Data Sheet 1_Identification of the key immune gene NR3C1 as a diagnostic biomarker in differentiating ovarian borderline tumors from benign tumors.zip

BackgroundThis 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.
MethodsWe 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.
ResultsWe 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 (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 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).
ConclusionOur 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.