Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, P.R. China.
Eur Rev Med Pharmacol Sci. 2013 Mar;17(6):758-66.
The aim of this study is to investigate the dysregulated biological functions that play important role in the occurrence and development of breast invasive ductal carcinoma (IDC).
We downloaded the gene expression profile data from gene expression omnibus (GEO) database, including 42 disease samples and 143 adjacent histological normal samples. Significance analysis of microarrays (SAM) was employed to identify differentially expressed genes (DEGs) between the normal and disease samples. Gene ontology (GO) function enrichment analysis was based on Software DAVID, followed by KEGG pathway enrichment analysis. TRANSFAC database and HPRD database were employed to construct the transcriptional regulatory network (Tnet) and protein-protein interaction (PPI) network, respectively.
We got a total of 1769 genes significantly differentially expressed, including 907 up-regulated genes and 862 down-regulated genes. Functional analysis revealed that hormone-responsive genes are related with the occurrence of cancer. Then, we successfully constructed IDC-specific Tnet and PPI network with DEGs response to hormone and obtained some hub genes, such as FOS and PIK3R1, in these networks. Besides, ten modules were found in these networks.
Hormone-responsive genes and modules may play an important role in the occurrence and development of IDC. Based on the findings above, we got a preliminary understand of the occurrence, development and metastasis of the IDC and possibly provided effective information on the biogenesis of IDC.
本研究旨在探讨在乳腺浸润性导管癌（IDC）发生和发展中起重要作用的失调生物学功能。
我们从基因表达综合数据库（GEO）下载了基因表达谱数据，包括 42 个疾病样本和 143 个相邻组织学正常样本。采用差异表达基因分析（SAM）鉴定正常和疾病样本之间的差异表达基因（DEGs）。基于 DAVID 软件进行基因本体（GO）功能富集分析，然后进行 KEGG 通路富集分析。分别使用 TRANSFAC 数据库和 HPRD 数据库构建转录调控网络（Tnet）和蛋白质-蛋白质相互作用（PPI）网络。
我们共得到 1769 个显著差异表达基因，其中 907 个上调基因和 862 个下调基因。功能分析表明，激素反应基因与癌症的发生有关。然后，我们成功构建了 IDC 特异性的 Tnet 和 PPI 网络，这些网络中的 DEGs 对激素有反应，并获得了一些核心基因，如 FOS 和 PIK3R1。此外，在这些网络中还发现了 10 个模块。
激素反应基因和模块可能在 IDC 的发生、发展中起重要作用。基于以上发现，我们初步了解了 IDC 的发生、发展和转移，可能为 IDC 的发生提供了有效的信息。
