Participants in the highest DII tertile had a greater gastric cancer risk compared to those in the lowest tertile . Stratification by sex revealed that men who were in the highest DII tertile showed a greater risk of intestinal type . Participants positive for H. pylori infection had higher risk of intestinal type . In this case-control study, we found a significantly positive association with a pro-inflammatory diet on gastric cancer risk, after adjusting for covariates. Future studies are suggested to prospectively examine the effect of a pro-inflammatory diet on gastric cancer risk.We aimed to investigate the association with the Dietary Inflammatory Index (DII™) on the risk of gastric cancer and whether histological type modifies this association. From March 2011 to December 2014, 388 cases and 776 controls were enrolled at the National Cancer Center. Utilizing a food frequency questionnaire, thirty-five food components were used to score the DII. The tertile distribution of DII for controls was as follows: T1: <0.96, T2: 0.96-2.97, and T3: ≥2.97. To investigate the association between DII and the gastric cancer risk, multivariable logistic models were constructed. In subgroup analyses, histological types including intestinal and diffuse types were examined. As the DII increased, gastric cancer risk increased ( Helicobacter pylori (H. pylori) positive infection, which occurs in approximately 54.4% .Table p-value for trend <0.001). Additionally, the variable ‘sex’ exhibited a significant interaction on the association with DII on gastric cancer risk .When stratified by sex, compared to the lowest tertile of DII, the highest tertile was associated with an increased risk of gastric cancer in women but not men . Women also showed a significantly increasing trend for gastric cancer risk as DII increased . Furthermore, H. pylori infection did not show a significant interaction .When stratified by the presence of Table H. pylori positive infection, those in the greatest tertile of DII had 2.16 times the risk of intestinal type gastric cancer than those in the lowest tertile . No other significance according to histological types was observed. Overall, individuals with H. pylori negative infection exhibited stronger associations than H. pylori positive individuals .Regarding sex differences, men showed a significantly increased risk for the intestinal type in the highest tertile of DII compared to the lowest tertile of DII, after adjusting for confounding factors . By contrast, women had a significantly increased risk of both intestinal and diffuse types associated with the highest DII tertile compared to the lowest DII tertile . Of the participants with H. pylori infection, individuals with DII scores in the highest tertile exhibited a greater gastric cancer risk. In the subgroup analyses based on histological type, men with higher DII values had a greater risk of the intestinal type, but women with higher DII scores had greater risks of both histological types. Additionally, when stratified by H. pylori infection, intestinal type was associated with greater DII scores only among individuals with H. pylori positive infection.In this case-control study of 1,164 Korean adults, higher diet-related inflammation as assessed by the DII was strongly associated with a greater risk of gastric cancer, even after adjusting for potential confounding variables. In particular, when stratified by sex, a significant association with a higher DII on gastric cancer risk was observed among women. Additionally, regardless of H. pylori infection or histological types of gastric cancer. Moreover, our study participants resided in a setting in which gastric cancer is the most prevalent cancer among men and the second most prevalent cancer after thyroid cancer among women [H. pylori positive infection and consumed different foods than the Italian population.Our findings regarding the greater gastric cancer risk associated with higher DII scores are consistent with previous epidemiological studies, including those of colorectal cancer , 22, esoH. pylori negative infection showed stronger associations between DII and the risk of gastric cancer than individuals with H. pylori positive infection. As pro-inflammatory or anti-inflammatory modulators, foods and dietary patterns play a role in increasing or decreasing the risk of gastric cancer. Previous studies related to the inflammatory potential of diets on gastric cancer have shown that dietary risk factors such as a high intake of salty foods or red meats increase the risk of gastric cancer [Unexpectedly, our current study indicated that individuals with c cancer , 28, whec cancer , includic cancer , has beec cancer . Moreovec cancer .The etiology of cancers is strongly linked to chronic inflammation as a main contributor to the development of carcinoma , 5. GastH. pylori, attributable to atrophic gastritis, chronic gastritis, and intestinal metaplasia, and related to well-differentiated and distinctive margins and the formation of tubules or glands [H. pylori but not with atrophic gastritis and intestinal metaplasia; in addition, the diffuse type is related to less differentiated, infiltrative to neighboring structures, indistinctive margins and family history. [The findings of our subgroup analyses based on different histological types are consistent with previous reports . Regardir glands , 35. Thehistory. , 35 The history. , indicathistory. .H. pylori infection, a major risk factor for gastric cancer, and we further investigated the associations stratified by H. pylori infection. However, this study also has limitations that must be considered when interpreting our results. This case-control study may have been subject to recall bias and selection bias. The cases might have had better recall than the controls, and the participants in the control group might have been more health-conscious than those in the case group. In addition, reverse causality is possible; some of the controls who reported having less pro-inflammatory diets had altered their diets due to subclinical symptoms of disease.This study has several strengths. First, a previously validated food frequency questionnaire (FFQ) was applied . Second,H. pylori positive infection. Our findings suggest that diet-related inflammation has an important role on gastric cancer risk. Future studies investigating genetic susceptibility to inflammation-related polymorphisms are suggested.In conclusion, in a case-control study of 388 cases and 776 controls, we observed a significantly positive association between a higher pro-inflammatory diet and a greater risk of gastric cancer, even after adjusting for potential confounding factors. In particular, in the subgroup analysis according to histological type, the risk of intestinal type gastric cancer increased as DII scores increased among men and individuals with H. pylori infection were excluded. At enrollment, 1,710 individuals agreed to participate. Of those, 50 individuals were excluded for providing incomplete dietary data. Of the remaining 1,660 participants, 9 were excluded for reporting an implausible range of total energy intake of <500 kcal or >5000 kcal. Eighty seven adults were additionally excluded from the remaining 1,651 due to missing information regarding BMI (n=9), H. pylori infection (n=1), physical activity (n=8), education (n=65), or family history of gastric cancer (n=4). Of the remaining 1,564 participants, 388 cases and 776 controls were finally included based on frequency matching by age (within 5 years) and sex using a 1:2 ratio. As a result, 1,164 participants were included in the final analyses  from March 2011 to December 2014. The details have been previously described . BrieflyA semi-quantitative FFQ including 106 food items was utilized to examine dietary intake. Usual food intake over the previous year was assessed by asking the study participants how frequently they ate each food item, with frequency rated as one of nine categories: never or rarely, once a month, two or three times a month, once or twice a week, three or four times a week, five or six times a week, once a day, twice a day, and three times a day. The validity as well as reproducibility of the FFQ have been previously confirmed and reported . CAN-PRO12, vitamin B6, β-carotene, carbohydrate, cholesterol, total fat, fiber, folic acid, garlic, ginger, Fe, Mg, monounsaturated fatty acids (MUFAs), niacin, n-3 fatty acids, n-6 fatty acids, onion, protein, polyunsaturated fatty acids (PUFAs), riboflavin, saturated fat, Se, thiamin, vitamin A, vitamin C, vitamin D, vitamin E, Zn, green/black tea, flavan-3-ol, flavonols, flavones, flavanones, isoflavones and anthocyanidins. The residual method was used to adjust for total energy intake [To obtain the DII score, dietary assessment data from the FFQ were utilized. The validity of the DII has previously been examined . The mety intake . As the y intake .H.pylori infection: a rapid urease test , histology, or serology.All participants were asked to complete self-administered questionnaires that included items on demographics, lifestyle characteristics, and medical history. A positive result on at least one of the following tests was sufficient to confirm 2), education, smoking (pack-years), drinking , physical activity, H. pylori infection, and a first-degree family history of gastric cancer. In particular, the covariate variable of smoking was measured in pack-years, which was calculated as the number of packs per day multiplied by the number of years of smoking. The variable of physical activity was measured with the question, ‘Do you regularly exercise? (Yes/No)’. Furthermore, for the subgroup analyses, histological types, i.e., intestinal and diffuse, were examined and additionally stratified by sex and H. pylori infection. A two-tailed p-value <0.05 was considered significant. Statistical analyses were performed using SAS version 9.3 .The general characteristics of the study sample were examined using t-tests and chi-square tests. The descriptive characteristics of the study participants were assessed according to the DII tertiles. The normality of the DII was checked. The distribution of the DII tertiles in the control group was as follows: T1: <0.96, T2: 0.96-2.97, and T3: ≥2.97. To investigate the association between DII and the risk of gastric cancer, multivariable logistic regression models were constructed to estimate ORs and 95% CIs. The potential confounding factors were total caloric intake, BMI (kg/m


Consumption of grilled meat conferred a significantly higher risk of breast cancer in all women (hazard ratio (HR) 1.77, 95% confidence interval (CI) 1.09–2.85) and in postmenopausal women . High-cholesterol food intake was associated with a higher risk in all women . Irregular meal intake was associated with an elevated risk in all women  and in premenopausal women . Our findings suggest that grilled meat and high-cholesterol food intake and irregular eating habits may be associated with a higher risk of breast cancer. Further studies with longer follow-up periods that include information on portion size, hormone receptor status, carcinogen levels in grilled meat, and a classification of foods by source are required.Breast cancer is the leading cause of cancer in females and has become a major global health priority. This prospective cohort study investigated the association of dietary factors, including food items and dietary habits, with the risk of breast cancer in Korean women. Study participants were women aged 30 years or older, recruited from the National Cancer Center in South Korea between August 2002 and May 2007. They were followed until December 2014 using the Korea Central Cancer Registry to identify breast cancer cases. Among 5046 non-pre-diagnosed cancer participants, 72 breast cancer cases were prospectively identified. Participants with breast cancer had a significantly higher educational level (college or higher: 58.3% vs. 39.5%,  Cancer is the leading cause of death worldwide and has become a major public health problem ,2. In KoDiet is considered a modifiable risk factor and accounts for approximately 35% of all cancer causes. Therefore, it is important to identify the dietary risk factors of cancer ,7,8. ThePrevious epidemiological studies have addressed the dietary risk factors of breast cancer, considering nutrient or food items alone and together with dietary patterns ,22,23,24Dietary factors tend to differ by population due to socioeconomic status, ethnicity, and culture . TherefoAccordingly, the aim of this prospective cohort study was to investigate the association of dietary factors with the risk of breast cancer among all women and in groups stratified by menopausal status: premenopausal and postmenopausal women.The source population included 14,531 men and women aged 30 years and older enrolled at the Center for Cancer Prevention and detection at the National Cancer Center in South Korea between August 2002 And May 2007. Male subjects were excluded, resulting in the selection of 6477 female subjects. A total of 1276 subjects were excluded for having an incomplete short-form Food Frequency Questionnaire (FFQ), and 155 subjects were excluded due to a diagnosis with any type of cancer before enrollment. Potential breast cancer cases  were ascertained by linkage to the 2014 KCCR database, which provides information on cancer incidence by following case classifications in Korea. after baseline examination of general characteristics and dietary factors (16 food groups and six dietary habits), the participants were followed up and contributed to the person-years until whichever came first: the date of any cancer diagnosis except for non-melanoma skin cancer (ICD-10 Code C44), death, or the end of the follow-up (December 2014). Seventy-two subjects developed breast cancer, and 4974 subjects were identified as breast cancer non-cases . Written2).At the time of enrollment, all participants were asked to complete self-administered questionnaires regarding socio-demographic characteristics , family history of cancer, cigarette and alcohol consumption, regular exercise habits, history of benign breast tumor, and reproductive factors . Height and weight were measured using an InBody 3.0  body composition analyzer or the X-SCAN PLUS II Body Composition Analyzer , and body mass index (BMI) was calculated as weight (kg)/height ; breakfast frequency ; meal time regularity ; meal speed ; frequency of overeating ; and the use of additional spices such as soy sauce, table salt, soybean paste, or hot pepper paste .t-test, chi-square test, or Fisher’s exact test. As the distribution of intake frequencies of each food item was skewed, the FFQ items were reclassified into two categories, low and high consumption, based on the median distribution of non-cases. A Cox proportional hazards model with person-years as the underlying time metric was used to evaluate the hazard ratio (HR) and 95% confidence interval (CI) of breast cancer for each dietary factor, including 16 food groups and six dietary habits. Multivariate Model 1 was adjusted for only significant factors of our study subjects’ general characteristics (2), family history of breast cancer , smoking status , alcohol consumption , physical activity , age at menarche , parity , oral contraceptive use , and benign breast tumor history  in premenopausal women. In all women, we additionally adjusted for hormone use and menopausal status , and age at menopause . In postmenopausal women, we additionally adjusted for hormone use  and age at menopause . All statistical analyses were performed using SAS software , and the level of significance was set at p < 0.05.The characteristics of the study subjects were compared using Student’s eristics , and Modp = 0.01) and the postmenopausal group , were more likely to be ever-smokers in the all  and premenopausal groups , and more frequently had a history of benign breast tumor in the all  and postmenopausal groups  than did non-cases  and Model 2 . A similar finding regarding irregular meal intake was observed in premenopausal women in both multivariate Model 1  and Model 2  ( = 0.03) , but not = 0.03) .This prospective cohort study investigated the impact of dietary factors on the risk of breast cancer. After adjusting for confounding factors, grilled meat intake was proportionally associated with breast cancer risk in the all and postmenopausal groups, high cholesterol food intake was associated with a higher risk of breast cancer in all women, and meal irregularity was positively associated with breast cancer risk in the all and premenopausal groups.The higher risk of breast cancer associated with grilled meat consumption is speculated to be due to carcinogenic mutagens such as heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs), which are highly abundant in meat cooked at high temperatures, especially that which is grilled or barbecued ,37. HCAsOther byproducts of grilled meat are advanced glycation end products (AGEs), which are created from a Maillard or browning reaction—non-enzymatic glycosylation of reducing sugars interacting with free amino groups of proteins, lipids, or nucleic acid ,52. By canalysis . One stuanalysis . Some stanalysis , Alzheimanalysis , and Baranalysis . Howeveranalysis ,57 and tanalysis  to prediHigh-cholesterol food intake was associated with a higher breast cancer risk in the all women in the age-adjusted model and in multivariate Model 2, but this association was not found in multivariate Model 1. The role of dietary cholesterol in breast cancer remains unclear ,60. HoweN-nitroso compounds (NOCs), which are potential carcinogens [In our study, the animal fat-rich food and fast food groups primarily consisted of processed meat products. Nitrate and nitrite, which are abundant in processed meats, are precursors of cinogens . Howevercinogens  and cohocinogens ,73, in pcinogens , and in cinogens . By contcinogens  and cohocinogens , in postcinogens ,77, and cinogens ,79.Light-colored, green-yellow, and salted vegetables and fruits were included in the category of fruits and vegetables. Although these food groups showed non-significant associations with the risk of breast cancer, several studies have indicated significant associations of foods other than salted vegetables. Vegetable intake may decrease the risk of breast cancer, but no association has been identified for fruits ,80,81. SWe found no association between cereal consumption and breast cancer risk. Several cohort studies of postmenopausal women also showed non-significant associations of whole grain  and refiDairy foods were not associated with breast cancer risk in our study. In a meta-analysis of 12 prospective studies, total dairy food intake was inversely associated with breast cancer, with low-fat dairy intake showing stronger associations in premenopausal women in particular ; howeverOf the six dietary habits assessed, meal irregularity was associated with a higher risk of breast cancer in all and premenopausal women. However, no previous studies have addressed the direct association between regular eating and breast cancer risk. One study identified an interaction between eating frequency and an inflammatory biomarker, C-reactive protein (CRP), as a putative factor associated with breast cancer. This study showed that a higher eating frequency was inversely proportional to serum CRP levels, suggesting that eating more frequently may lower systemic inflammation and subsequently reduce breast cancer risk .P for trend was significant in all women after adjusting for covariates. This result indicates that adding less spice may decrease the risk of breast cancer.Adding spices to food was not associated with breast cancer risk. In a case-control study, adding table salt was a non-significant factor . PsycholOur study has certain strengths: it is a cohort study with a follow-up period of more than nine years. We examined dietary factors at baseline when the subjects did not have breast cancer, so the reports were not subject to selection or recall bias. Additionally, as breast cancer is known to have a long latency, it is important to evaluate dietary patterns prior to the development of disease when environmental exposure may play a strong role , indicatSeveral limitations should also be mentioned: (1) Our FFQ data cover only baseline information and cannot reflect changes in dietary factors after baseline; (2) Additionally, residual confounding variables may have affected our results. Baseline nutritional status and genetic susceptibility may interact with diet and influence its relationship with breast cancer ; howeverIn conclusion, this study showed that breast cancer risk was proportionally associated with grilled meat intake in all women and in postmenopausal women, with high-cholesterol food intake in all women, and with irregular eating in all and premenopausal women. Further studies with longer follow-up periods that include information on portion size, hormone receptor status, levels of each carcinogen in grilled meat, and classification of food groups by the source are needed.


LIN28B for AM and rs16991615 in MCM8 for ANM; whereas in the AA group, only one SNP (rs2947411 in TMEM18) for AM was directionally consistent and nominally significant. In analysis of TRLS and NRLS, several SNPs were significant, including rs466639 in RXRG that was associated with both phenotypes in both AA and EA groups. None of the PRS was associated with breast cancer risk. Given the paucity of data available among AA populations, our study contributes to the literature of genetics of reproductive aging in AA women and highlights the importance of cross population replication of GWAS variants.Reproductive aging phenotypes, including age at menarche (AM) and age at natural menopause (ANM), are well-established risk factors for breast cancer. In recent years, many genetic variants have been identified in association with AM and ANM in genome-wide association studies among European populations. Using data from the Women’s Circle of Health Study (WCHS) of 1,307 European-American (EA) and 1,365 African-American (AA) breast cancer cases and controls, we aimed to replicate 53 earlier GWAS variants for AM and ANM in AA and EA groups and to perform analyses on total and net reproductive lifespan . Breast cancer risk was also examined in relation to a polygenic risk score (PRS) for each of the reproductive aging phenotypes. We replicated a number of variants in EA women, including rs7759938 in  Menarche and menopause are two fundamental physiological events in a woman’s life, respectively marking the beginning and the end of reproductive age. At those turning points, estrogen milieu inside a woman’s body undergoes drastic changes, and it is the lifetime exposure to sex hormones that is long speculated to have a profound impact on women’s health, extended from reproduction to hormone-related morbidities including breast cancer. Early age at menarche (AM) and late age at natural menopause (ANM) are well-established risk factors for breast cancer. The risk was estimated to increase by a factor of 1.050 for each year earlier at menarche and by 1.029 for each year older at menopause[As two complex numeric human traits, the timing of AM and ANM varies greatly among individuals. They are under the influence of an intricate set of social, environmental and genetic factors. It was estimated that genetic inheritance explains over half of the variation (heritability for AM at 53–74% and for ANM at 44–65%)–9, whichRacial/ethnic minority populations, particularly African Americans (AA), are under-represented in the GWAS of reproductive aging phenotypes in previous studies. Among the published GWAS, only one on AM and one In the Women’s Circle of Health Study (WCHS), which enrolled a similar number of AA and EA women, with and without breast cancer, we aimed to replicate earlier GWAS findings in both AA and EA women, as well as to examine them with breast cancer risk in the two groups.The WCHS was designed to study genetic and non-genetic factors in association with early and aggressive breast cancer risk in EA and AA women. A more detailed description of the study population has been published elsewhere–38. In bpost hoc for the clustering plots, to ensure call robustness.Our goal was to replicate single nucleotide polymorphisms (SNPs) that were previously identified in GWAS of AM and ANM in EA populations. Therefore, we selected 53 SNPs from 5 GWAS published by June 2011, 24–26 wA panel of ancestry informative markers (AIMs) developed by the Black Women’s Health Study was included as part of the customized genotyping array and the data were used to ascertain genetic ancestry and to control for population admixture. EstimatThe final dataset included 2,672 women, consisting of 658 EA breast cancer cases, 649 EA controls, 621 AA cases, and 744 AA controls. All analyses were performed separately for AA and EA women. T-tests or chi-square tests were used to compare descriptive characteristics between breast cancer cases and controls in univariate analyses. The two primary phenotypes, AM and ANM, were obtained via self-reported menstrual and pregnancy history. Women were defined as postmenopausal if they reported that they had ceased menstruation naturally for at least 12 consecutive months and those who had hysterectomy or oophorectomy were excluded from analysis of ANM, TRLS, and NRLS (n = 356). In addition to AM and ANM, we also computed another two phenotypes, total reproductive lifespan (TRLS) and and net reproductive lifespan (NRLS), using the numeric variables of AM and ANM. We considered TRLS to be the difference between ANM and AM, and NRLS to be equal to TRLS with time spent on all pregnancies and breastfeeding subtracted. Except for AM, the other three phenotypes were analyzed only among postmenopausal women. For AM analyses, 1365 AA women and 1307 EA women contributed in these analyses. For ANM/TRLS/NRLS analyses, 381 AA women and 465 EA women were included in the analyses.Each single SNP was related to one of the four reproductive aging phenotypes, AM, ANM, TRLS and NRLS, using generalized linear modeling (GLM) with adjustment chosen a priori for birth year, estimated proportion of European ancestry, smoking (not for AM), and number of pregnancies (not for AM or NRLS) ensuring a parsimonious model. Additive genetic models were tested, with the common allele in the EA group designated for both AA and EA groups as the reference allele. Genotype analyses were first conducted in the case and control groups separately, which produced estimates of similar magnitude in the same direction. Thus, all analyses were conducted with case-control combined and the case-control status was included as a covariate in the GLMs. For the purpose of our study, replication of previous GWAS hits was defined by nominal significance of p≤0.05 and concordance in direction of effect for the same phenotype.To test whether SNPs associated with reproductive aging had an impact on breast cancer risk, a polygenic risk score (PRS) was calculated for each of the four reproductive phenotypes, combining the effects of all SNPs associated with the phenotype at a nominal p-value <0.10. PRS for each phenotype and for each individual was calculated as a sum of the number of risk alleles, weighted by the regression coefficients from GLM. To align SNPs with opposite directions in their associations with a phenotype, the reference allele of those with a negative regression coefficient was flipped, so the regression coefficient became positive and could be tallied with other SNPs. As a result, a higher PRS indicated an older AM or ANM, or a longer TRLS and NRLS. Each PRS was then categorized into quartiles based on the distribution among controls, and related to breast cancer case-control status with a priori adjustment for age, estimated proportion of European ancestry, and family history of breast cancer, ensuring a parsimonious model. All analyses were performed using SAS 9.4 .LIN28B, was one of the most consistent variants in previous GWAS for AM. In our study, rs7759938 was associated with a 0.2 year of delay in menarche per copy of the variant G allele (p = 0.004). In addition, rs4843747 in 16q24.2–3, which was associated with later ANM in a previous GWAS, was shown to be significantly associated with a 0.2-year delay in AM per copy of the variant A allele (p = 0.01) in EA women in our study. In AA women, 20 out of 37 SNPs selected for association with AM were in the same direction as in the previous GWAS ; yet only rs2947411 in TMEM18 reached a nominal significance (p = 0.03) in our study. rs757647 in KDM3B, which was associated with earlier AM in EA women in our study, was also significant in AA women but in an opposite direction.In EA women, 31 out of 37 SNPs selected for association with AM were in the same direction as in the previous GWAS , including 4 with a nominal significance of p≤0.05 and another 5 with a suggestive significance of p≤0.10 . The topMCM8 was also one of the most significant SNPs identified in GWAS for ANM, with each copy of the variant A allele associated with 1.3-year delay in the onset of menopause. rs236114, another SNP in MCM8, was also replicated with ANM in EA women in our study with a suggestive level of significance (p = 0.08). In addition, two SNPs previously associated with AM, rs7821178 in PXMP3 and rs6589964 in BSX, were associated with ANM in EA women in our study. In AA women, none of the selected SNPs for association with ANM were in the same direction and reached a nominal significance. Instead, five SNPs previously associated with AM were significant with a nominal p≤0.10, including rs1859345 in SPOCK.In EA women, 2 out of 16 SNPs selected for association with ANM were in the same direction as in the previous GWAS and reached a nominal significance of p≤0.05 . The topMCM8 in EA women and rs1859345 in SPOCK in AA women, both of which were also associated with ANM and NRLS. Their effect sizes were similar across the three phenotypes . The top SNP associated with NRLS in EA was rs1079866 in INHBA, which was also associated with AM in our analysis. Per copy of the variant G allele was associated with a 0.9 year shorter in NRLS, whereas it was associated with only a 0.2-year delay in AM. Another noteworthy SNP is rs466639 in RXRG, which was associated with both TRLS and NRLS in both AA and EA women. Its effect size appeared to be slightly larger for NRLS  than for TRLS (0.8 to 1.0 year), the latter of which was similar to its effect size for ANM in AAs (0.8 year per copy).Tables The correlations between the PRS for each reproductive aging phenotype and the phenotype itself were highly significant as expected (p≤0.0002); yet the strength of the correlations was weak (correlation coefficient 0.16–0.21) . None ofLIN28B and rs16991615 in MCM8. Nevertheless, the replication of the same set of SNPs in AA women was not as successful. Only one out of the 37 selected SNPs for AM was significant in the same direction, and none of the 16 selected SNPs for ANM was significant. In analyses of TRLS and NRLS, a number of SNPs associated with AN or ANM in previous GWAS were associated with the two reproductive lifespan phenotypes, and most of the associations were consistent between the two phenotypes with similar effect sizes. We found little evidence for a strong impact of those reproductive aging-associated SNPs on breast cancer risk. Our analysis suggests that the overlap of genetic determinants of reproductive aging phenotypes between AA and EA populations may be limited, and highlights the importance of evaluating GWAS signals initially identified from EA populations in AA populations, before they can be generalized across populations.In a breast cancer case-control study with similar number of AA and EA women, we replicated several SNPs identified in previous GWAS of AM and ANM in the EA group, including the top GWAS signals rs7759938 in LIN28B for AM and rs16991615 in MCM8 for ANM, was mostly unsuccessful[In the past decade, GWAS has been a powerhouse in discovering common genetic variants reliably associated with a wide range of complex phenotypes and common diseases. To date, the only GWAS on AM and ANM among AA women did not identify any variants at a genome-wide significant level (5e-8), 23, whiuccessful, 21. Thiuccessful, but it uccessful–44. Consuccessful, 23. HowRXRG was associated with TRLS and NRLS in both AA and EA women with a similar effect size. This SNP was initially identified in association with AM in Europeans, with an effect size of 4.2 months earlier in AM per copy of the variant T allele[RXRG, which encodes retinoid X receptor gamma, a nuclear receptor that dimerizes with the receptors for retinoic acid, thyroid hormone and vitamin D and increases their DNA binding and transcriptional activity[Although most of the identified associations with AM and ANM in our study did not overlap between AA and EA women, a phenomenon we have repeatedly observed in previous studies of breast cancer risk, 45, 46, T allele. We repl activity. However activity. The var activity.To our knowledge, the literature on the overlapping of genetic underpinning between AM and ANM has been rather limited. In an early study in 1998 by Sneider et al., no correlation was found between age at menopause and age at menarche, suggesting distinct genetic mechanisms for the two phenotypes. In anothth and 5th highest quintiles of PRS for AM were associated with increased breast cancer risk, with a borderline significant trend; yet no association was found with PRS for ANM. In a recent large study of PRS based on 375 autosomal AM variants, increasing AM was associated with lower risk of breast cancer, particularly estrogen receptor (ER) positive cancer, as well as ovarian cancer and endometrial cancer, which was independent of the effects of those variants on obesity[Given the well-established associations of early AM and late ANM with breast cancer risk, it is a reasonable next step to examine whether GWAS SNPs for reproductive aging are associated with breast cancer risk, an approach known as Mendelian randomization analysis that is less likely to be biased by confounding factors and can be used to infer causality. In the first Mendelian randomization analysis for breast cancer risk, He and colleagues examined 19 AM SNPs and 17 ANM SNPs, individually and as PRS for early AM to combine the effects of multiple markers, in the ReproGen Consortium. Two AM n obesity. The lacα = 0.05) after controlling for multiple comparisons via FDR correction. Therefore, our results should be interpreted with caution of small sample size. This is particularly relevant to our speculation of different genetic underpinning for AM and ANM based on the marked lower replication success rate in AAs than in EAs. Due to limited statistical power, we evaluated the concordance of the direction of associations between our study and previous GWAS literature, as an additional criterion for replication success, which is not stringent statistically speaking. However, our study is among the first to include both AA and EA women from a well-characterized study for breast cancer health disparities. Given the challenges in conducting fully powered GWAS among AA populations and the paucity of data available, our study contributes to the literature for genetics of reproductive aging phenotypes in AA women and highlights the importance of cross-race/ethnicity replication of GWAS variants. Another strength is the inclusion of reproductive lifespans in our analysis, which may have a higher impact on the risk of breast cancer than either AM or ANM alone.We noted a few limitations in our study. Because genotyping was performed in 2011, which predated a number of large-scale GWAS for AM and ANM, our analysis included only variants identified from earlier studies and missed a substantial proportion of variants known to date. The sample size was also limited, especially for analysis of phenotypes among postmenopausal women. In addition, none of the SNPs for the four phenotypes passed the significance threshold Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Circadian disruption caused by exposure to light at night (LAN) has been proposed as a risk factor for breast cancer and a reason for secular increases in incidence. Studies to date have largely been ecological or case-control in design and findings have been mixed.We investigated the relationship between LAN and breast cancer risk in the UK Generations Study. Bedroom light levels and sleeping patterns at age 20 and at study recruitment were obtained by questionnaire. Analyses were conducted on 105 866 participants with no prior history of breast cancer. During an average of 6.1 years of follow-up, 1775 cases of breast cancer were diagnosed. Cox proportional hazard models were used to calculate hazard ratios (HRs), adjusting for potential confounding factors.in situ  breast cancer, or oestrogen-receptor (ER) positive ; or negative  tumours separately. The findings did not differ by menopausal status. Adjusting for sleep duration, sleeping at unusual times (non-peak sleep) and history of night work did not affect the results. Night waking with exposure to light, occurring around age 20, was associated with a reduced risk of premenopausal breast cancer .There was no association between LAN level and breast cancer risk overall : 0.88–1.15), or for invasive  or In this prospective cohort analysis of LAN, there was no evidence that LAN exposure increased the risk of subsequent breast cancer, although the suggestion of a lower breast cancer risk in pre-menopausal women with a history of night waking in their twenties may warrant further investigation. Breast cancer is the most common cancer diagnosed in women worldwide and incidence continues to rise. Internationally, the highest rates are seen in economically developed countries (In 1978 This ‘light at night’ (LAN) theory has stimulated epidemiological investigations. To date, most studies have been either ecological in design, correlating cancer incidence rates in populations with estimates of outdoor ambient LAN , a cohort study of >113 000 women from the UK, recruited at ages ⩾16 years since 2003. Recruitment involved a baseline postal questionnaire about established and potential breast cancer risk factors, and donation of a blood sample. Participants are followed up approximately every 3 years, by postal or online questionnaires, to obtain updated risk factor and outcome information , or the date after which ‘flagging’ notification was not yet complete (taken to be 1 March 2014). If the follow-up questionnaire was not returned and the woman was not covered by ‘flagging’ (<1.2% of the cohort), the follow-up was truncated at the date of her last returned questionnaire.Only a small proportion of women reported LAN in the lightest category (‘light enough to read’ 0.96% for the year before recruitment and 1.92% at age 20). This group was therefore combined with the adjacent group . Cox proportional hazards regression  using attained age as the implicit timescale was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer risk in relation to LAN exposure. To control for potential confounding by other breast cancer risk factors, we adjusted for year of birth, history of benign breast disease, history of breast cancer in a first-degree relative, socioeconomic score, age at menarche, age at first birth, parity, duration of breastfeeding, oral contraceptive (OC) use before menopause, hormone replacement therapy (HRT) use, menopausal status, age at menopause, pre-menopausal and post-menopausal body mass index, alcohol consumption, smoking and leisure time physical activity , with time-varying data incorporated for age at first birth, parity, menopausal status, age at menopause, OC use and HRT use. Socioeconomic score was based on place of residence , which are reported in the text. Analyses were also adjusted for ever night shift work, sleep duration, and non-peak sleep to investigate their potential impact.Results are presented by oestrogen-receptor (ER) status of breast cancer and menopausal status during follow-up. Age at menopause was assumed to be 50 years for women whose menopausal status or age at menopause was not known. Analyses presented in the tables are for invasive plus P-values were two-sided.All statistical tests were performed using Stata/IC version 14.0  and all n=809) had died and 1.2% (n=1323) were lost to follow-up . During follow-up, 1775 breast cancers (1503 invasive and 272 in situ) were diagnosed. ER status was ascertained for 99% of invasive and 58% of in situ cases; the latter was lower because hormone receptor assays are often not undertaken for in situ lesions in the UK. Histology was ascertained for 95% of cases. Further details of breast cancer cases are described in Of the 113 207 women recruited to the GS during June 2003 – June 2012, 6581 were excluded from analysis because of prior breast cancer, 14 due to prior bilateral mastectomy and 746 due to missing LAN information, leaving 105 866 women included in the analysis. Their mean age at recruitment was 46.5 years (range 16–102 years) and mean follow-up was 6.1 years (standard deviation=1.0). Follow-up by questionnaire (96.3%) or ‘flagging’ (1.7%) was complete for 98% of women, 0.8% (n=102 972) of participants in this analysis reported LAN information at age 20. A greater proportion of women reported waking at night and turning on lights or going into a bright room during the year before recruitment than at age 20 . Seventeen percent of participants reported a history of night shift work during the 10 years before recruitment (we did not have consistent information on shift work before that).Characteristics of the cohort are shown in vs lowest LAN level at recruitment was 1.00 (95% CI: 0.81–1.24) and at age 20 was 0.91 (95% CI: 0.73–1.13). Similarly, there was no significant association between LAN and breast cancer risk when the analysis was limited to post-menopausal follow-up  for invasive and HR=0.96 (95% CI: 0.83–1.11) for in situ breast cancer for the highest vs lowest LAN level at recruitment (not in Table). Similarly, there was no association between LAN and the risk of different morphological subtypes of breast cancer: for the highest vs lowest level LAN at recruitment, ductal HR=1.00 (95% CI: 0.86–1.16), lobular HR=0.92 (95% CI: 0.66–1.28) and other types HR=1.41 (95% CI: 0.85–2.35) (not in Table).When analyses were repeated to investigate the risk of invasive and P=0.04; P=0.03), but not ER-negative  cancers and no effect for post-menopausal breast cancer.There was no relationship between reported night waking with exposure to light in the year before recruitment and risk of breast cancer , duration of sleep  or non-peak sleep  (not in Table).There was no impact on our results when analyses comparing the highest LAN It has been hypothesised that suppression of nocturnal pineal melatonin production in response to LAN might explain the rises in breast cancer rates that have accompanied industrialisation and electrification in westernised countries . ExposurClearly, if circadian disruption of melatonin plays a substantial role in the aetiology of breast cancer, it is of major public health importance. Following early epidemiologic studies .In our analysis of over 105 000 UK women, we found no evidence of a relationship between self-reported level of domestic exposure to LAN and subsequent raised risk of breast cancer. Published ecological studies using satellite data to derive LAN exposure in Israel, South Korea and worldwide have tended to show high light levels associated with a 30–70% increased risk of breast cancer  and the extent of window covering blocking light entry. The LAN measure we used takes in all of these factors, whereas a residential address only gives potential information on external light. Thus a woman in a dark rural area with no street lighting might nevertheless leave her bedroom light on at night and sleep with high LAN, and a woman in a city centre might use shutters or blinds to keep out external light and hence sleep in total darkness. A potential limitation of our study is that it uses self-reported LAN exposure information. However, since this information was ascertained before breast cancer occurrence, it should not have biased the results. Misclassification would be likely to have diluted any true relation, but the lack of any sign of raised risk does not suggest a relationship.In conclusion, we found no evidence of an association between LAN exposure and raised risk of breast cancer in this large UK-based cohort study. Although our findings raise the possibility of a protective effect in pre-menopausal women who reported night waking with exposure to light at age 20, this was a subset analysis with modest statistical significance, based on relatively few breast cancer cases, has not been reported elsewhere, and has no plausible mechanism, so cannot be taken as strong evidence unless confirmed independently.


Family history is an important risk factor for breast cancer incidence, but the parameters conventionally used to categorize it are based solely on numbers and/or ages of breast cancer cases in the family and take no account of the size and age-structure of the woman’s family.Using data from the Generations Study, a cohort of over 113,000 women from the general UK population, we analyzed breast cancer risk in relation to first-degree family history using a family history score (FHS) that takes account of the expected number of family cases based on the family’s age-structure and national cancer incidence rates.Ptrend < 0.0001) with greater FHS. There was a 3.5-fold (95% CI 2.56–4.79) range of risk between the lowest and highest FHS groups, whereas women who had two or more relatives with breast cancer, the strongest conventional familial risk factor, had a 2.5-fold (95% CI 1.83–3.47) increase in risk. Using likelihood ratio tests, the best model for determining breast cancer risk due to family history was that combining FHS and age of relative at diagnosis.Breast cancer risk increased significantly  contains supplementary material, which is available to authorized users. Breast cancer is the most common cancer in women and the leading cause of cancer-related deaths in women worldwide . In addiIn assessing risk of breast cancer, the categorization of family history as a risk factor for breast cancer has ranged from presence or absence of a family history –13 to moThe Generations Study (GS) is a prospective cohort study that began recruiting women aged 16 or older from the general population of the United Kingdom in 2003 and now comprises over 113,000 women who completed an extensive questionnaire and provided consent . The firn = 6604) or who reported that they did not know about their biological parents or siblings (n = 3905) were excluded. This left 103,738 participants who formed the analysis cohort. Breast cancers occurring in GS cohort participants were reported by these participants in their follow-up questionnaires, and later confirmed by cancer registry records, general practitioners, pathology records, or through ‘flagging’ at the National Health Service Central Registers .The current analytic cohort is based on women who joined the study between June 2003 and June 2012, inclusive. Participants who had been diagnosed with breast cancer before entering the study , and for these women, year of birth was estimated . For analysis, we considered for each participant, all female full first-degree relatives as her ‘family cohort’ . Each relative in such a family cohort was considered to enter risk at her own date of birth and to leave risk at her year of breast cancer diagnosis, year of death (for mothers only), or the year the participant’s recruitment questionnaire was received , whichever occurred earliest.Information about first-degree relatives’ dates of birth, cancer history, and, for parents only, dates of death was provided by the participants in their recruitment questionnaire. There were 294,100 recorded first-degree female relatives of participants in this analysis cohort. For a small proportion of relatives, year of birth was not stated, or the stated year was impossible  for that family. We will refer to this SIR as the ‘Family History Score’ (FHS).We assessed risk of breast cancer in GS participants in relation to the FHS of their family, by calculating hazard ratios (HR) using Cox-proportional hazards regression with left truncation and right censoring, with age as the underlying time scale . The FHSTo observe the impact of unknown vital status in sisters and daughters , we conducted sensitivity analyses reducing the follow-up time (and hence expecteds) for sisters and daughters of GS participants in line with mortality rates in women of similar ages to the sisters and daughters without a history of breast cancer.Because of the recruitment method , about 2For the main analyses, in situ diagnoses in participants were included together with invasive breast cancers, since ductal carcinoma in situ (DCIS) is widely considered to be a precursor of invasive breast cancer . Women wA likelihood ratio test was used to compare the contribution of the FHS to models with the presence of a family history of breast cancer (yes/no), the number of relatives with breast cancer , the type of relative(s) with breast cancer , and the age of the youngest relative with breast cancer none, <45, ≥45), i.e., measures of family history used in previous epidemiological studies , ≥45, i.. All staAs of 30 August 2015, of the 103,738 GS participants, 1,474 were diagnosed with invasive breast cancer during follow-up and 259 with in situ diagnoses, giving a total of 1733 who reported breast cancer, with 99.8% confirmed from medical records. Follow-up questionnaires were completed by 96.3% of participants, and cancer status known from flagging for a further 1.7%. The remaining participants had either died (0.8%) or were lost to follow-up (1.2%). Total follow-up was 627,944 person-years, an average of 6.1 years per cohort member.Descriptive characteristics of women in the GS cohort are shown in Table Ptrend <0.0001). Participants in the highest FHS category had a relative risk of 3.50  compared with those with no affected relatives.The relative risks of breast cancer in participants in relation to FHS are shown in Table Ptrend < 0.0001; and FHS ≥ 100 HR = 3.61 95% CI 1.69–7.72, Ptrend = 0.0001, respectively). In sensitivity analyses after reducing follow-up time in sisters and daughters without a history of breast cancer (see Methods), there was no change in relative risks of breast cancer . When sensitivity analyses were conducted with only one participant proband included if more than one family member had joined the cohort (see Methods), there was still a similar increase in risk . Analysis of invasive breast cancer only (n = 1474 cases) also showed a significant increasing trend . Omitting participants for whom date of birth was missing for any relatives had no material effect on the results.The analysis in Table P < 0.0001), while the breast cancer risk in participants with two or more relatives diagnosed with breast cancer more than doubled . About 5% of participants with two or more relatives with breast cancer (n = 41) fell into the highest FHS score group, 10% into the FHS 50 ≤ 100 group (n = 72), and 85% (n = 656) had a FHS below 50.Table P < 0.0001) or sister only  with breast cancer. Participants who reported a relative diagnosed with breast cancer before age 45 had a relative risk of 2.47  which was significantly higher (P < 0.001) than those with an affected relative over age 45 . None of these measures showed as great a risk discrimination as the FHS.Breast cancer risks were similar in participants with a mother only , the number of relatives with breast cancer (P = 0.001), the type of relative with breast cancer (P < 0.001), and the age of relative at breast cancer diagnosis (P = 0.01). Conversely, addition of binary family history or number of affected relatives to a model with FHS showed some evidence of significant improvement , but the addition of type of relative to the FHS showed no significant improvement (P = 0.35). The best combination of variables was one with relative age at breast cancer diagnosis and FHS, for which the addition of either in the presence of the other showed a highly significant improvement (P = 0.006 and P = 0.01).Likelihood ratio tests comparing models with and without the addition of the FHS to models with the measures of family history in Table Family history is an important breast cancer risk factor, and one that can cause considerable anxiety to women . It is tBecause it is a continuous variable, the FHS allows for discrimination across the full spectrum of family histories in participants, while conventional discrete variables are confined to two or three categories of risk, with most of those with a positive family history falling into the lowest risk positive family history group . The highest FHS group had a greater relative risk than any of the highest risk groups from conventional categorizations of family history. The majority of participants who had two or more relatives with breast cancer were not in the highest FHS group, but instead fell into other, lower FHS groups, reflecting that multiple affected family members may not indicate a very high risk if a woman comes from a very large family.The addition of the FHS measure from Table As with any observational study, there were some limitations. Reports of family history of breast cancer were provided by participants in questionnaires and were unconfirmed, but there is evidence that information from women reporting breast cancer in their first-degree relatives is likely to be highly accurate, with 99% specificity and 96% sensitivity reported , 38–40.Another limitation was that vital status was only collected for parents in the baseline questionnaire. All sisters and daughters of participants therefore had to be considered alive, and those who had not been diagnosed with breast cancer were censored at the date the participant’s recruitment questionnaire was completed. For this reason, some family expected numbers are likely to be slightly overestimated, and subsequently the FHS slightly underestimated. Sensitivity analyses with reduced follow-up time for sisters and daughters, however, showed no material effect on the results.As stated above, about 28% of participants had a first-degree relative who was also a GS participant, but after removing from analyses all participating relatives who joined the GS after the first participating relative , results were essentially unchanged.Most relatives of participants (70.3%) were born before 1971. Therefore, estimated incidence rates were used for some calendar years for the majority of relatives when calculating the expected number of family breast cancers, since data on national rates do not exist before 1971. However, many of the person-years before 1971 were at young ages when breast cancer is uncommon. Thus, any errors consequent on these national rate estimations are likely to have been slight, and anyway non-differential, and therefore unlikely to have influenced the relative risks materially. This applies more so to the estimates of national breast cancer mortality rates before 1911: only 3.8% of participants’ relatives were alive before 1911.The FHS methodology could potentially be incorporated into risk prediction models for breast cancer, which currently use the number of first-degree relatives with breast cancer , 41–46. Supplementary material 1 (DOCX 19 kb)Below is the link to the electronic supplementary material.


FEN1) polymorphisms (rs4246215 and rs174538) with breast cancer risk in northwest Chinese women. We conducted a case-control study with 560 breast cancer patients and 583 age-matched healthy controls from Northwest China. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to estimate the associations. We found a significantly reduced risk of breast cancer associated with T allele of rs4246215 , especially in postmenopausal women . Furthermore, the polymorphism showed a decreased association with larger tumor size . For rs174538, we did not find any difference in all genetic models. However, rs174538 was associated with lymph node metastasis  and estrogen receptor status . Haplotype analysis showed that Trs4246215Grs174538 haplotype was a protective factor of breast cancer . Our results suggest that FEN1 polymorphisms may reduce the risk of breast cancer in Chinese women.This study aimed to assess the associations of two common Flap endonuclease 1 ( Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer death among females worldwide . It was FEN1) has been well characterized as a key factor in ensuring genomic stability and protecting tissues from tumorigenesis [FEN1, located on chromosome 11q12, contains two exons and one intron. FEN1 is essential for the long-patch pathway, which conducts a repair tract of at least two nucleotides. It plays an important role in the following processes: DNA repair by removing the 5′-flaps generated by Pol δ/ε [FEN1 expression is related to the development and progression of various cancers [FEN1 overexpression is common in breast [FEN1 might be a marker of tumor progression for many types of tumors [Genome stability is vital in the transmission of genetic information, and DNA molecule is vulnerable to factors that can induce DNA damage or block replication . Flap stigenesis . Human F Pol δ/ε , primer  Pol δ/ε , and the cancers . Previoun breast , predictf tumors .FEN1 may confer susceptibility to cancer. Two SNPs, 4150G>T  and −69G>A , were identified after thoroughly resequencing the FEN1 locus in 30 Han Chinese healthy volunteers [FEN1 and breast cancer. Therefore, we conducted a case-control study to investigate the associations of the FEN1 rs4246215 and rs174538 polymorphisms with BC risk in the Northwest Chinese population.Previous studies have suggested that single nucleotide polymorphisms (SNPs) in lunteers . And, uplunteers . It was P = 0.612) or the distribution of menopausal status (P = 0.716). However, the BMI was significantly different between BC patients and healthy controls (P = 0.038).Basic clinical characteristics of BC patients and the demographic characteristic of both the patients and healthy controls are presented in Table FEN1 rs4246215 and rs174538 polymorphisms are shown in Table P = 0.253, 0.922 for rs4246215 and rs174538, respectively). Compared with the GG genotype, the TT genotype frequency of the rs4246215 polymorphism among patients was significantly different from that of controls . The difference in the frequency distributions of T and G alleles among patients and controls was also significant . Compared to individuals with rs4246215 GG/GT genotypes, individual with TT genotypes had significantly decreased BC risk . These results suggested that the FEN1 rs4246215 polymorphism had a protective effect on BC. However, we did not observe any significant associations between the FEN1 rs174538 polymorphism and BC risk in any genetic model . However, there was no association between rs174538 and BC risk in either premenopausal patients or postmenopausal patients. Considering the difference of BMI in cases and controls, we infer that BMI may be a important factor of BC. Then, we performed a subgroup analysis by body mass index (BMI), choosing the overweight standard 24 kg/m2 as a cut-point. However, we did not observe any relationship between rs4246215 and breast cancer subjects with BMI≥ 24 kg/m2 (P = 0.10) or BMI< 24 kg/m2 (P = 0.26), as shown in Table Stratified analysis regarding the effect of rs4246215 and rs174538 polymorphisms on BC by menopausal status was displayed in Table FEN1 polymorphisms had an effect on the different clinical features of BC patients, we then analyzed the associations between the FEN1 polymorphisms and a series of clinic pathological parameters, including tumor size, lymph node metastasis, the status of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2). As shown in Table P = 0.02; dominant model: OR = 0.63, 95% CI = 0.44–0.90, P = 0.01). However, no significant association was detected in other clinical parameters of BC patients.To determine whether the P = 0.002; AA/GA vs. GG: OR = 0.61, 95% CI = 0.43–0.86, P = 0.005). Furthermore, the frequency of the variant genotypes of FEN1 rs174538 polymorphism was significantly higher in ER-positive patients .As shown in Table rs4246215Grs174538 haplotype, T rs4246215Grs174538 haplotype showed a decreased risk of breast cancer  about the association between the FEN1 and breast cancer risk. The authors indicated that rs174538GG and rs4246215GG genotypes were significantly correlated to increased risk for developing breast cancer compared with the mutation homozygote [In our study, we observed that variant genotypes of  studies . We searmozygote , which wFEN1 gene demonstrated a decreased association with tumor size. Furthermore, the variant genotypes of rs174538 were associated with negative lymph node metastasis and ER-positive status. These results suggested that the two polymorphisms in the FEN1 gene are related to the development and progression of BC, and may help to accurately predict the clinical course of BC. In addition, the rs4246215 polymorphism showed an increased association with postmenopausal status. We observed that patients with cancer have lower BMI in this study, it may be inferred that patients with BC lost weight after the onset of the cancer. We conducted the stratified analysis by BMI, but we did not observe any association between BMI and the two polymorphism.We also observed that the variant rs4246215 genotypes in the rs4246215Grs174538 haplotype may decrease the breast cancer risk. But, in Lv's et al. [rs4246215 Ars174538, Trs4246215Ars174538 and Grs4246215Grs174538 haplotypes were associated with increased risks of developing breast cancer compared with the Trs4246215Grs174538 haplotype. This was broadly consistent with our study. But, we did not found Grs4246215Ars174538, and Trs4246215Ars174538 haplotypes had any associations with breast cancer. The differences may be attributed to the geographical and life style differences in northwest and east Chinese women. Thus, more large well-designed repeated studies are needed to evaluate the results.Moreover, we did haplotype anayses and find the T s et al.  study, tOur study had some limitations. First, all people recruited in this study were from one hospital in the northwest China. Larger sample size and multi center experiment are needed for further verification. Second, more predisposing factors should be investigated, such as high-dose radiation exposure, and postmenopausal obesity. Third, we hypothesized that the two polymorphisms may enhance the expression of FEN1 gene via up-regulating the mRNA, thus strengthen the DNA repair ability of the injured breast cells. But, further experiments on cell or animal level are needed to explain the specific mechanisms.FEN1 polymorphisms have effects in reducing the BC risk in northwest Chinese women. Further functional studies and large population-based prospective studies are still required to elucidate the influence of FEN1 polymorphisms on BC.In summary, our case-control study indicates that The Institutional Review Board of the Xi'an Jiaotong University  approved the study. At the time of recruitment, written informed consent was obtained from all participants involved in the study.A total of 560 sporadic BC patients were recruited between January 2013 and October 2014 at the Second Affiliated Hospital of Xi'an Jiaotong University, China. The patients were recruited without age restrictions. All of the patients had pathologically confirmed. Patients were excluded if they had received chemotherapy or radiotherapy before surgery (guaranteeing the accuracy of tumor information we collected) or had other types of cancer. For comparison, 583 cancer-free controls were randomly selected from participants who were seeking health care in the outpatient departments at the hospital and were frequency-matched to the patients with age (± 5 years). All participants were interviewed with a self-administered questionnaire after obtaining the written informed consent.FEN1 according to the data on the Chinese population from HapMap (http://www.hapmap.org), were selected in our study. Sequenom MassARRAY Assay Design 3.0 Software  was used to design a multiplexed SNP MassEXTEND assay [Peripheral venous blood sample of about 2 mL was collected into tubes containing ethylene diamine tetraacetie acid from each participant and then stored at −80 °C for later use. The DNA concentration was measured by spectrophotometry , as described in our previous studies –24. Two ND assay . SNP genND assay , 26.2 test was used to examine the differences in the distributions of demographic characteristics, selected variables, and genotypes distributions of the two SNPs between the patients and controls. Hardy-Weinberg equilibrium (HWE) was tested by chi-square test for each SNP before the analysis. We conducted a case-control study for all of the subjects with adjustment for age and body mass index (BMI), and then performed the stratified analyses by menopausal status and BMI. The associations between FEN1 rs4246215 and rs174538 polymorphisms and breast cancer susceptibility were estimated by odds ratios (ORs) and 95% confidence intervals (CIs). Corresponding primers used for each SNP in our study are listed in Table P-value < 0.05 was considered as the criterion of statistical significance, and all statistical tests were two-sided. Phase2.1 software (download from http://stephenslab.uchicago.edu/phase/download.html) was used to conduct all common haplotypes and χ2 test was used to estimate the ORs and 95% CIs for each haplotype.The Student t-test or the χ


MAGEC3) reached chromosome-wide significance  advancing the age of onset by 6.7 years. In addition to the well-known contribution of BRCA, we demonstrate that a genetic locus on the X-chromosome contributes to ovarian cancer risk. An X-linked pattern of inheritance has implications for genetic risk stratification. Women with an affected paternal grandmother and sisters of affected women are at increased risk for ovarian cancer. Further work is required to validate this variant and to characterize carrier families.Given prior evidence that an affected woman conveys a higher risk of ovarian cancer to her sister than to her mother, we hypothesized that there exists an X-linked variant evidenced by transmission to a woman from her paternal grandmother via her father. We ascertained 3,499 grandmother/granddaughter pairs from the Familial Ovarian Cancer Registry at the Roswell Park Cancer Institute observing 892 informative pairs with 157 affected granddaughters. We performed germline X-chromosome exome sequencing on 186 women with ovarian cancer from the registry. The rate of cancers was 28.4% in paternal grandmother/granddaughter pairs and 13.9% in maternal pairs consistent with an X-linked dominant model  and inconsistent with an autosomal dominant model . Paternal grandmother cases had an earlier age-of-onset versus maternal cases  independent of BRCA1/2 status. Reinforcing the X-linked hypothesis, we observed an association between prostate cancer in men and ovarian cancer in his mother and daughters . Unaffected mothers with affected daughters produced significantly more daughters than sons . We performed exome sequencing in reported BRCA negative cases from the registry. Considering age-of-onset, one missense variant (rs176026 in  Our article uses the largest familial study of ovarian cancer to argue that there exists an ovarian cancer susceptibility gene on the X-chromosome acting independently of BRCA1 and BRCA2. This observation implies that there may be many cases of seemingly sporadic ovarian cancer that are actually inherited; for example, only daughters who inherit risk from their fathers. This X-linked pattern implies novel ways to prioritize families for screening even without additional testing—sisters must both be carriers or neither; fathers of women with potentially inherited ovarian cancer may receive new attention. In addition, we found evidence that other cancers affect fathers and sons in these families. Using sequencing technology, we isolated a candidate gene, MAGEC3, that may be associated with earlier onset of ovarian cancer. The further study of this gene and the X-linked pattern will require additional study. In a systematic review, Stratton and colleagues noted that, “not explicable in terms of any genetic model,” an affected woman’s sisters are at higher risk of disease than their mother [A history of ovarian cancer among first-order relatives remains the strongest and best-characterized predictor of ovarian cancer risk –3 and a r mother . We propBRCA1 and XCI [Genetic evidence of X-linkage has appeared in cytogenetic studies where loss of X-chromosome inactivation (XCI) can be visualized by loss of heterochromatin based Barr bodies . Studies and XCI ,16, espe and XCI , are mix and XCI ,18,19.Fig 1).The Familial Ovarian Cancer Registry housed at Roswell Park Cancer Institute , for over 35 years comprises over 50,000 participants and 5,600 cancers in 2,600 families. To leverage the deep pedigree data in this study, we reasoned that, if the disease allele passes through the father’s side of the family, it could be inferred by disease in a woman’s father’s mother. That is, by considering the frequency of disease transmission in grandmother/granddaughter pairs with an intermediate son/father. Under an autosomal dominant model, an affected grandmother  passes the disease allele to her granddaughter with probability 1/4. This means that data previously presented by affected mothers and sisters are not able to discriminate between autosomal and X-linked models and these effects have been previously indistinguishable by segregation analysis due to disease censoring in fathers. In the X-linked dominant model, while a maternal grandmother again passes the disease allele to her granddaughter with probability 1/4, a paternal grandmother passes the allele to her granddaughter with probability 1/2 due to deterministic transmission by the obligate carrier son/father. Therefore, we might discriminate between autosomal versus X-linked models by considering the rate of cancers in granddaughters with exactly one affected grandmother. An autosomal dominant model predicts an equal rate of cancers in maternal-lineage and paternal-lineage pairs while an X-linked dominant model predicts that paternal-lineage families will have twice the rate of cancers . The autosomal dominant model predicted too many maternal cancers and too few paternal cancers . The X-linked effect was robust versus ascertainment bias; we repeated the analysis excluding the granddaughters who were probands and observed a nearly identical relative risk .Among granddaughters with one affected grandmother , we obseWe observed a significant acceleration in the development of disease in granddaughters with an affected paternal grandmother versus maternal grandmother . Granddaughters with an affected maternal grandmother were not more likely to manifest early onset disease versus women with two unaffected grandmothers . While it does not affect the paternal/maternal effect conclusion, the subset of grandmother-granddaughter pairs possesses a higher risk than the average woman in the registry reflecting the selection bias towards families with more genetic follow up.We considered whether men in the path of transmission were more likely to develop other cancers. In the grandmothers-granddaughter trios with affected granddaughters, the intermediate father was more likely to report a prostate cancer diagnosis if his mother had had ovarian cancer  implying that the three generation pattern—ovary, prostate, ovary—was unusually common.BRCA1 mutation carriers, when the mother was unaffected thereby favoring paternal transmission, there was a strong bias towards daughters . Among BRCA1 mutation families there was no difference in sex ratio when the mother was affected . This number is close to the expected population ratio suggesting minimal ascertainment bias. There were insufficient BRCA2 carrying families to make an assessment.Because cancer-causing germline variants on X might affect the fitness of offspring and lead to an imbalance in sex birth ratio, we examined whether families were more likely to report female offspring. Removing the probands from the analysis, among BRCA negative families, there was a strong bias towards producing daughters when their mother was unaffected , putatively due to an allele transmitted by the father. The effect was attenuated for affected mothers but still significantly favored daughters . Conservatively, this number establishes a baseline for reporting bias in these families. Among Among children of men who reported cancers other than prostate cancer, the sex ratio was consistent with no sex bias  and, while men with prostate cancer reported an excess of daughters , the effect was shy of statistical significance.To estimate the potential impact of X-linked disease, we evaluated the kinship-based likelihood for autosomal dominant and X-linked models  for 1,38Fig 2, where in the first family, 4 of 5 daughters at risk developed ovarian cancers between the ages of 43 and 51 (a sixth daughter underwent prophylactic oophorectomy). This situation favors X-linkage: assuming a carrier penetrance of 0.65 [We illustrated this imbalanced likelihood in the pedigrees represented in  of 0.65  and a noBRCA1 coding region for 159 affected registry women who reported a negative BRCA test. The set comprised 49 cases with an affected mother only, 46 with an affected sister only, and 7 with both. Among the 2,161 common variants, one exceeded the chromosome-wide significance level  at position ChrX:140,983,127 (GRCh37/hg19) with LOD = 4.91. This position mapped to rs176026, a missense SNP  in the MAGEC3 gene on Xq27.2.To identify candidate X-linked loci, we sequenced the germline X-chromosome exome and 2 = 0.086, p = 0.96) . Notably, all women with A/G genotypes developed cancer before age 53. The A/G effect was pronounced for women with affected sisters only  with a median 11.0 years earlier onset (p<0.001). While women who either carry the rs176026 risk allele or were from BRCA1 variant families had similar age-of-onset (43 years), rs176026 carriers without BRCA1 variants had a stronger hazard  suggesting that the X-linked variant has the stronger effect in these families.We observed 138 ovarian cancer cases with the A/A genotype, 20 women with the A/G genotype and 1 woman with the G/G genotype (Hardy-Weinberg Equilibrium (HWE), X = 0.96) . While t2 = 0.7395, D’ = 1.0) another misssense SNP 63 base pairs away. Within the MAGEC3 coding sequence, two intronic SNPs  were also weaky linked to rs176026  and retained an age of onset effect. Altogether 28 women had a variant in any of rs176026 (N = 21), rs176024 (N = 14), rs73577987 (N = 18), or rs73577990 (N = 11) with a median age of onset of 53.4 , more than 9.4 years earlier than the rest of the familial cases . The frequency of the possible haploblock  was 1.96% (15/766) in all European populations, 0.7% (7/1003) in all African populations, and 26.8% (205/764) in all East Asian populations.The candidate SNP was in strong linkage with rs176024 (RHOXA13 motif and a PU.1 (SPI1) motif. rs176024 directly affects a predicted ERα binding motif reducing the PWM score 80%. While intronic, rs73577987 affected a STAT motif and an EWSR1-FLI1 motif; rs73577990 affected BDP1, ERα, NRSF and PU.1 motifs. Functional prediction suggested that rs176026 was more likely to be deleterious to secondary protein structure: PolyPhen-2 predicted rs176026 to have a probably damaging effect  [MAGEC3 [S1C Fig). In contrast, rs176024 was scored benign .HaploReg (v4) analysis noted that the rs176026 alternate allele increase the binding affinity for a ty 0.96)  and to a [MAGEC3  forcing MAGEC3  possesses two copies of the MAGE homology domain (MHD) that defines members of the MAGE gene duplication family. Unlike other cancer-testis antigens, which are expected to have no expression in normal tissue, MAGEC3 appears to have low to moderate expression in a range of normal tissues tested by the GTEx project [S2 Fig). Using the classic CT antigen MAGEA1 to provide a reference for lower limit of detection, we saw that MAGEC3 had a median expression 83x (brain) higher than MAGEA1, 47.5x (blood vessel endothelium), 9.2x (ovary) . Results were similar for classic CT antigens NY-ESO-1 (CTAG1B), MAGEA3, MAGEC1 while in contrast CT-like antigens  showed higher expression in other tissues.The canonical isoform of  project  MAGEC3 may perform a tumor suppressive function like many inherited cancer genes.In contrast, the TCGA mRNA data the level was 1.2x higher  and 0.80x . Notably in the cancers, there were three groups present: the set around the unity line where MAGEC3 expression was at the limit of detection, a set left of the line (7.1% 23/306) where MAGEA1 is likely expressed, a set right of the line  where MAGEC3 may be expressed. Even for the fraction of tumors expressing MAGEC3, the level was 2.65 fold lower than normal tissue (t-test p = 0.0009). These patterns are consistent with the idea that S4 Fig). These frequencies are correlated with the IARC reported incidence of ovarian cancer  especially when excluding the Chinese American (CHD), African American (ASW) and Mexican ancestry (MEX) populations living in the United States (r = 0.858). The 1000 genomes populations were similar (r = 0.903); notably the Japanese in Tokyo, Japan (JPT), Masaii in Kinyawa, Kenya (MKK) and Luhya in Webuye, Kenya (LWK) had unusually high incidence of ovarian cancer given their minor allele frequency.As we noted, the G allele frequency for rs176026 in the CEU HapMap study was 5%; in the African population (YRI), G is the major allele with 65% frequency  pair. We conclude that the ascertainment bias due to an unaffected mother is minimal and the notion that the relevant pairs represent paternal lineage is well supported.We considered whether the increased frequency of granddaughter cancers might be a result of ascertainment. Because two qualifying cancer cases are required to register a family, if a mother cannot contribute the case, then one may suspect the granddaughter is a qualifying case. In paternal grandmother/granddaughter pairs (N = 229), we noted that 164 granddaughters did not have cancer and could not be qualifying cases. The second qualifying case was a sister or paternal aunt in 79.1% of cases. In paternal pairs, 42.3% had an affected paternal aunt and 6.7% an affected maternal aunt, confirming evidence of specific lineage. The paternal/maternal granddaughter cancer rate was similar when stratifying pairs by initial ascertainment by mother/daughter , sister/sister , or 2Reflecting a maternal-lineage ascertainment bias, there were more maternal than paternal pairs (663 versus 229). Mean accession identifiers for maternal and paternal grandmothers are 10.3 and 14.9, respectively , suggesting that we have preferentially contacted maternal grandmothers. The direction of this potential bias is consistent with our suspicion that paternal pairs are underreported and reduces the precision of the paternal-lineage cancer rate estimate. Granddaughters with ovarian cancer in maternal families were not older than paternal-lineage women , but unaffected women were 4.2 years older (p<0.001). While statistically significant, this difference was not likely to be the source of a 14% increase in cancer risk.The rate of granddaughter ovarian cancers was not different between families with and without BRCA1/2 mutations , therefore BRCA status cannot be a confounding variable for women ascertained for family history. Alternatively, we observed the risk- doubling effect when stratifying 864 pairs into site-specific ovary  or breast/ovary syndrome families ; there was no confounding by a family history of breast cancer. Therefore, an X-linked gene might confer ovarian cancer-specific risk independent of BRCA-type disease.We have presented evidence that there may exist an X-linked model of transmission of an ovarian cancer susceptibility gene. Our observations are supported by a large familial study and the novel use of grandmother/granddaughter pairs to observe an increased rate of cancer among paternal granddaughters, an earlier age of onset, and a bias towards families with more daughters. We sequenced the X chromosomes of a small number of registry members in order to isolate a candidate gene, but we cannot rule out the possibility that our reported variant is in linkage with the true variant. However, the segregation analysis and age of onset analyses do suggest that it is likely to lie on the X chromosome.Future studies are warranted to confirm the identity and function of the X-linked gene that contributes to familial transmission of ovarian cancer. Limitations of our study include the case-only design, which has required us to forgo investigating common variants. While the number of pedigrees in the registry is large, unrelated case-control studies are much larger and would likely yield other potential variants. Our study population is nearly exclusively Caucasian and our results may not extend to other populations. Our exome sequencing approach focuses on the coding regions of the X-chromosome only. This design is unable to identify intragenic variants and complex rearrangements not involving exons.2 = 0.48. The missing gene might be ovarian cancer specific.Evidence of X-linkage is not inconsistent with the prevailing autosomal dominant BRCA1/2 with polygenic weak variant effects model for ovarian cancer . Ramus aovarian cancer only, a mother’s ovarian cancer raised her daughters’ ovarian cancer risk  while a sister’s ovarian cancer nearly quadruples her sister’s risk (OR = 3.92) [We suspect that the difficulty identifying this missing heritability may be due, in part, to historically inconsistent disease definition. In our literature review, we noted that studies that ascertained patients for breast cancer first and then acquired family members with ovarian cancer only saw increased risk to mothers . Indeed, = 3.92) . TherefoMAGEC3 is a potential candidate for the X-linked gene near previous linkage loci and it possesses a missense variant with large effect, rare prevalence and is associated with earlier onset. While MAGEC3 is thought to be a cancer testis antigen, it shows some expression in normal heart, brain, fallopian tube and pituitary gland tissues suggesting that the loss of expression of MAGEC3 plays some role in cancer formation. We have previously shown that co-expression patterns of the MAGE genes are non-random in ovarian tumors [CTAG1B) signaling highly aggressive tumors [We have provided some evidence that n tumors  and thate tumors .NSE3) binds with NSE1 and complexes with SMC5/SMC6 to repair double strand breaks via homologous recombination [MAGEA [MAGEC2 [MAGEC3 directly as a putative X-linked tumor suppressor [MAGEC3 (Xq27.2) has been previously linked to these hereditary cancers [The only MHD-carrying yeast homologous gene  have been accessioned continuously from 1981 to present as described previously . BrieflyBRCA1 and/or BRCA2 positive if any one family member tests positive for a deleterious mutation. If every family member tests negative, the family is classified as BRCA negative.Considering women who (a) were at least 45 without disease at last contact or (b) had died without disease and those with confirmed ovarian cancer, we observed over 8,900 mother-daughter pairs and 27,000 sister-sister pairs. From large registry pedigrees, we ascertained 3,499 women with two grandmothers who possessed a recorded disease status. Of these granddaughters, 2,569 reported no affected grandmothers (73.4%), 892 had exactly one affected grandmother (25.5%) and 38 had two affected grandmothers (1.1%). These women came from large pedigrees where the average family under study has 27.3 individuals (range: 8–330). Of the 3,499 pairs, 619 belonged to high-risk families tested for BRCA mutations as previously described [10(0.05/2161) = 4.636 on the log odds (LOD) scale. BRCA status was re-evaluated based on the sequencing results.Due to the age of the study cohort and availability of blood samples, we could not sequence all of the grandmother/granddaughter pairs. We focused on 159 women who reported a negative BRCA test and had an available DNA sample. DNA samples were whole-exome sequenced using Agilent SureSelect Human All Exome 50Mb kits v3 and v5. Raw sequence reads were aligned to the Human Reference Genome (NCBI Build 37) using the Burrows-Wheeler Aligner (BWA) , Picard Ovarian cancer incidence data was downloaded from the IARC website . HapMap n + Wc women with ovarian cancer among a sistership of N = Nn + Nc women, where the subscripts n and c refer to non-carriers and variant carriers. The likelihood of W given the probability of disease in carriers (pc) and non-carriers (pn) can be constructed by assuming that, conditional on Nn and Nc, Wn and Wc are simply binomial random variables. Under an autosomal model, Nc is Binomial . Under the X-linked model, P(Nc = N) = P(Nc = 0). Evaluating these likelihoods by enumerating admissible combinations of  is straightforward.Assume that we observe W = Whttps://www.gtexportal.org/) aligning reported female cases only with known tissue sample types. We normalized the MAGEC3 RNAseq levels to MAGEA1 levels on a per sample basis. TCGA ovary data on the quantile normalized HuEx array were downloaded from the GDAC Firehose (https://gdac.broadinstitute.org/) and cBioPortal’s normalized RNAseq Z-scores.We downloaded GTEx v7 data . It can be shown that the X-linked likelihood was maximized by a granddaughter cancer rate of 14.0%. Goodness-of-fit was tested versus a chi-square distribution. Pedigree likelihoods were evaluated using the kinship2  algorithS1 FigMAGEC3 affects the backbone between two MAGE homology domains (C) leading to a predicted conformational change and loss of function.X-chromosome wide exome sequencing (A) yielded a single SNP associated with earlier age of ovarian cancer onset (B). The variant in in (TIF)Click here for additional data file.S2 FigMAGEC1 and MAGEC2 show classic cancer testis antigen patterns while MAGEC3 shows moderate levels of expression in most tissues. (B) Log10 RPKM RNAseq data again shows MAGEC3 has moderate expression in a variety of tissues.(A) (TIF)Click here for additional data file.S3 FigRelative expression to MAGEA1, which is not expressed in normal tissue, is nearly 100x higher in brain tissue. The TCGA categories are ovarian tumors measured by array and RNA sequencing.(TIF)Click here for additional data file.S4 FigG/A allele relative frequencies by population and geographic location (A); CHD and ASW are the Chinese American and African American populations in Denver and southwest USA. Sorted by allele frequency and expected genotype frequency (B). Alelle frequency is correlated with national incidence of ovarian cancer in HapMap and 1000 genomes (C).(TIF)Click here for additional data file.