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Identification of key biomarkers in Angelman syndrome by a multi-cohort analysis.

ABSTRACT: The Angelman Syndrome (AS) is an extreme neurodevelopmental disorder without effective treatments. While most patients with this disease can be diagnosed by genetic testing, there are still a handful of patients have an unrecognized genetic cause for their illness. Thus, novel approaches to clinical diagnosis and treatment are urgently needed. The aim of this study was to identify and characterize differentially expressed genes involved in AS and built potential diagnostic panel for AS by NGS sequencing. A multi-cohort analysis framework was used to analyze stem cell-derived neurons from AS patients in GSE160747 dataset. We identified three differentially expressed genes (ACTN1, ADAMTS2, SLC30A8) differentiates AS patients from controls. Moreover, we validated the expression patterns of these genes in GSE146640, GSE120225. Receiver operating characteristic (ROC) curves analysis demonstrated that these genes could function as potential diagnostic biomarkers [AUC = 1 (95% CI 1-1)]. This study may provide new approach for diagnosing patients with AS and helping to develop novel therapies in treating AS patients.

SUBMITTER: Li Y

PROVIDER: S-EPMC9424609 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Identification of key biomarkers in Angelman syndrome by a multi-cohort analysis.

Li Yong Y Shu Junhua J Cheng Ying Y Zhou Xiaoqing X Huang Tao T

Frontiers in medicine 20220816

The Angelman Syndrome (AS) is an extreme neurodevelopmental disorder without effective treatments. While most patients with this disease can be diagnosed by genetic testing, there are still a handful of patients have an unrecognized genetic cause for their illness. Thus, novel approaches to clinical diagnosis and treatment are urgently needed. The aim of this study was to identify and characterize differentially expressed genes involved in AS and built potential diagnostic panel for AS by NGS se ...[more]

PMID: 36052323

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Project description:Objectives: To assess sleep quality and timing in children with Angelman syndrome (AS) with sleep problems using questionnaires and actigraphy and contrast sleep parameters to those of typically developing (TD) children matched for age and sex. Methods: Week-long actigraphy assessments were undertaken with children with AS (n = 20) with parent-reported sleep difficulties and compared with age and sex matched TD controls. The presence of severe sleep problems was assessed using the modified Simonds and Parraga sleep questionnaire. Sleep hygiene was measured using the Family Inventory of Sleep Habits. Results: Actigraphy and parent-completed sleep diary data indicated that children with AS had significantly earlier bedtimes (p = .003, Cohen d = .47) and poorer sleep efficiency (78%, p = .04, d = .33) than TD children (84%). No significant differences in total sleep time, sleep onset latency or wake after sleep onset were found between the two groups. The expected relationship between later bedtimes and increasing age found for the TD group (p < .001, β.78) was not evidenced for the AS group (p = .09, β.39). Considerable inter-individual and night to night variation in actigraphy assessed total sleep time and wake after sleep onset was found for children with AS compared to TD children. Parent report indicated that a greater proportion of children with AS had severe night waking problems compared to TD children (81 versus 5%). No significant differences in sleep hygiene and excessive daytime sleepiness were found between the two groups (p > .05). Conclusions: This study reports the largest objective dataset of sleep quality parameters in children with AS. Sleep quality in this group was characterised by poor efficiency and significant intra- and inter-individual variability that warrants further investigation. This variability should inform assessment and intervention for sleep in children with AS, as averages of total sleep, even across a 7 day period may not capture the difficulties with night waking highlighted by parental questionnaire report.

Dataset Information

Identification of key biomarkers in Angelman syndrome by a multi-cohort analysis.

Publications

Identification of key biomarkers in Angelman syndrome by a multi-cohort analysis.

Similar Datasets

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