Genomic assessment of follicular marker genes as pregnancy predictors for human IVF.

  • Date de publication : 2010-01-08


Hamel M, Dufort I, Robert C, Léveillé MC, Leader A, Sirard MA. Genomic assessment of follicular marker genes as pregnancy predictors for human IVF. Mol. Hum. Reprod. 2010;16:87-96. doi: 10.1093/molehr/gap079. PubMed PMID: 19778949.

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Mot(s) Clé(s)

adult cell cycle proteins female fertilization in vitro gtp-binding proteins gtpase-activating proteins genomics granulosa cells humans ovarian follicle phosphoglycerate kinase polymerase chain reaction pregnancy pregnancy outcome principal component analysis rgs proteins single embryo transfer


Embryo selection efficiency in human IVF procedure is still suboptimal as shown by low pregnancy rates with single embryo transfer (SET). Bidirectional communication between the oocyte and follicular cells (FC) is essential to achieve developmental competence of the oocyte. Differences in the gene expression profile of FCs from follicles leading to pregnancy could provide useful markers of oocyte developmental competence. FCs were recovered by individual follicle puncture. FC expression levels of potential markers were assessed by Q-PCR with an intra-patient and an inter-patient analysis approach. Using gene expression, a predictive model of ongoing pregnancy was investigated. Using intra-patient analysis, four candidate genes, phosphoglycerate kinase 1 (PGK1), regulator of G-protein signalling 2 (RGS2), regulator of G-protein signalling 3 (RGS3) and cell division cycle 42 (CDC42) showed a difference between FCs from follicles leading to a pregnancy or developmental failure. The best predictors for ongoing pregnancy were PGK1 and RGS2. Additionally, inter-patient analysis revealed differences in FC expression for PGK1 and CDC42 between follicles leading to a transferred embryo with positive pregnancy results and those with negative results. Both inter-patient and intra-patient approaches must be taken into consideration to delineate gene expression variations in the context of follicular competence. A predictor model using biomarkers could improve the efficiency of predicting developmental competence of oocytes. These new approaches provide useful tools in the context of embryo selection and in the improvement of pregnancy rates with SET.