Deregulated estrogen receptor signaling and DNA damage response in breast tumorigenesis

Novel frontiers in urogenital cancers: from molecular bases to preclinical models to tailor personalized treatments in ovarian and prostate cancer patients

Over the last few decades, the incidence of urogenital cancers has exhibited diverse trends influenced by screening programs and geographical variations. Among women, there has been a consistent or even increased occurrence of endometrial and ovarian cancers; conversely, prostate cancer remains one of the most diagnosed malignancies, with a rise in reported cases, partly due to enhanced and improved screening efforts.Simultaneously, the landscape of cancer therapeutics has undergone a remarkable evolution, encompassing the introduction of targeted therapies and significant advancements in traditional chemotherapy. Modern targeted treatments aim to selectively address the molecular aberrations driving cancer, minimizing adverse effects on normal cells. However, traditional chemotherapy retains its crucial role, offering a broad-spectrum approach that, despite its wider range of side effects, remains indispensable in the treatment of various cancers, often working synergistically with targeted therapies to enhance overall efficacy.For urogenital cancers, especially ovarian and prostate cancers, DNA damage response inhibitors, such as PARP inhibitors, have emerged as promising therapeutic avenues. In BRCA-mutated ovarian cancer, PARP inhibitors like olaparib and niraparib have demonstrated efficacy, leading to their approval for specific indications. Similarly, patients with DNA damage response mutations have shown sensitivity to these agents in prostate cancer, heralding a new frontier in disease management. Furthermore, the progression of ovarian and prostate cancer is intricately linked to hormonal regulation. Ovarian cancer development has also been associated with prolonged exposure to estrogen, while testosterone and its metabolite dihydrotestosterone, can fuel the growth of prostate cancer cells. Thus, understanding the interplay between hormones, DNA damage and repair mechanisms can hold promise for exploring novel targeted therapies for ovarian and prostate tumors.In addition, it is of primary importance the use of preclinical models that mirror as close as possible the biological and genetic features of patients' tumors in order to effectively translate novel therapeutic findings "from the bench to the bedside".In summary, the complex landscape of urogenital cancers underscores the need for innovative approaches. Targeted therapy tailored to DNA repair mechanisms and hormone regulation might offer promising avenues for improving the management and outcomes for patients affected by ovarian and prostate cancers.

Activation of the YY1-UGT2B7 Axis Promotes Mammary Estrogen Homeostasis Dysregulation and Exacerbates Breast Tumor Metastasis

Metastasis is the most common pathway of cancer death. The lack of effective predictors of breast cancer metastasis is a pressing issue in clinical practice. Therefore, exploring the mechanism of breast cancer metastasis to uncover reliable predictors is very important for the clinical treatment of breast cancer patients. In this study, tandem mass tag quantitative proteomics technology was used to detect protein content in primary breast tumor tissue samples from patients with metastatic and nonmetastatic breast cancer at diagnosis. We found that the high expression of yin-yang 1(YY1) is strongly associated with poor prognosis in high-grade breast cancer. YY1 expression was detected in both clinical tumor tissue samples and tumor tissue samples from mammary-specific polyomavirus middle T antigen overexpression mouse model mice. We demonstrated that upregulation of YY1 expression was closely associated with breast cancer metastasis and that high YY1 expression could promote the migratory invasive ability of breast cancer cells. Mechanistically, YY1 directly binds to the UGT2B7 mRNA initiation sequence ATTCAT, thereby transcriptionally regulating the inhibition of UGT2B7 expression. UGT2B7 can regulate the development of breast cancer by regulating estrogen homeostasis in the breast, and the abnormal accumulation of estrogen, especially 4-OHE2, promotes the migration and invasion of breast cancer cells, ultimately causing the development of breast cancer metastasis. In conclusion, YY1 can regulate the UGT2B7-estrogen metabolic axis and induce disturbances in estrogen metabolism in breast tumors, ultimately leading to breast cancer metastasis. Disturbances in estrogen metabolism in the breast tissue may be an important risk factor for breast tumor progression and metastasis SIGNIFICANCE STATEMENT: In this study, we propose for the first time a regulatory relationship between YY1 and the UGT2B7/estrogen metabolism axis and explore the molecular mechanism. Our study shows that the YY1/UGT2B7/estrogen axis plays an important role in the development and metastasis of breast cancer. This study further elucidates the potential mechanisms of YY1-mediated breast cancer metastasis and the possibility and promise of YY1 as a predictor of cancer metastasis.

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

BACKGROUND AND OBJECTIVES

Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes.

RESULTS

The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases.

CONCLUSION

MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

BACKGROUND

Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software.

CONCLUSIONS

This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

In vitro study to identify ligand-independent function of estrogen receptor-α in suppressing DNA damage-induced chondrocyte senescence

In osteoarthritis (OA), chondrocytes undergo many pathological alternations that are linked with cellular senescence. However, the exact pathways that lead to the generation of a senescence-like phenotype in OA chondrocytes are not clear. Previously, we found that loss of estrogen receptor-α (ERα) was associated with an increased senescence level in human chondrocytes. Since DNA damage is a common cause of cellular senescence, we aimed to study the relationship among ERα levels, DNA damage, and senescence in chondrocytes. We first examined the levels of ERα, representative markers of DNA damage and senescence in normal and OA cartilage harvested from male and female human donors, as well as from male mice. The influence of DNA damage on ERα levels was studied by treating human chondrocytes with doxorubicin (DOX), which is an often-used DNA-damaging agent. Next, we tested the potential of overexpressing ERα in reducing DNA damage and senescence levels. Lastly, we explored the interaction between ERα and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. Results indicated that the OA chondrocytes contained DNA damage and displayed senescence features, which were accompanied by significantly reduced ERα levels. Overexpression of ERα reduced the levels of DNA damage and senescence in DOX-treated normal chondrocytes and OA chondrocytes. Moreover, DOX-induced the activation of NF-κB pathway, which was partially reversed by overexpressing ERα. Taken together, our results demonstrated the critical role of ERα in maintaining the health of chondrocytes by inhibiting DNA damage and senescence. This study also suggests that maintaining the ERα level may represent a new avenue to prevent and treat OA.

Obesity and endocrine-related cancer: The important role of IGF-1

Obesity is increasingly becoming a global epidemic of concern and is considered a risk factor for several endocrine-related cancers. Moreover, obesity is associated with cancer development and poor prognosis. As a metabolic abnormality, obesity leads to a series of changes in insulin, IGF-1, sex hormones, IGFBPs, and adipokines. Among these factors, IGF-1 plays an important role in obesity-related endocrine cancers. This review describes the role of obesity in endocrine-related cancers, such as prostate cancer, breast cancer and pancreatic cancer, focusing on the mechanism of IGF-1 and the crosstalk with estrogen and adipokines. In addition, this review briefly introduces the current status of IGF-1R inhibitors in clinical practice and shows the prospect of IGF-1R inhibitors in combination with other anticancer drugs.

Intercommunication between Voltage-Gated Calcium Channels and Estrogen Receptor/Estrogen Signaling: Insights into Physiological and Pathological Conditions

Voltage-gated calcium channels (VGCCs) and estrogen receptors are important cellular proteins that have been shown to interact with each other across varied cells and tissues. Estrogen hormone, the ligand for estrogen receptors, can also exert its effects independent of estrogen receptors that collectively constitute non-genomic mechanisms. Here, we provide insights into the VGCC regulation by estrogen and the possible mechanisms involved therein across several cell types. Notably, most of the interaction is described in neuronal and cardiovascular tissues given the importance of VGCCs in these electrically excitable tissues. We describe the modulation of various VGCCs by estrogen known so far in physiological conditions and pathological conditions. We observed that in most in vitro studies higher concentrations of estrogen were used while a handful of in vivo studies used meager concentrations resulting in inhibition or upregulation of VGCCs, respectively. There is a need for more relevant physiological assays to study the regulation of VGCCs by estrogen. Additionally, other interacting receptors and partners need to be identified that may be involved in exerting estrogen receptor-independent effects of estrogen.

The Interplay between the Cellular Response to DNA Double-Strand Breaks and Estrogen

Cancer development is often connected to impaired DNA repair and DNA damage signaling pathways. The presence of DNA damage in cells activates DNA damage response, which is a complex cellular signaling network that includes DNA repair, activation of the cell cycle checkpoints, cellular senescence, and apoptosis. DNA double-strand breaks (DSBs) are toxic lesions that are mainly repaired by the non-homologous end joining and homologous recombination repair (HRR) pathways. Estrogen-dependent cancers, like breast and ovarian cancers, are frequently associated with mutations in genes that play a role in HRR. The female sex hormone estrogen binds and activates the estrogen receptors (ERs), ERα, ERβ and G-protein-coupled ER 1 (GPER1). ERα drives proliferation, while ERβ inhibits cell growth. Estrogen regulates the transcription, stability and activity of numerus DDR factors and DDR factors in turn modulate ERα expression, stability and transcriptional activity. Additionally, estrogen stimulates DSB formation in cells as part of its metabolism and proliferative effect. In this review, we will present an overview on the crosstalk between estrogen and the cellular response to DSBs. We will discuss how estrogen regulates DSB signaling and repair, and how DDR factors modulate the expression, stability and activity of estrogen. We will also discuss how the regulation of HRR genes by estrogen promotes the development of estrogen-dependent cancers.

Hormonal contraception and risk of breast cancer and breast cancer in situ among Swedish women 15–34 years of age: A nationwide register-based study

Background

Evidence on a possible association between newer hormonal contraceptives (HC) and risk of breast cancer remains inconclusive, especially as concerns progestogen-only methods.

Methods

In this nationwide prospective cohort study, all Swedish women aged 15–34 at study start on January 1^st 2005, or who turned 15 years during the study period, were followed until December 31^st 2017. Using information from seven National Registers, we assessed the risk ratio of developing breast cancer and breast cancer in situ in relation to different HC using Poisson regression. We adjusted the analyses for several known confounders of breast cancer.

Findings

This cohort included 1.5 million women providing more than 14 million person-years. During the study period, 3842 women were diagnosed with breast cancer. Compared with never users of any HC, we found no increased risk of developing breast cancer among current users of any combined HC, IRR 1.03 (0.91–1.16), whereas current users of progestogen-only methods had an increased risk of developing breast cancer, IRR 1.32 (1.20–1.45). Across all types of HC, the risk of developing breast cancer appeared to be highest the first five years of use (combined HC IRR 1.39 (1.14–1.69); progestogen-only methods IRR 1.74 (1.44–2.10). The risk disappeared ten years after the women stopped using HC. The absolute risk of breast cancer per 100,000 women-years was 22.4 for never users, 10.9 for current users of combined HC, and 29.8 for current users of progestogen-only methods.

Interpretation

Current use of progestogen-only methods is associated with a small increased risk of developing breast cancer, whereas we could only detect an increased risk among users of combined HC during the first five years of use. This may partly be explained by a selective prescription of progestogen-only methods to women with risk factors for breast cancer, like smoking or obesity. As the absolute risk of breast cancer was small, the many health benefits associated with HC must also be taken into account in contraceptive counselling.

Funding

This study was funded by the Swedish Cancer Society and by the Uppsala County Council, the Faculty of Medicine at Uppsala University.

A multivariate statistical test for differential expression analysis

Statistical tests of differential expression usually suffer from two problems. Firstly, their statistical power is often limited when applied to small and skewed data sets. Secondly, gene expression data are usually discretized by applying arbitrary criteria to limit the number of false positives. In this work, a new statistical test obtained from a convolution of multivariate hypergeometric distributions, the Hy-test, is proposed to address these issues. Hy-test has been carried out on transcriptomic data from breast and kidney cancer tissues, and it has been compared with other differential expression analysis methods. Hy-test allows implicit discretization of the expression profiles and is more selective in retrieving both differential expressed genes and terms of Gene Ontology. Hy-test can be adopted together with other tests to retrieve information that would remain hidden otherwise, e.g., terms of (1) cell cycle deregulation for breast cancer and (2) “programmed cell death” for kidney cancer.