Medical Intensive Care Unit, Guangdong Women and Children Hospital, Guangzhou, 510000 Guangdong, China.
Pathology department, Guangdong Women and Children Hospital, Guangzhou, 510000 Guangdong, China.
Dis Markers. 2022 Jun 7;2022:5782637. doi: 10.1155/2022/5782637. eCollection 2022.
Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE.
We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic model. We also analyzed immune cell infiltration. Finally, we verified the expression of the genes included in the predictive model via RT-PCR.
A total of 100 and 212 differently expressed genes were identified in the GSE75010 and GSE10588 datasets, respectively, and after overlapping with WGCNA results, 17 genes were identified. KEGG and GO analyses further indicated the involvement of these genes in bioprocesses, such as gonadotropin secretion, immune cell infiltration, and the SMAD and MAPK pathways. Additionally, protein-protein interaction network analysis identified 10 hub genes, six (, , , , , and ) of which were used to establish a logistic model for PE. RT-PCR analysis also confirmed that, except , these genes were upregulated in PE. Our results also indicated that macrophages played the most important role in immune cell infiltration in PE.
This study identified 10 hub genes in PE and used 6 of them to establish a logistic model and also analyzed immune cell infiltration. These findings may enhance the understanding of PE and enable the identification of potential therapeutic targets for PE.
子痫前期（PE）在全球范围内发病率较高，是一种对孕妇和新生儿有潜在危险的综合征。然而，其发病机制尚不清楚。本研究采用生物信息学分析，识别关键基因，建立逻辑模型，研究免疫细胞浸润，阐明 PE 的病理生理学。
我们从 GEO 数据库下载 GSE75010 和 GSE10588 数据集，进行加权基因共表达网络分析（WGCNA）以及基因本体（GO）和京都基因与基因组百科全书（KEGG）分析。使用在线检索相互作用基因的工具和 Cytoscape 软件来识别关键基因，然后用于建立逻辑模型。我们还分析了免疫细胞浸润。最后，通过 RT-PCR 验证预测模型中包含的基因的表达。
在 GSE75010 和 GSE10588 数据集中分别鉴定出 100 和 212 个差异表达基因，与 WGCNA 结果重叠后，鉴定出 17 个基因。KEGG 和 GO 分析进一步表明，这些基因参与了生物过程，如促性腺激素分泌、免疫细胞浸润以及 SMAD 和 MAPK 通路。此外，蛋白质-蛋白质相互作用网络分析鉴定出 10 个关键基因，其中 6 个（、、、、、和）用于建立 PE 的逻辑模型。RT-PCR 分析也证实，除了，这些基因在 PE 中均上调。我们的结果还表明，在 PE 中，巨噬细胞在免疫细胞浸润中起着最重要的作用。
本研究鉴定了 PE 中的 10 个关键基因，并使用其中的 6 个建立了逻辑模型，还分析了免疫细胞浸润。这些发现可能有助于加深对 PE 的理解，并为 PE 确定潜在的治疗靶点。
