Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.
Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.
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DOI：
10.1371/journal.pone.0111813
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Hunter KW
中科院分区：
文献类型：
--
作者：
Geiger TR;Ha NH;Faraji F;Michael HT;Rodriguez L;Walker RC;Green JE;Simpson RM;Hunter KW
保守共表达网络的鉴定是对具有共同分子或细胞功能的基因群进行聚类的有用工具。基因在网络中的相对重要性通常可以通过连接程度来推断，那些表现出高连接的基因更有可能与特定的分子功能相关。先前，我们利用跨物种网络分析确定了与乳腺癌远端无转移生存显著相关的两个网络模块。在这里，我们验证了一个高度相关的基因作为转移相关基因。Tpx2是雌激素受体阳性（ER+）乳腺癌增殖网络中最密切相关的基因，它能促进转移性疾病，但以肿瘤自主、增殖独立的方式发生。组织学分析表明，弥散性肿瘤细胞内TPX2水平的变化可能会影响继发部位休眠细胞向活跃增殖细胞的转变。这些结果支持了鉴别新的转移相关基因的共表达网络方法，为乳腺癌进展和转移性疾病的病因学提供了新的信息。
Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+) breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.
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影响因子：
8
作者：
通讯作者：
--
影响因子：
12.3
作者：
Herschkowitz JI;Simin K;Weigman VJ;Mikaelian I;Usary J;Hu Z;Rasmussen KE;Jones LP;Assefnia S;Chandrasekharan S;Backlund MG;Yin Y;Khramtsov AI;Bastein R;Quackenbush J;Glazer RI;Brown PH;Green JE;Kopelovich L;Furth PA;Palazzo JP;Olopade OI;Bernard PS;Churchill GA;Van Dyke T;Perou CM
通讯作者：
Perou CM
影响因子：
11.2
作者：
Schmidt, Marcus;Boehm, Daniel;Gehrmann, Mathias
通讯作者：
Gehrmann, Mathias
影响因子：
4.4
作者：
Hu, Zhiyuan;Fan, Cheng;Perou, Charles M.
通讯作者：
Perou, Charles M.
DOI：
10.1073/pnas.1117872109
发表时间：
2012-02-21
影响因子：
11.1
作者：
Hu, Ying;Wu, Gang;Hunter, Kent W.
通讯作者：
Hunter, Kent W.