Dataset Information

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis.

ABSTRACT:

Abstract

Obstructive sleep apnea (OSA) is a common chronic disease and increases the risk of cardiovascular disease, metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. This study aimed to identify potential key genes influence the mechanisms and consequences of OSA.Gene expression profiles related to OSA were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from OSA compared with normal tissues were screened using R software, followed by gene ontology (GO) and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network for these DEGs was constructed by STRING, and key hub genes were extracted from the network with plugins in Cytoscape. The hub genes were further validated in another GEO dataset and assessed by receiver operating characteristic (ROC) analysis and Pearson correlation analysis.There were 373 DEGs in OSA samples in relative to normal controls, which were mainly associated with olfactory receptor activity and olfactory transduction. Upon analyses of the PPI network, GDNF, SLC2A2, PRL, and SST were identified as key hub genes. Decreased expression of the hub genes was association with OSA occurrence, and exhibited good performance in distinguishing OSA from normal samples based on ROC analysis. Besides, the Pearson method revealed a strong correlation between hub genes, which indicates that they may act in synergy, contributing to OSA and related disorders.This bioinformatics research identified 4 hub genes, including GDNF, SLC2A2, PRL, and SST which may be new potential biomarkers for OSA and related disorders.

SUBMITTER: Cao Y

PROVIDER: S-EPMC7850694 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis.

Cao Yuanyuan Y Cai Xintian X Zhu Qing Q Li Nanfang N Li Nanfang N

Medicine 20210101 4

<h4>Abstract</h4>Obstructive sleep apnea (OSA) is a common chronic disease and increases the risk of cardiovascular disease, metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. This study aimed to identify potential key genes influence the mechanisms and consequences of OSA.Gene expression profiles related to OSA were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from OSA compare ...[more]

PMID: 33530245

Similar Datasets

Project description:BackgroundObstructive sleep apnea (OSA) is a complex and multi-gene inherited disease caused by both genetic and environmental factors. However, due to the high cost of diagnosis and complex operation, its clinical application is limited. This study aims to explore potential target genes associated with OSA and establish a corresponding diagnostic model.MethodsThis study used microarray datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) related to OSA and perform functional annotation and pathway analysis. The study employed multi-scale embedded gene co-expression network analysis (MEGENA) combined with least absolute shrinkage and selection operator (LASSO) regression analysis to select hub genes and construct a diagnostic model for OSA. In addition, the study conducted correlation analysis between hub genes and OSA-related genes, immunoinfiltration, gene set enrichment analysis (GSEA), miRNA network analysis, and identified potential transcription factors (TFs) and targeted drugs for hub genes. Finally, the study used chronic intermittent hypoxia (CIH) mouse model to simulate OSA hypoxic conditions and verify the expression of hub genes in CIH mice.ResultsIn this study, a total of 401 upregulated genes and 275 downregulated genes were identified, and enrichment analysis revealed that these differentially expressed genes may be associated with pathways such as vasculature development, cellular response to cytokine stimulus, and negative regulation of cell population proliferation. Through MEGENA combined with LASSO regression, seven OSA hub genes were identified, including C12orf54, FOS, GPR1, OR9A4, MYO5B, RAB39B, and KLHL4. The diagnostic model constructed based on these genes showed strong stability. The expression levels of hub genes were significantly correlated with the expression levels of OSA-related genes and mainly acted on pathways such as the JAK/STAT signaling pathway and the cytosolic DNA-sensing pathway. Drug-target predictions for hub genes were made using the Connectivity Map (CMap) database and the Drug-Gene Interaction database (Dgidb), which identified targeted therapeutic drugs for the hub genes. In vivo experiments showed that the hub genes were all decreasing in the OSA mouse model.ConclusionsThis study identified novel biomarkers for OSA and established a reliable diagnostic model. The transcriptional changes identified may help to reveal the pathogenesis, mechanisms, and sequelae of OSA.

Project description:BACKGROUND:Obstructive sleep apnea syndrome (OSAS) is common in adults. People with OSAS have a higher risk of experiencing traffic accidents and occupational injuries (OIs). We aimed to clarify the diagnostic performance of a three-channel screening device (ApneaLinkTM) compared with the gold standard of full-night attended polysomnography (PSG) among hospital outpatients not referred for sleep-related symptoms. Furthermore, we aimed to determine whether manual revision of the ApneaLinkTM autoscore enhanced diagnostic performance. METHODS:We investigated 68 patients with OI and 44 without OI recruited from the University Hospital Basel emergency room, using a cross-sectional study design. Participating patients spent one night at home with ApneaLinkTM and within 2 weeks slept for one night at the sleep laboratory. We reanalyzed all ApneaLinkTM data after manual revision. RESULTS:We identified significant correlations between the ApneaLinkTM apnea-hypopnea index (AHI) autoscore and the AHI derived by PSG (r = 0.525; p <0.001) and between the ApneaLinkTM oxygen desaturation index (ODI) autoscore and that derived by PSG (r = 0.722; p <0.001). The ApneaLinkTM autoscore showed a sensitivity and specificity of 82% when comparing AHI ≥5 with the cutoff for AHI and/or ODI ≥15 from PSG. In Bland Altman plots the mean difference between ApneaLinkTM AHI autoscore and PSG was 2.75 with SD ± 8.80 (β = 0.034), and between ApneaLinkTM AHI revised score and PSG -1.50 with SD ± 9.28 (β = 0.060). CONCLUSIONS:The ApneaLinkTM autoscore demonstrated good sensitivity and specificity compared with the gold standard (full-night attended PSG). However, Bland Altman plots revealed substantial fluctuations between PSG and ApneaLinkTM AHI autoscore respectively manually revised score. This spread for the AHI from a clinical perspective is large, and therefore the results have to be interpreted with caution. Furthermore, our findings suggest that there is no clinical benefit in manually revising the ApneaLinkTM autoscore.

Project description:Untreated and long-lasting obstructive sleep apnea (OSA) may lead to important vascular abnormalities, including endothelial cell (EC) dysfunction, hypertension, and atherosclerosis. We observed a correlation between microcirculatory reactivity and endothelium-dependent release of nitric oxide in OSA patients. Therefore, we hypothesized that OSA affects (micro)vasculature and we aimed to identify vascular gene targets of OSA that could possibly serve as reliable biomarkers of severity of the disease and possibly of vascular risk. Using quantitative RT-PCR, we evaluated gene expression in skin biopsies of OSA patients, mouse aortas from animals exposed to 4-week intermittent hypoxia (IH; rapid oscillations in oxygen desaturation and reoxygenation), and human dermal microvascular (HMVEC) and coronary artery endothelial cells (HCAEC) cultured under IH. We demonstrate a significant upregulation of endothelial nitric oxide synthase (eNOS), tumor necrosis factor-alpha-induced protein 3 (TNFAIP3; A20), hypoxia-inducible factor 1 alpha (HIF-1??? and vascular endothelial growth factor (VEGF) expression in skin biopsies obtained from OSA patients with severe nocturnal hypoxemia (nadir saturated oxygen levels [SaO2]<75%) compared to mildly hypoxemic OSA patients (SaO2 75%-90%) and a significant upregulation of vascular cell adhesion molecule 1 (VCAM-1) expression compared to control subjects. Gene expression profile in aortas of mice exposed to IH demonstrated a significant upregulation of eNOS and VEGF. In an in vitro model of OSA, IH increased expression of A20 and decreased eNOS and HIF-1? expression in HMVEC, while increased A20, VCAM-1 and HIF-1?expression in HCAEC, indicating that EC in culture originating from distinct vascular beds respond differently to IH stress. We conclude that gene expression profiles in skin of OSA patients may correlate with disease severity and, if validated by further studies, could possibly predict vascular risk in OSA patients.

Project description:Rationale: Obstructive sleep apnea (OSA) is common in pregnancy and associated with maternal and fetal complications. Early detection of OSA may have important implications for maternal-fetal well-being. A screening tool combining several methods of assessment may better predict OSA among pregnant women compared with tools that rely solely on self-reported information.Objectives: To develop a screening tool combining subjective and objective measures to predict OSA in pregnant women.Methods: This study is a secondary analysis using data collected from a completed cohort of pregnant women (n = 121 during the first and n = 87 during the third trimester). Participants underwent full polysomnography and completed the Multivariable Apnea Prediction Questionnaire. The Obstructive Sleep Apnea/Hypopnea Syndrome Score and Facco apnea predictive model were obtained. Logistic regression analysis and area under the curve (AUC) were used to identify models predicting OSA risk.Results: Participants' mean age was 27.4 ± 7.0 years. The prevalence of OSA during the first and third trimester was 10.7% and 24.1%, respectively. The final model predicting OSA risk consisted of body mass index, age, and presence of tongue enlargement. During the first trimester, the AUC was 0.86 (95% confidence interval [CI], 0.76-0.96). During the third trimester, the AUC was 0.87 (95% CI, 0.77-0.96). When the first-trimester data were used to predict third-trimester OSA risk, the AUC was 0.87 (95% CI, 0.77-0.97). This model had high sensitivity and specificity when used during both trimesters. The negative posttest probabilities (probability of OSA given a negative test result) ranged from 0.03 to 0.07.Conclusions: A new model consisting of body mass index, age, and presence of tongue enlargement provided accurate screening of OSA in pregnant women, particularly African-Americans. This tool can be easily and rapidly administered in busy clinical practices without depending on patients' awareness of experiencing apnea symptoms.

Dataset Information

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis.

Abstract

Publications

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets