STAT1 and IL-7 as potential diagnostic biomarkers for distinguishing high-grade from low-grade serous ovarian cancer: a multi-cohort analysis

Background: High-Grade Serous Ovarian Carcinoma (HGSOC) and Low-Grade Serous Ovarian Carcinoma (LGSOC) are distinct subtypes of epithelial ovarian cancer with significant differences in pathogenesis and prognosis, posing challenges for precise diagnosis. Identifying reliable biomarkers is crucial for improving differential diagnosis and clinical management. Methods: Transcriptome RNA-seq data of HGSOC and LGSOC were obtained from the GEO database (GSE27651, GSE126132). Differentially expressed immune-related genes (DIRGs) were identified. Functional enrichment analysis and protein-protein interaction (PPI) network construction were performed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE) algorithms were used to select predictive genes. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, and a nomogram was developed. Findings were validated in an independent dataset and via immunohistochemistry (IHC). The CIBERSORT algorithm assessed correlations between key DIRGs and tumor-infiltrating immune cells, with false discovery rate (FDR) correction applied for multiple testing. Results: expression exhibited a negative correlation trend with neutrophils (ρ = -0.372, raw P = 0.0048, q = 0.100). Conclusion: are consistently differentially expressed between HGSOC and LGSOC and may serve as ancillary diagnostic biomarkers in histologically ambiguous cases. However, their clinical utility-particularly in multi-gene combinations-requires prospective validation.