ArticleAlgorithms for molecular biology : AMB2013
Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell.
Article in Algorithms for molecular biology : AMB, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 47 citations in OpenAlex.
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- Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer.Bioinformatics and biology insights · 2024Article
- Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators.Plants (Basel, Switzerland) · 2023Article
- Exercise training attenuates angiotensin II-induced cardiac fibrosis by reducing POU2F1 expression.Journal of sport and health science · 2023Article
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- Acquired small cell lung cancer resistance to Chk1 inhibitors involves Wee1 up-regulation.Molecular oncology · 2021Article
- G-Tric: generating three-way synthetic datasets with triclustering solutions.BMC bioinformatics · 2021Article
- CD47 (Cluster of Differentiation 47).Atlas of genetics and cytogenetics in oncology and haematology · 2021Article
- TriRNSC: triclustering of gene expression microarray data using restricted neighbourhood search.IET systems biology · 2020Article
- The emerging role of BET inhibitors in breast cancer.Breast (Edinburgh, Scotland) · 2020Review
- Coordinated analysis of exon and intron data reveals novel differential gene expression changes.Scientific reports · 2020Article
- Downregulation of 15-hydroxyprostaglandin dehydrogenase during acquired tamoxifen resistance and association with poor prognosis in ERα-positive breast cancer.Exploration of targeted anti-tumor therapy · 2020Article
- Evaluation of classification and forecasting methods on time series gene expression data.PloS one · 2020Article
- Gene co-expression analysis for functional classification and gene-disease predictions.Briefings in bioinformatics · 2018Article
- Targeting bromodomain and extraterminal proteins in breast cancer.Pharmacological research · 2018Review
- TRIQ: a new method to evaluate triclusters.BioData mining · 2018Article
- The Oct1 transcription factor and epithelial malignancies: Old protein learns new tricks.Biochimica et biophysica acta · 2016Review
- Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes.BMC bioinformatics · 2015Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEstrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm δ-TRIMAX that aims to find genes that are coexpressed over subset of samples across a subset of time points. Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold δ.
resultsWe have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor α. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1.
conclusionsThus our integrative approach provides insights into breast cancer prognosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.