Objective: Mullerian low grade serous carcinoma (LGSC) and high grade serous carcinoma (HGSC) have distinct molecular profiles, clinical behavior and treatment response. Our objective was to study the biological profiles of these carcinomas.
Methods: This study examines publicly available gene expression profiles of LGSC and HGSC to identify differentially expressed genes and key pathways involved in carcinogenesis and chemotherapy response.
Results: Our analysis supports the hypothesis that serous mullerian carcinoma develop through two different pathways yielding two distinct malignancies, namely LGSC and HGSC. Furthermore, genes potentially involved in chemotherapeutic resistance of LGSC were identified. Suppressing the levels of these genes/proteins may increase clinical response to standard chemotherapy in patients with LGSC.
Conclusion: In summary, this review shows the molecular profile of LGSC and HGSC through multi-center analysis of gene expression profiles of these tumors. The gene signatures of these neoplasms may potentially be used to develop disease-specific, targeted therapy for LGSC and HGSC.
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http://dx.doi.org/10.1016/j.ygyno.2012.12.009 | DOI Listing |
Curr Issues Mol Biol
August 2024
Department of Obstetrics and Gynecology, Shimane University Faculty of Medicine, Izumo 693-8501, Japan.
This research describes an eight-year case-series of ovarian carcinoma by surgical (pTNM) staging and surgical procedure, explores the characteristics of ovarian surface epithelial cell (OSEC) tumours by histopathological type in a single centre of reference. Material and survival analysis with overall survivor probabilities for n=263 patients for 12 months and 60-month tumour free survival status (TFS). Results by staging (pTNM stage classification), histotype and for poor surgical candidate (PSC) status are shown.
View Article and Find Full Text PDFJ Pathol
October 2024
Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.
J Cancer Res Clin Oncol
July 2024
Department of Electronics and Communication Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, 495009, India.
This study presents a robust approach for the classification of ovarian cancer subtypes through the integration of deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages the powerful feature extraction capabilities of EfficientNet-B0, utilizing its deep features for subsequent fine-grained classification using the fine-KNN approach. The UBC-OCEAN dataset, encompassing histopathological images of five distinct ovarian cancer subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell ovarian carcinoma (CC), endometrioid carcinoma (EC), low-grade serous carcinoma (LGSC), and mucinous carcinoma (MC), served as the foundation for our investigation.
View Article and Find Full Text PDFOncology
November 2024
Institute of Pathology, Kantonsspital Baden AG, Baden, Switzerland.
Introduction: Genomic characterization of serous ovarian carcinoma (SOC), which includes low-grade serous carcinoma (LGSC) and high-grade serous carcinoma (HGSC), remains necessary to improve efficacy of platinum-based chemotherapy. The aim of this study was to investigate the genomic variations in these SOC groups, also in relation to chemoresponse.
Methods: Forty-five samples of SOC were retrospectively analyzed by next-generation sequencing on DNA/RNA extracts from formalin-fixed, paraffin-embedded (FFPE) tumor samples obtained at diagnosis.
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