基于文本挖掘的骨关节炎药物发现。
Text Mining-Based Drug Discovery in Osteoarthritis.
机构信息
Department of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou 350007, Fujian, China.
School of Clinical Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650032, Yunnan, China.
出版信息
J Healthc Eng. 2021 Apr 14;2021:6674744. doi: 10.1155/2021/6674744. eCollection 2021.
BACKGROUND
Osteoarthritis (OA) is a chronic and degenerative joint disease, which causes stiffness, pain, and decreased function. At the early stage of OA, nonsteroidal anti-inflammatory drugs (NSAIDs) are considered the first-line treatment. However, the efficacy and utility of available drug therapies are limited. We aim to use bioinformatics to identify potential genes and drugs associated with OA.
METHODS
The genes related to OA and NSAIDs therapy were determined by text mining. Then, the common genes were performed for GO, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis. Using the MCODE plugin-obtained hub genes, the expression levels of hub genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR). The confirmed genes were queried in the Drug Gene Interaction Database to determine potential genes and drugs.
RESULTS
The qRT-PCR result showed that the expression level of 15 genes was significantly increased in OA samples. Finally, eight potential genes were targetable to a total of 53 drugs, twenty-one of which have been employed to treat OA and 32 drugs have not yet been used in OA.
CONCLUSIONS
The 15 genes (including PTGS2, NLRP3, MMP9, IL1RN, CCL2, TNF, IL10, CD40, IL6, NGF, TP53, RELA, BCL2L1, VEGFA, and NOTCH1) and 32 drugs, which have not been used in OA but approved by the FDA for other diseases, could be potential genes and drugs, respectively, to improve OA treatment. Additionally, those methods provided tremendous opportunities to facilitate drug repositioning efforts and study novel target pharmacology in the pharmaceutical industry.
背景
骨关节炎（OA）是一种慢性退行性关节疾病，可导致僵硬、疼痛和功能下降。在 OA 的早期阶段，非甾体抗炎药（NSAIDs）被认为是一线治疗药物。然而，现有药物治疗的疗效和实用性有限。我们旨在使用生物信息学方法来识别与 OA 相关的潜在基因和药物。
方法
通过文本挖掘确定与 OA 和 NSAIDs 治疗相关的基因。然后，对常见基因进行 GO、KEGG 通路分析和蛋白质-蛋白质相互作用（PPI）网络分析。使用 MCODE 插件获得的枢纽基因，使用定量实时聚合酶链反应（qRT-PCR）验证枢纽基因的表达水平。在药物基因相互作用数据库中查询确认基因，以确定潜在的基因和药物。
结果
qRT-PCR 结果显示，OA 样本中 15 个基因的表达水平显著升高。最终，有 8 个潜在基因可靶向总共 53 种药物，其中 21 种药物已用于治疗 OA，32 种药物尚未用于 OA。
结论
这 15 个基因（包括 PTGS2、NLRP3、MMP9、IL1RN、CCL2、TNF、IL10、CD40、IL6、NGF、TP53、RELA、BCL2L1、VEGFA 和 NOTCH1）和 32 种药物，尽管尚未用于 OA，但已获得 FDA 批准用于治疗其他疾病，可分别作为潜在的基因和药物，以改善 OA 的治疗效果。此外，这些方法为药物重新定位工作和研究制药行业中新型靶标药理学提供了巨大的机会。
Zotero 插件