Biomarkers capable of detecting and targeting epithelial ovarian cancer cells for diagnostics and therapeutics would be extremely valuable. Ovarian cancer is the deadliest reproductive malignancy among women in the U.S., killing over 14 000 women each year. Both the lack of presenting symptoms and high mortality rates illustrate the need for earlier diagnosis and improved treatment of this disease. The glycosyltransferase enzyme GnT-III encoded by the Mgat3 gene is responsible for the addition of GlcNAc (N-acetylglucosamine) to form bisecting N-linked glycan structures. GnT-III mRNA expression is amplified in ovarian cancer tissues compared with normal ovarian tissue. We use a lectin capture strategy coupled to nano-ESI-RPLC-MS/MS to isolate and identify the membrane glycoproteins and unique glycan structures associated with GnT-III amplification in human ovarian cancer tissues. Our data illustrate that the majority of membrane glycoproteins with bisecting glycosylation are common to both serous and endometrioid histological subtypes of ovarian cancer, and several have been reported to participate in signaling pathways such as Notch, Wnt, and TGFβ.
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http://dx.doi.org/10.1021/pr501174p | DOI Listing |
J Ultrasound
January 2025
, Costa Contina street n. 19, 66054, Vasto, Chieti, Italy.
Aim: o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease.
Material And Methods: ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review.
Results: new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC.
Ann Surg Oncol
January 2025
Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of infection. This study aimed to develop a machine-learning model to predict postoperative infection after cytoreductive surgery (CRS) and HIPEC with splenectomy.
Methods: The study enrolled patients in the national TriNetX database and at the Johns Hopkins Hospital (JHH) who underwent splenectomy during CRS/HIPEC from 2010 to 2024.
Abdom Radiol (NY)
January 2025
Hanyang University Guri Hospital, Guri-si, Korea, Republic of.
Purpose: Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, facilitating risk stratification based on morphological features for malignancy assessment, which is essential for proper management. However, systematic determination of inter-reader reliability in O-RADS US categorization remains unexplored. This study aimed to systematically determine the inter-reader reliability of O-RADS US categorization and identify the factors that affect it.
View Article and Find Full Text PDFCancer
February 2025
Division of Hematology/Oncology, University of Illinois Chicago, Chicago, Illinois, USA.
Cancer Med
January 2025
Department of Obstetrics and Gynecology, Keio University School of Medicine, Tokyo, Japan.
Background: Borderline ovarian tumors (BOTs) differ from ovarian carcinomas in their clinical presentation and behavior, yet their molecular characteristics remain poorly understood. This study aims to address this gap by integrating whole-exome sequencing (WES) and RNA sequencing (RNA-seq) to compare BOTs with high-grade serous carcinoma (HGSC), endometrioid carcinoma (EC), and clear-cell carcinoma (CCC).
Objective: To elucidate the molecular features of BOTs and evaluate their similarities and differences in comparison to HGSC, EC, and CCC.
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