新的治疗靶点的发现,定义为药物可以与之相互作用以诱导治疗益处的蛋白质,通常是药物发现的第一步,也是最重要的一步。目标发现的一种解决方案是目标重新定位,一种依赖于对新疾病的已知目标进行再利用的策略,导致新的治疗方法,副作用少,潜在的药物协同作用。生物网络已成为集成异构数据并促进预测生物学或治疗特性的强大工具。因此,它们被广泛用于通过表征潜在的候选者来预测新的治疗靶点,通常基于它们在蛋白质-蛋白质相互作用(PPI)网络中的相互作用,以及它们与疾病相关基因的接近程度。然而,对PPI网络的过度依赖以及潜在靶标必须在已知基因附近的假设可能会引入偏差,从而限制这些方法的有效性.本研究以两种方式解决了这些限制。首先,通过利用多层网络,其中包含额外的信息,如基因调控,代谢物相互作用,代谢途径,和一些疾病特征,如差异表达基因,突变基因,副本编号变更,和结构变体。第二,通过使用几种方法从网络中提取相关特征,包括与疾病相关基因的接近度,但也没有偏见的方法,如基于传播的方法,拓扑度量,和模块检测算法。以前列腺癌为例,最佳特征被识别并用于训练机器学习算法,以预测前列腺癌的5个新的有希望的治疗目标:IGF2R,C5AR,RAB7、SETD2和NPBWR1。
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.