摘要
由一名新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-生物技术的新研究-生物信息学是一篇报道的主题。据《新闻周刊》编辑从重庆发回的新闻报道，“先兆子痫是一种以高血压和蛋白尿为特征的妊娠相关疾病，严重影响患者的健康和生活质量。然而，巨噬细胞在先兆子痫中的分子机制尚不清楚。”作者引用重庆医科大学第一附属D医院的一篇研究文章:“本研究利用生物信息学分析方法,对子痫前期发病的关键生物标志物进行了鉴定,并将GEO数据库中的GSE75010和GSE74341数据集合并进行差异分析,构建了基于巨噬细胞含量的加权基因共表达网络分析仪(WGCNA)。”免疫浸润分析采用CIBERSORT法,R Package‘ClusterProfiler’进行功能富集,并利用CMap数据库进行潜在药物预测。通过WG CNA分析发现70个差异表达的NETs基因,367个巨噬细胞相关基因,机器学习识别出3个关键基因:FNBP1L、NMUR1和PP 14571,这3个关键基因与免疫细胞含量显著相关,并丰富了多种信号通路。这些基因在PE患者中表达上调,建立了与M2巨噬细胞浸润相关的3个关键基因的表达模式,为了解PE的发病机制和治疗提供了潜在的靶点。此外,CM AP结果提示TNPB、多柔比星、Tyrphos Tin AG 825和坦奈司米霉素等4种潜在的药物是可能的。研究子痫前期中三个关键基因的表达水平对子痫前期的防治有重要意义,认为这些基因在子痫前期患者母胎免疫微环境的调节中起着重要作用。这些FER基因的相关通路为探索PSIA的分子机制和确定治疗靶点提供了潜在的途径。
Abstract
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."
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NSTL