ArticleMetabolic brain disease2022
Identification of methylation markers for diagnosis of autism spectrum disorder.
Article in Metabolic brain disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.Epigenomics · 2025Article
- Efficacy and safety ofFrontiers in nutrition · 2024Article
- Differential DNA Methylation from Autistic Children Enriches Evidence for Genes Associated with ASD and New Candidate Genes.Brain sciences · 2023Article
- Antioxidant Behavioural Phenotype in theGenes · 2023Article
- Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder.International journal of molecular sciences · 2023Article
- DNA Methylation Profiles ofInternational journal of molecular sciences · 2022Article
- Article
- Construction of an immune-related ceRNA network to screen for potential diagnostic markers for autism spectrum disorder.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is a hereditary heterogeneous neurodevelopmental disorder characterized by social and speech dysplasia. We collected the expression profiles of ASD in GSE26415, GSE42133 and GSE123302 from the gene expression omnibus (GEO) database, as well as methylation data of GSE109905. Differentially expressed genes (DEGs) between ASD and controls were obtained by differential expression analysis. Enrichment analysis identified the biological functions and signaling pathways involved by common genes in three groups of DEGs. Protein-protein interaction (PPI) networks were used to identify genes with the highest connectivity as key genes. In addition, we identified methylation markers by associating differentially methylated positions. Key methylation markers were identified using the least absolute shrink and selection operator (LASSO) model. Receiver operating characteristic curves and nomograms were used to identify the diagnostic role of key methylation markers for ASD. A total of 57 common genes were identified in the three groups of DEGs. These genes were mainly enriched in Sphingolipid metabolism and PPAR signaling pathway. In the PPI network, we identified seven key genes with higher connectivity, and used qRT-PCR experiments to verify the expressions. In addition, we identified 31 methylation markers and screened 3 key methylation markers (RUNX2, IMMP2L and MDM2) by LASSO model. Their methylation levels were closely related to the diagnostic effects of ASD. Our analysis identified RUNX2, IMMP2L and MDM2 as possible diagnostic markers for ASD. Identifying different biomarkers and risk genes will contribute to the diagnosis of ASD and the development of new clinical and drug treatments.
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