Breast cancer classification: linking molecular mechanisms to disease prognosis

NKAIN1, as an oncogene, promotes the proliferation and metastasis of breast cancer, affecting its prognosis

Na, K-ATPase interaction (NKAIN) is a transmembrane protein family, which can interact with Na, K-ATPase β1 subunit. NKAIN1 plays an important role in alcohol-dependent diseases such as endometrial and prostate cancers. However, the relationship between NKAIN1 and human breast cancer has not been studied. Hence, this study aimed to explore the relationship between NKAIN1 expression and breast cancer. Data used in this study were mainly from the Cancer Genome Atlas, including differential expression analysis, Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, multiple Cox regression analysis, co-expression gene analysis, and gene set enrichment analysis. Analyses were performed using reverse transcription-quantitative polymerase chain reaction, western blot analysis, and immunohistochemistry on 46 collected samples. The knockdown or overexpression of NKAIN1 in vitro in MCF-7 and MDA-MB-231 cell lines altered the proliferation and migration abilities of tumor cells. In vivo experiments further confirmed that NKAIN1 knockdown effectively inhibited the proliferation and migration of cancer cells. Therefore, our study identified NKAIN1 as an oncogene that is highly expressed in breast cancer tissues. The findings highlight the potential of NKAIN1 as a molecular biomarker of breast cancer.

Machine Learning-Supported Diagnosis of Small Blue Round Cell Sarcomas Using Targeted RNA Sequencing

Small blue round cell sarcomas (SBRCSs) are a heterogeneous group of tumors with overlapping morphologic features but markedly varying prognosis. They are characterized by distinct chromosomal alterations, particularly rearrangements leading to gene fusions, whose detection currently represents the most reliable diagnostic marker. Ewing sarcomas are the most common SBRCSs, defined by gene fusions involving EWSR1 and transcription factors of the ETS family, and the most frequent non-EWSR1-rearranged SBRCSs harbor a CIC rearrangement. Unfortunately, currently the identification of CIC::DUX4 translocation events, the most common CIC rearrangement, is challenging. Here, we present a machine-learning approach to support SBRCS diagnosis that relies on gene expression profiles measured via targeted sequencing. The analyses on a curated cohort of 69 soft-tissue tumors showed markedly distinct expression patterns for SBRCS subgroups. A random forest classifier trained on Ewing sarcoma and CIC-rearranged cases predicted probabilities of being CIC-rearranged >0.9 for CIC-rearranged-like sarcomas and <0.6 for other SBRCSs. Testing on a retrospective cohort of 1335 routine diagnostic cases identified 15 candidate CIC-rearranged tumors with a probability >0.75, all of which were supported by expert histopathologic reassessment. Furthermore, the multigene random forest classifier appeared advantageous over using high ETV4 expression alone, previously proposed as a surrogate to identify CIC rearrangement. Taken together, the expression-based classifier can offer valuable support for SBRCS pathologic diagnosis.

Cross-platform gene expression profiling of breast cancer: Exploring the relationship between breast cancer grades and gene expression pattern

Gene expression profiling is a powerful tool that has been extensively used to investigate the underlying biology and etiology of diseases, including cancer. Microarray gene expression analysis enables simultaneous measurement of thousands of mRNA levels. Sophisticated computational approaches have evolved in parallel with the rapid progress in bioassay technologies, enabling more effective analysis of the large and complex datasets that these technologies produce. In this study, we utilized systems biology approaches to examine gene expression profiles across different grades of breast cancer progression. We conducted a meta-analysis of publicly available microarray data to elucidate the molecular mechanisms underlying breast cancer grade classification. Our results suggest that while grade index is commonly used for evaluating cancer progression status in the clinic, the complexity of molecular mechanisms, histological characteristics, and other factors related to patient outcomes raises doubts about the utility of breast cancer grades as a foundation for formulating treatment protocols. Our study underscores the importance of advancing personalized strategies for breast cancer classification and management. More research is crucial to refine diagnostic tools and treatment modalities, aiming for greater precision and tailored care in patient outcomes.

Artificial intelligence in breast imaging: Current situation and clinical challenges

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

DDX1 is a prognostic biomarker and correlates with immune infiltrations in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading lethal malignant tumors worldwide. DEAD-box (DDX) family helicases are implicated in numerous human cancers. However, the role of DDX1 in HCC has not yet been fully elucidated. We downloaded gene expression data and clinical information data of HCC from The Cancer Genome Atlas and International Cancer Genome Consortium (ICGC) database and conducted subsequent analyses using the R package and online portal. The results revealed that HCC tissues had higher DDX1 expression compared with either paired or unpaired normal tissues. The increased DDX1 expression was closely related to the advanced pathological grade and histologic grade of HCC. Further analysis suggested that patients with high DDX1 expression contributed to poor prognosis The Cox regression analysis revealed that the expression level of DDX1 was an independent prognostic factor for HCC. In addition, an ICGC cohort was used for external validation. The cBio-Portal, MethSurv, and UALCAN database were used for evaluating the genomic mechanism. Moreover, the Tumor Immune Estimation Resource dataset and QUANTISEQ algorithm revealed that DDX1 expression positively correlates with immune infiltrating cells. We also identified the DDX1-related differentially expressed genes (DEGs) and explored their biological functions by GO, KEGG, and GSEA analyses, which indicated that DDX1 may regulate the progression of HCC. In general, increased DDX1 expression predicts a poor prognosis and drives the progression of HCC.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Mebendazole prevents distant organ metastases in part by decreasing ITGβ4 expression and cancer stemness

Breast cancer is the most diagnosed cancer among women. Approximately 15-20% of all breast cancers are highly invasive triple-negative breast cancer (TNBC) and lack estrogen, progesterone, and ERBB2 receptors. TNBC is challenging to treat due to its aggressive nature with far fewer targeted therapies than other breast cancer subtypes. Current treatments for patients with TNBC consist of cytotoxic chemotherapies, surgery, radiation, and in some instances PARP inhibitors and immunotherapy. To advance current therapeutics, we repurposed mebendazole (MBZ), an orally available FDA-approved anthelmintic that has shown preclinical efficacy for cancers. MBZ has low toxicity in humans and efficacy in multiple cancer models including breast cancer, glioblastoma multiforme, medulloblastoma, colon cancer, pancreatic and thyroid cancer. MBZ was well-tolerated in a phase I clinical trial of adults recently diagnosed with glioma. We determined that the half-maximal inhibitory concentration (IC₅₀) of MBZ in four breast cancer cell lines is well within the range reported for other types of cancer. MBZ reduced TNBC cell proliferation, induced apoptosis, and caused G2/M cell cycle arrest. MBZ reduced the size of primary tumors and prevented lung and liver metastases. In addition, we uncovered a novel mechanism of action for MBZ. We found that MBZ reduces integrin β4 (ITGβ4) expression and cancer stem cell properties. ITGβ4 has previously been implicated in promoting "cancer stemness," which may contribute to the efficacy of MBZ. Collectively, our results contribute to a growing body of evidence suggesting that MBZ should be considered as a therapeutic to slow tumor progression and prevent metastasis.

Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model

Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper's ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper's ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper's ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

Breast Cancer: A Global Concern, Diagnostic and Therapeutic Perspectives, Mechanistic Targets in Drug Development

Cancer is a complex multifactorial process, unchecked and abrupt division, and cell growth—conventional chemotherapy, along with radiotherapy, is used to treat breast cancer. Due to reduce efficacy and less survival rate, there is a particular need for the discovery of new active anticancer agents. Natural resources such as terrestrial/marine plants or organisms are a promising source for the generation of new therapeutics with improving efficacy. The screening of natural plant extracts and fractions, isolations of phytochemicals, and mechanistic study of those potential compounds play a remarkable role in the development of new therapeutic drugs with increased efficacy. Cancer is a multistage disease with complex signaling cascades. The initial study of screening whole extracts or fractions and later the isolation of secondary compounds and their mechanism of action study gives a clue of potential therapeutic agents for future drug development. The phytochemicals present in extracts/fractions produce remarkable effects due to synergistically targeting multiple signals. In this review, the molecular targets of extracts/ fractions and isolated compounds highlighted. The therapeutic agent's mechanistic targets in drug development focused involves; i) Induction of Apoptosis, ii) modulating cell cycle arrest, iii) Inhibition or suppression of invasion and metastasis and iv) various other pro-survival signaling pathways. The phytochemicals and their modified analogs identified as future potential candidates for anticancer chemotherapy.

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.

Methods

We downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).

Results

There were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.

Conclusions

These results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

A common goal to CARE: Cancer Advocates, Researchers, and Clinicians Explore current treatments and clinical trials for breast cancer brain metastases

Breast cancer is the most commonly diagnosed cancer in women worldwide. Approximately one-tenth of all patients with advanced breast cancer develop brain metastases resulting in an overall survival rate of fewer than 2 years. The challenges lie in developing new approaches to treat, monitor, and prevent breast cancer brain metastasis (BCBM). This review will provide an overview of BCBM from the integrated perspective of clinicians, researchers, and patient advocates. We will summarize the current management of BCBM, including diagnosis, treatment, and monitoring. We will highlight ongoing translational research for BCBM, including clinical trials and improved detection methods that can become the mainstay for BCBM treatment if they demonstrate efficacy. We will discuss preclinical BCBM research that focuses on the intrinsic properties of breast cancer cells and the influence of the brain microenvironment. Finally, we will spotlight emerging studies and future research needs to improve survival outcomes and preserve the quality of life for patients with BCBM.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Zebrafish Avatar to Develop Precision Breast Cancer Therapies

BACKGROUND

Zebrafish (Danio rerio) is a vertebrate that has become a popular alternative model for the cellular and molecular study of human tumors and for drug testing and validating approaches. Notably, zebrafish embryos, thanks to their accessibility, allow rapid collection of in vivo results prodromal to validation in the murine models in respect to the 3R principles. The generation of tumor xenograft in zebrafish embryos and larvae, or zebrafish avatar, represents a unique opportunity to study tumor growth, angiogenesis, cell invasion and metastatic dissemination, interaction between tumor and host in vivo avoiding immunogenic rejection, representing a promising platform for the translational research and personalized therapies.

OBJECTIVE

In this mini-review we report recent advances in breast cancer research and drug testing that took advantage of the zebrafish xenograft model using both breast cancer cell lines and patient's biopsy.

CONCLUSION

Patient derived xenograft, together with the gene editing, the omics biotechnology, the in vivo time lapse imaging and the high-throughput screening that are already set up and largely used in zebrafish, could represent a step forward towards precision and personalized medicine in the breast cancer research field.

Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

PROTACs: A Hope for Breast Cancer Patients?

BACKGROUND

Breast Cancer (BC) is the most widely recognized disease in women. A massive number of women are diagnosed with breast cancer and many lost their lives every year. Cancer is the subsequent driving reason for dying, giving rise to it one of the current medication's most prominent difficulties.

OBJECTIVES

The main objective of the study is to examine and explore novel therapy (PROTAC) and its effectiveness against breast cancer.

METHODS

The literature search was done across Medline, Cochrane, ScienceDirect, Wiley Online, Google Scholar, PubMed, Bentham Sciences from 2001 to 2020. The articles were collected; screened, segregated, and selected papers were included for writing the review article.

RESULTS AND CONCLUSION

A novel innovation emerged around two decades ago that has great potential to not only overcome the limitations but also can provide future direction for the treatment of many diseases which has presently not many therapeutic options available and regarded as incurable with traditional techniques; that innovation is called PROTAC (Proteolysis Targeting Chimera) and able to efficaciously ubiquitinate and debase cancer encouraging proteins by noncovalent interaction. PROTACs are constituted of two active regions isolated by a linker and equipped for eliminating explicit undesirable protein. It is empowering greater sensitivity to "drug-resistant targets" as well as a more prominent opportunity to influence non-enzymatic function. PROTACs have been demonstrated to show better target selectivity contrasted with traditional small-molecule inhibitors. So far, the most investigation into PROTACs possesses particularly concentrated on applications to cancer treatment including breast cancer, the treatment of different ailments may profit from this blossoming innovation.

The prognostic value of Piezo1 in breast cancer patients with various clinicopathological features

The effects of piezo-type mechanosensitive ion channel component 1 (Piezo1) in sensing extracellular mechanical stress have been well investigated. Recently, Piezo1's vital role in cancerogenesis has been demonstrated by many studies. Nonetheless, the prognostic value of Piezo1 in cancer still remains unexplored and unclear. This article aims to investigate the prognostic value of Piezo1 in breast cancer. Human Protein Atlas and the Cancer Genome Atlas (TCGA) databases were used to examine Piezo1 expression in different human tissues and human cell lines. The discrepancies of Piezo1 mRNA expression in breast cancer patients with different clinicopathological features were assessed using bc-GenExMiner. The prognostic value of Piezo1 in breast cancer patients was evaluated using Kaplan-Meier plotter. Piezo1 mRNA was extensively expressed in human tissues and cell lines, particularly in breast and cancerous breast cancer cell line MCF7. High Piezo1 expression was found correlated with poor prognosis of breast cancer. Survival analysis further confirmed unfavorable prognosis of high Piezo1 expression in breast cancer patients with lymph node positive, estrogen receptor positive, Grade 2 (Scarff-Bloom-Richardson grading system), luminal A, and human epidermal growth factor receptor 2 overexpression, respectively. This study suggested that Piezo1 can serve as a prognostic indicator of breast cancer.

Combining bioinformatics analysis and experiments to explore CARNS1 as a prognostic biomarker for breast cancer

BACKGROUND

Breast cancer is a heterogeneous malignant disease, which has variation in clinical behaviors. High-throughput technologies have added important genetic alternative and biological change information for breast cancer. CARNS1 is an important ATPases. It can catalyze the synthesis of carnosine, which has antiproliferative activity in cancer. Here, we hypothesize that CARNS1 plays an essential role in the development of breast cancer.

METHODS

The expressions of CARNS1 in breast cancer were data-mined and analyzed from TCGA (the Cancer Genome Atlas) and GEO (the Gene Expression Omnibus) databases. The correlation of CARNS1 expression with clinical characteristics and the diagnostic capability of CARNS1 were assessed. Experimental studies were conducted in two cohorts (n = 60) of breast cancer patients by qRT-PCR and immunohistochemical analysis.

RESULTS

CARNS1 was significantly downregulated in breast cancer. The expression was correlated with tumor molecular and histological types, T and M stages, and vital status. Kaplan-Meier survival analysis showed that the downregulation of CARNS1 was significantly related to poor overall survival and relapse-free survival. Moreover, these scenarios have been extended to ER, PR, and HER2 positive patients. Univariate and multivariate analysis showed that CARNS1 can be considered as an independent prognostic predictor for patients with breast cancer. Experimental data supported that the protein and mRNA levels of CARNS1 in breast cancer are indeed significantly downregulated.

CONCLUSION

Our findings have demonstrated that CARNS1 acts as a tumor suppressor gene and may be an independent prognostic indicator for breast cancer patients.

Shikonin promotes ubiquitination and degradation of cIAP1/2-mediated apoptosis and necrosis in triple negative breast cancer cells

Background

Shikonin (SKO) is a natural naphthoquinone derived from Chinese herbal medicine Arnebiae Radix with high development potentials due to its anti-inflammatory and anti-tumor activities. Overwhelming evidences have indicated that SKO can induce both necrosis and apoptosis in cancer cells, while the mechanisms for triple negative breast cancer cells is still need to be disclosed.

Methods

In this study, kinds of molecular biological technologies, including flow-cytometry, Western blot, immunoprecipitation, enzyme-linked immunosorbent assay (ELISA) as well as real-time quantitative PCR (RT-qPCR), were applied for investigation on the underlying mechanisms of SKO induced necrosis and apoptosis for MDA-MB-231 cells. Inhibitors were also used for validation ofthe key signaling pathways involved in SKO triggered necrosis and apoptosis.

Results

We found that SKO significantly triggered necrosis and apoptosis of MDA-MB-231 cells in both a concentration- and time-dependent manner. Mechanism studies demonstrated that SKO significantly promoted the autoubiquitination levels and facilitated the proteasome dependent degradation of cellular inhibitor of apoptosis protein 1 (cIAP1) and cIAP2 in MDA-MB-231 cells. Autoubiquitination and degradation of cIAP1 and cIAP2 induced by SKO further led to significant decreased ubiquitination and inactivation of RIP1, which played an important role in inhibition of pro-survival and accelerating of necrosis of MDA-MB-231 cells. Treatment with proteasome inhibitor lactacystin significantly rescued the cell viability induced by treatment of SKO.

Conclusions

Our results demonstrate that SKO promotes the autoubiquitination and degradation of cIAP1 and cIAP2, which further induces the decrease of the ubiquitination of RIP1 to inhibit the activation of pro-survival signaling pathways and accelerate the necrosis of MDA-MB-231 cells. The disclosed mechanisms of SKO induced necrosis and apoptosis in our study is firstly reported, and it is believed that SKO could be considered as a potential candidate and further developed for the treatment of triple negative breast cancer.

Physical Activity as an Imperative Support in Breast Cancer Management

Breast cancer (BC) is the most common malignancy and the second cause of cancer-related death among women. It is estimated that 9 in 10 cases of BC are due to non-genetic factors, and approximately 25% to 30% of total breast cancer cases should be preventable only by lifestyle interventions. In this context, physical activity represents an excellent and accessible approach not only for the prevention, but also for being a potential support in the management of breast cancer. The present review will collect the current knowledge of physical activity in the background of breast cancer, exploring its systemic and molecular effects, considering important variables in the training of these women and the evidence regarding the benefits of exercise on breast cancer survival and prognosis. We will also summarize the various effects of physical activity as a co-adjuvant therapy in women receiving different treatments to deal with its adverse effects. Finally, we will reveal the impact of physical activity in the enhancement of quality of life of these patients, to conclude the central role that exercise must occupy in breast cancer management, in an adequate context of a healthy lifestyle.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity

MOTIVATION

Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account.

RESULTS

In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/Zhangxf-ccnu/NETI2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Breast cancer prognosis signature: linking risk stratification to disease subtypes

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

Exploring the classification of cancer cell lines from multiple omic views

BACKGROUND

Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial.

METHODS

In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines.

RESULTS

Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines.

CONCLUSIONS

The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.

Mining the prognostic significance of the GINS2 gene in human breast cancer using bioinformatics analysis

A number of studies have demonstrated the crucial functions of GINS2 within the GINS complex in various types of cancer. However, the molecular mechanisms and prognostic value of GINS2 in breast cancer remain unknown. The present study used; BC-GenExMiner, COSMIC, UCSC Xena, The Human Protein Atlas, GEPIA, cBioPortal, GeneMANIA, TIMER and Oncomine, in order to investigate gene expression, co-expression, clinical parameters and mutations in GINS2 in patients with breast cancer. Furthermore, the present study assessed the prognostic value of GINS2 in patients with breast cancer via the Kaplan-Meier plotter database. The results of the present study demonstrated that the mRNA levels of GINS2 were significantly higher in breast cancer tissue compared with normal tissue. In addition, high mRNA expression levels of GINS2 were associated with high Scarff-Bloom-Richardson status grades, a basal-like status and age (≤51 years); however, it was not associated with lymph node metastasis. The survival analysis revealed that increased GINS2 mRNA levels were associated with a worse prognosis for relapse-free survival in all patients with breast cancer, particularly in those with estrogen receptor-positive and progesterone receptor-positive subtypes. In addition, a positive association between the GINS2, CENPM and MCM4 genes was confirmed. The results of the present study suggest that GINS2 could be used as a potential prognostic biomarker for breast cancer. Nevertheless, further studies are necessary to confirm the effects of GINS2 on the pathogenesis and development of patients with breast cancer.

Variability in hospital treatment costs: a time-driven activity-based costing approach for early-stage invasive breast cancer patients

OBJECTIVES

Using a standardised diagnostic and generic treatment path for breast cancer, and the molecular subtype perspective, we aim to measure the impact of several patient and disease characteristics on the overall treatment cost for patients. Additionally, we aim to generate insights into the drivers of cost variability within one medical domain.

DESIGN, SETTING AND PARTICIPANTS

We conducted a retrospective study at a breast clinic in Belgium. We used 14 anonymous patient files for conducting our analysis.

RESULTS

Significant cost variations within each molecular subtype and across molecular subtypes were found. For the luminal A classification, the cost differential amounts to roughly 166%, with the greatest treatment cost amounting to US$29 780 relative to US$11 208 for a patient requiring fewer medical activities. The major driver for these cost variations relates to disease characteristics. For the luminal B classification, a cost difference of roughly 242% exists due to both disease-related and patient-related factors. The average treatment cost for triple negative patients amounted to US$26 923, this is considered to be a more aggressive type of cancer. The overall cost for HER2-enriched is driven by the inclusion of Herceptin, thus this subtype is impacted by disease characteristics. Cost variability across molecular classifications is impacted by the severity of the disease, thus disease-related factors are the major drivers of cost.

CONCLUSIONS

Given the cost challenge in healthcare, the need for greater cost transparency has become imperative. Through our analysis, we generate initial insights into the drivers of cost variability for breast cancer. We found evidence that disease characteristics such as severity and more aggressive cancer forms such as HER2-enriched and triple negative have a significant impact on treatment cost across the different subtypes. Similarly, patient factors such as age and presence of gene mutation contribute to differences in treatment cost variability within molecular subtypes.

Analysis of H3K4me3 and H3K27me3 bivalent promotors in HER2+ breast cancer cell lines reveals variations depending on estrogen receptor status and significantly correlates with gene expression

BACKGROUND

The role of histone modifications is poorly characterized in breast cancer, especially within the major subtypes. While epigenetic modifications may enhance the adaptability of a cell to both therapy and the surrounding environment, the mechanisms by which this is accomplished remains unclear. In this study we focus on the HER2 subtype and investigate two histone trimethylations that occur on the histone 3; the trimethylation located at lysine 4 (H3K4me3) found in active promoters and the trimethylation located at lysine 27 (H3K27me3) that correlates with gene repression. A bivalency state is the result of the co-presence of these two marks at the same promoter.

METHODS

In this study we investigated the relationship between these histone modifications in promoter regions and their proximal gene expression in HER2+ breast cancer cell lines. In addition, we assessed these patterns with respect to the presence or absence of the estrogen receptor (ER). To do this, we utilized ChIP-seq and matching RNA-seq from publicly available data for the AU565, SKBR3, MB361 and UACC812 cell lines. In order to visualize these relationships, we used KEGG pathway enrichment analysis, and Kaplan-Meyer plots.

RESULTS

We found that the correlation between the three types of promoter trimethylation statuses (H3K4me3, H3K27me3 or both) and the expression of the proximal genes was highly significant overall, while roughly a third of all genes are regulated by this phenomenon. We also show that there are several pathways related to cancer progression and invasion that are associated with the bivalent status of the gene promoters, and that there are specific differences between ER+ and ER- HER2+ breast cancer cell lines. These specific differences that are differentially trimethylated are also shown to be differentially expressed in patient samples. One of these genes, HIF1AN, significantly correlates with patient outcome.

CONCLUSIONS

This study highlights the importance of looking at epigenetic markings at a subtype specific level by characterizing the relationship between the bivalent promoters and gene expression. This provides a deeper insight into a mechanism that could lead to future targets for treatment and prognosis, along with oncogenesis and response to therapy of HER2+ breast cancer patients.

ExoBCD: a comprehensive database for exosomal biomarker discovery in breast cancer

Effective and safe implementation of precision oncology for breast cancer is a vital strategy to improve patient outcomes, which relies on the application of reliable biomarkers. As 'liquid biopsy' and novel resource for biomarkers, exosomes provide a promising avenue for the diagnosis and treatment of breast cancer. Although several exosome-related databases have been developed, there is still lacking of an integrated database for exosome-based biomarker discovery. To this end, a comprehensive database ExoBCD (https://exobcd.liumwei.org) was constructed with the combination of robust analysis of four high-throughput datasets, transcriptome validation of 1191 TCGA cases and manual mining of 950 studies. In ExoBCD, approximately 20 900 annotation entries were integrated from 25 external sources and 306 exosomal molecules (49 potential biomarkers and 257 biologically interesting molecules). The latter could be divided into 3 molecule types, including 121 mRNAs, 172 miRNAs and 13 lncRNAs. Thus, the well-linked information about molecular characters, experimental biology, gene expression patterns, overall survival, functional evidence, tumour stage and clinical use were fully integrated. As a data-driven and literature-based paradigm proposed of biomarker discovery, this study also demonstrated the corroborative analysis and identified 36 promising molecules, as well as the most promising prognostic biomarkers, IGF1R and FRS2. Taken together, ExoBCD is the first well-corroborated knowledge base for exosomal studies of breast cancer. It not only lays a foundation for subsequent studies but also strengthens the studies of probing molecular mechanisms, discovering biomarkers and developing meaningful clinical use.

O-6-methylguanine DNA methyltransferase is a favorable biomarker with proliferation suppressive potential in Breast Cancer

Background: The O⁶-methylguanine-DNA methyltransferase (MGMT) is a highly effective enzyme capable of repairing DNA damage to maintain genomic stability. Until recently, reports on the expression and potential role of MGMT in breast cancer remain controversial. This study is intended to elucidate the prognostic significance and potential function of MGMT in breast cancer. Materials and methods: The immunohistochemistry assay and a series of public databases were utilized to determine the relevance between MGMT expression and clinicopathological characteristics, as well as survival outcomes in patients with breast cancer. The western blotting, qRT-PCR, proliferation, colony formation and transwell assays were used to investigate the potential function of MGMT in breast cancer cells. Results: The immunohistochemistry analysis and public cancer databases exploration demonstrated that MGMT expression was significantly related to estrogen receptor (ER) positivity in breast cancer. Positive expression of MGMT predicts a longer distant-free survival (DFS) and overall survival (OS) in patients with breast cancer, especially in ER-positive tumor. The mRNA level of MGMT was significantly associated with those of ESR1, GATA3 and FOXA1 in ER-positive breast tumor. Down-regulation of MGMT expression enhanced the proliferative and invasive capacities of breast cancer cells through PTEN/AKT pathway. Conclusions: MGMT is a favorable biomarker with proliferation suppressive potential in ER-positive breast cancer. Future study on targeted modulation of MGMT in the treatment of breast cancer is warranted.

MRPS30-DT Knockdown Inhibits Breast Cancer Progression by Targeting Jab1/Cops5

Longnoncoding RNAs (lncRNAs) are significantly correlated with cancer pathogenesis, development, and metastasis. Microarray analysis showed that lncRNA MRPS30-DT is overexpressed in breast carcinoma; however, the function of MRPS30-DT in breast cancer tumorigenesis remains unclear. In situ hybridization and immunohistochemical analysis were used to evaluate the expression levels of MRPS30-DT and Jab1 in clinical samples of breast carcinoma and their relation to survival outcome. qRT-PCR was used to measure MRPS30-DT and Jab1 mRNA expressions. Protein levels were detected using Western blot. Cell proliferation and invasion ability were evaluated via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), colony formation, and transwell assays. MRPS30-DT was knocked down in breast cancer cells to investigate its potential functional roles in cell growth and metastasis in vitro and in vivo. We found that MRPS30-DT was upregulated in breast cancer specimens and was accompanied by high Jab1 expression compared with that of paired para-carcinoma tissues. Knocking down MRPS30-DT significantly inhibited cancer cell proliferation and invasion and induced apoptosis in breast cancer cells. Similarly, knocking down MRPS30-DT in MDA-MB-231 cells significantly suppressed tumor growth. Furthermore, knocking down MRPS30-DT markedly reduced Jab1 expression in breast cancer cells and murine carcinoma. Statistical analyses suggested that high MRPS30-DT or Jab1 levels in breast cancer patients were positively correlated with poor prognoses. These data indicate the possible mechanisms of MRPS30-DT and Jab1 in breast cancer; thus, MRPS30-DT and Jab1 may be novel prognostic biomarkers and potential therapeutic targets for breast cancer treatment.

The Genomic Landscape of Centromeres in Cancers

Centromere genomics remain poorly characterized in cancer, due to technologic limitations in sequencing and bioinformatics methodologies that make high-resolution delineation of centromeric loci difficult to achieve. We here leverage a highly specific and targeted rapid PCR methodology to quantitatively assess the genomic landscape of centromeres in cancer cell lines and primary tissue. PCR-based profiling of centromeres revealed widespread heterogeneity of centromeric and pericentromeric sequences in cancer cells and tissues as compared to healthy counterparts. Quantitative reductions in centromeric core and pericentromeric markers (α-satellite units and HERV-K copies) were observed in neoplastic samples as compared to healthy counterparts. Subsequent phylogenetic analysis of a pericentromeric endogenous retrovirus amplified by PCR revealed possible gene conversion events occurring at numerous pericentromeric loci in the setting of malignancy. Our findings collectively represent a more comprehensive evaluation of centromere genetics in the setting of malignancy, providing valuable insight into the evolution and reshuffling of centromeric sequences in cancer development and progression.

A high-risk luminal A dominant breast cancer subtype with increased mobility

Purpose

Breast cancer is a heterogeneous disease, and although advances in molecular subtyping have been achieved in recent years, most subtyping strategies target individual genes independent of one another and primarily concentrate on proliferative markers. The contributions of biological processes and immune patterns have been neglected in breast cancer subtype stratification.

Methods

We performed a gene set variation analysis to simplify the information on biological processes using hallmark terms and to decompose immune cell data using the immune cell gene terms on 985 breast invasive ductal/lobular carcinoma RNAseq samples in the TCGA database.

Results

The samples were gathered into three clusters following implementation of the t-SNE and DBSCAN algorithms and were categorized as ‘hallmark-tsne’ subtypes. Here, we identified a high-risk luminal A dominant breast cancer subtype (C3) that displayed increased motility, cancer stem cell-like features, a higher expression of hormone/luminal-related genes, a lower expression of proliferation-related genes and immune dysfunction. With regard to immune dysfunction, we observed that the motility-increased C3 subtype exhibited high granulocyte colony stimulating factor (G-CSF) expression accompanied by neutrophil aggregation. Cancer cells that produce high levels of G-CSF can stimulate neutrophils to form neutrophil extracellular traps, which promote cancer cell migration. This finding sheds light on one potential explanation for why the C3 subtype correlates with poor prognosis.

Conclusions

The hallmark-tsne subtypes confirmed again that even the luminal A subtype is heterogeneous and can be further subdivided. The biological processes and immune heterogeneity of breast cancer must be understood to facilitate the improvement of clinical treatments.

Electronic supplementary material

The online version of this article (10.1007/s10549-019-05135-w) contains supplementary material, which is available to authorized users.

[Differences in expression profiles of circular RNA between luminal breast cancer cells and normal breast cells]

OBJECTIVE

To investigate the differences in the expression profiles of circular RNA (circRNA) between luminal breast cancer cells and normal breast cells.

METHODS

Total RNA extracted from luminal breast cancer cells MCF7 and normal breast cells MCF10A was digested with Rnase R to remove linear RNAs and enrich circRNAs. The enriched circRNAs were amplified and transcribed into fluorescent cRNAs using a random priming method, and were hybridized onto the circRNA hybridization array. The circRNA expression profiles of MCF7 and MCF10A cells were analyzed using Agilent Feature Extraction software. Quantile normalization and subsequent data processing were performed, and volcano plot filtering and hierarchical clustering were utilized to analyze the circRNA expression patterns. The expressions of 3 circRNAs with significant log fold changes were validated using qPCR.

RESULTS

The hybridization array data revealed significant differences in the circRNA expression profiles between MCF7 and MCF10A cells. Compared with those of MCF10A cells, the 12910 circRNAs expressed in MCF7 cells showed 5964 up-regulated, 81 consistently regulated, and 6865 down-regulated circRNAs; 343 circRNAs showed a log fold change by more than 2 folds, among which 213 circRNAs were up-regulated and 130 were down-regulated. Nine circRNAs showed differential expressions by more than 2 folds, including 8 up-regulated ones, namely hsa_circRNA_061260 (6.02 folds), hsa_circRNA_103933 (5.96 folds), hsa_circRNA_005239 (5.84 folds), hsa_circRNA_100689 (5.69 folds), hsa_circRNA_004087 (5.60 folds), hsa_circRNA_104420 (5.25 folds), hsa_circRNA_104421 (5.13 folds) and hsa_circRNA_101222 (5.03 folds); only one circRNA was down-regulated, namely hsa_circRNA_104864 (5.09 folds). The expressions of hsa_circRNA_100689, hsa_circRNA_005239 and hsa_circRNA_104864 were further validated by qPCR, which yielded consistent results with the microarray data.

CONCLUSIONS

The circRNA expression profiles differ significantly between luminal breast cancer cells and normal breast cells. These differentially expressed circRNAs may serve as potential novel targets for the diagnosis of luminal breast cancer.

An isomiR expression panel based novel breast cancer classification approach using improved mutual information

Background

Gene expression-based profiling has been used to identify biomarkers for different breast cancer subtypes. However, this technique has many limitations. IsomiRs are isoforms of miRNAs that have critical roles in many biological processes and have been successfully used to distinguish various cancer types. Biomarker isomiRs for identifying different breast cancer subtypes has not been investigated. For the first time, we aim to show that isomiRs are better performing biomarkers and use them to explain molecular differences between breast cancer subtypes.

Results

In this study, a novel method is proposed to identify specific isomiRs that faithfully classify breast cancer subtypes. First, as a null hypothesis method we removed the lowly expressed isomiRs from small sequencing data generated from diverse breast cancers types. Second, we developed an improved mutual information-based feature selection method to calculate the weight of each isomiR expression. The weight of isomiR measures the importance of a given isomiR in classifying breast cancer subtypes. The improved mutual information enables to apply the dataset in which the feature is continuous data and label is discrete data; whereby, the traditional mutual information cannot be applied in this dataset. Finally, the support vector machine (SVM) classifier is applied to find isomiR biomarkers for subtyping.

Conclusions

Here we demonstrate that isomiRs can be used as biomarkers in the identification of different breast cancer subtypes, and in addition, they may provide new insights into the diverse molecular mechanisms of breast cancers. We have also shown that the classification of different subtypes of breast cancer based on isomiRs expression is more effective than using published gene expression profiling. The proposed method provides a better performance outcome than Fisher method and Hellinger method for discovering biomarkers to distinguish different breast cancer subtypes. This novel technique could be directly applied to identify biomarkers in other diseases.

Obesity and breast cancer - Role of estrogens and the molecular underpinnings of aromatase regulation in breast adipose tissue

One in eight women will develop breast cancer over their lifetime making it the most common female cancer. The cause of breast cancer is multifactorial and includes hormonal, genetic and environmental cues. Obesity is now an accepted risk factor for breast cancer in postmenopausal women, particularly for the hormone-dependent subtype of breast cancer. Obesity, which is characterized by an excess accumulation of body fat, is at the origin of chronic inflammation of white adipose tissue and is associated with dramatic changes in the biology of adipocytes leading to their dysfunction. Inflammatory factors found in the breast of obese women considerably impact estrogen signaling, mainly by driving changes in aromatase expression the enzyme responsible for estrogen production, and therefore promote tumor formation and progression. There is thus a strong link between adipose inflammation and estrogen biosynthesis and their signaling pathways converge in obese patients. This review describes how obesity-related factors can affect the risk of hormone-dependent breast cancer, highlighting the different molecular mechanisms and metabolic pathways involved in aromatase regulation, estrogen production and breast malignancy in the context of obesity.

An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

BACKGROUND

Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).

RESULTS

We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs.

CONCLUSIONS

A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.

Prognostic values of distinct CBX family members in breast cancer

Chromobox (CBX) family proteins are canonical components in polycomb repressive complexes 1 (PRC1), with epigenetic regulatory function and transcriptionally repressing target genes via chromatin modification. A plethora of studies have highlighted the function specifications among CBX family members in various cancer, including lung cancer, colon cancer and breast cancer. Nevertheless, the functions and prognostic roles of distinct CBX family members in breast cancer (BC) remain elusive. In this study, we reported the prognostic values of CBX family members in patients with BC through analysis of a series of databases, including CCLE, ONCOMINE, Xena Public Data Hubs, and Kaplan-Meier plotter. It was found that the mRNA expression of CBX family members were noticeably higher in BC than normal counterparts. CBX2 was highly expressed in Basal-like and HER-2 subtypes, while CBX4 and CBX7 expressions were enriched in Luminal A and Luminal B subtypes of BC. Survival analysis revealed that CBX1, CBX2 and CBX3 mRNA high expression was correlated to worsen relapse-free survival (RFS) for all BC patients, while CBX4, CBX5, CBX6 and CBX7 high expression was correlated to better RFS in this setting. Noteworthily, CBX1 and CBX2 were associated with chemoresistance whereas CBX7 was associated with tamoxifen sensitivity, as well as chemosensitivity in breast tumors. Therefore, we propose that CBX1, CBX2 and CBX7 are potential targets for BC treatment. The results might be beneficial for better understanding the complexity and heterogeneity in the molecular underpinning of BC, and to develop tools to more accurately predict the prognosis of patients with BC.

Gene regulatory pattern analysis reveals essential role of core transcriptional factors' activation in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. Genome-scale molecular characteristics and regulatory mechanisms that distinguish TNBC from other subtypes remain incompletely characterized.

Results

By combining gene expression analysis and PANDA network, we defined three different TF regulatory patterns. A core TNBC-Specific TF Activation Driven Pattern (TNBCac) was specifically identified in TNBC by computational analysis. The essentialness of core TFs (ZEB1, MZF1, SOX10) in TNBC was highlighted and validated by cell proliferation analysis. Furthermore, 13 out of 35 co-targeted genes were also validated to be targeted by ZEB1, MZF1 and SOX10 in TNBC cell lines by real-time quantitative PCR. In three breast cancer cohorts, non-TNBC patients could be stratified into two subgroups by the 35 co-targeted genes along with 5 TFs, and the subgroup that more resembled TNBC had a worse prognosis.

Methods

We constructed gene regulatory networks in breast cancer by Passing Attributes between Networks for Data Assimilation (PANDA). Co-regulatory modules were specifically identified in TNBC by computational analysis, while the essentialness of core translational factors (TF) in TNBC was highlighted and validated by in vitro experiments. Prognostic effects of different factors were measured by Log-rank test and displayed by Kaplan-Meier plots.

Conclusions

We identified a core co-regulatory module specifically existing in TNBC, which enabled subtype re-classification and provided a biologically feasible view of breast cancer.

Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma

Gene expression data were analyzed in order to identify critical genes in breast invasive carcinoma (BRCA). Data from 1,073 BRCA samples and 99 normal samples were analyzed, which were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method and a functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. Relevant microRNAs (miRNAs), transcription factors (TFs) and associated small molecule drugs were revealed by Fisher's exact test. Furthermore, protein-protein interaction (PPI) information was downloaded from the Human Protein Reference Database. Interactions with a Pearson's correlation coefficient >0.5 were identified and PPI networks were subsequently constructed. A survival analysis was also conducted according to the Kaplan-Meier method. Initially, the 1,073 BRCA samples were clustered into seven groups, and 5,394 DEGs that were identified in ≥4 groups were selected. These DEGs were involved in the cell cycle, ubiquitin-mediated proteolysis, oxidative phosphorylation and human immunodeficiency virus infection. In addition, TFs, including Sp1 transcription factor, DAN domain BMP antagonist family member 5, MYCN proto-oncogene, bHLH transcription factor and cAMP responsive element binding protein (CREB)1, were identified in the BRCA groups. Seven PPI networks were subsequently constructed and the top 10 hub genes were acquired, including RB transcriptional corepressor 1, inhibitor of nuclear factor (NF)-κB kinase subunit γ, NF-κB subunit 2, transporter 1, ATP binding cassette subfamily B member, CREB binding protein and proteasome subunit α3. A significant difference in survival was observed between the two combined groups (groups-2, −4 and −5 vs. groups-1, −3, −6 and −7). In conclusion, numerous critical genes were detected in BRCA, and relevant miRNAs, TFs and small molecule drugs were identified. These findings may advance understanding regarding the pathogenesis of BRCA.

Endophilin A2 promotes HER2 internalization and sensitivity to trastuzumab-based therapy in HER2-positive breast cancers

Background

Human epidermal growth factor receptor-2 (HER2) is amplified and a clinical target in a subset of human breast cancers with high rates of metastasis. Targeted therapies involving the antibody trastuzumab and trastuzumab-emtansine (T-DM1) have greatly improved outcomes for HER2-positive (HER2+) breast cancer patients. However, resistance to these targeted therapies can develop and limit their efficacy. Here, we test the involvement of the endocytic adaptor protein endophilin A2 (Endo II) in HER2+ breast cancer models, and their responses to treatments with trastuzumab and T-DM1.

Methods

Endo II expression in human breast tumors and lymph node metastases were analyzed by immunohistochemistry. Stable silencing of Endo II was achieved in HER2+ cancer cell lines (SK-BR-3 and HCC1954) to test Endo II effects on HER2 levels, localization and signaling, cell motility and tumor metastasis. The effects of Endo II silencing on the responses of HER2+ cancer cells to trastuzumab or T-DM1 treatments were tested using real-time cell motility and cytotoxicity assays.

Results

High Endo II protein expression was detected in HER2-positive tumors, and was linked to worse overall survival in node-positive HER2+ breast cancers at the mRNA level. Stable silencing of Endo II in HER2+ cell lines led to elevated levels of HER2 on the cell surface, impaired epidermal growth factor-induced HER2 internalization, and reduced signaling to downstream effector kinases Akt and Erk. Endo II silencing also led to decreased migration and invasion of HER2+ cancer cells in vitro, and impaired lung seeding following tail vein injection in mice. In addition, Endo II silencing also impaired HER2 internalization in response to Trastuzumab, and led to reduced cytotoxicity response in HER2+ cancer cells treated with T-DM1.

Conclusions

Our study provides novel evidence of Endo II function in HER2+ cancer cell motility and trafficking of HER2 that relates to effective treatments with trastuzumab or T-DM1. Thus, differential expression of Endo II may relate to sensitivity or resistance to trastuzumab-based therapies for HER2+ cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0900-z) contains supplementary material, which is available to authorized users.

Low expression of PinX1 is associated with malignant behavior in basal-like breast cancer

Human Pinx1 protein, associated with shelterin proteins, is widely revealed as a haploinsufficient tumor suppressor. Growing evidence has manifested the deregulation of PinX1 in distinct cancers. Nonetheless, the loss status of PinX1 and its diagnostic, prognostic and clinicopathological significance in Basal-like breast cancer are still unclear. In the present study, the PinX1 expression levels of breast cancer tissues were investigated by qRT-PCR and immunoblotting assays. Then immunohistochemistry (IHC) was performed to detect PinX1 expression on a tissue microarray. The optimal threshold for PinX1 positivity was determined by receiver operating characteristic (ROC) curve analysis. To clarify the probable role of PinX1 in BLBC, the PinX1 knockout and stably over-expressed MDA-MB-231 cell lines were constructed by the CRISPR-Cas9 system and gene transfection. The association of PinX1 expression with cell proliferation, migration and apoptosis of MDA-MB-231 cells were observed by CCK-8 assay, wound healing assay, Transwell assay, flow cytometric analysis and immunoblotting of the cleaved caspase-3 protein level. Our results showed that both PinX1 mRNA and protein expression were downregulated in breast cancer tissues (P<0.05). In IHC analysis, the optimal cut-off parameter for PinX1 positive expression was 62.5% (the AUC was 0.749, P<0.01). PinX1 positivity was 76.9% (10/14) in luminal subtypes, 50% (5/10) in Her2-enriched breast cancer and 27.3% (9/33) in basal-like subtypes. Besides, in 59 invasive ductal breast carcinomas, PinX1 expression was inversely related to histology grade (P<0.05) while it was positively associated with PR status (P<0.05) and ER status (P<0.05). These results indicated that low expression of PinX1 correlated with aggressive clinicopathological significance of breast cancer, especially in the basal-like subtype. Besides, we identified that overexpression of PinX1 inhibited the proliferation rates and migration ability and increased the apoptosis rates of BLBC. Our findings demonstrated that low expression of PinX1 was associated with malignant behaviors in basal-like subtype of breast cancer. PinX1 is likely a feasible biomarker and molecular target of BLBC.

Palmitoylation: a protein S-acylation with implications for breast cancer

Protein S-acylation is a reversible post-translational lipid modification that involves linkage of a fatty acid chain predominantly to a cysteine amino acid via a thioester bond. The fatty acid molecule is primarily palmitate, thus the term 'palmitoylation' is more commonly used. Palmitoylation has been found to modulate all stages of protein function including maturational processing, trafficking, membrane anchoring, signaling range and efficacy, and degradation. In breast cancer, palmitoylation has been shown to control the function of commonly dysregulated genes including estrogen receptors, the epidermal growth factor (EGF) family of receptors, and cancer stem cell markers. Importantly, palmitoylation is a critical factor controlling the formation of complexes at the plasma membrane involving tetraspanins, integrins, and gene products that are key to cell-cell communication. During metastasis, cancer cells enhance their metastatic capacity by interacting with stroma and immune cells. Although aberrant palmitoylation could contribute to tumor initiation and growth, its potential role in these cell-cell interactions is of particular interest, as it may provide mechanistic insight into metastasis, including cancer cell-driven immune modulation. Compelling evidence for a role for aberrant palmitoylation in breast cancer remains to be established. To this end, in this review we summarize emerging evidence and highlight pertinent knowledge gaps, suggesting directions for future research.

The prognostic potential of alternative transcript isoforms across human tumors

BACKGROUND

Phenotypic changes during cancer progression are associated with alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival.

METHODS

Using information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor (ER) status and melanoma tumors with proliferative and invasive phenotypes.

RESULTS

Transcript isoform signatures accurately separate early from late-stage groups and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures in retrospective data and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to ER status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup.

CONCLUSIONS

Our analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis and may potentially contribute towards current strategies of precision cancer medicine.

Identification of novel biomarkers associated with poor patient outcomes in invasive breast carcinoma

Breast carcinoma (BC) corresponds to 23 % of all cancers in women, with 1.38 million new cases and 460,000 deaths worldwide annually. Despite the significant advances in the identification of molecular markers and different modalities of treatment for primary BC, the ability to predict its metastatic behavior is still limited. The purpose of this study was to identify novel molecular markers associated with distinct clinical outcomes in a Brazilian cohort of BC patients. We generated global gene expression profiles using tumor samples from 24 patients with invasive ductal BC who were followed for at least 5 years, including a group of 15 patients with favorable outcomes and another with nine patients who developed metastasis. We identified a set of 58 differentially expressed genes (p ≤ 0.01) between the two groups. The prognostic value of this metastasis signature was corroborated by its ability to stratify independent BC patient datasets according to disease-free survival and overall survival. The upregulation of B3GNT7, PPM1D, TNKS2, PHB, and GTSE1 in patients with poor outcomes was confirmed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) in an independent sample of patients with BC (47 with good outcomes and eight that presented metastasis). The expression of BCL2-associated agonist of cell death (BAD) protein was determined in 1276 BC tissue samples by immunohistochemistry and was consistent with the reduced BAD mRNA expression levels in metastatic cases, as observed in the oligoarray data. These findings point to novel prognostic markers that can distinguish breast carcinomas with metastatic potential from those with favorable outcomes.

Micronutrients Involved in One-Carbon Metabolism and Risk of Breast Cancer Subtypes

Background

Vitamins involved in one-carbon metabolism are hypothesized to influence breast cancer (BC) risk. However, epidemiologic studies that examined associations between B vitamin intake and BC risk have provided inconsistent results. We prospectively examined, in the Italian ORDET cohort, whether B vitamin consumption was associated with risk of BC and BC subtypes.

Methods

After a mean follow-up of 16.5 years, 391 BCs were diagnosed among 10,786 cohort women. B vitamin intakes were estimated from food frequency questionnaires. Cox proportional hazard models adjusted for energy intake and confounders, estimated hazard ratios (HR) with 95% confidence intervals (CIs) for BC according to intake.

Results

RRs were 0.61 (95% CI 0.38–0.97 highest vs. lowest quartile; P trend 0.025) for thiamine; 0.48 (95% CI 0.32–0.71; P trend <0.001) for riboflavin; 0.59 (95% CI 0.39–0.90; P trend 0.008) for vitamin B6, and 0.65 (95% CI 0.44–0.95; P trend 0.021) for folate. As regards risk of BC subtypes, high riboflavin and folate were significantly associated with lower risk of estrogen receptor positive (ER+) and progesterone receptor positive (PR+) cancers, and high thiamine was associated with lower risk of ER-PR- cancers. High riboflavin was associated with lower risk of both HER2+ and HER2- cancers, high folate with lower risk of HER2- disease, and high thiamine with HER2+ disease.

Conclusions

These findings support protective effects of thiamine and one-carbon metabolism vitamins (folate, riboflavin, and vitamin B6) against BC in general; while folate may also protect against ER+PR+ and HER2- disease; and thiamine against ER-PR-, and HER2+ disease.

Complex-based analysis of dysregulated cellular processes in cancer

Background

Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions. Therefore, complexes aggregate the effect of individual genes (proteins) and can be used to gain a better understanding of cancer mechanisms. Here, we observe that complexes show considerable changes in their expression, in turn directed by the concerted action of transcription factors (TFs), across cancer conditions. We seek to gain novel insights into cancer mechanisms through a systematic analysis of complexes and their transcriptional regulation.

Results

We integrated large-scale protein-interaction (PPI) and gene-expression datasets to identify complexes that exhibit significant changes in their expression across different conditions in cancer. We devised a log-linear model to relate these changes to the differential regulation of complexes by TFs. The application of our model on two case studies involving pancreatic and familial breast tumour conditions revealed: (i) complexes in core cellular processes, especially those responsible for maintaining genome stability and cell proliferation (e.g. DNA damage repair and cell cycle) show considerable changes in expression; (ii) these changes include decrease and countering increase for different sets of complexes indicative of compensatory mechanisms coming into play in tumours; and (iii) TFs work in cooperative and counteractive ways to regulate these mechanisms. Such aberrant complexes and their regulating TFs play vital roles in the initiation and progression of cancer.

Conclusions

Complexes in core cellular processes display considerable decreases and countering increases in expression, strongly reflective of compensatory mechanisms in cancer. These changes are directed by the concerted action of cooperative and counteractive TFs. Our study highlights the roles of these complexes and TFs and presents several case studies of compensatory processes, thus providing novel insights into cancer mechanisms.