Sleep & breathing = Schlaf & Atmung · 2022
Evaluation of obstructive sleep apnea: an analysis based on aberrant genes.
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Liao Jianhong, Gao Xiang, Shi Yunhan, Li Yanru, Han Demin
Аннотация
BACKGROUND: Obstructive sleep apnea (OSA) is an upstream disorder that frequently causes multisystem disorders. Much research has revealed the pathogenesis of OSA, but there is still a lack of research on the complications caused by OSA.
METHODS: The mRNA expression and methylation dataset based on peripheral blood mononuclear cells (PBMCs) were downloaded from the Gene Expression Omnibus (GEO) database. All differential expressed genes (DEGs) were ranked using the Robust Rank Aggregation (RRA) algorithm. A weighted gene co-expression network analysis (WGCNA) was constructed. Subsequently, we used immune infiltration, enrichment analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis for apnea and hypopnea index (AHI) and hypertension and excessive daytime sleepiness (EDS) and constructed diagnostic model using random forest algorithm.
RESULTS: In the present study, we identified 318 DEGs in PBMCs involved in pathogenesis or continuous positive airway pressure (CPAP) therapy. Pathway enrichment identified DEGs associated with protein regulation and metabolism. Notably, through intra group analysis, we found that the immune disorder was more significant for OSA in males, non-daytime sleepy, or non-hypertensive OSA. The area under the ROC curve of model for EDS prediction is 0.889 and 0.852 for hypertension. Notably, we found that the diagnostic model had a high linear predictive value for AHI.
CONCLUSIONS: Our results indicate that PBMCs are a significant component of alterations in OSA and are expected to explain the mechanism of multisystem diseases caused by OSA. The present study provides new insights for symptom evaluation, classification and treatment of OSA from the molecular level.
METHODS: The mRNA expression and methylation dataset based on peripheral blood mononuclear cells (PBMCs) were downloaded from the Gene Expression Omnibus (GEO) database. All differential expressed genes (DEGs) were ranked using the Robust Rank Aggregation (RRA) algorithm. A weighted gene co-expression network analysis (WGCNA) was constructed. Subsequently, we used immune infiltration, enrichment analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis for apnea and hypopnea index (AHI) and hypertension and excessive daytime sleepiness (EDS) and constructed diagnostic model using random forest algorithm.
RESULTS: In the present study, we identified 318 DEGs in PBMCs involved in pathogenesis or continuous positive airway pressure (CPAP) therapy. Pathway enrichment identified DEGs associated with protein regulation and metabolism. Notably, through intra group analysis, we found that the immune disorder was more significant for OSA in males, non-daytime sleepy, or non-hypertensive OSA. The area under the ROC curve of model for EDS prediction is 0.889 and 0.852 for hypertension. Notably, we found that the diagnostic model had a high linear predictive value for AHI.
CONCLUSIONS: Our results indicate that PBMCs are a significant component of alterations in OSA and are expected to explain the mechanism of multisystem diseases caused by OSA. The present study provides new insights for symptom evaluation, classification and treatment of OSA from the molecular level.