Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by diverse symptoms affecting social interaction, communication, and behavior. This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder. METHODS: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms. A nomogram prediction model was developed based on these signatures, and we performed Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and immune cell infiltration analysis to assess the impact of BLI subtypes and their underlying mechanisms. RESULTS: Our findings revealed 17 differentially expressed BLI genes in children with ASD, with BLNK, MAPK8, PRKCQ, and TNFSF12 identified as potential biomarkers. The nomogram demonstrated high diagnostic accuracy for ASD. We delineated two distinct molecular subtypes (Cluster 1 and Cluster 2), with GSVA indicating that Cluster 2 showed upregulation of immune- and inflammation-related pathways. This cluster exhibited increased levels of antimicrobial agents, chemokines, cytokines, and TNF family cytokines, alongside activation of bacterial lipoprotein-related pathways. A significant correlation was found between these pathways and distinct immune cell subtypes, suggesting a potential mechanism for neuroinflammation and immune cell infiltration in ASD. CONCLUSION: Our research highlights the role of BLI-associated genes in the immune responses of individuals with ASD, indicating their contribution to the disorder's typification. The interplay between bacterial components, genetic predisposition, and immune dysregulation offers new insights for understanding ASD and developing personalized interventions.