Department of Physiology, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Department of Medical Informatics Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Front Endocrinol (Lausanne). 2023 Sep 7;14:1193992. doi: 10.3389/fendo.2023.1193992. eCollection 2023.
Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.
Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.
Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.
In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.
多囊卵巢综合征（PCOS）是一种常见的内分泌和生殖系统疾病，目前缺乏准确的诊断策略。研究发现，细胞坏死性凋亡在生殖和内分泌疾病中起着关键作用，但它在 PCOS 中的作用尚不清楚。本研究旨在寻找与细胞坏死性凋亡相关的差异诊断基因（NDDGs），构建诊断模型评估 PCOS 的进展，并探索潜在的治疗药物。
通过合并基因表达数据集和加权基因共表达网络分析（WGCNA）以及细胞坏死性凋亡基因集筛选与 PCOS 相关的差异表达基因。使用最小绝对收缩和选择算子（LASSO）回归分析构建与细胞坏死性凋亡相关的基因特征。使用列线图进行独立风险分析。通过 GeneMANIA 数据库和基因集富集分析（GSEA）对 NDDGs 的通路富集进行分析。基于 ssGSEA 算法分析估计免疫微环境分析。使用比较毒理学基因组数据库（CTD）探索 NDDGs 的潜在治疗药物。通过 GSE84958、小鼠模型和临床样本验证 NDDGs 的表达。
确定了 4 个与细胞坏死性凋亡相关的特征基因（IL33、TNFSF10、BCL2 和 PYGM），可用于定义 PCOS 的细胞坏死性凋亡。训练集和验证集诊断风险模型的曲线下面积（AUC）分别为 0.940 和 0.788。富集分析表明，NDDGs 富集在 B 细胞、T 细胞和自然杀伤细胞等免疫相关信号通路中。免疫微环境分析显示，NDDGs 与 13 种明显不同的免疫细胞显著相关。根据对临床有益的特征构建了列线图。从数据库中挖掘了白藜芦醇、维 A 酸、槲皮素、姜黄素等几种化合物作为 PCOS 的治疗药物。验证集、动物模型和临床样本中 NDDGs 的表达与训练集的结果一致。
本研究鉴定了 4 个 NDDGs，可用于评估 PCOS 的进展和预后，并为 PCOS 的治疗提供潜在靶点。
