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ORIGINAL ARTICLE
Year : 2014  |  Volume : 10  |  Issue : 4  |  Page : 1013-1018

Bioinformatics analysis of aggressive behavior of breast cancer via an integrated gene regulatory network


1 Department of Surgery, People's Hospital of Linzi District, Affiliated to Binzhou Medical College, Zibo, China
2 Department of Radiology, People's Hospital of Linzi District, Affiliated to Binzhou Medical College, Zibo, China
3 Department of Radiation Oncology, People's Hospital of Linzi District, Affiliated to Binzhou Medical College, Zibo, China

Correspondence Address:
Gang Yu
Department of Radiation Oncology, People's Hospital of Linzi District, Affiliated to Binzhou Medical College, Zibo 255400
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.137971

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Background: Breast cancer is one of the most frequently diagnosed cancers in women. Though death from this disease is mainly caused by the metastases of the aggressive cancer cells, few studies have expounded the aggressive behavior of breast cancer. Materials and Methods: We downloaded the gene expression profiles of GSE40057, including four aggressive and six less-aggressive breast cancer cell lines, from Gene Expression Omnibus and identified the differentially expressed genes (DEGs) between the aggressive and less-aggressive samples. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Then, motifs and modules of the network were identified. Modules were further analyzed at a functional level using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to study the aggressive behavior of breast cancer. Results: A total of 764 DEGs were found and two modules were filtered from the integrated gene regulatory network. Totally two motifs and modules for DEGs were identified. Significant GO terms associated with cell proliferation and hormone stimulus of the modules were found and the target genes identified were  CAV1, CD44, and TGFβR2. The KEGG pathway analysis discovered that CAV1 and FN1 were significantly enriched in focal adhesion, extracellular matrix (ECM)-receptor interaction, and pathways in cancer. Conclusion: Aggressive behavior of breast cancer was proved to be related to cell proliferation and hormone stimulus. Genes such as CAV1, CD44, TGFβR2, and FN1 might be potential targets to diagnose the aggressive behavior of breast cancer cells.

Abstract in Chinese

通过完整的基因调控网络对乳腺癌侵袭性行为的生物信息学分析 摘要 背景:乳腺癌是妇女最常见的癌症之一。虽然这种疾病的死亡主要是由侵袭性的癌细胞转移引起的,很少有研究阐述了乳腺癌的侵袭性行为。 材料和方法:我们下载gse40057基因表达谱,包括四个侵袭性的和六个低侵袭性的乳腺癌细胞株,从基因表达谱确定了侵袭性和低侵袭性样本间的差异表达基因(DEGs)。一个完整的基因调控网络的构建包括差异表达基因,微RNA(miRNAs)和转录因子。然后,对网络的图案和模块进行鉴定。基于基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路,对模块在功能性水平进一步分析,以研究乳腺癌的侵袭行为。 结果:从完整的基因调控网络中,共有764个差异表达基因被发现,两个模块被过滤出来。两个关于差异表达基因的主题和模块被确定。重要的基因本体论术语与细胞增殖的激素刺激相关模块关联,并确定目标基因CAV1,CD44和TGF R2。KEGG通路分析发现,CAV1和FN1在病灶黏附(作用),细胞外基质(ECM)-受体相互作用,和癌症通路上非常丰富。 结论:乳腺癌侵略行为被证明是与细胞增殖和激素的刺激。基因如CAV1、CD44、TGFR2和FN1可能是诊断乳腺癌细胞的侵袭行为的潜在目标。 关键词:攻击性行为,乳腺癌,基因调控网络



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