Introduction: It has been previously shown that B7-H4, one of the B7 family members that serve as negative regulators of T cell function, has altered expression levels in a variety of cancers, overexpression of B7-H4 promotes cellular transformation. However, there is still lack of adequate evidence to establish a direct connection between B7-H4 expression and malignant transformation.
Methods: Herein, we constructed pE-green fluorescent protein-N1/B7-H4 mammalian expression vector and transfected into B7-H4-negative human ovarian cancer cell line SKOV3. Cellular proliferation, apoptosis, adhesion, motility, and invasion were examined in vitro. Cells injected subcutaneously into severe combined immunodeficient mouse were analyzed for the possible functions of B7-H4 in ovarian tumorigenesis in vivo.
Results: Fluorescence microscopy studies confirmed that the B7-H4-green fluorescent protein localizes in the cytoplasm of SKOV3/B7-H4 cells, whereas green fluorescent protein is uniformly distributed throughout the cell. B7-H4 promoted cellular proliferation rate and increased cell adhesion, migration, and invasion. In addition, SKOV3 cells expressing B7-H4 gained growth advantage in the xenograft model in vivo.
Conclusions: These studies demonstrate that B7-H4 directly promotes malignant transformation of ovarian cancer cell line, and provides a potential therapeutic strategy for targeting B7-H4 to inhibit progression of human ovarian cancers.
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http://dx.doi.org/10.1111/IGC.0b013e3181ad0fa2 | 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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