Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-04-02 DOI:10.1016/j.cca.2025.120238

Erlin Hu , Xiaoni Kuang , Sun Zhaohui , Sifeng Wang , Tuoyu Gan , Wenjuan zhou , Zhu Ming , Yuxia Cheng , Chunhua Ye , Kang Yan , Xiaohui Gong , Tuanmei Wang , Xiangwen Peng

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引用次数: 0

Abstract

Background

Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Methods

We used data independent acquisition (DIA) mass spectrometry to compare protein expression in serum from 99 Chinese children with autism spectrum disorders with 70 healthy children.

Results

We identified 347 downregulated and 394 upregulated proteins. Based on bioinformatics analysis, differential proteins were enriched in the immune system, immune disease, cell motility, and focal adhesion. Machine learning revealed a model with eight proteins (IGH c1898_heavy_IGHV3-33_IGHD3-9_IGHJ4, LYZ, IGL c1860_light_IGLV8-61_IGLJ2, SERPINA10, IG c1421_light_IGKV1-27_IGKJ4, rheumatoid factor RF-ET1, IGL c600_light_IGKV4-1_IGKJ4, and SELL) that were mostly associated with immunity, and accurate for diagnosis of autism. The protein family was verified by a logic-regression leave-one cross-validation method with bidirectional feature screening. The accuracy of this model was 0.9527, and the kappa coefficient was 0.9025.

Conclusions

Our study showed that immunity is closely related to the onset of autism and can be used for early screening of patients. A model with eight proteins (IGH c1898_heavy_IGHV3-33_IGHD3-9_IGHJ4, LYZ, IGL c1860_light_IGLV8-61_IGLJ2, SERPINA10, IG c1421_light_IGKV1-27_IGKJ4, rheumatoid factor RF-ET1, IGL c600_light_IGKV4-1_IGKJ4, and SELL), which are mostly associated with immunity, is accurate for diagnosis of autism.

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独立获取数据的蛋白质组学和机器学习发现，与免疫相关的蛋白质是早期诊断自闭症的潜在分子标记。

背景：自闭症的早期诊断对其治疗至关重要，但到目前为止，还没有明确的儿童早期诊断的分子标志物。方法：采用数据独立采集（DIA）质谱法比较99例中国自闭症谱系障碍儿童与70例健康儿童血清蛋白表达。结果：共鉴定出347个下调蛋白和394个上调蛋白。基于生物信息学分析，在免疫系统、免疫疾病、细胞运动和局灶黏附中富集了差异蛋白。机器学习发现了一个包含8个蛋白（IGH c1898_heavy - ighv3 - 33_ighd3 - 9_ighj4、LYZ、IGL c1860_light_IGLV8-61_IGLJ2、SERPINA10、IGL c1421_light_IGKV1-27_IGKJ4、类风湿因子RF-ET1、IGL c600_light_IGKV4-1_IGKJ4和SELL）的模型，这些蛋白主要与免疫相关，能够准确诊断自闭症。通过双向特征筛选的逻辑回归左一交叉验证方法对蛋白家族进行验证。该模型的精度为0.9527，kappa系数为0.9025。结论：我们的研究表明，免疫与自闭症的发病密切相关，可用于患者的早期筛查。一个包含8个蛋白（IGH c1898_heavy_IGHV3-33_IGHD3-9_IGHJ4、LYZ、IGL c1860_light_IGLV8-61_IGLJ2、SERPINA10、IGL c1421_light_IGKV1-27_IGKJ4、类风湿因子RF-ET1、IGL c600_light_IGKV4-1_IGKJ4、SELL）的模型能够准确诊断自闭症，这些蛋白主要与免疫相关。

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.