Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell.
Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell.
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DOI：
10.1186/1748-7188-8-9
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发表时间：
2013-03-23
期刊：
影响因子：
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通讯作者：
Wingender E
中科院分区：
文献类型：
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作者：
Bhar A;Haubrock M;Mukhopadhyay A;Maulik U;Bandyopadhyay S;Wingender E
雌激素是一种化学信使，对许多乳腺癌有影响，因为它有助于细胞生长和分裂。这些癌症通常被称为雌激素反应性癌症，其中雌激素受体占据细胞表面。乳腺癌的成功治疗需要了解基因表达，识别肿瘤标志物，获取细胞通路的知识等。在本文中，我们介绍了我们提出的三聚算法δ-TRIMAX，旨在找到在一个子集的时间点的样本的子集上共表达的基因。在这里，我们介绍了一种新的均方残差这样的3D数据集。我们提出的算法产生三聚体，具有低于阈值δ的均方残差得分。我们已经在一个模拟数据集和一个真实数据集上应用了我们的算法。真实生活数据集是雌激素诱导的乳腺癌细胞系中的时间序列数据集。为了建立属于所得三聚体的基因的生物学意义，我们进行了基因本体，KEGG途径和转录因子结合位点富集分析。此外，我们通过计算其特征基因来代表每个所得的三聚体，并验证其特征基因是否在雌激素反应的早期、中期和晚期也有差异表达。我们还确定了枢纽基因，为每个所得的三聚体，并验证枢纽基因是否被发现与乳腺癌。通过我们的分析，CCL 2、CD 47、NFIB、BRD 4、HPGD、CSNK 1 E、NPC 1 L1、PTEN、PTPN 2和ADAM 9被鉴定为已知与乳腺癌相关的枢纽基因。其他基因也被确定为枢纽基因，可能与乳腺癌或雌激素反应元件有关。TFBS富集分析还揭示了转录因子POU 2F 1与编码雌激素受体α的ESR 1启动子区结合。转录因子E2 F1与共表达基因MCM 7、ANAPC 1和WEE 1的启动子区结合。因此，我们的综合方法提供了对乳腺癌预后的见解。
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. 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. Thus our integrative approach provides insights into breast cancer prognosis.