Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell

Gene Co-Expression Analysis of Multiple Brain Tissues Reveals Correlation of FAM222A Expression with Multiple Alzheimer's Disease-Related Genes

Background

Alzheimer's disease (AD) is the most common form of dementia in the elderly marked by central nervous system (CNS) neuronal loss and amyloid plaques. FAM222A, encoding an amyloid plaque core protein, is an AD brain atrophy susceptibility gene that mediates amyloid-β aggregation. However, the expression interplay between FAM222A and other AD-related pathway genes is unclear.

Objective

Our goal was to study FAM222A's whole-genome co-expression profile in multiple tissues and investigate its interplay with other AD-related genes.

Methods

We analyzed gene expression correlations in Genotype-Tissue Expression (GTEx) tissues to identify FAM222A co-expressed genes and performed functional enrichment analysis on identified genes in CNS system.

Results

Genome-wide gene expression profiling identified 673 genes significantly correlated with FAM222A (p < 2.5×10-6) in 48 human tissues, including 298 from 13 CNS tissues. Functional enrichment analysis revealed that FAM222A co-expressed CNS genes were enriched in multiple AD-related pathways. Gene co-expression network analysis for identified genes in each brain region predicted other disease associated genes with similar biological function. Furthermore, co-expression of 25 out of 31 AD-related pathways genes with FAM222A was replicated in brain samples from 107 aged subjects from the Aging, Dementia and TBI Study.

Conclusion

This gene co-expression study identified multiple AD-related genes that are associated with FAM222A, indicating that FAM222A and AD-associated genes can be active simultaneously in similar biological processes, providing evidence that supports the association of FAM222A with AD.

Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators

To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.

miR-6087 Might Regulate Cell Cycle–Related mRNAs During Cardiomyogenesis of hESCs

MicroRNAs (miRNAs) are small noncoding RNAs that act as negative regulators of gene expression at the post-transcriptional level, promoting mRNA degradation or translation repression. Despite the well-described presence of miRNAs in various human tissues, there is still a lack of information about the relationship between miRNAs and the translation regulation in human embryonic stem cells (hESCs) during cardiomyogenesis. Here, we investigate RNA-seq data from hESCs, focusing on distinct stages of cardiomyogenesis and searching for polysome-bound miRNAs that could be involved in translational regulation. We identify miR-6087 as a differentially expressed miRNA at latest steps of cardiomyocyte differentiation. We analyzed the coexpression pattern between the differentially expressed mRNAs and miR-6087, evaluating whether they are predicted targets of the miRNA. We arranged the genes into an interaction network and identified BLM, RFC4, RFC3, and CCNA2 as key genes of the network. A post hoc analysis of the key genes suggests that miR-6087 could act as a regulator of the cell cycle in hESC during cardiomyogenesis.

Exercise training attenuates angiotensin II-induced cardiac fibrosis by reducing POU2F1 expression

PURPOSE

Exercise training protects against heart failure. However, the mechanism underlying the protective effect of exercise training on angiotensin II (Ang II)-induced cardiac fibrosis remains unclear.

METHODS

An exercise model involving C57BL/6N mice and 6 weeks of treadmill training was used. Ang II (1.44 mg/kg/day) was administered to induce cardiac fibrosis. RNA sequencing and bioinformatic analysis were used to identify the key factors mediating the effects of exercise training on cardiac fibrosis. Primary adult mouse cardiac fibroblasts (CFs) were used in vitro. Adeno-associated virus serotype 9 was used to overexpress POU domain, class 2, transcription factor 1 (POU2F1) in vivo.

RESULTS

Exercise training attenuated Ang II-induced cardiac fibrosis and reversed 39 gene expression changes. The transcription factor regulating the largest number of these genes was POU2F1. Compared to controls, POU2F1 was shown to be significantly upregulated by Ang II, which is itself reduced by exercise training. In vivo, POU2F1 overexpression nullified the benefits of exercise training on cardiac fibrosis. In CFs, POU2F1 promoted cardiac fibrosis. CCAAT enhancer-binding protein β (C/EBPβ) was predicted to be the transcription factor of POU2F1 and verified using a dual-luciferase reporter assay. In vivo, exercise training activated AMP-activated protein kinase (AMPK) and alleviated the increase in C/EBPβ induced by Ang II. In CFs, AMPK agonist inhibited the increase in C/EBPβ and POU2F1 induced by Ang II, whereas AMPK inhibitor reversed this effect.

CONCLUSION

Exercise training attenuates Ang II-induced cardiac fibrosis by reducing POU2F1. Exercise training inhibits POU2F1 by activating AMPK, which is followed by the downregulation of C/EBPβ, the transcription factor of POU2F1.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

Acquired small cell lung cancer resistance to Chk1 inhibitors involves Wee1 up-regulation

Platinum‐based chemotherapy has been the cornerstone treatment for small cell lung cancer (SCLC) for decades, but no major progress has been made in the past 20 years with regard to overcoming chemoresistance. As the cell cycle checkpoint kinase 1 (Chk1) plays a key role in DNA damage response to chemotherapeutic drugs, we explored the mechanisms of acquired drug resistance to the Chk1 inhibitor prexasertib in SCLC. We established prexasertib resistance in two SCLC cell lines and found that DNA copy number, messengerRNA (mRNA) and protein levels of the cell cycle regulator Wee1 significantly correlate with the level of acquired resistance. Wee1 small interfering RNA (siRNA) or Wee1 inhibitor reversed prexasertib resistance, whereas Wee1 transfection induced prexasertib resistance in parental cells. Reverse phase protein microarray identified up‐regulated proteins in the resistant cell lines that are involved in apoptosis, cell proliferation and cell cycle. Down‐regulation of CDK1 and CDC25C kinases promoted acquired resistance in parental cells, whereas down‐regulation of p38MAPK reversed the resistance. High Wee1 expression was significantly correlated with better prognosis of resected SCLC patients. Our results indicate that Wee1 overexpression plays an important role in acquired resistance to Chk1 inhibition. We also show that bypass activation of the p38MAPK signaling pathway may contribute to acquired resistance to Chk1 inhibition. The combination of Chk1 and Wee1 inhibitors may provide a new therapeutic strategy for the treatment of SCLC.

G-Tric: generating three-way synthetic datasets with triclustering solutions

BACKGROUND

Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations [Formula: see text] features [Formula: see text] contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output.

RESULTS

G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters.

CONCLUSIONS

Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric's potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

TriRNSC: triclustering of gene expression microarray data using restricted neighbourhood search

Computational analysis of microarray data is crucial for understanding the gene behaviours and deriving meaningful results. Clustering and biclustering of gene expression microarray data in the unsupervised domain are extremely important as their outcomes directly dominate healthcare research in many aspects. However, these approaches fail when the time factor is added as the third dimension to the microarray datasets. This three-dimensional data set can be analysed using triclustering that discovers similar gene sets that pursue identical behaviour under a subset of conditions at a specific time point. A novel triclustering algorithm (TriRNSC) is proposed in this manuscript to discover meaningful triclusters in gene expression profiles. TriRNSC is based on restricted neighbourhood search clustering (RNSC), a popular graph-based clustering approach considering the genes, the experimental conditions and the time points at an instance. The performance of the proposed algorithm is evaluated in terms of volume and some performance measures. Gene Ontology and KEGG pathway analysis are used to validate the TriRNSC results biologically. The efficiency of TriRNSC indicates its capability and reliability and also demonstrates its usability over other state-of-art schemes. The proposed framework initiates the application of the RNSC algorithm in the triclustering of gene expression profiles.

CD47 (Cluster of Differentiation 47)

CD47, also known as integrin-associated protein, is a constitutively and ubiquitously expressed transmembrane receptor. CD47 is conserved across amniotes including mammals, reptiles, and birds. Expression is increased in many cancers and, in non-malignant cells, by stress and with aging. The up-regulation of CD47 expression is generally epigenetic, whereas gene amplification occurs with low frequency in some cancers. CD47 is a high affinity signaling receptor for the secreted protein thrombospondin-1 (THBS1) and the counter-receptor for signal regulatory protein-α (SIRPA, SIRPα) and SIRPγ (SIRPG). CD47 interaction with SIRPα serves as a marker of self to innate immune cells and thereby protects cancer cells from phagocytic clearance. Consequently, higher CD47 correlates with a poor prognosis in some cancers, and therapeutic blockade can suppress tumor growth by enhancing innate antitumor immunity. CD47 expressed on cytotoxic T cells, dendritic cells, and NK cells mediates inhibitory THBS1 signaling that further limits antitumor immunity. CD47 laterally associates with several integrins and thereby regulates cell adhesion and migration. CD47 has additional lateral binding partners in specific cell types, and ligation of CD47 in some cases modulates their function. THBS1-CD47 signaling in non-malignant cells inhibits nitric oxide/cGMP, calcium, and VEGF signaling, mitochondrial homeostasis, stem cell maintenance, protective autophagy, and DNA damage response, and promotes NADPH oxidase activity. CD47 signaling is a physiological regulator of platelet activation, angiogenesis and blood flow. THBS1/CD47 signaling is frequently dysregulated in chronic diseases.

Evaluation of classification and forecasting methods on time series gene expression data

Time series gene expression data is widely used to study different dynamic biological processes. Although gene expression datasets share many of the characteristics of time series data from other domains, most of the analyses in this field do not fully leverage the time-ordered nature of the data and focus on clustering the genes based on their expression values. Other domains, such as financial stock and weather prediction, utilize time series data for forecasting purposes. Moreover, many studies have been conducted to classify generic time series data based on trend, seasonality, and other patterns. Therefore, an assessment of these approaches on gene expression data would be of great interest to evaluate their adequacy in this domain. Here, we perform a comprehensive evaluation of different traditional unsupervised and supervised machine learning approaches as well as deep learning based techniques for time series gene expression classification and forecasting on five real datasets. In addition, we propose deep learning based methods for both classification and forecasting, and compare their performances with the state-of-the-art methods. We find that deep learning based methods generally outperform traditional approaches for time series classification. Experiments also suggest that supervised classification on gene expression is more effective than clustering when labels are available. In time series gene expression forecasting, we observe that an autoregressive statistical approach has the best performance for short term forecasting, whereas deep learning based methods are better suited for long term forecasting.

Downregulation of 15-hydroxyprostaglandin dehydrogenase during acquired tamoxifen resistance and association with poor prognosis in ERα-positive breast cancer

Aim:

Tamoxifen (TAM) resistance remains a clinical issue in breast cancer. The authors previously reported that 15-hydroxyprostaglandin dehydrogenase (HPGD) was significantly downregulated in tamoxifen-resistant (TAMr) breast cancer cell lines. Here, the authors investigated the relationship between HPGD expression, TAM resistance and prediction of outcome in breast cancer.

Methods:

HPGD overexpression and silencing studies were performed in isogenic TAMr and parental human breast cancer cell lines to establish the impact of HPGD expression on TAM resistance. HPGD expression and clinical outcome relationships were explored using immunohistochemistry and in silico analysis.

Results:

Restoration of HPGD expression and activity sensitised TAMr MCF-7 cells to TAM and 17β-oestradiol, whilst HPGD silencing in parental MCF-7 cells reduced TAM sensitivity. TAMr cells released more prostaglandin E₂ (PGE₂) than controls, which was reduced in TAMr cells stably transfected with HPGD. Exogenous PGE₂ signalled through the EP4 receptor to reduce breast cancer cell sensitivity to TAM. Decreased HPGD expression was associated with decreased overall survival in ERα-positive breast cancer patients.

Conclusions:

HPGD downregulation in breast cancer is associated with reduced response to TAM therapy via PGE₂-EP4 signalling and decreases patient survival. The data offer a potential target to develop combination therapies that may overcome acquired tamoxifen resistance.

Coordinated analysis of exon and intron data reveals novel differential gene expression changes

RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level. This coordinated analysis of exon and intron data offers strong evidence for significant differences that distinguish the profiles of the exon-only expression data from the combined exon and intron data. One advantage of our proposed method, called matched change characterization for exons and introns (MEI), is its straightforward applicability to existing archived data using small modifications to standard RNA-Seq pipelines. Using MEI, we demonstrate that when data are examined for changes in variability across control and case conditions, novel differential changes can be detected. Notably, when MEI criteria were employed in the analysis of an archived data set involving polyarthritic subjects, the number of differentially expressed genes was expanded by sevenfold. More importantly, the observed changes in exon and intron variability with statistically significant false discovery rates could be traced to specific immune pathway gene networks. The application of MEI analysis provides a strategy for incorporating the significance of exon and intron variability and further developing the role of using both exons and intron sequencing counts in studies of gene regulatory processes.

The emerging role of BET inhibitors in breast cancer

Bromodomain and extraterminal domain (BET) proteins are epigenetic molecules that regulate the expression of multiple genes involved in carcinogenesis. Breast cancer is an heterogenous disease emerging from aberrant gene expression and epigenetic alteration patterns. Amplification or overexpression of BET proteins has been identified in breast tumors highlighting their clinical significance. Development of BET inhibitors that disrupt BET protein binding to acetylated lysine residues of chromatin and suppress transcription of various oncogenes has shown promising results in breast cancer cells and xenograft models. Currently, Phase I/II clinical trials explore safety and efficacy of BET inhibitors in solid tumors and breast cancer. Treatment-emergent toxicities have been reported, including thrombocytopenia and gastrointestinal disorders. Preliminary results demonstrated greater response rates to BET inhibitors in combination with already approved anticancer agents. Consistently, BET inhibition sensitized breast tumors to chemotherapy drugs, hormone therapy and PI3K inhibitors in vitro. This article aims to review all existing preclinical and clinical evidence regarding BET inhibitors in breast cancer.

The “Gene Cube”: A Novel Approach to Three-dimensional Clustering of Gene Expression Data

Background:

A very popular technique for isolating significant genes from cancerous tissues is the application of various clustering algorithms on data obtained by DNA microarray experiments.

Aim:

The objective of the present work is to take into consideration the chromosomal identity of every gene before the clustering, by creating a three-dimensional structure of the form Chromosomes×Genes×Samples. Further on, the k-Means algorithm and a triclustering technique called δ- TRIMAX, are applied independently on the structure.

Materials and Methods:

The present algorithm was developed using the Python programming language (v. 3.5.1). For this work, we used two distinct public datasets containing healthy control samples and tissue samples from bladder cancer patients. Background correction was performed by subtracting the median global background from the median local Background from the signal intensity. The quantile normalization method has been applied for sample normalization. Three known algorithms have been applied for testing the “gene cube”, a classical k-means, a transformed 3D k-means and the δ-TRIMAX.

Results:

Our proposed data structure consists of a 3D matrix of the form Chromosomes×Genes×Samples. Clustering analysis of that structure manifested very good results as we were able to identify gene expression patterns among samples, genes and chromosomes. Discussion: to the best of our knowledge, this is the first time that such a structure is reported and it consists of a useful tool towards gene classification from high-throughput gene expression experiments.

Conclusion:

Such approaches could prove useful towards the understanding of disease mechanics and tumors in particular.

TRIQ: a new method to evaluate triclusters

Background

Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO).

Results

We propose TRIQ, a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster.

Conclusions

TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Targeting bromodomain and extraterminal proteins in breast cancer

Breast cancer is a collection of distinct tumor subtypes that are driven by unique gene expression profiles. These transcriptomes are controlled by various epigenetic marks that dictate which genes are expressed and suppressed. During carcinogenesis, extensive restructuring of the epigenome occurs, including aberrant acetylation, alteration of methylation patterns, and accumulation of epigenetic readers at oncogenes. As epigenetic alterations are reversible, epigenome-modulating drugs could provide a mechanism to silence numerous oncogenes simultaneously. Here, we review the impact of inhibitors of the Bromodomain and Extraterminal (BET) family of epigenetic readers in breast cancer. These agents, including the prototypical BET inhibitor JQ1, have been shown to suppress a variety of oncogenic pathways while inducing minimal, if any, toxicity in models of several subtypes of breast cancer. BET inhibitors also synergize with multiple approved anti-cancer drugs, providing a greater response in breast cancer cell lines and mouse models than either single agent. The combined findings of the studies discussed here provide an excellent rationale for the continued investigation of the utility of BET inhibitors in breast cancer.

The Oct1 transcription factor and epithelial malignancies: Old protein learns new tricks

The metazoan-specific POU domain transcription factor family comprises activities underpinning developmental processes such as embryonic pluripotency and neuronal specification. Some POU family proteins efficiently bind an 8-bp DNA element known as the octamer motif. These proteins are known as Oct transcription factors. Oct1/POU2F1 is the only widely expressed POU factor. Unlike other POU factors it controls no specific developmental or organ system. Oct1 was originally described to operate at target genes associated with proliferation and immune modulation, but more recent results additionally identify targets associated with oxidative and cytotoxic stress resistance, metabolic regulation, stem cell function and other unexpected processes. Oct1 is pro-oncogenic in multiple contexts, and several recent reports provide broad evidence that Oct1 has prognostic and therapeutic value in multiple epithelial tumor settings. This review focuses on established and emerging roles of Oct1 in epithelial tumors, with an emphasis on mechanisms of transcription regulation by Oct1 that may underpin these findings. This article is part of a Special Issue entitled: The Oct Transcription Factor Family, edited by Dr. Dean Tantin.

Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes

Background

Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits.

Results

In this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm δ-TRIMAX. Its aim is to make optimal use of δ-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA- δ-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation.

Conclusions

EMOA- δ-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0635-8) contains supplementary material, which is available to authorized users.

"Upstream Analysis": An Integrated Promoter-Pathway Analysis Approach to Causal Interpretation of Microarray Data

A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators.

Germ-line MTHFR C677T, FV H1299R and PAI-1 5G/4G variations in breast carcinoma

BACKGROUND

Various oncogenes related to cancer have been extensively studied and several polymorphisms have been found to be associated with breast cancer. The current report outlines analysis of germ-line polymorphisms for C677T, A1298C (MTHFR), Leiden, R2 (FV) and 5G/4G (PAI-1) in Turkish breast cancer patients. We studied 51 cases diagnosed with invasive ductal and operable with lymph node-positive breast cancer and 106 women as a control group.

MATERIALS AND METHODS

Peripheric blood-DNA samples were used for genotyping by StripAssay technique which is based on the reverse-hybridization principle and real-time PCR methods and results were compared statistically.

RESULTS

The frequency of the MTHFR gene 677T and 1298A alleles were significantly higher in cancer patients than in the healthy subjects. The T allele frequency in codon 677 was 2.3-fold and C allele frequency was 3.1-fold increased in BC when compared to the control group for the MTHFR gene. Both differences were statistically significant (OR: 2.295, CI: 1.283-4.106), p<0.006 and (OR: 3.131, CI:1.826-5.369), p<0.0001 respectively. The R2 allele frequency of FV gene was 5.1-fold increased in the current BC when compared to the control group and that difference was also statistically significant (OR: 5.133, CI: 1.299-20.28), p<0.02.

CONCLUSIONS

The present data suggest that germ-line polymorphisms of C677T, C1298A for MTHFR and R2 for FV are associated in breast cancer and may be additional prognostic markers related to breast cancer survival. The results now need to be confirmed in a larger group of patients.

Transcriptional regulation prediction of antiestrogen resistance in breast cancer based on RNA polymerase II binding data

Background

Although endocrine therapy impedes estrogen-ER signaling pathway and thus reduces breast cancer mortality, patients remain at continued risk of relapse after tamoxifen or other endocrine therapies. Understanding the mechanisms of endocrine resistance, particularly the role of transcriptional regulation is very important and necessary.

Methods

We propose a two-step workflow based on linear model to investigate the significant differences between MCF7 and OHT cells stimulated by 17β-estradiol (E2) respect to regulatory transcription factors (TFs) and their interactions. We additionally compared predicted regulatory TFs based on RNA polymerase II (PolII) binding quantity data and gene expression data, which were taken from MCF7/MCF7+E2 and OHT/OHT+E2 cell lines following the same analysis workflow. Enrichment analysis concerning diseases and cell functions and regulatory pattern analysis of different motifs of the same TF also were performed.

Results

The results showed PolII data could provide more information and predict more recognizably important regulatory TFs. Large differences in TF regulatory mode were found between two cell lines. Through verified through GO annotation, enrichment analysis and related literature regarding these TFs, we found some regulatory TFs such as AP-1, C/EBP, FoxA1, GATA1, Oct-1 and NF-κB, maintained OHT cells through molecular interactions or signaling pathways that were different from the surviving MCF7 cells. From TF regulatory interaction network, we identified E2F, E2F-1 and AP-2 as hub-TFs in MCF7 cells; whereas, in addition to E2F and E2F-1, we identified C/EBP and Oct-1 as hub-TFs in OHT cells. Notably, we found the regulatory patterns of different motifs of the same TF were very different from one another sometimes.

Conclusions

We inferred some regulatory TFs, such as AP-1 and NF-κB, cooperated with ER through both genomic action and non-genomic action. The TFs that were involved in both protein-protein interactions and signaling pathways could be one of the key resistant mechanisms of endocrine therapy and thus also could be new treatment targets for endocrine resistance. Our flexible workflow could be integrated into an existing analytical framework and guide biologists to further determine underlying mechanisms in human diseases.