Bioinformatic Analysis of Human Cumulus Cells to Unravel Cellular's Processes that Could Be Used to Establish Oocyte Quality Biomarkers with Clinical Application.

Metadata analysis of public microarray datasets using bioinformatics tools has been successfully used in several biomedical fields in the search for biomarkers. In reproductive science, there is an urgent need for the establishment of oocyte quality biomarkers that could be used in the clinical environment to increase the chances of successful outcomes in treatment cycles. Adaptive cellular processes observed in cumulus oophorus cells reflect the conditions of the follicular microenvironment and may thus bring relevant information of oocyte's conditions. Here we analyzed human cumulus cells gene expression datasets in search of predictors of oocyte quality, a strategy which uncovered several cellular processes positively and negatively associated with embryo development and pregnancy potential. Secondly, the expression levels of genes that were present in the majority of processes observed were validated in house with clinical samples. Our data confirmed the association of the selected biomarkers with blastocyst formation and pregnancy potential rates, independently of patients' clinical characteristics such as diagnosis, age, BMI, and stimulation protocol applied. This study shows that bioinformatic analysis of cellular processes can be successfully used to elucidate possible oocyte quality biomarkers. Our data reinforces the need to consider clinical characteristics of patients when selecting relevant biomarkers to be used in the clinical environment and suggests a combination of positive (PTGS2) and negative (CYPB1) quality biomarkers as a robust strategy for a complementary oocyte selection tool, potentially increasing assisted reproduction success rates. Also, GPX4 expression as pregnancy potential biomarker is indicated here as a possibility for further investigations.