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通过生物信息学分析鉴定乳腺癌相关成纤维细胞中受分子特征调控的差异表达基因。

Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis.

作者信息

Vastrad Basavaraj, Vastrad Chanabasayya, Tengli Anandkumar, Iliger Sudhir

机构信息

Department of Pharmaceutics, SET's College of Pharmacy, Dharwad, Karnataka, 580002, India.

Department of Computer Science, Karnataka University, Dharwad, Karnataka, 580002, India.

出版信息

Arch Gynecol Obstet. 2018 Jan;297(1):161-183. doi: 10.1007/s00404-017-4562-y. Epub 2017 Oct 23.

DOI:10.1007/s00404-017-4562-y
PMID:29063236
Abstract

OBJECTIVE

Breast cancer is a severe risk to public health and has adequately convoluted pathogenesis. Therefore, the description of key molecular markers and pathways is of much importance for clarifying the molecular mechanism of breast cancer-associated fibroblasts initiation and progression. Breast cancer-associated fibroblasts gene expression dataset was downloaded from Gene Expression Omnibus database.

METHODS

A total of nine samples, including three normal fibroblasts, three granulin-stimulated fibroblasts and three cancer-associated fibroblasts samples, were used to identify differentially expressed genes (DEGs) between normal fibroblasts, granulin-stimulated fibroblasts and cancer-associated fibroblasts samples. The gene ontology (GO) and pathway enrichment analysis was performed, and protein-protein interaction (PPI) network of the DEGs was constructed by NetworkAnalyst software.

RESULTS

Totally, 190 DEGs were identified, including 66 up-regulated and 124 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell-cell signalling and negative regulation of cell proliferation; molecular function (MF), including insulin-like growth factor II binding and insulin-like growth factor I binding; cellular component (CC), including insulin-like growth factor binding protein complex and integral component of plasma membrane; the down-regulated DEGs were significantly enriched in BP, including cell adhesion and extracellular matrix organization; MF, including N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase activity and calcium ion binding; CC, including extracellular space and extracellular matrix. WIKIPATHWAYS analysis showed the up-regulated DEGs were enriched in myometrial relaxation and contraction pathways. WIKIPATHWAYS, REACTOME, PID_NCI and KEGG pathway analysis showed the down-regulated DEGs were enriched endochondral ossification, TGF beta signalling pathway, integrin cell surface interactions, beta1 integrin cell surface interactions, malaria and glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulphate. The top 5 up-regulated hub genes, CDKN2A, MME, PBX1, IGFBP3, and TFAP2C and top 5 down-regulated hub genes VCAM1, KRT18, TGM2, ACTA2, and STAMBP were identified from the PPI network, and subnetworks revealed these genes were involved in significant pathways, including myometrial relaxation and contraction pathways, integrin cell surface interactions, beta1 integrin cell surface interaction. Besides, the target hsa-mirs for DEGs were identified. hsa-mir-759, hsa-mir-4446-5p, hsa-mir-219a-1-3p and hsa-mir-26a-5p were important miRNAs in this study.

CONCLUSIONS

We pinpoint important key genes and pathways closely related with breast cancer-associated fibroblasts initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying breast cancer-associated fibroblasts occurrence and progression, holding promise for acting as molecular markers and probable therapeutic targets.

摘要

目的

乳腺癌对公众健康构成严重威胁,其发病机制错综复杂。因此,描述关键分子标志物和信号通路对于阐明乳腺癌相关成纤维细胞启动和进展的分子机制至关重要。从基因表达综合数据库下载乳腺癌相关成纤维细胞基因表达数据集。

方法

共使用9个样本,包括3个正常成纤维细胞、3个颗粒蛋白刺激的成纤维细胞和3个癌相关成纤维细胞样本,以鉴定正常成纤维细胞、颗粒蛋白刺激的成纤维细胞和癌相关成纤维细胞样本之间的差异表达基因(DEGs)。进行基因本体(GO)和通路富集分析,并通过NetworkAnalyst软件构建DEGs的蛋白质-蛋白质相互作用(PPI)网络。

结果

共鉴定出190个DEGs,其中66个上调基因和124个下调基因。GO分析结果显示,上调的DEGs在生物过程(BP)中显著富集,包括细胞-细胞信号传导和细胞增殖的负调控;分子功能(MF),包括胰岛素样生长因子II结合和胰岛素样生长因子I结合;细胞成分(CC),包括胰岛素样生长因子结合蛋白复合物和质膜整合成分;下调的DEGs在BP中显著富集,包括细胞粘附和细胞外基质组织;MF,包括N-乙酰半乳糖胺4-硫酸酯6-O-磺基转移酶活性和钙离子结合;CC,包括细胞外空间和细胞外基质。WIKIPATHWAYS分析显示上调的DEGs富集于子宫肌层舒张和收缩通路。WIKIPATHWAYS、REACTOME、PID_NCI和KEGG通路分析显示下调的DEGs富集于软骨内骨化、转化生长因子β信号通路、整合素细胞表面相互作用、β1整合素细胞表面相互作用、疟疾和糖胺聚糖生物合成-硫酸软骨素/硫酸皮肤素。从PPI网络中鉴定出前5个上调的枢纽基因CDKN2A、MME、PBX1、IGFBP3和TFAP2C以及前5个下调的枢纽基因VCAM1、KRT18、TGM2、ACTA2和STAMBP,子网络显示这些基因参与重要通路,包括子宫肌层舒张和收缩通路、整合素细胞表面相互作用、β1整合素细胞表面相互作用。此外,还鉴定了DEGs的靶标hsa-mirs。hsa-mir-759、hsa-mir-4446-5p、hsa-mir-219a-1-3p和hsa-mir-26a-5p是本研究中的重要miRNA。

结论

通过对DEGs进行一系列生物信息学分析,我们确定了与乳腺癌相关成纤维细胞启动和进展密切相关的重要关键基因和通路。这些筛选出的基因和通路为乳腺癌相关成纤维细胞发生和进展的更详细分子机制提供了依据,有望作为分子标志物和潜在治疗靶点。

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