Obstetrics/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.
Graduate Program in Genetics and Molecular Biology, Gene Therapy Center and Bioinformatics Core, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
J Assist Reprod Genet. 2021 Aug;38(8):2007-2020. doi: 10.1007/s10815-021-02141-0. Epub 2021 Mar 31.
To study the use of in silica model to better understand and propose new markers of ovarian response to controlled ovarian stimulation before IVF.
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.
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.
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.
CRD42020197185.
研究使用计算机模拟来更好地理解和提出新的卵巢对体外受精前控制性卵巢刺激反应的标志物。
系统评价和计算机模拟使用生物信息学。在系统评价过程中选择了 103 篇论文后，我们对所有纳入论文进行了 GRADE 资格认证，以进行基于证据的质量评估。我们使用功能蛋白质网络相互作用在硅模型中纳入了 57 个基因。此外，将这些结果导入 Cytoscape 以构建蛋白质-蛋白质相互作用网络。因此，使用 MCODE 进行了聚类分析，并将其导出到 BINGO 插件以确定基因本体论。使用 Bonferroni 校正检验，p 值<0.05 被认为具有统计学意义。
计算机模拟是稳健的，呈现出一个与排卵相关的基因网络，包含 87 个节点（基因）和 348 个边（基因之间的相互作用）。与网络中心度有关，网络的平均介数值为 102.54；平均接近度为 0.007；平均度数为 8.0。此外，具有较高介数的基因是 PTPN1。具有较高接近度的基因是 SRD5A1 和 HSD17B3，具有较低接近度的基因是 GDF9。最后，具有较高度数值的基因是 UBB；该基因参与调节 TP53 活性途径。
本系统评价表明，我们不能在体外受精前使用任何遗传标志物进行控制性卵巢刺激。此外，计算机模拟是理解和寻找个体化体外受精新标志物的有用工具。
CRD42020197185。
