Abstract:
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases.
摘要:
自闭症谱系障碍(ASD)是一种复杂的神经发育疾病,其特征是大脑连通性和功能改变。在这项研究中,我们采用先进的生物信息学和可解释的人工智能来分析与ASD相关的基因表达,使用来自五个GEO数据集的数据。在351名神经典型对照和358名自闭症患者中,我们鉴定了3,339个差异表达基因(DEGs),其调整后的p值(≤0.05)。随后的荟萃分析确定了342DEG(调整后的p值≤0.001),包括所有数据集的19个上调基因和10个下调基因。共有的基因,致病性单核苷酸多态性(SNPs),染色体位置,并研究了它们对生物学途径的影响。我们确定了潜在的生物标志物(HOXB3,NR2F2,MAPK8IP3,PIGT,SEMA4D,和SSH1)通过文本挖掘,值得进一步调查。此外,我们阐明了RPS4Y1和KDM5D基因在神经发生和神经发育中的作用。我们的分析检测到1,286个与ASD相关疾病相关的SNP,其中14个高风险SNP位于染色体10和X。我们强调了与FGFR抑制剂相关的潜在错义SNP,这表明它可以作为靶向治疗反应性的有希望的生物标志物。我们可解释的AI模型将MID2基因鉴定为潜在的ASD生物标志物。这项研究揭示了重要的基因和潜在的生物标志物,为复杂疾病中的新基因发现提供了基础。