emergentec biodevelopment GmbH, Vienna, Austria.
Proteomics Clin Appl. 2011 Jun;5(5-6):354-66. doi: 10.1002/prca.201000136. Epub 2011 Apr 14.
For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects.
Molecular features on DN were retrieved from public domain Omics studies and by mining scientific literature, patent text and clinical trial specifications. Molecular feature sets were consolidated on a human protein interaction network and interpreted on the level of molecular pathways in the light of the pathophysiology of the disease and its clinical context defined as associated biomarkers and drug targets.
About 1000 gene symbols each could be assigned to the pathophysiological description of DN and to the clinical context. Direct feature comparison showed minor overlap, whereas on the level of molecular pathways, the complement and coagulation cascade, PPAR signaling, and the renin-angiotensin system linked the disease descriptor space with biomarkers and targets.
Only the combined molecular feature landscapes closely reflect the clinical implications of DN in the context of hypertension and diabetes. Omics data integration on the level of interaction networks furthermore provides a platform for identification of pathway-specific biomarkers and therapy options.
对于糖尿病肾病 (DN) 等表型复杂的疾病，整合多个组学来源有望更好地描述疾病的病理生理学，为新的诊断和治疗方法提供基础，同时也为个性化治疗提供重要依据。
从公共领域的组学研究和科学文献、专利文本以及临床试验规范中检索到与 DN 相关的分子特征。将分子特征集整合到人类蛋白质相互作用网络上，并根据疾病的病理生理学及其临床背景（定义为相关生物标志物和药物靶点），在分子途径层面上进行解释。
大约有 1000 个基因符号可分别用于描述 DN 的病理生理学和临床背景。直接进行特征比较的话，重叠度较小，而在分子途径层面上，补体和凝血级联、过氧化物酶体增殖物激活受体信号通路和肾素-血管紧张素系统将疾病描述符空间与生物标志物和靶点联系起来。
只有综合的分子特征图谱才能在高血压和糖尿病的背景下，紧密反映 DN 的临床意义。在交互网络层面上进行组学数据整合，还为鉴定特定途径的生物标志物和治疗方案提供了一个平台。
