Table6_A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities.CSV

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation “component”) could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

复杂疾病与多种细胞、生理及临床表型（phenotype）密切相关。为加深对疾病机制的认知并提升此类疾病的临床治疗能力，阐明特定疾病表型（尤其是与多种疾病相关的表型）的分子基础与治疗路径至关重要。炎症过程便是这类典型表型之一，广泛参与多种健康问题，包括缺血性心脏病、脑卒中、癌症、糖尿病、慢性肾脏病、非酒精性脂肪肝病（non-alcoholic fatty liver disease）以及自身免疫性与神经退行性疾病。尽管上述每种疾病的发病机制可能涉及数百个基因，但筛选出参与特定表型（如炎症‘组分’）的基因，将有助于我们理解跨疾病背景下该表型背后的基因与通路，并预测可靶向该表型的潜在药物。本研究提出一种整合基因相互作用网络、疾病/性状-基因关联以及药物靶点信息的计算方法以实现该目标。我们将该方法应用于筛选对应慢性炎症的复杂疾病基因特征，并借助SAveRUNNER对药物进行优先级排序，以揭示全新的治疗机遇。