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

Burnik Papler T, Vrtacnik Bokal E, Lovrecic L, Kopitar AN, Maver A - PLoS ONE (2015)

Bottom Line: 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 consent with microarray data as there were no significant differences in gene expression of genes selected for validation.In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Reproduction, Division of Gynaecology, University Medical Centre Ljubljana, Slajmerjeva 3, SI-1000 Ljubljana, Slovenia.

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 consent with microarray data as there were no significant differences in gene expression of genes selected for validation. In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.

No MeSH data available.


Predictive performance of expression biomarkers in granulosa and cumulus cells according to microarray data.AUC- area under the curve; GC- granulosa cells; CC- cumulus cells.
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pone.0115865.g003: Predictive performance of expression biomarkers in granulosa and cumulus cells according to microarray data.AUC- area under the curve; GC- granulosa cells; CC- cumulus cells.

Mentions: We have subsequently tested the performance of gene expression signature of GC and CC in prediction of the non-P versus P outcome. Prediction performance of gene expression based prediction model, as determined by cross-validation testing, failed to surpass AUC values of 0.5 in CC, even when the number of modeled features reached 100 genes. Similarly, in GC, AUC performance of models with up to 100 included genes failed to surpass AUC performance values of 0.6 (Fig. 3).


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

Burnik Papler T, Vrtacnik Bokal E, Lovrecic L, Kopitar AN, Maver A - PLoS ONE (2015)

Predictive performance of expression biomarkers in granulosa and cumulus cells according to microarray data.AUC- area under the curve; GC- granulosa cells; CC- cumulus cells.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359149&req=5

pone.0115865.g003: Predictive performance of expression biomarkers in granulosa and cumulus cells according to microarray data.AUC- area under the curve; GC- granulosa cells; CC- cumulus cells.
Mentions: We have subsequently tested the performance of gene expression signature of GC and CC in prediction of the non-P versus P outcome. Prediction performance of gene expression based prediction model, as determined by cross-validation testing, failed to surpass AUC values of 0.5 in CC, even when the number of modeled features reached 100 genes. Similarly, in GC, AUC performance of models with up to 100 included genes failed to surpass AUC performance values of 0.6 (Fig. 3).

Bottom Line: 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 consent with microarray data as there were no significant differences in gene expression of genes selected for validation.In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Reproduction, Division of Gynaecology, University Medical Centre Ljubljana, Slajmerjeva 3, SI-1000 Ljubljana, Slovenia.

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 consent with microarray data as there were no significant differences in gene expression of genes selected for validation. In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study.

No MeSH data available.