PubTransformer

A site to transform Pubmed publications into these bibliographic reference formats: ADS, BibTeX, EndNote, ISI used by the Web of Knowledge, RIS, MEDLINE, Microsoft's Word 2007 XML.

MicroRNA expression profiling and bioinformatics analysis of dysregulated microRNAs in obstructive sleep apnea patients.

Abstract 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.
PMID
Related Publications

MicroRNA expression profile of surgical removed mandibular bone tissues from patients with mandibular prognathism.

Screening the key microRNAs and transcription factors in prostate cancer based on microRNA functional synergistic relationships.

Bioinformatics analysis of microRNA comprehensive regulatory network in B- cell acute lymphoblastic leukemia.

Bioinformatics analysis of miRNA expression profile between primary and recurrent glioblastoma.

Bioinformatics identification of potentially involved microRNAs in Tibetan with gastric cancer based on microRNA profiling.

Authors

Mayor MeshTerms
Keywords
Journal Title medicine
Publication Year Start




PMID- 28834917
OWN - NLM
STAT- In-Process
DA  - 20170823
LR  - 20170823
IS  - 1536-5964 (Electronic)
IS  - 0025-7974 (Linking)
VI  - 96
IP  - 34
DP  - 2017 Aug
TI  - MicroRNA expression profiling and bioinformatics analysis of dysregulated
      microRNAs in obstructive sleep apnea patients.
PG  - e7917
LID - 10.1097/MD.0000000000007917 [doi]
AB  - 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.
FAU - Li, Kun
AU  - Li K
AD  - Department of Otolaryngology, Beijing Anzhen Hospital, Capital Medical
      University, Beijing, China.
FAU - Wei, Peng
AU  - Wei P
FAU - Qin, Yanwen
AU  - Qin Y
FAU - Wei, Yongxiang
AU  - Wei Y
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Medicine (Baltimore)
JT  - Medicine
JID - 2985248R
EDAT- 2017/08/24 06:00
MHDA- 2017/08/24 06:00
CRDT- 2017/08/24 06:00
AID - 10.1097/MD.0000000000007917 [doi]
AID - 00005792-201708250-00054 [pii]
PST - ppublish
SO  - Medicine (Baltimore). 2017 Aug;96(34):e7917. doi: 10.1097/MD.0000000000007917.