Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell
Open accessAbstract
BACKGROUND: Estrogen 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. RESULTS: We 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. CONCLUSIONS: Thus our integrative approach provides insights into breast cancer prognosis.
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Differential co-expression analysis identifies genes with varying co-expression partners under different conditions.
“An increasingly used method that goes beyond traditional co-expression networks is differential co-expression analysis [4–7]. This approach identifies genes with varying co-expression partners under different conditions, such as disease states [4, 8–10], tissue types [11] and developmental stages [12], because these genes are more likely to be regulators that underlie phenotypic differences.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [6]
The Background (page unavailable) describes identifying genes coexpressed over subsets of experimental conditions, and the Conclusion applies that approach to estrogen-induced breast cancer cells. The paper does not show genes changing co-expression partners between disease states, so it does not fully support the differential co-expression claim. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA three-dimensional clustering method (triclustering) effectively identified several known breast cancer genes in a breast cancer cell line.
“This method effectively identified several known breast cancer genes in a breast cancer cell line by identifying hub genes in triclusters differentially expressed between cancer samples at early and late time points using the eigengene changes between the samples of each tricluster [6].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [6]
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Cai, H., & Marais, D. L. D. (2022) · Research Square · doi:10.21203/rs.3.rs-1220378/v1
Haubrock, M. (2021) · doi:10.53846/goediss-8880
Self-citationThe functional evolution of collembolan Ubx on the regulation of abdominal appendage formation
Liang, Y., & Luan, Y. (2024) · Development Genes and Evolution · doi:10.1007/s00427-024-00718-0
- Authors
- Bhar, A., Haubrock, M., Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Wingender, E.
- Affiliations
- Universitätsmedizin Göttingen (DE)
- University of Kalyani (IN)
- Jadavpur University (IN)
- Indian Statistical Institute (IN)
- Corresponding author
- Bhar, Anirban
- Maulik, Ujjwal
- Bandyopadhyay, Sanghamitra
- Wingender, Edgar
- ORCID
- Venue
- Algorithms for Molecular Biology
- Published
- 2013-03-23
- Volume
- 8
- Issue
- 1
- Pages
- 9-9
- ISSN
- 1748-7188 (ISSN-L)
- PMC
- PMC3827943
- OpenAlex
- W2116476540
- Cited by
- 44
- References
- 49
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 5 · 2022: 5 · 2021: 5 · 2020: 8 · 2019: 3 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 6 · 2014: 1 · 2013: 1
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Algorithms for Molecular Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- triclustering, gene co-expression, temporal gene expression, estrogen receptor alpha, ESR1, hub genes, eigengenes, transcription factor binding sites, POU2F1, E2F1, breast cancer prognosis
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