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Peritoneal tumor spread in serous ovarian cancer-epithelial mesenchymal status and outcome

GSE293643 Homo sapiens Expression profiling by high throughput sequencing 42 samples 2025/04/03 GPL11154
Summary
In this study we aimed to analyze the biological mechanisms underlying apparently different modes of peritoneal tumor spread in serous ovarian cancer: miliary (widespread, millet-like lesions) versus non-miliary (bigger, exophytically growing implants). Tumor tissues and ascites from 23 chemotherapy naive patients were analyzed by RNA-sequencing and flow cytometry. On the basis of differential gene expression between miliary and non-miliary, gene signatures were developed. A calculated tumor spread factor revealed a significant independent negative impact of miliary spread on overall survival (HR 3.77; CI95 1.14–12.39; p = 0.029) in an independent cohort of 165 serous ovarian cancer patients. Comparing previously published epithelial-mesenchymal transition (EMT) gene signatures, non-miliary spread correlated significantly with a reduced epithelial status. We conclude that serous ovarian cancer is a heterogeneous disease with distinct modes of peritoneal tumor spread, differing not only in clinical appearance, but also in molecular characteristics and outcome. EMT, peritoneal inflammation status, and therapeutic options are discussed. Significance: More than half of serous epithelial ovarian cancer patients present with a newly described type of intraperitoneal tumor spread, associated with differences in the inflammation status, activated oncogenic pathways, lack of EMT, and thus reduced overall survival. Both, the diminished immune reaction and the enhanced epithelial and malignant characteristics of the tumor cells open new avenues for therapeutic options and strategies, like Catumaxomab, already in clinical use.
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NCBI GEO page ↗ Paper (PMID 25991672) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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