Objective: The crosstalk between tumor microenvironment (TME) and cancer cells plays a critical role in the occurrence and development of ovarian cancer. Imprinted gene MEST is a tumor-promoting factor that modulates several carcinogenic signaling pathways. This study aimed to investigate the expression pattern of MEST and its association with immune cell infiltration.
Methods: The transcriptome data of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database was utilized, and the expression and immune characteristics of MEST were verified by immunohistochemistry of ovarian cancer specimens. Kaplan-Meier Plotter was used to assess the prognostic value in patients with ovarian cancer.
Results: We found that high expression of MEST was associated with diminished immune cell infiltration and worse prognosis of ovarian cancer patients in independent cohorts. There was a positive correlation between MEST and BRCA1 expression. The MESTBRCA1 ovarian cancer group was correlated with lower infiltration of CD4 cells, CD57 cells, CD68 cells and MPO cells, had worse overall survival (OS) in TCGA (HR = 1.57, p = 0.0004) and GSE27651 (HR = 4.27, p = 0.0002) cohorts, and predicted poor progress free survival (PFS) in GSE9891 (HR = 1.76, p = 0.0098) and GSE15622 (HR = 4.80, p = 0.0121) cohorts. Moreover, the expression of PD-L1 predicted unfavorable OS (HR = 2.48, p = 0.0415) and PFS (HR = 2.36, p = 0.0215) in MESTBRCA1 ovarian cancer group in GSE9891 cohort.
Conclusion: These findings suggest that the co-expression of MEST and BRCA1 may be an ideal combination for predicting the prognosis and response to immunotherapy in patients with ovarian cancer.
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http://dx.doi.org/10.1016/j.ygyno.2022.01.010 | DOI Listing |
Endometriosis is a chronic disease characterised by the presence of endometrial tissue outside the uterine cavity, affecting 5-15% of women, especially those of reproductive age. The disease may manifest itself as dysmenorrhoea, dyspareunia, sterility and chronic pelvic pain, among other symptoms. Although it is not malignant, it shares some characteristics with cancer and can lead to epithelial ovarian carcinoma.
View Article and Find Full Text PDFJ Immunother Cancer
January 2025
Medical Oncology, Sarah Cannon Research Institute, Nashville, Tennessee, USA.
Background: SL-172154 is a hexameric fusion protein adjoining the extracellular domain of SIRPα to the extracellular domain of CD40L via an inert IgG-derived Fc domain. In preclinical studies, a murine equivalent SIRPα-Fc-CD40L fusion protein provided superior antitumor immunity in comparison to CD47- and CD40-targeted antibodies. A first-in-human phase I trial of SL-172154 was conducted in patients with platinum-resistant ovarian cancer.
View Article and Find Full Text PDFEur J Obstet Gynecol Reprod Biol
January 2025
Department of Obstetrics and Gynecology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2-5-1 Shikata-cho kitaku, Okayama 700-8558, Japan. Electronic address:
Introduction: Exosomes play an important role in regulating physiological processes and mediating the systemic dissemination of various types of cancer. We investigated the association of exosomal tetraspanins CD9, CD63, and CD81 in patients with ovarian cancer (OC).
Material And Methods: We measured the plasma tetraspanins CD9, CD63, and CD81 by enzyme-linked immunosorbent assay in 91 patients who underwent treatment for OC between April 2018 and March 2024.
Eur J Oncol Nurs
December 2024
Dept of Gynecology and Obstetrics and CCC Munich, LMU University Hospital, LMU Munich, Germany; Bavarian Cancer Research Center (BZKF), Munich, Germany. Electronic address:
Purpose: The increase of oral tumor therapies (OTT) poses new challenges in patient care. Within CAMPA (Care improvement for advanced or metastatic breast and ovarian cancer patients treated with PARP-inhibitors), additional nursing support for patients treated with PARP-inhibitors was developed.
Methods: Additional nursing support (1 year) was evaluated in breast and gynecooncological cancer patients at an academic and a non-academic outreach center.
Biometrics
January 2025
Department of Biostatistics, University of Michigan at Ann Arbor, Ann Arbor, MI 48109, United States.
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.
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