背景:胶质母细胞瘤(GBM)是最侵袭性和最常见的恶性原发性脑肿瘤;然而,治疗仍然是一个重大挑战。这项研究旨在通过开发包含异构类型的生物医学数据的综合性罕见疾病概况网络来确定GBM的药物再利用或重新定位候选药物。
方法:我们通过从NCATSGARD知识图谱(NGKG)中提取和整合与GBM相关疾病相关的生物医学信息,开发了基于胶质母细胞瘤的生物医学概况网络(GBPN)。我们进一步基于模块化类对GBPN进行聚类,从而产生多个聚焦子图,名为mc_GBPN。然后,我们通过在mc_GBPN上执行网络分析来识别高影响力节点,并验证那些可能是GBM的潜在药物再利用或重新定位候选的节点。
结果:我们开发了具有1,466个节点和107,423个边缘的GBPN,因此具有41个模块化类的mc_GBPN。从mc_GBPN中确定了十个最有影响力的节点的列表。这些特别包括利鲁唑,干细胞疗法,大麻二酚,和VK-0214,具有治疗GBM的证据。
结论:我们的GBM靶向网络分析使我们能够有效地确定药物再利用或重新定位的潜在候选药物。将通过使用其他不同类型的生物医学和临床数据以及生物学实验进行进一步验证。这些发现可以减少胶质母细胞瘤的侵入性治疗,同时通过缩短药物开发时间表显着降低研究成本。此外,这个工作流程可以扩展到其他疾病领域。
Glioblastoma (GBM) is the most aggressive and common malignant primary brain tumor; however, treatment remains a significant challenge. This study aims to identify drug repurposing or repositioning candidates for GBM by developing an integrative rare disease profile network containing heterogeneous types of biomedical data.
We developed a Glioblastoma-based Biomedical Profile Network (GBPN) by extracting and integrating biomedical information pertinent to GBM-related diseases from the NCATS GARD Knowledge Graph (NGKG). We further clustered the GBPN based on modularity classes which resulted in multiple focused subgraphs, named mc_GBPN. We then identified high-influence nodes by performing network analysis over the mc_GBPN and validated those nodes that could be potential drug repurposing or repositioning candidates for GBM.
We developed the GBPN with 1,466 nodes and 107,423 edges and consequently the mc_GBPN with forty-one modularity classes. A list of the ten most influential nodes were identified from the mc_GBPN. These notably include Riluzole, stem cell therapy, cannabidiol, and VK-0214, with proven evidence for treating GBM.
Our GBM-targeted network analysis allowed us to effectively identify potential candidates for drug repurposing or repositioning. Further validation will be conducted by using other different types of biomedical and clinical data and biological experiments. The findings could lead to less invasive treatments for glioblastoma while significantly reducing research costs by shortening the drug development timeline. Furthermore, this workflow can be extended to other disease areas.