Racial variation in breast tumor promoter methylation in the Carolina Breast Cancer Study

Novel DNA methylation-based epigenetic signatures in colorectal cancer from peripheral blood leukocytes

Colorectal cancer (CRC) is a multifactorial disease characterized by accumulation of multiple genetic and epigenetic alterations, transforming colonic epithelial cells into adenocarcinomas. Alteration of DNA methylation (DNAm) is a promising biomarker for predicting cancer risk and prognosis, but its role in CRC tumorigenesis is inconclusive. Notably, few DNAm studies have used pre-diagnostic peripheral blood (PB) DNA, causing difficulty in postulating the underlying biologic mechanism of CRC initiation. We conducted epigenome-wide association (EWA) scans in postmenopausal women from Women's Health Initiative (WHI) with their pre-diagnostic DNAm in PB leukocytes (PBLs) to prospectively evaluate CRC development. Our site-specific DNAm analyses across the genome adjusted for DNAm-age, leukocyte heterogeneities, as well as body mass index, diabetes, and insulin resistance. We validated 20 top EWA-CpGs in 2 independent CRC tissue datasets. Also, we detected differentially methylated regions (DMRs) associated with CRC, further mapped to transcriptomic profile, and finally conducted a Gene Set Enrichment Analysis. We detected multiple novel CpGs validated across WHI and tissue datasets. In particular, 2 CpGs (B4GALNT4cg10321339, SV2Bcg18144285) had the strongest effect on CRC risk. Results from our DMR scans contained MIR663cg06007966, which was also validated in EWA analyses. Also, we detected 1 methylome region in PEG10 of Chr7 shared across datasets. Our findings reflect both novel and well-established epigenomic and transcriptomic sites in CRC, warranting further functional validations. Our study contributes to better understanding of the complex interrelated mechanisms on the methylome underlying CRC tumorigenesis and suggests novel preventive DNAm-targets in PBLs for detecting at-risk individuals for CRC development.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning

BACKGROUND

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

METHODS

We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision.

RESULTS

wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes.

CONCLUSIONS

The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping

Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by analyzing multi-omics data. However, the high noise levels in omics data often pose challenges in capturing consistent representations and adequately integrating their information. This paper proposed a novel variational autoencoder-based deep learning model, named Deeply Integrating Latent Consistent Representations (DILCR). Firstly, multiple independent variational autoencoders and contrastive loss functions were designed to separate noise from omics data and capture latent consistent representations. Subsequently, an Attention Deep Integration Network was proposed to integrate consistent representations across different omics levels effectively. Additionally, we introduced the Improved Deep Embedded Clustering algorithm to make integrated variable clustering friendly. The effectiveness of DILCR was evaluated using 10 typical cancer datasets from The Cancer Genome Atlas and compared with 14 state-of-the-art integration methods. The results demonstrated that DILCR effectively captures the consistent representations in omics data and outperforms other integration methods in cancer subtyping. In the Kidney Renal Clear Cell Carcinoma case study, cancer subtypes were identified by DILCR with significant biological significance and interpretability.

From Race to Racism and Disparities to Equity: An Actionable Biopsychosocial Approach to Breast Cancer Outcomes

PURPOSE

Racial disparities in outcomes of breast cancer in the United States have widened over more than 3 decades, driven by complex biologic and social factors. In this review, we summarize the biological and social narratives that have shaped breast cancer disparities research across different scientific disciplines in the past, explore the underappreciated but crucial ways in which these 2 strands of the breast cancer story are interwoven, and present 5 key strategies for creating transformative interdisciplinary research to achieve equity in breast cancer treatment and outcomes.

DESIGN

We first review the key differences in tumor biology in the United States between patients racialized as Black versus White, including the overrepresentation of triple-negative breast cancer and differences in tumor histologic and molecular features by race for hormone-sensitive disease. We then summarize key social factors at the interpersonal, institutional, and social structural levels that drive inequitable treatment. Next, we explore how biologic and social determinants are interwoven and interactive, including historical and contemporary structural factors that shape the overrepresentation of triple-negative breast cancer among Black Americans, racial differences in tumor microenvironment, and the complex interplay of biologic and social drivers of difference in outcomes of hormone receptor positive disease, including utilization and effectiveness of endocrine therapies and the role of obesity. Finally, we present 5 principles to increase the impact and productivity of breast cancer equity research.

RESULTS

We find that social and biologic drivers of breast cancer disparities are often cyclical and are found at all levels of scientific investigation from cells to society. To break the cycle and effect change, we must acknowledge and measure the role of structural racism in breast cancer outcomes; frame biologic, psychosocial, and access factors as interwoven via mechanisms of cumulative stress, inflammation, and immune modulation; take responsibility for the impact of representativeness (or the lack thereof) in genomic and decision modeling on the ability to accurately predict the outcomes of Black patients; create research that incorporates the perspectives of people of color from inception to implementation; and rigorously evaluate innovations in equitable cancer care delivery and health policies.

CONCLUSIONS

Innovative, cross-disciplinary research across the biologic and social sciences is crucial to understanding and eliminating disparities in breast cancer outcomes.

Landmark Series: The Cancer Genome Atlas and the Study of Breast Cancer Disparities

Race-related variation in breast cancer incidence and mortality are well-documented in the United States. The effect of genetic ancestry on disparities in tumor genomics, risk factors, treatment, and outcomes of breast cancer is less understood. The Cancer Genome Atlas (TCGA) is a publicly available resource that has allowed for the recent emergence of genome analysis research seeking to characterize tumor DNA and protein expression by ancestry as well as the social construction of race and ethnicity. Results from TCGA based studies support previous clinical evidence that demonstrates that American women with African ancestry are more likely to be afflicted with breast cancers featuring aggressive biology and poorer outcomes compared with women with other backgrounds. Data from TCGA based studies suggest that Asian women have tumors with favorable immune microenvironments and may experience better disease-free survival compared with white Americans. TCGA contains limited data on Hispanic/Latinx patients due to small sample size. Overall, TCGA provides important opportunities to define the molecular, biologic, and germline genetic factors that contribute to breast cancer disparities.

Outcomes in breast cancer-does ethnicity matter?

Ethnic or racial differences in breast cancer (BC) survival outcomes have been reported, but current data are largely restricted to comparisons between African Americans and non-Hispanic whites. Most analyses have traditionally been based on self-reported race which may not always be accurate, or are oversimplified in their classification. With increasing globalization, quantification of the genetic ancestry from genomic data may offer a solution to infer the complex makeup from admixture of races. Focusing on the larger and the latest studies, we will discuss recent findings on the differing host and tumor biology that may be driving these disparities, in addition to the extrinsic environmental or lifestyle factors. Socioeconomic disparities with lower cancer literacy may lead to late presentation, poorer adherence to treatment, and other lifestyle factors such as unhealthy diet, obesity, and inadequate physical activity. These hardships may also result in greater allostatic load, which is in turn associated with aggressive BC features in disadvantaged populations. Epigenetic reprogramming may mediate the effects of the environment or lifestyle factors on gene expression, with ensuing differences in BC characteristics and outcome. There is increasing evidence that germline genetics can influence somatic gene alterations or expression, as well as modulate the tumor or immune microenvironment. Although the precise mechanisms remain elusive, this may account for the varying distribution of different BC subtypes across ethnicities. These gaps in our knowledge highlight the need to interrogate the multiomics landscape of BC in diverse populations, ideally in large-scale collaborative settings with standardized methodology for the comparisons to be statistically robust. Together with improving BC awareness and access to good quality health care, a holistic approach with insights of the biological underpinnings is much needed to eradicate ethnic disparities in BC outcomes.

DNA methylation in peripheral blood leukocytes for the association with glucose metabolism and invasive breast cancer

BACKGROUND

Insulin resistance (IR) is a well-established factor for breast cancer (BC) risk in postmenopausal women, but the interrelated molecular pathways on the methylome are not explicitly described. We conducted a population-level epigenome-wide association (EWA) study for DNA methylation (DNAm) probes that are associated with IR and prospectively correlated with BC development, both overall and in BC subtypes among postmenopausal women.

METHODS

We used data from Women's Health Initiative (WHI) ancillary studies for our EWA analyses and evaluated the associations of site-specific DNAm across the genome with IR phenotypes by multiple regressions adjusting for age and leukocyte heterogeneities. For our analysis of the top 20 IR-CpGs with BC risk, we used the WHI and the Cancer Genomic Atlas (TCGA), using multiple Cox proportional hazards and logit regressions, respectively, accounting for age, diabetes, obesity, leukocyte heterogeneities, and tumor purity (for TCGA). We further conducted a Gene Set Enrichment Analysis.

RESULTS

We detected several EWA-CpGs in TXNIP, CPT1A, PHGDH, and ABCG1. In particular, cg19693031 in TXNIP was replicated in all IR phenotypes, measured by fasting levels of glucose, insulin, and homeostatic model assessment-IR. Of those replicated IR-genes, 3 genes (CPT1A, PHGDH, and ABCG1) were further correlated with BC risk; and 1 individual CpG (cg01676795 in POR) was commonly detected across the 2 cohorts.

CONCLUSIONS

Our study contributes to better understanding of the interconnected molecular pathways on the methylome between IR and BC carcinogenesis and suggests potential use of DNAm markers in the peripheral blood cells as preventive targets to detect an at-risk group for IR and BC in postmenopausal women.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy. In stage 1, we obtain a composite kernel borrowing the cancer integration via multi-kernel learning (CIMLR) idea by optimizing the kernel parameters for individual omics data type. In stage 2, we obtain a final fused kernel through a weighted linear combination of individual kernels learned from stage 1 using an unsupervised multiple kernel learning method. Based on the final fusion kernel, k-means clustering is applied to identify cancer subtypes. Simulation studies show that hMKL outperforms the one-stage CIMLR method when there is data heterogeneity. hMKL can estimate the number of clusters correctly, which is the key challenge in subtyping. Application to two real data sets shows that hMKL identified meaningful subtypes and key cancer-associated biomarkers. The proposed method provides a novel toolkit for heterogeneous multi-omics data integration and cancer subtypes identification.

Comprehensive analysis of roles of atrial-fibrillation-related genes in lung adenocarcinoma using bioinformatic methods

Atrial fibrillation (AF) is the most common tachyarrhythmia in the world. Lung cancer is the leading cause of cancer deaths in 93 countries. Previous studies demonstrated that the prevalence of AF was higher in patients with lung cancer. However, research on the associations between AF and lung cancer is still rare. In the present study, we first identified AF-related genes using weighted gene correlation network analysis. We then analyzed the expression profiles, prognosis, immune infiltration, and methylation characteristics of these genes in LUAD patients using bioinformatics analysis. We found several AF-related genes, including CBX3, BUB1, DSC2, P4HA1, and CYP4Z1, which differently expressed between tumor and normal tissues. Survival analysis demonstrated that CYP4Z1 was positively correlated with overall survival in LUAD patients, while CBX3, BUB1, DSC2, and P4HA1 were negatively correlated. Moreover, we found that the methylation level of DSC2 in normal lung tissues was significantly higher than that in tumor tissues, and six methylation sites in the DNA sequences of DSC2 were identified negatively correlated with its expression levels. Immune infiltration analysis suggested that levels of immune cell infiltration were related to gene expression levels in varying degrees. We identified AF-related genes and found these genes were correlated with prognosis, immune infiltration, and methylation levels in lung cancer patients. We also constructed a risk signature based on these genes in LUAD patients. We hoped that the current study could provide a novel insight into roles of AF-related genes in lung cancer patients.

Racial Disparity in Quadruple Negative Breast Cancer: Aggressive Biology and Potential Therapeutic Targeting and Prevention

Simple Summary

Quadruple negative breast cancer (QNBC), a subgroup of triple negative BC, has emerged as a highly aggressive BC subtype that disproportionately afflicts and impacts Black/African-American (AA) women. In this article, we review molecular distinctions in Black/AA and White/European-American (EA) QNBC biology as well as address potential non-genetic risk factors that could be underlying this racially disparate burden. We aim to provide deeper insight and provide a framework for novel discovery of actionable therapeutic targets and identify lifestyle changes to improve outcomes for Black/AA QNBC patients.

Abstract

Black/African-American (AA) women, relative to their White/European-American (EA) counterparts, experience disproportionately high breast cancer mortality. Central to this survival disparity, Black/AA women have an unequal burden of aggressive breast cancer subtypes, such as triple-negative breast cancer (ER/PR-, HER2-wild type; TNBC). While TNBC has been well characterized, recent studies have identified a highly aggressive androgen receptor (AR)-negative subtype of TNBC, quadruple-negative breast cancer (ER/PR-, HER2-wildtype, AR-; QNBC). Similar to TNBC, QNBC disproportionately impacts Black/AA women and likely plays an important role in the breast cancer survival disparities experienced by Black/AA women. Here, we discuss the racial disparities of QNBC and molecular signaling pathways that may contribute to the aggressive biology of QNBC in Black/AA women. Our immediate goal is to spotlight potential prevention and therapeutic targets for Black/AA QNBC; ultimately our goal is to provide greater insight into reducing the breast cancer survival burden experienced by Black/AA women.

Epigenetic Determinants of Racial Disparity in Breast Cancer: Looking beyond Genetic Alterations

Simple Summary

A substantial disparity in breast cancer incidence and mortality exists between African American (AA) and European American (EA) women. However, the basis for these disparities is poorly understood. In this article, we describe that gene–environment interactions mediated through epigenetic modifications may play a significant role in racial disparities in BC incidence and outcomes. Our in silico analyses and an in-depth literature survey suggest that there exists a significant difference in epigenetic patterns between AA and EA women with breast cancer. Herein, we describe the environmental factors that contribute to these epigenetic changes, which may underlie the disparate racial burden in patients with breast cancer. We suggest that AA women with higher basal epigenetic changes, may have higher pre-disposition to cancer onset, and an aggressive disease course. Pre-existing racial differences in epigenetic profiles of breast tissues raises the possibility of examining these profiles for early diagnosis.

Abstract

Breast cancer (BC) is the most commonly diagnosed cancer in women. Despite advancements in BC screening, prevention, and treatment, BC incidence and mortality remain high among African American (AA) women. Compared with European American (EA) women, AA women tend to be diagnosed with more advanced and aggressive tumors and exhibit worse survival outcomes. Most studies investigating the determinants of racial disparities in BC have focused on genetic factors associated with African ancestry. However, various environmental and social stressors over an individual’s life course can also shape racial stratification in BC. These social and environmental exposures result in long-term changes in gene expression mediated by epigenetic mechanisms. Epigenetics is often portrayed as an intersection of socially patterned stress and genetic expression. The enduring nature of epigenetic changes makes them suitable for studying the effects of different environmental exposures over an individual’s life course on gene expression. The role of differential social and environmental exposures in racial disparities in BC suggests varied epigenetic profiles or signatures associated with specific BC subtypes in AA and EA women. These epigenetic profiles in EA and AA women could be used as biomarkers for early BC diagnosis and disease prognosis and may prove valuable for the development of targeted therapies for BC. This review article discusses the current state of knowledge regarding epigenetic differences between AA and EA women with BC. We also discuss the role of socio-environmental factors, including psychosocial stress, environmental toxicants, and dietary factors, in delineating the different epigenetic profiles in AA and EA patients with BC.

A biosocial return to race? A cautionary view for the postgenomic era

Recent studies demonstrating epigenetic and developmental sensitivity to early environments, as exemplified by fields like the Developmental Origins of Health and Disease (DOHaD) and environmental epigenetics, are bringing new data and models to bear on debates about race, genetics, and society. Here, we first survey the historical prominence of models of environmental determinism in early formulations of racial thinking to illustrate how notions of direct environmental effects on bodies have been used to naturalize racial hierarchy and inequalities in the past. Next, we conduct a scoping review of postgenomic work in environmental epigenetics and DOHaD that looks at the role of race/ethnicity in human health (2000–2021). Although there is substantial heterogeneity in how race is conceptualized and interpreted across studies, we observe practices that may unwittingly encourage typological thinking, including: using DNA methylation as a novel marker of racial classification; neglect of variation and reversibility within supposedly homogenous racial groups; and a tendency to label and reify whole groups as pathologized or impaired. Even in the very different politico‐economic and epistemic context of contemporary postgenomic science, these trends echo deeply held beliefs in Western thinking which claimed that different environments shape different bodies and then used this logic to argue for essential differences between Europeans and non‐Europeans. We conclude with a series of suggestions on interpreting and reporting findings in these fields that we feel will help researchers harness this work to benefit disadvantaged groups while avoiding the inadvertent dissemination of new and old forms of stigma or prejudice.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

A transdisciplinary approach to understand the epigenetic basis of race/ethnicity health disparities

Health disparities correspond to differences in disease burden and mortality among socially defined population groups. Such disparities may emerge according to race/ethnicity, socioeconomic status and a variety of other social contexts, and are documented for a wide range of diseases. Here, we provide a transdisciplinary perspective on the contribution of epigenetics to the understanding of health disparities, with a special emphasis on disparities across socially defined racial/ethnic groups. Scientists in the fields of biological anthropology, bioinformatics and molecular epidemiology provide a summary of theoretical, statistical and practical considerations for conducting epigenetic health disparities research, and provide examples of successful applications from cancer research using this approach.

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data

The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.

SOX2 and Bcl-2 as a Novel Prognostic Value in Hepatocellular Carcinoma Progression

Sex-determining region Y-box 2 (SOX2) is a stem cell transcription factor and a major regulator of self-renewal and pluripotency of cancer stem cells (CSCs). In many types of cancer, SOX2 is dysregulated due to overexpression associated with tumor progression and low survival rate. Many HCC cases encounter recurrence and metastasis which might be due to CSCs and also apoptosis. Since little is known about the expression pattern of SOX2 and apoptotic genes in HCC, we aimed to determine the prognostic significance of SOX2, Bax, and Bcl-2 in clinicopathological features, tumor progression, and survival rate of the HCC patients. The expression of SOX2, Bax, and Bcl-2 were evaluated using qRT-PCR in 53 formalin-fixed, paraffin-embedded tissues (FFPE) of patients and 44 controls. Correlation of these genes was analyzed with clinicopathological features and tumor progression. The correlationship between SOX2 expression and ALBI grade as prognostic indicators were calculated. Survival rates were determined by Kaplan–Meier survival curves. SOX2 and Bcl-2 were remarkably overexpressed in HCC patients compared to controls (p = 0.04 and p = 0.003, respectively). A significant association was found for both SOX2 and Bcl-2 overexpression with TNM staging (p = 0.02, p = 0.04) and tumor grading (p = 0.01, p = 0.003), respectively. A significant correlation was observed: patients with SOX2 overexpression had a lower 5-year overall survival rate (p = 0.04); however, there was no significant association between Bcl-2 and survival (p = 0.5). Collectively, overexpression of SOX2 and Bcl-2, alone or combined, may be a potential marker to evaluate prognosis and response to HCC treatment.

TUSC3 induces drug resistance and cellular stemness via Hedgehog signaling pathway in colorectal cancer

Tumor suppressor candidate 3 (TUSC3) is a coding gene responsible for N-glycosylation of many critical proteins. TUSC3 gene plays an oncogenic role in colorectal cancer (CRC), however, the role of TUSC3 in drug resistance of CRC is still unclear. The aim of this study is to investigate the biological function and molecular mechanism of TUSC3 in CRC drug resistance. The expression of TUSC3 in CRC is positively correlated to tumor stage in 90 paired clinical samples, and negatively associated with overall survival and disease-free survival of CRC patients. In vitro, TUSC3 promotes the formation of stemness and induces the drug resistance to 5-fluorouracil and cis-dichlorodiammineplatinum(II) in CRC cells. The tissue microarray assay and bioinformatic analysis indicate that TUSC3 may promote the expression of CD133 and ABCC1 via Hedgehog signaling pathway. Treatment of Hedgehog signaling pathway agonist or inhibitor in TUSC3-silenced or TUSC3-overexpressed cells reverse the effects of TUSC3 in cellular stemness phenotype and drug resistance. Meanwhile, coimmunoprecipitation and immunofluorescence assays indicate a tight relationship between TUSC3 and SMO protein. Our data suggest that TUSC3 promotes the formation of cellular stemness and induces drug resistance via Hedgehog signaling pathway in CRC.

Genetic ancestry plays a central role in population pharmacogenomics

Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD (http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/).

Hsin-Chou Yang et al. examine population structure in several genomic databases and identify that pharmacogenetic loci are enriched for markers of genetic ancestry. Their results suggest that genetic ancestry must be carefully considered in population pharmacogenetics studies.

Cancer health disparities in racial/ethnic minorities in the United States

There are well-established disparities in cancer incidence and outcomes by race/ethnicity that result from the interplay between structural, socioeconomic, socio-environmental, behavioural and biological factors. However, large research studies designed to investigate factors contributing to cancer aetiology and progression have mainly focused on populations of European origin. The limitations in clinicopathological and genetic data, as well as the reduced availability of biospecimens from diverse populations, contribute to the knowledge gap and have the potential to widen cancer health disparities. In this review, we summarise reported disparities and associated factors in the United States of America (USA) for the most common cancers (breast, prostate, lung and colon), and for a subset of other cancers that highlight the complexity of disparities (gastric, liver, pancreas and leukaemia). We focus on populations commonly identified and referred to as racial/ethnic minorities in the USA-African Americans/Blacks, American Indians and Alaska Natives, Asians, Native Hawaiians/other Pacific Islanders and Hispanics/Latinos. We conclude that even though substantial progress has been made in understanding the factors underlying cancer health disparities, marked inequities persist. Additional efforts are needed to include participants from diverse populations in the research of cancer aetiology, biology and treatment. Furthermore, to eliminate cancer health disparities, it will be necessary to facilitate access to, and utilisation of, health services to all individuals, and to address structural inequities, including racism, that disproportionally affect racial/ethnic minorities in the USA.

Molecular and Cellular Factors Associated with Racial Disparity in Breast Cancer

Recent studies have demonstrated that racial differences can influence breast cancer incidence and survival rate. African American (AA) women are at two to three fold higher risk for breast cancer than other ethnic groups. AA women with aggressive breast cancers show worse prognoses and higher mortality rates relative to Caucasian (CA) women. Over the last few years, effective treatment strategies have reduced mortality from breast cancer. Unfortunately, the breast cancer mortality rate among AA women remains higher compared to their CA counterparts. The focus of this review is to underscore the racial differences and differential regulation/expression of genetic signatures in CA and AA women with breast cancer. Moreover, immune cell infiltration significantly affects the clinical outcome of breast cancer. Here, we have reviewed recent findings on immune cell recruitment in the tumor microenvironment (TME) and documented its association with breast cancer racial disparity. In addition, we have extensively discussed the role of cytokines, chemokines, and other cell signaling molecules among AA and CA breast cancer patients. Furthermore, we have also reviewed the distinct genetic and epigenetic changes in AA and CA patients. Overall, this review article encompasses various molecular and cellular factors associated with breast cancer disparity that affects mortality and clinical outcome.

Greater Survival Improvement in African American vs. Caucasian Women with Hormone Negative Breast Cancer

Background: African American women have not benefited equally from recently improved breast cancer survival. We investigated if this was true for all subsets.

Methods: We identified 395,170 patients with breast adenocarcinoma from the SEER database from 1990 to 2011 with designated race, age, stage, grade, ER and PR status, marital status and laterality, as control. We grouped patients into two time periods, 1990-2000 and 2001-2011, three age categories, under 40, 40-69 and ≥ 70 years and two stage categories, I-III and IV. We used the Kaplan-Meier and logrank tests to compare survival curves. We stratified data by patient- and tumor-associated variables to determine co-variation among confounding factors using the Pearson Chi-square test and Cox proportional hazards regression to determine hazard ratios (HR) to compare survival.

Results: Stage I-III patients of both races ≥ 70 years old, African American widowed patients and Caucasians with ER- and PR- tumors had worse improvements in survival in 2001-2011 than younger, married or hormone receptor positive patients, respectively. In contrast, African Americans with ER- (Cox HR 0.70 [95% CI 0.65-0.76]) and PR- (Cox HR 0.67 [95% CI 0.62-0.72]) had greater improvement in survival in 2001-2011 than Caucasians with ER- (Cox HR 0.81 [95% CI 0.78-0.84]) and PR- disease (Cox HR 0.75 [95% CI 0.73-0.78]). This was not associated with changes in distribution of tumor or patient attributes.

Conclusions: African American women with stage I-III ER- and PR- breast cancer had greater improvement in survival than Caucasians in 2001-2011. This is the first report of an improvement in racial disparities in survival from breast cancer in a subset of patients.

Differences in Genome-wide DNA Methylation Profiles in Breast Milk by Race and Lactation Duration

Black women in the United States are disproportionately affected by early-onset, triple-negative breast cancer. DNA methylation has shown differences by race in healthy and tumor breast tissues. We examined associations between genome-wide DNA methylation levels in breast milk and breast cancer risk factors, including race, to explain how this reproductive stage influences a woman's risk for, and potentially contributes to racial disparities in, breast cancer. Breast milk samples and demographic, behavioral, and reproductive data, were obtained from cancer-free, uniparous, and lactating U.S. black (n = 57) and white (n = 82) women, ages 19-44. Genome-wide DNA methylation analysis was performed on extracted breast milk DNA using the Infinium HumanMethylation450 BeadChip. Statistically significant associations between breast cancer risk factors and DNA methylation beta values, adjusting for potential confounders, were determined using linear regression followed by Bonferroni Correction (P < 1.63 × 10-7). Epigenetic analysis in breast milk revealed statistically significant associations with race and lactation duration. Of the 284 CpG sites associated with race, 242 were hypermethylated in black women. All 227 CpG sites associated with lactation duration were hypomethylated in women who lactated longer. Ingenuity Pathway Analysis of differentially methylated promoter region CpGs by race and lactation duration revealed enrichment for networks implicated in carcinogenesis. Associations between DNA methylation and lactation duration may offer insight on its role in lowering breast cancer risk. Epigenetic associations with race may mediate social, behavioral, or other factors related to breast cancer and may provide insight into potential mechanisms underlying racial disparities in breast cancer incidence.

Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences

Recent advances in high-throughput sequencing have accelerated the accumulation of omics data on the same tumor tissue from multiple sources. Intensive study of multi-omics integration on tumor samples can stimulate progress in precision medicine and is promising in detecting potential biomarkers. However, current methods are restricted owing to highly unbalanced dimensions of omics data or difficulty in assigning weights between different data sources. Therefore, the appropriate approximation and constraints of integrated targets remain a major challenge. In this paper, we proposed an omics data integration method, named high-order path elucidated similarity (HOPES). HOPES fuses the similarities derived from various omics data sources to solve the dimensional discrepancy, and progressively elucidate the similarities from each type of omics data into an integrated similarity with various high-order connected paths. Through a series of incremental constraints for commonality, HOPES can take both specificity of single data and consistency between different data types into consideration. The fused similarity matrix gives global insight into patients' correlation and efficiently distinguishes subgroups. We tested the performance of HOPES on both a simulated dataset and several empirical tumor datasets. The test datasets contain three omics types including gene expression, DNA methylation, and microRNA data for five different TCGA cancer projects. Our method was shown to achieve superior accuracy and high robustness compared with several benchmark methods on simulated data. Further experiments on five cancer datasets demonstrated that HOPES achieved superior performances in cancer classification. The stratified subgroups were shown to have statistically significant differences in survival. We further located and identified the key genes, methylation sites, and microRNAs within each subgroup. They were shown to achieve high potential prognostic value and were enriched in many cancer-related biological processes or pathways.

Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data

Motivation

Integrating different omics profiles is a challenging task, which provides a comprehensive way to understand complex diseases in a multi-view manner. One key for such an integration is to extract intrinsic patterns in concordance with data structures, so as to discover consistent information across various data types even with noise pollution. Thus, we proposed a novel framework called 'pattern fusion analysis' (PFA), which performs automated information alignment and bias correction, to fuse local sample-patterns (e.g. from each data type) into a global sample-pattern corresponding to phenotypes (e.g. across most data types). In particular, PFA can identify significant sample-patterns from different omics profiles by optimally adjusting the effects of each data type to the patterns, thereby alleviating the problems to process different platforms and different reliability levels of heterogeneous data.

Results

To validate the effectiveness of our method, we first tested PFA on various synthetic datasets, and found that PFA can not only capture the intrinsic sample clustering structures from the multi-omics data in contrast to the state-of-the-art methods, such as iClusterPlus, SNF and moCluster, but also provide an automatic weight-scheme to measure the corresponding contributions by data types or even samples. In addition, the computational results show that PFA can reveal shared and complementary sample-patterns across data types with distinct signal-to-noise ratios in Cancer Cell Line Encyclopedia (CCLE) datasets, and outperforms over other works at identifying clinically distinct cancer subtypes in The Cancer Genome Atlas (TCGA) datasets.

Availability and implementation

PFA has been implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/PFApackage_0.1.rar .

Contact

lnchen@sibs.ac.cn , liujuan@whu.edu.cn or zengtao@sibs.ac.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Endometrial cancer disparities: a race-conscious critique of the literature

Racial disparities in endometrial cancer are stark and have increased over the past decade. While the disparities are well documented, intervention work to address the mortality gap is nonexistent. This review critiques how race has been conceptualized to explain the causes of endometrial cancer disparities, assesses gaps in knowledge production, and proposes new research priorities. Using public health critical race praxis, a research approach for examining racial disparities and knowledge production processes, we reviewed the endometrial cancer disparities literature from 1995 through 2016. Using systematic search methods, 133 unique records were identified and 48 studies critiqued. We found that a narrow definition of race as a purely biological construct is common throughout the literature. This appears to result in an underemphasis on the role of modifiable, nonbiological contributors to racial disparities and a lack of follow-up work to address these contributors. Key knowledge gaps identified were the role of health care systems in early diagnosis, a lack of intervention studies to address persistent treatment inequity by race, and the near absence of qualitative work to understand the perspectives of Black women diagnosed with endometrial cancer. We conclude with an iterative demonstration of the public health critical race praxis and suggest new routes of inquiry to broaden the scope of research priorities to understand and improve the outcomes of Black women with endometrial cancer.

TUSC3: functional duality of a cancer gene

Two decades ago, following a systematic screening of LOH regions on chromosome 8p22, TUSC3 has been identified as a candidate tumor suppressor gene in ovarian, prostate and pancreatic cancers. Since then, a growing body of evidence documented its clinical importance in various other types of cancers, and first initial insights into its molecular function and phenotypic effects have been gained, though the precise role of TUSC3 in different cancers remains unclear. As a part of the oligosaccharyltransferase complex, TUSC3 localizes to the endoplasmic reticulum and functions in final steps of N-glycosylation of proteins, while its loss evokes the unfolded protein response. We are still trying to figure out how this mechanistic function is reconcilable with its varied effects on cancer promotion. In this review, we focus on cancer-related effects of TUSC3 and envisage a possible role of TUSC3 beyond endoplasmic reticulum.

Using Genetic Technologies To Reduce, Rather Than Widen, Health Disparities

Evidence shows that both biological and nonbiological factors contribute to health disparities. Genetics, in particular, plays a part in how common diseases manifest themselves. Today, unprecedented advances in genetically based diagnoses and treatments provide opportunities for personalized medicine. However, disadvantaged groups may lack access to these advances, and treatments based on research on non-Hispanic whites might not be generalizable to members of minority groups. Unless genetic technologies become universally accessible, existing disparities could be widened. Addressing this issue will require integrated strategies, including expanding genetic research, improving genetic literacy, and enhancing access to genetic technologies among minority populations in a way that avoids harms such as stigmatization.

The association between socioeconomic factors and breast cancer-specific survival varies by race

Although racial disparity is well described for oncologic outcomes, factors associated with survival within racial groups remains unexplored. The objective of this study is to determine whether breast cancer survival among White or Black patients is associated with differing patient factors. Women diagnosed with breast cancer from 1998 through 2012 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. Cox proportional hazard logistic regression was used to estimate cause-specific survival in the combined cohort, and separate cohorts of Black or White patients only. Main outcomes included cause-specific survival in cohorts of Black only, White only, or all patients adjusted for demographic and oncologic factors. A total of 406,907 Black (10.8%) or White (89.2%) patients diagnosed with breast cancer from 1998 through 2012 were isolated. Cancer-specific survival analysis of the combined cohort showed significantly decreased hazard ratio (H.R.) in patients from the higher economic quartiles (Q1: 1.0 (ref), Q2: 0.95 (p<0.01), Q3: 0.94 (p<0.01), Q4: 0.87 (p<0.001)). Analysis of the White only cohort showed a similar relationship with income (Q1: 1.0 (ref), Q2: 0.95 (p<0.01), Q3: 0.95 (p<0.01), Q4: 0.86 (p<0.001)). However, analysis of the Black only cohort did not show a relationship with income (Q1: 1.0 (ref), Q2: 1.04 (p = 0.34), Q3: 0.97 (p = 0.53), Q4: 1.04 (p = 0.47)). A test of interaction confirmed that the association between income and cancer-specific survival is dependent on patient race, both with and without adjustment for demographic and oncologic characteristics (p<0.01). While median county income is positively associated with cancer-specific survival among White patients, this is not the case with Black patients. Similar findings were noted for education level. These findings suggest that the association between socioeconomic status and breast cancer survival commonly reported in the literature is specific to White patients. These findings provide insight into differences between White and Black patients in cancer-specific survival.

Analysis of Tumor Biology to Advance Cancer Health Disparity Research

Cancer mortality rates in the United States continue to decline. Reductions in tobacco use, uptake of preventive measures, adoption of early detection methods, and better treatments have resulted in improved cancer outcomes for men and women. Despite this progress, some population groups continue to experience an excessive cancer burden when compared with other population groups. One of the most prominent cancer health disparities exists in prostate cancer. Prostate cancer mortality rates are highest among men of African ancestry when compared with other men, both in the United States and globally. This disparity and other cancer health disparities are largely explained by differences in access to health care, diet, lifestyle, cultural barriers, and disparate exposures to carcinogens and pathogens. Dietary and lifestyle factors, pathogens, and ancestry-related factors can modify tumor biology and induce a more aggressive disease. There are numerous examples of how environmental exposures, like tobacco, chronic stress, or dietary factors, induce an adverse tumor biology, leading to a more aggressive disease and decreased patient survival. Because of population differences in the exposure to these risk factors, they can be the cause of cancer disparities. In this review, we will summarize recent advances in our understanding of prostate and breast cancer disparities in the United States and discuss how the analysis of tumor biology can advance health disparity research.

Promoter methylation of TCF21 may repress autophagy in the progression of lung cancer

Lung cancer is a leading cause of cancer mortality worldwide. Promoter methylation of transcription factor 21 (TCF21) was frequently observed in the early stage of non-small cell lung cancer (NSCLC). However, clinical relevance and molecular functions of TCF21 in NSCLC progression remain unclear. In this study, we analyzed the associations between TCF21 expression and clinicopathological features in 100 patients with NSCLC and revealed the underlying molecular mechanisms of TCF21 methylation on cell viability, apoptosis and invasion of H1299 cells. We found that the expression of TCF21 was significantly regulated by its methylation level in patients with NSCLC and was associated with tumor stage, metastasis and invasion. Demethylation of H1299 cells by 5-aza-2'-deoxycytine (5-Aza) demonstrated that a higher level of TCF21 expression led to remarkable decreases of cell viability and invasion ability but an increase of cell apoptosis. Accordingly, TCF21 knockdown showed converse results to high expression of TCF21. TCF21 knockdown cells exhibited significantly upregulated ATG-9, BECLIN-1, and LC3-I/II expressions but decreased p62 expression compared to wildtype cells. Inhibition of autophagy by 3-methyladenine (3-MA) elevated TCF21 expression and increased cell apoptosis. TCF21 expression is clinically related to the progress of lung cancer and may inhibit autophagy by suppressing ATG-9 and BECLIN-1. In turn, autophagy may also play an important role in regulation TCF21 expression.

Racial health disparities in ovarian cancer: not just black and white

Ovarian cancer (OC) is the most lethal gynecological malignancy, which disproportionately affects African American (AA) women. Lack of awareness and socioeconomic factors are considered important players in OC racial health disparity, while at the same time, some recent studies have brought focus on the genetic basis of disparity as well. Differential polymorphisms, mutations and expressions of genes have been reported in OC patients of diverse racial and ethnic backgrounds. Combined, it appears that neither genetic nor the socioeconomic factors alone might explain the observed racially disparate health outcomes among OC patients. Rather, a more logical explanation would be the one that takes into consideration the combination and/or the interplay of these factors, perhaps even including some environmental ones. Hence, in this article, we attempt to review the available information on OC racial health disparity, and provide an overview of socioeconomic, environmental and genetic factors, as well as the epigenetic changes that can act as a liaison between the three. A better understanding of these underlying causes will help further research on effective cancer management among diverse patient population and ultimately narrow health disparity gaps.

Associations of two-pore domain potassium channels and triple negative breast cancer subtype in The Cancer Genome Atlas: systematic evaluation of gene expression and methylation

Objectives

It is unclear whether 2-pore domain potassium channels are novel molecular markers with differential expression related to biologically aggressive triple-negative type breast tumors. Our objective was to systematically evaluate associations of 2-pore domain potassium channel gene expression and DNA methylation with triple-negative subtype in The Cancer Genome Atlas invasive breast cancer dataset. Methylation and expression data for all fifteen 2-pore domain potassium family genes were examined for 1040 women, and associations with triple-negative subtype (vs. luminal A) were evaluated using age/race adjusted generalized-linear models, with Bonferroni-corrected significance thresholds. Subtype associated CpG loci were evaluated for functionality related to expression using Spearman’s correlation.

Results

Overexpression of KCNK5, KCNK9 and KCNK12, and underexpression of KCNK6 and KCNK15, were significantly associated with triple-negative subtype (Bonferroni-corrected p < 0.0033). A total of 195 (114 hypomethylated and 81 hypermethylated) CpG loci were found to be significantly associated with triple-negative subtype (Bonferroni-corrected p < 8.22 × 10⁻⁸). Significantly negatively correlated expression patterns that were differentially observed in triple-negative vs. luminal A subtype were demonstrated for: KCNK2 (gene body: cg04923840, cg13916421), KCNK5 (gene body: cg05255811, cg18705155, cg09130674, cg21388745, cg00859574) and KCNK9 (TSS1500: cg21415530, cg12175729; KCNK9/TRAPPC9 intergenic region: cg17336929, cg25900813, cg03919980). CpG loci listed for KCNK5 and KCNK9 all showed relative hypomethylation for probability of triple-negative vs. luminal A subtype. Triple-negative subtype was associated with distinct 2-pore domain potassium channel expression patterns. Both KCNK5 and KCNK9 overexpression appeared to be functionally related to CpG loci hypomethylation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-017-2777-4) contains supplementary material, which is available to authorized users.

Interethnic DNA methylation difference and its implications in pharmacoepigenetics

AIM

This is the first systematic study to examine the population differentiation effect of DNA methylation on the treatment response and drug absorption, distribution, metabolism and excretion in multiple tissue types and cancer types.

MATERIALS & METHODS

We analyzed the whole methylome and transcriptome data of primary tumor tissues of four cancer types (breast, colon, head & neck and uterine corpus) and lymphoblastoid cell lines for African and European ancestry populations.

RESULTS

Ethnicity-associated CpG sites exhibited similar methylation patterns in the two studied populations, but the patterns differed between tumor tissues and lymphoblastoid cell lines. Ethnicity-associated CpG sites may have triggered gene expression, influenced drug absorption, distribution, metabolism and excretion, and showed tumor-specific patterns of methylation and gene regulation.

CONCLUSION

Ethnicity should be carefully accounted for in future pharmacoepigenetics research.

SOX2 regulates multiple malignant processes of breast cancer development through the SOX2/miR-181a-5p, miR-30e-5p/TUSC3 axis

BACKGROUND

High levels of SOX2 protein are correlated with increased dissemination of breast cancer. However, the underlying molecular mechanisms are not fully understood.

METHODS

In this study we investigate the role of SOX2 in breast cancer metastasis using multiple in vitro and in vivo assays including cell culture, shRNA-mediated knockdown, wound healing, colony formation, transwell chamber, xenograft and tail vein injection. Moreover, western blot, immunostaining, microarray and real-time PCR were used to determine the change of protein and miRNA levels. Luciferase assays were also used to evaluate activity which TUSC3 is a target of miR-181a-5p and miR-30e-5p, and the clinical survival relevance was analyzed by Kaplan-Meier analysis.

RESULTS

We identified a novel pathway involving SOX2 regulation of microRNAs to control the proliferation and migration of breast cancer cells. shRNA-mediated knockdown of SOX2 inhibits breast cancer cell expansion and migration. More importantly, we found that these changes are accompanied by significant reduction in the levels of two microRNAs, miR-181a-5p and miR-30e-5p. Overexpression of these two microRNAs leads to reduced protein levels of Tumor Suppressor Candidate 3 (TUSC3) in breast cancer cells; mutations of the potential binding sites in the 3'-UTR of TUSC3 abrogate the inhibitory effects of the microRNAs. We further found that upregulation of TUSC3 expression leads to reduced proliferation and migration of breast cancer cells. In human breast cancer samples the levels of TUSC3 protein are inversely correlated with those of SOX2 protein.

CONCLUSIONS

Taken together, our work reveals a novel SOX2-mediated regulatory axis that plays critical roles in the proliferation, migration and invasiveness of breast cancer cells. Targeting this axis may provide beneficial effect in the treatment of breast cancer.

TUSC3: a novel tumour suppressor gene and its functional implications

The tumour suppressor candidate 3 (TUSC3) gene is located on chromosome region 8p22 and encodes the 34 kD TUSC3 protein, which is a subunit of the oligosaccharyl transferase responsible for the N‐glycosylation of nascent proteins. Known to be related to autosomal recessive mental retardation for several years, TUSC3 has only recently been identified as a potential tumour suppressor gene. Based on the structure and function of TUSC3, specific mechanisms in various diseases have been investigated. Several studies have demonstrated that TUSC3 is an Mg²⁺‐transporter involved in magnesium transport and homeostasis, which is important for learning and memory, embryonic development and testis maturation. Moreover, dysfunction or deletion of TUSC3 exerts its oncological effects as a modulator by inhibiting glycosylation efficiency and consequently inducing endoplasmic reticulum stress and malignant cell transformation. In this study, we summarize the advances in the studies of TUSC3 and comment on the potential roles of TUSC3 in diagnosis and treatment of TUSC3‐related diseases, especially cancer.

Epigenetic basis of cancer health disparities: Looking beyond genetic differences

Despite efforts at various levels, racial health disparities still exist in cancer patients. These inequalities in incidence and/or clinical outcome can only be explained by a multitude of factors, with genetic basis being one of them. Several investigations have provided convincing evidence to support epigenetic regulation of cancer-associated genes, which results in the differential transcriptome and proteome, and may be linked to a pre-disposition of individuals of certain race/ethnicity to early or more aggressive cancers. Recent technological advancements and the ability to quickly analyze whole genome have aided in these efforts, and owing to their relatively easy detection, methylation events are much well-characterized, than the acetylation events, across human populations. The early trend of investigating a pre-determined set of genes for differential epigenetic regulation is paving way for more unbiased screening. This review summarizes our current understanding of the epigenetic events that have been tied to the racial differences in cancer incidence and mortality. A better understanding of the epigenetics of racial diversity holds promise for the design and execution of novel strategies targeting the human epigenome for reducing the disparity gaps.

Identification of Biomarkers for Breast Cancer Using Databases

Breast cancer is one of the major causes of cancer death in women. Many studies have sought to identify specific molecules involved in breast cancer and understand their characteristics. Many biomarkers which are easily measurable, dependable, and inexpensive, with a high sensitivity and specificity have been identified. The rapidly increasing technology development and availability of epigenetic informations play critical roles in cancer. The accumulated data have been collected, stored, and analyzed in various types of databases. It is important to acknowledge useful and available data and retrieve them from databases. Nowadays, many researches utilize the databases, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Surveillance, Epidemiology and End Results (SEER), and Embase, to find useful informations on biomarkers for breast cancer. This review summarizes the current databases which have been utilized for identification of biomarkers for breast cancer. The information provided by this review would be beneficial to seeking appropriate strategies for diagnosis and treatment of breast cancer.

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.