Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

Anupama Reddy, Conway C. Huang, Huiqing Liu, C. DeLisi, M. Nevalainen, S. Szalma, G. Bhanot
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引用次数: 6

Abstract

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.
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稳健的基因网络分析显示STAT5a网络的改变是前列腺癌的一个标志。
我们开发了一种通用方法,从微阵列数据集中基因之间的配对相关性中识别基因网络,并将其应用于来自69个原发性前列腺肿瘤的公共前列腺癌基因表达数据。我们将节点度定义为与节点显著相关的基因数量,并将枢纽基因定义为与节点度最高的基因。在VisANT (http://visant.bu.edu/)中使用转录因子结合信息作为生物过滤器来修剪相关网络。采用严格的排列检验确定枢纽基因的可靠性。正常前列腺样本、非裔美国人(AA)和欧裔美国人(EA)前列腺癌样本的单独网络被生成并进行了比较。我们发现相同的中枢控制着AA和EA网络的疾病进展。结合AA和EA样本,我们建立了低低(<7)和高(≥7)Gleason分级肿瘤的网络。将其主要枢纽与正常样本的网络进行比较,发现了两种与疾病相关的变化:(i)与正常网络中的程度相比,一些枢纽基因在肿瘤网络中的程度增加,这表明这些基因与癌症调节控制的获得有关(例如,可能开启致癌基因)。(ii)与在正常网络中的程度相比,一些枢纽在肿瘤网络中的程度降低了,这表明这些基因与癌症中调节控制的丧失有关(例如,可能的肿瘤抑制基因的丧失)。一个惊人的结果是,在AA和EA肿瘤样本中,与正常前列腺网络相比,STAT5a、CEBPB和EGR1是获得邻居的主要枢纽。相反,与正常前列腺网络相比,HIF-lα是前列腺癌网络中失去连接的主要枢纽。我们还发现这些中心的程度从正常到低级别到高级别疾病逐渐变化,这表明这些中心是前列腺癌的主要调节因子,并标志着疾病进展。STAT5a被确定为一个中心枢纽,在前列腺癌网络中有大约120个邻居,而在正常前列腺网络中只有81个邻居。STAT5a的120个邻居中,有57个是已知的癌症相关基因,已知参与与肿瘤发生相关的功能通路。我们的方法是通用的,可以很容易地扩展到识别和研究与任何两种表型相关的网络。
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