BACKGROUND: Cumulus cell (CC) gene expression is suggested as a non-invasive analysis method to predict oocyte competence. There are, however, important between-patient differences in CC gene expression. These can be compensated when expression results are combined with patient and cycle characteristics using a multiple regression analysis model. METHODS: From ICSI patients stimulated with GnRH antagonist and recombinant FSH (n= 25) or GnRH agonist and highly purified menotrophin (n= 20), CC were collected and oocytes were individually fertilized and cultured. CC were analyzed for the expression of Syndecan 4 (SDC4), Prostaglandin-endoperoxide synthase 2 (PTGS2), Versican (VCAN), Activated leukocyte cell adhesion molecule, Gremlin 1, transient receptor potential cation channel, subfamily M, member 7 (TRPM7), Calmodulin 2 and Inositol 1,4,5-trisphosphate 3-kinase A (ITPKA) using quantitative PCR. Results were analyzed in relation to the stimulation protocol. Within-patient variation in gene expression was related to oocyte maturity and developmental potential. Models predictive for normal embryo or blastocyst development and pregnancy in single embryo transfer cycles were developed. RESULTS: Mature oocytes have higher PTGS2 and lower VCAN expression in their cumulus. All genes except VCAN had a positive correlation with good embryo or blastocyst morphology and were used to develop predictive models for embryo or blastocyst development (P< 0.01). Specific models were obtained for the two stimulation protocols. In both groups, better cleavage-stage embryo prediction relied on TRPM7 and ITPKA expression and pregnancy prediction relied on SDC4 and VCAN expression. In the current data set, the use of CC expression for pregnancy prediction resulted in a sensitivity of >70% and a specificity of >90%. CONCLUSIONS: Multivariable models based on CC gene expression can be used to predict embryo development and pregnancy.
BACKGROUND: Cumulus cell (CC) gene expression is suggested as a non-invasive analysis method to predict oocyte competence. There are, however, important between-patient differences in CC gene expression. These can be compensated when expression results are combined with patient and cycle characteristics using a multiple regression analysis model. METHODS: From ICSI patients stimulated with GnRH antagonist and recombinant FSH (n= 25) or GnRH agonist and highly purified menotrophin (n= 20), CC were collected and oocytes were individually fertilized and cultured. CC were analyzed for the expression of Syndecan 4 (SDC4), Prostaglandin-endoperoxide synthase 2 (PTGS2), Versican (VCAN), Activated leukocyte cell adhesion molecule, Gremlin 1, transient receptor potential cation channel, subfamily M, member 7 (TRPM7), Calmodulin 2 and Inositol 1,4,5-trisphosphate 3-kinase A (ITPKA) using quantitative PCR. Results were analyzed in relation to the stimulation protocol. Within-patient variation in gene expression was related to oocyte maturity and developmental potential. Models predictive for normal embryo or blastocyst development and pregnancy in single embryo transfer cycles were developed. RESULTS: Mature oocytes have higher PTGS2 and lower VCAN expression in their cumulus. All genes except VCAN had a positive correlation with good embryo or blastocyst morphology and were used to develop predictive models for embryo or blastocyst development (P< 0.01). Specific models were obtained for the two stimulation protocols. In both groups, better cleavage-stage embryo prediction relied on TRPM7 and ITPKA expression and pregnancy prediction relied on SDC4 and VCAN expression. In the current data set, the use of CC expression for pregnancy prediction resulted in a sensitivity of >70% and a specificity of >90%. CONCLUSIONS: Multivariable models based on CC gene expression can be used to predict embryo development and pregnancy.
Authors: William A Stocker; Kelly L Walton; Dulama Richani; Karen L Chan; Kiri H Beilby; Bethany J Finger; Mark P Green; Robert B Gilchrist; Craig A Harrison Journal: J Biol Chem Date: 2020-04-29 Impact factor: 5.157
Authors: T Adriaenssens; I Van Vaerenbergh; W Coucke; I Segers; G Verheyen; E Anckaert; M De Vos; J Smitz Journal: J Assist Reprod Genet Date: 2019-01-09 Impact factor: 3.412
Authors: C Scarica; D Cimadomo; L Dovere; A Giancani; M Stoppa; A Capalbo; F M Ubaldi; L Rienzi; R Canipari Journal: J Assist Reprod Genet Date: 2019-02-01 Impact factor: 3.412