Dataset Information

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers.

ABSTRACT: Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA-mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.

SUBMITTER: Shen L

PROVIDER: S-EPMC5175196 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers.

Shen Li L Lin Yuxin Y Sun Zhandong Z Yuan Xuye X Chen Luonan L Shen Bairong B

Scientific reports 20161221

Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes w ...[more]

PMID: 28000768

Similar Datasets

Project description:BackgroundEarly identification and treatment of individuals with autism spectrum disorder (ASD) improves outcomes, but specific evidence needed to individualize treatment recommendations is lacking. Biomarkers that could be routinely measured within the clinical setting could potentially transform clinical care for patients with ASD. This demonstration project employed collection of biomarker data during regular autism specialty clinical visits and explored the relationship of biomarkers with clinical ASD symptoms.MethodsEighty-three children with ASD, aged 5-10 years, completed a multi-site feasibility study integrating the collection of biochemical (blood serotonin, urine melatonin sulfate excretion) and clinical (head circumference, dysmorphology exam, digit ratio, cognitive and behavioral function) biomarkers during routine ASD clinic visits. Parents completed a demographic survey and the Aberrant Behavior Checklist-Community. Cognitive function was determined by record review. Data analysis utilized Wilcoxon two-sample tests and Spearman correlations.ResultsParticipants were 82% male, 63% White, 19% Hispanic, with a broad range of functioning. Group means indicated hyperserotonemia. In a single regression analysis adjusting for race and median household income, higher income was associated with higher levels of blood serotonin and urine melatonin sulfate excretion levels (p = 0.004 and p = 0.04, respectively). Melatonin correlated negatively with age (p = 0.048) and reported neurologic problems (p = 0.02). Dysmorphic status correlated with higher reported stereotyped behavior (p = 0.02) and inappropriate speech (p = 0.04).ConclusionThis demonstration project employed collection of multiple biomarkers, allowed for examination of associations between biochemical and clinical measures, and identified several findings that suggest direction for future studies. This clinical research model has promise for integrative biomarker research in individuals with complex, heterogeneous neurodevelopmental disorders such as ASD.

Project description:Autism Spectrum Disorder (ASD) lacks validated measures of core social functions across development stages suitable for clinical trials. We assessed the concurrent validity between ASD clinical measures and putative biomarkers of core deficits, and their feasibility of implementation in human studies. Datasets from two adult ASD studies were combined (observational study [n = 19] and interventional study baseline data [n = 19]). Potential biomarkers included eye-tracking, olfaction, and auditory and visual emotion recognition assessed via the Affective Speech Recognition test (ASR) and Reading-the-Mind-in-the-Eyes Test (RMET). Current functioning was assessed with intelligence quotient (IQ), adaptive skill testing, and behavioral ratings. Autism severity was determined by the Autism Diagnostic Observation Scale-2 and Social Communication Interaction Test (SCIT). Exploratory measures showed varying significant associations across ASD severity, adaptive skills, and behavior. Eye tracking endpoints showed little relationship to adaptive ability but correlated with severity and behavior. ASR scores significantly correlated with most adaptive behavior domains, as well as severity. Olfaction predicted visual and auditory emotion recognition. SCIT scores related moderately to multiple severity domains, and was the only measure not related with IQ. RMET accuracy was less related to ASD features. Eye tracking, SCIT, and ASR showed high test-retest reliability. We documented associations of proximal biomarkers of social functioning with multiple ASD dimensions. With the exception of SCIT, most correlations were modest, limiting utility as proxy measures of social communication. Feasibility and reliability were high for eye-tracking, ASR, and SCIT. Overall, several novel experimental paradigms showed potential as social biomarkers or surrogate markers in ASD. Autism Research 2018, 11: 1567-1579. © 2018 The Authors. Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc. LAY SUMMARY: More accurate measurements of treatment effects are needed to help the development of new drug treatments for autism spectrum disorders (ASD). This study evaluates the relationship between assessments designed to measure behaviors associated with social communication and cognition in ASD with clinical and diagnostic assessments of symptom severity as well as their implementation. The assessments including eye-tracking, auditory and visual social stimuli recognition, and olfaction identification showed potential for use in the evaluation of treatments for social difficulties in ASD.

Dataset Information

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers.

Publications

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets