Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031, Taiwan; Faculty of Pharmacy, University of Ahmad Dahlan, Yogyakarta, 55164, Indonesia.
Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, 11031, Taiwan; Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, 11031, Taiwan.
Pharmacol Res. 2020 Nov;161:105203. doi: 10.1016/j.phrs.2020.105203. Epub 2020 Sep 17.
Even though many genetic risk loci for human diseases have been identified and comprehensively cataloged, strategies to guide clinical research by integrating the extensive results of genetic studies and biological resources are still limited. Moreover, integrative analyses that provide novel insights into disease biology are expected to be especially useful for drug discovery. Herein, we used text mining of genetic studies on colorectal cancer (CRC) and assigned biological annotations to identified risk genes in order to discover novel drug targets and potential drugs for repurposing. Risk genes for CRC were obtained from PubMed text mining, and for each gene, six functional and bioinformatic annotations were analyzed. The annotations include missense mutations, cis-expression quantitative trait loci (cis-eQTL), molecular pathway analyses, protein-protein interactions (PPIs), a genetic overlap with knockout mouse phenotypes, and primary immunodeficiency (PID). We then prioritized the biological risk candidate genes according to a scoring system of the six functional annotations. Each functional annotation was assigned one point, and those genes with a score ≥2 were designated "biological CRC risk genes". Using this method, we revealed 82 biological CRC risk genes, which were mapped to 128 genes in an expanded PPI network. Further utilizing DrugBank and the Therapeutic Target Database, we found 21 genes in our list that are targeted by 166 candidate drugs. Based on data from ClinicalTrials.gov and literature review, we found four known target genes with six drugs for clinical treatment in CRC, and three target genes with nine drugs supported by previous preclinical results in CRC. Additionally, 12 genes are targeted by 32 drugs approved for other indications, which can possibly be repurposed for CRC treatment. Finally, analysis from Connectivity Map (CMap) showed that 18 drugs have a high potential for CRC.
尽管已经确定并全面编目了许多人类疾病的遗传风险基因座，但整合遗传研究的广泛结果和生物资源以指导临床研究的策略仍然有限。此外，综合分析有望为药物发现提供对疾病生物学的新见解。在此，我们使用对结直肠癌 (CRC) 的遗传研究进行文本挖掘，并将生物注释分配给已确定的风险基因，以发现新的药物靶点和潜在的再利用药物。CRC 的风险基因从 PubMed 文本挖掘中获得，并且对于每个基因，分析了六个功能和生物信息学注释。这些注释包括错义突变、顺式表达数量性状基因座 (cis-eQTL)、分子途径分析、蛋白质-蛋白质相互作用 (PPIs)、与敲除小鼠表型的遗传重叠以及原发性免疫缺陷 (PID)。然后，我们根据六个功能注释的评分系统对生物风险候选基因进行优先级排序。每个功能注释分配一分，得分≥2 的那些基因被指定为“生物 CRC 风险基因”。使用这种方法，我们揭示了 82 个生物 CRC 风险基因，这些基因映射到扩展的 PPI 网络中的 128 个基因。进一步利用 DrugBank 和治疗靶点数据库，我们在列表中发现了 21 个基因，这些基因被 166 种候选药物靶向。根据 ClinicalTrials.gov 和文献综述的数据，我们发现 CRC 中有四个已知的靶基因和六种药物用于临床治疗，有三个靶基因和 9 种药物在 CRC 的以前临床前研究中得到支持。此外，12 个基因被 32 种用于其他适应症的药物靶向，这些药物可能可用于 CRC 的治疗。最后，Connectivity Map (CMap) 的分析表明，有 18 种药物对 CRC 具有较高的潜力。
