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 and genetic burden measures are associated with tumor characteristics in invasive breast carcinoma

The development and progression of invasive breast cancer is characterized by alterations to the genome and epigenome. However, the relationship between breast tumor characteristics, disease subtypes, and patient outcomes with the cumulative burden of these molecular alterations are not well characterized. We determined the average departure of tumor DNA methylation from adjacent normal breast DNA methylation using Illumina 450K methylation data from 700 invasive breast tumors and 90 adjacent normal breast tissues in The Cancer Genome Atlas. From this we generated a novel summary measure of altered DNA methylation, the DNA methylation dysregulation index (MDI), and examined the relation of MDI with tumor characteristics and summary measures that quantify cumulative burden of genetic mutation and copy number alterations. Our analysis revealed that MDI was significantly associated with tumor stage (P = 0.017). Across invasive breast tumor subtypes we observed significant differences in genome-wide DNA MDIs (P = 4.9E–09) and in a fraction of the genome with copy number alterations (FGA) (P = 4.6E–03). Results from a linear regression adjusted for subject age, tumor stage, and estimated tumor purity indicated a positive significant association of MDI with both MCB and FGA (P = 0.036 and P < 2.2E–16). A recursively partitioned mixture model of all 3 somatic alteration burden measures resulted in classes of tumors whose epigenetic and genetic burden profile were associated with the PAM50 subtype and mutations in TP53, PIK3CA, and CDH1. Together, our work presents a novel framework for characterizing the epigenetic burden and adds to the understanding of the aggregate impact of epigenetic and genetic alterations in breast cancer.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

DNA methylation-based biomarkers in serum of patients with breast cancer

Alterations of genetic and epigenetic features can provide important insights into the natural history of breast cancer. Although DNA methylation analysis is a rapidly developing field, a reproducible epigenetic blood-based assay for diagnosis and follow-up of breast cancer has yet to be successfully developed into a routine clinical test. The aim of this study was to review multiple serum DNA methylation assays and to highlight the value of those novel biomarkers in diagnosis, prognosis and prediction of therapeutic outcome. Serum is readily accessible for molecular diagnosis in all individuals from a peripheral blood sample. The list of hypermethylated genes in breast cancer is heterogeneous and no single gene is methylated in all breast cancer types. There is increasing evidence that a panel of epigenetic markers is essential to achieve a higher sensitivity and specificity in breast cancer detection. However, the reported percentages of methylation are highly variable, which can be partly explained by the different sensitivities and the different intra-/inter-assay coefficients of variability of the analysis methods. Moreover, there is a striking lack of receiver operating characteristic (ROC) curves of the proposed biomarkers. Another point of criticism is the fact that 'normal' patterns of DNA methylation of some tumor suppressor and other cancer-related genes are influenced by several factors and are often poorly characterized. A relatively frequent methylation of those genes has been observed in high-risk asymptomatic women. Finally, there is a call for larger prospective cohort studies to determine methylation patterns during treatment and follow-up. Identification of patterns specific for a differential response to therapeutic interventions should be useful. Only in this way, it will be possible to evaluate the predictive and prognostic characteristics of those novel promising biomarkers.     

Promoter hypermethylation of p16INK4A, p14ARF, CyclinD2 and Slit2 in serum and tumor DNA from breast cancer patients

Epigenetic mechanisms such as DNA methylation play important role in cancer. Epigenetic alterations involved in the onset and progression of breast cancer may serve as biomarkers for early detection and prediction of disease prognosis. Furthermore, using body fluids such as serum offers a non-invasive method to procure multiple samples for biomarker analyses. The aim of this study is to determine the correlation between methylation status of multiple cancer genes, p16(INK4A), p14(ARF), Cyclin D2 and Slit2 in invasive ductal carcinoma of the breast and paired serum DNA and clinicopathological parameters. Of the 36 breast cancer patients investigated, 31 (86%) tumors and 30 (83%) paired sera showed methylation of at least one of these 4 genes. Methylation frequencies varied from 27% for CyclinD2, 44% for p16(INK4A), 47% for p14(ARF) to 58% for Slit2. There was concordance between DNA methylation in tumor and paired serum DNA of each gene. This study underscores the potential utility of DNA methylation based screening of serum as a surrogate marker for tumor DNA methylation status of these genes in breast cancer. Further, expression profile of p16(INK4A) could be linked to epigenetic events, thus suggesting this pathway as a potential target for therapeutic strategies based on reversal of epigenetic silencing.     

基因启动子甲基化对转录因子结合的抑制作用分析方法

基因启动子甲基化对转录因子结合的抑制作用是一种有效的基因转录调控机制.尽管基因启动子甲基化水平已经可以通过实验测量,但仍未有有效的方法利用这些数据定量分析甲基化对转录因子结合的影响.设计一个通用模型来描述基因启动子甲基化对转录因子结合的抑制作用.在特定细胞环境下,通过基因表达与转录因子在基因启动子上结合值之间的相关性分析,实现模型参数求取,并基于该模型进行甲基化对转录因子结合的抑制作用分析.神经细胞生物实验数据测试证明了该方法的有效性.     

Leptin and high glucose stimulate cell proliferation in MCF-7 human breast cancer cells: reciprocal involvement of PKC-alpha and PPAR expression

Glucose concentration may be an important factor in breast cancer cell proliferation, and the prevalence of breast cancer is high in diabetic patients. Leptin may also be an important factor since plasma levels of leptin correlated with TNM staging for breast cancer patients. The effects of glucose and leptin on breast cancer cell proliferation were evaluated by examining cell doubling time, DNA synthesis, levels of cell cycle related proteins, protein kinase C (PKC) isozyme expression, and peroxisome proliferator-activated receptor (PPAR) subtypes were determined following glucose exposure at normal (5.5 mM) and high (25 mM) concentrations with/without leptin in MCF-7 human breast cancer cells. In MCF-7 cells, leptin and high glucose stimulated cell proliferation as demonstrated by the increases in DNA synthesis and expression of cdk2 and cyclin D1. PKC-alpha, PPARgamma, and PPARalpha protein levels were up-regulated following leptin and high glucose treatment in drug-sensitive MCF-7 cells. However, there was no significant effect of leptin and high glucose on cell proliferation, DNA synthesis, levels of cell cycle proteins, PKC isozymes, or PPAR subtypes in multidrug-resistant human breast cancer NCI/ADR-RES cells. These results suggested that hyperglycemia and hyperleptinemia increase breast cancer cell proliferation through accelerated cell cycle progression with up-regulation of cdk2 and cyclin D1 levels. This suggests the involvement of PKC-alpha, PPARalpha, and PPARgamma.     

Do Breast Cancer Cell Lines Provide a Relevant Model of the Patient Tumor Methylome?

It is well documented that tumor cells undergo dramatic genetic and epigenetic changes during initial establishment as cell lines and in subsequent serial passaging, and that the resultant cell lines may have evolved significantly from the primary tumors from which they were derived. This has potential implications due to their widespread use in drug response experiments and studies of genomic function. One approach to optimizing the design of such cell line studies is to identify and use the cell lines that faithfully recapitulate critical features of primary tumors. To evaluate the epigenetic fidelity of breast cancer cell lines in the context of primary tumors, we performed methylation profiling of 55 well-characterized breast cancer cell lines on the Illumina HumanMethylation27 BeadChip platform, and compared them to publicly available methylation profiles of primary breast tumors. We found that the DNA methylation profiles of breast cancer cell lines largely retain the features that characterize primary tumors, although there are crucial differences as well. We describe these similarities and differences between primary tumors and breast cancer cell lines in detail, and develop a quantitative measure of similarity that is used to score each cell line with respect to how faithfully its methylation profile mirrors that of primary tumors.     

Shelterin complex gene: Prognosis and therapeutic vulnerability in cancer

Telomere encompasses a (TTAGGG)n tandem repeats, and its dysfunction has emerged as the epicenter of driving carcinogenesis by promoting genetic instability. Indeed, they play an essential role in stabilizing chromosomes and therefore protecting them from end-to-end fusion and DNA degradation. Telomere length homeostasis is regulated by several key players including shelterin complex genes, telomerase, and various other regulators. Targeting these regulatory players can be a good approach to combat cancer as telomere length is increasingly correlated with cancer initiation and progression. In this review, we have aimed to describe the telomere length regulator's role in prognostic significance and important drug targets in breast cancer. Moreover, we also assessed alteration in telomeric function by various telomere length regulators and compares this to the regulatory mechanisms that can be associated with clinical biomarkers in cancer. Using publicly available software we summarized mutational and CpG island prediction analysis of the TERT gene breast cancer patient database. Studies have reported that the TERT gene has prognostic significance in breast cancer progression however mechanistic approaches are not defined yet. Interestingly, we reported using the UCSC Xena web-based tool, we confirmed a positive correlation of shelterin complex genes TERF1 and TERF2 in recurrent free survival, indicating the critical role of these genes in breast cancer prognosis. Moreover, the epigenetic landscape of DNA damage repair genes in different breast cancer subtypes also being analyzed using the UCSC Xena database. Together, these datasets provide a comprehensive resource for shelterin complex gene profiles and define epigenetic landscapes of DNA damage repair genes which reveals the key role of shelterin complex genes in breast cancer with the potential to identify novel and actionable targets for treatment.