Integrating Network Analysis and Machine Learning Identifies Key Autism Spectrum Disorder Genes Linked to Immune Dysregulation and Therapeutic Targets.

Scientists used computer analysis to study genes associated with autism. They identified 10 important genes that might help diagnose autism and found that some of these genes are connected to immune system problems. One gene called MGAT4C showed particularly good potential for helping diagnose autism. The study also suggested some existing drugs that might be helpful for treating autism, though these would need testing in real patients.

This bioinformatics study analyzed gene expression data to identify autism-associated genes and potential therapeutic targets. Researchers used computational methods including network analysis, machine learning, and immune correlation analysis on the GSE18123 dataset. Ten key genes were identified through random forest analysis, with MGAT4C showing particularly strong diagnostic potential (AUC = 0.730). The study revealed connections between autism genes and immune system dysfunction, and predicted potential therapeutic compounds through drug connectivity mapping.

Functional enrichment analysis linked identified genes to relevant biological pathways. The research aims to bridge basic genetic discoveries with clinical applications in autism treatment development.

Identifies potential biomarkers for autism diagnosis and therapeutic targets for treatment development. The immune system connections suggest new research directions. However, all findings require validation in clinical populations before translation to practice. Drug predictions need experimental testing and clinical trials.

Study is computational analysis only without clinical validation. Sample size not reported. Drug predictions require experimental validation. Findings based on single dataset (GSE18123). No information provided about participant characteristics or demographics.

Understanding the genetic mechanisms and identifying potential therapeutic targets are essential for clarifying Autism Spectrum Disorder (ASD) etiology and improving treatments. This study aims to bridge the gap between basic transcriptomic discoveries and clinical applications in ASD research. Differentially expressed genes (DEGs) of GSE18123 datase were identified. A protein-protein interaction (PPI) network was constructed.

Functional enrichment analysis was performed to link genetic loci to relevant biological pathways. Connectivity Map (CMap) analysis was used to predict potential drugs. Furthermore, immune infiltration correlation analysis explored associations between key genes and immune cell subpopulations. Diagnostic performance of top genes was evaluated by receiver operating characteristic (ROC) analysis.

The functional enrichment analysis successfully revealed relevant biological processes associated with ASD, while the CMap analysis predicted potential drugs that were consistent with some clinical trial results. Random forest analysis selected ten key feature genes (,,,,,,,,, and) with the highest importance scores for autism prediction. Immune infiltration analysis showed significant correlations in genes and multiple immune cell types, demonstrating complex pleiotropic associations within the immune microenvironment. ROC curve analysis indicated that most top genes had strong discriminatory power in differentiating ASD from controls, particularly MGAT4C (AUC = 0.730), highlighting its potential as a robust biomarker.

This study effectively bridges the basic transcriptomic discoveries and clinical applications in ASD research. The findings contribute to a better understanding of the etiology of ASD and provide potential therapeutic leads. Future research could focus on validating these potential drugs in clinical studies, as well as further exploring the biological functions of the identified genes to develop more targeted and effective treatments for ASD.