整合生物信息学和机器学习,揭示慢性阻塞性肺疾病和2型糖尿病的共同机制和生物标志物。

IF 3.6 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Shen Jiran, Wang Jiling, Zhou Sijing, Zhang Binbin, Li Pulin, Han Rui, Fei Guanghe, Cao Chao, Wang Ran
{"title":"整合生物信息学和机器学习,揭示慢性阻塞性肺疾病和2型糖尿病的共同机制和生物标志物。","authors":"Shen Jiran, Wang Jiling, Zhou Sijing, Zhang Binbin, Li Pulin, Han Rui, Fei Guanghe, Cao Chao, Wang Ran","doi":"10.1093/postmj/qgae186","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link between the two diseases, the pathophysiological mechanisms they share are not fully understood.</p><p><strong>Methods: </strong>In this study, the co-expressed genes of COPD and T2DM in Gene Expression Omnibus database were identified by bioinformatics method, and the functional enrichment analysis was performed. Machine learning algorithms were used to identify biomarkers. The diagnostic value of these biomarkers was assessed by receiver operating characteristic analysis, and their relationship to immune cells was investigated by immunoinfiltration analysis. Finally, real-time quantitative polymerase chain reaction was performed.</p><p><strong>Results: </strong>A total of five overlapping genes were obtained, focusing on pathways associated with insulin resistance and inflammatory mediators. The machine learning method identified three biomarkers: matrix metalloproteinase 9, laminin α4, and differentially expressed in normal cells and neoplasia domain containing 4 C, all of which were shown to have high diagnostic values by receiver operating characteristic analysis. Immunoinfiltration analysis showed that it was associated with a variety of immune cells. In addition, the real-time quantitative polymerase chain reaction results confirmed agreement with our bioinformatics analysis.</p><p><strong>Conclusions: </strong>Our study sheds light on the common pathogenesis and biomarkers of both diseases, and these findings have potential implications for the development of new diagnostic and treatment strategies for COPD and T2DM. Key message What is already known on this topic?  Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) often coexist as comorbidities. However, the exact mechanistic link between the two diseases remains complex, multifactorial, and not fully understood. What this study adds?  Three biomarkers, including matrix metalloproteinase, laminin α4, and differentially expressed in normal cells and neoplasia domain containing 4 C, were identified as key co-expression hub genes in COPD and T2DM. How this study might affect research, practice or policy?  Future studies may benefit from incorporating a larger sample set to further explore and validate the diagnostic and therapeutic effects of these core genes.</p>","PeriodicalId":20374,"journal":{"name":"Postgraduate Medical Journal","volume":" ","pages":"535-544"},"PeriodicalIF":3.6000,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating bioinformatics and machine learning to unravel shared mechanisms and biomarkers in chronic obstructive pulmonary disease and type 2 diabetes.\",\"authors\":\"Shen Jiran, Wang Jiling, Zhou Sijing, Zhang Binbin, Li Pulin, Han Rui, Fei Guanghe, Cao Chao, Wang Ran\",\"doi\":\"10.1093/postmj/qgae186\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link between the two diseases, the pathophysiological mechanisms they share are not fully understood.</p><p><strong>Methods: </strong>In this study, the co-expressed genes of COPD and T2DM in Gene Expression Omnibus database were identified by bioinformatics method, and the functional enrichment analysis was performed. Machine learning algorithms were used to identify biomarkers. The diagnostic value of these biomarkers was assessed by receiver operating characteristic analysis, and their relationship to immune cells was investigated by immunoinfiltration analysis. Finally, real-time quantitative polymerase chain reaction was performed.</p><p><strong>Results: </strong>A total of five overlapping genes were obtained, focusing on pathways associated with insulin resistance and inflammatory mediators. The machine learning method identified three biomarkers: matrix metalloproteinase 9, laminin α4, and differentially expressed in normal cells and neoplasia domain containing 4 C, all of which were shown to have high diagnostic values by receiver operating characteristic analysis. Immunoinfiltration analysis showed that it was associated with a variety of immune cells. In addition, the real-time quantitative polymerase chain reaction results confirmed agreement with our bioinformatics analysis.</p><p><strong>Conclusions: </strong>Our study sheds light on the common pathogenesis and biomarkers of both diseases, and these findings have potential implications for the development of new diagnostic and treatment strategies for COPD and T2DM. Key message What is already known on this topic?  Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) often coexist as comorbidities. However, the exact mechanistic link between the two diseases remains complex, multifactorial, and not fully understood. What this study adds?  Three biomarkers, including matrix metalloproteinase, laminin α4, and differentially expressed in normal cells and neoplasia domain containing 4 C, were identified as key co-expression hub genes in COPD and T2DM. How this study might affect research, practice or policy?  Future studies may benefit from incorporating a larger sample set to further explore and validate the diagnostic and therapeutic effects of these core genes.</p>\",\"PeriodicalId\":20374,\"journal\":{\"name\":\"Postgraduate Medical Journal\",\"volume\":\" \",\"pages\":\"535-544\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Postgraduate Medical Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/postmj/qgae186\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Postgraduate Medical Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/postmj/qgae186","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

摘要

背景:慢性阻塞性肺疾病(COPD)和2型糖尿病(T2DM)的发病率呈上升趋势。虽然有证据表明这两种疾病之间存在联系,但它们共有的病理生理机制尚不完全清楚。方法:本研究采用生物信息学方法对基因表达Omnibus数据库中COPD和T2DM共表达基因进行鉴定,并进行功能富集分析。使用机器学习算法来识别生物标志物。通过受体操作特征分析评估这些生物标志物的诊断价值,并通过免疫浸润分析研究它们与免疫细胞的关系。最后进行实时定量聚合酶链反应。结果:共获得5个重叠基因,重点关注与胰岛素抵抗和炎症介质相关的通路。机器学习方法鉴定出基质金属蛋白酶9、层粘连蛋白α4、正常细胞和含4c的瘤变结构域差异表达3种生物标志物,通过受体工作特征分析显示,这3种生物标志物具有较高的诊断价值。免疫浸润分析显示与多种免疫细胞有关。此外,实时定量聚合酶链反应结果与我们的生物信息学分析结果一致。结论:我们的研究揭示了这两种疾病的共同发病机制和生物标志物,这些发现对COPD和T2DM的新诊断和治疗策略的发展具有潜在的意义。关于这个话题我们已经知道了什么?慢性阻塞性肺疾病(COPD)和2型糖尿病(T2DM)常作为合并症共存。然而,这两种疾病之间的确切机制联系仍然是复杂的,多因素的,并没有完全理解。这项研究补充了什么?发现基质金属蛋白酶、层粘连蛋白α4、正常细胞和含4c的瘤变结构域差异表达的3个生物标志物是COPD和T2DM的关键共表达枢纽基因。这项研究将如何影响研究、实践或政策?未来的研究可能会受益于纳入更大的样本集,以进一步探索和验证这些核心基因的诊断和治疗效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating bioinformatics and machine learning to unravel shared mechanisms and biomarkers in chronic obstructive pulmonary disease and type 2 diabetes.

Background: Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link between the two diseases, the pathophysiological mechanisms they share are not fully understood.

Methods: In this study, the co-expressed genes of COPD and T2DM in Gene Expression Omnibus database were identified by bioinformatics method, and the functional enrichment analysis was performed. Machine learning algorithms were used to identify biomarkers. The diagnostic value of these biomarkers was assessed by receiver operating characteristic analysis, and their relationship to immune cells was investigated by immunoinfiltration analysis. Finally, real-time quantitative polymerase chain reaction was performed.

Results: A total of five overlapping genes were obtained, focusing on pathways associated with insulin resistance and inflammatory mediators. The machine learning method identified three biomarkers: matrix metalloproteinase 9, laminin α4, and differentially expressed in normal cells and neoplasia domain containing 4 C, all of which were shown to have high diagnostic values by receiver operating characteristic analysis. Immunoinfiltration analysis showed that it was associated with a variety of immune cells. In addition, the real-time quantitative polymerase chain reaction results confirmed agreement with our bioinformatics analysis.

Conclusions: Our study sheds light on the common pathogenesis and biomarkers of both diseases, and these findings have potential implications for the development of new diagnostic and treatment strategies for COPD and T2DM. Key message What is already known on this topic?  Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) often coexist as comorbidities. However, the exact mechanistic link between the two diseases remains complex, multifactorial, and not fully understood. What this study adds?  Three biomarkers, including matrix metalloproteinase, laminin α4, and differentially expressed in normal cells and neoplasia domain containing 4 C, were identified as key co-expression hub genes in COPD and T2DM. How this study might affect research, practice or policy?  Future studies may benefit from incorporating a larger sample set to further explore and validate the diagnostic and therapeutic effects of these core genes.

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
Postgraduate Medical Journal
Postgraduate Medical Journal 医学-医学:内科
CiteScore
8.50
自引率
2.00%
发文量
131
审稿时长
2.5 months
期刊介绍: Postgraduate Medical Journal is a peer reviewed journal published on behalf of the Fellowship of Postgraduate Medicine. The journal aims to support junior doctors and their teachers and contribute to the continuing professional development of all doctors by publishing papers on a wide range of topics relevant to the practicing clinician and teacher. Papers published in PMJ include those that focus on core competencies; that describe current practice and new developments in all branches of medicine; that describe relevance and impact of translational research on clinical practice; that provide background relevant to examinations; and papers on medical education and medical education research. PMJ supports CPD by providing the opportunity for doctors to publish many types of articles including original clinical research; reviews; quality improvement reports; editorials, and correspondence on clinical matters.
×
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:604180095
Book学术官方微信