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Obstructive Sleep Apnea and Dementia-Common Gene Associations through Network-Based Identification of Common Driver Genes

Background: Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population. The relationship between neurodegenerative diseases such as dementia and sleep disorders have long attracted clinical attention; however, no comprehensive data exists elucidating common gene expression between the two di...

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Detalles Bibliográficos
Autores principales: Jeong, Hyun-Hwan, Chandrakantan, Arvind, Adler, Adam C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8069301/
https://www.ncbi.nlm.nih.gov/pubmed/33918603
http://dx.doi.org/10.3390/genes12040542
Descripción
Sumario:Background: Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population. The relationship between neurodegenerative diseases such as dementia and sleep disorders have long attracted clinical attention; however, no comprehensive data exists elucidating common gene expression between the two diseases. The objective of this study was to (1) demonstrate the practicability and feasibility of utilizing a systems biology approach called network-based identification of common driver genes (NICD) to identify common genomic features between two associated diseases and (2) utilize this approach to identify genes associated with both OSA and dementia. Methods: This study utilized 2 public databases (PCNet, DisGeNET) and a permutation assay in order to identify common genes between two co-morbid but mutually exclusive diseases. These genes were then linked to their mechanistic pathways through Enrichr, producing a list of genes that were common between the two different diseases. Results: 42 common genes were identified between OSA and dementia which were primarily linked to the G-coupled protein receptor (GPCR) and olfactory pathways. No single nucleotide polymorphisms (SNPs) were identified. Conclusions: This study demonstrates the viability of using publicly available databases and permutation assays along with canonical pathway linkage to identify common gene drivers as potential mechanistic targets for comorbid diseases.