AbstractPulmonary arterial hypertension (PAH) is a life-threatening disease with a poor prognosis, and metabolic abnormalities play a critical role in its development. This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH.

摘要肺动脉高压（PAH）是一种危及生命的疾病，预后不良，代谢异常在其发展中起着至关重要的作用。这项研究使用代谢组学，机器学习算法和生物信息学来筛选与PAH诊断相关的潜在代谢生物标志物。

In this study, plasma samples were collected from 17 patients diagnosed with idiopathic pulmonary arterial hypertension (IPAH) and 20 healthy controls. Plasma metabolomic profiling was performed by high-performance liquid chromatography-mass spectrometry. Gene profiles of PAH patients were obtained from the GEO database.

在这项研究中，从17名诊断为特发性肺动脉高压（IPAH）的患者和20名健康对照中收集血浆样本。通过高效液相色谱-质谱法进行血浆代谢组学分析。PAH患者的基因谱是从GEO数据库获得的。

Key differentially expressed metabolites (DEMs) and metabolism-related genes were subsequently identified using machine learning algorithms. Twenty differential plasma metabolites associated with IPAH were identified (VIP score > 1 and p < 0 0.05), and enrichment analysis revealed the arginine biosynthesis pathway as the most altered pathway.

随后使用机器学习算法鉴定了关键的差异表达代谢物（DEM）和代谢相关基因。鉴定出20种与IPAH相关的差异血浆代谢物（VIP评分>1和p<0.05），富集分析显示精氨酸生物合成途径是改变最多的途径。

Using machine learning models, including least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM), we extracted key metabolites that correlated with clinical phenotypes. Our results suggested that five metabolites, kynurenine, homoserine, tryptophan, AMP, and spermine, are potential biomarkers for IPAH.

使用机器学习模型，包括最小绝对收缩和选择算子（LASSO），随机森林（RF）和支持向量机（SVM），我们提取了与临床表型相关的关键代谢物。我们的研究结果表明，犬尿氨酸，高丝氨酸，色氨酸，AMP和精胺五种代谢物是IPAH的潜在生物标志物。

Bioinformatics analysis also identified 3 metabolism-related genes, MAPK6, SLC7A11 and CDC42BPA, that are strongly correlated with pulmonary hypertension, demonstrating strong predictive power and clinical relevance. Our findings revealed some key genes associated with metabolism in PH, and provided crucial information about complex metabolic reprogramming signals and may lead to the identification of useful metabolic biomarkers for the diagno.

生物信息学分析还确定了与肺动脉高压密切相关的3个代谢相关基因MAPK6，SLC7A11和CDC42BPA，显示出强大的预测能力和临床相关性。我们的研究结果揭示了一些与PH代谢相关的关键基因，并提供了有关复杂代谢重编程信号的关键信息，并可能导致鉴定诊断有用的代谢生物标志物。

IntroductionPulmonary arterial hypertension (PAH) is a rare and severe condition characterized by progressive remodeling of the pulmonary vasculature, which ultimately leads to failure of the right ventricle (RV) and ultimately mortality if left untreated1,2. While conventional diagnostic approaches and therapeutic strategies have proven effective in certain cases, they have been unable to fully address the complex pathophysiology of PAH.

引言肺动脉高压（PAH）是一种罕见且严重的疾病，其特征是肺血管系统进行性重塑，最终导致右心室（RV）衰竭，如果不及时治疗，最终导致死亡1,2。虽然传统的诊断方法和治疗策略在某些情况下已被证明是有效的，但它们无法完全解决PAH的复杂病理生理学。

The five-year and seven-year survival rates for individuals diagnosed with PAH are approximately 57% and 49%, respectively3. Early diagnosis of PAH is of significant importance to patients. Consequently, there is an urgent need to elucidate the potential mechanisms underlying PAH and to identify associated biomarkers.In recent years, research into the potential causes of PAH has focused on the aberrant regulation of metabolic pathways.

被诊断患有PAH的个体的五年和七年生存率分别约为57%和49%3。PAH的早期诊断对患者具有重要意义。因此，迫切需要阐明PAH的潜在机制并鉴定相关的生物标志物。近年来，对PAH潜在原因的研究集中在代谢途径的异常调节上。

Metabolomics is becoming an increasingly important tool for investigating the nature of metabolic abnormalities associated with PAH. This approach allows the identification of novel biomarkers and therapeutic targets4,5. Furthermore, the integration of metabolomic data with other omics technologies, including genomics, transcriptomics, and proteomics, has begun to enhance our understanding of the pathogenesis of PAH.

代谢组学正在成为研究与PAH相关的代谢异常性质的越来越重要的工具。这种方法允许鉴定新的生物标志物和治疗靶标4,5。此外，代谢组学数据与其他组学技术（包括基因组学，转录组学和蛋白质组学）的整合已经开始增强我们对PAH发病机制的理解。

This approach has led to the discovery of new metabolic pathways that were previously not associated with this disease6,7. The utilization of these comprehensive omics strategies is likely to elucidate the intricate etiology of PAH and facilitate the development of novel therapeutic interventions.A number of metabolomic studies of PAH have been conducted, and the results have already provided valuable insights into the potential for new pathway markers that could be used to aid in the diagno.

这种方法导致发现了以前与这种疾病无关的新代谢途径6,7。这些综合组学策略的利用可能会阐明PAH的复杂病因，并促进新型治疗干预措施的开发。已经进行了许多PAH的代谢组学研究，结果已经为可能用于辅助诊断的新途径标记的潜力提供了有价值的见解。

Data availability

数据可用性

The gene expression profiles of GSE131793, GSE113439 and GSE53408 were downloaded from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). The datasets of metabolite are available from the corresponding author on reasonable request.

从gene expression Omnibus（GEO）下载了GSE131793、GSE113439和GSE53408的基因表达谱(https://www.ncbi.nlm.nih.gov/geo/)。代谢物的数据集可根据合理的要求从通讯作者处获得。

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PubMed Google ScholarHai-Kuo ZhengView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsZ.H.K. conceived the study, designed the experiments, acquired the funding and drafted the article. Y.C. performed the bioinformatics analyses, interpreted the data and wrote the manuscript. L.Y.H. was responsible for bioinformatics analysis.

PubMed谷歌学术贡献。H、 K.构思了这项研究，设计了实验，获得了资金并起草了这篇文章。Y、 C.进行了生物信息学分析，解释了数据并撰写了手稿。五十、 Y.H.负责生物信息学分析。

All authors Z.H.K., Y.C. and L.Y.H. contributed to revising the manuscript critically for important intellectual content and have given final approval for the version to be published. Each author agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Corresponding authorCorrespondence to.

所有作者Z.H.K.，Y.C.和L.Y.H.都对稿件的重要知识内容进行了批判性修改，并最终批准了该版本的发布。每位作者同意对作品的各个方面负责，以确保与作品任何部分的准确性或完整性有关的问题得到适当的调查和解决。对应作者对应。

Hai-Kuo Zheng.Ethics declarations

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Competing interests

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The authors declare no competing interests.

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Ethical approval

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for research involving human subjects was obtained from the Ethics Committee of the China-Japan Union Hospital of Jilin University. All methods were performed in accordance with the relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardians.

涉及人类受试者的研究来自吉林大学中日联合医院伦理委员会。所有方法均按照相关指南和规定进行。所有受试者和/或其法定监护人均已获得知情同意。

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Reprints and permissionsAbout this articleCite this articleYang, C., Liu, YH. & Zheng, HK. Identification of metabolic biomarkers in idiopathic pulmonary arterial hypertension using targeted metabolomics and bioinformatics analysis.

转载和许可本文引用本文Yang，C.，Liu，YH。＆郑，香港。使用靶向代谢组学和生物信息学分析鉴定特发性肺动脉高压的代谢生物标志物。

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KeywordsIdiopathic pulmonary arterial hypertensionMetabolomicsBiomarkersMachine learningBiochemical pathways

关键词糖尿病性肺动脉高压代谢组学生物标志物机器学习生化途径