Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.
Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.
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基于差分网络模型揭示复杂疾病个性化功能失调基因网络
DOI：
10.1186/s12967-015-0546-5
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发表时间：
2015-06-13
影响因子：
7.4
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Yu X;Zeng T;Wang X;Li G;Chen L
在复杂疾病的传统分析中，假设对照样本和病例样本具有很高的纯度。然而，由于疾病样本的异质性，许多疾病基因甚至不总是一致地上调/下调，导致被低估。这个问题将严重影响有效的个性化诊断或治疗。表达式方差和协方差可以以网络方式解决这样的问题。但是，这些分析总是需要多个样本，而不是一个样本，这在临床实践中通常不适用于每个个体。为了提取个体患者的共同和特定网络特征，本文提出了一种新的差异网络模型，即个性化功能障碍基因网络，该模型可以同时整合具有不同特征的基因，如具有差异基因表达（DEG）的基因、具有差异表达方差（DEVG）的基因和具有差异表达协方差（DECG）的基因对，来构建个性化的功能失调的网络。该模型对差分信息使用了一种新的类椭圆形测量，即，差异评分（DEVC），以重建正常和患病样本组之间的差异表达网络;并进一步定量评估每个个体的患者特异性网络中的不同特征基因。这种基于DEVC的差异表达网络（DEVC-net）已被应用于前列腺癌和糖尿病等复杂疾病的研究。(1)这些疾病的差异基因网络具有一种新的双色拓扑结构，其非中心子网络主要由控制各种生物过程的基因/蛋白质组成。(2)差异表达的方差/协方差比差异表达的方差/协方差更能提供新的信息源，可用于识别具有区分能力的基因或基因对，而传统方法忽略了这一点。(3)更重要的是，DEVC-net可以有效地测量个体在同一样本中不同特征基因及其网络或模块的表达状态或活性。所有这些结果都支持DEVC-网确实具有明显的优势，可以有效地提取一个样本的基因/蛋白质网络的区分性解释特征（即，个性化功能障碍网络），即使当疾病样本是异质的，并且因此除了常规的个体基因之外，还可以提供新的特征，如基因对，以分析个性化诊断和预后，更好地理解潜在的生物学机制。
In the conventional analysis of complex diseases, the control and case samples are assumed to be of great purity. However, due to the heterogeneity of disease samples, many disease genes are even not always consistently up-/down-regulated, leading to be under-estimated. This problem will seriously influence effective personalized diagnosis or treatment. The expression variance and expression covariance can address such a problem in a network manner. But, these analyses always require multiple samples rather than one sample, which is generally not available in clinical practice for each individual. To extract the common and specific network characteristics for individual patients in this paper, a novel differential network model, e.g. personalized dysfunctional gene network, is proposed to integrate those genes with different features, such as genes with the differential gene expression (DEG), genes with the differential expression variance (DEVG) and gene-pairs with the differential expression covariance (DECG) simultaneously, to construct personalized dysfunctional networks. This model uses a new statistic-like measurement on differential information, i.e., a differential score (DEVC), to reconstruct the differential expression network between groups of normal and diseased samples; and further quantitatively evaluate different feature genes in the patient-specific network for each individual. This DEVC-based differential expression network (DEVC-net) has been applied to the study of complex diseases for prostate cancer and diabetes. (1) Characterizing the global expression change between normal and diseased samples, the differential gene networks of those diseases were found to have a new bi-coloured topological structure, where their non hub-centred sub-networks are mainly composed of genes/proteins controlling various biological processes. (2) The differential expression variance/covariance rather than differential expression is new informative sources, and can be used to identify genes or gene-pairs with discriminative power, which are ignored by traditional methods. (3) More importantly, DEVC-net is effective to measure the expression state or activity of different feature genes and their network or modules in one sample for an individual. All of these results support that DEVC-net indeed has a clear advantage to effectively extract discriminatively interpretable features of gene/protein network of one sample (i.e. personalized dysfunctional network) even when disease samples are heterogeneous, and thus can provide new features like gene-pairs, in addition to the conventional individual genes, to the analysis of the personalized diagnosis and prognosis, and a better understanding on the underlying biological mechanisms.
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影响因子：
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DOI：
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