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8,942 results for “Prostate”
Supplementary material: CYB561 supports the neuroendocrine phenotype in castration-resistant prostate cancer
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YY1 enhances HIF-1α stability in tumor-associated macrophages to suppress anti-tumor immunity of prostate cancer
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Data from: Reinterpretation of prostate cancer pathology by Appl1, Sortilin and Syndecan-1 biomarkers
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Prostate-specific deletion of Cdh1 induces murine prostatic inflammation and bladder overactivity
<p>Benign Prostatic Hyperplasia (BPH) is an age-related debilitating prostatic disease that is frequently associated with prostatic inflammation and bothersome lower urinary tract symptoms (LUTS). Animal models have shown that formalin- and bacterial-induced prostatic inflammation can induce bladder dysfunction; however, the underlying mechanisms contributing to prostatic inflammation in BPH and bladder dysfunction are not clear. We previously reported that E-cadherin expression in BPH is down-regulated in hyperplastic nodules compared to expression in adjacent normal tissues. Here, we explored the potential consequences of prostatic E-cadherin down-regulation on the prostate and bladder <i>in vivo</i> using an inducible murine model of prostate luminal epithelial-specific deletion of <i>Cdh1</i>. The PSA-CreER<sup>T2</sup> transgenic mouse strain expressing tamoxifen-inducible CreER<sup>T2</sup> recombinase driven by a 6-kb human PSA promoter/enhancer was crossed with the B6.129-<i>Cdh1</i><sup>tm2Kem</sup>/J mouse to generate bigenic PSA-CreER<sup>T2</sup>/<i>Cdh1</i><sup>-/-</sup> mice. Deletion of E-cadherin was induced by transient administration of tamoxifen when mice reached sexual maturity (7 weeks of age). At 21-23 weeks of age, the prostate, bladder, and prostatic urethra were examined histologically, and bladder function was assessed using Void Spot Assays and cystometry. Mice with <i>Cdh1</i> deletion had increased prostatic epithelial hyperplasia, inflammation, and stromal changes at 21-23 weeks of age, as well as changes in bladder voiding function compared to age-matched controls. Thus, loss of E-cadherin in the murine prostate could result in prostatic defects that are characteristic of BPH and lower urinary tract symptoms, suggesting that E-cadherin down-regulation could be a driving force in human BPH development and progression.</p>
Data from: Probabilistic interfractional motion carbon ion radiation therapy dose distribution for prostate cancer shows rectum sparing with moderate target coverage degradation
Purpose: This observational study investigates the influence of interfractional motion on clinical target volume (CTV) coverage, planning target volume (PTV) margins, and rectum tissue sparing in carbon ion radiation therapy (CIRT). It reports dose coverage to target structures and organs at risk in the presence of interfractional motion, investigates rectal tissue sparing, and provides recommendations for further lowering the rate of toxicity. We also propose probabilistic DVH for consideration in treatment planning to represent probable dose to the clinic's patient population. Methods: At Gunma University Hospital intensity-modulated x-ray therapy (IMXT, aka IMRT) prostate cancer patients are positioned on a table which is shifted twice based on cone-beam computed tomography (CBCT) to align bones and then align prostate tissue to isocenter. These shifts thereby contain interfractional motion. 1306 such tableshifts from 85 patients were collected. Normal probability distributions were fit to the difference between bone-matching and prostate-matching CBCT-to-planning CT tableshifts (i.e. interfractional motion). Between 2011 and 2016 CIRT prostate patients were treated with PTV1 and PTV2 margins as follows: PTV1 extends the prostate contour by 10/10, 5/10, 6/6 mm in the right/left, posterior/anterior, and superior/inferior directions, respectively, and the proximal seminal vesicles contour by 5 mm superiorly and inferiorly, 3 mm right and left. PTV2 reduces PTV1 posteriorly along a straight line to exclude the rectum and reduces the superior and inferior margins by 6 mm. From those treated with these margins, 40 patients' beam data were selected to create probable interfractional motion: The previously fit normal probability distributions were randomly sampled 2000 times per patient and beams shifted to simulate this motion. These shifted dose distributions were scaled down proportionately in magnitude and summed to obtain probable blurred dose distributions. Results: Probable dose to rectum is substantially less than planned for doses higher than 10 Gy(RBE). Absolute DVH show that mean clinical target volumes are about 138-670 cm3 smaller for a given probable dose than planned doses higher than 57 Gy(RBE) after accounting for standard error. Cumulative DVH show mean CTV fraction receiving a given probable dose is less than the mean fraction receiving the corresponding planned dose for doses larger than 52 Gy(RBE), up to 19% less at 57.4 Gy(RBE). Our PTV1 margins generally cover 95% of interfractional motion but seminal vesicles and inferior prostate receive less dose than planned due to insufficient PTV2 margins. Conclusion: Assuming rigidly shifting interfractional motion around the prostate region and neglecting minor changes in soft tissue stopping power, interfractional motion resulted in underdosing or tissue sparing in all cases. Given our low rates of relapse and recurrence, it appears less curative dose is needed than previously thought or else the target may be smaller than previously thought. In-room CT may be useful to lower dose, shrink target margins, conduct PET auto-activation dose verification studies and account for interfractional motion.
FIGURE 6. Polycystis ali Schockaert, 1982 A, Prostate stylet type I in Polycystididae (Rhabditophora: Rhabdocoela: Kalyptorhynchia) from the Indian Ocean, with the description of twelve new species
FIGURE 6. Polycystis ali Schockaert, 1982 A, Prostate stylet type I, side view (from a specimen from Kenya). B, Prostate stylet type I, view from below (from a specimen from Kenya). Polycystis elsae n. sp. C, Prostate stylet type I (from the holotype).
cis-mQTL results in prostate benign tissue of European ancestry patients and African ancestry patients
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cis-mQTL results in prostate cancer tissue of European ancestry patients and African ancestry patients
<p>cis-mQTL results of in tumor tissues</p>
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
Biomarkers in Metastatic Castration-Resistant Prostate Cancer for efficiency of Immune Checkpoint Inhibitors
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Datasets: Carbon sources and pathways for citrate secreted by human prostate cancer cells determined by NMR tracing and metabolic modeling
<p>Zipped NMR datasets:</p> <p>data1 LNCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate</p> <p>data2 LNCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data3 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data4 VCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data5 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data6 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data7 VCaP medium with [2-<sup>13</sup>C]pyruvate -glucose +aspartate</p> <p>data8 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data9 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data10 VCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data11 LNCaP and VCaP medium with and without zinc(II)</p> <p>data12 LNCaP and VCaP medium with and without zinc(II)</p> <p>data13 LNCaP and VCaP medium with and without zinc(II)</p> <p>data14 LNCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data15 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data16 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data17 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data18 VCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data19 VCaP medium with [1,6-<sup>13</sup>C<sub>2</sub>]glucose</p> <p>data20 VCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data21 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data22 LNCaP medium with [2-<sup>13</sup>C]pyruvate +citr. Spiking</p> <p>data23 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data24 LNCaP medium with [5-<sup>13</sup>C]glutamine</p> <p>data25 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data26 VCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data27 VCaP medium with [2-<sup>13</sup>C]pyruvate -glucose +aspartate</p> <p>data28 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data29 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data30 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data31 LNCaP medium with [2-<sup>13</sup>C]pyruvate</p> <p>data32 VCaP medium with [2-<sup>13</sup>C]pyruvate -glucose +aspartate</p> <p>data33 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data34 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data35 LNCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate</p> <p>data36 VCaP medium with [U-<sup>13</sup>C<sub>4</sub>]aspartate -glucose +pyruvate</p> <p>data37 LNCaP medium with [5-<sup>13</sup>C]glutamine + citr. spiking</p>
Roles of m5C RNA modification patterns in biochemical recurrence and tumor microenvironment characterization of prostate cancer
<p>The datasets included in the manuscript 'Roles of m5C RNA modification patterns in biochemical recurrence and tumor microenvironment characterization of prostate cancer'</p>
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
<p>The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.</p>
Data of FigS1, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS1, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS1.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains three files in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2-1-3 .csv), three files in sps-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2-1-3 .sps), all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M1.pdf) and one in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M.txt).</p> <p> </p>
Bacterial RNA Virus MS2 Exposure Increases the Expression of Cancer Progression genes in LNCaP Prostate Cancer Cell Line
<p><strong>Supplementary Figure 1. </strong>Network of protein-protein interactions in LNCaP cells according to their interaction with T4 and M13 phages (DNA phages). The network of protein-protein interactions in Homo Sapiens was constructed using the string protein-protein interaction network v 11.5 [35] with the overexpressed genes (highest confidence level setting) in LNCaP cells. Interactions of overexpressed protein genes were mapped with the highest confidence cut-off of (0.7-0.9). In the resulting protein association network, proteins are presented as nodes connected by lines with varying thicknesses representing the highest confidence level of (0.7-0.9). It is observed that there is strong experimentally determining evidence for the link between SRC and other overexpressed genes, including MAPKs and other integrins by adding more nodes until the highest confidence level of (0.7-0.9) among all overexpressed genes was obtained based on known interactions.</p> <p> </p> <p><strong>Supplementary Figure 2. </strong>Enrichr web server-based gene set enrichment analysis for overexpressed cancer progression genes in LNCaP cells treated with (A) MS2 and (B) T4 and M13 phages; which predicts caveolin-mediated endocytosis for MS2 bacterial virus (with a p-value of 0.000001399). Similarly, an integrin-mediated signaling pathway (with a p-value of 0.000007588) based on overexpression of most integrin genes and caveolin-mediated endocytosis (with a p-value of 0.000001799) were predicted for both T4 and M13 phages based on GO Biological process 2021.</p>
Potential drug targets for benign prostatic hyperplasia: A proteome-wide Mendelian randomization study
<p>Additional file 3</p>
Synergistic antitumor interaction of valproic acid and simvastatin and docetaxel in Prostate cancer cells
<p>Synergistic antitumor interaction of valproic acid, simvastatin and docetaxel in prostate cancer cells on cell migration, cell cycle perturbation, apoptosis, 3D cell culture models and stem marker reduction in<em> in vivo</em> model.</p>
PROSTATE
<p>Summary</p> <p>Prostate transversal T2-weighted magnetic resonance images (MRIs) acquired on a 3.0T Siemens TrioTim using only a pelvic phased-array coil were acquired for prostate cancer detection. The data was provided to TCIA as part of an <a href="https://wiki.cancerimagingarchive.net/display/Public/NCI-ISBI+2013+Challenge+-+Automated+Segmentation+of+Prostate+Structures">ISBI challenge competition in 2013</a>.</p> <p> </p> <ul> <li><a href="https://wiki.cancerimagingarchive.net/display/Public/Prostate-3T#68827855b363215b8f94d6696b808667125bc1b"><strong>Data Access</strong></a></li> <li><a href="https://wiki.cancerimagingarchive.net/display/Public/Prostate-3T#688278512300d8cec7b49538ff489bb108277a0"><strong>Detailed Description</strong></a></li> <li><a href="https://wiki.cancerimagingarchive.net/display/Public/Prostate-3T#6882785035760770f9440c5a72d69914bf9b3f6"><strong>Citations & Data Usage Policy</strong></a></li> <li><a href="https://wiki.cancerimagingarchive.net/display/Public/Prostate-3T#688278576f85f43b21d4b64a72f4a2630f09702"><strong>Versions</strong></a></li> </ul> <p>Citations & Data Usage Policy </p> <p>Users of this data must abide by the <a href="https://wiki.cancerimagingarchive.net/x/c4hF">TCIA Data Usage Policy</a> and the <a href="https://creativecommons.org/licenses/by/3.0/">Creative Commons Attribution 3.0 Unported License</a> under which it has been published. Attribution should include references to the following citations:</p> <p>Data Citation</p> <p>Litjens, Geert, Futterer, Jurgen, & Huisman, Henkjan. (2015). <strong>Data From Prostate-3T.</strong> The Cancer Imaging Archive. DOI: <a href="https://doi.org/10.7937/K9/TCIA.2015.QJTV5IL5">10.7937/K9/TCIA.2015.QJTV5IL5</a></p> <p>TCIA Citation</p> <p>Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. (2013) <strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository</strong>, Journal of Digital Imaging, Volume 26, Number 6 pp 1045-1057. DOI: <a href="https://doi.org/10.1007/s10278-013-9622-7">10.1007/s10278-013-9622-7</a></p> <p>Other Publications Using This Data</p> <ul> <li><a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=21267207">Bloch N, Rusu M, et al. (2015) NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures. TCIA. St. Louis, MO</a></li> </ul> <p>TCIA maintains<a href="https://www.cancerimagingarchive.net/publications/"> a list of publications</a> that leverage our data. If you have a publication you'd like to add, please<a href="http://www.cancerimagingarchive.net/support/"> contact the TCIA Helpdesk</a>.</p>
Unravelling the metabolic alterations induced by Zika infection in prostate epithelial (PNT1a) and adenocarcinoma (PC-3) cell lines
<p>Unravelling the metabolic alterations induced by Zika infection in prostate epithelial (PNT1a) and adenocarcinoma (PC-3) cell lines</p>
nnU-Net weights for Prostate segmentation used in PI-CAI Challenge - Phase 1
<p>nnU-Net weights for Prostate segmentation used in PI-CAI Challenge - Phase 1.</p> <p>A baseline nnU-Net was trained to segment the prostate gland, using the 1500 cases training data from PI-CAI dataset. The network took as input 3 modalities (T2w, ADC, high DWIb), and the ground truth was AI-derived prostate masks.</p> <p>See Abstract "Deep learning for detection and diagnosis of prostate cancer from bpMRI and PSA: Guerbet’s contribution to the PI-CAI 2022 Grand Challenge" for further information.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.