***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND Common genetic variants in the Toll-like receptor 4 ( TLR4 ) , which is involved in inflammation and immune response pathways , may be important for prostate cancer . METHODS In a large nested case-control study of prostate cancer in the Physicians ' Health Study ( 1982 - 2004 ) , 10  single nucleotide polymorphisms ( SNPs ) were selected and genotyped to capture common variation within the TLR4 gene as well as 5 kb up and downstream . Unconditional logistic regression was used to assess associations of these SNPs with total prostate cancer incidence , and with prostate cancers defined as advanced stage/lethal ( T3/T4 , M1/N1(T1-T4 ) , lethal ) or high Gleason grade ( 7 ( 4 + 3 ) or greater ) . Cox-proportional hazards regression was used to assess progression to metastases and death among prostate cancer cases . RESULTS The study included 1,267 controls and 1,286 incident prostate cancer cases , including 248 advanced stage/lethal and 306 high grade cases . During a median follow-up of 10.6 years , 183 men died of prostate cancer or developed distant metastases . No statistically significant associations between the TLR4 SNPs were found for total prostate cancer incidence , including SNPs for which an association was reported in other published studies . Additionally , there were no significant associations with TLR4 SNPS and the incidence of advanced stage/lethal , or high grade cancers ; nor was there evidence among prostate cancer cases for associations of TLR4 SNPs with progression to prostate cancer specific mortality or bony metastases . CONCLUSIONS Results from this prospect i ve nested case-control study suggest that genetic variation across TLR4 alone is not strongly associated with prostate cancer risk or mortality We examined whether selected polymorphisms in 11 c and i date genes and serum levels of insulin-like growth factor I ( IGF-I ) help predict the presence of prostate cancer among patients prescreened with prostate-specific antigen ( PSA ) and digital rectal exam ( DRE ) .  We studied 1031 consecutive men who underwent one or more prostate biopsies because of an elevated PSA level ( > 4 ng/ml ) or an abnormal DRE . Eleven c and i date genes were examined , including the and rogen receptor , SRD5A2 , CYP17 , CYP3A4 , vitamin D receptor , PSA , GST-T1 , GST-M1 , GST-P1 , IGF-I , and IGF binding protein 3 . We also measured serum IGF-I levels before biopsy . Of the 1031 men , 483 had cancer on any biopsy ( cases ) and 548 men had no cancer ( controls ) . Age , ethnicity , total PSA , and DRE result were strongly predictive of the presence of prostate cancer . The mean IGF-I level for cases ( 119.4 ng/ml ) was lower than for controls ( 124.4 ng/ml , P = 0.05 ) and were not predictive for the presence of prostate cancer . We found no associations between the and rogen receptor , SRD5A2 , CYP17 , CYP3A4 , vitamin D receptor , GST-M1 , GST-P1 , and IGF binding protein 3 genotypes and prostate cancer risk . The adjusted odds ratios for having prostate cancer for patients with the GST-T1 and IGF-I variant alleles were 1.64 ( 95 % confidence interval , 1.1 - 2.4 ; P = 0.01 ) and 1.70 ( 95 % confidence interval , 1.1 - 2.7 ; P = 0.02 ) , respectively . Nine of 11 c and i date genes were not significantly predictive for prostate cancer in a clinical setting . The GST-T1 and IGF-I polymorphisms demonstrated modest associations with prostate cancer risk . IGF-I levels were not helpful in identifying patients with prostate cancer at the time of biopsy OBJECTIVE Insulin receptor substrate-1 ( IRS-1 ) acts as a docking protein between the insulin-like growth factor-1 ( IGF-1 ) receptor and intracellular signaling molecules in the IGF-1 signaling pathway . Accumulating data support a role of IGF-1 in prostate carcinogenesis . We assessed the influence of the most common IRS-1 gene polymorphism ( Gly972Arg ) on prostate cancer risk , alone and in combination with IGF-1 and other components in the IGF-1 signaling pathway . MATERIAL S AND METHODS In a nested case-control study within the Physicians ' Health Study , the IRS-1 polymorphism was assayed from prospect ively collected sample s from 564 incident prostate cancer cases and 758 controls matched on age and smoking . We calculated relative risks ( RR ) and 95 % confidence intervals ( CI ) using conditional logistic regression . RESULTS Among the controls , 0.8 % were homozygous ( AA ) and 12 % were heterozygous ( GA ) for the polymorphic allele . There was no association between carriage of the A allele and total prostate cancer risk ( RR = 1.1 95 % CI = 0.8 - 1.5 ) , advanced disease ( stage C or D or lethal prostate cancer , RR = 1.3 95 % CI = 0.8 - 2.3 ) , or plasma IGF-1 levels . We explored possible interactions with body mass index and components in the IGF-1 pathway including IGFBP3 , PI3k , and PTEN but none of these factors influenced the relation between IRS-1 genotype and prostate cancer risk . CONCLUSIONS Our data do not support an association between carriage of the variant IRS-1 gene and prostate cancer risk A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results observed and the results expected in each study , under the assumption that each study estimates the same common treatment effect . A large total deviation indicates that a single common treatment effect is unlikely . Any pooled estimate calculated must account for the between- study heterogeneity . In practice , this test has low sensitivity for detecting heterogeneity , and it has been suggested that a liberal significance level , such as 0.1 , should be used [ 6 ] . Estimating a Common Effect The questions that this step tries to answers are , 1 ) To the extent that data are similar , what is their best common point estimate of a therapeutic effect , and 2 ) how precise is this estimate ? The mathematical process involved in this step generally involves combining ( pooling ) the results of different studies into an overall estimate . Compared with the results of individual studies , pooled results can increase statistical power and lead to more precise estimates of treatment effect . Each study is given a weight according to the precision of its results . The rationale is that studies with narrow CIs should be weighted more heavily than studies with greater uncertainty . The precision is generally expressed by the inverse of the variance of the estimate of each study . The variance has two components : the variance of the individual study and the variance between different studies . When the between- study variance is found to be or assumed to be zero , each study is simply weighted by the inverse of its own variance , which is a function of the study size and the number of events in the study . This approach characterizes a fixed-effects model , as exemplified by the Mantel-Haenszel method [ 7 , 8 ] or the Peto method [ 9 ] for dichotomous data . The Peto method has been particularly popular in the past . It has the advantage of simple calculation ; however , although it is appropriate in most cases , it may introduce large biases if the data are unbalanced [ 10 , 11 ] . On the other h and , r and om-effects models also add the between- study variance to the within- study variance of each individual study when the pooled mean of the r and om effects is calculated . The r and om-effects model most commonly used for dichotomous data is the DerSimonian and Laird estimate of the between- study variance [ 12 ] . Fixed- and r and om-effects models for continuous data have also been described [ 13 ] . Pooled results are generally reported as a point estimate and CI , typically a 95 % CI . Other quantitative techniques for combining data , such as the Confidence Profile Method [ 14 ] , use Bayesian methods to calculate posterior probability distributions for effects of interest . Bayesian statistics are based on the principle that each observation or set of observations should be viewed in conjunction with a prior probability describing the prior knowledge about the phenomenon of interest [ 15 ] . The new observations alter this prior probability to generate a posterior probability . Traditional meta- analysis assumes that nothing is known about the magnitude of the treatment effect before r and omized trials are performed . In Bayesian terms , the prior probability distribution is noninformative . Bayesian approaches may also allow the incorporation of indirect evidence in generating prior distributions [ 14 ] and may be particularly helpful in situations in which few data from r and omized studies exist [ 16 ] . Bayesian analyses may also be used to account for the uncertainty introduced by estimating the between- study variance in the r and om-effects model , leading to more appropriate estimates and predictions of treatment efficacy [ 17 ] . Exploring and Explaining Heterogeneity The next important issue is whether the common estimate obtained in the previous step is robust . Sensitivity analyses determine whether the common estimate is influenced by changes in the assumptions and in the protocol for combining the data . A comparison of the results of fixed- and r and om-effects models is one such sensitivity analysis [ 18 ] . Generally , the r and om-effects model produces wider CIs than does the fixed 
    Output:
    
However , overall results failed to present a positive association between polymorphisms in genes of PI3K/Akt pathway and PCa risk .
Conclusion : Our work suggests that polymorphisms in genes of PI3K/Akt Signaling Pathway are not risk factor for PCa .