利用多种机器学习算法探索作为肺动脉高压生物标志物的 IRGs。

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Jiashu Yang, Siyu Chen, Ke Chen, Junyi Wu, Hui Yuan
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引用次数: 0

摘要

背景:肺动脉高压(PAH)是一种预后差、死亡率高的严重疾病,临床上缺乏简单灵敏的诊断生物标志物。本研究旨在利用基因组学研究确定新型 PAH 诊断生物标志物:我们对包括 PAH 和炎症反应基因(IRGs)在内的大型转录组数据集进行了全面分析,并结合 113 种机器学习模型评估诊断潜力。我们开发了基于枢纽基因的临床诊断模型,并通过校准曲线、临床决策曲线和 ROC 曲线评估了这些基因的有效性。我们还建立了一个 PAH 动物模型来验证枢纽基因的表达模式:在 113 种机器学习算法中,Lasso + LDA 模型的 AUC 最高,达到 0.741。在 PAH 组和正常对照组之间观察到了中枢基因 CTGF、DDR2、FGFR2、MYH10 和 YAP1 的不同表达谱。利用这些中心基因建立的诊断模型显示出很高的准确性,AUC 为 0.87。其中 MYH10 的 AUC 为 0.8,诊断效果最好。动物实验证实 PAH 组和对照组之间 CTGF、DDR2、FGFR2、MYH10 和 YAP1 的表达存在差异(P < 0.05);结论:我们成功地建立了 PAH 的诊断模型:我们利用IRGs成功建立了PAH诊断模型,显示出卓越的诊断性能。CTGF、DDR2、FGFR2、MYH10 和 YAP1 可作为 PAH 的新型分子诊断标记物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring IRGs as a Biomarker of Pulmonary Hypertension Using Multiple Machine Learning Algorithms.

Background: Pulmonary arterial hypertension (PAH) is a severe disease with poor prognosis and high mortality, lacking simple and sensitive diagnostic biomarkers in clinical practice. This study aims to identify novel diagnostic biomarkers for PAH using genomics research.

Methods: We conducted a comprehensive analysis of a large transcriptome dataset, including PAH and inflammatory response genes (IRGs), integrated with 113 machine learning models to assess diagnostic potential. We developed a clinical diagnostic model based on hub genes, evaluating their effectiveness through calibration curves, clinical decision curves, and ROC curves. An animal model of PAH was also established to validate hub gene expression patterns.

Results: Among the 113 machine learning algorithms, the Lasso + LDA model achieved the highest AUC of 0.741. Differential expression profiles of hub genes CTGF, DDR2, FGFR2, MYH10, and YAP1 were observed between the PAH and normal control groups. A diagnostic model utilizing these hub genes was developed, showing high accuracy with an AUC of 0.87. MYH10 demonstrated the most favorable diagnostic performance with an AUC of 0.8. Animal experiments confirmed the differential expression of CTGF, DDR2, FGFR2, MYH10, and YAP1 between the PAH and control groups (p < 0.05); Conclusions: We successfully established a diagnostic model for PAH using IRGs, demonstrating excellent diagnostic performance. CTGF, DDR2, FGFR2, MYH10, and YAP1 may serve as novel molecular diagnostic markers for PAH.

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来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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