Pan Yuyan, Chen Zhiwei, Qi Fazhi, Liu Jiaqi
Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Big Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University, Shanghai, China.
Ann Transl Med. 2021 Feb;9(4):347. doi: 10.21037/atm-21-218.
Keloids (KL) and hypertrophic scars (HS) are forms of abnormal cutaneous scarring characterized by excessive deposition of extracellular matrix and fibroblast proliferation. Currently, the efficacy of drug therapies for KL and HS is limited. The present study aimed to investigate new drug therapies for KL and HS by using computational methods.
Text mining and GeneCodis were used to mine genes closely related to KL and HS. Protein-protein interaction analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The selection of drugs targeting the genes closely related to KL and HS was carried out using Pharmaprojects. Drug-target interaction prediction was performed using DeepPurpose, through which candidate drugs with the highest predicted binding affinity were finally obtained.
Our analysis using text mining identified 69 KL- and HS-related genes. Gene enrichment analysis generated 25 genes, representing 7 pathways and 130 targeting drugs. DeepPurpose recommended 14 drugs as the final drug list, including 2 phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitors, 10 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitors and 2 vascular endothelial growth factor A (VEGFA) antagonists.
Drug discovery using text mining and DeepPurpose may be a powerful and effective way to identify drugs targeting the genes related to KL and HS.
瘢痕疙瘩（KL）和增生性瘢痕（HS）是异常皮肤瘢痕形成的形式，其特征在于细胞外基质过度沉积和成纤维细胞增殖。目前，针对KL和HS的药物治疗效果有限。本研究旨在通过计算方法研究针对KL和HS的新药物治疗方法。
使用文本挖掘和GeneCodis挖掘与KL和HS密切相关的基因。使用蛋白质相互作用检索工具（STRING）和Cytoscape进行蛋白质-蛋白质相互作用分析。使用Pharmaprojects选择针对与KL和HS密切相关基因的药物。使用DeepPurpose进行药物-靶点相互作用预测，最终获得预测结合亲和力最高的候选药物。
我们使用文本挖掘的分析确定了69个与KL和HS相关的基因。基因富集分析产生了25个基因，代表7条途径和130种靶向药物。DeepPurpose推荐了14种药物作为最终药物清单，包括2种磷脂酰肌醇-4,5-二磷酸3-激酶（PI3K）抑制剂、10种前列腺素内过氧化物合酶2（PTGS2）抑制剂和2种血管内皮生长因子A（VEGFA）拮抗剂。
使用文本挖掘和DeepPurpose进行药物发现可能是识别针对与KL和HS相关基因的药物的一种强大而有效的方法。
