per CMS group in colorectal adenomas and cancers. “Invasive Front” (C) and “Central Tumor” (D) enrichment was calculated using the ssGSEA algorithm.Click here for additional data file.Table S1. Availability of the study dataTable S2. Comparison of the CMS classification of the TCGA data set in the current study to the original TCGA CMS labelsTable S3. CMS classification of colorectal cancers from the study datasetTable S4. MSI samples in the study dataset: association between CMS classes in CRCs and differentiation grade, stage and MSI statusTable S5. CMS classification of adenomas from the study datasetTable S6. Cancer‐associated events (CAEs): DNA copy number aberrations and the risk of progression for adenomas in the study datasetTable S7. Fisher exact test results for the association analysis in adenomas from the study datasetTable S8. CMS classification of adenomas and cancers from the validation setTable S9. CMS classification of colorectal adenomas from the study dataset performed with Single Sample PredictorTable S10. Comparison of the CMS classification of colorectal adenomas by the study approach (random forest CMS classifier) and single sample predictorClick here for additional data file.