{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li N"],"funding":["National Natural Science Foundation of China","Ministry of Science and Technology of the People' s Republic of China"],"pagination":["639"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10135462"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(4)"],"pubmed_abstract":["<h4>Background</h4>Obstructive sleep apnea (OSA) is highly prevalent yet underdiagnosed. This study aimed to develop a predictive signature, as well as investigate competing endogenous RNAs (ceRNAs) and their potential functions in OSA.<h4>Methods</h4>The GSE135917, GSE38792, and GSE75097 datasets were collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) and differential expression analysis were used to identify OSA-specific mRNAs. Machine learning methods were applied to establish a prediction signature for OSA. Furthermore, several online tools were used to establish the lncRNA-mediated ceRNAs in OSA. The hub ceRNAs were screened using the cytoHubba and validated by real-time qua"],"journal":["Biomolecules"],"pubmed_title":["Integrative Analysis and Experimental Validation of Competing Endogenous RNAs in Obstructive Sleep Apnea."],"pmcid":["PMC10135462"],"funding_grant_id":["2021ZD0201900","81970870"],"pubmed_authors":["Liu Y","Guan J","Zhu Y","Yin S","Zhang X","Gao Z","Wang X","Liu F","Li N"],"additional_accession":[]},"is_claimable":false,"name":"Integrative Analysis and Experimental Validation of Competing Endogenous RNAs in Obstructive Sleep Apnea.","description":"<h4>Background</h4>Obstructive sleep apnea (OSA) is highly prevalent yet underdiagnosed. This study aimed to develop a predictive signature, as well as investigate competing endogenous RNAs (ceRNAs) and their potential functions in OSA.<h4>Methods</h4>The GSE135917, GSE38792, and GSE75097 datasets were collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) and differential expression analysis were used to identify OSA-specific mRNAs. Machine learning methods were applied to establish a prediction signature for OSA. Furthermore, several online tools were used to establish the lncRNA-mediated ceRNAs in OSA. The hub ceRNAs were screened using the cytoHubba and validated by real-time qua","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2025-04-22T05:11:53.333Z","creation":"2025-04-05T21:17:10.635Z"},"accession":"S-EPMC10135462","cross_references":{"pubmed":["37189386"],"doi":["10.3390/biom13040639"]}}