Literature DB >> 27634905

Association between 8q24 (rs13281615 and rs6983267) polymorphism and breast cancer susceptibility: a meta-analysis involving 117,355 subjects.

Yafei Zhang1, Xianling Zeng2, Hongwei Lu1, Hong Ji1, Enfa Zhao3, Yiming Li1.   

Abstract

Published data on the association between 8q24 polymorphism and breast cancer (BC) risk are inconclusive. Thus, we conducted a meta-analysis to evaluate the relationship between 8q24 (rs13281615 and rs6983267) polymorphism and BC risk. We searched PubMed, EMBASE, Web of science and the Cochrane Library up to August 13, 2015 for relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Twenty-six studies published from 2008 to 2014, with a total of 52,683 cases and 64,672 controls, were included in this meta-analysis. The pooled results showed that there was significant association between 8q24 rs13281615 polymorphism and BC risk in any genetic model. In the subgroup analysis by ethnicity, the effects remained in Asians and Caucasians. However, no genetic models reached statistical association in Africans. There was no association in any genetic model in rs6983267. This meta-analysis suggests that 8q24 rs13281615 polymorphism is a risk factor for susceptibility to BC in Asians, Caucasians and in overall population, While, there was no association in Africans. The rs6983267 polymorphism has no association with BC risk in any genetic model. Further large scale multicenter epidemiological studies are warranted to confirm this finding.

Entities:  

Keywords:  8q24; breast cancer; meta-analysis; rs13281615; rs6983267

Mesh:

Year:  2016        PMID: 27634905      PMCID: PMC5356534          DOI: 10.18632/oncotarget.12009

Source DB:  PubMed          Journal:  Oncotarget        ISSN: 1949-2553


INTRODUCTION

Breast cancer (BC), one of the most frequently encountered malignant tumors in women, has become the main reason of tumor-associated death in our word [1], whose incidence rate is increasing year by year and accompanied by younger trend in the world [2, 3]. The etiology of BC is very complex, many factors cause it's occurrence and development, the specific mechanism has not been clarified. Epidemiology and basic etiology studies have shown that BC's occurrence and progression is closely related to multiple factors interaction between genetic, endocrine and external environment [4-6]. The genetic markers of BC are mainly expressed in two forms, that is, the higher external rate mutation gene and the lower external rate polymorphism gene. Compared with familial BC, most sporadic BC is associated with lower external rate polymorphism genes [7, 8]. The distribution of these genes in the population is more than 1%, and the genetic variation of these genes (Single nucleotide polymorphism, SNP) is mainly by changing the expression level and function of the protein and the modified factor inside and outside the cell, thus affecting the genetic susceptibility of BC [8]. In recent years, many BC-associated gene loci have been demonstrated by GWAS, 8q24 (rs13281615 and rs6983267) is the gene mutation site selected by this technique [7]. Previous functional studies have reported the association between 8q24 (rs13281615 and rs6983267) polymorphism and BC risk [9-29]. Because of differences in race and region, studies of this site are not entirely consistent with the conclusion that whether the site is associated with the risk of BC. To clarify the role of 8q24 (rs13281615 and rs6983267) polymorphism in BC risk, five meta-analysis [30-34] on the associations between 8q24 (rs13281615 and rs6983267) polymorphism and BC had been carried out. However, the results remain inconclusive and number of their studies included in their meta-analysis about BC is small. In the subgroup of their analyses the sample size is extremely small, and some just no subgroup. Therefore, we decided to carry out this meta-analysis on all the included case-control researches to make a more accurate assessment of the relationship. Moreover, we performed a subgroup analysis stratified by ethnicity.

RESULTS

Characteristics of included papers

The specific search process is shown in Figure 1. A total of 447 references were preliminarily identified at first based on our selection strategy. We also identified 2 papers [11, 29] through other source. 347 records left after removing repeated studies. We refer to titles or abstracts of all the included literatures, and then removed obviously irrelevant papers. In the end, the whole of the rest of the papers were checked based on the inclusion and exclusion criteria. Finally, 21 studies [9-29] on 8q24 (rs13281615 and rs6983267) polymorphism and the occurrence of BC were eventually included in our study, including 52,683 cases and 64,672 controls. Characteristics of eligible analysis are shown in Table 1. The 21 case-control papers were published between 2008 and 2014, among them, 1 study was performed in African, 7 in Asian, 12 in Caucasians and 1 in both African and Caucasians. All studies were case-controlled.
Figure 1

Flow chart of studies selection in this meta-analysis

Table 1

Characteristics of the studies included in the meta-analysis

First authorYearCountryEthnicitySource of controlsNumber(case/control)HWE
rs13281615
Antoniou[9]2009MixedCaucasiansNested7787/66620.100
Bai[10]2014ChinaAsianPB280/2870.086
Barnholtz-Sloan[11]2010United StatesCaucasiansPB1223/11170.220
Barnholtz-Sloan[11]2010United StatesAfricanPB736/6580.580
Campa[12]2011USA and EuropeCaucasiansPB8302/116150.106
Chan[13]2012SingaporeAsianPB1174/14630.047
Elematore[14]2014ChileCaucasiansPB347/8010.993
Fletcher[15]2008United KingdomCaucasiansPB1470/13410.366
Garcia-Closas[16]2008MixedCaucasiansNested15084 /221050.672
Gorodnova[17]2010RussianCaucasiansPB140/1740.710
Harlid[18]2012EuropeanCaucasiansPB3545/50070.045
Jiang[19]2011ChinaAsianPB493/5101.000
Latif[20]2010BritishCaucasiansHB919/3430.639
Li[29]2011ChinaAsianHB558/6350.748
Long[21]2010ChinaAsianPB2945/29810.985
McInerney[22]2009IrelandCaucasiansPB917/9930.096
Mizoo[23]2012JapanAsianPB466/4580.252
Shan[24]2012TunisiaAfricanPB639/3650.497
Tamimi[25]2010SwedenCaucasiansPB661/711<0.001
Teraoka[26]2011Denmark and USACaucasiansPB606/11940.041
Zhang[28]2014ChinaAsianHB482/5270.089
rs6983267
Fletcher[15]2008United KingdomCaucasiansPB1480/13360.453
McInerney[22]2009IrelandCaucasiansPB945/9570.349
Wokolorczyk[27]2008CanadaCaucasiansPB1006/19100.266
Zhang[28]2014ChinaAsianHB478/5220.046

HWE: Hardy-Weinberg equilibrium; PB: population based; HB: hospital-based; SNP: single nucleotide polymorphism.

HWE: Hardy-Weinberg equilibrium; PB: population based; HB: hospital-based; SNP: single nucleotide polymorphism.

Meta-analysis results

Table 2 and Table 3 shows the 8q24 (rs13281615 and rs6983267) polymorphisms genotype distribution and allele frequencies in in case group and control group. Main results of our study were shown in Table 4. A total of 21 studies with 52,683 cases and 64,672 controls were included. As shown in Table 4, The pooled results indicated that the correlation between 8q24 rs13281615 polymorphism and the occurrence of BC was significant in any genetic model: Allele model (OR = 1.10, 95% CI = 1.07-1.13, P < 0.00001), dominant model (OR = 1.13, 95% CI = 1.08-1.18, P < 0.00001) recessive model (OR = 1.13, 95% CI = 1.08-1.18, P < 0.00001) homozygous genetic model (OR = 1.20, 95% CI = 1.13-1.27, P < 0.00001) heterozygote comparison (OR = 1.09, 95% CI = 1.06-1.12, P < 0.00001).
Table 2

rs13281615 polymorphisms genotype distribution and allele frequency in cases and controls

First authorGenotype (N)Allele frequency (N)
CaseControlCaseControl
TotalGGAGAATotalGGAGAAGAGA
Antoniou778713963872251966621158331721876664891056337691
Bai28063152652876215867278282282292
Barnholtz-Sloan12232306043891117194519404106413829071327
Barnholtz-Sloan736130387219658123331204647825577739
Campa83021764404424941161521935609381375729032999513235
Chan117431755430314633646934061188116014211505
Elematore34713114868801255394152410284904698
Fletcher147030573043513412256294871340160010791603
Garcia-Closas1508429217284487922105377310682765013126170421822825982
Gorodnova140426335174388452147133160188
Harlid3545719172311035007884235717663161392941255889
Jiang493125247121510127255128497489509511
Latif919185464270343561601278341004272414
Li558162285111635173313149609507659611
Long29457961470679298174514917453062282829812981
McInerney91717846727299318245635582310118201166
Mizoo4661802117545817720675571361560356
Shan6392013031353659617693705573368362
Tamimi661223263175711273277161709613823599
Teraoka606140292174119421362335857264010491339
Zhang48214324891527124283120534430531523
Table 3

rs6983267 polymorphisms genotype distribution and allele frequency in cases and controls

First authorGenotype (N)Allele frequency (N)
CaseControlCaseControl
TotalGGAGAATotalGGAGAAGAGA
Fletcher148033873440813363126533711410155012771395
McInerney945230464251957245464248924966954960
Wokolorczyk10062545072451910513977420101599720031817
Zhang478151218109522183233106520436599445
Table 4

Meta-analysis results

ComparisonsOR95%CIP valueHeterogeneityEffects model
I2P value
Allele model
rs132816151.101.07-1.13P < 0.0000149%0.006R
African1.080.96-1.220.1858%0.12F
Asian1.081.03-1.140.0010%0.77F
Caucasians1.111.06-1.16P < 0.0000167%0.0004R
rs69832670.950.89-1.010.100%0.66F
Dominant model
rs132816151.131.08-1.18P < 0.0000139%0.03R
African1.130.94-1.360.180%0.34F
Asian1.111.03-1.210.0090%0.92F
Caucasians1.141.07-1.21P < 0.000163%0.002R
rs69832670.940.85-1.040.240%0.63F
Recessive model
rs132816151.131.08-1.18P < 0.0000130%0.10R
African1.090.89-1.320.4060%0.11F
Asian1.111.03-1.200.0080%0.79F
Caucasians1.151.08-1.22P < 0.0000150%0.02R
rs69832670.930.84-1.030.150%0.89F
Homozygous genetic model
rs132816151.201.13-1.27P < 0.0000143%0.02R
African1.160.92-1.470.2260%0.12F
Asian1.171.07-1.290.0010%0.77F
Caucasians1.221.12-1.32P < 0.0000162%0.002R
rs69832670.900.80-1.020.100%0.69F
Heterozygote comparison
rs132816151.091.06-1.12P < 0.0000122%0.17F
African1.120.93-1.360.240%0.68F
Asian1.081.00-1.180.060%0.98F
Caucasians1.111.05-1.170.000555%0.01R
rs69832670.960.86-1.070.450%0.74F

F-fixed effects model; R-random effects model.

F-fixed effects model; R-random effects model. The subgroup study stratified by ethnicity showed an increased BC risk in Asians (Allele model: OR = 1.08, 95% CI = 1.03-1.14, P = 0.001; dominant model: OR = 1.11, 95% CI = 1.03-1.21, P = 0.009; recessive model: OR = 1.11, 95% CI = 1.03-1.20, P = 0.008; homozygous genetic model: OR = 1.17, 95% CI = 1.07-1.29, P = 0.001; heterozygote comparison: OR = 1.08, 95% CI = 1.00-1.18, P = 0.06) and Caucasians (Allele model: OR = 1.11, 95% CI = 1.06-1.16, P < 0.00001; dominant model: OR = 1.14, 95% CI = 1.07-1.21, P < 0.0001; recessive model: OR = 1.15, 95% CI = 1.08-1.22, P < 0.00001; homozygous genetic model: OR = 1.22, 95% CI = 1.12-1.32, P < 0.00001; heterozygote comparison: OR = 1.11, 95% CI = 1.05-1.17, P = 0.0005). While, there was not any genetic model attained statistical correlation in Africans. There was no association in any genetic model in rs6983267 (Table 4).

Sensitivity analyses

As shown in Table 1, all the studies conformed to he balance of Hardy-Weinberg equilibrium (HWE) in controls except 5 studies (P < 0.05), however, after performing the sensitivity analyses, the overall outcomes were no statistically significant change when removing any of the articles, indicating that our study has good stability and reliability.

Detection for heterogeneity

Heterogeneity among studies was obtained by Q statistic. Random-effect models were applied if p-value of heterogeneity tests were less than 0.1 (p ≤ 0.1), otherwise, fixed-effect models were selected (Table 4).

Publication bias

We use Begg's funnel plot and Egger test to evaluate the published bias. As Figure 2 (rs13281615) and Figure 3 (rs6983267) indicated, the funnel plot is symmetrical, indicating that there is no significant publication bias in the total population. In our study, no significant publication bias was found in the Begg's test and Egger's test (P > 0.05).
Figure 2

Funnel plot on the association of rs13281615 variant and breast cancer in a fixed-effect model (heterozygote comparison).

Abbreviations: SE, standard error; OR, odds ratio; A vs. G, Allele model.

Figure 3

Funnel plot on the association of rs6983267 variant and breast cancer in a fixed-effect model (recessive model).

Abbreviations: SE, standard error; OR, odds ratio; A vs. G, Allele model.

Funnel plot on the association of rs13281615 variant and breast cancer in a fixed-effect model (heterozygote comparison).

Abbreviations: SE, standard error; OR, odds ratio; A vs. G, Allele model.

Funnel plot on the association of rs6983267 variant and breast cancer in a fixed-effect model (recessive model).

Abbreviations: SE, standard error; OR, odds ratio; A vs. G, Allele model.

DISCUSSION

A large amount of evidence suggests that genetics is important in determining the risk of cancer [35]. Related research is to search for the susceptibility genes associated with cancer. It is believed that SNP is the main cause of human genetic variation, which may increase individual risk to suffer cancer [14, 18, 22, 23]. With the development of medical science, hereditary susceptibility to cancer has caused people's great interest, and the study on the genetic polymorphism of the tumor is increasing. At present, BRCA1 and BRCA2 gene polymorphisms are generally considered to be associated with BC, but because of the low mutation rate, it can only be detected in the small part of BC patients [36]. In 2007, the whole genome association analysis identified many genetic polymorphisms that may be associated with the development of BC, which included rs6983267 and rs13281615 [37]. The 8q24 rs6983267 and rs13281615 bit is located in the non - base region of the dye - color 8q24, and Its function is not very clear. 8q24.12-24.13 location myelocytomatosis oncogene (MYC), MYC is a group of cancer genes, which are not expressed in normal cells, and expressed in some cancer cells. MYC can promote cell proliferation, immortalized, differentiation and transformation. It is an significant factor in the formation of BC, colon cancer and prostate cancer [38, 39]. Studies shown that the chromosome 8q24 region is related to the occurrence of prostate cancer and colon cancer, which can play a role in carcinogenesis through interacting with MYC gene amplification and over expression [40]. The change of some loci of a single gene may have limited impact on the ultimate effect of cancer, and it is needed to carry out large-scale studies in populations. Recently, a growing number of epidemiological studies have been carried out to explore the relationship between 8q24 (rs13281615 and rs6983267) polymorphisms and the occurrence of BC. Nevertheless, the conclusions are still inconclusive. Therefore, we carried out the meta-analysis on the whole included case-control researches to make a more accurate assessment of the relationship. In our study, 21 studies were eventually included, including 52,683 cases and 64,672 controls. And we assessed the relationship between 8q24 (rs13281615 and rs6983267) polymorphisms and the occurrence of BC. In the total population, the pooled results indicated that the correlation between 8q24 rs13281615 polymorphism and the occurrence of BC was significant in any genetic model (Table 4). It was partially consistent with the consequences of previous five meta-analysis [30-34], while the sample size was several times than theirs, make the results more convincing. BC is a disease with a difference in the incidence and mortality among different ethnic groups. Each increase of 25% European descent, the risk of developing BC will increase by 1.79 times [41]. Easton [42] et al believes that the incidence of the European population is higher than that of other ethnic groups, which are consistent with the results of this study. The subgroup study stratified by ethnicity showed an increased BC risk in Asians and Caucasians (Table 4). While, there was not any genetic model attained statistical correlation in Africans. There was no association in any genetic model in rs6983267 (Table 4). It was partially consistent with the Dai(2013)'s study [30] (with 11 papers containing 40,762 cases and 50,380 controls) and Pei(2013)'s [32] (with 12 eligible studies including 42,508 cases and 53,928 controls) findings. Another meta-analysis by Song(2012) [33] et al, including seven papers with 22,128 cases and 29,276 controls, they found no relationship between rs13281615 polymorphism and BC susceptibility in Asians. This contradiction may be caused by sample sizes and racial differences. Our meta-analysis has some limitations in the following aspects. First, our study is a summary of the data. Due to the lack of the original data needed, we could not evaluate the cancer susceptibility stratified by age, Sex, environment, hormone level, menopause age and other risk factors. We also cannot analyze these studies to analyze the potential interaction of gene - environment and gene - gene. Secondly, we just included the published papers in our study, there may still be some published studies in line with the conditions. Moreover, our study is a summary of the data. We did not verify it from the level of basic experiments. Data from a large sample of multiple centers is still needed to confirm the relationship between 8q24 (rs13281615 and rs6983267) polymorphisms and BC risk. In summary, our study suggests that 8q24 rs13281615 polymorphism could increase the risk of BC in Asians, Caucasians and in overall population, While, there was no association in Africans. The rs6983267 polymorphism has no relationship with the occurrence of BC in any genetic model. Data from a large sample of multiple centers is still needed to confirm our findings.

MATERIALS AND METHODS

Literature searching strategy

We searched PubMed, EMBASE, Web of science, the Cochrane Library for relevant studies published before August 13, 2015. The following keywords were used: rs6983267/rs13281615/8q24, variant*/genotype/polymorphism/SNP, breast AND (cancer or carcinom* or neoplasm* or tumor) and the combined phrases for all genetic studies on the association between the 8q24 (rs13281615 and rs6983267) polymorphism and BC risk. The reference lists of all articles were also manually screened for potential studies. Abstracts and citations were screened independently by two authors, all the agreed articles need a second screen for full-text reports. The searching was done without restriction on language.

Selection and exclusion criteria

Inclusion criteria: A study was included in this meta-analysis if it met the following criteria: i) independent case-control studies for humans; ii) the study evaluated the association between 8q24 (rs13281615 and rs6983267) polymorphism and BC risk; iii) has available genotype frequencies in cancer cases and control subjects for risk estimate. We excluded comments, editorials, systematic reviews or studies lacking sufficient data. If the publications were duplicated or shared in more than one study, the most recent publications were included. All identified studies were screened by two investigators independently. What's more, there was no limitation for publication language.

Data extraction and synthesis

We used endnote bibliographic software to construct an electronic library of citations identified in the literature search. All the PubMed, EMBASE, Web of science and the Cochrane Library searches were performed using Endnote; duplicates were found automatically by endnote and deleted manually. All data extraction were checked and calculated twice according to the inclusion criteria listed above by two independent investigators. Data extracted from the included studies were as follows: First author, year of publication, country, ethnicity, Source of controls, Genotyping method, number of cases and controls and evidence of HWE in controls. A third reviewer would participate if some disagreements were emerged, and a final decision was made by the majority of the votes.

Statistical analysis

All statistical analyses were performed using STATA version 11.0 software (StataCorp LP, College Station, TX) and Review Manage version 5.2.0 (The Cochrane Collaboration, 2012). Hardy-Weinberg equilibrium (HWE) was assessed by χ2 test in the control group of each study [43]. The strength of associations between the 8q24 (rs13281615 and rs6983267) polymorphism and BC risk were measured by odds ratio (ORs) with 95% confidence interval (CIs). Z test was used to assess the significance of the ORs, I2 and Q statistics was used to determine the statistical heterogeneity among studies. A random-effect model was used if p value of heterogeneity tests was no more than 0.1 (p ≤ 0.1), otherwise, a fixed-effect model was selected [43, 44]. Sensitivity analyses were performed to assess the stability of the results. We used Begg's funnel plot and Egger's test to evaluate the publication bias [45, 46]. The strength of the association was estimated in the allele model, the dominant model, the recessive model, the homozygous genetic model, and the heterozygous genetic model, respectively. p < 0.05 was considered statistically significant. We performed subgroup according to Ethnicity.
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1.  8q24 prostate, breast, and colon cancer risk loci show tissue-specific long-range interaction with MYC.

Authors:  Nasim Ahmadiyeh; Mark M Pomerantz; Chiara Grisanzio; Paula Herman; Li Jia; Vanessa Almendro; Housheng Hansen He; Myles Brown; X Shirley Liu; Matt Davis; Jennifer L Caswell; Christine A Beckwith; Adam Hills; Laura Macconaill; Gerhard A Coetzee; Meredith M Regan; Matthew L Freedman
Journal:  Proc Natl Acad Sci U S A       Date:  2010-05-07       Impact factor: 11.205

2.  Association between polymorphisms within the susceptibility region 8q24 and breast cancer in a Chinese population.

Authors:  Yu Zhang; Pengfei Yi; Wei Chen; Jie Ming; Beibei Zhu; Zhi Li; Na Shen; Wei Shi; Juntao Ke; Qunzi Zhao; Xuzai Lu; Xueqiong Xun; Li Liu; Ranran Song; Hui Guo; Rong Zhong; Liming Liang; Tao Huang; Xiaoping Miao
Journal:  Tumour Biol       Date:  2014-01-11

3.  Meta-analysis in clinical trials.

Authors:  R DerSimonian; N Laird
Journal:  Control Clin Trials       Date:  1986-09

4.  Operating characteristics of a rank correlation test for publication bias.

Authors:  C B Begg; M Mazumdar
Journal:  Biometrics       Date:  1994-12       Impact factor: 2.571

5.  Allelic imbalance at an 8q24 oncogenic SNP is involved in activating MYC in human colorectal cancer.

Authors:  Keishi Sugimachi; Atsushi Niida; Ken Yamamoto; Teppei Shimamura; Seiya Imoto; Hisae Iinuma; Yoshiaki Shinden; Hidetoshi Eguchi; Tomoya Sudo; Masahiko Watanabe; Junichi Tanaka; Shinei Kudo; Kazuo Hase; Masato Kusunoki; Kazutaka Yamada; Yasuhiro Shimada; Kenichi Sugihara; Yoshihiko Maehara; Satoru Miyano; Masaki Mori; Koshi Mimori
Journal:  Ann Surg Oncol       Date:  2014-01-06       Impact factor: 5.344

6.  Novel genetic markers of breast cancer survival identified by a genome-wide association study.

Authors:  Xiao Ou Shu; Jirong Long; Wei Lu; Chun Li; Wendy Y Chen; Ryan Delahanty; Jiarong Cheng; Hui Cai; Ying Zheng; Jiajun Shi; Kai Gu; Wen-Jing Wang; Peter Kraft; Yu-Tang Gao; Qiuyin Cai; Wei Zheng
Journal:  Cancer Res       Date:  2012-01-09       Impact factor: 12.701

Review 7.  Breast Cancer: Epidemiology and Etiology.

Authors:  ZiQi Tao; Aimin Shi; Cuntao Lu; Tao Song; Zhengguo Zhang; Jing Zhao
Journal:  Cell Biochem Biophys       Date:  2015-06       Impact factor: 2.194

8.  Distribution of FGFR2, TNRC9, MAP3K1, LSP1, and 8q24 alleles in genetically enriched breast cancer patients versus elderly tumor-free women.

Authors:  Tatiana V Gorodnova; Ekatherina Sh Kuligina; Grigory A Yanus; Anna S Katanugina; Svetlana N Abysheva; Alexandr V Togo; Evgeny N Imyanitov
Journal:  Cancer Genet Cytogenet       Date:  2010-05

9.  Low penetrance breast cancer predisposition SNPs are site specific.

Authors:  Niall Mcinerney; Gabrielle Colleran; Andrew Rowan; Axel Walther; Ella Barclay; Sarah Spain; Angela M Jones; Stephen Tuohy; Catherine Curran; Nicola Miller; Michael Kerin; Ian Tomlinson; Elinor Sawyer
Journal:  Breast Cancer Res Treat       Date:  2008-11-13       Impact factor: 4.872

10.  Genome-wide association study identifies novel breast cancer susceptibility loci.

Authors:  Douglas F Easton; Karen A Pooley; Alison M Dunning; Paul D P Pharoah; Deborah Thompson; Dennis G Ballinger; Jeffery P Struewing; Jonathan Morrison; Helen Field; Robert Luben; Nicholas Wareham; Shahana Ahmed; Catherine S Healey; Richard Bowman; Kerstin B Meyer; Christopher A Haiman; Laurence K Kolonel; Brian E Henderson; Loic Le Marchand; Paul Brennan; Suleeporn Sangrajrang; Valerie Gaborieau; Fabrice Odefrey; Chen-Yang Shen; Pei-Ei Wu; Hui-Chun Wang; Diana Eccles; D Gareth Evans; Julian Peto; Olivia Fletcher; Nichola Johnson; Sheila Seal; Michael R Stratton; Nazneen Rahman; Georgia Chenevix-Trench; Stig E Bojesen; Børge G Nordestgaard; Christen K Axelsson; Montserrat Garcia-Closas; Louise Brinton; Stephen Chanock; Jolanta Lissowska; Beata Peplonska; Heli Nevanlinna; Rainer Fagerholm; Hannaleena Eerola; Daehee Kang; Keun-Young Yoo; Dong-Young Noh; Sei-Hyun Ahn; David J Hunter; Susan E Hankinson; David G Cox; Per Hall; Sara Wedren; Jianjun Liu; Yen-Ling Low; Natalia Bogdanova; Peter Schürmann; Thilo Dörk; Rob A E M Tollenaar; Catharina E Jacobi; Peter Devilee; Jan G M Klijn; Alice J Sigurdson; Michele M Doody; Bruce H Alexander; Jinghui Zhang; Angela Cox; Ian W Brock; Gordon MacPherson; Malcolm W R Reed; Fergus J Couch; Ellen L Goode; Janet E Olson; Hanne Meijers-Heijboer; Ans van den Ouweland; André Uitterlinden; Fernando Rivadeneira; Roger L Milne; Gloria Ribas; Anna Gonzalez-Neira; Javier Benitez; John L Hopper; Margaret McCredie; Melissa Southey; Graham G Giles; Chris Schroen; Christina Justenhoven; Hiltrud Brauch; Ute Hamann; Yon-Dschun Ko; Amanda B Spurdle; Jonathan Beesley; Xiaoqing Chen; Arto Mannermaa; Veli-Matti Kosma; Vesa Kataja; Jaana Hartikainen; Nicholas E Day; David R Cox; Bruce A J Ponder
Journal:  Nature       Date:  2007-06-28       Impact factor: 49.962

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Review 2.  Global Inequities in Precision Medicine and Molecular Cancer Research.

Authors:  Thomas M Drake; Stephen R Knight; Ewen M Harrison; Kjetil Søreide
Journal:  Front Oncol       Date:  2018-09-04       Impact factor: 6.244

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4.  Variants in the 8q24 region associated with risk of breast cancer: Systematic research synopsis and meta-analysis.

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