P value: 0.00625). In stratified analysis, we found another three SNPs were significantly associated within different subgroups. However, after Bonferroni correction , there were no statistically significant was observed. In gene-environment interaction analysis, we observed gene-environment interactions played a potential role of in the risk of breast cancer. These findings provide new insight into the associations between the genetic susceptibility and fine classifications of breast cancer. Based on these results, we encourage further large series studies and functional research to confirm these finding.Some of the new breast cancer susceptibility loci discovered in recent Genome-wide association studies (GWASs) have not been confirmed in Chinese populations. To determine whether eight novel Single-Nucleotide Polymorphisms (SNPs) have associations with breast cancer risk in women from southeast China, we conducted a case-control study of 1,156 breast cancer patients and 1,256 healthy controls. We first validated that the SNPs rs12922061, rs2290203, and rs2981578 were associated with overall breast cancer risk in southeast Chinese women, with the per-allele OR of 1.209 (95%CI: 1.064-1.372), 1.176 (95%CI: 1.048-1.320), and 0.852 (95%CI: 0.759-0.956), respectively. Rs12922061 and rs2290203 even passed the threshold for Bonferroni correction ( Breast cancer is the most common type of cancer that is diagnosed and the most common cause of cancer deaths among females worldwide . In ChinBreast cancer is a complex disease which results from both genetic factors and traditional risk factors. There are a number of traditional risk factors that have been reported to be associated with breast cancer, including age, age at menarche, reproductive and menstrual history, Body Mass Index (BMI), alcohol intake, smoking, physical activity, benign breast diseases, oral contraceptives, and hormone therapy –13. AlthIn this study, we conducted a case-control study of 1,156 breast cancer patients and 1,256 healthy controls from a southeast Chinese population to investigate the associations between eight novel GWAS-identified independent genetic susceptibility loci and the risk of breast cancer –22. In aP≤0.05). There were no statistical differences in the other risk factors between patients and controls (P>0.05). With regard to the ER/PR status of the breast cancer patients, there were 778 (67.3%) ER positive cases and 709 (61.3%) PR positive cases included in this study.A total of 1,156 breast cancer patients and 1,126 health controls were selected for this study, and their characteristics are summarized in Table P>0.05). After adjusting for age, age at menarche, and family history of breast cancer, we found three of the eight SNPs are significantly associated with overall breast cancer risk. The SNP rs12922061 is most strongly associated with breast cancer risk. The per-allele OR is 1.209  for the rs12922061 T allele, 1.176  for the rs2290203 G allele, and 0.852  for the rs2981578 T allele. Rs12922061 and rs2290203 even passed the threshold for Bonferroni correction . We had power of 85.14%, 80.24%, and 78.50% to detect an OR of 1.209, 1.176, and 0.852 for rs12922061, rs2290203, and rs2981578 with the frequency of 0.276, 0.482, and 0.450 respectively. In addition, two SNPs  have a marginally significant association with breast cancer risk. Specifically, rs2981578 and rs10474352 are associated with a decreased risk, which is contrary to the previous reports [Table  reports , 19. We P=0.031). However, for Luminal or HER-2 overexpression type cases, rs12922061 have a more significant effect . Meanwhile, among women of a younger age, with lower BMI, lower education, a younger age at menarche, postmenopausal status, older age at menopause, more menstruation years, older age at first live birth, longer period of breast feeding, ER positive, HR positive, and with Luminal or HER-2 overexpression type, the association for rs2290203 is significant, regardless of the family history of breast cancer. Moreover, regardless of the family history of breast cancer, rs2981578 shows a protective effect in women of a younger age, with lower BMI, higher education, younger age at menarche, premenopausal status, fewer menstruation years, younger age at first live birth, fewer live births, shorter period of breast feeding, ER positive, HR positive, and Luminal type or HER-2 overexpression type. After heterogeneity analysis, we observed that there are heterogeneities between the two subgroups in age and BMI . Nevertheless, rs2981578 presents a more significant protective effect in the group with a younger age and the group with a lower BMI .The results of stratified analysis are displayed in Table P=0.047), between the subgroup of breast subtype for rs9693444 (P=0.042), and between the subgroup of breast subtype for rs4951011 (P=0.042). Only rs4951011 shows a more significant association with the Basal-like type of breast cancer (P=0.005). No heterogeneities were observed in the rest of the subgroups. After Bonferroni correction , there were no statistically significant was observed in stratified analysis.Although, the other loci are not associated with overall breast cancer risk, there still are significant associations between these loci and different subgroups. The SNP rs10474352 shows a significant protective effect in the women of a younger age at menarche, those with a longer period of breast feeding, those without a family history of breast cancer, who are ER negative, and HR negative. In addition, rs10816625 is significantly associated with the women of an older age, a higher BMI, lower education, younger age at first live birth and who are ER positive and HR positive. Moreover, rs2296067, rs9693444, and rs4951011 are associated with women of an older age at first live birth, shorter period of breast feeding, and Basal-like type, respectively. There are heterogeneities between the subgroup of age at first live birth for rs2296067 (P trend =3.97×10−4).As shown in Table P=0.046, P=0.035, and P=0.007, respectively). Additionally, rs10816625 has an interaction with the age at the first live birth for the risk of breast cancer (P=0.017). Moreover, there is a significant interaction between rs9693444 and the length of the breast feeding period for breast cancer risk (P=0.032)  gene, which encodes the PRC1 protein and is suspected of being strictly regulated in a cancer-specific manner. The PRC1 protein is a mitotic spindle midzone-associated protein and is a substrate of a cyclin-dependent kinase [PRC1 gene is down-regulated by p53, whereas in p53 defective cells, it is over-expressed [PRC1 gene is significantly higher in breast tumor tissue, compared with adjacent normal tissue [PRC1 gene could be a predictor of the poor prognosis for breast cancer patients [PRC1 gene, but it does relate to the PCCD1 gene  [PCCD1 gene.The SNP rs2290203 was discovered in an East Asian GWAS, and has been confirmed in European populations . In thist kinase . The PRCxpressed . Also, tl tissue . A studypatients . However2290203) , 27. We FGFR2) gene. In 2007 this gene was reported to be associated with breast cancer in women of European descent [FGFR2 gene, is a receptor tyrosine kinase, which is an essential part of the signaling pathway of the growth and differentiation for cells in breast tissue [FGFR2 gene [The SNP rs2981578 is located in intron 2 of the fibroblast growth factor receptor 2  [For rs2296067, rs4951011, and rs9693444, no significant ORs were observed in the present study. However, there were still significant associations between these loci and different subgroups. Moreover, it has been previously reported that rs9693444 is associated with breast cancer risk in Chinese women (44×10−4) . We beliSeveral limitations need to be taken into consideration in this study. Above all, the sample size in our study is still limiting and that will affect the sensitivity and accuracy of the results. Particularly in the stratified analysis, the incidences of some epidemiological characteristics were not numerous enough and cannot be used to efficiently analyze our data. In addition, as our study was a hospital based case-control study, there was a certain selection bias compared with the general population. The self-reported life-style factors of participation might also have a recall bias. Therefore, in the next few years, we will expand the sample size and perform a large series study to improve the sensitivity and accuracy of the study, aiming to reduce these biases, and better understand the relationship between breast cancer risk and these susceptibility loci.In conclusion, our study is the first study to validate that rs12922061 on 16q12.2, rs2290203 on 15q26.1, and rs2981578 on 10q26.13 are associated with overall breast cancer risk in southeast Chinese women. The SNPs rs12922061 and rs2290203 even passed the threshold for Bonferroni correction. In addition, the three other SNPs  are found to have a significant association within different subgroups. Moreover, gene-environment interaction analysis revealed that there are interactions between rs2981578 and the age, BMI, and family history of breast cancer, between rs10816625 and the age at the first live birth, and between rs9693444 and the length of the breast feeding period. These findings may provide new insight into the association between genetic susceptibility and the fine classifications of breast cancer, which would guide clinical therapy in the future. Finally, it is certain that further large series studies and functional research are still warranted.All study participants were genetically unrelated Chinese females from Fujian province. There are 1,166 breast cancer patients and 1,258 healthy controls in this hospital-based case-control study. Patients were randomly enrolled from Fujian Medical University Union Hospital, Fujian, China, between January 2005 and December 2015, and each case was histopathologically confirmed by at least two oncologists. Estrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER-2) status of patients were evaluated by immunohistochemical analysis. It was considered to be a positive result when the percentage of stained cancer cell nucleus were ≥10%. The rest of the clinicopathological data was obtained from medical records. Healthy controls were selected from people who were undergoing routine health examinations in the same hospital during the corresponding period. Controls were age-matched (±5 years) healthy individuals without breast cancer and other cancers. After written informed consent was obtained, each study participant was personally interviewed face-to-face by trained interviewers to collect information that included demographic data, menstrual, reproductive and breastfeeding history, environmental exposure history, previous benign breast disease history, and their family history of breast cancer. Subsequently, a 3-ml peripheral blood sample was collected. This study was approved by the Ethics Committee of Fujian Medical University Union Hospital.P≤5.0×10−8). The SNP rs13294895 was excluded owing to have an minor allele frequency (MAF) <0.1 in Han Chinese from Beijing from HapMap. The MAF of rs140068132 in Han Chinese from Beijing from HapMap have not been reported, therefore rs140068132 was removed. A further SNP rs3803662 has been incorporated in our foregoing experiments. Thus, the remaining eight SNPs were selected for genotyping, namely 16q12.2-rs12922061 (CASC16), 15q26.1-rs2290203 (PRC1), 10q26.13-rs2981578 (FGFR2), 5q14.3-rs10474352 (ARRDC3), 9q31.2-rs10816625 (LOC105376214), 9-rs2296067 (KDM4C), 8p12- rs9693444 (DUSP4-MIR3148), and 1q32.1-rs4951011 (ZC3H11A).We selected 23 novel GWAS-identified SNPs by reviewing the available literature between January 2013 and December 2015 , 38–40. Genomic DNA was extracted from leukocytes from EDTA anti-coagulated whole blood using the Whole-Blood DNA Extraction Kit , according to the manufacturer's protocol. The concentration of the DNA samples was quantified with an Epoch Microplate Spectrophotometer , and quality of DNA samples was determined by agarose gel electrophoresis. Qualified DNA samples were genotyped by SNPscan, which is a high-throughput SNPs genotyping technology . Finally, the raw date was analyzed by the GeneMapper 4.0 Software . A five percent sample of both the cases and controls were randomly selected as blinded duplicates for quality assessment purposes and 100% agreement was obtained. Due to DNA quality or quantity, genotyping of ten cases (0.86%) and two controls (0.16%) failed. The call rate for per-SNP was 99.5%. After removing all data from these 12 participants, there were 1,156 cases and 1,256 controls in the final analyses.2 tests, for categorical variables. Genotype data of control samples were evaluated for consistency with the Hardy-Weinberg equilibrium (HWE) by a goodness-of-fit χ2 test. The associations between SNPs and the risk of breast cancer were assessed by computing odds ratios (ORs) and 95% confidence interval (CIs) using conditional logistic regression models (co-dominant model and additive model) with adjustment for potential confounders such as age, age at menarche, and family history of breast cancer. The power of the study was carried out by using the Quanto, version 1.2.4, with the disease risk in the Chinese population was 268.6 per 100000. The date was then stratified into fourteen subgroups , and assessed the cumulative effect of multiple genetic risk variants by calculating OR and 95%CI with adjustment for potential confounders as described above. Furthermore, the genetic-environment multiplicative interaction analysis was applied to explore the interactions between susceptibility loci and traditional risk factors, and it was performed by a multinomial logistic regression model. All of the statistical analyses were two-sided and a P value equal to or less than 0.05 was taken as the significance level. The Bonferroni correction was adopted to correct multiple comparisons. Analyses were carried out by using the Statistical Package for the Social Sciences Differences in demographic characteristics, risk factors and frequencies of alleles and genotypes between cases and controls were evaluated by t-test, for continuous variables, or χ


A steadily increasing pattern of breast cancer mortality has been reported in South Korea since the late 1980s. This paper explored the trends of educational inequalities of female breast cancer mortality between 1983 and 2012 in Korea, and conducted age-period-cohort (APC) analysis by educational level.Age-standardized mortality rates of breast cancer per 100,000 person-years were calculated. Relative index of inequality (RII) for breast cancer mortality was used as an inequality measure. APC analyses were conducted using the Web tool for APC analysis provided by the Division of Cancer Epidemiology and Genetics at the U.S. National Cancer Institute.An increasing trend in breast cancer mortality among Korean women between 1983 and 2012 was due to the increased mortality of the lower education groups , not the highest education group. The breast cancer mortality was higher in women with a tertiary education than in women with no education or a primary education during 1983-1992, and the reverse was true in 1993-2012. Consequently, RII was changed from positive to negative associations in the early 2000s. The lower education groups had the increased breast cancer mortality and significant cohort and period effects between 1983 and 2012, whereas the highest group did not.APC analysis by socioeconomic position used in this study could provide an important clue for the causes on breast cancer mortality. The long-term monitoring of socioeconomic patterning in breast cancer risk factors is urgently needed.The online version of this article (doi:10.1186/s12939-017-0554-6) contains supplementary material, which is available to authorized users. Breast cancer was the leading cause of cancer death and the most commonly diagnosed cancer among women worldwide in 2012 .Breast cancer was viewed as a “cancer of affluence” because the incidence and mortality of breast cancer was positively related to high socioeconomic position and more developed countries , 5. AlthAge-period-cohort (APC) effect analysis can provide clues of the cause of the disease through describing temporal variations of disease incidence or mortality. APC effect on breast cancer has been analyzed in many studies including those done in Korea –16. HoweIn this study we examined the trends of educational inequalities of female breast cancer mortality between 1983 and 2012 in Korea, and conducted APC analysis by educational level.http://mdis.kostat.go.kr/). Female breast cancer mortality data were categorized into seven 5-year age groups , six 5-year calendar periods , and three educational levels . The National Census data were used as the denominators (numbers of women at risk) and categorized into seven 5-year age groups and three educational levels for each census year. The 5-year birth cohort groups were defined by subtracting age from period based on the midpoint of the five-year age band and the mid-year of the five-year calendar period.Female breast cancer  mortality data (death certificate data) from 1983 to 2012 and the National Census data in 1985, 1990, 1995, 2000, 2005, and 2010 were obtained from Micro Data Service System provided by Statistics Korea  for each 5-year calendar period was estimated using Poisson regression and adjusted for age. Relative index of inequality (RII) for breast cancer mortality was used as an inequality measure. To obtain RII, we ordered the educational groups from highest (tertiary) to lowest  and calculated the cumulative percentage distribution of each educational group by 5-year age groups and 5-year calendar periods. Poisson regression model was used to estimate the RII. These analyses were performed using SAS software version 9.3 .http://analysistools.nci.nih.gov/apc/). Additional details of the Web tool are described elsewhere [APC analyses were conducted using the Web tool for APC analysis provided by Division of Cancer Epidemiology and Genetics at the U.S. National Cancer Institute . The estimated RII for breast cancer mortality comparing the lowest with the highest education categories was 0.72 (95% CI = 0.60 to 0.85) in 1988-1992 and 1.23 (95% CI = 1.10 to 1.36) in 2008-2012  and the secondary education group (p < 0.0001)  and the secondary education group (p < 0.0001) , not the highest education group. The breast cancer mortality was higher in women with a tertiary education than in women with no education or a primary education during 1983-1992, and the reverse was true in 1993-2012. Consequently, RII was changed from positive to negative associations in the early 2000s. We also found significant cohort and period effects in the lower education groups, while cohort and period effects were not observed in the highest education group.Breast cancer has been one of the well-known diseases with a positive social gradient , 18, 19.The change of the inequality pattern could be explained by the changes of the incidence, survival, and risk factors across education groups. Breast cancer incidence among Korean women increased during 1993-2012 . HoweverFurthermore, socioeconomic breast cancer mortality disparities could be partially explained by the risk factors related to the survival rate such as screening, tumor stage distribution at diagnosis, and treatment inequalities . The NatIn addition, several risk factors might have contributed to the mortality disadvantages among the less educated. For example, the prevalence of obesity in Korea was higher in the lower education group and the inequality for abdominal obesity by educational group increased between 1998 and 2007 . The ratAnalysis results showed that the lower education groups had the increased breast cancer mortality and significant cohort and period effects between 1983 and 2012, whereas the highest group did not. The risk factors which could accompany the cohort effects in breast cancer incidence are reproductive factors  and behavioural factors  . BehavioThis study also showed that women with a lower education had higher breast cancer death rates in more current cohorts in contrast to the highest education group. It means that if the risk factors with the different pattern of the prevalence by educational level were found, it would be reasonable that those factors were considered as main risk factors for the increase of breast cancer mortality. However, unfortunately, there were few studies that contained evidence to conclude what the main risk factors were. Reproductive factors including age at first birth were still less favorable for the higher education group in Korea , likewisThis study has limitations. First, this study is ecological, thereby we can only speculate on the etiologies of the observed changes. Second, population distribution of educational attainment among Korean women changed dramatically during 1983 to 2012. Only 6.2% of women attained a tertiary education during 1983-1987. The percentage increased to 38.9% in 2008-2012, while women with no or a primary education only accounted for 7.2% in 2008-2012 Additional file 2: Table S2.Age-specific mortality rate per 100,000 person-years during1983-2012 in Korea. (DOCX 14 kb)Additional file 3: Table S3.Age-standardized (reference population = 2005 census) mortality rate of breast cancer by educational level among Korean women according to 5 year calendar periods between 1983 and 2012. (DOCX 13 kb)Additional file 4: Table S4.Rate ratios (95% CI) of periods and cohorts by educational level. (DOCX 14 kb)


BRCA mutation carriers. Whether low OPG levels contribute to the high breast cancer risk in this population is unknown. OPG concentrations were measured in plasma of 206 cancer-free BRCA mutation carriers using an enzyme-linked immunosorbent assay. Subjects were categorized as high vs. low based on the median of the entire cohort (95 ng/mL) and followed for a new diagnosis of breast cancer. Cumulative incidence by baseline plasma OPG concentration was estimated using Kaplan-Meier survival analysis. Cox proportional hazards models were used to estimate the adjusted hazard ratios for the association between plasma OPG and breast cancer risk. Over a mean follow-up period of 6.5 years (range 0.1–18.8 years), 18 incident breast cancer cases were observed. After ten years of follow-up, the cumulative incidence of breast cancer among women with low OPG was 21%, compared to 9% among women with high OPG (P-log rank = 0.046). After multivariate adjustment, women with high plasma OPG had a significantly decreased risk of developing breast cancer, compared to women with low OPG . These data suggest that low OPG levels are associated with an increased risk of BRCA-associated breast cancer. Targeting RANK signalling may represent a plausible, non-surgical prevention option for BRCA mutation carriers.Emerging evidence suggests a role of receptor activator of nuclear factor κB (RANK)/RANK ligand (RANKL) signaling in breast cancer development. Lower osteoprotegerin (OPG) levels, the endogenous decoy receptor for RANKL which competes with RANK for binding of RANKL, has been reported among  BRCA1 or BRCA2, face a high lifetime risk of breast cancer, compared to women in the general population ) compared to the OPG concentrations of women who did not develop breast cancer (117.9 pg/ml [7.4–547.7]) (P = 0.04). BRCA mutation carriers with low baseline OPG concentrations (≤ 95 pg/ml) had a significantly higher risk of developing breast cancer compared to those with high baseline OPG concentrations (> 95 pg/ml).Over a mean follow-up period of 6.5 years (range 0.1–18.8 years), 18 incident cases of primary invasive breast cancer were observed in the cohort. Thirteen (13%) of the women with low OPG developed breast cancer compared to 6 (6%) of the women with high OPG levels . Results were similar in the multivariate analysis adjusting for potential confounders including BRCA mutation type, oophorectomy, breastfeeding and oral contraceptive use  for BRCA1 and 0.10  for BRCA2 mutation carriers (data not shown).Table BRCA1 or BRCA2 mutation. Women with low OPG levels were more likely to develop breast cancer compared to women with high levels. These findings are timely given the emerging importance of RANK/RANKL-signaling in mammary tumour development, which may be of more relevance for BRCA mutation carriers ) and indirectly (via RANKL) involved in normal mammary gland development as well as mammary tumorigenesis [in vivo administration of a synthetic progesterone upregulates RANKL levels in mammary epithelial cells [et al., demonstrated that treatment of brca1/p53-deficient mice with the progesterone antagonist mifepristone prevented mammary tumorigenesis [Evidence from experimental studies strongly support a role of progesterone-mediated RANKL signaling in the development of igenesis , 11, 23.igenesis , 11, 23.al cells . Poole eigenesis .BRCA1 mutation carriers who have an increased propensity to develop breast tumours with stem cell-like properties [BRCA1-associated breast tumours [et al., showed that breast cells isolated from BRCA1 mutation carriers exhibit an expanded luminal progenitor subpopulation compared to normal mammary tissue [et al., demonstrated that RANK expression was highest in the luminal progenitor cells  of BRCA1 mutation carriers compared to tissue from both wild-type and BRCA2 mutation carriers [Brca1 deficient mice  treated with a RANKL inhibitor (15). Importantly, these findings were validated in a concomitant study led by Sigl and colleagues [Of particular importance are studies demonstrating that progesterone-driven RANKL-signalling is implicated in the regulation and expansion of mammary stem and progenitor cells –13. Joshoperties . Specifi tumours . Accordiy tissue . In a recarriers . They alnhibitor . ImportaBRCA mutation carriers is ∼2% between the ages of 30 and 60. Ideally, a drug intervention would need to occur early in adulthood and have no serious or long-term side effects. Quantifying OPG levels could help stratify women who are at a higher risk of developing disease who may be targets for alternative chemoprevention options. The low endogenous levels of OPG coupled with the significant lifetime risks of breast and ovarian cancer in BRCA mutation carriers, strongly support the use of an anti-RANKL monoclonal antibody like denosumab for the prevention of hereditary breast cancer while simultaneously maintaining bone density. Last year, authors of the adjuvant denosumab  in breast cancer trial (ABCSG-18) published no additional toxicity and a favorable safety profile of adjuvant denosumab therapy  among women with early breast cancer treated with aromatase inhibitors compared to the placebo group [The annual risk for breast cancer in bo group . Furtherbo group . An effeBRCA mutation type. Although we did not have data on timing of blood collection, Widschwendter et al. clearly showed no variation of OPG within the menstrual cycle and there was no difference in OPG levels by menopausal status in our cohort of BRCA mutation carriers (data not shown). We did not evaluate levels of RANKL in our analysis. Despite this, the recent findings from Nolan and colleagues of increased RANK in BRCA1 luminal progenitors suggests that it may be of interest to evaluate circulating RANKL levels in future studies with a much larger number of women.Our study has several limitations. The sample size was small, the number of incident cases was low and the finding was of marginal statistical significance. We were not sufficiently powered to conduct robust analyses subdivided by BRCA mutation. Pending validation in a larger study, the integration of OPG levels may help identify those women who may benefit from treatment with RANKL blockade to prevent breast cancer. Inhibiting RANK-signaling by an existing monoclonal antibody  to simultaneously prevent breast cancer and maintain bone health  represents an important opportunity that warrants exploration in a primary prevention trial of BRCA mutation carriers.Notwithstanding these limitations, this is the first report of circulating OPG and breast cancer risk among women with a BRCA1 and BRCA2 mutation carriers [BRCA1 or BRCA2 mutations because of a personal or family history of breast and/or ovarian cancer. Mutation detection was conducted using a range of techniques, but all nucleotide sequences were confirmed by direct sequencing of DNA. The study was approved by the institutional ethics review boards of the host institutions and all study subjects provided written informed consent.We identified potentially eligible women from a longitudinal study of carriers . For theAll study subjects completed a baseline questionnaire at the time of study enrolment. These were either mailed to each study participant or administered over the phone by a genetic counsellor or research assistant at the time of genetic testing and collected information regarding family history, reproductive and medical histories, preventive oophorectomy and mastectomy, and select lifestyle factors such as alcohol and coffee consumption. Follow-up questionnaires were completed every two years thereafter to update information on relevant reproductive, medical and lifestyle information, and to ascertain incident cancers.Blood samples were collected at the time of genetic testing and baseline questionnaire administration into EDTA- or citrate-containing tubes by venipuncture, processed and separated into plasma and DNA and stored at −80°C and 4°C respectively, at the Women's College Hospital.n = 284), those who had previously undergone a prophylactic bilateral mastectomy (n = 11), and those who did not have a baseline plasma sample available (n = 250). After these exclusions, a total of 206 participants were available for the plasma OPG analysis.Women were eligible for inclusion in the current study if they were between the ages of 18–70 years at the time of enrolment and completed at least one follow-up questionnaire. Of the 751 women who were initially eligible, we excluded those who had a previous history of breast or ovarian cancer  according to the manufacturer's protocol. Briefly, all patient plasma samples were run in duplicate. The concentration of OPG was calculated as the average of duplicate samples  and subsequently converted to a total OPG concentration upon comparison to OPG standards provided in the kit.t-test and χ2 test were used to evaluate differences in the baseline characteristics of the women with high vs. low OPG levels.Plasma OPG was categorized as high or low based on the median distribution in the entire cohort (≤ 95 and > 95 ng/mL). The student's th, 2016.Participants were followed from date of completion of the baseline questionnaire until either the date of their breast cancer diagnosis, date of prophylactic mastectomy, date of ovarian cancer diagnosis, date of death or date of completion of their last follow-up questionnaire. The follow-up period of this analysis was from the date of baseline questionnaire until June 26BRCA mutation type (BRCA1 or BRCA2), oophorectomy (yes/no), breastfeeding  and oral contraceptive use . All analyses were conducted using SAS version 9.4 software . All P values were 2-sided and were considered statistically significant if P ≤ 0.05.We used the Kaplan-Meier survival analysis to estimate the cumulative incidence of breast cancer in women with high vs. low plasma OPG levels. Incidence rates in the two groups were compared using a log-rank test. We employed Cox proportional hazards models to estimate the hazard ratio (HR) and 95% confidence interval (CI) associated with plasma OPG levels. The multivariable model was adjusted for age at blood draw (continuous),


Breast cancer is the most common cancer in women. However, it remains unproven whether psychological factors have an influence on breast cancer incidence. In our earlier study, subjects possessing two personality traits, decisiveness and “ikigai” (a Japanese word meaning something that makes one’s life worth living), showed a significantly lower risk of developing breast cancer, although no psychological factors have been convincingly demonstrated to have an influence on breast cancer development in other studies. Therefore, we conducted this follow-up analysis to confirm the association between breast cancer incidence and psychological traits, using the final dataset of a large-scale prospective cohort study in Japan. We identified 209 cases of incident breast cancer out of a maximum 21-year follow-up of 29,098 Japanese women. Cox proportional hazard regression analysis, adjusted for the same potential confounders used in our previous study, did not reveal any significant relationships between breast cancer incidence and four psychological traits: having “ikigai”, decisiveness, ease of anger arousal, and perceived stress. Our finding is consistent with previous studies, and suggests that the psychological traits are unlikely to be an important risk factor for breast cancer. Breast cancer is the most common cancer in women worldwide3456891112131415161415192122Two potential mechanisms are proposed to explain how these psychological factors work to increase cancer risk. The first is via altering the immune and endocrine systems, and the second is via altering behaviors such as sleep, excecise, diet, smoking, alchohol consumption, drug use, and poor adherence to medical regimens24Recently, an elaborate meta-analysis using pooled data from six cohort studies revealed that personality was not associated with an increased risk of all cancers and site-specific cancers including breast cancere.g., mean follow-up of 5.4 years in the former meta-analysis and follow-up of 7–9 years in our previous study). Empirical studies demonstrated that it may take approximately 14 years for breast cancer to grow from the first tumor cell into a detectable lump and, thus, studies with these time frames are likely to underestimate associations with breast cancer incidence30One limitation of these studies was the relatively short follow-up period  aged 40–79 were enrolled between 1988 and 1990. The details of the study procedure were described elsewhereThis study excluded 23 study areas where information on cancer incidence at follow-up or psychological traits at baseline were not available. Furthermore, 247 subjects were excluded because of positive histories of breast cancer. Another 9,268 subjects were excluded because all of their data on psychological traits at baseline were missing. Thus, a total of 29,098 women were included in this study.Informed consent was obtained individually from each participant, except in a few study areas where informed consent was obtained at the group level after the purpose of the study and confidentiality of the data had been explained to community leaders. The JACC Study begun before the ethical guideline was first established by Japanese government in 2002, and the Japanese ethical guideline allows such established epidemiological studies to continue without obtaining additional personal informed consents3334At baseline, we used a self-administered questionnaire to obtain information on demographics. medical history, tabacco and alcohol use, psychical activity, psychological traits, and other lifestyle factors. Psychological traits were measured using four questions at balseline: having “ikigai” , decisiveness (do you make decisions quickly?), ease of anger (are you easily angered?), and perceived stress (do you experience stress during your daily life?), with three- or four-point Likert-type response options .Potential confounders were also measured using the questionnaire at baseline; they are detailed in the statistical analysis section below.Follow-up was conducted from enrollment until the end of 2009 in all areas, except three areas where follow-up was terminated at the end of 1999. During this period, population registries in the municipalities were used to ascertain the residential and vital status of the subjects. In Japan, registration of death is required by Family Registration Law, and theoretically provides complete mortality data. The cause and date of death of subjects were annually or biannually confirmed by death certificates in each area. The date of move-out of cohort members from the study area was also annually or biannually verified by the investigator in cooperation with the local governmental office.We ascertained the incidence of cancer by linking to records of population-based cancer registries, supplemented by a systematic review of death certificates. In some study areas, medical records in major local hospitals were also reviewedAccording to psychological traits responses, the baseline subject characteristics were summarized as percentage for categorical data and mean for continuous data with corresponding 95% confidence intervals (95% CI). Psychological trait variables were treated as ordinal variables for all analyses. and Spearman’s correlation coefficients among psychological traits were estimated with 95% CI. The person-years of follow-up were calculated from the baseline to the following events: diagnosis of breast cancer, death from any causes, emigration outside the study area, or the end of follow-up (the latter three events were treated as censored).2). In the Cox regression analysis, missing values for each covariate were treated as an additional category in the variable. The proportional hazards assumption was checked by visual inspection of the log-log Kaplan–Meier curves.The hazard rations (HR) with the 95% CI for breast cancer incidence were estimated using Cox proportional hazards regression models. For estimating HR, we used age (using 10-year age groups) as an independent variable. We also used the following potential confounding variables used in the previous studyp-values were two-sided, and the significance level was set to 0.05. All analyses were performed using SAS version 9.3 .All Correlations are moderate among the four psychological traits as shown in Baseline characteristics of the study population are summarized in During a maximum 21-year follow-up , 209 cases of incident breast cancer were ascertained. As shown in Our large-scale prospective cohort study with 19–21 years of follow-up did not reveal any significant relationships between breast cancer incidence and the four psychological traits: having “ikigai”, decisiveness, ease of anger arousal, and perceived stress.In the previous study using the first 7–9 years follow-up data of the same cohort, two psychological traits. having “ikigai” and decisiveness, had a nominally significant association with breast cancer incidence38399 cells, and the doubling time for primary breast cancer tumors is estimated to be 105–270 days301316A clinically detectable tumor contains at least 10The construct “ikigai” indicates the source of value in one’s life or the things that make one’s life worthwhile. This construct is noteworthy because it was repeatedly shown to be associated with mortality in another study using the same cohort in the current42432842Therefore, future studies should follow-up for a period long enough to grasp the whole picture of breast cancer development, using well-validated questionnaires to measure psychological traits.This large-scale prospective cohort study failed to find any significant association between the four psychological traits examined and breast cancer incidence in Japan. The fact that the psychological traits are unlikely to be an important risk factor for breast cancer may be a comfort to women who are otherwise concerned about the risk of personality traits leading to breast cancer.How to cite this article: Sawada, T. et al. The influence of personality and perceived stress on the development of breast cancer: 20-year follow-up of 29,098 Japanese women. Sci. Rep.6, 32559; doi: 10.1038/srep32559 (2016).


It indicated a striking life event led to a 1.726-fold increased HR  for breast cancer with adjustment for age, and a 1.811-fold increased HR  with adjustment for age, BMI, and late age at menopause. High BMI  and a family history of breast cancer  increased the risk of breast cancer, and late age at menopause decreased the risk for breast cancer . Our findings indicate a significant association between striking life events and breast cancer in Chinese women, especially in the subpopulation with high BMI or family history of breast cancer.The current case-control study investigated the relationship between striking life events and breast cancer in Chinese women. A total of 265 primary breast cancer patients and 265 controls were enrolled with matching for age and completed questionnaires. Logistic regression analysis was used. Thirty-nine breast cancer patients reported striking life events and twenty-four of the controls reported striking life events. The number of striking life events was significantly greater in patients ( Breast cancer has a high morbidity and mortality throughout the world, accounts for 23% of all malignant tumors, and is a major cause of cancer deaths among women . The numPrevious research has placed greater emphasis on certain of these traditional risk factors. Remarkably, few studies have examined the role of psychological factors on breast cancer. These psychological factors include personality traits, behavioral characteristics, and the presence of psychological stress, crucial life events, and social support. Very little research has examined the potential effect of striking life events on breast cancer. A striking life event is defined as an event that can lead to an acute stress disorder or acute anxiety disorder, conditions characterized by adverse and anguishing experiences and physiological responses. Striking life events include a change in marital status, such as separation, divorce, or widowhood; death of a spouse, child, or close relative; illness of friend; personal health problems; and change in financial status –9. In 18A more recent prospective cohort study found that chronic stress in women was associated with an increased incidence of breast cancer, possibly due to inhibition of estrogen synthesis . Based oThe differences in these previous studies may be due to differences in the ways of thinking, life style, culture, and philosophy between Eastern and Western women. For example, in Western societies, anxiety was more commonly reported than in non-Western societies. About ten percent of people in North America and Europe were experiencing clinical anxiety compared to approximately eight percent in the Middle East and six percent in Asia. The opposite was true for depression, depression was the lowest in America but highest in certain areas of Asia . It seemWe enrolled 265 patients with breast cancer and 265 age-matched controls, all of whom completed the questionnaire and were profiled Table . Due to Among the 265 cases with breast cancer, 39 patients reported striking events in the 3 years before the onset of breast cancer, 7 cases reported 2 striking events, and no cases reported more than 2 striking events. Eight of the patients had striking events but did not provide detailed descriptions; however, after receiving explanations from the researchers, these 8 patients classified their events as related to feelings , economics , or family .p = 0.0441).The overall incidence of striking life events was only 17.4% among cases and 9% among controls. Table p = 0.0001) and breast cancer in a first-degree relative  were strongly associated with breast cancer. Interestingly, post-menopausal status was a protective factor . Analysis of the relationship between striking life events and breast cancer indicated a marginally insignificant association .Table Forest plots Figure  show thep = 0.0423). The difference implied that we never really appreciated the depth and bitterness of the conflict in the relationship between breast cancer greater levels of stress, thus women with breast cancer were more likely to have greater levels of stress.Analysis of rank variables rather than continuous variables may yield different results. Thus, we re-analyzed these data by use of multivariable logistic regression of rank variables Table . These rForest plots Figure  show thep = 0.0480) in the age-adjusted analysis and marginally insignificant in the multivariable analysis (p = 0.0609). Subgroup analysis of women with late menarche also indicated a significant association of striking life events with breast cancer .Finally, we examined the correlation of striking life events and breast cancer with correction for age alone, with correction for multiple variables, and with correction for multiple variables in a subgroup analysis of women with late menarche Table . The HR Many recent studies have identified risk factors for breast cancer, but few studies have examined the association of life events with this cancer. In the Finnish Twin Cohort, a national modification of a standardized life event inventory was used, examining accumulation of life events and individual life events and placing emphasis on the 5 years. The multivariable adjusted hazard ratio for breast cancer per one-event increase in the total number of life events was 1.07, and divorce/separation, death of a husband, and death of a close relative or friend were all associated with increased risk of breast cancer, seemed a role for life events in breast cancer etiology through hormonal or other mechanisms . In factInterestingly, some previous studies of the effect of psychological or life events on breast cancer did not control for confounding variables . Other sRemarkably, the overall incidence of striking life events was only 17.4% among cases and 9% among controls. These data differ from the findings of several previous reports because these other studies also considered neighborhood disputes, poor relationships with a boss or colleague, and academic failure as striking or severe life events. Although these other items had a psychological impact, they were more akin to a bad mood or depressed state. Thus, we did not include these items, but only considered a depressed state in the risk assessment model.Psychological factors that potentially increase the risk of breast cancer can be classified as chronic or acute. In our study, we evaluated striking life events that caused acute psychological trauma and emphasized the importance of the underlying psychological stimuli. In fact, previous research that analyzed specific types of stressful life events, such as major life events, concluded that there was a significant correlation between breast cancer and stressful life events . Thus, sThere is still some dispute about how to define a “striking life event”. An earlier report used the Holmes-Rahe scale to classify striking life events . This scIt is also important to define the time when a striking life event occurred. A previous study examined events that occurred one year before the diagnosis of breast cancer, and found that 82% women recalled at least one striking life event, and 56% women thought back the occurrence of major events . In the p = 0.0480) but adjustment for multiple factors led to no significant correlation , and the classification of variable grade after regression analysis led to a significant correlation . Thus, the association observed is limited to a certain category of life events.The present study compared the characteristics of 265 cases with breast cancer and 265 controls. The most important finding was the positive association of striking life events with breast cancer . Our comparison of the results of the univariate and multivariate analysis indicated that confounding factors were very important, and that striking life events lost statistical significance when there was no control for confounding. Adjustment for age alone led to a significant correlation . However, the HR for this association was lower in the univariate analysis, indicating that confounding factors weakened the relationship. This may be the reason for the conflicting results in previous reports. Remarkably, adjustment for age alone led to a significant association of striking life events with breast cancer . Although there was no such relationship in multivariate regression analysis , it was a participant again in regression analysis after classification of variable . This demonstrates that the relationship of striking life events with breast cancer rises to statistical significance only when considering the influence of certain confounding variables.In present study, the most important finding was the positive association between striking life events and breast cancer in multivariate analysis  was the most common, followed by marriage-related issues, and then the economic issues (such as unemployment or major property damage). The incidence of each different class of striking events was similar in cases and controls. However, the overall number of striking life events was greater in our cases than controls. Moreover, our multivariate analysis indicated that striking life events had a more modest effect in increasing the risk of breast cancer that reported by Kruk et al. (HR: 5.09) and Chen et al. (HR: 11.64), but similar to our previous meta-analysis , 36. TheIn addition, the present study indicated associations of breast cancer with menopausal status, family history of breast cancer, and BMI. Our finding that early menopause was a protective factor is in concert with other reports  and our in situ (stage 0) would not be included because the most recent version of breast cancer staging considers breast cancer in situ as a precancerous lesion.A possible direction of bias in present study is that researchers administered the questionnaires to most breast cancer patients, and these patients were given ample time to explain the nature of different striking life events, but the controls completed the questionnaires by themselves with limited time. This may have contributed to the greater number of striking life events in cases than controls. Second, the time from diagnosis to data collection may have been shorter in our cases than controls. Previous studies used survey intervals of 2 years or 7.7 months, and both groups of researchers noted that a longer interval would lead to greater recall bias , 36. MeaSome additional limitations of this study should be noted. First, we did not analyze individual personality characteristics, different methods of coping, or the use of social support systems. Some previous reports found that a tendency for anger or other negative behaviors was an important and independent risk factor for breast cancer, and proposed that venting or suppression of anger may have been a key factor , 40. SecThis 1:1 age-matched case-control study was carried out in Peking Union Medical College Hospital of Peking Union Medical College from January 2011 to July 2012. Each case was age-matched with a control whose age differed by less than 3 years. The cases were 26 to 69 years-old , had pathological diagnoses of invasive breast cancer, had no psychiatric illnesses, and received radical breast resection from May 2011 to Dec 2011 in the Department of Breast Disease. Each case had full understanding of her disease and was able to independently complete the survey form. A life event inventory was included in the questionnaire. The controls were healthy individuals selected from the Physical Examination Center or Department of Breast Disease in Peking Union Medical College. These women were 27 to 71 years-old , did not have invasive breast cancer, and had no psychiatric illnesses. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols and data collection were approved by the Peking Union Medical College Hospital Medical Ethics Committee (No. S-406). The informed consent was obtained from all subjects. Sample size calculations were carried out before starting the study according to the previous formula .The present study focused on the effect of psychologically striking events, including illness or death of relatives, divorce or separation, emotional trauma, unemployment, and significant loss of property. Each patient self-reported the psychological effect of these different stressors. Thus, the death of a relative may have led to depression and even forced some individuals into early retirement due to the psychological stress. However, other individuals may have experienced no psychological trauma following the death of a relative if they were not living together or if the relative was elderly and had a natural death. A series of previous studies were as referenced methodological issues to form the final questionnaire content , 17, 21.It was also necessary to consider possible risk factors of breast cancer outside the psychologically striking events to avoid confounding errors. However, the risk factors for breast cancer vary in different countries, and there appear to be unique risk factors for Chinese women. Thus, we considered factors based on the previous literature and our own previous report, the largest survey of Chinese women, which was funded by the Chinese 11th Five-Year Research Plan (No. 2006BAI02A09) .Thus, we considered height, weight, body mass index (BMI), age, age at menarche, level of stress (scored 0 to 9), history of breast biopsy , presence of a family member  with breast cancer, use of oral contraceptives for more than 1 year, and post-menopausal status . Although previous studies of Chinese women did not report associations of childbearing and lactation with breast cancer, other research has classified these two items as risk factors, so our present survey also considered them.Among cases and controls, all information was collected within 12 months of the diagnosis of breast cancer (cases) or during an interview (controls). The presence of striking life events was assessed by asking several questions: “In the 3 years before your diagnosis of breast cancer, were there any mentally or spiritually stressful events?” “Please describe which of the listed items were not psychological blows to you.” “Was your specific psychological blow excluded the listed items?” “If you would rather not describe the specific psychological blow, please classify the event into 1 of 3 categories.”Additional information was collected from controls and cases including age, height, weight, age at menarche, stress, history of surgery, family history, menopausal status, use of oral contraceptives, and striking events within the previous 3 years.Among cases, a single investigator administered the questionnaire to most patients (82%) to reduce the possibility of judgment bias by the researcher. Other cases required help for completion of forms, and this was strictly screened by the same researcher. The rejection rate was 37%. Among controls, information was from a large database, and each case was age-matched (maximum difference of 3 years) with a control. These data were collected following a short-term training of doctors and nurses by use of unified standards and methods to reduce sampling bias in information acquisition. Moreover, a double entry method was used for data input; one method used Epidate 3.0 software in which the variable was assigned a value and the other used a breast cancer risk prediction model Figure  and auto2, 18.5–24, 24–28, or ≥ 28 kg/m2), stress scores , breast operation history , breast cancer history (with or without), use of oral contraceptives (never or ever), and post-menopausal (yes or no). Further adjustment was made with correction for multiple variables. Finally, in the analyses of individual life events, we also adjusted with correction for multiple variables in a subgroup analysis of women with late menarche. We retained subjects with missing values for any of the covariates by including them in the reference category for the relevant covariate.Results of the analyses were first adjusted only for age (continuous variable). The next set of analyses examined whether the age-adjusted estimates were confounded by menarche , body mass index  were the independent variables. The chi square test was used to assess the significance of differences between the 2 groups. Then, logistic regression analysis was used to assess the association of psychologically striking events and each categorical variables with the occurrence of breast cancer, respectively. Continuous variable was changed to rank variable, and striking life events were ranked into with or without events. Multiple logistic regression analysis was used to assess the association of psychologically striking events and other variables with the occurrence of breast cancer. The odds ratios (ORs) and 95% confidence intervals (CI) were calculated. A result was considered statistically significant if the The present study of Chinese women indicated a positive relationship between striking life events and breast cancer. Breast cancer was also associated with obesity, family history of breast cancer, late menarche, late menopause, and overall stress. Specially, the association is not universal but applies to a certain impact of life events.We have all necessary consent from any patients involved in the study, including consent to participate in the study where appropriate. We confirmed that all experiments were performed in accordance with relevant guidelines and regulations. The present study and data collection were approved by the Peking Union Medical College Hospital Medical Ethics Committee (No. S-406).


Significant limitations exist in our ability to predict breast cancer risk at the individual level. Circulating microRNAs (C-miRNAs) have emerged as measurable biomarkers (liquid biopsies) for cancer detection. We evaluated the ability of C-miRNAs to identify women most likely to develop breast cancer by profiling miRNA from serum obtained long before diagnosis. 24 breast cancer cases and controls (matched for risk and age) were identified from women enrolled in the High-Risk Breast Program at the UVM Cancer Center. Isolated RNA from serum was profiled for over 2500 human miRNAs. The miRNA expression data were input into a stepwise linear regression model to discover a multivariable miRNA signature that predicts long-term risk of breast cancer. 25 candidate miRNAs were identified that individually classified cases and controls based on statistical methodologies. A refined 6-miRNA risk-signature was discovered following regression modeling that distinguishes cases and controls  in this cohort. A functional relationship between miRNAs that cluster together when cases are contrasted against controls was suggested and confirmed by pathway analyses. The discovered 6 miRNA risk-signature can discriminate high-risk women who ultimately develop breast cancer from those who remain cancer-free, improving current risk assessment models. Future studies will focus on functional analysis of the miRNAs in this signature and testing in larger cohorts. We propose that the combined signature is highly significant for predicting cancer risk, and worthy of further screening in larger, independent clinical cohorts. Several models are available for estimation of breast cancer risk; the most widely used include the Gail , Claus , and IntMicroRNAs (miRNAs) have emerged as promising biomarkers as they are stable in circulation and found in most body fluids , 8. CircSignificant differences in miRNAs have been found between cancer patients and healthy controls –20, suggWe postulate that there are measurable differences in C-miRNAs within serum obtained from women at high risk for breast cancer who eventually develop tumors, and those who are at high risk, but remain cancer-free. To assess this, we analyzed global miRNA levels from serum obtained from women at high risk for breast cancer years prior to cancer development. Here we present our patient classification algorithm and a preliminary risk signature of 6 miRNAs that, when combined, discriminate cases from controls with high accuracy and precision. Although studies have provided short term risk assessment (i.e. early detection) no studies have yet evaluated the potential of liquid biopsy to predict breast cancer development years before cancer identification –28.Twenty-four of the HRBP participants who were diagnosed with breast cancer met criteria to be included as cases. Most (66.7%) were at increased risk due to strong family history. The majority developed breast cancers that were less than 2 cm (87.5%) and lymph node negative (86.4%). Table The levels of 2578 mature human miRNAs (miRBase v20) were interrogated in banked serum samples from the study cohort of 48 women. We developed a standardized method for serum miRNA expression analysis encompassing all steps from RNA isolation through generation of background normalized data Figure . As confCandidate miRNAs were selected for further development of a risk signature using two distinct techniques. Area under the ROC curves (AUC), a measure of the classification accuracy, and associated 95% confidence intervals were generated for each of the 2578 interrogated miRNAs . Twenty The identified 25 candidate miRNAs Figure  were useFinal models were generated separately for the top model-selected miRNAs, 6 for AUC set and 6 for ANOVA set Figure , utiliziFormula 1: AUC-selected miRNA-modeled risk scoreRisk score = (-1.062 x hsa-miR-3124-5p) + (-0.32 x hsa-miR-1184) + (-0.33 x hsa-miR-4423-3p) + (0.621 x hsa-miR-4529-3p) + (-0.626 x hsa-miR-7855-5p) + (0.359 x hsa-miR-4446-3p)Formula 2: ANOVA-selected miRNA-modeled risk scoreRisk score = (-0.274 x hsa-miR-1184) + (-1.305 x hsa-miR-766-3p) + (-0.393 x hsa-miR-4423-3p) + (0.601 x hsa-miR-4727-3p) + (0.229 x hsa-miR-208a-5p)These formula-generated risk scores were applied to miRNA levels from each case and control for discriminatory power. Figure We further evaluated the expression of all model-identified miRNAs used for risk score calculation, a combined 9 individual miRNAs, in all 48 patient samples Figure . Two-thiwww.ingenuity.com). No targets exist within IPA for hsa-miR-7855-5p. Thus, it was excluded from pathway analysis. We used the comparison analysis feature in IPA to discover biological pathways regulated by the remaining 5 miRNAs in combination. Regulation of the Epithelial-Mesenchymal Transition Pathway, VEGF Signaling, and Molecular Mechanisms of Cancer were within the top 5 Ingenuity Canonical Pathways indicating enrichment in genes targeted by risk signature miRNA in cancer-related biological processes.   developed cancer more than 2 years after serum collection). The median follow-up of women in our database is 8.7 years and the control group has been followed for longer (a mean of 11.2 years), suggesting that these controls are truly cancer-free. For this analysis, we used the most comprehensive miRNA microarray available (miRBase v20 – 2578 human miRNAs). Prior studies regarding early detection and risk have largely screened miRNAs known to be associated with breast cancer . Use of There are limitations of our study which must be acknowledged. Our study is small and validation in a larger, more diverse dataset is critical. We have secured access to samples from 3 large randomized chemoprevention trials  and will be testing our findings in the placebo arms of these trials. The AUC that we have identified is quite high and we anticipate that testing in a validation cohort will result in some loss of accuracy. Elimination of the 3 miRNAs that were poorly expressed  is a potential limitation of our model and this will be addressed in future validation studies by using a larger number of cases and controls and screening for all 25 candidate miRNAs  at the University of Vermont Cancer Center is a prospective cohort of women at increased risk for developing breast cancer due to one or more of the following risk factors: a strong family history, benign breast disease, prior irradiation for Hodgkin's disease, a known pathogenic mutation in a breast cancer-causing gene, and/or a modeled lifetime breast cancer risk of over 20% at time of enrollment. All participants are recruited from the high-risk breast clinic where they receive screening recommendations according to individual risk and clinical guidelines. Enrollees provide written informed consent to be included in the HRBP database and re-contacted every 4 years thereafter for follow-up. At baseline and subsequent follow-up visits, blood samples are obtained and data collected via questionnaires and medical records to update reproductive and family histories, breast imaging results, lifestyle and health behaviors. Medical records and pathology reports are reviewed at each visit to ascertain incident breast cancers in the cohort. Serum is obtained from coagulated whole blood samples by centrifugation at 1811 g for 10 minutes. Serum aliquots are stored at -80°C within 1 hour of blood draw. Since 2003, 605 women have been enrolled in the HRBP and followed for a median of 8.7 years (range 0.06 – 13.7 years) and enrollment is ongoing. To date, forty-four women have been diagnosed with breast cancer, for an age-adjusted invasive breast cancer incidence of 367/100,000 women years .For the current study, cases and controls were identified from the above cohort. All HRBP participants who were diagnosed with invasive breast cancer more than 6 months after enrollment were selected as cases, with the following exceptions: we excluded women who developed either non-invasive (Stage 0) or metastatic (Stage IV) breast cancer, had a pathogenic mutation in a cancer-causing gene, were diagnosed with another cancer prior to their diagnosis of breast cancer, or for whom serum was not available. Controls were randomly selected from HRBP participants who were cancer free and matched on both age at serum sampling (+/- 3.5 years) and qualifying risk factor for HRBP enrollment (i.e. strong family history or benign breast disease).Data were abstracted from subjects’ electronic medical records to complete information needed for risk model calculation , to calculate the Charlson Comorbidity Index , and to Total RNA was isolated from patient serum using a standardized protocol . Serum fExactly 8μL of isolated RNA was prepared for Affymetrix GeneChip miRNA v4 microarrays (Thermo Fisher Scientific) following manufacturer's recommended protocol except for an increased hybridization time of 42 hours to increase signal-to-noise ratios. The data have been deposited into GEO datasets (GSE98181).www.netaffx.com - Thermo Fisher Scientific). The command line code used is: apt-probeset-summarize -c miRNA-4_0.clf -p miRNA-4_0-st-v1.pgf -b miRNA-4_0-st-v1.bgp --qc-probesets miRNA-4_0-st-v1.qcc -a pm-gcbg,sea -a pm-gcbg,dabgRaw *.CEL data files were processed individually using the apt-probeset-summarize function within the Affymetrix Power Tools v1.18.0 (APT) software package (Thermo Fisher Scientific). Simplified Expression Analysis (SEA) algorithm was used to summarize probesets and the Detectable Above BackGround (DABG) algorithm was used to assign p-values to probeset intensities. All annotation and library files for the miRNA v4 GeneChips were downloaded via the NetAffx Analysis Center  and 95% confidence interval (CI) values were generated for each tested parameter  using the pROC v1.8 R/CRAN package . The com2 expression values for all 2578 mature miRNAs as well as clinical classification as case or control for each patient were imported into R. Univariate Cox proportional hazards (CoxPH) models for each miRNA were generated starting with an otherwise blank model and the p-value calculated using the coxph function of the survival v2.39-5 R/CRAN package [The normalized log package , 54. Pri package  was usedThe 48 patient samples were randomly divided into a 1000 different training sets of 32 and validation sets of 16. For each training set, a CoxPH model was built as described, signature miRNAs identified, and risk scores and AUCs calculated. Using these signature miRNAs, risk scores and AUCs were calculated for the validation set.MicroRNAs identified in at least 500 models with a validation set AUC > 0.8 in greater than 50% of the models were selected and used to generate a new CoxPH model with associated risk scores. All 48 patient samples were used in final model generation.www.ingenuity.com) and core analyses run using default parameters. A comparison analysis combining each individual miRNA target analysis was run. The top 5 Ingenuity Canonical Pathways and Diseases and Biological Functions were identified based on maximum values of the negative logarithms of p-values.Interaction networks for each miRNA were identified in Ingenuity Pathway Analysis , R v3.x, or Spotfire v2 (Tibco). Density plots were generated using the sm.density.compare function of the sm v2.2-5.4 R/CRAN package . All R sWe have identified a 6-miRNA signature which can discriminate between high-risk women who do and do not develop breast cancer over a median follow-up of 8.7 years. The accuracy of this signature is greater than that of any published risk models. Given the accessibility and stability of circulating miRNAs, they have the potential to significantly improve breast cancer risk prediction by identifying women at greatest risk who can be offered aggressive screening, prevention and clinical trials. Future studies by our group will focus on testing in larger, independent cohorts from completed clinical trials and functional analysis of the miRNAs which make up this signature.


Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures . PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence.2.5 within 3 years before mammogram β = 0.05, 95% CI –0.16, 0.27; PM2.5–10 β = 0, 95%, CI –0.15, 0.16; PM10 β = 0.02, 95% CI –0.10, 0.13) and postmenopausal women . Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m3 increase in PM2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density .Recent PM and distance to roadways were not associated with mammographic density in premenopausal women  contains supplementary material, which is available to authorized users. In 2013, 66.8% of women in the United States aged ≥ 40 years had undergone a mammogram within the previous 2 years , 6.The World Health Organization’s International Agency for Research on Cancer classified ambient air pollution and particulate matter (PM) as Group 1 human carcinogens largely based on results from lung cancer studies . HoweverGiven the large geographical scope of the nationwide US-based Nurses’ Health Study (NHS) and NHSII cohorts and the ability to control for well-established breast cancer risk factors and predictors of mammographic density, we investigated associations between PM exposures at one’s residential address and distance to roadways in relation to a continuous measure of mammographic density in women without breast cancer. We assessed the associations separately for premenopausal and postmenopausal women and within regions of the United States.n = 5371, controls n = 7469) and the NHSII  were established to evaluate blood-based biomarkers of risk. Around the time of blood draw, the majority of the NHS (80.1%) and NHSII (89.2%) participants reported having a mammogram in the last 2 years on the 1990 and 1999 questionnaires, respectively. Film mammograms were collected from women who were participants in the nested breast cancer case–control studies and who also reported mammography around the time of blood collection. We successfully obtained mammograms from approximately 80% of the eligible participants from the NHS  and the NHSII  [The NHS cohort was established in 1976, enrolling 121,700 married, female US nurses between the ages of 30 and 55 years who resided in 11 states at entry  . The NHS = 1833) , 26. The = 1833) , 26.2) and absolute nondense area (cm2).Film mammograms of the cranio-caudal views of each breast were digitized with a Lumysis 85 laser film scanner for all NHS mammograms and for the first two batches of NHSII mammograms, and with a VIDAR CAD PRO Advantage scanner  for the third batch of NHSII mammograms. The correlation between percent density measures from the two scanners was 0.88 . Trainedi.Particulate matter2.5), thoracic particles less than 10 μm (PM10), and coarse particles between 2.5 and 10 μm (PM2.5–10). Particulate matter comes from various sources and the varying contribution of these sources is different in each region of the United States. These sources generally include motor vehicle emissions, tire fragments, road dust, industrial and agricultural combustion, wood burning, pollens and molds, forest fires, volcanic emissions, and sea spray [In the current study, the primary exposures of interest were PM levels 1 year before the year of the mammogram and the cumulative average PM for up to 3 years prior to the mammogram year. Particulate matter is classified into three size fractions, including fine particles less than 2.5 μm in aerodynamic diameter (PMea spray . We defiea spray : Northea2.5 and PM10 were generated using geographic information system (GIS)-based spatio-temporal models to account for spatial and meteorological variation over time [2.5 data were not directly measured before 1999, and therefore we derived PM2.5 levels before 1999 from the PM10 levels before 1999 using the 1999 ratio of PM2.5:PM10 [2.5–10) was calculated as the difference between PM10 and PM2.5 estimates. The models were evaluated for predictive accuracy using a 10-set cross-validation approach; cross-validation correlation coefficients were high for PM2.5 (R2 = 0.77) and moderate for PM10 (R2 = 0.58) and PM2.5–10 (R2 = 0.46) [Briefly, predictions of ambient PM were available across the 48 conterminous United States . Predictions of monthly average PMver time . The monver time . PM2.5 d2.5:PM10 . Coarse  = 0.46) .ii.Proximity to roadwaysSecondary exposures of interest included proximity between one’s residential address in the year before the mammogram to various types of major roadways as a proxy for traffic-related exposures. For women with a street-segment level geocoded address, proximity of residential address to nearest roadways was calculated in meters using GIS software and the ESRI StreetMap Pro 2007 road network data. Distances to three different types of roadways were classified based on the US Census Feature Class Code road classification system. The types of roadways included: A1 , A2 , and A3 .Residential addresses were updated biennially in both the NHS and the NHSII as part of the questionnaire mailing process. By the mid-1990s, participants resided in all 50 states . Study pn = 2362 for NHS, n = 1833 for NHSII). Our analyses were further restricted to women who had a screening film mammogram dated between January 1990 and December 2008 and those with data available on estimated PM exposures living in the conterminous United States . Because menopause status and BMI are the strongest predictors of mammographic density, we excluded women with missing or dubious menopause status (n = 308) or with missing BMI data (n = 70) at the time of mammogram. The final study sample comprised 3258 participants .To study the influence of PM and proximity to road on normal breast tissue composition, we restricted the analyses to NHS and NHSII participants without breast cancer for whom mammographic density data were available , categories of parity and age at first birth , categories of hormonal therapy use , history of biopsy-confirmed benign breast disease, age at menopause , categories of breast feeding status , age at menarche (years), oral contraceptive use among premenopausal women only , family history of breast cancer, race/ethnicity , BMI at age 18 , alcohol consumption (g/day), physical activity (MET hours/week), Census tract-level median house value and median income based on values from the 2000 Census, region of residence , and date of mammogram to account for the strong decreasing trends in PM levels over time.We considered the following variables in the multivariable-adjusted models: cohort/batch , age at mammogram (years), BMI at mammogram  [Analyses were carried out separately for women who were premenopausal and postmenopausal at the time of the mammogram. Mammographic density measures were square-root transformed to achieve the statistical assumptions for linear regression. Multivariable linear regression was conducted to estimate the average difference in square-root-transformed breast density measures for a 10-μg/mt group) . In the We used the likelihood ratio test (LRT) to determine whether the associations between the exposures and percent density varied by cohort and by region of residence. In addition to the PM results presented across the entire United States, estimates from multivariable models were presented separately for each region. We used nonparametric restricted cubic regression splines  to deterAll analyses were conducted in SAS version 9.4 .2.5 was 16.9 μg/m3 , of PM2.5–10 was 10.6 μg/m3 , and of PM10 was 27.7 μg/m3 . The highest median PM2.5 level was in the Midwest  followed by the Northeast , the West , and the South . The highest median PM2.5–10 level was in the West  followed by the South , the Midwest , and the Northeast . There were no statistically significant interactions with PM exposures and cohort for the multivariable models among premenopausal or among postmenopausal women (LRT p > 0.15).The 1989 median level of PMn = 1624) in the highest year-adjusted quintiles of PM2.5 1 year before the mammogram were more likely to be overweight/obese at age 18, to have never breast fed, to be current oral contraceptive users, and were less likely to have a history of benign breast disease or family history of breast cancer compared to women in the lowest quintile  in the highest year-adjusted quintiles of PM2.5 1 year before the mammogram were similar in terms of most mammographic density predictors compared to women in the lowest quintile . Among postmenopausal women in the Midwest and the South, the results were null and not statistically significant for PM exposures and density measures . For the estimates of untransformed percent mammographic density, a 10-μg/m3 increase in cumulative PM2.5–10 up to 3 years before the mammogram was associated with an average difference of –2.0 percentage points  in the Northeast was associated with a percent mammographic density 3.4 percentage points higher percent mammographic density  among postmenopausal women. Unexpectedly, recent coarse particulate matter (PM2.5–10) in the West showed a weaker, inverse association with percent density .In summary, we observed no overall association between residential PM exposures or proximity to roadways and mammographic density in premenopausal and postmenopausal women residing across the conterminous United States. This is consistent with the reported null overall associations between PM and breast cancer incidence in prospective cohort studies from the Danish Nurse Cohort, the NHSII, and the Sister Study Cohort –19. Upon2.5 absorbance but null associations for small increases in PM2.5, PM2.5–10, and PM10 exposures. In the five registries in the US-based Breast Cancer Surveillance Consortium [2.5 at the participants’ zip code was positively associated with categorical measures of mammographic density based on the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS) categories. In addition to the methodologic differences in measurement of mammographic density and air pollution across previous studies, the inconsistent findings suggest that geospatial variation in air pollution composition may account for the different findings in Denmark, the Netherlands, and the United States. In the current study, we observed regional differences among postmenopausal women with positive associations between PM2.5 and mammographic density in the Northeastern United States, but null findings between PM2.5 and mammographic density in the Midwest, South, and West. Regional differences have also been noted in cardiovascular disease outcomes, with stronger associations with PM2.5 in the Northeast [2.5 levels, composition, and sources across regions in the United States.Three studies based in Denmark, in the Netherlands, and in five registries in the US Breast Cancer Surveillance Consortium  reported inconsistent findings between air pollution exposures and mammographic density –23, poteortheast . This ma2.5) are primarily from combustion sources, organic compounds, and metals that can penetrate the small airways and alveoli deep in the lung [2.5 composition consists of sulfates, nitrates, ammonium, elemental carbon, organic carbon, Na+, and silicon and the remaining ~ 20% is a catch-all category consisting largely of many minerals and metals ; however, the distribution of these major components differs across the United States [2.5 composition in the Western United States was higher in organic carbon, elemental carbon, nitrates, and silicon [2.5 levels were highest in the Eastern United States [2.5 and PM2.5–10 [2.5 in the Northeast than in other regions as well as the half-life and biological plausibility of PM2.5 to induce systemic changes may be germane to variation in breast tissue composition seen only with PM2.5 among postmenopausal women residing in the Northeast. Future studies of PM and postmenopausal breast cancer risk should be aware of the differences in PM composition across regions of the United States.Fine particles  and is largely composed of soil, street dust, fly ash from uncontrolled combustion, nitrates, sulfates, crustal oxides , sea salt, pollen, fungal spores, insect fragments, other bioaerosols, and automobile debris. The atmospheric half-life of PM2.5–10 is shorter than that of PM2.5 ranging from minutes to hours and PM2.5–10 can penetrate the extrathoracic and upper tracheobronchial regions [2.5 measurement estimations, coarse PM2.5–10 estimates had lower cross-validation R2 coefficients across all US regions (PM2.5R2 = 0.77 versus PM2.5–10R2 = 0.46) and within regions , which suggests that PM2.5–10 estimates had more error on average than PM2.5 estimates [While we observed largely null PM regions . Comparestimates .x/NO2 were not available in these cohorts. In this study, we used spatio-temporal PM modeling that can reduce classical error; this technique has been reported to be more strongly correlated with personal PM exposure than using PM values from a nearest monitor [There are several limitations and strengths of the study. Exposure measurement error is often a challenge. Several types of error can contribute to measurement error of PM. Instead of collecting personal exposure data that are not feasible on a large epidemiologic scale, we used predictions from spatio-temporal modeling. These data are subject to both Berkson error, which results in imprecision, and classical error that usually results in attenuated estimates toward the null , 42. A c monitor . Lastly, monitor –48. In s monitor , 49–54. This study does not provide evidence that PM in the United States is associated with breast density variation. However, there is suggestive evidence that fine PM in the Northeast United States may influence breast tissue composition for postmenopausal women. Furthermore, this study cannot rule out the potential relationship of PM exposures during earlier time windows of exposure and mammographic density.


Patients with postpartum breast cancer have been reported to have a poor prognosis. The present study aimed to evaluate the pregnancy-related risk factors of postpartum breast cancer in Korea. We collected patient data from the Korea National Health Insurance (KNHI) Claims Database of the Health Insurance Review and Assessment Service (HIRA) for the 2009–2013 period. We evaluated the pregnancy-related risk factors for postpartum breast cancer in two population groups. For Group 1 (women who had given birth during the 2010–2012 period), data on those who were diagnosed with breast cancer from childbirth to 1-year postpartum were extracted. For Group 2, we extracted the data of women who gave birth in 2010 and traced them until December 31, 2013. In Group 1, 1,384,551 deliveries and 317 postpartum breast cancer patients were recorded in Korea between January 1, 2010, and December 31, 2012. Women aged ≥35 years  and those who gave birth via cesarean delivery  were considered to be at a higher risk for breast cancer. Lower risk was noted in primiparous women . In Group 2, the data of 457,924 women who gave birth in 2010 were traced until December 31, 2013. Among them, 655 patients were diagnosed with breast cancer, and age ≥35 years and cesarean delivery were associated with an higher risk of breast cancer, whereas primiparous status was associated with a lower risk of breast cancer. In conclusion, older age (≥35 years) and cesarean delivery are significant risk factors for postpartum breast cancer, and primiparous women have a lower risk of developing postpartum breast cancer. Breast cancer is the most common cancer in women. The risk of breast cancer is closely influenced by various reproductive factors. Epidemiological studies have demonstrated dual opposing effects of pregnancy on the mother’s risk of breast cancer ,2. Full-With improvement in treatment modalities and early detection, the overall survival of patients with breast cancer has improved. However, PABC in young women is still a burden for patients, families, and physicians because it is known to have a poorer prognosis. Several previous studies reported that PABCs are potentially large advanced stage tumors with high histological grades and they tend to have negative hormone receptor status –15. In aTherefore, identification of predictive or risk factors is necessary for the early detection of PABC in pregnant women and during postpartum period. Since women in their pregnancy and postpartum visit the hospital more frequently than those who are not pregnant, the investigation of PABC-related risk factors, which can be detected during pregnancy or postpartum, would improve the diagnosis of breast cancer in these patients. Therefore, we investigated the pregnancy-associated risk factors of PABC such as obstetric complications, modality of delivery, twin pregnancy, and parity for the early prediction of PABC. However, it is difficult to know whether obstetric factors in patients diagnosed with breast cancer during pregnancy are related to the cause or outcome of cancer. Therefore, using the nationwide database, we focused on investigation of the pregnancy-related risk factors only in patients diagnosed with breast cancer in the postpartum period. We investigated the differences between characteristics of women diagnosed with breast cancer within a year after childbirth and those who were not diagnosed with breast cancer. Then, we conducted investigation on pregnancy-related risk factors in this population. In addition, we traced the data of patients diagnosed with breast cancer after childbirth in 2010 over a maximum time period of 4 years. The number of cases of postpartum breast cancer over time and pregnancy-related risk factors in this population were also investigated.The study data were collected from the Korea National Health Insurance (KNHI) Claims Database of the Health Insurance Review and Assessment Service (HIRA) for the 2009–2013 period. In Korea, 97% of the population is obligated to enroll in the KNHI program. Healthcare providers are required by the health insurance policies to allow the review of medical costs incurred, by HIRA. The remaining 3% of the population is under the Medical Aid Program. Thus, the HIRA database contains information on all claims for approximately 50 million Koreans, and nearly all of the information concerning the volume of disease can be obtained from this centralized database, with the exception of procedures that are not covered by insurance, such as cosmetic surgery. Many epidemiological analyses have been published using this database. According to the Act on the Protection of Personal Information Maintained by Public Agencies, HIRA prepares the claims data by concealing the individuals’ identities. The database we received included an unidentifiable code representing each individual, together with information on their age, diagnosis, and a list of prescribed procedures.The diagnosis and procedure codes from the tenth revision of the International Classification of Diseases (ICD-10) were used to identify women who gave birth and those diagnosed with breast cancer during the study period. The current study included women who had given birth during the 2010–2012 period only. The study population was divided into two groups for analysis: Group 1 and Group 2. For Group 1, investigation of different characteristics and obstetric risk factors among women who had given birth during the 2010–2012 period and who had been diagnosed with postpartum breast cancer within 1 year after childbirth was performed. Included women were classified as having postpartum breast cancer if they were diagnosed with breast cancer  in the period from the time of delivery to 1-year postpartum, and underwent surgery and/or chemotherapy for breast cancer. Since patients who were diagnosed with breast cancer before delivery could also make medical claim on breast cancer after delivery, we added one additional criterion for the exclusion of patients who were diagnosed with breast cancer before delivery. Therefore, only patients who were diagnosed with breast cancer  after delivery and also did not have medial claim for breast cancer before delivery, beginning January 1, 2009 were confirmed as patients who were diagnosed with postpartum breast cancer. For the Group 2, we investigated the incidence of postpartum breast cancer over a time period of maximum 4 years in women who gave birth in 2010. Therefore, the data of women who gave birth in 2010 were traced until December 31, 2013, and they were classified as having postpartum breast cancer if they were diagnosed with breast cancer  until December 31, 2013 and underwent surgery and/or chemotherapy for breast cancer. Furthermore, only patients who did not have a medial claim for breast cancer before delivery, beginning January 1, 2009 were confirmed as patients having postpartum breast cancer.The data collected from the database on women’s characteristics were age, multiple pregnancies (defined as twin or higher-order gestation), number of deliveries, delivery mode , complications of pregnancy including gestational diabetes mellitus, and pre-eclampsia.Student’s t-test was performed to compare the continuous variables between postpartum breast cancer group and non-breast cancer group, while Pearson chi-square test was used to compare categorical variables. To evaluate the risk of breast cancer development after delivery, multivariate logistic regression analysis was performed including the development of breast cancer after delivery as the outcome within the entire study population. All statistical analyses were performed using SPSS software, version 20 , and a P value <0.05 was considered statistically significant.In Group 1, 1,384,551 deliveries were recorded in Korea between January 1, 2010 and December 31, 2012. Among them, 317 postpartum breast cancer patients who were diagnosed with breast cancer within 365 days after childbirth were recorded. The number of postpartum breast cancer patients was counted for each quarter. The number of patients diagnosed with breast cancer increased with time; 63 patients were diagnosed with breast cancer within 89 days after childbirth, 60 patients within 90–179 days, 85 patients within 180–269 days, and 109 patients within 270–365 days . Table 1PABC is clinically significant because it affects younger population and has aggressive features ,16–18. IWithin a year after childbirth, the number of postpartum breast cancer patients increased during the later period. More mothers were diagnosed with breast cancer during the 9–12 months after delivery than during the 0–3 and 4–6 months after delivery. According to the study on the rate of exclusive breast-feeding in Korean mothers using Korea National Health and Nutrition Examination Survey, the exclusive breast-feeding rate dropped sharply following the 6 months after childbirth, showing 60.9% at 1 month, 35.6% at 6 months, and 1.2% at 12 months after birth. After mAdditionally, the data of mothers who delivered in 2010 were traced until December 31, 2013. In the present analysis, while the number of patients diagnosed with postpartum breast cancer increased until the third year, this number slightly decreased in the fourth year. Previous studies reported that the timeframe within which the risk of breast cancer reaches its highest level is approximately 5 years after delivery . Due to In the present study, age of ≥35 years and cesarean delivery were found to be significant risk factors of postpartum breast cancer. Recently, the incidence rates of breast cancer in young women have significantly increased worldwide ,21. AlthCesarean delivery was also identified as a significant risk factor for postpartum breast cancer in the present analysis. Little is known about the relationship between cesarean delivery and breast cancer. Kalan et al. reported in their analysis of 985 breast cancer patients that breast cancer in patients with a history of cesarean delivery had aggressive biological features . The exaAlthough the exact mechanism of protective effect of oxytocin on breast cancer is not fully elucidated, low oxytocin exposure during gestation, delivery, or lactation is possibly associated with cancer development through several mechanisms . OxytociParity is reported to show opposite effects in young age-onset and old age-onset breast cancer. A higher number of childbirths is a well-known protective factor against postmenopausal breast cancer . HoweverThe present study had several limitations. First, individual cancer characteristics could not be obtained because this study was based on the insurance claim data from the Korea National Health Insurance Claims Database of the HIRA, a database designed for cost claim issues, not for research. In addition, we could not analyze patient characteristics such as age at menarche, body mass index, family history of breast cancer, lactation, oral contraceptive use, and alcohol consumption in detail. Moreover, we could not investigate the characteristics of labor such as the cause and type of cesarean delivery, and whether planned or following labor. Nevertheless, this is the first study including all maternities in Korea over a period of 3 years. In addition, the cancer patients included in the present study were tightly followed up by the hospital because they were registered and supported by the government through the critically ill patients benefit program of Korea. Therefore, the number of patients and extracted data are reliable.The special clinical or biological characteristics of PABC have been identified after extensive investigations. Earlier diagnosis is an important determinant of prognosis in patients with PABC that have worse prognoses than patients with non-PABC. Therefore, breast cancer should be considered in patients having the risk factors analyzed in this study. Careful self-examination, physician’s caution, and active examination are required for early diagnosis. Moreover, diagnostic tools having higher sensitivity and specificity compared to the existing ones should be developed for patients in this condition. Subsequently, special screening plans should be designed for mothers who visit the hospital frequently owing to pregnancy and delivery.In conclusion, old age (≥35 years), cesarean delivery are significant risk factors for postpartum breast cancer, and primiparous women have a lower risk of developing postpartum breast cancer. Mothers and physicians should exercise caution concerning breast cancer especially in the presence of these risk factors.