这项研究检查了雌激素化合物如双酚A(BPA)的影响,雌二醇(E2),和玉米赤霉烯酮(ZEA)对人类卵巢癌,专注于构建风险模型,进行基因集变异分析(GSVA),并评估免疫浸润。差异基因表达分析确定了暴露于BPA的人卵巢细胞中980个共享的差异表达基因(DEG),E2和ZEA,表明核糖体生物发生和RNA加工的破坏。使用癌症基因组图谱卵巢癌(TCGA-OV)数据集,开发了基于最小绝对收缩和选择算子(LASSO)的风险模型,其中包含预后基因4-羟基苯基丙酮酸双加氧酶样(HPDL),Thy-1细胞表面抗原(THY1),和肽酶抑制剂3(PI3)。该模型有效地将卵巢癌患者分为高风险和低风险类别,显示总生存率的显著差异,疾病特异性生存,和无进展生存期。GSVA分析将HPDL表达与细胞周期相关的途径联系起来,DNA损伤,修复,而THY1和PI3与细胞凋亡有关,缺氧,和增殖途径。免疫浸润分析显示HPDL高表达组和低表达组不同的免疫细胞谱,THY1和PI3,表明它们对肿瘤微环境的影响。研究结果表明,雌激素化合物显着改变卵巢癌的基因表达和致癌途径。整合HPDL的风险模型,THY1和PI3提供了一个强大的预后工具,通过GSVA和免疫浸润分析,可以深入了解这些基因与肿瘤微环境之间的相互作用,建议个性化治疗的潜在目标。
This study examines the impact of estrogenic compounds like bisphenol A (BPA), estradiol (E2), and zearalenone (ZEA) on human ovarian cancer, focusing on constructing a risk model, conducting Gene Set Variation Analysis (GSVA), and evaluating immune infiltration. Differential gene expression analysis identified 980 shared differentially expressed genes (DEGs) in human ovarian cells exposed to BPA, E2, and ZEA, indicating disruptions in ribosome biogenesis and RNA processing. Using the Cancer Genome Atlas Ovarian Cancer (TCGA-OV) dataset, a least absolute shrinkage and selection operator (LASSO)-based risk model was developed incorporating prognostic genes 4-Hydroxyphenylpyruvate Dioxygenase Like (HPDL), Thy-1 Cell Surface Antigen (THY1), and Peptidase Inhibitor 3 (PI3). This model effectively stratified ovarian cancer patients into high-risk and low-risk categories, showing significant differences in overall survival, disease-specific survival, and progression-free survival. GSVA analysis linked HPDL expression to pathways related to the cell cycle, DNA damage, and repair, while THY1 and PI3 were associated with apoptosis, hypoxia, and proliferation pathways. Immune infiltration analysis revealed distinct immune cell profiles for high and low expression groups of HPDL, THY1, and PI3, indicating their influence on the tumor microenvironment. The findings demonstrate that estrogenic compounds significantly alter gene expression and oncogenic pathways in ovarian cancer. The risk model integrating HPDL, THY1, and PI3 offers a strong prognostic tool, with GSVA and immune infiltration analyses providing insights into the interplay between these genes and the tumor microenvironment, suggesting potential targets for personalized therapies.