The role of CD133 (Prominin-1) as a cancer stem cell marker may be useful for therapeutic approaches and prognostication in prostate cancer patients. We investigated the stem-cell-related function and biological features of a subpopulation of CD133+ cells isolated from established primary human prostate cancer cell lines. The CD133+ cells sorted from human prostate cancer 22Rv1 exhibited high clonogenic and tumorigenic capabilities, sphere forming capacity and serially reinitiated transplantable tumors in NOD-SCID mice. Gene profiling analysis of CD133+ cells showed upregulation of markers of stem cell differentiation (CD44, Oct4, SOX9 and Nanog), epithelial-to-mesenchymal transition (c-myc and BMI1), osteoblastic differentiation (Runx2), and skeletal morphogenesis (BMP2), compared to side population of CD133- cells. These cells are highly malignant and resistant to γ-radiation and chemotherapeutic drug, docetaxel. Importantly, a docetaxel-resistant subclone was more enriched in CD133+ cells with significant increase in Runx2 expression, compared to CD133- cells. Furthermore, knockdown of Runx2 in these cells resulted in differential response to chemotherapy, sensitizing them to increased cell death. These results demonstrate therapy-resistant population with stem-like features are distinct subpopulation of malignant cells that resides within parental cell lines. The molecular signature of CD133+ cells may lead to identification of novel therapeutic targets and prognostic markers in the treatment of prostate cancer.
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http://dx.doi.org/10.1016/j.canlet.2018.05.014 | 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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