Gene Expression: Protein Interaction Systems Network Modeling Identifies Transformation-Associated Molecules and Pathways in Ovarian Cancer
Gene Expression: Protein Interaction Systems Network Modeling Identifies Transformation-Associated Molecules and Pathways in Ovarian Cancer
复制标题
DOI：
10.1158/0008-5472.can-10-0447
复制
发表时间：
2010-06-15
期刊：
影响因子：
11.2
通讯作者：
Kalra, Rajkumar S.
中科院分区：
文献类型：
--
作者：
Bapat, Sharmila A.;Krishnan, Anagha;Kalra, Rajkumar S.
同一起源的癌症中多种不同的遗传缺陷导致肿瘤表型和患者治疗反应的异质性，但相关的分子机制仍然难以捉摸。在这里，我们在系统水平上表明，浆液性卵巢癌的标志是与特定基因集相关的互连模块的激活，该特定基因集源自三个独立的肿瘤特异性基因表达数据集。网络预测算法与预先建立的蛋白质相互作用网络和已知功能相结合，证实了与卵巢癌相关的基因作为预测生物标志物的重要性，此外还纯粹基于互连性“发现”新基因，其精确参与仍有待研究。拷贝数改变和异常表观遗传调控被鉴定并验证为对基因表达的显着影响。更重要的是，已经确定了以 c-Myc 激活、改变的视网膜母细胞瘤信号传导和 p53/细胞周期/DNA 损伤修复途径为中心的三个功能模块参与转化相关事件。进一步的研究将赋予一组预测个体和肿瘤特异性途径的特异性标记物以重要意义并帮助其设计。用这个新兴领域的说法，这种基因中心相互作用的网络可能会定义个性化的治疗决策。癌症研究； 70（12）； 4809-19。 (C)2010 AACR。
Multiple, dissimilar genetic defects in cancers of the same origin contribute to heterogeneity in tumor phenotypes and therapeutic responses of patients, yet the associated molecular mechanisms remain elusive. Here, we show at the systems level that serous ovarian carcinoma is marked by the activation of interconnected modules associated with a specific gene set that was derived from three independent tumor-specific gene expression data sets. Network prediction algorithms combined with preestablished protein interaction networks and known functionalities affirmed the importance of genes associated with ovarian cancer as predictive biomarkers, besides "discovering" novel ones purely on the basis of interconnectivity, whose precise involvement remains to be investigated. Copy number alterations and aberrant epigenetic regulation were identified and validated as significant influences on gene expression. More importantly, three functional modules centering on c-Myc activation, altered retinoblastoma signaling, and p53/cell cycle/DNA damage repair pathways have been identified for their involvement in transformation-associated events. Further studies will assign significance to and aid the design of a panel of specific markers predictive of individual-and tumor-specific pathways. In the parlance of this emerging field, such networks of gene-hub interactions may define personalized therapeutic decisions. Cancer Res; 70( 12); 4809-19. (C)2010 AACR.