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

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.

CCL2 and CCL5 Are Novel Therapeutic Targets for Estrogen-Dependent Breast Cancer

“…These results were corroborated in experimental breast cancer models, in which E2 significantly increased the levels of CCL2 and CCL5 in vivo. This effect may be a result of a direct gene regulation via an estrogen responsive element as computational data on large genome datasets suggest an ERa-binding site in the promoter region of the CCL2 (44). In addition, E2 may indirectly affect chemokine expression by interacting with the transcription factors AP-1 and NF-kB that bind to promoter regions of CCL2 and CCL5 (45)(46)(47).…”

Germ-line MTHFR C677T, FV H1299R and PAI-1 5G/4G Variations in Breast Carcinoma

“…Literature findings showed that, the risk factors are mainly derived from environmental carcinogens, hormonal, genetic parameters, and obesity in BC tumorigenesis . The genetic parameters are focused on; oncogenes, epigenetic alterations in tumour supressor genes, oncogenic viruses and many other intrinsic and/or extrinsic environmental factors may directly play crucial role in BC inititiation and/or progression (Werner and Bruchim, 2012;Bhar et al, 2013). In contrast, limited findings are reported about failure of germ-line functional gene effect on familiar BC.…”

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

“…The tumor suppressor BRCA1 may collaborate with Oct1 in Gadd45a regulation [109] and act as an Oct1 cofactor at the Esr1 promoter [110]. Esr1 encodes ERalpha and is an Oct1 target gene [111]. Oct1 and BRCA1 have been documented to interact [109, 112, 113].…”