新生儿败血症的代谢生物标志物:利用代谢组学与机器学习相结合进行鉴定。

IF 4.6 2区 生物学 Q2 CELL BIOLOGY Frontiers in Cell and Developmental Biology Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI:10.3389/fcell.2024.1491065
Zhaonan Bian, Xinyi Zha, Yanru Chen, Xuting Chen, Zhanghua Yin, Min Xu, Zhongxiao Zhang, Jihong Qian
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引用次数: 0

摘要

背景:败血症是一种与新生儿和婴儿死亡相关的常见疾病,目前,血液培养是诊断败血症的金标准方法,但其阳性率较低,且需要2天以上的时间才能培养出来。同时,令人遗憾的是,临床上还缺乏用于早期及时诊断婴儿败血症和判断该疾病严重程度的特异性生物标志物:方法:采用基于液相色谱-质谱联用技术(LC-MS)的血清代谢组学方法,对 18 名患有合并症的败血症婴儿、25 名无合并症的败血症婴儿和 25 名患有非感染性疾病的婴儿的样本进行了评估。通过多变量统计分析筛选出了含量不同的代谢物。此外,还进行了最小绝对收缩和选择算子(LASSO)和支持向量机递归特征消除(SVM-RFE)分析,以确定患败血症和未感染婴儿的关键代谢物。采用随机森林算法确定有合并症和无合并症脓毒症婴儿的主要不同含量代谢物。生成接收者操作特征曲线(ROC)用于生物标记物价值测试。最后,进行了代谢通路分析,以探索与已确定的差异丰富代谢物相关的代谢和信号通路:结果:共有189种代谢物在感染性婴儿和非感染性婴儿之间存在显著差异,137种代谢物在有合并症和无合并症的败血症婴儿之间存在差异。利用 LASSO 和 SVM-RFE 分析筛选出主要的差异丰富代谢物后,保留了己胺、硫酸精神苷、溶菌酶 (18:1(9Z)/0:0)、2,4,6-三溴苯酚和 25-肉桂酰-伏尔加苷用于婴儿败血症的诊断。ROC曲线分析显示,己胺的曲线下面积(AUC)为0.9200,硫酸精神苷为0.9749,溶菌酶(18:1 (9Z)/0:0)为0.9684,2,4,6-三溴苯酚为0.7405,25-肉桂酰伏尔加苷为0.8893,所有代谢物的组合为1.000。将有合并症的脓毒症婴儿与无合并症的脓毒症婴儿进行比较,利用随机森林算法确定了四种最重要的内源性代谢物,即 12-氧代-20-三羟基白三烯 B4、二氢缬氨酸、PA(8:0/12:0)和 2-庚硫醇。对这四种关键的差异丰度代谢物进行的 ROC 曲线分析表明,这四种代谢物的 AUC 均为 1。通路分析表明,苯丙氨酸、酪氨酸和色氨酸的生物合成、苯丙氨酸代谢和卟啉代谢在婴儿败血症中发挥着重要作用:通过对血清代谢物谱进行鉴定,并应用机器学习方法确定了有合并症的脓毒症婴儿、无合并症的脓毒症婴儿和无感染性疾病的婴儿中主要的不同含量的代谢物。这些研究结果有望促进婴儿败血症的早期诊断并确定疾病的严重程度。
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Metabolic biomarkers of neonatal sepsis: identification using metabolomics combined with machine learning.

Background: Sepsis is a common disease associated with neonatal and infant mortality, and for diagnosis, blood culture is currently the gold standard method, but it has a low positivity rate and requires more than 2 days to develop. Meanwhile, unfortunately, the specific biomarkers for the early and timely diagnosis of sepsis in infants and for the determination of the severity of this disease are lacking in clinical practice.

Methods: Samples from 18 sepsis infants with comorbidities, 25 sepsis infants without comorbidities, and 25 infants with noninfectious diseases were evaluated using a serum metabolomics approach based on liquid chromatography‒mass spectrometry (LC‒MS) technology. Differentially abundant metabolites were screened via multivariate statistical analysis. In addition, least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses were conducted to identify the key metabolites in infants with sepsis and without infections. The random forest algorithm was applied to determine key differentially abundant metabolites between sepsis infants with and without comorbidities. Receiver operating characteristic (ROC) curves were generated for biomarker value testing. Finally, a metabolic pathway analysis was conducted to explore the metabolic and signaling pathways associated with the identified differentially abundant metabolites.

Results: A total of 189 metabolites exhibited significant differences between infectious infants and noninfectious infants, while 137 distinct metabolites exhibited differences between septic infants with and without comorbidities. After screening for the key differentially abundant metabolites using LASSO and SVM-RFE analyses, hexylamine, psychosine sulfate, LysoPC (18:1 (9Z)/0:0), 2,4,6-tribromophenol, and 25-cinnamoyl-vulgaroside were retained for the diagnosis of infant sepsis. ROC curve analysis revealed that the area under the curve (AUC) was 0.9200 for hexylamine, 0.9749 for psychosine sulfate, 0.9684 for LysoPC (18:1 (9Z)/0:0), 0.7405 for 2,4,6-tribromophenol, 0.8893 for 25-cinnamoyl-vulgaroside, and 1.000 for the combination of all metabolites. When the septic infants with comorbidities were compared to those without comorbidities, four endogenous metabolites with the greatest importance were identified using the random forest algorithm, namely, 12-oxo-20-trihydroxy-leukotriene B4, dihydrovaltrate, PA (8:0/12:0), and 2-heptanethiol. The ROC curve analysis of these four key differentially abundant metabolites revealed that the AUC was 1 for all four metabolites. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and porphyrin metabolism play important roles in infant sepsis.

Conclusion: Serum metabolite profiles were identified, and machine learning was applied to identify the key differentially abundant metabolites in septic infants with comorbidities, septic infants without comorbidities, and infants without infectious diseases. The findings obtained are expected to facilitate the early diagnosis of sepsis in infants and determine the severity of the disease.

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来源期刊
Frontiers in Cell and Developmental Biology
Frontiers in Cell and Developmental Biology Biochemistry, Genetics and Molecular Biology-Cell Biology
CiteScore
9.70
自引率
3.60%
发文量
2531
审稿时长
12 weeks
期刊介绍: Frontiers in Cell and Developmental Biology is a broad-scope, interdisciplinary open-access journal, focusing on the fundamental processes of life, led by Prof Amanda Fisher and supported by a geographically diverse, high-quality editorial board. The journal welcomes submissions on a wide spectrum of cell and developmental biology, covering intracellular and extracellular dynamics, with sections focusing on signaling, adhesion, migration, cell death and survival and membrane trafficking. Additionally, the journal offers sections dedicated to the cutting edge of fundamental and translational research in molecular medicine and stem cell biology. With a collaborative, rigorous and transparent peer-review, the journal produces the highest scientific quality in both fundamental and applied research, and advanced article level metrics measure the real-time impact and influence of each publication.
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