Chen Shiying, Ke Yumin, Xie Yajing, Zhou Zhimei, Chen Weihong, Huang Li, Sheng Liying, Wang Yueli, Liu Shunlan, Wu Zhuna
Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Front Cell Dev Biol. 2025 Sep 1;13:1602192. doi: 10.3389/fcell.2025.1602192. eCollection 2025.
This study aims to evaluate novel immune-related biomarkers for distinguishing borderline ovarian tumors (BOTs) from Benign ovarian tumors (BeOTs), addressing the diagnostic challenges posed by their intermediate biological behavior between benign and malignant neoplasms.
We obtained the microarray expression profiles from the datasets (GSE4122 + GSE6822 + GSE36668) in the Gene Expression Omnibus (GEO) database and integrated them with the immune-related genes in the ImmPort database. Differentially immune-related genes (DIRGs) underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein-protein interaction (PPI) network was built to explore the connection. Candidate biomarkers were identified using the Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), with their diagnostic ability evaluated using Receiver operating characteristic (ROC) curves. A nomogram was constructed to predict BOTs. To validate the diagnostic potential and expression profiles, immunohistochemistry (IHC) analysis was performed in conjunction with the evaluation of an independent test group. We characterized the infiltration profiles of 22 immune cell types in BOTs through the CIBERSORT algorithm.
We identified 26 DIRGs between BOTs and BeOTs. These DIRGs were primarily associated with the positive regulation of transferase activity, the positive regulation of epithelial cell proliferation, and the positive regulation of the MAPK cascade. KEGG analysis indicated enrichment of Rap1 and PI3K-Akt signaling pathways. FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUC = 0.883, AUC = 0.789, AUC = 0.760, AUC = 0.783) and further validated in the test group (AUC = 0.917, AUC = 0.900, AUC = 0.867, AUC = 0.833). Low expression of NR3C1 and GNAI1 and high expression of FGFR3 and PDGFA are associated with the development of BOTs. In addition, NR3C1 negatively correlated with CD4 memory resting T cells, as well as positively correlated with T cells gamma delta (P < 0.05).
Our study findings suggested that NR3C1 may serve as an immune-related diagnostic biomarker for BOTs, offering a novel perspective for investigating the development and diagnosis of BOTs.
本研究旨在评估用于区分卵巢交界性肿瘤（BOTs）与卵巢良性肿瘤（BeOTs）的新型免疫相关生物标志物，以应对它们在良性和恶性肿瘤之间的中间生物学行为所带来的诊断挑战。
我们从基因表达综合数据库（GEO）中的数据集（GSE4122 + GSE6822 + GSE36668）获取微阵列表达谱，并将其与ImmPort数据库中的免疫相关基因整合。对差异免疫相关基因（DIRGs）进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。构建蛋白质-蛋白质相互作用（PPI）网络以探索其联系。使用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）识别候选生物标志物，并使用受试者工作特征（ROC）曲线评估其诊断能力。构建列线图以预测BOTs。为验证诊断潜力和表达谱，结合独立测试组的评估进行免疫组织化学（IHC）分析。我们通过CIBERSORT算法对BOTs中22种免疫细胞类型的浸润谱进行了特征分析。
我们在BOTs和BeOTs之间鉴定出26个DIRGs。这些DIRGs主要与转移酶活性的正调控、上皮细胞增殖的正调控以及MAPK级联的正调控相关。KEGG分析表明Rap1和PI3K-Akt信号通路富集。发现FGFR3、GNAI1、NR3C1和PDGFA对BOTs具有潜在诊断价值（AUC = 0.883，AUC = 0.789，AUC = 0.760，AUC = 0.783），并在测试组中进一步验证（AUC = 0.917，AUC = 0.900，AUC = 0.867，AUC = 0.833）。NR3C1和GNAI1的低表达以及FGFR3和PDGFA的高表达与BOTs的发生发展相关。此外，NR3C1与CD4记忆静止T细胞呈负相关，与γδT细胞呈正相关（P < 0.05）。
我们的研究结果表明，NR3C1可能作为BOTs的一种免疫相关诊断生物标志物，为研究BOTs的发生发展和诊断提供了新的视角。
