系统生物学和机器学习识别肺癌和胃食管反流病的基因重叠。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Sanjukta Dasgupta
{"title":"系统生物学和机器学习识别肺癌和胃食管反流病的基因重叠。","authors":"Sanjukta Dasgupta","doi":"10.1089/omi.2024.0150","DOIUrl":null,"url":null,"abstract":"One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease (GERD) pose a significant burden on planetary health, but also the coexistence of GERD in patients with LC is often associated with a poor prognosis. This study reports on the genetic overlaps between these two conditions using systems biology-driven bioinformatics and machine learning-based algorithms. A total of nine hub genes including IGHV1-3, COL3A1, ITGA11, COL1A1, MS4A1, SPP1, MMP9, MMP7, and LOC102723407 were found to be significantly altered in both LC and GERD as compared with controls and with pathway analyses suggesting a significant association with the matrix remodeling pathway. The expression of these genes was validated in two additional datasets. Random forest and K-nearest neighbor, two machine learning-based algorithms, achieved accuracies of 89% and 85% for distinguishing LC and GERD, respectively, from controls using these hub genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of doxycycline to matrix metalloproteinase 7 (binding affinity: -6.8 kcal/mol). The present study is the first of its kind that combines in silico and machine learning algorithms to identify the gene signatures that relate to both LC and GERD and promising drug candidates that warrant further research in relation to therapeutic innovation in LC and GERD. Finally, this study also suggests upstream regulators, including microRNAs and transcription factors, that can inform future mechanistic research on LC and GERD.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.\",\"authors\":\"Sanjukta Dasgupta\",\"doi\":\"10.1089/omi.2024.0150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease (GERD) pose a significant burden on planetary health, but also the coexistence of GERD in patients with LC is often associated with a poor prognosis. This study reports on the genetic overlaps between these two conditions using systems biology-driven bioinformatics and machine learning-based algorithms. A total of nine hub genes including IGHV1-3, COL3A1, ITGA11, COL1A1, MS4A1, SPP1, MMP9, MMP7, and LOC102723407 were found to be significantly altered in both LC and GERD as compared with controls and with pathway analyses suggesting a significant association with the matrix remodeling pathway. The expression of these genes was validated in two additional datasets. Random forest and K-nearest neighbor, two machine learning-based algorithms, achieved accuracies of 89% and 85% for distinguishing LC and GERD, respectively, from controls using these hub genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of doxycycline to matrix metalloproteinase 7 (binding affinity: -6.8 kcal/mol). The present study is the first of its kind that combines in silico and machine learning algorithms to identify the gene signatures that relate to both LC and GERD and promising drug candidates that warrant further research in relation to therapeutic innovation in LC and GERD. Finally, this study also suggests upstream regulators, including microRNAs and transcription factors, that can inform future mechanistic research on LC and GERD.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1089/omi.2024.0150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1089/omi.2024.0150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

摘要

一体健康 "和 "地球健康 "强调的是将几种复杂的人类疾病以及人类和地球生态系统健康联系起来的共同分子机制。例如,肺癌(LC)和胃食管反流病(GERD)不仅对地球健康造成重大负担,而且肺癌患者同时患有胃食管反流病往往预后不佳。本研究利用系统生物学驱动的生物信息学和基于机器学习的算法,报告了这两种疾病之间的基因重叠。研究发现,与对照组相比,LC 和胃食管反流病的九个中心基因(包括 IGHV1-3、COL3A1、ITGA11、COL1A1、MS4A1、SPP1、MMP9、MMP7 和 LOC102723407)都发生了显著改变,并且通路分析表明这些基因与基质重塑通路有显著关联。这些基因的表达在另外两个数据集中得到了验证。随机森林和 K 最近邻这两种基于机器学习的算法利用这些中枢基因区分 LC 和胃食管反流病与对照组的准确率分别达到了 89% 和 85%。此外,通过分子对接确认了强力霉素与基质金属蛋白酶7的结合亲和力(结合亲和力:-6.8 kcal/mol),从而确定了潜在的药物靶点。本研究是同类研究中首例结合硅学和机器学习算法来确定与半结肠癌和胃食管反流病相关的基因特征以及有希望的候选药物的研究,这些候选药物在半结肠癌和胃食管反流病的治疗创新方面值得进一步研究。最后,本研究还提出了上游调控因子,包括微RNA和转录因子,为今后有关LC和胃食管反流病的机理研究提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.
One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease (GERD) pose a significant burden on planetary health, but also the coexistence of GERD in patients with LC is often associated with a poor prognosis. This study reports on the genetic overlaps between these two conditions using systems biology-driven bioinformatics and machine learning-based algorithms. A total of nine hub genes including IGHV1-3, COL3A1, ITGA11, COL1A1, MS4A1, SPP1, MMP9, MMP7, and LOC102723407 were found to be significantly altered in both LC and GERD as compared with controls and with pathway analyses suggesting a significant association with the matrix remodeling pathway. The expression of these genes was validated in two additional datasets. Random forest and K-nearest neighbor, two machine learning-based algorithms, achieved accuracies of 89% and 85% for distinguishing LC and GERD, respectively, from controls using these hub genes. Additionally, potential drug targets were identified, with molecular docking confirming the binding affinity of doxycycline to matrix metalloproteinase 7 (binding affinity: -6.8 kcal/mol). The present study is the first of its kind that combines in silico and machine learning algorithms to identify the gene signatures that relate to both LC and GERD and promising drug candidates that warrant further research in relation to therapeutic innovation in LC and GERD. Finally, this study also suggests upstream regulators, including microRNAs and transcription factors, that can inform future mechanistic research on LC and GERD.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信