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自闭症谱系障碍中感觉知觉异常的预测模型。

Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder.

机构信息

Department of Children's and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, China.

出版信息

Int J Mol Sci. 2023 Jan 25;24(3):2367. doi: 10.3390/ijms24032367.

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers for the diagnosis of ASD sensory perception abnormality are currently elusive. We sought to identify potential biomarkers related to ASD sensory perception abnormality to construct a prediction model that could facilitate the early identification of and screening for ASD. Differentially expressed genes in ASD were obtained from the Gene Expression Omnibus database and were screened for genes related to sensory perception abnormality. After enrichment analysis, the random forest method was used to identify disease-characteristic genes. A prediction model was constructed with an artificial neural network. Finally, the results were validated using data from the dorsal root ganglion, cerebral cortex, and striatum of the BTBR T+ Itpr3tf/J (BTBR) ASD mouse model. A total of 1869 differentially expressed genes in ASD were screened, among which 16 genes related to sensory perception abnormality were identified. According to enrichment analysis, these 16 genes were mainly related to actin, cholesterol metabolism, and tight junctions. Using random forest, 15 disease-characteristic genes were screened for model construction. The area under the curve of the training set validation result was 0.999, and for the model function validation, the result was 0.711, indicating high accuracy. The validation of BTBR mice confirmed the reliability of using these disease-characteristic genes for prediction of ASD. In conclusion, we developed a highly accurate model for predicting ASD sensory perception abnormality from 15 disease-characteristic genes. This model provides a new method for the early identification and diagnosis of ASD sensory perception abnormality.

摘要

自闭症谱系障碍 (ASD) 是一种神经发育障碍,其临床表型具有异质性。患者常伴有异常的感觉感知,进一步影响 ASD 的核心表型,显著降低其生活质量。然而,目前用于诊断 ASD 感觉感知异常的生物标志物仍难以确定。本研究旨在寻找与 ASD 感觉感知异常相关的潜在生物标志物,构建预测模型,以辅助 ASD 的早期识别和筛查。从基因表达综合数据库中获取 ASD 的差异表达基因,并筛选与感觉感知异常相关的基因。通过富集分析,采用随机森林法识别疾病特征基因。利用人工神经网络构建预测模型。最后,使用 BTBR 自闭症模型的背根神经节、大脑皮层和纹状体数据进行验证。共筛选出 1869 个 ASD 差异表达基因,其中鉴定出 16 个与感觉感知异常相关的基因。根据富集分析,这 16 个基因主要与肌动蛋白、胆固醇代谢和紧密连接有关。利用随机森林筛选出 15 个疾病特征基因进行模型构建。训练集验证结果的曲线下面积为 0.999,模型功能验证的结果为 0.711,表明准确率较高。BTBR 小鼠的验证证实了使用这些疾病特征基因预测 ASD 的可靠性。综上所述,本研究从 15 个疾病特征基因中开发了一种预测 ASD 感觉感知异常的高准确性模型。该模型为 ASD 感觉感知异常的早期识别和诊断提供了新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8de4/9916460/f54f4a1f66a9/ijms-24-02367-g001.jpg

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