Purpose: We developed and describe a practical method by which primary prostate cancer specimens can be screened for recurrent chromosomal translocations, which is a potential source of fusion genes, as well as a process by which identified translocations can be mapped to define the genes involved.
Materials And Methods: A series of 7 prostate cancer cell lines and 25 transiently established primary cell cultures, which were sourced from tissue harvested at 16 radical prostatectomies and 9 channel transurethral prostate resections, were screened for chromosomal translocations using multiplex-fluorescence in situ hybridization technology. A series of fluorescence in situ hybridization based breakpoint mapping experiments were performed to identify candidate genes involved in regions associated with recurrent translocation.
Results: Our analysis identified the repetition of 2 translocations in prostate cancer lines, that is t(1;15) and t(4;6), at a frequency of 28% and 57%, respectively. More significantly 4 of the 25 subsequently established primary cultures (16%) also revealed a t(4;6) translocation. Using the LNCaP cell line the breakpoints involved were mapped to the t(4;6)(q22;q15) region and a number of candidate genes were identified.
Conclusions: We found that the t(4;6) translocation is also a repeat event in primary cell cultures from malignant prostate cancer. Breakpoint mapping showed that the gene UNC5C loses its promoter and first exon as a direct result of the translocation in the 4q22 region. As such, we identified it as a possible contributor to a putative fusion gene in prostate cancer.
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http://dx.doi.org/10.1016/j.juro.2007.01.001 | DOI Listing |
PLoS One
January 2025
Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom.
Introduction: Undiagnosed chronic disease has serious health consequences, and variation in rates of underdiagnosis between populations can contribute to health inequalities. We aimed to estimate the level of undiagnosed disease of 11 common conditions and its variation across sociodemographic characteristics and regions in England.
Methods: We used linked primary care, hospital and mortality data on approximately 1.
Ann Nucl Med
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
The purpose of this systematic review was to evaluate the role of PSMA PET/CT in intermediate-risk prostate cancer (PCa) patients, to determine whether it could help improve treatment strategy and prognostic stratification. A systematic literature search up to May 2024 was conducted in the PubMed, Embase and Scopus databases. Articles with mixed risk patient populations, review articles, editorials, letters, comments, or case reports were excluded.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Allegheny Health Network, Neuroscience Institute, Pittsburgh, PA, United States.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
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