Ovarian cancer of epithelial origin is associated with the highest mortality rate of all gynecologic malignancies. Since no symptoms or signs are manifested at the early stages of the disease, it is no surprise that in 75% of patients peritoneal metastases are found during primary surgery. Despite advances in conservative treatment methods (invasive and noninvasive), screening for early detection of the disease is not yet available, and the overall survival rate is as low as 5-15%. Recent studies in molecular biology have drawn attention to different research directions in ovarian cancer and have contributed much to our understanding of this disease and its underlying pathologic mechanisms. This review is intended to highlight some of the new aspects of this research, specifically: hereditary ovarian cancer, genetic background in terms of chromosomal changes, DNA anomalies, oncogenes, tumor-suppressor genes, peptide growth factors and cytokines, invasiveness and metastasis, and finally, drug resistance. No breakthrough has as yet occurred in any of the subjects screened in this review, but results are promising. The clinical application of the steadily increasing knowledge in the biology of ovarian cancer may assist in the development of new treatment modalities that will improve survival.
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J Ovarian Res
January 2025
Department of Medical Genetics, National Taiwan University Hospital, 19F, No. 8, Chung-Shan South Road, Taipei City, Taiwan.
Background: The homologous recombination deficiency (HRD) test is an important tool for identifying patients with epithelial ovarian cancer (EOC) benefit from the treatment with poly(adenosine diphosphate-ribose) polymerase inhibitor (PARPi). Using whole exome sequencing (WES)-based platform can provide information of gene mutations and HRD score; however, the clinical value of WES-based HRD test was less validated in EOC.
Methods: We enrolled 40 patients with EOC in the training cohort and 23 in the validation cohort.
J Ovarian Res
January 2025
Department of Health Education, Nanjing Municipal Center for Disease Control and Prevention, No.3, Zizhulin Road, Nanjing, Jiangsu Province, 210003, China.
Background: PARP inhibitors (PARPis) have shown promising effectiveness for ovarian cancer. This network meta-analysis (PROSPERO registration number CRD42024503390) comprehensively evaluated the effectiveness and safety of PARPis in platinum-sensitive recurrent ovarian cancer (PSROC).
Methods: Articles published before January 6, 2024 were obtained from electronic databases.
J Transl Med
January 2025
Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
Background: The evidence on the relationship of dietary antioxidant nutrients with the survival of ovarian cancer (OC) remains scarce.
Objective: This study aimed to investigate these associations in a prospective cohort of Chinese patients with OC.
Methods: In this prospective cohort study, patients with epithelial OC completed a food frequency questionnaire at diagnosis and 12 months post-diagnosis, and were followed from 2015 to 2023.
JMIRx Med
January 2025
Department of Biochemistry and Medical Genetics, Cancer Center, University of Illinois Chicago, 900 s Ashland, Chicago, IL, 60617, United States, 1 8479124216.
Background: The causes of breast cancer are poorly understood. A potential risk factor is Epstein-Barr virus (EBV), a lifelong infection nearly everyone acquires. EBV-transformed human mammary cells accelerate breast cancer when transplanted into immunosuppressed mice, but the virus can disappear as malignant cells reproduce.
View Article and Find Full Text PDFNPJ Precis Oncol
January 2025
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
Histopathology foundation models show great promise across many tasks, but analyses have been limited by arbitrary hyperparameters. We report the most rigorous single-task validation study to date, specifically in the context of ovarian carcinoma morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained encoders and fourteen foundation models, each trained with 1864 whole slide images and validated through hold-out testing and two external validations (the Transcanadian Study and OCEAN Challenge).
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