Find research datasets worth reusing

Data of Fig9, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"

Data of Fig9, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples” The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig9.PNG). The Corresponding raw data and subsequent data analysis obtained for  immunohistochemistry contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M.pdf) and one file in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt)

Data of FigS3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"

Data of FigS3, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples” The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS3.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3_M .txt), six files in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-1-6 .csv) and one file in sps-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-4 .sps). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M 1.pdf).

Data of FigS5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"

Data of FigS5, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples” The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS5.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M.txt).

Data of FigS6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"

Data of FigS6, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples” The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS6.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains two files in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1-2_M.txt). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf).

opencc-by-4.0Jul 2022View details →

Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy

This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a  sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.

opencc-by-4.0Feb 2022View details →

Digital Pathology Dataset for Prostate Cancer Diagnosis

Links to code and Patterns paper: 1. <a href="https://10.5281/zenodo.7152962">Multi-lens Neural Machine (MLNM) Code</a> 2. <a href="https://www.cell.com/patterns/fulltext/S2666-3899(22)00274-4">An AI-assisted Tool For Efficient Prostate Cancer Diagnosis in Low-grade and Low-volume Cases</a> Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified. Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (<a href="https://computationalpathologygroup.github.io/ASAP/">ASAP</a>). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated.   Whole Slide Image Dataset Whole Slide Image dataset containing 99 images in SVS format with corresponding annotations in XML format are provided in WSI.zip.  Available patient grading for the WSIs are provided in 'gleason_score_mapped.txt'.  These XML annotations can be parsed using the code in official repository. Cropped Image Dataset Patches of size 512 × 512 pixels were cropped from the WSI (Whole Slide Image Dataset) at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images. This dataset is used to train the two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows: <ol> <li>gland_segmentation_dataset.zip</li> <li>gland_classification_dataset.zip</li> </ol> Table 1: The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. <table> <tbody> <tr> <td>   </td> <td> #Slides </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td>   </td> <td> Train </td> <td> Valid </td> <td> Test </td> <td> Total </td> </tr> <tr> <td> Prostatectomy </td> <td> 17 </td> <td> 8 </td> <td> 15 </td> <td> 40 </td> </tr> <tr> <td> Biopsy </td> <td> 26 </td> <td> 13 </td> <td> 20 </td> <td> 59 </td> </tr> <tr> <td> Total </td> <td> 43 </td> <td> 21 </td> <td> 35 </td> <td> 99 </td> </tr> <tr> <td>   </td> <td> #Patches </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td>   </td> <td> Train </td> <td> Valid </td> <td> Test </td> <td> Total </td> </tr> <tr> <td> Prostatectomy </td> <td> 7795 </td> <td> 3753 </td> <td> 7224 </td> <td> 18772 </td> </tr> <tr> <td> Biopsy </td> <td> 5559 </td> <td> 4028 </td> <td> 5981 </td> <td> 15568 </td> </tr> <tr> <td> Total </td> <td> 13354 </td> <td> 7781 </td> <td> 13205 </td> <td> 34340 </td> </tr> </tbody> </table> Table 2: The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. GS: Gleason Score. B: Benign. M: Malignant. <table> <tbody> <tr> <td>   </td> <td> #Slides (GS  3+3:3+4:4+3) </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td>   </td> <td> Train </td> <td> Valid </td> <td> Test </td> <td> Total </td> </tr> <tr> <td> Biopsy </td> <td> 10:9:1 </td> <td> 3:7:0 </td> <td> 6:10:0 </td> <td> 19:26:1 </td> </tr> <tr> <td>   </td> <td> #Patches (B:M) </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td>   </td> <td> Train </td> <td> Valid </td> <td> Test </td> <td> Total </td> </tr> <tr> <td> Biopsy </td> <td> 1557:2277 </td> <td> 1216:1341 </td> <td> 1543:2718 </td> <td> 4316:6336 </td> </tr> </tbody> </table> NB: Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study.

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Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation

Background: Prostate-related quality of life can be assessed with a variety of different questionnaires. The 50-item Expanded Prostate Cancer Index Composite (EPIC) and the International Prostate Symptom Score (IPSS) are two widely used options. The goal of this study was, therefore, to develop and validate a model that is able to convert between the EPIC and the IPSS to enable comparisons across different studies. Methods: Three hundred forty-seven consecutive patients who had previously received radiotherapy and surgery for prostate cancer at two institutions in Switzerland and Germany were contacted via mail and instructed to complete both questionnaires. The Swiss cohort was used to train and internally validate different machine learning models using fourfold cross-validation. The German cohort was used for external validation. Results: Converting between the EPIC Urinary Irritative/Obstructive subscale and the IPSS using linear regressions resulted in mean absolute errors (MAEs) of 3.88 and 6.12, which is below the respective previously published minimal important differences (MIDs) of 5.2 and 10 points. Converting between the EPIC Urinary Summary and the IPSS was less accurate with MAEs of 5.13 and 10.45, similar to the MIDs. More complex model architectures did not result in improved performance in this study. The study was limited to the German versions of the respective questionnaires. Conclusions: Linear regressions can be used to convert between the IPSS and the EPIC Urinary subscales. While the equations obtained in this study can be used to compare results across clinical trials, they should not be used to inform clinical decision-making in individual patients. Trial registration This study was retrospectively registered on clinicaltrials.gov on January 14th, 2022, under the registration number NCT05192876.

opencc-zeroJul 2024View details →

opencc-by-4.0Nov 2018View details →

Metabolomic Profiling of Zinc Accumulating Prostate Cancer Cells: dataset of metabolomics and transctiptomics

In this study, we focused on the metabolism of prostate cancer cells forced to accumulate zinc. Because levels of metabolites involved in Krebs and methionine cycle can participate in non-metabolic processes such as changes in gene expression, a panel of 371 genes connected with key steps of carcinogenesis was designed and expression levels of these genes were assessed to determine, which pathways are changed due to the long-term zinc treatment. As a model of prostate cancerogenesis, wild-type and zinc accumulating cell lines PNT1A, 22Rv1, and PC-3 were used. Metabolite profiles were examined using liquid chromatography triple quadrupole mass spectrometry. Quantification of total intracellular zinc was performed by atomic absorption spectrometry and gene expression investigated by cDNA microarray.  For description of creation of zinc-resistant cell lines see Holubova et al, Metallomics 2014, DOI 10.1039/C4MT00065J   Description of dataset Total 6 files are included: Krebs.metabolites.csv: table of metabolomic data (in ppm) of wild type/untreated/zinc-resistant cells. Krebs.metabolites.medium.csv: table of metabolomic data (in fold change compared to medium) in cultivation media of abovementioned cells. RNA_array_fold_p.csv: processed results of microarray displayed as mean log2 fold change (resistant - WT) and p level RNA_array_raw_22Rv1.csv, RNA_array_raw_PC-3.csv, RNA_array_raw_PNT1A.csv raw data from microarray reader for WT and resistant cells.

opencc-by-4.0Jun 2019View details →

Integrated analysis of miRNA landscape and cellular networking pathways in stage-specific prostate cancer

Figure S1. Heat map of miRNA-microarray. Expression of miRNAs differentially expressed and assessed in microarray analysis of RNA isolated from four different cell lines of prostate cancer (LNCaP, PC3, DU145, 22Rv1) compared with control cell line of prostate cancer (PrEc). The red color depicts high and green color showed a lower level of expression at p value <0.01. Figure S2A. Drug resistance by drug efflux. The internetworking relationship with miRNAs and plasma membrane protein P-glycoprotein (Pgp-plasma membrane protein). The miRNA-130a and miR-181a are downregulated in this pathway (green color) and linked with Pg and BCRP (breast cancer resistant protein). miR-133a and miR379 are involved in regulating the expression of MRP2. While miR-298, miR27a, miR331-5p and miR-130a are involved in regulating the expression of poly-glycoprotein (P-gp). Figure S2B. Epithelial mesenchymal transition pathway. The miR-200b was downregulated during early-stage prostate cancer and was involved in inhibiting Jagged-2 (JAG2), one of the NOTCH ligands. Figure S2C. Adipogenesis pathway. The expression of miR-326 was upregulated during the metastatic stage of prostate cancer, (red color) and involved in regulating the expression of CCAT/enhancer binding protein β (C/EBPβ), directly linked with the nuclear hormone receptor peroxisome proliferator-activated receptor-gamma (PPAR-γ). Figure S2D. Bone metamorphosis signaling pathway. The expression of miR-140, miR-145, and miR-155 was upregulated, along with miR-140 and miR-145 were associated with modulation of gene SOX9, and miR-155 was involved in regulating the gene FOXO3A. Figure S2E. Th1 pathway. In this pathway, the expression of miR-146a showed a higher level of expression (red color) and modulated the expression of NF-κB signaling. Figure S2F. Th1 and Th2 pathway. In this pathway, the expression of miR-146a showed a lower level of expression (green color) and may modulate the expression of NF-κB signaling. Table S1. List of differentially expressed miRNAs derived from LNCaP cells lines statistically significant as P < 0.001. Table S2. List of differentially expressed miRNAs derived from PC3 cells lines statistically significant as P < 0.001. Table S3. List of differentially expressed miRNAs derived from DU145 cells lines statistically significant as P < 0.001. Table S4. List of differentially expressed miRNAs derived from 22Rv1 cells lines statistically significant as P < 0.001.

opencc-by-4.0Jul 2018View details →

Data for Contrast Enhanced Prostate MRI paper

DICOM and MATLAB .mat data files used in paper: "Optimisation of contrast enhanced MR sequences for detection of prostate cancer" This data is referred to in the code as 20Mar2018. Associated MATLAB .m files are on the GitHub site:  <a href="https://github.com/UCL/CEPMRI-opt.">https://github.com/UCL/CEPMRI-opt </a>

opencc-by-4.0Dec 2013View details →

Fig. 4 in First Report of a Case of Prostatitis Due to Acanthamoeba in a Dog

Fig. 4. Phylogenetical analysis of the DF3 sequence of the Acanthamoeba strain isolated from the prostate revealed that the strain belonged to genotype T4.

opencc-by-4.0Feb 2023View details →

The PI-CAI Challenge: Zonal Segmentation for Prostate MRI (A. Karagöz et al., 2022)

The zonal prostate segmentations have been generated by the algorithm from (A. Karagöz et al., 2022). See their <a href="https://rumc-gcorg-p-public.s3.amazonaws.com/evaluation-supplementary/642/88bd85e5-ca10-4979-875e-eba7476491d3/Hevi_picai_report.pdf">method description</a> for details. The segmentations indicate the peripheral zone as label 1, and the transition zone as label 2. Softmax predictions for these cases are stored as prostate_gland_sm_pz.nii.gz for the peripheral zone, and prostate_gland_sm_tz.nii.gz for the transition zone, in a folder with the subject id as the name.

opencc-by-4.0Dec 2022View details →

Genetically adjusted PSA levels for prostate cancer screening

Prostate-specific antigen (PSA) screening for prostate cancer remains controversial because it increases overdiagnosis and overtreatment of clinically insignificant tumors. Accounting for genetic determinants of constitutive, non-cancer PSA variation has potential to improve screening utility. We discovered 128 genome-wide significant associations (P<5×10-8) in a multi-ancestry GWAS meta-analysis of 95,768 men and developed a PSA polygenic score (PGSPSA) that explains 9.61% of constitutive PSA variation. Here we provide full GWAS summary statistics for the multi-ancestry meta-analysis and ancestry-stratified summary statistics for ~1.2 million variants used to derive a genome-wide PGSPSA.

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ETV4 mediates dosage-dependent prostate tumor initiation and cooperates with p53 loss to generate prostate cancer

The mechanisms underlying ETS-driven prostate cancer initiation and progression remain poorly understood due to a lack of model systems that recapitulate this phenotype. We generated a genetically engineered mouse with prostate-specific expression of the ETS factor, ETV4, at lower and higher protein dosages through mutation of its degron. Lower-level expression of ETV4 caused mild luminal cell expansion without histologic abnormalities and higher-level expression of stabilized ETV4 caused prostatic intraepithelial neoplasia (mPIN) with 100% penetrance within 1 week. Tumor progression was limited by p53-mediated senescence and Trp53 deletion cooperated with stabilized ETV4. The neoplastic cells expressed differentiation markers such as Nkx3.1 recapitulating luminal gene expression features of untreated human prostate cancer. Single-cell and bulk RNA-sequencing showed stabilized ETV4 induced a novel luminal-derived expression cluster with signatures of the cell cycle, senescence, and epithelial to mesenchymal transition. These data suggest that ETS overexpression alone, at sufficient dosage, can initiate prostate neoplasia.

opencc-zeroMar 2023View details →

opencc-by-nc-sa-4.0Apr 2023View details →

MR to Ultrasound Registration for Prostate Challenge - Dataset

Multimodal image registration between pre-operative and intra-operative imaging enables the fusion of clinically important information during many surgical and interventional tasks. The registration of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images assists prostate biopsy and focal therapy, arguably having transformed prostate cancer patient care to a less invasive and more localized diagnostic, monitoring and treatment pathway. Though, even with great progress having been made by the community in the past two decades, challenges remain in this application. First, paired MRI and TRUS data from a sizable patient cohort are not routinely stored in clinical practice, and publicly-accessible data is scarce and low-quality. Second, annotating anatomical and pathological landmarks on both images - critical in representing corresponding locations for validation - requires expert domain knowledge and experience from multiple disciplines including urology, radiology and pathology. In addition to its prevalence-warranted clinical importance, this is also a unique application that saw a wide range of registration algorithms proposed and housed intriguing debates such as rigid-versus-nonrigid and FLE-versus-TRE. Both feature- and intensity-based classical methods and unsupervised or segmentation-driven learning methods have been innovated with some most technically interesting approaches in the field such as biomechanical regularisation and statistical motion modelling. The mu-Reg challenge aims to provide well-curated, yet real-world clinical data, with more than a hundred paired MR and TRUS images, annotated carefully by researchers and clinicians with more than 15 years of experience working with this application. The outcome of the challenge includes one of the first multimodal imaging data, facilitated with expert annotations for validation, for benchmarking advancement in registration methodology, as well as for future research in managing the most common non-skin cancer in men. --- The training and validation data may be used within the research remit of this challenge and in further research-related publications. The training and validation data are not to be used commercially. However, if the desired use is unclear, the organizers ask that those accessing the data refrain from further use or distribution outside of this challenge. --- Challenge information is accessible at: https://muregpro.github.io/

opencc-by-4.0Aug 2023View details →

Dataset on prognostic factors in patients with metastatic castration-resistant prostate cancer undergoing radioligand therapy with [177Lu]Lu-PSMA-617

This upload provides Open Data associated with the publication "Prognostic value of the De Ritis ratio for overall survival in patients with metastatic castration-resistant prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy" by Gaal S et al. (2023). The upload contains the anonymized dataset of 91 patients analyzed in this publication with all variables that are required to reproduce the results. However, to fully comply with requirements for data anonymization, the patients' age was categorized into groups spanning 5 years each. A dataset with the age variable in exact years can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request. Besides the dataset, this upload provides a dictionary to explain all variables and their categories.

closedIPD-NOFeb 2026View details →

A Couple Support Intervention for Prostate Cancer

ClinicalTrials.gov study NCT01842438. IPD Sharing: NO. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

68Ga-PSMA-R2 in Patients With Biochemical Relapse (BR) and Metastatic Prostate Cancer (mPCa)

ClinicalTrials.gov study NCT03490032. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

A Study of Rucaparib Versus Physician's Choice of Therapy in Participants With Metastatic Castration-resistant Prostate Cancer and Homologous Recombination Gene Deficiency

ClinicalTrials.gov study NCT02975934. IPD Sharing: YES. Countries: 12. Publications: 3.