No specific gene expression signature in human granulosa and cumulus cells for prediction of oocyte fertilisation and embryo implantation

PloS One
Tanja Burnik PaplerAleš Maver

Abstract

In human IVF procedures objective and reliable biomarkers of oocyte and embryo quality are needed in order to increase the use of single embryo transfer (SET) and thus prevent multiple pregnancies. During folliculogenesis there is an intense bi-directional communication between oocyte and follicular cells. For this reason gene expression profile of follicular cells could be an important indicator and biomarker of oocyte and embryo quality. The objective of this study was to identify gene expression signature(s) in human granulosa (GC) and cumulus (CC) cells predictive of successful embryo implantation and oocyte fertilization. Forty-one patients were included in the study and individual GC and CC samples were collected; oocytes were cultivated separately, allowing a correlation with IVF outcome and elective SET was performed. Gene expression analysis was performed using microarrays, followed by a quantitative real-time PCR validation. After statistical analysis of microarray data, there were no significantly differentially expressed genes (FDR<0,05) between non-fertilized and fertilized oocytes and non-implanted and implanted embryos in either of the cell type. Furthermore, the results of quantitative real-time PCR were in cons...Continue Reading

Associated Clinical Trials

References

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Citations

Apr 26, 2018·Molecular Reproduction and Development·Alan M O'DohertyLynne C O'Shea
Nov 7, 2016·Reproduction : the Official Journal of the Society for the Study of Fertility·Richard J Kordus, Holly A LaVoie
Jun 4, 2019·Molecular Biology Reports·Azita FaramarziMasoumeh Golestan Jahromi
Mar 7, 2020·Systems Biology in Reproductive Medicine·Qiuzi ShenLing Zhang
Feb 26, 2020·Human Reproduction Update·Purificación Hernández-VargasFrancisco Domínguez
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Aug 22, 2021·Reproductive Biomedicine Online·Ashley W TiegsRichard T Scott

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Datasets Mentioned

BETA
GSE55654

Methods Mentioned

BETA
PCR
flow cytometry
Feature Extraction

Software Mentioned

FE
R
limma
Bioconductor
Agilent Feature Extraction ( FE )
FlowJo

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