确定COPD和败血症之间的共同诊断生物标志物和治疗靶点:生物信息学和机器学习方法。

IF 2.7 3区 医学 Q2 RESPIRATORY SYSTEM
Xinyi Li, Yuyang Xiao, Meng Yang, Xupeng Zhang, Zhangchi Yuan, Zaiqiu Zhang, Hanyong Zhang, Lin Liu, Mingyi Zhao
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引用次数: 0

摘要

背景:有证据表明慢性阻塞性肺疾病(COPD)和脓毒症之间存在双向关联,但潜在的机制尚不清楚。本研究旨在利用孟德尔随机化(Mendelian randomization, MR)和生物信息学方法探索共享的诊断基因、潜在机制以及免疫细胞在copd -败血症关系中的作用,同时确定潜在的治疗药物。方法:使用全基因组关联数据进行两样本MR分析,以评估COPD和败血症的遗传预测。使用双向双样本MR分析定量免疫细胞介导的效应。采用差异表达基因(DEG)分析和加权基因共表达网络分析(WGCNA)鉴定共同基因。通过功能富集分析来探讨这些基因的生物学作用。LASSO和SVM-RFE算法确定了共享的诊断基因,并使用受试者工作特征(ROC)曲线对其进行评估。使用CIBERSORT分析免疫细胞浸润,使用NetworkAnalyst构建转录因子(TF)和miRNA网络。使用DSigDB进行药物预测,分子对接验证潜在药物。结果:三种免疫细胞类型被确定为COPD和败血症之间的介质,这些细胞介导的基因预测效应率分别为6.5%、12.8%和3.9%。共鉴定出33个重叠基因,其中AIM2和RNF125为关键诊断基因。免疫浸润分析显示单核细胞、巨噬细胞、血浆和树突状细胞失调。监管网络分析确定了九个关键的共同监管机构。确定了10个潜在的药物靶点,其中7个通过分子对接验证。结论:AIM2和RNF125可作为诊断性生物标志物,已鉴定的免疫细胞亚群可介导copd -脓毒症的联系,为潜在的治疗靶点提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

Background: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanisms remain unclear. This study aimed to explore shared diagnostic genes, potential mechanisms, and the role of immune cells in the COPD-sepsis relationship using Mendelian randomization (MR) and bioinformatics approaches, while also identifying potential therapeutic drugs.

Methods: Two-sample MR analysis was performed using genome-wide association data to assess genetically predicted COPD and sepsis. Immune cell-mediated effects were quantified using a two-way two-sample MR analysis. Differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were used to identify common genes. Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration was analyzed with CIBERSORT, while transcription factor (TF) and miRNA networks were constructed using NetworkAnalyst. Drug predictions were made using DSigDB, and molecular docking validated potential drugs.

Results: Three immune cell types were identified as mediators between COPD and sepsis, with genetically predicted effects mediated by these cells at rates of 6.5%, 12.8%, and 3.9%. A total of 33 overlapping genes were identified, and AIM2 and RNF125 were highlighted as key diagnostic genes. Immune infiltration analysis revealed dysregulated monocyte, macrophage, plasma, and dendritic cells. Regulatory network analysis identified nine key co-regulators. Ten potential drug targets were identified, with seven validated via molecular docking.

Conclusion: AIM2 and RNF125 may serve as diagnostic biomarkers, and identified immune cell subsets could mediate the COPD-sepsis connection, offering insights into potential therapeutic targets.

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来源期刊
CiteScore
4.80
自引率
10.70%
发文量
372
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed journal of therapeutics and pharmacology focusing on concise rapid reporting of clinical studies and reviews in COPD. Special focus will be given to the pathophysiological processes underlying the disease, intervention programs, patient focused education, and self management protocols. This journal is directed at specialists and healthcare professionals
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