ArticleSleep & breathing = Schlaf & Atmung2019
Bioinformatics analysis to reveal the key genes related to obstructive sleep apnea.
Article in Sleep & breathing = Schlaf & Atmung, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Molecular mechanisms of bamboo-derived miRNA-mediated gene regulation and dietary adaptation in giant pandas.BMC genomics · 2025Article
- Identification and molecular mechanism of palmitoylation-related biomarkers in obstructive sleep apnea.Frontiers in neurology · 2025Article
- Article
- Identification of the Novel Gene Markers Based on the Gene Profile among Different Severity of Obstructive Sleep Apnea.Computational and mathematical methods in medicine · 2022Article
- [Research status of Epstein Barr virus in children with adenotonsillar hypertrophy].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2021Review
- Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeObstructive sleep apnea (OSA) is induced by obstruction of the upper airway, which can raise multiple health risks. This study is designed to reveal the key genes involved in OSA.
methodsGSE38792 was extracted from Gene Expression Omnibus database, including ten visceral adipose tissues from OSA patients and eight visceral adipose tissues from normal controls. Differential expression analysis was conducted using limma package, and then the functions of the differentially expressed genes (DEGs) were analyzed using DAVID database, followed by protein-protein interaction (PPI) network, and integrated regulatory network analysis was performed using Cytoscape software.
resultsA total of 368 DEGs (176 upregulated and 192 downregulated) were identified in OSA samples. Epstein-Barr virus infection (involving IL10RB, MAPK9, and MAPK10) and olfactory transduction were the main pathways separately enriched for the upregulated genes and the downregulated genes. After the PPI network was built, the top ten network nodes (such as TXN) were selected according to node degrees. Two significant PPI network modules were identified. Moreover, the integrated regulatory network was constructed.
conclusionIL10RB, MAPK9, MAPK10, and TXN might function in the pathogenesis of OSA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.