{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T02:39:47Z","timestamp":1790735987447,"version":"4.1.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T00:00:00Z","timestamp":1753401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Ovarian cancer (OV) is the deadliest gynecologic malignancy owing to its late diagnosis and high metastatic propensity. Current biomarkers lack sufficient sensitivity and specificity for the detection of early-stage cancer. To address this gap, we integrated single\u2010cell transcriptomic profiling of tumor tissues with analysis of circulating exosomal RNA, aiming to uncover candidate markers that reflect tumor heterogeneity and metastatic potential and that may serve as sensitive, non\u2010invasive diagnostics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We integrated single-cell RNA sequencing (scRNA-seq) data from primary tumors and metastatic lesions with bulk tissue transcriptomes and plasma-derived exosomal RNA sequencing (RNA-seq). Differentially expressed genes (DEGs) shared across tumor cells, metastatic subpopulations, and exosomes were identified through intersection analysis. Candidate genes were validated in clinical specimens using qPCR and immunohistochemistry. We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. Tumor cell differentiation states were evaluated using CytoTRACE, and intercellular communication was analyzed with CellChat.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Intersection analysis highlighted 52 overlapping DEGs, of which SCNN1A and EFNA1 emerged as the top prognostic indicators. Both genes were significantly upregulated in tumor tissues, metastatic foci, and plasma exosomes (<jats:italic>P<\/jats:italic> &amp;lt; 0.01). The exosome-based Adaboost model had an area under the curve of 0.955 in an independent test cohort. Single-cell subcluster analyses revealed high SCNN1A\/EFNA1 expression correlated with stem-like differentiation states and enriched pathways associated with immune evasion and adhesion. CellChat analysis demonstrated that highly differentiated tumor cells extensively engaged with fibroblasts and endothelial cells, implying their role in niche formation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>By coupling single-cell, bulk tissue, and exosomal transcriptomics, we elucidated the key molecular drivers of OV metastasis and established SCNN1A and EFNA1 as promising non-invasive biomarkers. This multi-omics framework provides an effective strategy for early detection and a better understanding of metastatic progression in OV.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1630794","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T05:25:59Z","timestamp":1753421159000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer 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