Brahma-related gene 1 (BRG1) is one of two mutually exclusive ATPases that function as the catalytic subunit of human SWItch/Sucrose NonFermentable (SWI/SNF) chromatin remodeling enzymes. BRG1 has been identified as a tumor suppressor in some cancer types but has been shown to be expressed at elevated levels, relative to normal tissue, in other cancers. Using TCGA (The Cancer Genome Atlas) prostate cancer database, we determined that BRG1 mRNA and protein expression is elevated in prostate tumors relative to normal prostate tissue. Only 3 of 491 (0.6%) sequenced tumors showed amplification of the locus or mutation in the protein coding sequence, arguing against the idea that elevated expression due to amplification or expression of a mutant BRG1 protein is associated with prostate cancer. Kaplan-Meier survival curves showed that BRG1 expression in prostate tumors inversely correlated with survival. However, BRG1 expression did not correlate with Gleason score/International Society of Urological Pathology (ISUP) Grade Group, indicating it is an independent predictor of tumor progression/patient outcome. To experimentally assess BRG1 as a possible therapeutic target, we treated prostate cancer cells with a biologic inhibitor called ADAADi (active DNA-dependent ATPase A Domain inhibitor) that targets the activity of the SNF2 family of ATPases in biochemical assays but showed specificity for BRG1 in prior tissue culture experiments. The inhibitor decreased prostate cancer cell proliferation and induced apoptosis. When directly injected into xenografts established by injection of prostate cancer cells in mouse flanks, the inhibitor decreased tumor growth and increased survival. These results indicate the efficacy of pursuing BRG1 as both an indicator of patient outcome and as a therapeutic target.
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http://dx.doi.org/10.1002/jcp.28161 | 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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