Obstructive sleep apnea (OSA) is a common chronic obstructive sleep disease in clinic. The purpose of our study was to use bioinformatics analysis to identify microRNAs (miRNAs) that are differentially expressed between OSA patients and healthy controls.Serum samples were collected from OSA patients and healthy controls. To better reveal the sample specificity of differentially expressed microRNAs, supervised hierarchical clustering was conducted. We used the microT-CDS and TargetScan databases to predict target genes of the differentially expressed microRNAs and selected the common genes. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to evaluate many coexpression relationships. Moreover, we used these potential microRNA-target pairs and coexpression relationships to construct a regulatory coexpression network using Cytoscape software. Functional analysis of microRNA target genes was conducted with FunRich.A total of 104 microRNAs that were differentially expressed between OSA patients and healthy controls were identified. Supervised hierarchical clustering was conducted based on the expression of the 104 microRNAs in the OSA patients and healthy controls. Overall, 6621 potential target genes were predicted, and 119 target genes were screened based on coexpression coefficients in the STRING database. A regulatory coexpression network was constructed that included 23 differentially expressed microRNAs and 18 of the most related potential target genes. Metabolic signaling pathways were the most highly enriched category. Differentially expressed microRNAs, such as hsa-miR-485-5p, hsa-miR-107, hsa-miR-574-5p, and hsa-miR-199-3p, might participate in OSA. The target gene CAD might also be closely related to OSA.Our results may provide a basis for the pathogenesis of OSA and the study of disease diagnosis, prevention, and treatment. However, more experiments are needed to verify these predictions.