ABCC9, NKAPL,and TMEM132C are potential diagnostic and prognostic markers in triple‐negative breast cancer

Triple‐negative breast cancer (TNBC) is a highly heterogeneous disease. The aim of this study is to identify the diagnostic and poor prognostic signatures in TNBC by exploring the aberrant DNA methylation and gene expression. Differential expression and methylation analysis of the TNBC and paracancer samples from The Cancer Genome Atlas were performed. Gene set enrichment and protein–protein interaction (PPI) network analysis was used to explore the mechanisms of TNBC. Methylation‐gene expression correlation analysis was performed, and multivariate Cox analysis and receiver operating characteristics analysis were used to further screen the hub genes for TNBC. We identified 1,525 differentially expressed genes and 150 differentially methylated genes between TNBC and paracancer samples. About 96.64% of the methylation sites were located on the CpG island. A total of 17 Gene Ontology biological process terms and 18 signal pathways were significantly enriched. GNG4, GNG11, PENK, MAOA, and AOX1 were identified as the core genes of the PPI network. Methylation‐expression correlations revealed that ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) showed promise as diagnostic and prognostic markers in TNBC. ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) were potential diagnostic and prognostic markers in TNBC.     

The prediction of tumor and normal tissues based on the DNA methylation values of ten key sites

Abnormal DNA methylation can alter the gene expression to promote or inhibit tumorigenesis in colon adenocarcinoma (COAD). However, the finding important genes and key sites of abnormal DNA methylation which result in the occurrence of COAD is still an eventful task. Here, we studied the effects of DNA methylation in the 12 types of genomic features on the changes of gene expression in COAD, the 10 important COAD-related genes and the key abnormal DNA methylation sites were identified. The effects of important genes on the prognosis were verified by survival analysis. Moreover, it was shown that the important genes were participated in cancer pathways and were hub genes in a co-expression network. Based on the DNA methylation levels in the ten sites, the least diversity increment algorithm for predicting tumor tissues and normal tissues in seventeen cancer types are proposed. The better results are obtained in jackknife test. For example, the predictive accuracies are 94.17 %, 91.28 %, 89.04 % and 88.89 %, respectively, for COAD, rectum adenocarcinoma, pancreatic adenocarcinoma and cholangiocarcinoma. Finally, by computing enrichment score of infiltrating immunocytes and the activity of immune pathways, we found that the genes are highly correlated with immune microenvironment.     

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.     

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.     

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.     

Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer

Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour-infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour-infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up-regulated differentially expressed genes between them were enriched in immune system processes, immune-related KEGG pathways and biological processes. In addition, many important immune-related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune-related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.     

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.     

Key genes and pathways in tumor-educated dendritic cells by bioinformatical analysis

Specific tumor microenvironment signaling might prevent the maturation of dendritic cells (DCs) with tolerogenic and immunosuppressive potential accounting for antigen-specific unresponsiveness in the lymphoid organs and in the periphery. In the present study, dendritic cells treated with LLC lung cancer cell or 4T1 breast cancer cell culture supernatants significantly down-regulated the expression of co-stimulatory molecules MHC-II, CD40, CD80, but up-regulated the inhibitory molecule PD-L1/L2, VISTA, and increased the messengerRNA levels of interleukin (IL)-6, arginase I, and IL-10, but decreased tumor necrosis factor-α and IL-12a. RNA was isolated from the dendritic cells with or without tumor supernatant stimulation and RNA sequencing was done. Then the differential expression genes were sorted, the candidate genes were analyzed and pathway enrichment analysis was done, and the associated protein–protein interaction network (PPI) was established. After integrated bioinformatical analysis, 405 (279 up-regulated and 126 down-regulated) consistently differential expression genes were identified. Using gene ontology and pathway analysis, it was found that differential expression genes were mainly enriched in the immune response, cell–cell interaction, hemostasis, and cell surface interactions with the vascular wall. The PPI data demonstrated that 236 nodes were classified with 1072 edges, and the most remarkable three modules involved 53 central node genes associated with cell survival, cell-substrate adhesion, chemotaxis, migration, immune response, and complement receptor mediated signaling pathway. These findings revealed the immune status of dendritic cells in the tumor environment.     

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.     

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.     

DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.     

Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

Background

Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.

Methods

Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.

Results

The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.

Conclusions

In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.

     

Robust prediction of gene regulation in colorectal cancer tissues from DNA methylation profiles

DNA methylation is recognized as one of several epigenetic regulators of gene expression and as potential driver of carcinogenesis through gene-silencing of tumor suppressors and activation of oncogenes. However, abnormal methylation, even of promoter regions, does not necessarily alter gene expression levels, especially if the gene is already silenced, leaving the exact mechanisms of methylation unanswered. Using a large cohort of matching DNA methylation and gene expression samples of colorectal cancer (CRC; n = 77) and normal adjacent mucosa tissues (n = 108), we investigated the regulatory role of methylation on gene expression. We show that on a subset of genes enriched in common cancer pathways, methylation is significantly associated with gene regulation through gene-specific mechanisms. We built two classification models to infer gene regulation in CRC from methylation differences of tumor and normal tissues, taking into account both gene-silencing and gene-activation effects through hyper- and hypo-methylation of CpGs. The classification models result in high prediction performances in both training and independent CRC testing cohorts (0.92<AUC<0.97) as well as in individual patient data (average AUC = 0.82), suggesting a robust interplay between methylation and gene regulation. Validation analysis in other cancerous tissues resulted in lower prediction performances (0.69<AUC<0.90); however, it identified genes that share robust dependencies across cancerous tissues. In conclusion, we present a robust classification approach that predicts the gene-specific regulation through DNA methylation in CRC tissues with possible transition to different cancer entities. Furthermore, we present HMGA1 as consistently associated with methylation across cancers, suggesting a potential candidate for DNA methylation targeting cancer therapy.     

Radiation-induced epigenetic DNA methylation modification of radiation-response pathways

DNA methylation can regulate gene expression and has been shown to modulate cancer cell biology and chemotherapy resistance. Therapeutic radiation results in a biological response to counter the subsequent DNA damage and genomic stress in order to avoid cell death. In this study, we analyzed DNA methylation changes at >450,000 loci to determine a potential epigenetic response to ionizing radiation in MDA-MB-231 cells. Cells were irradiated at 2 and 6 Gy and analyzed at 7 time points from 1–72 h. Significantly differentially methylated genes were enriched in gene ontology categories relating to cell cycle, DNA repair, and apoptosis pathways. The degree of differential methylation of these pathways varied with radiation dose and time post-irradiation in a manner consistent with classical biological responses to radiation. A cell cycle arrest was observed 24 h post-irradiation and DNA damage, as measured by γH2AX, resolved at 24 h. In addition, cells showed low levels of apoptosis 2–48 h post-6 Gy and cellular senescence became significant at 72 h post-irradiation. These DNA methylation changes suggest an epigenetic role in the cellular response to radiation.     

Biological Characteristics and Genetic Heterogeneity between Carcinoma-Associated Fibroblasts and Their Paired Normal Fibroblasts in Human Breast Cancer

Background

The extensional signals in cross-talk between stromal cells and tumor cells generated from extracellular matrix molecules, soluble factor, and cell-cell adhesion complexes cooperate at the extra- and intracellular level in the tumor microenvironment. CAFs are the primary type of stromal cells in the tumor microenvironment and play a pivotal role in tumorigenesis and development. Hitherto, there is hardly any systematic analysis of the intrinsic relationship between CAFs function and its abnormal signaling pathway. The extreme complexity of CAFs’ features and their role in tumor development are needed to be further investigated.

Methodology/Principal Findings

We primary cultured CAFs and NFs from early stages of breast cancer tissue and identified them using their biomarker by immunohistochemistry for Fibronectin, α-SMA and FAP. Microarray was applied to analyze gene expression profiles of human breast CAFs and the paired NFs. The Up-regulated genes classified by Gene Ontology, signal pathways enriched by DAVID pathway analysis. Abnormal signaling pathways in breast cancer CAFs are involved in cell cycle, cell adhesion, signal transduction and protein transport being reported in CAFs derived from other tumors. Significantly, the altered ATM signaling pathway, a set of cell cycle regulated signaling, and immune associated signaling are identified to be changed in CAFs.

Conclusions/Significance

CAFs have the vigorous ability of proliferation and potential of invasion and migration comparing with NFs. CAFs could promote breast cancer cell invasion under co-culture conditions through up-regulated CCL18 and CXCL12. Consistently with its biologic behavior, the gene expression profiling analyzed by microarray shows that some of key signaling pathways, such as cell cycle, cell adhesion, and secreting factors play an important role in CAFs. The altered ATM signaling pathway is abnormally active in the early stage of breast cancer. The set of immune associated signaling may be involved in tumor cell immune evasion.