Transcriptomic analysis and competing endogenous RNA network in the human endometrium between proliferative and mid-secretory phases

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Title
Transcriptomic analysis and competing endogenous RNA network in the human endometrium between proliferative and mid-secretory phases
Author(s)
S L Yu; T H Kim; Y H Han; Y Kang; D U Jeong; Dong Chul Lee; J Kang; S R Park
Bibliographic Citation
Experimental and Therapeutic Medicine, vol. 21, no. 6, pp. 660-660
Publication Year
2021
Abstract
Successful embryo implantation is the first step for establishing natural pregnancy and is dependent on the crosstalk between the embryo and a receptive endometrium. However, the molecular signaling events for successful embryo implantation are not entirely understood. To identify differentially expressed transcripts [long-noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs] and competing endogenous RNA (ceRNA) networks associated with endometrial receptivity, the current study analyzed gene expression profiles between proliferative and mid-secretory endometria in fertile women. A total of 247 lncRNAs, 67 miRNAs and 2,154 mRNAs were identified as differentially expressed between proliferative and mid-secretory endometria. Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that these differentially expressed genes were significantly enriched for ‘cell adhesion molecules.’ Additionally, 98 common mRNAs were significantly involved in tryptophan metabolism, metabolic pathways and FoxO signaling. From the differentially expressed lncRNA/miRNA/mRNA ceRNA network, hub RNAs that formed three axes were identified: The DLX6-AS1/miR-141 or miR-200a/OLFM1 axis, the WDFY3-AS2/miR-135a or miR-183/STC1 axis, and the LINC00240/miR-182/NDRG1 axis. These may serve important roles in the regulation of endometrial receptivity. The hub network of the current study may be developed as a candidate marker for endometrial receptivity.
Keyword
Proliferative endometriumMid-secretory endometriumEndometrial receptivityRNA sequencingCompeting endogenous RNA network
ISSN
1792-0981
Publisher
Spandidos Publ Ltd
Full Text Link
http://dx.doi.org/10.3892/etm.2021.10092
Type
Article
Appears in Collections:
Division of A.I. & Biomedical Research > Genomic Medicine Research Center > 1. Journal Articles
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