Gene expression and DNA methylation analyses suggest that immune process-related ADCY6 is a prognostic factor of luminal-like breast cancer

Breast cancer is a malignant tumor that seriously threatens women's health, and luminal-like cancer subtypes account for the majority of the cases. The purpose of this study was to investigate the relationships among DNA methylation, gene expression profile, and the tumor-immune microenvironment of luminal-like breast cancer, and to identify the potential key genes that regulate immune cell infiltration in luminal-like breast cancer. The ESTIMATE algorithm was applied to calculate immune scores and stromal scores of patients with breast cancer. Kaplan-Meier curves were generated for survival analysis. The clusterProfile package was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Correlations between ADCY6 expression and immune cell infiltration-related pathways were analyzed by gene set variation analysis. R software was used for the statistical analysis and figure generation. Disease-free survival was higher in the immune score-high group than it was in the immune score-low group, while the stromal score had no correlation with prognosis. There were 515 genes that differed in both gene expression and DNA methylation levels, and these genes were mainly enriched in immune process-related pathways. ADCY6 was enriched in module A of the PPI network. Patients with downregulation and hypermethylation of ADCY6 associated with a better prognosis. ADCY6 expression was negatively correlated with the activation of immune process-related signaling pathways, immune checkpoint receptors, and ligands, except for CLEC4G. DNA methylation was found to be involved in the regulation of the key cellular pathways of luminal-like breast cancer immune cell infiltration. Additionally, ADCY6 was identified as a prognostic factor involved in the DNA methylation-regulated immune processes in luminal-like breast cancer.     

CD24 Overexpression Is Associated with Poor Prognosis in Luminal A and Triple-Negative Breast Cancer

CD24 is associated with unfavourable prognoses in various cancers, but the prevalence of CD24 expression and its influence on clinical outcome in subtypes of breast cancers remain unclear. CD24 expression was analyzed by immunohistochemistry in 747 breast cancer tissues, and DNA methylation and histone modification status in the promoter region of CD24 were assessed using bisulfite sequencing and chromatin immunoprecipitation assay. 213 (28.5%) samples exhibited high CD24 expression in the membrane and/or cytoplasm of breast cancer cells, and CD24 overexpression was significantly correlated with the presence of lymph node metastasis and more advanced pathological stage. Patients with CD24-high tumours had significantly shorter patient survival than those with CD24-low tumours. Importantly, multivariate analysis that included tumour size, lymph node metastasis and chemotherapy demonstrated that high CD24 expression is independently associated with poorer survival in luminal A and triple-negative breast cancer (TNBC) subtypes. Furthermore, CD24 gene expression was associated with histone acetylation independent of DNA methylation, suggesting its epigenetic regulation in breast cancer. Our results suggest that CD24 overexpression is an independent unfavourable prognostic factor in breast cancer, especially for luminal A and TNBC subtypes, and CD24 may be a promising therapeutic target for specific subtypes of breast cancer.     

Epigenetic changes of CXCR4 and its ligand CXCL12 as prognostic factors for sporadic breast cancer

Chemokines and their receptors are involved in the development and cancer progression. The chemokine CXCL12 interacts with its receptor, CXCR4, to promote cellular adhesion, survival, proliferation and migration. The CXCR4 gene is upregulated in several types of cancers, including skin, lung, pancreas, brain and breast tumors. In pancreatic cancer and melanoma, CXCR4 expression is regulated by DNA methylation within its promoter region. In this study we examined the role of cytosine methylation in the regulation of CXCR4 expression in breast cancer cell lines and also correlated the methylation pattern with the clinicopathological aspects of sixty-nine primary breast tumors from a cohort of Brazilian women. RT-PCR showed that the PMC-42, MCF7 and MDA-MB-436 breast tumor cell lines expressed high levels of CXCR4. Conversely, the MDA-MB-435 cell line only expressed CXCR4 after treatment with 5-Aza-CdR, which suggests that CXCR4 expression is regulated by DNA methylation. To confirm this hypothesis, a 184 bp fragment of the CXCR4 gene promoter region was cloned after sodium bisulfite DNA treatment. Sequencing data showed that cell lines that expressed CXCR4 had only 15% of methylated CpG dinucleotides, while the cell line that not have CXCR4 expression, had a high density of methylation (91%). Loss of DNA methylation in the CXCR4 promoter was detected in 67% of the breast cancer analyzed. The absence of CXCR4 methylation was associated with the tumor stage, size, histological grade, lymph node status, ESR1 methylation and CXCL12 methylation, metastasis and patient death. Kaplan-Meier curves demonstrated that patients with an unmethylated CXCR4 promoter had a poorer overall survival and disease-free survival. Furthermore, patients with both CXCL12 methylation and unmethylated CXCR4 had a shorter overall survival and disease-free survival. These findings suggest that the DNA methylation status of both CXCR4 and CXCL12 genes could be used as a biomarker for prognosis in breast cancer.     

Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes

Oncogenic transformation of normal cells often involves epigenetic alterations, including histone modification and DNA methylation. We conducted whole-genome bisulfite sequencing to determine the DNA methylomes of normal breast, fibroadenoma, invasive ductal carcinomas and MCF7. The emergence, disappearance, expansion and contraction of kilobase-sized hypomethylated regions (HMRs) and the hypomethylation of the megabase-sized partially methylated domains (PMDs) are the major forms of methylation changes observed in breast tumor samples. Hierarchical clustering of HMR revealed tumor-specific hypermethylated clusters and differential methylated enhancers specific to normal or breast cancer cell lines. Joint analysis of gene expression and DNA methylation data of normal breast and breast cancer cells identified differentially methylated and expressed genes associated with breast and/or ovarian cancers in cancer-specific HMR clusters. Furthermore, aberrant patterns of X-chromosome inactivation (XCI) was found in breast cancer cell lines as well as breast tumor samples in the TCGA BRCA (breast invasive carcinoma) dataset. They were characterized with differentially hypermethylated XIST promoter, reduced expression of XIST, and over-expression of hypomethylated X-linked genes. High expressions of these genes were significantly associated with lower survival rates in breast cancer patients. Comprehensive analysis of the normal and breast tumor methylomes suggests selective targeting of DNA methylation changes during breast cancer progression. The weak causal relationship between DNA methylation and gene expression observed in this study is evident of more complex role of DNA methylation in the regulation of gene expression in human epigenetics that deserves further investigation.     

Comprehensive characterization of pathological stage-related genes of papillary thyroid cancer along with survival prediction

It is crucial to understand the differences across papillary thyroid cancer (PTC) stages, so as to provide a basis for individualized treatments. Here, comprehensive function characterization of PTC stage-related genes was performed and a new prognostic signature was developed for advanced patients. Two gene modules were confirmed to be closely associated with PTC stages and further six hub genes were identified that yield excellent diagnostic efficiency between tumour and normal tissues. Genetic alteration analysis indicates that they are much conservative since mutations in the DNA of them rarely occur, but changes of DNA methylation on these six genes show that 12 DNA methylation sites are significantly associated with their corresponding genes' expression. Validation data set testing also suggests that these six stage-related hub genes would be probably potential biomarkers for marking four stages. Subsequently, a 21-mRNA-based prognostic risk model was constructed for PTC stage III/IV patients and it could effectively predict the survival of patients with strong prognostic ability. Functional analysis shows that differential expression genes between high- and low-risk patients would promote the progress of PTC to some extent. Moreover, tumour microenvironment (TME) of high-risk patients may be more conducive to tumour growth by ESTIMATE analysis.     

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.     

Integrated Genomic and Epigenomic Analysis of Breast Cancer Brain Metastasis

The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.     

Bone Marrow Stromal Antigen 2 (BST-2) DNA Is Demethylated in Breast Tumors and Breast Cancer Cells

Background

Bone marrow stromal antigen 2 (BST-2) is a known anti-viral gene that has been recently identified to be overexpressed in many cancers, including breast cancer. BST-2 is critical for the invasiveness of breast cancer cells and the formation of metastasis in vivo. Although the regulation of BST-2 in immune cells is unraveling, it is unknown how BST-2 expression is regulated in breast cancer. We hypothesized that meta-analyses of BST-2 gene expression and BST-2 DNA methylation profiles would illuminate mechanisms regulating elevated BST-2 expression in breast tumor tissues and cells.

Materials and Methods

We performed comprehensive meta-analyses of BST-2 gene expression and BST-2 DNA methylation in The Cancer Genome Atlas (TCGA) and various Gene Expression Omnibus (GEO) datasets. BST-2 expression levels and BST-2 DNA methylation status at specific CpG sites on the BST-2 gene were compared for various breast tumor molecular subtypes and breast cancer cell lines.

Results

We show that BST-2 gene expression is inversely associated with the methylation status at specific CpG sites in primary breast cancer specimens and breast cancer cell lines. BST-2 demethylation is significantly more prevalent in primary tumors and cancer cells than in normal breast tissues or normal mammary epithelial cells. Demethylation of the BST-2 gene significantly correlates with its mRNA expression. These studies provide the initial evidence that significant differences exist in BST-2 DNA methylation patterns between breast tumors and normal breast tissues, and that BST-2 expression patterns in tumors and cancer cells correlate with hypomethylated BST-2 DNA.

Conclusion

Our study suggests that the DNA methylation pattern and expression of BST-2 may play a role in disease pathogenesis and could serve as a biomarker for the diagnosis of breast cancer.     

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient‐specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient‐specific subpathway activity profiles using a greedy search algorithm. A four‐subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high‐risk and low‐risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e‐13) and test set (median survival of 75 vs 101 months, P = 4.17e‐5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four‐subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four‐subpathway signature may be a useful biomarker for breast cancer prognosis.     

Aberrant Keap1 methylation in breast cancer and association with clinicopathological features

Keap1 (Kelch-like ECH-associated protein 1) is an adaptor protein that mediates the ubiquitination/degradation of genes regulating cell survival and apoptosis under oxidative stress conditions. We determined methylation status of the KEAP1 promoter in 102 primary breast cancers, 14 pre-invasive lesions, 38 paired normal breast tissues and 6 normal breast from reductive mammoplasty by quantitative methylation specific PCR (QMSP). Aberrant promoter methylation was detected in 52 out of the 102 primary breast cancer cases (51%) and 10 out of 14 pre-invasive lesions (71%). No mutations of the KEAP1 gene were identified in the 20 breast cancer cases analyzed by fluorescence based direct sequencing. Methylation was more frequent in the subgroup of patients identified as ER positive-HER2 negative tumors (66.7%) as compared with triple-negative breast cancers (35%) (p = 0.05, Chi-square test). The impact of the interactions between Er, PgR, Her2 expression and KEAP1 methylation on mortality was investigated by RECPAM multivariable statistical analysis, identifying four prognostic classes at different mortality risks. Triple-negative breast cancer patients with KEAP1 methylation had higher mortality risk than patients without triple-negative breast cancer (HR = 14.73, 95%CI: 3.65–59.37). Both univariable and multivariable COX regressions analyses showed that KEAP1 methylation was associated with a better progression free survival in patients treated with epirubicin/cyclophosfamide and docetaxel as sequential chemotherapy (HR = 0.082; 95%CI: 0.007–0.934). These results indicate that aberrant promoter methylation of the KEAP1 gene is involved in breast cancerogenesis. In addition, identifying patients with KEAP1 epigenetic abnormalities may contribute to disease progression prediction in breast cancer patients.     

Epigenetic impact of long-term shiftwork: pilot evidence from circadian genes and whole-genome methylation analysis

Epigenetic association studies have demonstrated differential promoter methylation in the core circadian genes in breast cancer cases relative to cancer-free controls. The current pilot study aims to investigate whether epigenetic changes affecting breast cancer risk could be caused by circadian disruption through exposure to light at night. Archived DNA samples extracted from whole blood of 117 female subjects from a prospective cohort conducted in Denmark were included in this study. A polymerase chain reaction (PCR)-based method was used for detection of gene-promoter methylation, whereas genome-wide methylation analysis was performed using the Illumina Infinium Methylation Chip. Long-term shiftwork resulted in the same promoter hypomethylation of CLOCK and hypermethylation of CRY2, as was previously observed in breast cancer case-control studies. Genome-wide methylation analysis further discovered widespread methylation alterations in shiftworkers, including changes in many methylation- and cancer-relevant genes. Pathway analysis of the genes with altered methylation patterns revealed several cancer-related pathways. One of the top three networks generated was designated as "DNA replication, recombination, and repair, gene expression, behavior" with ESR1 (estrogen receptor α) featured most prominently in the network, underscoring the potential breast cancer relevance of the genes differentially methylated in long-term shiftworkers. These results, although exploratory, demonstrate the first evidence of the cancer-relevant epigenetic effects of night shiftwork, which warrant further investigation. Considering there are millions of shiftworkers worldwide, understanding the effects of this exposure may lead to novel strategies for cancer prevention and new policies regulating shiftwork.     

Gene expression and pathologic response to neoadjuvant chemotherapy in breast cancer

Pathologic complete response after neoadjuvant systemic treatment appears to be a valid surrogate for better overall survival in breast cancer patients. Currently, together with standard clinicopathologic assessment, novel molecular biomarkers are being exhaustively tested in order to look into the heterogeneity of breast cancer. The aim of our study was to examine an association between 23-gene real-time-PCR expression assay including ABCB1, ABCC1, BAX, BBC3, BCL2, CASP3, CYP2D6, ERCC1, FOXC1, GAPDH, IGF1R, IRF1, MAP2, MAPK 8, MAPK9, MKI67, MMP9, NCOA3, PARP1, PIK3CA, TGFB3, TOP2A, and YWHAZ receptor status of breast cancer core biopsies sampled before neoadjuvant chemotherapy (anthracycline and taxanes) and pathologic response. Core-needle biopsies were collected from 42 female patients with inoperable locally advanced breast cancer or resectable tumors suitable for downstaging, before any treatment. Expressions of 23 genes were determined by means of TagMan low density arrays. Analysis of variance was used to select genes with discriminatory potential between receptor subtypes. We introduced a correction for false discovery rates (presented as q values) due to multiple hypothesis testing. Statistical analysis showed that seven genes out of a 23-gene real-time-PCR expression assay differed significantly in relation to pathologic response regardless of breast cancer subtypes. Among these genes, we identified: BAX (p = 0.0146), CYP2D6 (p = 0.0063), ERCC1 (p = 0.0231), FOXC1 (p = 0.0048), IRF1 (p = 0.0022), MAP2 (p = 0.0011), and MKI67 (p = 0.0332). The assessment of core biopsy gene profiles and receptor-based subtypes, before neoadjuvant therapy seems to predict response or resistance and to define new signaling pathways to provide more powerful classifiers in breast cancer, hence the need for further research.     

Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis

Background

Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development.

Results

We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma.

Conclusions

This work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0435-x) contains supplementary material, which is available to authorized users.     

Specific 50'CpG island methylation signatures of FHIT and p16 genes and their potential diagnostic relevance in Indian breast cancer patients

Even after tremendous molecular studies, early detection,more accurate and sensitive diagnosis, and prognosis of breast cancer appear to be a riddle so far. To stab the enigma, this study is designed to envisage DNA methylation signatures as cancer-specific and stage-specific biomarkers in Indian patients. Rigorous review of scattered scientific reports on aberrant DNA methylation helped us to select and analyze a potential tumor suppressor gene pair (FHIT and p16 genes) in breast cancer patients. Methylation signatures from 232 primary sporadic breast cancer patients were pinpointed by methylation-specific PCR (MSP). To increase the sensitivity, we combined both MSP and expression studies (RT-PCR and Northern blotting) in a reproducible manner. Statistical analysis illustrated that hypermethylation of FHIT gene ( p < 0.0001) and p16 gene ( p=0.04) may be used as a potential diagnostic marker to diagnose the early and locally advanced stages of breast cancer. Additionally, the study authenticates the dependency of methylation and expressional loss of p16 gene on FHIT gene silencing. This observation not only describes the severity of disease when both genes are silenced but also drives to speculate the molecular cross talk between two genes or genetic pathways dictated by them separately.     

Identification of prognostic signature in cancer based on DNA methylation interaction network

Background

The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.

Methods

In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.

Results

We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme).In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.

Conclusions

A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.