Typically, ovarian cancer remains restricted to the peritoneal cavity. Because of this unique localization, the study of ovarian cancer is particularly suitable for immune analysis and for the development of immunotherapy. Here we report that peritoneal fluid from patients with ovarian or other intra-abdominal cancers contained significantly elevated levels of interleukin 10 (IL-10) (542 +/- 77 pg/ml, N = 35), compared with peritoneal fluid from patients with benign gynecological conditions (34.2 +/- 7.5 pg/ml, N = 63) (P < 0.001). Peritoneal fluid IL-10 levels did not correlate with histology, tumor stage, grade, or prognosis. IL-10 levels were also elevated in the serum of patients with intra-abdominal cancer (1353 +/- 906, N = 8). Established ovarian cancer cell lines (N = 5) did not produce any detectable IL-10. Investigation of the cell surface phenotype of the cells in the peritoneal cavity indicated the presence of significant amounts of activated immune cells. The presence of cytokines such as IL-10 in the peritoneal cavity of ovarian cancer bearing patients could be important in the growth and development of cancer, more specifically, in relation to host immune responsiveness.
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http://dx.doi.org/10.1016/1043-4666(92)90082-3 | DOI Listing |
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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