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The expression of transforming growth factor alpha (TGFalpha), amphiregulin (AR) and cripto-1 (CR-1) was assessed by immunohistochemistry in 83 specimens (59 primary ovarian tumors and 24 extra-ovarian carcinomas) that were obtained from 68 ovarian carcinoma patients. Within the 59 primary tumors, 54 (92%) expressed immunoreactive TGFalpha, 45 (76%) expressed AR, and 28 (47%) expressed CR-1. The expression of AR and CR-1 mRNAs in the ovarian carcinomas was also demonstrated by RT-PCR analysis. Seventeen extra-ovarian specimens (71%) were found to express CR-1, whereas AR and TGFalpha were expressed respectively in 21 (87%) and 22 (92%) extra-ovarian tissues. In 15 cases for whom both ovarian and extra-ovarian tissues were available, a statistically significant higher expression of CR-1 was found in extra-ovarian specimens. A statistically significant correlation was found between AR expression in the ovarian carcinomas and both low grade and low proliferative activity. Finally, expression of TGFalpha was predictive of longer progression-free survival. These data strongly suggest that the EGF-related peptides might be involved in the pathogenesis and outcome of human ovarian cancer.
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Bull Cancer
September 2025
Département d'oncologie médicale, centre Léon-Bérard (CLB-UNICANCER), université Claude-Bernard (UCB Lyon 1), Lyon, France. Electronic address:
Granulosa cell tumors (GCTs) are rare ovarian neoplasms, accounting for 2-5% of all ovarian cancers. Two histological types have been described: juvenile (JGCT) and adult (AGCT), the latter accounting for around 95% of the GCTs. AGCTs are mostly diagnosed at an early stage and commonly have a good prognosis.
View Article and Find Full Text PDFJMIR Cancer
September 2025
iCARE Secure Data Environment & Digital Collaboration Space, NIHR Imperial Biomedical Research Centre, London, United Kingdom.
Background: Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users.
View Article and Find Full Text PDFJCO Precis Oncol
September 2025
Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, CA.
Purpose: mutations are classically seen in non-small cell lung cancers (NSCLCs), and EGFR-directed inhibitors have changed the therapeutic landscape in patients with -mutated NSCLC. The real-world prevalence of -mutated ovarian cancers has not been previously described. We aim to determine the prevalence of pathogenic or likely pathogenic mutations in ovarian cancer and describe a case of -mutated metastatic ovarian cancer with a durable response to osimertinib, an EGFR-directed targeted therapy.
View Article and Find Full Text PDFCien Saude Colet
August 2025
Departamento de Ciências Farmacêuticas, Universidade Federal de São Paulo. São Paulo SP Brasil.
The scope of this study was to conduct an analysis on the effect of the Age-Period-Cohort (APC) on ovarian cancer mortality in the South and Northeast regions of Brazil. The APC models were estimated by Poisson regression through estimable functions in women aged 30 and over residing in the states of the South and Northeast regions. Upon estimating the APC models, a positive gradient was found in mortality rates with advancing age in all locations The South region showed a reduction in the risk of death in the last two periods (RR2010-2014 0.
View Article and Find Full Text PDFCancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
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