Mapping Gene Networks in Colon Cancer

描述（由申请人提供）：结肠癌，以及一般的癌症，是一种基因组疾病，总是与基因的异常表达相关。目前尚不清楚的是，这些基因是如何在形成癌症表型的操作网络中相互联系的。对癌症相关基因的网络连接的理解提供了识别可用于治疗干预的关键分支点的潜力。该应用程序的目标是绘制来自NCI-60面板的一系列明确定义的结肠癌细胞系中的癌症基因网络。这将通过实施涉及表型、基因表达谱、转录因子结合分析和基因相互作用计算建模的迭代过程来完成。最终的网络模型将通过在称为RNA干扰（RNAi）的过程中使用称为短发夹RNA （sirna）的分子探针干扰癌细胞系来进行测试。网络中被RNAi敲除导致癌症表型缺失的基因被假设可以调节下游效应基因的表达。癌细胞系中的这种扰动将通过随后一轮的基因表达谱、转录因子结合分析和计算建模来检查，从而使我们能够逐步改进我们的癌症网络模型。修订后的模型的有效性将通过随后一轮的RNAi扰动和癌症表型筛选进行仔细检查，并重复迭代过程。最后，我们的网络中基因的生物学意义将与分期人类结肠癌（正常结肠癌、腺瘤、Dukes' B、C和D期样本以及肝转移）的表达谱数据进行交叉验证。我们的研究目标总结如下：(1)构建入侵基因网络图谱；(2)构建（抗）粘附基因网络图谱；(3)构建入侵和（抗）粘附基因网络的概率模型。

DESCRIPTION (provided by applicant): Colon cancer, and cancer in general, is a disease of the genome which invariably is associated with aberrant expression of genes. What is not clearly understood is how these genes are interconnected in the context of an operational network that underlies the cancer phenotype. An understanding of the network connections of cancer-related genes offers the potential to identify critical branch points amendable to therapeutic intervention. The goal of this application is to map cancer gene networks in a well-defined series of colon cancer cell lines from the NCI-60 panel. This will be accomplished by implementing an iterative process involving phenotyping, gene expression profiling, transcription factor binding analysis, and computational modeling of gene interactions. The resulting network model will be tested by perturbing cancer cell lines with molecular probes called short hairpin RNAs (siRNAs) in a process known as RNA interference (RNAi). Genes within the network whose knockdown by RNAi leads to a loss-of-cancer phenotype are hypothesized to regulate the expression of downstream effector genes. This perturbation in cancer cell lines will be examined by a subsequent round of gene expression profiling, transcription factor binding analysis and computational modeling, thereby allowing us to progressively improve our cancer network model. The validity of the revised model will be scrutinized with an ensuing round of RNAi perturbation and loss-of- cancer phenotype screening, and the iterative process is repeated. Lastly, the biological significance of genes in our networks will be cross-validated with expression profiling data of staged human colon cancers (normal colon, adenomas, Dukes' B, C and D stage samples, and liver metastases). Our research objectives are summarized as follows: (1) Construct a map of the invasion gene network, (2) Construct a map of the (anti-)adhesion gene network, and (3) Build probabilistic models of the invasion and (anti-) adhesion gene networks.