Recognition of pivotal immune genes NR1H4 and IL4R as diagnostic biomarkers in distinguishing ovarian clear cell cancer from high-grade serous cancer

Yumin Ke; Meili Liang; Zhimei Zhou; Yajing Xie; Li Huang; Liying Sheng; Yueli Wang; Xinyan Zhou; Zhuna Wu

doi:10.3389/fmolb.2025.1600808

Recognition of pivotal immune genes NR1H4 and IL4R as diagnostic biomarkers in distinguishing ovarian clear cell cancer from high-grade serous cancer

Front Mol Biosci. 2025 Jun 27:12:1600808. doi: 10.3389/fmolb.2025.1600808. eCollection 2025.

Authors

Yumin Ke^#¹, Meili Liang^#¹, Zhimei Zhou¹, Yajing Xie¹, Li Huang¹, Liying Sheng¹, Yueli Wang¹, Xinyan Zhou², Zhuna Wu¹

Affiliations

¹ Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
² Department of Gynecology and Obstetrics, Qingtongxia People's Hospital, Wuzhong, China.

^# Contributed equally.

Abstract

Objective: 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.

Methods: 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.

Results: 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_NR1H4 = 0.809; AUC_IL4R = 0.840). In the validation cohort, AUC_NR1H4 = 0.848 and AUC_IL4R = 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.

Conclusion: 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.

Keywords: IL4R; NR1H4; bioinformatics analysis; immune infiltration; ovarian clear cell carcinoma.