N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions , DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue . Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster ( Breast cancer is the most common cancer among females worldwide and the leading cause of cancer-related mortality in middle-aged women Phenotypic and molecular heterogeneity often limits the utility of classical dichotomous case-control analyses. These can prove challenging to delineate or simplistic for understanding the underlying molecular subtypes. In these instances, alternative strategies, such as unsupervised and data-driven methods, can allow for novel hypotheses and the finding of translational biomarkers. Our ambition is to yield unexpected patterns in the data to deliver subgroups that can then readily be linked to molecular phenotypes, clinical risk factors, and potentially stratified intervention. Machine learning-based clustering is one approach to achieve such explorative, data-driven subtyping. It has been applied successfully in other disease areas, such as diabetes With access to the Swedish prospective population-based KARMA (Karolinska Mammography Project for Risk Prediction of Breast Cancer) cohort ,19, we aN = 183) after entering the cohort. Controls were 1:2 matched to each case based on age at last regular screening mammogram and study site (The source population was the KARMA (Karolinska Mammography Project for Risk Prediction of Breast Cancer) Cohort consisting of 70,877 women visiting any of four Swedish mammography units during 2011–2013 ,19. All udy site .Fig. 1OvThe median time from blood draw to breast cancer diagnosis was 23 days (range 0–588 days). Twelve of the incident cases had been diagnosed with breast cancer in the past . For all, the previous breast tumor was in the other breast than the tumor that was detected after sampling. 2 controls had previous breast cancer diagnoses, 6 and 16 years before study entry.In addition, 19 cases and 10 controls had been diagnosed with other types of cancer before sampling . An additional set of 60 samples from 30 cancer-free individuals from the KARMA cohort were included for quality control (named ‘doubles’). These 30 individuals were sampled on two separate occasions with a median time interval of 19.1 months (range 10.7–19.9) between sampling times.Supplementary Table S1). MHT information was also available from the KARMA questionnaire. It was merged with the drug registry used for analysis. In instances where both registry and questionnaire data was available, registry data took precedence.Raw (unprocessed) digital mammograms for each study participant were collected at KARMA study enrolment as previously described ,21. AddiNon-fasting EDTA (ethylenediaminetetraacetic acid) plasma samples of peripheral blood were collected between January 2011 and September 2012 from the KARMA participants at the study enrolment We used antibody suspension bead arrays (SBA) to determine protein profiles in plasma samples. The SBAs were generated using carboxylated magnetic beads  as previously described elsewhere in Supplementary Fig. S1) as previously described in Additional file 1. The 422 antibodies included in the first bead array (SBA1) targeted 295 protein-encoding genes annotated to extracellular matrix (www.uniprot.org) N = 27), laminins (N = 21), matrix metalloproteases (N = 21), metallopeptidases (N = 18), and proteoglycans (N = 16). Most of the antibodies (82%) in SBA1 targeted secreted proteins. The 347 antibodies in SBA2 included 243 antibodies (127 proteins) targeting breast cancer-related proteins from literature, 62 antibodies towards 55 proteins with strong expression in breast tissue according to RNAseq data (www.proteinatlas.org), 39 antibodies towards 11 proteins with indicative associations to breast cancer from previous screenings and 3 controls. The 350 antibodies against 241 protein-encoding genes included in the third suspension bead array (SBA3) were selected based on possible relationship to mammographic breast density, cancer development and/or progression, tissue composition and/or remodeling. Due to overlap between the different arrays, the total number was 1073 unique antibodies targeting 701 unique proteins. This included sets of paired antibodies with common protein targets.We used antibodies derived from the Human Protein Atlas Supplementary Fig. S2).The generated raw protein profile data was normalized and annotated as follows. Antibody-specific probabilistic quotient normalization (Abs-PQN) Empty > mean(MFISample) + 3 × sd(MFISample)). Replicated samples were also excluded before the analyses.A set of 96 duplicated samples was used to assess technical variation and to confirm the reproducibility of antibody profiles within all three SBAs. Prior to statistical analyses, the data were annotated based on assay performance using three criteria. Internal controls and antibodies were excluded from proceeding analyses if they showed low reproducibility in replicated analyses (as rho<0.7), correlation to human IgG levels (rho>0.5), or elevated background levels in assays with sample-free buffers , and MHT use (yes/no) in addition to BMI and entry date. In model 3, smoking (packs/year), alcohol (grams/week), and childbirth (yes/no) were included as exposure variables in addition to the variables in model 2. Due to missing values for BMI (4 missing), area-based density (20 missing), MHT usage (5 missing), smoking (3 missing), alcohol (2 missing), and childbirth (1 missing), 540 samples  were analyzed in model 1, 490 samples  were analyzed in model 2 and 484  were analyzed in model 3. Statistical modeling was performed using the ”clogit” function of the "survival" R package (version 3.1.8) ,27.We performed an unsupervised archetype clustering of the proteomics data to identify clusters of individuals with similar protein profiles. These profiles were subsequently associated with clinical risk factors and other traits.. After the archetypal analysis, each participant was assigned to the archetype they had the highest probability of belonging to. To validate the clusters, we tested the stability of the clusters when the data was changed slightly The quality-controlled proteomics data sets were linearly adjusted for BMI, entry date, and age at sampling. Clustering was performed using archetypal analysis where each participant can be described as a combination of archetypes representing extremes in the data. Archetypal analysis was performed using the “archetypal“ function of the “archetypal” R package (version 1.1.0) . The Wilcoxon rank-sum test was used for continuous variables and Fisher's exact test for categorical variables. Testing of the influence of potential genetic components between the clusters was done by the absolute values of PRS in the clusters as a continuous variable. All P-values were two-sided and considered statistically significant if <0.05.We compared the clusters to investigate how the differences in protein levels driving the clustering materialized at the clinical level. Similarly, we compared the genetic predisposition to breast cancer to assess if the differences in protein levels might be genetically driven. Details on the genetic data and calculation of polygenic risk scores (PRSs) are given in the supplementary material t-test. The resulting p-values were corrected for multiple comparisons using Benjamini-Hochberg adjustment, resulting in false discovery rates (FDRs) for each protein. To shortlist representative proteins for a cluster, we selected the union of those with the lowest p-values and the highest (positive or negative) difference in relative abundance.To rank the proteins driving a cluster, we first performed differential abundance analysis comparing a cluster to the remaining samples using a two-tailed Next, we performed pathway analysis to summarize the potential functions of differentially abundant circulating proteins. We began by applying Over-Representation Analysis (ORA) using two criteria for protein selection; proteins with an FDR < 0.05 and the top 50 proteins with the lowest p-value, using the “gost” function of the “gprofiler2” R package (version 0.1.8) The levels of the selected proteins in all participants were associated with the variables dense area (adjusted for BMI and age) and MHT status  using linear and logistic regression, respectively. All data handling and statistical analyses were performed in R version 3.6.0.Supplementary Table S2). Cases and controls had similar BMI, but cases had a higher absolute area-based breast density (p = 0.0045). 74.9% of cases were postmenopausal, with similar proportions for controls. 48.1% of cases and 46.7% of controls had never taken MHT, with similar numbers for statin use. Most of the tumors were positive for ER (74.9%) and PR (59.6%), only a few confirmed HER2 positive (7.7%). More than half of the tumors were invasive (54.1%) with histological grade ≥2 (76.5%) but without lymph node invasion (78.1%). Women were recruited at four centers, but no differences between sampling centers were observed at the protein level .The selected study population consisted of 183 cases and 366 matched controls , as wellp < 0.05 in at least one of the three conditional logistic regression models tested (data not shown). However, none remained significant after adjustment for multiple testing (FDR > 0.05).A set of 54 proteins were associated with case-control status with a nominal Supplementary Fig. S4) and by associating protein levels with BMI, age and entry date in a combined linear model. The linear association resulted in significant (p < 0.05) associations for 305, 415, and 57 proteins for BMI, age, and entry date, respectively. Thus, when considering both the overall impact on the measured proteins and the effect on individual proteins, the age of the women had the strongest influence on the measured proteins, followed by BMI and with a limited effect of entry date. The experimental proteomics data were therefore adjusted for BMI, age of the women, and study entry date prior to further analyses. Five individuals lacked information on BMI and were therefore excluded, leaving 573 samples  for analysis. 552 unique antibodies with 552 unique targets were left after removing antibodies with the same target .Before clustering, we adjusted the proteomics data for a selected set of covariates. The impact of BMI, age of the women at sampling, and study entry date (as a proxy for sample age) on the protein data were analyzed by projecting the data to two dimensions using Uniform Manifold Approximation and Projection (UMAP)  that would balance simplicity with adequate stratification of the data. This resulted in 5 clusters with 19, 113, 115, 144, and 182 participants, respectively (A–D), representing 3.3%, 19.7%, 20.0%, 25.1% and 31.8% of all tested subjects.To identify patterns in the proteomics data that grouped individuals into clusters, we performed archetypal analysis. We applied the Unit Invariant Knee method to identify the optimal number of clusters  . The MJI for cluster 1–5 were 0.70 ± 0.29, 0.60 ± 0.12, 0.56 ± 0.16, 0.58 ± 0.14, and 0.61 ± 0.14 (Supplementary Table S3). To further assess the quality of the clustering, we determined the cluster membership of pairs of replicated samples and pairs of samples collected on different occasions from the same individual (double samples). We observed that replicate sample pairs significantly more often belonged to the same cluster than double sample pairs . This is in line with the difference in measured protein levels between replicate sample pairs being of purely technical origin. In contrast, differences in measured protein levels of the double pairs can be of both technical and biological origin due to the time elapsed between samplings. In addition, doubles pairs belonged more often to the same cluster than random pairs of samples. Thus, also showing that the protein profiles of the individual women did not substantially change between samplings. Taken together, this indicated that the clustering captures groups of individuals with similar protein profiles.The mean Jaccard index (MJI) was calculated to assess cluster stability by repeating the clustering 150 times on bootstrap samples, randomly resampled with replacement. The MJIs between the most similar clusters for each clustering were summarized by taking the mean (and Supplementary Table S4). Women belonging to cluster 1 had distinct clinical characteristics. Given that cluster 1 was the most stable cluster (MJI = 0.70) and the cluster with a unique protein profile, we focused the remaining analyses on this cluster. Cluster 1 consisted of women of a higher age than clusters 2 and 4 , despite the proteomics data being adjusted for age prior to archetype clustering. Consequently, 18 of the 19 women (95%) in cluster 1 were postmenopausal, while all other clusters included 71% to 77% of postmenopausal women. BMI and BMI-adjusted area-based breast density were not significantly different across clusters . Cluster 1 had a mean and median dense area of 25.8 cm2 and 21.2 cm2, respectively . Cluster 1 also had a significantly greater proportion of women who had taken MHT compared to the other clusters . Additionally, the proportion of women who had previously taken MHT prior to study entry but were not taking MHT at the time of blood sampling, was also significantly higher in cluster 1 . We observed no significant difference between clusters regarding the time from last MHT to study entry . Cluster 1 contained a higher proportion of cases who had taken MHT ever (100% of cases) compared to other clusters (approximately 50% of cases) .There was a significantly greater proportion of breast cancer cases in cluster 1 compared to clusters 2, 3, and 4 . Lastly, we compared PRSs across clusters and found no significant difference. Also, no significant differences were observed when comparing only cases in cluster 1 with cases in the remaining clusters. Additionally, when comparing the PRS of all cases to all controls, the PRS was slightly higher for cases. However, this difference was not statistically significant. This could be due to the small sample sizes. .Given that 101 of the women were currently using or had previously been treated with statins and that statin use has previously been shown to affect the plasma proteome ,38, we wp < 0.05; data not shown). In fact, 16 of 18 postmenopausal women from the original cluster 1 (88% overlap) were grouped together again. This supports the utility of the chosen approach.Given that several cases and controls had previous cancer diagnoses, we reran the clinical comparison of the clusters where these individuals were excluded to ascertain that such previous cancer and related treatment was not driving the differences observed. We did not observe any major changes resulting from excluding these individuals (data not shown). Applied to only the subset of postmenopausal women, the archetypal analysis again found a small cluster of 16 women. This cluster resembled cluster 1 in terms of higher frequencies of cases and previous MHT users (A). Distinct patterns reflecting the differences in protein levels can be observed for all clusters but are most apparent for cluster 1. The differential abundance analysis comparing the protein profiles of women in cluster 1 with all other individuals yielded 393  proteins with higher levels, of which 245 had an FDR < 0.05. In contrast, there were 159  proteins with lower levels, 73 of which had an FDR < 0.05. There were no significantly enriched pathways neither from the ORA over-representation analysis nor the GSEA gene set enrichment analysis. However, this investigation was likely biased by the already highly selective design to target only a particular set of proteins in the circulation.Differences in protein levels between the clusters were reflected in a heatmap A. DistinB), there were 16 more abundant (p < 0.05) and MHT status  when performing linear regression and logistic regression, respectively. CCR7, MMRN1, HNRNPA2B1, RBBP8, ACOX3, TJP3, and MMP15 were associated with adjusted breast density , but not MTH status . MFI levels of PTCH1 and ZP4 were lower in cases than in controls and significantly lower if MHT had been used .To provide insights into the proteomic signatures of cluster 1, we shortlisted those proteins unifying the lowest p-values and largest relative abundance differences. Compared to the rest of the participants and choosing the union of the 25 most significant and 25 most differentially abundant proteins of cluster 1 B, there abundant  and 15 labundant . The levApplying an unsupervised analysis approach on plasma proteomic data from women of the KARMA breast cancer risk cohort, we identified a subset of individuals enriched by previous MHT users and a greater proportion of breast cancers. The women in this cluster were also older, predominantly postmenopausal, and had a larger mammographic dense area relative to their age. Characterization of circulating proteins driving the cluster found lower levels of proteins involved in cell adhesion and immunoregulation, and higher levels of proteins associated with DNA integrity, cell fate, metabolism, and the female reproductive system. This supports their putative roles in the development of breast cancer or mediation of risk factors.At first, we attempted to use a traditional biomarker discovery approach to compare cases and controls. This did, however, not reveal any proteomic profiles to be significantly different between the two groups. Even though studies have suggested blood-based biomarkers for diagnosis, our observations are in line with previous literature reporting few or no protein biomarkers for overall early detection of breast cancer . Likely,A data-driven archetypal analysis was then used as an unsupervised approach to identify proteomic-based clusters. These were then linked to phenotypic or genotypic traits. This enabled the identification of associations between clusters of women with similar plasma profiles and risk factors for breast cancer. By clustering the participants on their proteomics data, we found one stable cluster (MJI = 0.7). In contrast, the assigned members of the remaining clusters showed higher interchangeability when the data was perturbed (MJI ≤ 0.61). Cluster assignments with lower MJI scores should therefore be interpreted with greater caution In cluster 1, in which 79% of all women were previous MHT users, we also found an overrepresentation of breast cancers, with 58% being cases compared to 28–35% in the other clusters. This confirms previous knowledge that the use of MHT is associated with an increased 5-year risk of breast cancer among postmenopausal women Investigating the proteins driving cluster 1, we found lower levels of circulating proteins regulating DNA repair/integrity  and cell fate/replication , which play a role in cancer development. Concordantly, RBBP8, TJP3, and HNRNPA2 were also significantly negatively associated with mammographic density. Individuals in cluster 1 had higher circulating levels of proteins that may be linked to mammographic breast density and the accompanying mechanical stiffness. This included the cell junction and adhesion molecules CLDN15, ITGB7, F11R, and its receptor ITGAL, which are potentially involved in sensing stiffness in the breast tissue and activating cellular downstream signaling pathways to maintain tissue homeostasis PTCH1 gene have been associated with poor prognosis and increased recurrence of breast cancer In addition to the mentioned candidates of cluster 1, we also found decreased levels of proteins related to the female tissues: ZP4 and PTCH1. Across clusters, the two proteins were decreased for cases compared to controls and in MHT treated compared to untreated women. Both proteins are expressed in female tissues, and we found both proteins to be negatively associated with mammographic density. Interestingly, these were the only two cluster-1-specific proteins that were also significantly associated with MHT use. We, therefore, hypothesize that MHT might negatively affect the expression in female tissues and thereby affect the plasma abundance of these proteins. ZP4 was selected for inclusion in this study due to its role in the extracellular matrix (SBA1). It is primarily expressed by the ovary, placenta, and other tissues ,56. ZP4 Strengths of our study reside in the utilized exploratory affinity-based proteomic assay. It provides novel opportunities for high-throughput screening for circulating proteins associated to risk factors, indicative for disease development in selected phenotypes. The experimental design allows combining different protein assays into one multiplexed approach. The method is attractive due to its minimal consumption of sample volumes. The method provided us with relative protein quantities in plasma that allowed an in-depth comparative analysis across thousands of samples Weaknesses in our study can be seen in the low number of breast cancer cases available from prospective population studies. An initial sampling of participants was based on a classical case-control design with two matched controls for each breast cancer case. Therefore, the cohort of women included in this study was enriched for breast cancer cases compared to the general population. However, this enrichment of cases increased the chances of observing effects related to risk factors and case-control status where much larger numbers of participants would otherwise have been needed. Furthermore, we used plasma to identify proteomic signatures associated with breast cancer risk factors and early detection. As previously discussed Our findings suggest that the use of MHT may leave long-lasting fingerprints in the circulating proteome. Effects of the treatment could be detected in the circulating proteome even years after discontinuation. These effects were especially apparent for proteins associated with mammographic density, breast tissue composition, tumor development and progression, and the female reproductive system. Like previous studies, we did not identify immediately applicable plasma protein biomarkers for an early detection of breast cancer. Instead, we identified circulating proteins associated with previous MHT use, connecting to a higher frequency of women with breast tumors, greater age, and relatively greater mammographic density. The findings obtained from profiling population samples provide novel biological insights into putative pathological processes associated with MHT usage and breast cancer risk. Collectively, this suggests that rather than looking for biomarkers secreted by a developing tumor for early breast cancer detection, proteomic characterization of plasma might currently be more successfully aimed at identifying biomarkers that modify or explain the effects of known risk factors. Unsupervised analysis approaches may aid in this endeavor by providing novel hypotheses. Our findings need to be further validated in plasma and cellular assays with breast or other female tissue. Still, they convey that further integration of health and treatment trajectories needs to be considered when judging molecular phenotypes of disease.Cecilia E. Thomas: Conceptualization, Visualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Leo Dahl: Visualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Sanna Byström: Conceptualization, Data curation, Writing – original draft, Writing – review & editing. Yan Chen: Formal analysis, Writing – review & editing, Writing – original draft. Mathias Uhlén: Writing – review & editing, Writing – original draft. Anders Mälarstig: Writing – review & editing, Writing – original draft. Kamila Czene: Writing – review & editing, Writing – original draft. Per Hall: Supervision, Conceptualization, Writing – original draft, Writing – review & editing. Jochen M. Schwenk: Conceptualization, Visualization, Supervision, Writing – original draft, Writing – review & editing. Marike Gabrielson: Conceptualization, Visualization, Methodology, Formal analysis, Writing – review & editing, Writing – original draft.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:MU is one of the founders of Atlas Antibodies AB, a company that sells Human Protein Atlas antibodies used in this study. JMS acknowledge a relationship with Atlas Antibodies AB. The other authors declare no conflict of interest.


The association between the Dietary Inflammatory Index (DII) and breast cancer risk has been widely reported in recent years, but there is still controversy about whether a pro-inflammatory diet is a risk factor for breast cancer. We conducted a meta-analysis to investigate the relationship between the DII and breast cancer risk in pre-menopausal and post-menopausal women. We comprehensively searched PubMed, Embase and the Cochrane Library in January 2021 to identify articles reporting an association between the DII and breast cancer risk. A pooled analysis was conducted with 14 studies covering 312,885 participants. Overall, women in the most pro-inflammatory diet category were at greater risk for breast cancer than those in the most anti-inflammatory category . This association was strong in both pre-menopausal women  and post-menopausal women . Thus, a strong and independent association was observed between a pro-inflammatory diet (assessed using the DII score) and breast cancer risk, irrespective of menopausal status. Further studies will be required to determine the relationship between a pro-inflammatory diet and different subtypes of breast cancer. Breast cancer has become the most common cancer among women, and the number of new cases has been increasing rapidly . Other tIn addition to autoimmune diseases and infections, several risk factors related to lifestyle may lead to chronic inflammation. One such risk factor is the dietary composition. Saturated fats, refined carbohydrates and red meat may exert pro-inflammatory effects, while soy products and phytochemicals may exert anti-inflammatory effects due to their influence on oxidative stress and other pathways .The Dietary Inflammatory Index (DII) was created to comprehensively investigate the inflammatory potential of the diet in the general population with adjustment for dietary differences around the world. This index was developed through an extensive review of over 6500 articles published from 1950 to 2010. Forty-five food parameters  were evaluated, and six inflammatory biomarkers were used to calculate the inflammatory characteristics of each food parameter. A food parameter was considered pro-inflammatory if its intake was associated with significant increases in inflammatory biomarkers, or anti-inflammatory if it was associated with significant decreases in inflammatory biomarkers. A score was assigned to each food parameter based on its association with inflammatory biomarkers in the literature, and the score was weighted based on study design. The scores were then adjusted to the global standard mean intake, converted into proportions (0-1) and distributed around zero, with higher DII scores indicating pro-inflammatory effects and lower DII scores suggesting anti-inflammatory effects  OR “diet”[All Fields] OR “dietary”[All Fields] OR “dietaries”[All Fields]) AND (“inflammation”[MeSH Terms] OR “inflammation”[All Fields] OR “inflammations”[All Fields] OR “inflammation s”[All Fields]) AND (“abstracting and indexing”[MeSH Terms] OR (“abstracting”[All Fields] AND “indexing”[All Fields]) OR “abstracting and indexing”[All Fields] OR “index”[All Fields] OR “indexed”[All Fields] OR “indexes”[All Fields] OR “indexing”[All Fields] OR “indexation”[All Fields] OR “indexations”[All Fields] OR “indexe”[All Fields] OR “indexer”[All Fields] OR “indexers”[All Fields] OR “indexs”[All Fields])) OR “DII”[All Fields]) AND (“breast neoplasms”[MeSH Terms] OR (“breast”[All Fields] AND “neoplasms”[All Fields]) OR “breast neoplasms”[All Fields] OR (“breast”[All Fields] AND “cancer”[All Fields]) OR “breast cancer”[All Fields]). All articles published before January 1, 2021 were screened. Additional studies were identified from the retrieved articles.in vitro studies or articles published in a form other than a clinical trial.The titles and abstracts of the retrieved articles were screened for relevant studies, and the full texts of studies of interest were screened by two independent researchers. Clinical trials were included if they: 1) assessed the relationship between the DII and the risk of breast cancer in humans and 2) reported quantitative data. Studies were excluded if they: 1) only included patients who had already been diagnosed with breast cancer; 2) investigated the prognosis of breast cancer; 3) did not report quantitative data or 4) were conference abstracts, animal studies, cadaveric studies, Two researchers extracted the data and assessed the quality of the included studies independently. The corresponding author was sought to resolve any disagreement. Data including the sample size, study design, population characteristics, RR, OR, 95% CI and other relevant details were recorded and verified. The Newcastle-Ottawa Scale was used to assess the quality of the included studies.2 and a Galbraith plot. To address the high between-study heterogeneity, an influence analysis was conducted in which each study was removed in turn to examine the results of the pooled analysis.Statistical analyses were performed using Stata 16 . Multivariable adjusted RRs were pooled to compare the most pro-inflammatory and most anti-inflammatory diets using a random effects model due to high heterogeneity. Because the incidence of breast cancer was low, the ORs reported in case-control studies were considered equal to RRs, in accordance with the Cochrane Handbook for Systematic Reviews. Subgroup analyses were conducted when no less than four studies could be included. Heterogeneity was assessed with ISince our study is a meta-analysis, an Ethical Review Committee Statement is not required.The analysis was mainly conducted in Guizhou Provincial People’s Hospital.


Breast cancer (BC) is a heterogeneous disease and is one of the most common malignancy affecting women worldwide while colorectal cancer (CRC) is estimated to be the third common cancer and second leading cause of cancer related death globally. Both BC and CRC involve multiple genetic and epigenetic alterations in genes belonging to various signaling pathways including NOTCH that has been implicated in the development of these cancers. We investigated four single nucleotide polymorphisms, each in genes encoding NOTCH1-4 receptors for their role in susceptibility to breast and colorectal cancers in Saudi population. In this case-control study, TaqMan genotypic analysis of rs3124591 in NOTCH1 and rs3820041 in NOTCH4 did not exhibit association with breast as well as colorectal cancers. However, a strong association of rs11249433 which is in close proximity to NOTCH2 was observed with breast cancer susceptibility especially with those having an early onset of the disease. Interestingly, the rs1043994 located in NOTCH3 showed gender preference and was found to be significantly associated with colorectal cancers in males. Validation of these findings in bigger populations of different ethnicities may prove beneficial in identifying rs11249433 and rs1043994 as genetic screening markers for early detection of breast and colorectal carcinomas, respectively. Breast cancer (BC) is a heterogeneous and one of the common disease affecting women worldwide. BC accounts for 11.7 percent 2.261 million) of all new cancer cases and 6.9 percent 684,996) of all cancer deaths globally . About 1,996 of a1 millionIn vitro overexpression of NOTCH1 has been implicated in colony formation, proliferation and tumorsphere formation of CRC cells. Additionally, NOTCH1 promotes growth and development of colon cancers in vivo [Several studies indicate that genetic variations are associated with risk of BC , 8 and p in vivo .Efforts are ongoing to identify reliable biomarkers for predicting human malignant neoplastic diseases. Genetic variation analysis has the potential to be utilized for screening and identifying novel prognosis genes. Single-nucleotide polymorphisms (SNPs) is one of the most common type of genetic variation that may serve as potential and distinctive genetic markers. In the present study, based on the associations with cancers from previous literature, we examined the influence of four SNPs, rs3124591, rs11249433, rs1043994, and rs3830041 which are located within or in close proximity of NOTCH1, NOTCH2, NOTCH3 and NOTCH4 genes, respectively on BC and CRC susceptibility by comparing the genotypic distribution of these SNPs in cancer cases to that of healthy subjects from Saudi Arabia.n = 190; controls n = 70), NOTCH2 related rs11249433 , NOTCH3 rs1043994 , NOTCH4 rs3830041 . Pretreatment blood samples from breast cancer patients were collected at King Fahad Medical City, Riyadh.The study subjects included in this case-control study comprised of women with pathologically confirmed breast cancer and age-matched female controls of Saudi Arabian ethnicity without any history of cancer. The median age at the time of breast cancer diagnosis was 53 years. The number of cases and controls examined for each SNP were as follows: NOTCH1 rs3124591 , NOTCH2 related rs11249433 , NOTCH3 rs1043994 , NOTCH4 rs3830041 . The median age at diagnosis of CRC was 58 years. Age-matched controls were recruited after diagnostic exclusion of cancer and cancer-related diseases. Blood samples from CRC patients were taken prior to receiving treatment at King Khalid University Hospital, Riyadh.The number of CRC cases and controls included in this study for each SNP were as follows: NOTCH1 rs3124591 (cases Ethylenediaminetetraacetic acid (EDTA) containing vacutainers were used to collect approximately 3 ml of blood samples from each study participants. Genomic DNA isolation was performed utilizing QIAmp DNA blood mini kit  as per the manufacturer’s instructions. Spectrophotometric quantitation and purity of the extracted DNA was done on NanoDrop 8000 .A total of four single nucleotide polymorphism in NOTCH1, NOTCH2, NOTCH3 and NOTCH4 receptor genes were selected from previous literature –24. TaqMhttps://ihg.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl. Genetic association of each SNP with breast and colorectal cancer were determined by case-control comparisons using the chi-square test and odds ratios (OR), and 95% CI. A p-value of ≤ 0.05 was considered as significant. Additionally, since we analyzed 4 SNPs in total, Bonferroni’s correction for multiple comparison was applied with an α = 0.0125 considered as significant.Frequencies for the three genotypes and alleles for each SNP were computed and tests for deviation from Hardy-Weinberg equilibrium and tests for association were performed using publicly available web-based tool at We examined the genotypes of four germline SNPs residing in NOTCH1 - rs3124591, NOTCH2 - rs11249433, NOTCH3 - rs1043994, and NOTCH4 - rs3830041 to determine their association with breast and colorectal cancer risk in Saudi Arabian patients. The rs3124591 is a 3 Prime UTR variant in NOTCH1, rs11249433 resides in a linkage disequilibrium block neighboring NOTCH2, rs1043994 is a synonymous variant coding for alanine at protein position 202 of NOTCH3 and rs3830041 is an intronic variant in NOTCH4 receptor.p = 0.046275) and NOTCH2 related rs11249433 (p = 0.000095) in breast cancers deviated from Hardy-Weinberg equilibrium . The other three SNPs, rs3124591 (NOTCH1), rs1043994 (NOTCH3), and rs3830041 (NOTCH4) were not significantly associated with the predisposition of breast carcinoma in the overall analysis   SNP was found to be significantly associated with early onset (≤53 years of age at diagnosis) of breast cancer. The GA genotype of NOTCH2 related rs11249433 SNP conferred about 4.7 fold higher risk of developing carcinoma of the breast before or till the age of 53 years relative to those having GG genotype  . This as0.00707) .Colorectal cancer cases and controls were segregated according to the median age at the time of disease diagnosis as ≤58 years and >58 years. None of the four SNPs examined in the NOTCH receptors were found to be significantly associated with the age of onset of colorectal cancers .2 = 4.01, p = 0.04514)  . The othNotch signaling pathway is highly conserved molecular cell signaling pathway which plays important roles in proliferation, differentiation, cell fate specification, homeostasis and angiogenesis. Additionally, notch signaling is considered as one of the most common pathway implicated in cancer metastasis . SeveralSeveral studies have shown that SNPs in NOTCH receptors are linked to risk and prognosis of a number of diseases. For examples, SNPs in NOTCH1 and NOTCH2 are associated with risk of breast carcinoma , 22. GenIn the overall analysis, except for rs11249433 which is in close proximity to NOTCH2 gene that showed significant association with breast cancer, none of the other SNPs were found to confer increased risk either in breast or colorectal cancers in our population. Investigations on the association of NOTCH1 rs3124591, NOTCH3 rs1043994, and NOTCH4 rs3830041 with the risk of human cancers are rare, however, several studies have shown a link between NOTCH2 related rs11249433 and increased risk of breast cancer especially in women of European ancestry –21. The Our data showed that rs1043994 in NOTCH3 although not significantly associated with breast or colorectal cancer in the overall analysis was having a statistically significant association with colorectal cancers in males. The GA heterozygote males of this SNP were at approximately 2-fold higher risk of developing colorectal cancers compared to GG homozygotes. Colorectal cancer is the predominant cancer in Saudi Arabian males. As cancer risk can be influenced by differential gene expression pattern between men and women as a result of differences in their hormonal and genetic factors, the association of colorectal cancer in men could be attributed to NOTCH3 - rs1043994 variants. Gender related differences in the prognosis of several cancers including colorectal cancer have been reported –41. A liin situ [in situ but not in invasive ductal carcinoma was observed [In Chinese population, while the association of NOTCH2-rs11249433 and NOTCH3-rs1043994 was lacking with breast cancer risk, NOTCH1-rs3124591 was significantly associated with invasive ductal carcinoma and ductal carcinoma in situ . Furtherobserved . Our datobserved . A recenobserved . HoweverWhile there are few reports in the literature on the correlation of the four NOTCH receptor SNPs that we examined on breast cancer, this is the first study to screen these SNPs in colorectal cancers. Screening of larger population of different ethnicity validating our findings on the association of NOTCH2-rs11249433 with breast cancer particularly in younger women and NOTCH3-rs1043994 with colorectal cancer in men would prove beneficial in utilizing these variants as genetic markers for early diagnosis and management of these malignancies.


Obesity is a rising health epidemic in breast cancer survivors and associated with multiple negative health sequalae and increased mortality. Delay Discounting (DD) is a behavioral economic measure of an individual’s valuation of future outcomes. While higher DD correlates with obesity in the general adult population, valuation of the future may impact cancer survivors differently due to their unique experiences. We assessed cross-sectional associations between DD, BMI, and healthy lifestyle behaviors in an exploratory analysis of 89 women with hormone receptor positive non-metastatic breast cancer. We found higher DD to be associated with obesity and decreased frequency of vegetable consumption. Future studies should investigate DD as a therapeutic target for novel behavioral interventions in breast cancer survivors affected by obesity. This may improve valuation of the future, increase healthy lifestyle behaviors, and facilitate weight loss to promote overall health and longevity in this population.p < 0.01), independent of age, race, income, time since diagnosis, and menopausal status. There was no significant association between DD and fruit consumption or exercise frequency. Vegetable consumption was significantly associated with lower DD . Higher DD is associated with obesity and decreased frequency of vegetable consumption in BC survivors. Future studies should investigate DD as a therapeutic target for behavioral interventions to facilitate weight loss and promote longevity in this population.Obesity in breast cancer (BC) survivors is associated with increased mortality. Delay discounting (DD) is a behavioral economic measure of how individuals value future outcomes. Higher DD correlates with obesity in the general population. Valuation of the future may be associated with obesity differently in cancer survivors. This study evaluated the relationship between DD and obesity in BC survivors. We report an exploratory analysis assessing cross-sectional associations between DD, BMI, and lifestyle behaviors  related to obesity in 89 women with hormone receptor positive non-metastatic BC. Multivariate linear regression analysis examined demographic and lifestyle behavior variables associated with both BMI and DD. Greater willingness to wait for larger, delayed rewards (lower DD) was significantly associated with lower BMI (standardized beta = −0.32;  World-wide, breast cancer (BC) is the most commonly diagnosed cancer in women, with an estimated 2.3 million new cases in 2020 . OutcomeObesity is a rising health epidemic in the United States  and exceDelay discounting (DD) is a behavioral economic measure that describes the degree to which individuals value future outcomes . When gip = 0.01), suggesting that DD is elevated soon after diagnosis and decreases with time [Valuation of the future in cancer survivors may be altered by a number of unique factors, such as an adjusted mortality perception, financial toxicity from cancer diagnoses and treatment, and cancer-related psychosocial consequences. For instance, cancer survivors may experience psychologic effects including posttraumatic stress and a fear of cancer recurrence ,35. The ith time . The mulFocused investigation of DD in cancer survivors is critical to better elucidate its role as a potential therapeutic target for weight loss in this population. In a study of 1001 survivors across several types of cancers, including breast, lung, sarcoma, genitourinary, and others, participants were recruited to assess associations between DD and multiple lifestyle behaviors . Lower Dp = 0.005) [A pilot study from our investigational team was the first to study DD specifically in a BC survivor cohort and demonstrated DD as a predictor of adjuvant endocrine therapy non-adherence in 89 participants. This study found that treatment adherence was higher in participants showing greater willingness to wait for larger, delayed rewards   . These f= 0.005) ,26,27,28Patients were recruited from a large, community-based healthcare system in the Roanoke, VA area. Key eligibility criteria included: (1) women with stage 1–3 non-recurrent hormone receptor positive invasive BC treated with curative intent in the last five years, (2) currently prescribed or recommended adjuvant oral endocrine therapy  with or without ovarian suppression, and (3) 18–80 years old. Patients who were prescribed endocrine therapy for metastatic disease and those suffering from physical  or cognitive  impairments that may interfere with medication self-administration were excluded. All participants completed informed consent prior to study enrollment. The Carilion Clinic Institutional Review Board approved all study procedures.The primary aim of this study was to evaluate cross-sectional associations between DD and endocrine therapy non-adherence. We previously reported these findings which demonstrate that greater rates of DD were significantly associated with poorer adherence to endocrine therapy . To asse2. During a single 90–120 min session, participants completed a questionnaire to collect information encompassing demographics, cancer diagnoses and treatment, and healthy lifestyle behaviors. The latter included information on consumption of fruit and vegetables  as well as exercise frequency . Next, participants completed the five-trial, adjusting-delay task [Weight and height were collected by self-report. BMI was calculated based on the formula: BMI = weight (kg)/height (m)lay task  to asseslay task ,28,33. Tlay task . Thus, alay task ,40. The p-values. A p-value < 0.05 was considered significant in this study.Demographic variables are summarized using means (standard deviations), medians (interquartile ranges), and frequencies (percentages), where appropriate. Univariate linear regression analysis was performed to identify explanatory variables, including demographics and healthy lifestyle behaviors , associated with BMI. To identify an optimal subset of factors associated with BMI, we used multivariate regression and performed an exhaustive model selection search and identified the final model as that with the lowest Bayesian Information Criterion (BIC). Similarly, multivariate regression with model selection were performed to predict discounting (ED50 $100 and ED50 $1000). Results are reported using standardized betas and 2 (SD 6.6). A total of 29 (33%) patients in this cohort were overweight; 38 (43%) patients were obese. Adjuvant aromatase inhibitor  was the most common endocrine therapy prescribed , with the remainder receiving adjuvant tamoxifen .A total of 89 participants completed this study, of which details have been previously published . Table 1p = 0.002; ED50 $1000, standardized beta = −0.28, p = 0.008) and this effect was independent of age, race, income, time since diagnosis, and menopausal status (p < 0.001), fruit consumption , and vegetable consumption  were significantly associated with lower BMI. Model selection identified the most probable model to include ED50 $100 , exercise frequency , and fruit consumption .For DD, the mean natural log ED50 was 5.38 (1.89) and 5.69 (2.14) for ED50 $100 and $1000, respectively. For context, these values correspond to raw values of 217.02 and 295.89 days, respectively, indicating that the $100 and $1000 rewards lost half of their subjective value in approximately 7–10 months. High ED50 values  were associated with lower BMI  and fruit consumption  were not associated with DD rate; however, vegetable consumption was significantly associated with lower DD rate . Upon model selection, only age  and household income  were associated with DD rate.Linear regression models were used to estimate ED50 from health behaviors. Exercise frequency . In addition, we did not find a consistent, statistically significant difference between DD rate and healthy lifestyle behaviors assessed in this study. While increased vegetable consumption was associated with lower discounting, there was no significant relationship between discounting and the other lifestyle behaviors. This may be related to the small sample size. Additionally, the potential to introduce recall bias exists with the use of self-report questionnaires. Future studies should incorporate validated, higher-resolution measures to provide a more comprehensive understanding of the relationship between discounting, physical activity, and energy consumption. For example, portion size, calorie consumption, diet quality, and exercise intensity/time are measures which can provide greater precision to estimate net energy balance and may clarify the link between discounting and lifestyle behaviors. Therefore, further investigation to assess the role of physical activity and dietary factors as a potential mediator of the association between DD and obesity should be pursued using robust measurements  and in a larger cohort of patients.The study population also lacked socioeconomic and racial diversity and the majority of participants were post-menopausal. Additionally, there was limited diversity in the BC subtype, as all participants presented with hormone receptor positive BC. Advanced BC stage was also underrepresented (4% with stage 3 BC). Given that research supports obesity is associated with multiple negative health sequalae in BC survivors regardless of receptor subtypes and stage ,7,8,51, Future studies should investigate the effect of toxicities related to anti-cancer therapies on the uptake of healthy lifestyle behaviors and influence on DD. This is highly relevant in the modern era of breast cancer management given the rapid expansion of adjuvant treatments in cancer survivors to decrease recurrence risk; these treatments are not without side effects. We previously reported the adverse effects and associated severity related to endocrine therapy in this patient cohort . The preThe current study suggests an association between DD and obesity in BC survivors and supports DD as a potential therapeutic target for weight loss. Additional research to elucidate the role of this behavioral mechanism in unhealthy lifestyle choices related to positive energy balance is indicated. Additionally, this relationship should be assessed in a more diverse BC survivorship cohort to confirm reproducibility. These findings support the potential for innovative behavioral therapies that target DD to promote weight loss and the associated long-term health benefits in BC survivors affected by increased weight. A randomized clinical trial by our research team implementing remotely delivered EFT intervention by smartphone is currently ongoing with these objectives (NCT05012176). If this strategy is feasible and efficacious to promote weight loss, improve diet quality, and reduce systemic inflammatory markers, additional investigation will be pursued across multiple cancer survivorship cohorts, as weight reduction in obesity is a pervasive target in optimal cancer survivorship care.


No direct association was found between the carriage of HLA-DRB1*11 and 1*12 alleles and breast cancer risk. However, further investigation of other HLA alleles involved in the occurrence of breast cancer may provide more information.Several factors contribute to the development of breast cancer, including the immune system. This study is aimed to characterize the carriage of human leukocyte antigen (HLA)-DRB1*11 and 1*12 alleles in patients with breast cancer. This case-control study consisted of 96 histologically diagnosed breast cancer cases and 102 controls . A multiplex polymerase chain reaction (PCR) was used to characterize the carriage of HLA-DRB1*11 and 1*12 alleles. The HLA-DRB1*11 allele was present in 26.59% of cases and 22.55% of controls. The HLA-DRB1*12 allele was present in 56.63% of cases and 55.88% of controls. This study found no direct association between the carriage of the HLA-DRB1*11 and HLA-DRB1*12 alleles and the occurrence of breast cancer. In addition, the deletion of the HLA-DRB1*11 allele is associated  with obesity/overweight (OR = 0.13; 95% CI [0.01–1.14]; and  At the same time, female breast cancer was ranked the most diagnosed type of cancer in the world. In fact, 2.3 million new cancer cases were recorded, representing 11.7% of all diagnosed cancers, with 68,496 deaths, representing 6.9% of cancer deaths ,6. Severhttp://hla.alleles.org/nomenclature/stats.html” ; and pulation ,51.Thus, these different studies show divergent or even contradictory results depending on the populations investigated, hence the need for more in-depth studies in different populations to establish meta-analyses that can be used for very advanced risk analyses.This work is a preliminary study of the involvement of HLA gene system alleles in breast cancer in women of Burkina Faso. In addition, we are aware of the statistical bias due to the size of our study population. For this last point, it is useful to emphasize that access to women with breast cancer in Burkina Faso remains somewhat difficult because cancer care is still being improved through universal health insurance and the creation of cancer services in the country’s regions.5This study explored the frequency of HLA-DRB1*11 and 1*12 alleles and their involvement in breast cancer. Deleting the HLA-DRB1*11 allele is associated with obesity/overweight, which is a risk for cancer development. However, the relatively early age of breast cancer diagnosis requires further investigation of the other HLA gene alleles. In addition, further investigation of a wide range of HLA gene alleles in a large population may provide more information.


Previous observational studies have provided conflicting results on the association between serum iron status and the risk of breast cancer. Considering the relevance of this relationship to breast cancer prevention, its elucidation is warranted.We used a two-sample Mendelian randomisation (MR) study to explore the causal relationship between serum iron status and the risk of breast cancer.To select single nucleotide polymorphisms (SNPs) that could be used as instrumental variables for iron status, we used the Genetics of Iron Status consortium, which includes 11 discovery and 8 replication cohorts, encompassing 48,972 individuals of European descent. Moreover, we used the OncoArray network to select SNPs that could be considered instrumental variables for the outcome of interest (breast cancer); this dataset included 122,977 individuals of European descent with breast cancer and 105,974 peers without breast cancer. Both conservative  and liberal (SNPs associated with the levels of at least one iron status marker) approaches were used as part of the MR analysis. For the former, we used an inverse-variance weighted (IVW) method, whereas for the latter, we used the IVW, MR-Egger regression, weighted median and simple mode methods.P = 0.030). Nevertheless, the levels of the other iron status markers showed no association with the risk of breast cancer or its subtypes (P > 0.05).When the conservative approach was used, iron status showed no significant association with the risk of breast cancer or any of its subtypes. However, when the liberal approach was used, transferrin levels were found to be positively associated with the risk of ER-negative breast cancer based on the simple mode method (OR for MR, 1.225; 95% CI, 1.064, 1.410; In our MR study, the liberal approach suggested that changes in the concentration of transferrin could increase the risk of ER-negative breast cancer, although the levels of other iron status markers had no effect on the risk of breast cancer or its subtypes. This should be verified in future studies.The online version contains supplementary material available at 10.1186/s12263-021-00691-7. The morbidity of breast cancer is increasing, affecting the quality of life of patients and their families and increasing their economic burden. Oxidative stress, a known cause of breast cancer, may have a role in this process . Iron isPrevious epidemiological studies have reported that higher levels of iron may be associated with a modestly increased risk of breast cancer , 8. In cPrevious studies have reported the presence of confounding factors, such as post-natal living environment, behaviour and habits, social status and environmental factors, that affect the association between iron status and breast cancer risk . Indeed,Mendelian randomisation (MR) — a process in which genetic variations closely related to the exposure variable are used as instrumental variables — can help overcome the limitations of traditional epidemiological studies and allow us to make causal inferences regarding the effect of a particular exposure on an outcome . BecauseTo our knowledge, there have been no MR-based studies examining the relationship between iron status and breast cancer risk, even though the delineation of this relationship could have significant utility in breast cancer prevention and treatment. To this end, in the present study, we used MR to investigate whether iron status is related to the incidence of breast cancer using publicly available data from genome-wide association studies (GWASs).We applied two-sample MR to summary data from the respective GWASs Fig. . The ori-6, minor allele frequency (MAF) > 0.01 [We obtained summary data from the largest meta-GWAS containing information on iron status, the Genetics of Iron Status (GIS) consortium, and performed MR analysis. This database included data on serum iron levels, transferrin levels, ferritin levels, and transferrin saturation. Data from 11 discovery and 8 replication cohorts encompassing 48,972 individuals of European ancestry were collected . Benyami) > 0.01 .Publicly available summary data on breast cancer were obtained from the largest meta-GWAS containing data on breast cancer risk — the OncoArray network — which contains data regarding five cancers: breast, ovarian, prostate, lung, and colorectal cancer . The bre−12, and MAF > 0.01. This information has been previously reported [The thresholds of the population stratification and quality control for the GWAS analysis for the cohort were imputed into the score > 0.3, HWE ≥ 10reported , 17.P < 5 × 10−8) were selected from the GIS consortium (Tables 2 ≤ 0.01). None of the SNPs that were chosen as instrumental variables were correlated with breast cancer (P > 0.05). The corresponding statistical indicators (beta and SE) were obtained from the GIS consortium and OncoArray network databases. SNPs for iron status from the GIS consortium corresponded to the European population, with a sample size of 48,972 cases. SNPs for breast cancer from the OncoArray network database corresponded to the same European ethnic group, with a sample size of 122,977.Instrumental variables that showed a strong association with iron status P < 5 × 0−8 were m Tables , 3, 4 [1rs855791, rs1800562, rs1799945) strongly associated with the increasing concentrations of ferritin, serum iron and transferrin saturation and decreasing concentrations of transferrin (P < 5 × 10−8) were selected. Increased concentrations of ferritin and serum iron; increased transferrin saturation and decreased concentrations of transferrin would cause an improvement in systemic iron status [To select instrumental variables, we used two analysis methods: conservative and liberal variable analyses. For conservative variable analyses, 3 SNPs (P < 5 × 10−8) were selected. Previous study has shown that there is a causal relationship between body mass index (BMI) and breast cancer [P > 0.05).For liberal variable analyses, the SNPs strongly affiliated with at least one of the iron markers  in the GWAS  using the following formula: OR = exp (beta). The 95% confidence interval (CI) was computed as follows: CI = exp (beta ± 1.96 × SE). Due to the limited consistency in publicly available data, the relationship between instrumental variables and other potential confounders, such as exercise and drinking, was difficult to assess. Hence, we used the GWAS catalogue database (https://www.ebi.ac.uk/gwas) to search for other phenotypes  related to the selected instrumental variable SNPs and manually removed these SNPs from the MR analysis to rule out possible pleiotropic effects.Two-sample MR was performed for testing the causal relationship between iron status and breast cancer. Moreover, breast cancer was subdivided as ER-positive and ER-negative breast cancer. Conservative and liberal methods were used in the MR analyses. As part of the conservative analysis, we used the inverse-variance-weighted (IVW)  method tFor the sensitivity analyses, we used the IVW and MR-Egger tests to evaluate heterogeneity and displayed the results using forest plots showing the value for each SNP and Cochran’s Q statistics , 25. In All above analyses were performed by R, version 3.6.1.rs1800562, rs1799945, and rs855791). F statistics for all the instrumental variables ranged from 40  to 3346 , showing that all the SNPs were strong instrumental variables (Tables Tables P > 0.05) . In the liberal analysis, we found a positive correlation between transferrin levels and ER-negative breast cancer risk based on the simple mode . However, the status of other iron markers had no association with the risk of breast cancer or its subtypes (P > 0.05).Figure P > 0.05). Using the IVW method, we found no evidence of heterogeneity for the associations between the status of the four iron markers  and breast cancer risk . Moreover, when using the MR-Egger method in the liberal analysis, we did not identify aggregated directional pleiotropy between the levels of the four iron markers and breast cancer risk  , high-density lipoprotein cholesterol (HDL-C), triglyceride (TG) and total cholesterol (TCHO) levels; rs4921915 is also related with TG and TCHO, and rs1800562 and rs651007 are related with LDL-C and TCHO [rs174577, rs4921915, rs1800562 and rs651007) did not change the trends in the obtained results ; t cancer , 30.In addition, the simple mode approach is a new method wherein the mode-based estimate can be used to obtain a single causal estimation from multiple genetic instruments. It provides robustness to horizontal pleiotropy in a different manner than the IVW, MR-Egger and weighted median methods. The simple mode provides better detection capability than the MR-Egger estimate, although its detection capability is inferior to that of the IVW and weighted median methods . In addirs1800562 in HFE, rs174577 in FADS2, rs651007 in ABO and rs4921915 in NAT2 — related to LDL-C, TCHO, and/or TG levels, which have been reported to influence breast cancer risk [As there is some risk of pleotropic effects in MR estimates , we searcer risk , 32. NevBreast cancer is a heterogeneous disease with different histopathology and molecular subtypes, each with their own clinical prognosis and risk factors . PreviouThe change in iron status and the risk of breast cancer may be caused by common exposure factors. For example, inflammation affects iron status, increasing serum ferritin concentrations and reducing serum iron concentration , 38, andThere are some advantages to our study. We evaluated summary data from the largest meta-GWASs available, the GIS consortium and the OncoArray network. All the data extracted were from individuals of European descent, reducing any ancestry-related bias. In addition, we used two analysis methods to select instrumental variables — conservative and liberal analyses — which guarantees the robustness of our causal estimation.However, there are some limitations to our present study. First, due to the limitations of the publicly available GWAS databases, it was difficult to perform hierarchical analysis according to factors such as age and sex in the combined exposures and outcome databases. Second, we used liberal instruments, which although provide more power to the study, also make the study particularly vulnerable to effects of pleiotropy. In this study, although we tried to reduce the effects of pleiotropy, bias due to the unknown biological functions of the SNPs concerned with iron status may be inevitable. Moreover, under ideal condition, for MR analysis, a sample size as large as possible should be used to make the results more reliable. Although the data for iron status and breast cancer were obtained from the GWAS databases with the largest sample size in the world, the sample size may still not have been fully ideal. The cohorts used in our study are all from European races, which contributing to reducing the bias brought by races. However, it is unknown whether the results are suitable for other races. Therefore, more researches should be conducted.Our MR study indicates that changes in the serum transferrin concentration could increase the risk of ER-negative breast cancer, whereas the status of the other three iron statuses had no association with breast cancer. As the liberal instrument was relatively weak, these findings need to be verified in further studies.Additional file 1: Supplementary Fig. 1. The SNP effects on iron status biomarkers and breast cancer for Scatterplot. iron (a), transferrin (b), ferritin (c), and transferrin saturation (d). MR Egger, Mendelian randomization–Egger regression method.Additional file 2: Supplementary Fig. 2. The SNP effects on iron status biomarkers and ER-positive breast cancer for Scatterplot. iron (a), transferrin (b), ferritin (c), and transferrin saturation (d). MR Egger, Mendelian randomization–Egger regression method, ER, estrogen receptor.Additional file 3: Supplementary Fig. 3. The SNP effects on iron status biomarkers and ER-negative breast cancer for Scatterplot. iron (a), transferrin (b), ferritin (c), and transferrin saturation (d). MR Egger, Mendelian randomization–Egger regression method; ER, estrogen receptor.Additional file 4: Supplementary Fig. 4. Odds ratio of breast cancer, ER-positive breast cancer, and ER-negative breast cancer risk per standard deviation increase in iron excluding 1 SNP at per time estimated by the inverse variance weighted and Wald ratio (only for breast cancer). SNP: single nucleotide polymorphism; OR: odds ratio; 95% CI, 95% confidence interval; ER, estrogen receptor.Additional file 5: Supplementary Fig. 5. Odds ratio of breast cancer, ER-positive breast cancer, and ER-negative breast cancer risk per standard deviation increase in transferrin excluding 1 SNP at per time estimated by the inverse variance weighted. SNP: single nucleotide polymorphism; OR: odds ratio; 95% CI, 95% confidence interval; ER, estrogen receptor.Additional file 6: Supplementary Fig. 6. Odds ratio of breast cancer, ER-positive breast cancer, and ER-negative breast cancer risk per standard deviation increase in ferritin excluding 1 SNP at per time estimated by the inverse variance weighted. SNP: single nucleotide polymorphism; OR: odds ratio; 95% CI, 95% confidence interval; ER, estrogen receptor.Additional file 7: Supplementary Fig. 7. Odds ratio of breast cancer, ER-positive breast cancer, and ER-negative breast cancer risk per standard deviation increase in Transferrin saturation excluding 1 SNP at per time estimated by the inverse variance weighted and Wald ratio (only for breast cancer). SNP: single nucleotide polymorphism; OR: odds ratio; 95% CI, 95% confidence interval; ER, estrogen receptor.


A total of 170 cases and 220 age matched controls were recruited in this study. GAS5 lncRNA polymorphism was genotyped using tetra primers amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) method. Statistical analysis was performed using SPSS. The distribution of the genotype ins/ins, ins/del and del/del were %75.29, 21.76% and 2.94% and 52.27%, 39.55% and 8.81% in the cases and controls, respectively. The ins/del or del/del genotype had a significantly decreased risk of breast cancer as compared with the ins/ins genotype under a codominant model . Moreover, the deletion allele of this polymorphic site is associated with a protective effect . Our study provided the first evidence that the deletion allele of GAS5 rs145204276 may have a protective role in mediating individual susceptibility to breast cancer. However, further comprehensive studies are warranted in a larger sample.Long non-coding RNAs (lncRNAs) are RNA molecules (>200 nucleotides in length) with no protein-coding capacity. Recent studies have demonstrated that lncRNAs involve in the regulation of their target genes at transcriptional, post-transcriptional and epigenetic levels. The aim of this case-control study was to explore whether growth arrest-specific  Numerous risk factors, includ¬ing lower age of menarche, late age of first pregnancy, fewer pregnancies, shorter or no periods of breastfeeding, later menopause, obesity, alcohol consumption, inactivity, and hormone replacement therapy (HRT), have been reported to contribute to the pathogenesis of breast cancer . Yet, the underlying etiological mechanisms of breast cancer are not fully understood.Breast cancer ranks the first most common type of malignancies among females worldwide . On a global scale, breast cancer accounts for approximately 450,000 dead worldwide every year . In Iran, breast cancer accounts for 76% of all common female cancers, with an estimated prevalence rate of 23.65 per 100,000 . It has been shown that the age of breast cancer diagnosis in Iranian women is between 40 and 50 years . The etiology of breast cancer comprises interactions of genetic factors and multiple environmental components. In the last years, many variants of different genes have been associated with breast cancer risk worldwide; including Gene regulation plays an important role in cancer development. A large portion of the human genome would transcript into noncoding RNAs (ncRNAs) which can be categorized into two major groups: house-keeping ncRNAs  and regulatory ncRNAs  . Long non-coding RNAs (lncRNAs) are RNA molecules (>200 nucleotides in length) that usually with no protein-coding capacity . Recent studies have demonstrated that lncRNAs involve in the regulation of their target genes at transcriptional, post-transcriptional and epigenetic levels . lncRNAs participate in diverse biological processes, such as apoptosis, cell cycle, differentiation, migration, and cell proliferation; supporting the hypothesis that dysregulation of lncRNAs may be associated with human diseases . In addition, studies have also revealed that some lncRNAs may function as oncogenes or tumor suppressors . GAS5) gene, which was first identified in 1988 . The GAS5 gene is located on chromosome 1q25.1 that encodes several small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs) and lncRNA .GAS5 LncRNA is a transcript of the growth arrest-specific 5 . It should be noted that other mechanisms such as direct interaction with miR-21, CDK6, YBX1 and other proteins are also important . Downregulation of GAS5 has been reported in several malignancies, including breast, gastric and prostate cancer ; thus, it may function as a tumor suppressor . Although the exact mechanism of GAS5 lncRNA is not completely understood, it is suggested that it repress transcriptional activity by competing with DNA glucocorticoid receptor response element (GRE) for binding to the glucocorticoid receptors (GR) . In addition, GAS5 lncRNA gene polymorphisms have been reported to be an important predisposition factor to gastric cancer , acute myeloid leukemia  and hepatocellular carcinoma . However, the relationship between GAS5 lncRNA indel variation and the risk of breast cancer has not been investigated to date. Thus, the aim of this case-control study was to investigate the asso¬ciation between a 5-base pair indel polymorphism (rs145204276) of GAS5 lncRNA and breast cancer risk in Iranian population.A genetic polymorphism is a variation in DNA sequence that occurs in a population at a frequency greater than 1%, resulting in the existence of multiple alleles. The most famous polymorphic site of GAS5 is rs145204276, which contains a 5-base pair insertion/deletion (indel) polymorphism in the promoter region with a minor allele frequency (MAF) 0.12 . The indel polymorphisms likely have a great influence on human property and disease susceptibility . It has been shown that the relative GAS5 expression level in del/del genotype was significantly higher than ins/del and ins/ins genotype . In recent years, the SubjectsWe conducted a case-control study on 390 women (170 cases and 220 controls) who were recruited between August 2013 and December 2017. Breast cancer patients diagnosed either by imaging evidence, histopathologic documentation, with a minimum age of 35 years. Pathobiological features  were obtained from the medical records of the hospitals and collected. Patients who had previous radiotherapy or chemotherapy were excluded in the study. In addition, 220 age-matched healthy women who got a routine health checkup and normal mammography without any history of neoplasms or chronic disease were recruited as controls. Characteristics of all enrolled subjects, such as age, breast feeding history, menopausal status and using oral contraceptive pill (OCP) were collected. Written informed consent for the genetic analysis was obtained from each subject participating in the study. The study was conducted in accordance with the Declaration of Helsinki regarding the use of human samples.GenotypingBlood samples of breast cancer cases and controls were collected in EDTA-containing tubes and centrifuged at 1500 g for 10 minutes. Plasma and buffy coat were separated and stored at -20°C. Genomic DNA was extracted using GPP Solution Kit  according to the manufacturer’s recommendations. Purity and quantity of DNA were checked with a spectrophotometer  with A260/280 1.75–1.85. All DNA samples were dissolved in TE buffer  with a final concentration ranging from 55–365 ng/µl and stored at -20°C until use. 2, 2 mM of each dNTP, 0.5 mM/ml primer mix, and 1 U Taq DNA polymerase . The cycling profile was as follows: initial denaturation at 95°C for 6 minutes, 35 cycles of denaturation at 94°C for 35 seconds, annealing at 56°C  and 60°C  for 40 seconds, with a final extension step at 72°C for 2 minutes. The fragment lengths were 109 and 348 bp for ins and del alleles, respectively. The amplicons were electrophoresed with a 50 bp DNA ladder  on 1.5% agarose gel containing safe stain. All blood samples were genotyped successfully. Genotyping was performed in a blinded manner.The GAS5 lncRNA polymorphism was genotyped using tetra primers amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) method. Primers were designed by Oligo7 software . Primers were synthesized by MWG-Biotech . The primers sequences were as follows: Ins-F (5ʹ- GCAGAGACATGACCGTCCAC- 3ʹ) and ins-R (5ʹ-CCCCATCCCCAGAGCTTTCGTT-3ʹ) for ins allele; Del-F (5ʹ-AAAACCCGCAACATTCGCAAA-3ʹ) and del-R (5ʹ-CCCCATCCCCAGAGCTTTCGTC-3ʹ) for del allele. PCR was carried out on the thermal cycler  in a total volume of 20 µl, including 50 ng genomic DNA, 1X PCR buffer, 3 mM MgClStatistical analysisGAS5 lncRNA polymorphisms were estimated by gene counting. Within the cancer cases and controls, frequencies of GAS5 lncRNA different alleles and genotypes investigated were calculated using the chi-square test. Odds ratios (OR) were calculated to study the association between alleles or genotypes of GAS5 lncRNA and risk of breast cancer by the means of the Fisher’s exact test with the reference category being the homozygote of the major allele among the subjects. We also tested four different genetic models including codominant, dominant, recessive and overdominant. Statistical analyses were performed using SPSS version 16.0 for Windows . Significance was set at p<0.05. The OpenEpi software (www.OpenEpi.com) based on minor allele frequency (MAF) of SNP was used to calculate a required sample size. Calculations of Hardy-Weinberg equilibrium (HWE) were conducted using the chi-square test to compare the observed genotype frequencies with the expected frequencies among the case and control subjects. HWE graphically analyzed by using triangular ternary plot through “de Finetti” diagram (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). Allele frequencies of the The demographic characters of the subjects are presented in χ2-test (P=0.0001). The distribution of the genotype ins/ins, ins/del and del/del were %75.29, 21.76% and 2.94% and 52.27%, 39.55% and 8.81% in the cases and controls, respectively. As shown in Genotyping for rs145204276 of GAS5 lncRNA was performed in 170 breast cancer patients and 220 healthy controls. The Hardy–Weinberg analysis is shown in The association between clinic-pathological char¬acteristics of breast cancer patients with GAS5 polymorphism is shown in In this case-control study, we studied the impact of GAS5 lncRNA (rs145204276) polymorphism in breast cancer susceptibility in Iranian population. To our knowledge, this is the first study in Iranian population showing a significant association between the GAS5 lncRNA (rs145204276) del allele and decreased breast cancer susceptibility. Moreover, the ins/ins homozygous breast cancer patients presented significantly more often with Ductal carcinoma than patients carrying a del allele of GAS5. th CpG site as compared with those with genotype ins/ins.There are no previous reports about the prevalence of the rs145204276 variant in patients with breast cancer. A previous study conducted by Zheng et al. demonstrated that the deletion allele of rs145204276 was significantly associated with 21% decreased risk of colorectal adenocarcinoma and compared with the genotype ins/ins, both the genotype ins/del and del/del showed decreased susceptibility . However, a matched hospital-based case–control study with 813 colorectal cancer and 926 cancer-free controls supported the hypothesis that del allele of GAS5 indel polymorphism played a role in the pathogenesis of CRC in the Chinese population . A recently published data from Xu and colleagues showed that del/del genotype had a significantly higher expression of GAS5 in patients with osteosarcoma . In addition, cases with del/del genotype were found to have evidently hypermethylation at the 7Increasing evidence has suggested that lncRNAs are key players in various processes of breast cancer, such as proliferation, metastasis, angiogenesis and drug resistance . In addition to downregulation of GAS5 lncRNA in some tumors, suppression of GAS5 expression has been found to correlate with tumor size and advanced disease in lung , gastric , colon  and ovarian  cancers. GAS5 transcripts induce growth arrest and cell death in human breast cancer cell lines, suggesting that GAS5 lncRNA may have a tumor-suppressive role in breast cancer cells . However, GAS5 may act as a proto-oncogene in HCC . The GAS5 rs145204276 is a 5-bp indel polymorphism (-/AGGCA) in the gene promoter region. It has been shown that the deletion allele of rs145204276 could alter the methylation status of CpG islands which could increase the expression of lncRNA in lung cancer and hepatocellular carcinoma . Previous study showed that rs145204276 may contribute to hepatocarcinogenesis by affecting methylation status of the GAS5 promoter and subsequently its transcriptional activity. To date, there have been only two previous studies to establish an association between lncRNA genetic variants and breast cancer susceptibility in Iranian women. Hassanzarei and coworkers reported that HOX transcript antisense intergenic RNA (HOTAIR) rs920778, rs12826786, and rs1899663 polymorphisms may be associated with breast cancer risk in a sample of southeast Iranian population . In a case-control study involving 122 breast cancer cases and 200 normal age-match female, Khorshidi et al, suggested that breast cancer risk is significantly associated with ANRIL variants .Some limitations of this study should be noted. Firstly, this study only considers a north Iranian population that may limit the application of these findings to other populations. Second, the sample size was small; the results should be interpreted with caution. Given the diversity of rs145204276 polymorphism, a larger number of subjects are required to evaluate the role of GAS5 lncRNA polymor¬phism in breast cancer risk. Third, other polymorphisms of GAS5 and their possible interactions were not evaluated. However, numerous factors act individually and together to influence the risk of breast cancer. So, we should involve more factors in our future work.In conclusion, our study provided the first evidence that the del allele of GAS5 rs145204276 may have a protective role in mediating individual susceptibility to breast cancer. As breast cancer is a complex disease that involves environmental and genetic factors, further comprehensive studies are warranted in a larger sample, including investigation of combined effects of multiple loci other than one polymorphic site or single gene.


Mechanisms underlying the adiposity–cancer relationship are incompletely understood. We quantified the mediating roles of C‐reactive protein (CRP), leptin, fasting insulin, and estradiol in the effect of adiposity on estrogen receptor (ER)‐positive breast, endometrial, and colorectal cancer risk in postmenopausal women.We used a case–cohort study within the Women's Health Initiative Observational Study, analyzed as a cumulative sampling case–control study. The study included 188 breast cancer cases, 98 endometrial cancer cases, 193 colorectal cancer cases, and 285 controls. Interventional indirect and direct effects on the risk ratio (RR) scale were estimated using causal mediation analysis.2 was 1.87 . The indirect effect RRs were 1.38 (0.79–2.33) through leptin and CRP, 1.58 (1.17–2.43) through insulin, and 1.11 (0.98–1.30) through estradiol. The direct effect RR was 0.82 (0.39–1.68). For endometrial cancer, the total effect RR was 2.12 (1.12–4.00). The indirect effect RRs were 1.72 (0.85–3.98) through leptin and CRP, 1.42 (0.96–2.26) through insulin, and 1.24 (1.03–1.65) through estradiol. The direct effect RR was 0.70 (0.23–2.04). For colorectal cancer, the total effect RR was 1.70 (1.03–2.79). The indirect effect RRs were 1.04 (0.61–1.72) through leptin and CRP, 1.36 (1.00–1.88) through insulin, and 1.02 (0.88–1.17) through estradiol. The direct effect RR was 1.16 (0.58–2.43).For breast cancer, the total effect RR for BMI ≥30 versus ≥18.5–<25 kg/mLeptin, CRP, fasting insulin, and estradiol appear to mediate the effect of high BMI on cancer risk to different extents, with likely varying degrees of importance between cancers. These insights might be important in developing interventions to modify obesity‐associated cancer risk in postmenopausal women. Mechanisms underlying the effect of adiposity on increased cancer risk are incompletely understood. Here, we quantified the roles of leptin and C‐reactive protein, fasting insulin, and estradiol in explaining the effect of high BMI on estrogen receptor‐positive breast, endometrial, and colorectal cancers in postmenopausal women. We used a novel causal mediation analysis approach and data from a case–cohort study within the Women's Health Initiative Observational Study. The assessed biomarkers explained the increased risk of the three cancers to different extents, and their importance varied between cancers. These insights might be important in developing cancer prevention interventions for postmenopausal women with obesity.1Excess body fat is a risk factor for several cancers, including estrogen receptor (ER)‐positive postmenopausal breast, endometrial, and colorectal cancers.Chronic low‐grade inflammation is a known consequence of excess adiposity. In the obese state, the production of adipocytokines and cytokines is disturbed, characterized by increased secretion of leptin, one of the most abundant adipocytokines with pro‐inflammatory properties.difference method. This method would entail estimating the adiposity–cancer association first without, then with the biomarker (mediator) in the regression model, and taking the difference between the coefficients from the two models as an estimate of the mediating (indirect) effect.A better understanding of the role these biomarker pathways play in mediating the effect of adiposity on cancer risk and their potentially differing importance for different cancers might help to identify risk‐reducing targets for individuals with obesity. In the present study, we made use of an existing case–cohort study of obesity‐related biomarkers and breast, endometrial, and colorectal cancers among postmenopausal women within the Women's Health Initiative Observational Study (WHI‐OS).2The Women's Health Initiative Observational Study (WHI‐OS) included 93,676 postmenopausal women aged 50–79 years old, recruited from 40 centers across the United States from 1993 to 1998.2.1We made use of an existing case–cohort study derived from the baseline WHI participants, which investigated inflammation, fasting insulin, and estradiol and the risk of endometrial, breast, and colorectal cancers.For the present analysis, we treated these data as if the design followed an unmatched case–control study with cumulative sampling because methods for mediation analysis for estimation of interventional indirect effects have not been adapted to the case–cohort design. However, since outcome was rare in the sub‐cohort and a small proportion of participants died or were lost to follow‐up, we expected that analyzing the study as cumulative sampling case–control study to not materially influence the results.Our analytic dataset included women diagnosed with ER‐positive breast, endometrial, or colorectal cancer in the original case–cohort study as cases, and women in the sub‐cohort who had not been diagnosed with those cancers, and had not died or were not lost to follow‐up by the end of follow‐up period as controls, if they met the following additional eligibility criteria Table .2. Women with diabetes or unknown diabetes status were excluded because both diabetes and diabetes medication use might change the adiposity–insulin signaling relationship.Confounder Selection), or biomarkers (see Biomarkers). Only for endometrial cancer, controls with history of hysterectomy at baseline were also excluded , or had BMI <18.5 kg/mThe analytic dataset included 188 ER‐positive breast cancer cases (median follow‐up 3.6 years [interquartile range 2.4–4.8]), 98 endometrial cancer cases (median follow‐up 3.3 years [interquartile range 2.6–4.8]), 193 colorectal cancer cases (median follow‐up 3.8 years [interquartile range 2.7–5.3]), and 285 controls  (median follow‐up 7.0 years [interquartile range 5.9–7.9])  it was included as a categorical variable in all analyses. Women were categorized based on the WHO cutoff values .Body mass index (BMI) was the measure of adiposity (exposure) and to assist with interpretation of the interventional indirect effects  preliminary analyses to investigate BMI–biomarker associations within the controls ; (ii) preliminary analyses to investigate the BMI–cancer and biomarker–cancer associations ; and iii) causal mediation analyses to decompose the total BMI–cancer association into the interventional indirect (mediated) and direct effects.2.3.1The causal diagram in Figure For the controls, for each biomarker, the geometric mean ratio (GMR) and 95% confidence intervals (CIs) were estimated in relation to BMI using a linear regression model fitted to the log‐transformed biomarker variable and adjusted for the pre‐exposure confounders.risk=1(1+exp‐logodds).log(1samplingfractionofcontrols) was subtracted from the constant term to take the cumulative case–control design into account.Risk ratios (RRs) and 95% CIs were estimated from the logistic regression models for the BMI–cancer and biomarker–cancer associations from the logistic regression models based on the formula The linearity of the mediator–cancer associations was investigated by fitting the biomarker variables as restricted cubic splines (with two degrees of freedom).The mediation analysis decomposes the total effect of BMI on cancer risk into indirect effects through the biomarkers, and a direct effect, which is the part of the effect that remains unexplained by the biomarkers. We followed the decomposition approach proposed by Moreno‐Betancur et al. A simulation‐based, regression‐standardization approach was used to estimate the indirect and direct effects, where the expected outcomes under different scenarios were averaged over mediator distributions based on a large number of Monte Carlo draws . There were positive associations with fasting insulin and estradiol, with the larger RR observed for fasting insulin . There was also evidence for positive associations with leptin and CRP, and the associations were somewhat attenuated after adjusting for insulin . There were positive associations with leptin and CRP, which were slightly attenuated after adjusting for insulin, and with fasting insulin, and estradiol. The strongest association was observed for estradiol    for BMI ≥30 versus ≥18.5–<25 kg/m2) Table .2, as there was no evidence for association comparing BMI ≥25–<30 kg/m2 versus ≥18.5–<25 kg/m2.For all cancer sites, there was no strong evidence for departure from linearity for any of the biomarkers Table . For eac3.32, we estimated a positive indirect effect through leptin and CRP (RR 1.38) but with high uncertainty , and a stronger indirect effect through fasting insulin . The indirect effect through estradiol was weaker . The RR for direct effect was 0.82  , and a stronger indirect effect through fasting insulin . The point estimate was indicative of even a stronger indirect effect through leptin and CRP (RR 1.72), but there was high uncertainty around the estimated effect . The RR for direct effect was 0.70   and estradiol , with high uncertainty around the estimated effects, but there was a positive indirect effect through fasting insulin  The RR for direct effect was 1.16   were ≤9%, estradiol and insulin tests were performed in duplicates and tests with, respectively, >20% and >10% CV were repeatedWe excluded cases diagnosed within the first year after baseline to reduce the possibility of cancer having influenced measures of adiposity or biomarkers. However, we cannot rule out reverse causation for the adiposity–biomarker associations. They were assessed at baseline, and we did not adjust for prior biomarker measurements. Our analyses relied on no unmeasured exposure–mediator, exposure–outcome, and mediator–outcome confounding.2 increment in BMI 50.0; 95%CI, 23.2–76.6), 24% was mediated through estradiol , with a larger mediating effect (49%) observed for ER‐positive breast cancers. In women without diabetes, of the total effect , 66% was mediated through fasting insulin .2 1.75; 95%CI, 1.05–2.91), conducted as a case–control study within the Melbourne Collaborative Cohort Study (MCCS), 72% of the effect was explained by free estradiol  and 28% by fasting insulin .Our results generally agree with a previous study that used data from two case–cohort studies within the WHI‐OS, one of which was the same dataset as that used here, and performed a causal mediation analysis to estimate the natural indirect effect of estradiol and fasting insulin in mediating the association of BMI with postmenopausal breast cancer in women not using hormone therapy.2 1.50; 95% CI, 1.43–1.57), the indirect effect through triglyceride glucose product  was essentially null , suggesting that the biomarker did not explain any of the total BMI–endometrial cancer risk association. The study also observed no evidence of an association between TyG and endometrial cancer risk (HR per 1 standard deviation (SD) increase in TyG 1.04; 95% CI, 0.98–1.11). In our study, we observed an association between fasting insulin and endometrial cancer risk, which was in line with results from a Mendelian randomization study 2 2.51; 95%CI, 1.26–5.02), the largest indirect effect was through a range of biomarkers of inflammatory status . A negligible indirect effect was estimated through C‐peptide (used as a proxy measure for insulin resistance) , and an indirect effect comparable to what we observed for estradiol, through free estradiol and estrone .In a study of six European cohorts that used causal mediation analysis to explain the effect of adiposity on endometrial cancer risk in pre‐ and postmenopausal women,In the UK Biobank cohort, a sequential causal mediation analysis approach was used to explain the effect of adiposity on colorectal cancer risk in postmenopausal women through CRP, glycated hemoglobin , and sex hormone‐binding globulin (SHBG) and testosterone.In summary, among postmenopausal women, we observed that leptin and CRP, fasting insulin, and estradiol might mediate associations of general obesity with ER‐positive breast, endometrial, and colorectal cancers, with suggestion of varying degrees of importance for the biomarkers between the three cancers. Studies with larger sample sizes are needed to further investigate and produce results with more certainty. Ideally, future studies will take advantage of novel causal mediation analysis approaches, similar to the method used in the present study, to appropriately account for dependence between biomarkers and to establish relevant biological targets for future prevention‐based interventions.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the institutional review boards of all participating institutions. Participants provided written informed consent at enrollment.Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.The authors declare no conflict of interest.Table S1Click here for additional data file.