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21,150 results for “tumor”
Single-cell RNA-seq profiles of lung adenocarcinoma patients and tumor-bearing mice
<p>single-cell RNA sequencing (scRNA-seq) profiles from eight patients with lung adenocarcinoma (LUAD) and four samples of tumor tissues from tumor bearing mice were performed. By integrating other scRNA-seq data and clinical information, we identified activated adaptive immune responses in older patients, reflected by enriched dysfunctional T cell signature scores and immune checkpoint molecules. Our study shows increased efficacy of immune checkpoint blockade therapy in older patients, addressing the prominent role of age when considering immunotherapy.</p>
Cross-reactive CD8+ T cell responses to tumor-associated antigens (TAAs) and homologous microbiota-derived antigens (MoAs)
<p><strong><span>Background: </span></strong><span>We have recently shown extensive sequence and conformational homology between tumor-associated antigens (TAAs) and antigens derived from microorganisms (MoAs). The present study aimed to assess the breadth of T-cell recognition specific to MoAs and the corresponding TAAs in healthy subjects (HS) and patients with cancer (CP).</span></p> <p><strong><span>Method: </span></strong><span>A library of >100 peptide-MHC (pMHC) combinations was used to generate DNA-barcode labelled multimers. Homologous peptides were selected from the Cancer Antigenic Peptide Database, as well as Bacteroidetes/Firmicutes-derived peptides. They were incubated with CD8+ T cells from the peripheral blood of HLA-A*02:01 healthy individuals (n=10) and cancer patients (n=16). T cell recognition was identified using tetramer-staining analysis. Cytotoxicity assay was performed using as target cells TAP-deficient T2 cells loaded with MoA or the paired TuA.</span></p> <p><strong><span>Results: </span></strong><span>A total of 66 unique pMHC recognized by CD8+ T cells across all groups were identified. Of these, 21 epitopes from microbiota were identified as novel immunological targets. Reactivity against selected TAAs was observed for both HS and CP. pMHC tetramer staining confirmed CD8+ T cell populations cross-reacting with CTA SSX2 and paired microbiota epitopes. Moreover, PBMCs activated with the MoA where shown to release IFNγ as well as to exert cytotoxic activity against cells presenting the paired TuA.</span></p> <p><strong><span>Conclusions: </span></strong><span>Several predicted microbiota-derived MoAs are recognized by T cells in HS and CP. Reactivity against TAAs was observed also in HS, primed by the homologous bacterial antigens. CD8+ T cells cross-reacting with MAGE-A1 and paired microbiota epitopes were identified in three subjects. Therefore, the microbiota can elicit an extensive repertoire of natural memory T cells to TAAs, possibly able to control tumor growth (“natural anti-cancer vaccination”). In addition, non-self MoAs can be included in preventive/therapeutic off-the-shelf cancer vaccines with more potent anti-tumor efficacy than those based on TAAs.</span></p>
Comparison of spatial transcriptomics technologies used for tumor cryosections
<p>This repository contains data from a spatial transcriptomics (ST) analysis of brain tumor cryosections (medulloblastoma with extensive nodularity, MBEN). It is associated with the preprint by Rademacher, Huseynov, Bortolomeazzi et al. 2024, <em>bioRxiv</em>, <a href="https://doi.org/10.1101/2024.04.03.586404">https://doi.org/10.1101/2024.04.03.586404</a>, that has a full description of the work. In the study four imaging-based ST methods – RNAscope HiPlex, Molecular Cartography, MERFISH/Merscope, and Xenium – as well as sequencing-based ST (Visium) and single cell RNA sequencing of dissociated nuclei (snRNA-seq) were compared. The files provided here are described in readme.txt and include the transcript count matrices acquired on the Visium platform as well as Seurat objects of the data and analysis results for the comparison of the different technologies. The data for snRNA-seq, RNAscope HiPlex and Molecular Cartography included in the Seurat objects are based on the primary data acquired in a previous study (Ghasemi et al. 2024, Nat Commun, <a href="https://doi.org/10.1038/s41467-023-44117-x">https://doi.org/10.1038/s41467-023-44117-x</a>). </p>
Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Identification of intratumoral bacteria that enhance breast tumor metastasis
<p>This file contains the supporting datasets for the manuscript entitled Identification of intratumoral bacteria that enhance breast tumor metastasis, by Gerbec et al. File includes 8 supporting datasets as well as a legend file with the description of each individual dataset.</p>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
Novel Peptide-Based PET Probe for Non-invasive Imaging of C-X-C Chemokine Receptor Type 4 (CXCR4) in Tumors
<p>These are RAW data datasets of the following final paper</p> <p>Trotta, A.M., Aurilio, M., D'Alterio, C., Ieranò, C., Di Martino, D., Barbieri, A., Luciano, A., Gaballo, P., Santagata, S., Portella, L., Tomassi, S., Marinelli, L., Sementa, D., Novellino, E., Lastoria, S., Scala, S., Schottelius, M., Di Maro, S.</p> <p>Novel Peptide-Based PET Probe for Non-invasive Imaging of C-X-C Chemokine Receptor Type 4 (CXCR4) in Tumors, (2021) Journal of Medicinal Chemistry, 64 (6), pp. 3449-3461. ISSN 00222623</p> <p>https://doi.org/10.1021/acs.jmedchem.1c00066</p> <p>Abstract</p> <p>The recently reported CXCR4 antagonist 3 (Ac-Arg-Ala-[DCys-Arg-2Nal-His-Pen]-CO2H) was investigated as a molecular scaffold for a CXCR4-targeted positron emission tomography (PET) tracer. Toward this end, 3 was functionalized with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and 1,4,7-triazacyclononanetriacetic acid (NOTA). On the basis of convincing affinity data, both tracers, [68Ga]NOTA analogue ([68Ga]-5) and [68Ga]DOTA analogue ([68Ga]-4), were evaluated for PET imaging in “in vivo” models of CHO-hCXCR4 and Daudi lymphoma cells. PET imaging and biodistribution studies revealed higher CXCR4-specific tumor uptake and high tumor/background ratios for the [68Ga]NOTA analogue ([68Ga]-5) than for the [68Ga]DOTA analogue ([68Ga]-4) in both in vivo models. Moreover, [68Ga]-4 and [68Ga]-5 displayed rapid clearance and very low levels of accumulation in all nontarget tissues but the kidney. Although the high tumor/background ratios observed in the mouse xenograft model could partially derive from the hCXCR4 selectivity of [68Ga]-5, our results encourage its translation into a clinical context as a novel peptide-based tracer for imaging of CXCR4-overexpressing tumors.</p> <p> </p> <p> </p>
Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma
<p>Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma</p> <p> </p>
Tumor-Immune Microenvironment Revealed by Imaging Mass Cytometry in a Metastatic Sarcomatoid Urothelial Carcinoma with a Prolonged Response to Pembrolizumab - IMC data
<blockquote> <p>Sarcomatoid urothelial carcinoma (SUC) is a rare subtype of urothelial carcinoma (UC), that typically presents at an advanced stage compared to more common variants of UC. Locally advanced and metastatic UC have a poor long-term survival following progression on first-line platinum-based chemotherapy. Antibodies directed against the programmed cell death 1 protein (PD-1) or its ligand (PD-L1) are now approved to be used in these scenarios. The need for reliable biomarkers for treatment stratification is still under research. Here we present a novel case report of the first Image Mass Cytometry (IMC) analysis done in SUC to investigate the immune cell repertoire and PD-L1 expression in a patient who presented with metastatic SUC and experienced a prolonged response to the anti-PD1 immune checkpoint inhibitor pembrolizumab after progression on first line chemotherapy. This case report provides an important platform for translating these findings to a larger cohort of UC and UC variants.</p> </blockquote> <p>We make available TIFF files containing imaging mass cytometry data for 4 regions of interest of a sample of metastatic sarcomatoid urothelial carcinoma. The order of the axis in the image stacks is "CYX". The CSV files indicate the identity of the channels.</p>
Dataset for tumor infiltrating lymphocyte classification (304,097 image patches from TCGA)
<p>This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original images are from Abousamra et al. (2022) and Saltz et al. (2018), and the original whole slide images are from TCGA. This dataset is a subset of the data presented in Abousamra et al. (2022) (with new data partitions).</p> <p>If you use this dataset, please cite the following papers, as well as this Zenodo page.</p> <p>Abousamra, S., Gupta, M. D., Hou, L., Batiste, R., Zhao, T., Shankar, A., Rao, A., Chen, C., Samaras, D., Kurc, T., & Saltz, J. (2022). Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. <em>Frontiers in Oncology</em>, 5971. https://doi.org/10.3389/fonc.2021.806603</p> <p>Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., & Danilova, L. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. <em>Cell Reports</em>, <em>23</em>(1), 181-193.</p> <p> </p> <p>The acknowledgements from the <em>Frontiers in Oncology</em> and <em>Cell Reports</em> papers are included below:</p> <blockquote> <p>This work was supported by the National Institutes of Health (NIH) and National Cancer Institute (NCI) grants UH3-CA22502103, U24-CA21510904, 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 as well as generous private support from Bob Beals and Betsy Barton. AR and AS were partially supported by NCI grant R37-CA214955 (to AR), the University of Michigan (U-M) institutional research funds and also supported by ACS grant RSG-16-005-01 (to AR). AS was supported by the Biomedical Informatics & Data Science Training Grant (T32GM141746). This work was enabled by computational resources supported by National Science Foundation grant number ACI-1548562, providing access to the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center, and also a DOE INCITE award joint with the MENNDL team at the Oak Ridge National Laboratory, providing access to Summit high performance computing system. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.</p> </blockquote> <p> </p> <blockquote> <p>We are grateful to all the patients and families who contributed to this study. Funding from the Cancer Research Institute is gratefully acknowledged, as is support from National Cancer Institute (NCI) through U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845,U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672, U24CA180924, U24CA210950, U24CA215109, NCI Contract HHSN261201400007C, and Leidos Biomedical Contract 14X138. A.U.K.R. and P.S were supported by CCSG Bioinformatics Shared Resource P30 CA01667, ITCR U24 Supplement 1U24CA199461-01, a gift from Agilent technologies, CPRIT RP150578, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation XSEDE Science Gateways program under grant ACI-1548562 allocation TG-ASC130023. The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure and the Institute for Advanced Computational Science at Stony Brook University for access to the high-performance LIred and SeaWulf computing systems, the latter of which was supported by National Science Foundation grant (#1531492).</p> </blockquote> <p>------------------------------------</p> <p>This dataset includes 304,097 image patches. All images are 100 x 100 pixels at 0.5 micrometers per pixel. An image is TIL-positive if there are at least two TILs present.</p> <p>Refer to `images-tcga-tils-metadata.csv` for information about each image. That spreadsheet has the following columns:</p> <pre><code>partition,study,barcode,label,path,md5</code></pre> <p>Partition specifies which partition the image is part of (train, val, test). Study is the TCGA study the image is part of (e.g., acc for TCGA-ACC). Barcode is the TCGA participant barcode. This is used during partitioning, to ensure that images from the same participant are not present in different data partitions. Label is either til-negative or til-positive. An image is til-positive if there are at least two TILs in the image. Path is the path to the PNG image. All images are stored as PNG. Md5 is the md5 hash of the image. This can be used to ensure there are no duplicate images and to verify the integrity of images.</p> <p>There are study-specific directories in the directory `images-tcga-tils`, and there is a directory named `pancancer` that includes images from all the included TCGA studies. That directory uses symlinks to avoid storing duplicate data.</p> <p> </p>
MOLECULAR MIMICRY BETWEEN TUMOR ASSOCIATED ANTIGENS AND MICROBIOTA-DERIVED EPITOPES.
<p><strong>Background: </strong>The gut microbiota profile is unique for each individual and are composed by different bacteria species according to individual birth-to-infant transitions. In the last years, the local and systemic effects of microbiota on cancer onset, progression and response to treatments, such as immunotherapies, have been extensively described. Here we offer a new perspective, proposing a role for the microbiota based on the molecular mimicry of tumor-associated antigens by microbiome-associated antigens.</p> <p><strong>Methods:</strong> In the present study we looked for homology between published TAAs and non-self microbiota-derived epitopes. Blast search for sequence homology was combined with extensive bioinformatics analyses.</p> <p><strong>Results:</strong> Several evidences for homology between TAAs and microbiota-derived antigens have been found. Strikingly, three cases of 100% homology between the paired sequences has been identified. The predicted average affinity to HLA molecules of microbiota-derived antigens is very high (<100nM). The structural conformation of the microbiota-derived epitopes is, in general, highly similar to the corresponding TAA. In some cases, it is identical and contact areas with both HLA and TCR chains are indistinguishable. Moreover, the spatial conformation of TCR-facing residues can be identical in paired TAA and microbiota-derived epitopes, with exactly the same values of planar as well as dihedral angles.</p> <p><strong>Conclusions:</strong> The data reported in the present study show for the first time the high homology in the linear sequence as well as in structure and conformation between TAAs and peptides derived from microbiota species of the Firmicutes and the Bacteroidetes phyla, which together account for 90% of gut microbiota. Cross-reacting CD8<sup>+</sup> T cell responses are very likely induced. Therefore, the anti-microbiota T cell memory may turn out to be an anti-cancer T cell memory, able to control the growth of cancer developed during the lifetime if the expressed TAA is similar to the microbiota epitope. This may ultimately represent a relevant selective advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy.</p>
Data of FigS2, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS2, “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_FigS2.PNG). The corresponding raw data and subsequent data analysis obtained from proteomics analysis are provided as nine files in CSV format (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1-3_M1-3.csv). All further experiment related information provided as one meta-data-file (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1_M .txt) in txt format.</p>
Data of FigS4, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS4, “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_FigS4.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_6_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_6_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_1-3.csv).</p>
Data of Fig7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig7, “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_Fig7.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_1_M.txt), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf)</p> <p> </p>
Data of Fig8, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig8, “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_Fig8.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 .txt) 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_12_1_M1 .pdf)</p> <p> </p>
Data of Fig4, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig4, “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_Fig4.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_3_M_1.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_3_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_4_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_4_M_1.pdf).</p>
Data of Fig2, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig2, “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_Fig2.PNG). The corresponding raw data and subsequent data analysis obtained from proteomics analysis are provided as three files in CSV format (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1-3.csv). All further experiment related information provided as one meta-data-file (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1_M .txt) in txt format. 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_1_M_1 .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_1_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_2_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_2_M_1.pdf).</p>
Data of Fig5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig5, “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_Fig5.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_4_M_1.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_4_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_3.csv). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5_M .txt) and three files in CSV format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5-1-3.csv), all further experiment related information provided as three meta-data-files in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5_M_1-3.pdf).</p>
Data of Fig6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig6, “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_Fig6.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_5_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_5_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_1-3.csv).</p> <p> </p>
Data of Fig3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig3, “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_Fig3.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_2_M_1.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_2_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M_1.pdf).</p>
ScienceDex guides
Understand access before you commit
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.