Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2024-10-29 DOI:10.1016/j.cca.2024.120018

Xiaoxiao Tang , Xiaoqian Ran , Zhiyuan Liang , Hongbin Zhuang , Xi Yan , Chengyun Feng , Ayesha Qureshi , Yan Gao , Liming Shen

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引用次数: 0

Abstract

Background and aims

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention.

Materials and Methods

Sequential window acquisition of all theoretical spectra-mass spectrometry (SWATH-MS)-based proteomics technology and 30 DSM-V defined ASD cases versus age- and sex-matched controls were initially evaluated, and candidate biomarkers were screened using machine learning methods. Candidate biomarkers were validated by targeted proteomics multiple reaction monitoring (MRM) analysis using an independent group of 30 ASD cases vs. controls.

Results

Fifty-one differentially expressed proteins (DEPs) were identified by SWATH analysis. They were associated with the immune response, complements and coagulation cascade pathways, and apolipoprotein-related metabolic pathways. Machine learning analysis screened 10 proteins as biomarker combinations (TFRC, PPBP, APCS, ALDH1A1, CD5L, SPARC, FGG, SHBG, S100A9, and PF4V1). In the MRM analysis, four proteins (PPBP, APCS, FGG, and PF4V1) were significantly different between the groups, and their combination as a screening indicator showed high potential (AUC = 0.8087, 95 % confidence interval 0.6904–0.9252, p < 0.0001).

Conclusions

Our study provides data that suggests that a few plasma proteins have potential use in screening for ASD.

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利用血浆蛋白质组学结合机器学习方法筛选自闭症谱系障碍的生物标志物。

背景和目的：自闭症谱系障碍（ASD）是一种常见的儿童神经发育障碍。早期干预是有效的。研究自闭症谱系障碍的新型血液生物标志物有助于早期检测和干预：基于蛋白质组学技术的所有理论光谱-质谱（SWATH-MS）的顺序窗口采集和30个DSM-V定义的ASD病例与年龄和性别匹配的对照组进行了初步评估，并使用机器学习方法筛选了候选生物标志物。候选生物标记物通过靶向蛋白质组学多反应监测（MRM）分析进行了验证，分析对象是30个ASD病例与对照组：结果：通过SWATH分析确定了51种差异表达蛋白（DEPs）。这些蛋白质与免疫反应、补体和凝血级联途径以及脂蛋白相关代谢途径有关。机器学习分析筛选出 10 个蛋白质作为生物标记物组合（TFRC、PPBP、APCS、ALDH1A1、CD5L、SPARC、FGG、SHBG、S100A9 和 PF4V1）。在 MRM 分析中，有四种蛋白质（PPBP、APCS、FGG 和 PF4V1）在组间存在显著差异，它们的组合作为筛查指标显示出很高的潜力（AUC = 0.8087，95 % 置信区间为 0.6904-0.9252，p 结论）：我们的研究提供的数据表明，一些血浆蛋白具有筛查 ASD 的潜在用途。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.