Ke Yumin, Liang Meili, Zhou Zhimei, Xie Yajing, Huang Li, Sheng Liying, Wang Yueli, Zhou Xinyan, Wu Zhuna
Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Department of Gynecology and Obstetrics, Qingtongxia People's Hospital, Wuzhong, China.
Front Mol Biosci. 2025 Jun 27;12:1600808. doi: 10.3389/fmolb.2025.1600808. eCollection 2025.
Ovarian clear cell carcinoma (OCCC) is characterized by poor prognosis and limited early diagnostic markers. Identifying molecular distinctions between OCCC and the more common high-grade serous ovarian cancer (HGSC) is critical to developing targeted diagnostic and therapeutic strategies for improved clinical outcomes.
We retrieved the mRNA expression profiles of OCCC and HGSC from the Gene Expression Omnibus (GEO) database. To identify differentially immune-related genes (DIRGs) linked to OCCC. We assessed DIRGs functional enrichment and built a protein-protein interaction (PPI) to explore DIRGs interactions. Least Absolute Shrinkage and Selection Operator (LASSO) regression model and Multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE) methods were applied to identify predictive genes. The diagnostic performance of these candidate genes was evaluated using receiver operating characteristic (ROC) curves. A nomogram was constructed to predict OCCC. We further validated key DIRGs' diagnostic ability via a validation set and immunohistochemistry (IHC). The CIBERSORT algorithm was used to analyze correlations between DIRGs and immune cell types in OCCC.
We detected 10 DIRGs in OCCC compared to HGSC. These genes were mainly linked to collagen-rich extracellular matrix, Phosphoinositide-3 Kinase- Protein Kinase B (PI3K-AKT) pathway, and transcriptional dysregulation in cancer. Nuclear receptor subfamily 1 group H member 4 (NR1H4) and Interleukin-4 Receptor (IL4R) emerged as potential biomarkers for OCCC (AUC = 0.809; AUC = 0.840). In the validation cohort, AUC = 0.848 and AUC = 0.821, respectively. IHC revealed higher expression levels of NR1H4 and IL4R in OCCC (P < 0.05). Additionally, NR1H4 correlated positively with resting memory T cells and neutrophils, while IL4R correlated with resting Natural Killer (NK) cells and neutrophils.
NR1H4 and IL4R are promising immune-related diagnostic biomarkers for OCCC, with potential roles in neutrophil-mediated tumor microenvironment modulation. These findings enhance understanding of OCCC pathogenesis and provide a foundation for developing targeted diagnostic tools and immunotherapeutic strategies.
卵巢透明细胞癌（OCCC）预后较差且早期诊断标志物有限。识别OCCC与更常见的高级别浆液性卵巢癌（HGSC）之间的分子差异对于制定针对性的诊断和治疗策略以改善临床结局至关重要。
我们从基因表达综合数据库（GEO）中检索了OCCC和HGSC的mRNA表达谱。为了识别与OCCC相关的差异免疫相关基因（DIRGs）。我们评估了DIRGs的功能富集情况，并构建了蛋白质-蛋白质相互作用（PPI）网络以探索DIRGs之间的相互作用。应用最小绝对收缩和选择算子（LASSO）回归模型和多重支持向量机递归特征消除（mSVM-RFE）方法来识别预测基因。使用受试者工作特征（ROC）曲线评估这些候选基因的诊断性能。构建了一个列线图来预测OCCC。我们通过验证集和免疫组织化学（IHC）进一步验证了关键DIRGs的诊断能力。使用CIBERSORT算法分析DIRGs与OCCC中免疫细胞类型之间的相关性。
与HGSC相比，我们在OCCC中检测到10个DIRGs。这些基因主要与富含胶原蛋白的细胞外基质、磷酸肌醇-3激酶-蛋白激酶B（PI3K-AKT）通路以及癌症中的转录失调有关。核受体亚家族1组H成员4（NR1H4）和白细胞介素-4受体（IL4R）成为OCCC的潜在生物标志物（AUC = 0.809；AUC = 0.840）。在验证队列中，AUC分别为0.848和0.821。免疫组织化学显示OCCC中NR1H4和IL4R的表达水平较高（P < 0.05）。此外，NR1H4与静息记忆T细胞和中性粒细胞呈正相关，而IL4R与静息自然杀伤（NK）细胞和中性粒细胞相关。
NR1H4和IL4R是有前景的OCCC免疫相关诊断生物标志物，在中性粒细胞介导的肿瘤微环境调节中具有潜在作用。这些发现加深了对OCCC发病机制的理解，并为开发针对性的诊断工具和免疫治疗策略提供了基础。
