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An in silico model using prognostic genetic factors for ovarian response in controlled ovarian stimulation:
B S Eisele1, G C Villalba Silva2, C Bessow1
1Obstetrics/Gynecology Post-Graduate Program, Medical School, Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcellos, 2350-11 andar, Porto Alegre, Rio Grande do Sul, CEP 91003-001, Brazil.
Purpose:
To study the use of in silica model to better understand and propose new markers of ovarian response to controlled ovarian stimulation before IVF.
Methods:
A systematic review and in silica model using bioinformatics. After the selection of 103 papers from a systematic review process, we performed a GRADE qualification of all included papers for evidence-based quality evaluation. We included 57 genes in the silica model using a functional protein network interaction. Moreover, the construction of protein-protein interaction network was done importing these results to Cytoscape. Therefore, a cluster analysis using MCODE was done, which was exported to a plugin BINGO to determine Gene Ontology. A p value of < 0.05 was considered significant, using a Bonferroni correction test.
Results:
In silica model was robust, presenting an ovulation-related gene network with 87 nodes (genes) and 348 edges (interactions between the genes). Related to the network centralities, the network has a betweenness mean value = 102.54; closeness mean = 0.007; and degree mean = 8.0. Moreover, the gene with a higher betweenness was PTPN1. Genes with the higher closeness were SRD5A1 and HSD17B3, and the gene with the lowest closeness was GDF9. Finally, the gene with a higher degree value was UBB; this gene participates in the regulation of TP53 activity pathway.
Conclusions:
This systematic review demonstrated that we cannot use any genetic marker before controlled ovarian stimulation for IVF. Moreover, in silica model is a useful tool for understanding and finding new markers for an IVF individualization.
Prospero:
CRD42020197185.
