Biomarkers for early diagnosis of autism obtained through bioinformatics analysis of human blood sample

Abstract

Objective: autism spectrum disorder (ASD), a serious disorder that begins early in life, continues throughout the lifespan. Children with ASD who are diagnosed early are more responsive to therapeutic interventions and have less social and language impairment than children diagnosed later; however, current diagnostic measures are mostly applied to children older than one year and lack the appropriate biological markers for early diagnosis of ASD. Using bioinformatic analysis, this study explores the molecular information mechanism of ASD. Method: in this study, we used dataset GSE6575 from Gene Expression Omnibus (GEO) to analyze the mRNA expression profile of ASD, including 35 ASD samples and 12 normal control samples looking for different genes and we did enrichment analysis of those genes. We then used the STRING database to construct a protein–protein interaction (PPI) network of differential genes. Finally, Cytoscape plug-in cytoHubba was used to search for hub genes. The diagnostic value of the hub genes was verified by subject operating characteristic curves. Result: we looked for 50 different genes and did an enrichment analysis of those genes. The results of the enrichment analysis showed that these differential genes were mainly concentrated in the response to viruses, the immune regulation of inflammation and energy metabolism. Using Cytoscape plug-in cytoHubba, we found ten different genes. We drew ROC curves for all ten genes among which two genes, interleukin 2 receptor subunit beta (IL2Rβ) and perforin 1 (PRF1), had good sensitivity and specificity for the early diagnosis of autism. The areas under the ROC curves were 0.855, 0.830 for IL2Rβ, PRF1. Conclusion: data analysis using the GEO database can provide new insights into the etiology of ASD as well as some possible biomarkers and therapeutic targets for early diagnosis and treatment of ASD.

Keywords

autism spectrum disorder (ASD) / differentially expressed gene / early diagnosis / energy metabolism / immune regulation