Feature Engineering-Assisted Drug Repurposing on Disease-Drug Transcriptome Profiles in Gastric Cancer.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-04-04 DOI:10.1089/adt.2023.141

K. K. Kırboğa, M. Rudrapal

{"title":"Feature Engineering-Assisted Drug Repurposing on Disease-Drug Transcriptome Profiles in Gastric Cancer.","authors":"K. K. Kırboğa, M. Rudrapal","doi":"10.1089/adt.2023.141","DOIUrl":null,"url":null,"abstract":"Gastric cancer is one of the most common and deadly types of cancer in the world. To develop new biomarkers and drugs to diagnose and treat this cancer, it is necessary to identify the differences between the transcriptome profiles of gastric cancer and healthy individuals, identify critical genes associated with these differences, and make potential drug predictions based on these genes. In this study, using two gene expression datasets related to gastric cancer (GSE19826 and GSE79973), 200 genes that were ready for machine learning were selected, and their expression levels were analyzed. The best 100 genes for the model were chosen with the permutation feature importance method, and central genes, such as SCARB1, ETV3, SPATA17, FAM167A-AS1, and MTBP, which were shown to be associated with gastric cancer, were identified. Then, using the drug repurposing method with the Connectivity Map CLUE Query tools, potential drugs such as Forskolin, Gestrinone, Cediranib, Apicidine, and Everolimus, which showed a highly negative correlation with the expression levels of the selected genes, were identified. This study provides a method to develop new approaches to diagnosing and treating gastric cancer by comparing the transcriptome profiles of patients gastric cancer and performing a feature engineering-assisted drug repurposing analysis based on cancer data.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"21 6","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/adt.2023.141","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Gastric cancer is one of the most common and deadly types of cancer in the world. To develop new biomarkers and drugs to diagnose and treat this cancer, it is necessary to identify the differences between the transcriptome profiles of gastric cancer and healthy individuals, identify critical genes associated with these differences, and make potential drug predictions based on these genes. In this study, using two gene expression datasets related to gastric cancer (GSE19826 and GSE79973), 200 genes that were ready for machine learning were selected, and their expression levels were analyzed. The best 100 genes for the model were chosen with the permutation feature importance method, and central genes, such as SCARB1, ETV3, SPATA17, FAM167A-AS1, and MTBP, which were shown to be associated with gastric cancer, were identified. Then, using the drug repurposing method with the Connectivity Map CLUE Query tools, potential drugs such as Forskolin, Gestrinone, Cediranib, Apicidine, and Everolimus, which showed a highly negative correlation with the expression levels of the selected genes, were identified. This study provides a method to develop new approaches to diagnosing and treating gastric cancer by comparing the transcriptome profiles of patients gastric cancer and performing a feature engineering-assisted drug repurposing analysis based on cancer data.

查看原文本刊更多论文

胃癌疾病-药物转录组图谱的特征工程辅助药物再利用

胃癌是世界上最常见、最致命的癌症之一。为了开发诊断和治疗这种癌症的新生物标记物和药物，有必要确定胃癌与健康人转录组之间的差异，找出与这些差异相关的关键基因，并根据这些基因预测潜在的药物。本研究利用两个与胃癌相关的基因表达数据集（GSE19826 和 GSE79973），选择了 200 个可用于机器学习的基因，并对其表达水平进行了分析。利用置换特征重要性法选出了模型的最佳100个基因，并确定了与胃癌相关的中心基因，如SCARB1、ETV3、SPATA17、FAM167A-AS1和MTBP。然后，利用连接图CLUE查询工具的药物再利用方法，确定了与所选基因的表达水平呈高度负相关的潜在药物，如Forskolin、Gestrinone、Cediranib、Apicidine和Everolimus。这项研究通过比较胃癌患者的转录组图谱，并基于癌症数据进行特征工程辅助药物再利用分析，为开发诊断和治疗胃癌的新方法提供了一种方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.