首页|First Affiliated Hospital of Chongqing Medical University Reports Findings in Bi oinformatics (Mechanistic study of pre-eclampsia and macrophage-associated molec ular networks: bioinformatics insights from multiple datasets)
First Affiliated Hospital of Chongqing Medical University Reports Findings in Bi oinformatics (Mechanistic study of pre-eclampsia and macrophage-associated molec ular networks: bioinformatics insights from multiple datasets)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news reporting out of Chongqin g, People's Republic of China, by NewsRx editors, research stated, "Pre-eclampsi a is a pregnancy-related disorder characterized by hypertension and proteinuria, severely affecting the health and quality of life of patients. However, the mol ecular mechanism of macrophages in pre-eclampsia is not well understood." Our news journalists obtained a quote from the research from the First Affiliate d Hospital of Chongqing Medical University, "In this study, the key biomarkers d uring the development of pre-eclampsia were identified using bioinformatics anal ysis. The GSE75010 and GSE74341 datasets from the GEO database were obtained and merged for differential analysis. A weighted gene co-expression network analysi s (WGCNA) was constructed based on macrophage content, and machine learning meth ods were employed to identify key genes. Immunoinfiltration analysis completed b y the CIBERSORT method, R package 'ClusterProfiler' to explore functional enrich ment of these intersection genes, and potential drug predictions were conducted using the CMap database. Lastly, independent analysis of protein levels, localiz ation, and quantitative analysis was performed on placental tissues collected fr om both preeclampsia patients and healthy control groups. We identified 70 diffe rentially expressed NETs genes and found 367 macrophagerelated genes through WG CNA analysis. Machine learning identified three key genes: FNBP1L, NMUR1, and PP 14571. These three key genes were significantly associated with immune cell cont ent and enriched in multiple signaling pathways. Specifically, these genes were upregulated in PE patients. These findings establish the expression patterns of three key genes associated with M2 macrophage infiltration, providing potential targets for understanding the pathogenesis and treatment of PE. Additionally, CM ap results suggested four potential drugs, including Ttnpb, Doxorubicin, Tyrphos tin AG 825, and Tanespimycin, which may have the potential to reverse pre-eclamp sia. Studying the expression levels of three key genes in pre-eclampsia provides valuable insights into the prevention and treatment of this condition. We propo se that these genes play a crucial role in regulating the maternal-fetal immune microenvironment in PE patients, and the pathways associated with these genes of fer potential avenues for exploring the molecular mechanisms underlying preeclam psia and identifying therapeutic targets."
ChongqingPeople's Republic of ChinaA siaBioinformaticsBiotechnologyConnective Tissue CellsCyborgsEclampsiaEmerging TechnologiesGeneticsHealth and MedicineImmunologyInformation T echnologyMachine LearningMacrophagesMononuclear Phagocyte SystemMyeloid CellsObstetricsPhagocytesPreeclampsiaPregnancy ComplicationsPregnancy-Induced HypertensionWomen's Health
NETL
NSTL
