Insights into significant pathways and gene interaction networks underlying breast cancer cell line MCF-7 treated with 17β-estradiol (E2)

Robustness evaluations of pathway activity inference methods on gene expression data

BACKGROUND

With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.

RESULTS

Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.

CONCLUSION

However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

Colorectal cancer with low SLC35A3 is associated with immune infiltrates and poor prognosis

The expression level of SLC35A3 is associated with the prognosis of many cancers, but its role in colorectal cancer (CRC) is unclear. The purpose of our study was to elucidate the role of SLC35A3 in CRC. The expression levels of SLC35A3 in CRC were evaluated through tumor immune resource assessment (TIMER), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), Human Protein Atlas (HPA), qRT-PCR, and immunohistochemical evaluation. TCGA, GEO, and ICGC databases were used to analyze the diagnostic and prognostic value of SLC35A3 in CRC. A overall survival (OS) model was constructed and validated based on the expression level of SLC35A3 and multivariable analysis results. The cBioPortal tool was used to analyze SLC35A3 mutation in CRC. The UALCAN tool was used to analyze the promoter methylation level of SLC35A3 in colorectal cancer. In addition, the role of SLC35A3 in CRC was determined through GO analysis, KEGG analysis, gene set enrichment analysis (GSEA), immune infiltration analysis, and immune checkpoint correlation analysis. In vitro experiments validated the function of SLC35A3 in colorectal cancer cells. Compared with adjacent normal tissues and colonic epithelial cells, the expression of SLC35A3 was decreased in CRC tissues and CRC cell lines. Low expression of SLC35A3 was associated with N stage, pathological stage, and lymphatic infiltration, and it was unfavorable for OS, disease-specific survival (DSS), recurrence-free survival (RFS), and post-progression survival (PPS). According to the Receiver Operating Characteristic (ROC) analysis, SLC35A3 is a potential important diagnostic biomarker for CRC patients. The nomograph based on the expression level of SLC35A3 showed a better predictive model for OS than single prognostic factors and TNM staging. SLC35A3 has multiple types of mutations in CRC, and its promoter methylation level is significantly decreased. GO and KEGG analysis indicated that SLC35A3 may be involved in transmembrane transport protein activity, cell communication, and interaction with neurotransmitter receptors. GSEA revealed that SLC35A3 may be involved in energy metabolism, DNA repair, and cancer pathways. In addition, SLC35A3 was closely related to immune cell infiltration and immune checkpoint expression. Immunohistochemistry confirmed the positive correlation between SLC35A3 and helper T cell infiltration. In vitro experiments showed that overexpression of SLC35A3 inhibited the proliferation and invasion capability of colorectal cancer cells and promoted apoptosis. The results of this study indicate that decreased expression of SLC35A3 is closely associated with poor prognosis and immune cell infiltration in colorectal cancer, and it can serve as a promising independent prognostic biomarker and potential therapeutic target.

Quantitative expression of oestrogen receptor in breast cancer: Clinical and molecular significance

BACKGROUND

Oestrogen receptor (ER) positive breast cancer (BC) patients are eligible for endocrine therapy (ET), regardless of ER immunohistochemical expression level. There is a wide spectrum of ER expression and the response to ET is not uniform. This study aimed to assess the clinical and molecular consequences of ER heterogeneity with respect to ET-response.

METHODS

ER expression, categorised by percentage and staining intensity in a large BC cohort (n = 7559) was correlated with clinicopathological parameters and patient ET response. The Cancer Genome Atlas Data BC cohort (n = 1047) was stratified by ER expression and transcriptomic analysis completed to better understand the molecular basis of ER heterogeneity.

RESULTS

The quantitative proportional increase in ER expression was positively associated with favourable prognostic parameters. Tumours with 1-9% ER expression were characteristically similar to ER-negative (<1%) tumours. Maximum ET-response was observed in tumours with 100% ER expression, with responses significantly different to tumours exhibiting ER at < 100% and significantly decreased survival rates were observed in tumours with 50% and 10% of ER expression. The Histochemical-score (H-score), which considers both staining intensity and percentage, added significant prognostic value over ER percentage alone with significant outcome differences observed at H-scores of 30, 100 and 200. There was a positive correlation between ER expression and ESR1 mRNA expression and expression of ER-regulated genes. Pathway analysis identified differential expression in key cancer-related pathways in different ER-positive groups.

CONCLUSION

ET-response is statistically proportionally related to ER expression with significant differences observed at 10%, 50% and 100%. The H-score adds prognostic and predictive information.

Duality of the SVIL expression in bladder cancer and its correlation with immune infiltration

SVIL is a member of the villin/gelsolin superfamily and is responsible for encoding supervillin. It has been reported to be closely related to the occurrence and development of various tumors. However, the mechanism of SVIL in bladder cancer has not been reported yet. In this research, we evaluated the relationship between SVIL expression and bladder cancer in public dataset and examined the expression of SVIL in bladder cancer cell lines, tissue microarrays and patients in our cohort. Our work determined that the expression of SVIL in bladder cancer tissue was significantly lower than that in normal tissue. However, in bladder cancer tissues, the high expression of SVIL is significantly associated with poor prognosis. This kind of duality is very novel and has great research value. The expression level of SVIL can well predict the survival time of bladder cancer patients, and is an independent risk factor of bladder cancer patients. The expression of SVIL is also closely related to the immune tumor microenvironment of bladder cancer. Our research provides a basis for personalized therapeutic targets for bladder cancer.

Identification of a 5-gene-risk score model for predicting luminal A-invasive lobular breast cancer survival

Breast cancer is a devastating malignancy, among which the luminal A (LumA) breast cancer is the most common subtype. In the present study, we used a comprehensive bioinformatics approach in the hope of identifying novel prognostic biomarkers for LumA breast cancer patients. Transcriptomic profiling of 611 LumA breast cancer patients was downloaded from TCGA database. Differentially expressed genes (DEGs) between tumor samples and controls were first identified by differential expression analysis, before being used for the weighted gene co-expression network analysis. The subsequent univariate Cox regression and LASSO algorithm were used to uncover key prognostic genes for constructing multivariate Cox regression model. Patients were stratified into high-risk and low-risk groups according to the risk score, and subjected to multiple downstream analyses including survival analysis, gene set enrichment analysis (GSEA), inference on immune cell infiltration and analysis of mutation burden. Receiving operator curve analysis was also performed. A total of 7071 DEGs were first identified by edgeR package, pink module was found significantly associated with invasive lobular carcinoma (ILC). 105 prognostic genes and 9 predictors were identified, allowing the identification of a 5-key prognostic genes (LRRC77P, CA3, BAMBI, CABP1, ATP8A2) after intersection. These 5 genes, and the resulting Cox model, displayed good prognostic performance. Furthermore, distinct differences existed between two risk-score stratified groups at various levels. The identified 5-gene prognostic model will help deepen the understanding of the molecular and immunological mechanisms that affect the survival of LumA-ILC patients and guide and proper monitoring of these patients.

Screening and Biological Function Analysis of miRNA and mRNA Related to Lung Adenocarcinoma Based on Bioinformatics Technology

Objective. To screen the differentially expressed miRNAs (DEMs) and the differentially expressed gene mRNAs (DEGs) in lung adenocarcinoma (LUAD) from the TCGA database and to explore the relationship between miRNAs and the prognosis of lung adenocarcinoma and their biological functions. Methods. The RNA-seq and miRNA-seq data of lung adenocarcinoma samples were downloaded from the TCGA database for analysis, and the R program was used to screen for differentially expressed miRNAs and mRNAs. Then, the molecular functions, biological processes, cellular components, and signaling pathways involved in the occurrence and development of LUAD were analyzed using the functional accumulation analysis software of GSEA. The relationship between the integrated differentially expressed RNAs was analyzed by miRcode, TargetScan, and miRTarbase databases, and the miRNA-mRNA network was constructed. Result. A total of 516 differentially expressed miRNAs and 5464 differentially expressed mRNAs were identified in LUAD. The GSEA enrichment analysis showed that miRNAs and mRNAs were mainly enriched in extracellular structure organization, external encapsulating structure organization, extracellular matrix organization, and gated channel activity. They were mainly involved in neuroactive ligand-receptor interaction signaling pathway. Some miRNAs and mRNAs in clustering modules were found to be associated with the prognosis of LUAD. Four targeting networks consisting of 22 miRNAs and 531 mRNAs were constructed. Conclusion. The miRNA and mRNA related to the prognosis of LUAD were screened out, which provided a valuable preliminary basis for the follow-upin-depth clinical research and basic experimental research of LUAD.

Analysis of Breast Cancer Based on the Dysregulated Network

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

Multi-time scale transcriptomic analysis on the dynamic process of tamoxifen resistance development in breast cancer cell lines

BACKGROUND

Approximately 30% of breast cancer patients develop endocrine resistance after tamoxifen therapy. There still lacks a comprehensive understanding on the mechanism of tamoxifen resistance. This study aims to explore the dynamic process of ER + breast cancer resistance to tamoxifen through the time course transcriptomic analysis.

METHODS

The transcriptome profiles of human breast cancer cell line MCF-7 treated with tamoxifen at different time scales were collected from LINCS, SRA and GEO databases. Differentially expressed genes (DEGs) were identified in the short-term tamoxifen treatment and tamoxifen-resistant cell lines. The time course analysis was used to explore the dynamic development of tamoxifen resistance using the transcriptome profiles of tamoxifen-cultured MCF-7 for 1-12 weeks.

RESULTS

After the short-term treatment of MCF-7 with tamoxifen for 6 h or 24 h, the expression level of gene PRSS23 was significantly reduced. However, its expression recovered in the resistant cell lines. The time course analysis identified 9 clusters of the DEGs based on the temporal trend of their expression levels. Gene PRSS23 belongs to cluster 2 in which the expression levels were significantly down-regulated in the first 4 weeks but gradually recovered afterwards. Functional enrichment analysis of the DEGs in cluster 2 showed that they are significantly enriched in DNA replication, mismatch repair and cell cycle pathways. Their specific role in the resistance development needs to be further explored. The protein-protein interaction network analysis indicates that gene PRSS23 participates in the drug resistance by regulating multiple tamoxifen drug targets.

CONCLUSIONS

The acquired drug resistance in ER + breast cancer is a complex and dynamic biological process. PRSS23 plays an important role in the development of resistance and is a potential target for overcoming resistance.

Component Identification of Phenolic Acids in Cell Suspension Cultures of Saussureainvolucrata and Its Mechanism of Anti-Hepatoma Revealed by TMT Quantitative Proteomics

Saussurea involucrata (S. involucrata) had been reported to have anti-hepatoma function. However, the mechanism is complex and unclear. To evaluate the anti-hepatoma mechanism of S. involucrata comprehensively and make a theoretical basis for the mechanical verification of later research, we carried out this work. In this study, the total phenolic acids from S. involucrata determined by a cell suspension culture (ESPI) was mainly composed of 4,5-dicaffeoylquinic acid, according to the LC-MS analysis. BALB/c nude female mice were injected with HepG2 cells to establish an animal model of liver tumor before being divided into a control group, a low-dose group, a middle-dose group, a high-dose group, and a DDP group. Subsequently, EPSI was used as the intervention drug for mice. Biochemical indicators and differences in protein expression determined by TMT quantitative proteomics were used to resolve the mechanism after the low- (100 mg/kg), middle- (200 mg/kg), and high-dose (400 mg/kg) interventions for 24 days. The results showed that EPSI can not only limit the growth of HepG2 cells in vitro, but also can inhibit liver tumors significantly with no toxicity at high doses in vivo. Proteomics analysis revealed that the upregulated differentially expressed proteins (DE proteins) in the high-dose group were over three times that in the control group. ESPI affected the pathways significantly associated with the protein metabolic process, metabolic process, catalytic activity, hydrolase activity, proteolysis, endopeptidase activity, serine-type endopeptidase activity, etc. The treatment group showed significant differences in the pathways associated with the renin-angiotensin system, hematopoietic cell lineage, etc. In conclusion, ESPI has a significant anti-hepatoma effect and the potential mechanism was revealed.

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

Coexpression of Smac/DIABLO and Estrogen Receptor in breast cancer

BACKGROUND

Smac/DIABLO is a proapoptotic protein deregulated in breast cancer, with a controversial role as a tumor marker, possibly due to a lack of correlative mRNA and protein analyses.

OBJECTIVE

To investigate the association of Smac/DIABLO gene and protein levels with clinical variables in breast cancer patients.

METHODS

Smac/DIABLO mRNA expression was analyzed by qPCR in 57 frozen tissues, whereas protein levels were assessed by immunohistochemistry in 82 paraffin-embedded tissues. Survivin mRNA levels were also measured. In vitro assays were performed to investigate possible regulators of Smac/DIABLO.

RESULTS

Higher levels of Smac/DIABLO mRNA and protein were found in estrogen receptor (ER)-positive samples (p= 0.0054 and p= 0.0043, respectively) in comparison to ER-negative tumors. A negligible positive association was found between Smac/DIABLO and survivin expression. In vitro assays showed that Smac/DIABLO is not regulated by ER and, conversely, it does not participate in ER expression modulation.

CONCLUSIONS

mRNA and protein levels of Smac/DIABLO were increased in ER-positive breast tumors in comparison with ER-negative samples, although the mechanism of this regulation is still unknown. Public databases showed a possible clinical relevance for this association.

Identification of metastasis and prognosis-associated genes for serous ovarian cancer

Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.

Deregulation of extracellular matrix modeling with molecular prognostic markers revealed by transcriptome sequencing and validations in Oral Tongue squamous cell carcinoma

Oral Tongue Squamous Cell Carcinoma (OTSCC), a distinct sub-group of head and neck cancers, is characteristically aggressive in nature with a higher incidence of recurrence and metastasis. Recent advances in therapeutics have not improved patient survival. The phenomenon of occult node metastasis, even among the purportedly good prognosis group of early-stage and node-negative tongue tumors, leads to a high incidence of locoregional failure in OTSCC which needs to be addressed. In the current study, transcriptome analysis of OTSCC patients identified the key genes and deregulated pathways. A panel of 26 marker genes was shortlisted and validated using real-time PCR in a prospective cohort of 100 patients. The gene expression was correlated with clinicopathological features including occult node metastasis, survival, and therapeutic outcome. The up-regulation of a panel of 6 genes namely, matrix metalloproteinase 9 (MMP9), Laminin subunit Gamma 2 (LAMC2), Desmoglein 2 (DSG2), Plasminogen Activator Urokinase (PLAU), Forkhead Box M1 (FOXM1), and Myosin 1B (MYO1B) was associated with failure of treatment in the early stage (T1, T2). Up-regulation of Tenacin C (TNC) and Podoplanin (PDPN) was significantly correlated with occult node positivity. Immunohistochemical analysis of LAMC2, MMP9, and E-Cadherin (ECAD) confirmed these markers to be indicators of poor prognosis. We propose this panel of valuable prognostic markers can be clinically useful to identify poor prognosis and occult node metastasis in OTSCC patients.

Weighted dimensionality reduction and robust Gaussian mixture model based cancer patient subtyping from gene expression data

BACKGROUND

The heterogeneous nature of cancer necessitates subtyping of cancer patients into distinct and well separated subgroups. However, computational issues arise because gene expression data is noisy and contains outliers apart from being high dimensional. As such, an attempt to subtype cancer patients from gene expression data leads to highly overlapping Kaplan-Meier (KM) survival plots and thus clear distinction among the discovered subtypes becomes difficult. Here we attempt to achieve a greater separation among the subtypes through a robust clustering pipeline.

METHODS

We propose a robust framework to achieve a better separation among the discovered subtypes. Our framework is based on dimensionality reduction of a weighted gene expression matrix using t-distributed Stochastic Neighbor Embedding (t-SNE) and a robust Gaussian mixture model based clustering approach. Every gene is weighted according to the median absolute deviation (MAD) of the gene before dimensionality reduction. The results are quantified by measuring the minimum pairwise separation among the KM plots and minimum hazard ratio among the subtypes. We also introduce a novel method, called cumulative survival separation, to quantify the separation among the discovered subtypes.

RESULTS

To validate the proposed methodology we obtained five cancer gene expression datasets from The Cancer Genome Atlas (TCGA) and comparisons with Consensus Clustering (CC), Consensus non-negative matrix factorization (CNMF), fast density-aware spectral clustering (Spectrum) and Neighborhood based Multi-Omics clustering (NEMO) methodologies show that the proposed method is able to achieve a greater separation compared to the aforementioned methods in literature. For instance, the minimum pairwise life expectancy difference (in days) between the discovered subtypes for GBM is 61 days for the proposed methodology with MAD scores, whereas it is approximately 33, 19, 49 and 33 days only for CC, Spectrum, Nemo and CNMF respectively. Comparisons are also shown for the proposed framework with and without using the MAD scores and it is observed that MAD score significantly improves the subtype separation. Hazard ratio analysis also shows that the proposed methodology performs better. Furthermore, pathway over-representation analyses were carried to identify relevant genetic pathways which can be possible targets for treatment.

CONCLUSION

The results suggest that the use of median absolute deviation and a robust clustering methodology are helpful in achieving greater separation among the subtypes with better statistical and clinical significance.

Estrogens Determine Adherens Junction Organization and E-Cadherin Clustering in Breast Cancer Cells via Amphiregulin

Estrogens play an important role in the development and progression of human cancers, particularly in breast cancer. Breast cancer progression depends on the malignant destabilization of adherens junctions (AJs) and disruption of tissue integrity. We found that estrogen receptor alpha (ERα) inhibition led to a striking spatial reorganization of AJs and microclustering of E-Cadherin (E-Cad) in the cell membrane of breast cancer cells. This resulted in increased stability of AJs and cell stiffness and a reduction of cell motility. These effects were actomyosin-dependent and reversible by estrogens. Detailed investigations showed that the ERα target gene and epidermal growth factor receptor (EGFR) ligand Amphiregulin (AREG) essentially regulates AJ reorganization and E-Cad microclustering. Our results not only describe a biological mechanism for the organization of AJs and the modulation of mechanical properties of cells but also provide a new perspective on how estrogens and anti-estrogens might influence the formation of breast tumors.

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ERα inhibition causes adherens junction (AJ) reorganization through AREG and EGFR

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AJ reorganization coincides with microclustering of E-Cadherin at cell membranes

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AJ reorganization and microclustering of E-Cadherin are actomyosin dependent

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AJ reorganization correlates with increased cell stiffness and reduced motility

A topological approach for cancer subtyping from gene expression data

BACKGROUND

Gene expression data contains key information which can be used for subtyping cancer patients. However, computational methods suffer from 'curse of dimensionality' due to very high dimensionality of omics data and therefore are not able to clearly distinguish between the discovered subtypes in terms of separation of survival plots.

METHODS

To address this we propose a framework based on Topological Mapper algorithm. The novelty of this work is that we suggest a method for defining the filter function on which the mapper algorithm heavily depends. Survival analysis of the discovered cancer subtypes is carried out and evaluated in terms of minimum pairwise separation between the Kaplan-Meier plots. Furthermore, we present a method to measure the separation between the discovered subtypes based on hazard ratios.

RESULTS

Five cancer genomics datasets obtained from The Cancer Genome Atlas portal have been used for comparisons with Robust Sparse Correlation-Otrimle (RSC-Otrimle) algorithm and Similarity Network Fusion(SNF). Comparisons show that the minimum pairwise life expectancy difference (in days) between the discovered subtypes for lung, colon, breast, glioblastoma and kidney cancers is 107, 204, 20, 88 and 425 days, respectively, for the proposed methodology whereas it is only 69, 43, 6, 61 and 282 days for RSC-Otrimle and 9, 95, 18, 60 and 148 days for SNF. Hazard ratio analysis also shows that the proposed methodology performs better in four of the five datasets. A visual inspection of Kaplan-Meier plots reveals that the proposed methodology achieves lesser overlap in Kaplan-Meier plots especially for lung, breast and kidney cases. Furthermore, relevant genetic pathways for each subtype have been obtained and pathways which can be possible targets for treatment have been discussed.

CONCLUSION

The significance of this work lies in individualized understanding of cancer from patient to patient which is the backbone of Precision Medicine.

LOC134466 methylation promotes oncogenesis of endometrial carcinoma through LOC134466/hsa-miR-196a-5p/TAC1 axis

To investigate possible mechanism of abnormal methylation of long non-coding RNA (lncRNA) on endometrial carcinoma (EC) progression, we detected the genome methylation profiling of endometrial carcinoma by bioinformatic analysis. Accordingly, gene LOC134466 was chosen for the further research. We also found that TAC1 was the target gene of LOC134466 and miRNA, hsa-miR-196a-5p, might form a connection between LOC134466 and TAC1. The relationship was further proved by dual-luciferase reporter assay. In vitro studies, DNA methylation and expression were determined by MSP and qRT-PCR respectively. Cell proliferation, apoptosis and cell cycle were demonstrated by colony formation assay, Annexin V/PI double staining and flow cytometry. Besides, the function of LOC134466 and TAC1 in EC was further confirmed by Tumor Xenograft. Our results indicated that EC progression was promoted by hypermethylated LOC134466 and TAC1. Moreover, TAC1 transcription was regulated by LOC134466 via hsa-miR-196a-5p binding. LOC134466 and TAC1 demethylation by 5-Aza-2-Deoxycytidine inhibited EC cells proliferation and accelerated cell apoptosis. Furthermore, the expression of TACR1, TACR2 and TACR3 was remarkably decreased through LOC134466 and TAC1 treatments. Our findings establish a novel regulatory axis, LOC134466/hsa-miR-196a-5p/TAC1. Downregulation of the axis promoted EC development through TACR3, which further activated neuroactive ligand-receptor interaction.

Robust clustering of noisy high-dimensional gene expression data for patients subtyping

Motivation

One of the most important research areas in personalized medicine is the discovery of disease sub-types with relevance in clinical applications. This is usually accomplished by exploring gene expression data with unsupervised clustering methodologies. Then, with the advent of multiple omics technologies, data integration methodologies have been further developed to obtain better performances in patient separability. However, these methods do not guarantee the survival separability of the patients in different clusters.

Results

We propose a new methodology that first computes a robust and sparse correlation matrix of the genes, then decomposes it and projects the patient data onto the first m spectral components of the correlation matrix. After that, a robust and adaptive to noise clustering algorithm is applied. The clustering is set up to optimize the separation between survival curves estimated cluster-wise. The method is able to identify clusters that have different omics signatures and also statistically significant differences in survival time. The proposed methodology is tested on five cancer datasets downloaded from The Cancer Genome Atlas repository. The proposed method is compared with the Similarity Network Fusion (SNF) approach, and model based clustering based on Student's t-distribution (TMIX). Our method obtains a better performance in terms of survival separability, even if it uses a single gene expression view compared to the multi-view approach of the SNF method. Finally, a pathway based analysis is accomplished to highlight the biological processes that differentiate the obtained patient groups.

Availability and implementation

Our R source code is available online at https://github.com/angy89/RobustClusteringPatientSubtyping.

Supplementary information

Supplementary data are available at Bioinformatics online.

Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) are a subclass of non-protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non-small cell lung cancer plasma RNA-Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer-related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.

Network-guided prediction of aromatase inhibitor response in breast cancer

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.

Breast cancer is the second most common type of cancer in women, with an incidence rate of over 250,000 cases per year, and breast cancer cases show significant heterogeneity in clinical and omic measures. Estrogen receptor positive (ER+) tumors typically grow in response to estrogen, and in post menopausal women, estrogen is only produced in peripheral tissues via the aromatase enzyme. Inhibition of aromatase is often an effective treatment for ER+ tumors, but aromatase inhibitor therapy is not effective for all tumors, and causes of this heterogeneity in response are largely not known. In this work, we present a feature construction and classification method to predict response to aromatase inhibitor therapy. We use network smoothing techniques to combine tumor omic data into predictive features, which we use as input to standard machine learning algorithms. We train predictive models using clinical data, including high-quality clinical data from UPMC patients, and show that our method outperforms previous approaches in predicting response to aromatase inhibitor therapy.

The role of 17β‑estradiol‑induced upregulation of Piwi‑like 4 in modulating gene expression and motility in breast cancer cells

A majority of breast cancer cases are positive for the estrogen receptor (ER), which means that they can respond to the estrogen hormone to achieve growth. Hence, the ER signaling pathway has been extensively targeted in pharmaceutical research and development in order to suppress tumor growth. However, prevalent hormone therapy and targeted therapy often become ineffective as cancer cells ultimately develop resistance, suggesting that there could be unidentified signaling molecules and events that regulate breast cancer growth. Notably, recent studies have uncovered that Piwi-like (Piwil) proteins, which were initially found in germline cells, are expressed in a wide spectrum of human cancers, including breast cancers. Although Piwil proteins have been well established to silence retrotransposons and to promote heterochromatin formation in germline cells, their somatic functions in cancer cells remain largely unknown. In the present study, we profiled the expression of four Piwi homologs in an ER-positive breast cancer cell line, MCF-7, and found that only Piwil4 was upregulated by 17β-estradiol treatment. Notably, Piwil4 upregulation was not observed in an ER-positive but non-tumorigenic breast cancer cell line, MCF-12A. In addition, the induced expression of Piwil4 was dependent on estrogen/ERα signaling. To explore the biological significance of Piwil4 in breast cancer growth, we knocked down Piwil4 with multiple siRNAs and observed the suppressed expression of some canonical targets of ER. The knockdown of Piwil4 expression also decreased the migration and invasion capabilities of MCF-7 cells. Furthermore, the loss-of-function of Piwil4 reduced the motility of MCF-7 cells in wound-healing assays, which could be associated to decreased expression of vimentin and N-cadherin. Collectively, these findings revealed that Piwil4 is a novel regulator of ER signaling that could be targeted to inhibit breast cancer growth and migration.

Multiparameter Phenotypic Profiling in MCF-7 Cells for Assessing the Toxicity and Estrogenic Activity of Whole Environmental Water

Multi-parameter phenotypic profiling of small molecules is a powerful approach to their toxicity assessment and identifying potential mechanisms of actions. The present study demonstrates the application of image-based multi-parameter phenotypic profiling in MCF-7 cells to assess the overall toxicity and estrogenic activity of whole environmental water. Phenotypic profiling of 30 reference compounds and their complex mixtures was evaluated to investigate the cellular morphological outcomes to targeted biological pathways. Overall toxicity and estrogenic activity of environmental water samples were then evaluated by phenotypic analysis comparing with conventional bioassays and chemical analysis by multivariate analysis. The phenotypic analysis for reference compounds demonstrated that size and structure of cells related to biological processes like cell growth, death, and communication. The phenotypic alteration and nuclei intensity were selected as potential biomarkers to evaluate overall toxicity and estrogenic activities, respectively. The phenotypic profiles were associated with the chemical structure profiles in environmental water samples. Since the phenotypic parameters revealed multiple toxicity endpoints, it could provide more information that is relevant to assessing the toxicity of environmental water samples in compare with conventional bioassays. In conclusion, the image-based multi-parameters phenotypic analysis with MCF-7 cells provides a rapid and information-rich tool for toxicity evaluation and identification in whole water samples.

High expression of WISP1 in colon cancer is associated with apoptosis, invasion and poor prognosis

Colon cancer (CC) likes many epithelial-derived cancers, resulting from a complex tumorigenic process. However, the exactly mechanisms of development and progression of CC are still unknown. In this study, integrated analysis in the GSE33113 and Fudan University Shanghai Cancer Center Hospital datasets revealed that WISP1 expression was significantly increased in CC cases, positivity correlated with the advanced pathologic stage and a poor prognosis was more likely in CC patients with higher levels of WISP1. Downregulation of WISP1 inhibited cell proliferation and invasion through increasing apoptosis and blocking cell cycle at G1 phase in CC LOVO and RKO cells. Besides, Gene set enrichment analysis (GSEA) revealed that relative genes involved in the Cell adhesion molecules and Cytokine-cytokine receptor interaction pathways were enriched in WISP1-higher expression patients. Western blot analysis showed that Cell adhesion molecules pathway associated genes (ICAM- 1, VCAM-1, SDC2 and CDH2) and Cytokine-cytokine receptor interaction pathway associated genes (VEGFC, CCL18, CXCR4 and TGFBR1) were also modulated by WISP1 downregulation. Then, we found that the protein β-catenin was identified as a binding partner of WISP1 and mediated the functions of WISP1 through promoting cell proliferation and invasion in LOVO and RKO cells. Further in vivo tumor formation study in nude mice indicated that inhibition of WISP1 delayed the progress of tumor formation and inhibited PCNA expression. These results indicate that WISP1 could act as an oncogene and may serve as a promising therapeutic strategy for colon cancer.

Multiple mutations of lung squamous cell carcinoma shared common mechanisms

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell lung cancers which is the cause of 80% of all lung cancer deaths. The genes that highly mutated in patients with LUSC and their roles played in the tumorigenesis remains unknown. Data of patients with Lung squamous cell carcinoma (LUSC) were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed genes were identified between control and cancer samples. Patients and controls can be separated by mRNA expression level showing that the between-group variance and totally 1265 genes were differentially expressed between controls and patients. Top genes whose mutations highly occurred in patients with LUSC were identified, most of these genes were shown to be related with tumorigenesis in previous studies. All of the genes mostly mutated were independently correlated with expression levels of all genes. These mutations did not show the trend of co-occurrence. However, the influenced gene of these mutations had overlaps. After studying the intersection of these genes, a group of shared genes were identified. The shared pathways enriched which played critical role in LUSC were identified based on these shared genes. Different mutations had contribution to the progression of LUSC. Though these genes involved different specific mechanisms, most of them may share a common mechanism which is critical for LUSC. The results may suggest a neglected mechanism and also indicate a potential target for therapies.

Identification of differentially expressed genes in the development of osteosarcoma using RNA-seq

Objective

Osteosarcoma (OS) is a malignant bone tumor with high morbidity in young adults and adolescents. This study aimed to discover potential early diagnosis biomarkers in OS.

Results

In total, 111 differentially expressed genes (DEGs) were identified in primary OS compared with normal controls and 235 DEGs were identified in metastatic OS compared with primary OS. AURKB and PPP2R2B were the significantly up-regulated and down-regulated hub proteins, respectively, in the PPI protein-protein network (PPI) network of primary OS. ISG15 and BTRC were the significantly up-regulated and down-regulated hub proteins, respectively, in the network of metastatic OS. The DEGs in metastatic OS compared with primary OS were significantly enriched in the arachidonic acid metabolism, malaria, and chemokine signaling pathways. Finally, we employed quantitative real-time polymerase chain reaction (qRT-PCR) to validate the expression levels of candidate DEGs and the results indicated that our bioinformatics approach was acceptable.

Materials and Methods

The mRNA expression profiling of 20 subjects was obtained through high-throughput RNA-sequencing. DEGs were identified between primary OS and normal Control, and between primary OS and metastatic OS, respectively. Functional annotation and PPI networks were used to obtain insights into the functions of DEGs. qRT-PCR was performed to detect the expression levels of dysregulated genes in OS.

Conclusions

Our work might provide groundwork for the further exploration of tumorigenesis and metastasis mechanisms of OS.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Identification of Key Genes and Pathways in Tongue Squamous Cell Carcinoma Using Bioinformatics Analysis

BACKGROUND Tongue squamous cell carcinoma (TSCC) is a major type of oral cancers and has remained an intractable cancer over the past decades. The aim of this study was to identify differentially expressed genes (DEGs) during TSCC and reveal their potential mechanisms. MATERIAL AND METHODS The gene expression profiles of GSE13601 were downloaded from the GEO database. The GSE13601 dataset contains 57 samples, including 31 tongue SCC samples and 26 matched normal mucosa samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed; Cytoscape software was used for the protein-protein interaction (PPI) network and module analysis of the DEGs. RESULTS We identified a total of 1,050 upregulated DEGs (uDEGs) and 702 downregulated DEGs (dDEGs) of TSCC. The GO analysis results showed that uDEGs were significantly enriched in the following biological processes (BP): signal transduction, positive or negative regulation of cell proliferation, and negative regulation of cell proliferation. The dDEGs were significantly enriched in the following biological processes: signal transduction, cell adhesion, and apoptotic process. The KEGG pathway analysis showed that uDEGs were enriched in metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway, while the dDEGs were enriched in focal adhesion and ECM-receptor interaction. The top centrality hub genes RAC1, APP, EGFR, KNG1, AGT, and HRAS were identified from the PPI network. Module analysis revealed that TSCC was associated with significant pathways, including neuroactive ligand-receptor interaction, calcium signaling pathway, and chemokine signaling pathway. CONCLUSIONS The present study identified key genes and signal pathways, which deepen our understanding of the molecular mechanisms of carcinogenesis and development of the disease, and might be used as diagnostic and therapeutic molecular biomarkers for TSCC.

QuaDMutEx: quadratic driver mutation explorer

BACKGROUND

Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.

RESULTS

We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors.

CONCLUSIONS

Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license.

Identification of WISP1 as a novel oncogene in glioblastoma

Glioblastoma is the most common and aggressive primary brain tumor and has a high mortality in humans. However, mechanisms and factors involved in the progression of glioblastoma remain elusive. WISP1 (WNT1 inducible signaling pathway protein 1), has been suggested to be a critical regulator of cancer development. The aim of this study was to investigate the role of WISP1 in regulating the progression of glioblastoma. Clinicopathological characteristics of glioblastoma were assessed, and higher levels of WISP1 were positively associated with advanced clinical stage and a poor prognosis. Consistently, WISP1 expression was significantly upregulated in glioblastoma tissue and cell lines compared with normal tissue and cells. Additionally, inhibition of WISP1 greatly suppressed cell proliferation, migration, and invasion and promoted apoptosis and cell cycle arrest of glioblastoma cells. Further study indicated that downregulation of WISP1 suppressed cell proliferation associated with the gene expression of c‑myc and cyclin D1 and cellular signaling such as through the ERK pathway, while inhibiting epithelial-mesenchymal transition and MMP9. Finally, knockdown of WISP1 markedly suppressed in vivo tumor growth and sensitized glioblastoma cells to temozolomide. This study identified WISP1 as an oncogene in glioblastoma and suggests that WISP1 may serve as a potential molecular marker and treatment target for glioblastoma.

Combined analysis of ChIP-seq and gene microarray datasets identify the E2-mediated genes in ERα-dependent manner in osteosarcoma

Osteosarcoma is a common bone tumor which is affected by E2, the most representative estrogen. Gene regulation function of E2 is highly dependent on estrogen receptor. The purpose of this study was to explore the gene regulation patterns of E2 through estrogen receptor α (ESR1) in osteosarcoma based on the combined analysis of ChIP-seq and gene microarray. All of the datasets were downloaded from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) in E2 treated U2OS cells expressing ESR1 (U2OS-ERα) compared with those treated with vehicle were obtained based on R programming software. ESR1-specific binding sites (peaks) in E2 treated U2OS cells were identified through MACS. Overlaps between DEGs and ESR1 target genes which contained peaks in promoters were considered as reliable E2-mediated genes through ESR1 in osteosarcoma. Moreover, we conducted miRNA-Gene regulation analysis for those genes through miRWalk database to identify potential therapeutic targets for the genes. Functional enrichment analysis of DEGs indicated their potential involvement in cancer, and cell activity-related processes. Fifteen overlaps were identified between DEGs and target genes of ESR1, of which 12 were found to be regulated by miRNA. Several known estrogen response genes and novel genes were obtained in this study and they might provide potential therapeutic targets for osteosarcoma.

Screening of the prognostic targets for breast cancer based co-expression modules analysis

The purpose of the present study was to screen the prognostic targets for breast cancer based on a co-expression modules analysis. The microarray dataset GSE73383 was downloaded from the Gene Expression Omnibus (GEO) database, including 15 breast cancer samples with good prognosis and 9 breast cancer samples with poor prognosis. The differentially expressed genes (DEGs) were identified with the limma package. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Furthermore, the co-expression analysis of DEGs was conducted with weighted correlation analysis. The interaction associations were analyzed with the Human Protein Reference Database and BioGRID. The protein-protein interactions (PPI) network was constructed and visualized by Cytoscape software. A total of 491 DEGs were identified in breast cancer samples with poor prognosis compared with those with good prognosis, and they were enriched in 85 GO terms and 4 KEGG pathways. 368 DEGs were co-expressed with others, and they were clustered into 10 modules. Module 6 was the most relevant to the clinical features, and 21 genes and 273 interaction pairs were selected out. Abnormal expression levels of required for meiotic nuclear division 5 homolog A (RMND5A) and angiopoietin-like protein 1 (ANGPTL1) were associated with a poor prognosis. It was indicated that SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily D, member 1, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily D, member 1, dihydropyrimidinase-like 2, RMND5A and ANGPTL1 were potential prognostic markers in breast cancer, and the cell cycle may be involved in the regulation of breast cancer.

GRIK3: A novel oncogenic protein related to tumor TNM stage, lymph node metastasis, and poor prognosis of GC

Glutamate receptor, ionotropic, kainate 3 (GRIK3), as a member of the glutamate kainate receptor family, mainly participated in neuroactive ligand receptor interaction pathway. Other members of GRIK family were previously reported to regulate cellular migration, transformation, and proliferation in tumor. However, the mechanism of GRIK3 in tumor is still unclear. Therefore, the purpose of our study was to reveal the expression and clinical significance of GRIK3 in gastric cancer (GC). First, we performed the expression analysis and survival analysis of GRIK3 using The Cancer Genome Atlas (TCGA) database, and the results showed that the GRIK3 expressed differentially between gastric cancer tissues and the adjacent normal tissues and that higher expression of GRIK3 was associated with poor survival outcomes. And the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that GRIK3 mainly took part in cancer-related process. Subsequently, the validated immunohistochemistry showed that GRIK3 expressed higher in the GC tissues than in the matched normal tissues and the patients with overexpressed GRIK3 had worse survival outcomes. The univariate and multivariate analyses suggested that the expression of GRIK3 was an independent prognostic factor to predict GC prognosis. Furthermore, additional experiment showed that the lymph node metastasis tissues had higher GRIK3 expression than their matched primary GC tissues. These findings suggested that elevated GRIK3 expression could serve as an independent prognostic biomarker and a novel potential treatment target for patients with GC.

Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization

With the rapid advances in technologies of microarray and massively parallel sequencing, data of multiple omics sources from a large patient cohort are now frequently seen in many consortium studies. Effective multi-level omics data integration has brought new statistical challenges. One important biological objective of such integrative analysis is to cluster patients in order to identify clinically relevant disease subtypes, which will form basis for tailored treatment and personalized medicine. Several methods have been proposed in the literature for this purpose, including the popular iCluster method used in many cancer applications. When clustering high-dimensional omics data, effective feature selection is critical for better clustering accuracy and biological interpretation. It is also common that a portion of "scattered samples" has patterns distinct from all major clusters and should not be assigned into any cluster as they may represent a rare disease subcategory or be in transition between disease subtypes. In this paper, we firstly propose to improve feature selection of the iCluster factor model by an overlapping sparse group lasso penalty on the omics features using prior knowledge of inter-omics regulatory flows. We then perform regularization over samples to allow clustering with scattered samples and generate tight clusters. The proposed group structured tight iCluster method will be evaluated by two real breast cancer examples and simulations to demonstrate its improved clustering accuracy, biological interpretation, and ability to generate coherent tight clusters.

The complex nature of oestrogen signalling in breast cancer: enemy or ally?

The pleiotropic nature of oestradiol, the main oestrogen found in women, has been well described in the literature. Oestradiol is positioned to play a unique role since it can respond to environmental, genetic and non-genetic cues to affect genetic expression and cellular signalling. In breast cancer, oestradiol signalling has a dual effect, promoting or inhibiting cancer growth. The potential impact of oestradiol on tumorigenesis depends on the molecular and cellular characteristics of the breast cancer cell. In this review, we provide a broad survey discussing the cellular and molecular consequences of oestrogen signalling in breast cancer. First, we review the structure of the classical oestrogen receptors and resultant transcriptional (genomic) and non-transcriptional (non-genomic) signalling. We then discuss the nature of oestradiol signalling in breast cancer including the specific receptors that initiate these signalling cascades as well as potential outcomes, such as cancer growth, proliferation and angiogenesis. Finally, we examine cellular and molecular mechanisms underlying the dimorphic effect of oestrogen signalling in breast cancer.

Flaxseed and its components differentially affect estrogen targets in pre-neoplastic hen ovaries

Flaxseed has been studied for decades for its health benefits that include anti-cancer, cardio-protective, anti-diabetic, anti-inflammatory properties. The biologically active components that mediate these effects are the omega-3 fatty acids and the lignan, secoisolariciresinol diglucoside. We have previously shown that whole flaxseed supplemented diet decreases the severity and incidence of ovarian cancer while a 15% dose of flaxseed is most protective against inflammation and estrogen-induced chemical and genotoxicity. The objective of this study was to dissect the independent effects of the two flaxseed components on estrogen signaling and metabolism. Two and half year old hens were fed either a control diet, 15% whole flaxseed diet, defatted flax meal diet or 5% flax oil diet for 3 months after which the animals were sacrificed and blood and tissues were harvested. Whole flaxseed diet caused a decrease in expression of ERα. ERα target gene expression was assessed using RT(2) profiler PCR array. Some targets involved in the IGF/insulin signaling pathway (IRS1, IGFBP4, IGFBP5) were downregulated by flaxseed and its components. Flaxseed diet also downregulated AKT expression. A number of targets related to NF-kB signaling were altered by flaxseed diet including a series of targets implicated in cancer. Whole flaxseed diet also affected E2 metabolism by increasing CYP1A1 expression with a corresponding increase in the onco-protective E2 metabolite, 2-methoxyestradiol. The weak anti-estrogens, enterolactone, enterodiol and 2-methoxyestradiol, might be working synergistically to generate a protective effect on the ovaries from hens on whole flaxseed diet by altering the estrogen signaling and metabolism.

Oestrogen receptors interact with the α-catalytic subunit of AMP-activated protein kinase

We identified a novel interaction between the classical oestrogen receptors (ERα and ERβ) and the catalytic subunit of AMP-activated protein kinase (AMPK) in several cell types. In addition, we demonstrate that oestradiol (E2) activates AMPK through ERα and requires the upstream kinase complex liver kinase B (LKB1).

Normal and pathological stressors engage the AMP-activated protein kinase (AMPK) signalling axis to protect the cell from energetic pressures. Sex steroid hormones also play a critical role in energy metabolism and significantly modify pathological progression of cardiac disease, diabetes/obesity and cancer. AMPK is targeted by 17β-oestradiol (E2), the main circulating oestrogen, but the mechanism by which E2 activates AMPK is currently unknown. Using an oestrogen receptor α/β (ERα/β) positive (T47D) breast cancer cell line, we validated E2-dependent activation of AMPK that was mediated through ERα (not ERβ) by using three experimental strategies. A series of co-immunoprecipitation experiments showed that both ERs associated with AMPK in cancer and striated (skeletal and cardiac) muscle cells. We further demonstrated direct binding of ERs to the α-catalytic subunit of AMPK within the βγ-subunit-binding domain. Finally, both ERs interacted with the upstream liver kinase B 1 (LKB1) kinase complex, which is required for E2-dependent activation of AMPK. We conclude that E2 activates AMPK through ERα by direct interaction with the βγ-binding domain of AMPKα.

Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes

Multiple signal transduction pathways can affect each other considerably through crosstalk. However, the presence and extent of this phenomenon have not been rigorously studied. The aim of the present study was to identify strong and normal interactions between pathways in breast cancer and determine the main pathway. Five sets of breast cancer data were downloaded from the high-throughput Gene Expression Omnibus (GEO) and analyzed to identify differentially expressed (DE) genes using the Rank Product (RankProd) method. A list of pathways with differential expression was obtained by gene set enrichment analysis (GSEA) of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DE genes that overlapped between pathways were identified and a crosstalk network diagram based on the overlap of DE genes was constructed. A total of 1,464 DE genes and 26 pathways were identified. In addition, the number of DE genes that overlapped between specific pathways were determined, and the greatest degree of overlap was between the extracellular matrix (ECM)-receptor interaction and Focal adhesion pathways, which had 22 overlapping DE genes. Weighted pathway analysis of the crosstalk between pathways identified that Pathways in cancer was the main pathway in breast cancer.

DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method

MOTIVATION

The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.

RESULTS

Here, we propose a method for uncovering the dominant effects of cancer-driver genes (DEOD) based on a partial covariance selection approach. Inspired by a convex optimization technique, it estimates the dominant effects of candidate cancer-driver genes on the expression level changes of their target genes. It constructs a gene network as a directed-weighted graph by integrating DNA copy numbers, single nucleotide mutations and gene expressions from matched tumor samples, and estimates partial covariances between driver genes and their target genes. Then, a scoring function to measure the cancer-driver score for each gene is applied. To test the performance of DEOD, a novel scheme is designed for simulating conditional multivariate normal variables (targets and free genes) given a group of variables (driver genes). When we applied the DEOD method to both the simulated data and breast cancer data, DEOD successfully uncovered driver variables in the simulation data, and identified well-known oncogenes in breast cancer. In addition, two highly ranked genes by DEOD were related to survival time. The copy number amplifications of MYC (8q24.21) and TRPS1 (8q23.3) were closely related to the survival time with P-values = 0.00246 and 0.00092, respectively. The results demonstrate that DEOD can efficiently uncover cancer-driver genes.

The human toxome project

The Human Toxome Project, funded as an NIH Transformative Research grant 2011-2016, is focused on developing the concepts and the means for deducing, validating and sharing molecular pathways of toxicity (PoT). Using the test case of estrogenic endocrine disruption, the responses of MCF-7 human breast cancer cells are being phenotyped by transcriptomics and mass-spectroscopy-based metabolomics. The bioinformatics tools for PoT deduction represent a core deliverable. A number of challenges for quality and standardization of cell systems, omics technologies and bioinformatics are being addressed. In parallel, concepts for annotation, validation and sharing of PoT information, as well as their link to adverse outcomes, are being developed. A reasonably comprehensive public database of PoT, the Human Toxome Knowledge-base, could become a point of reference for toxicological research and regulatory test strategies.

Systems level analysis and identification of pathways and networks associated with liver fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.