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14,866 results for “cancer cell”
Association of Peripheral Monocytic-Myeloid-Derived Suppressor Cells with Molecular Subtypes in Single Center Endometrial Cancer Patients Receiving Carboplatin + Paclitaxel/Avelumab (MITO END-3 Trial)
<p><span>The MITO-END3 trial compared carboplatin and paclitaxel (</span><span>CP</span><span>) with avelumab plus carboplatin and paclitaxel (</span><span>CPA)</span><span> as first-line treatment in endometrial cancer (EC) patients and </span><span>demonstrated a significant interaction between avelumab response and mismatch repair status. To investigate prognostic/predictive biomarker, </span><span>twenty-nine MITO-END3-EC patients were evaluated at pre-treatment (B1) and at the end of CP/CPA treatment (B2) for </span><span>peripheral Myeloid derived suppressor cells (MDSC) and Tregs. </span><span>At B2, <span>effector</span> Tregs frequency was significantly higher in patients treated with CPA as compared to CP (p=0.038). Both treatments (CP/CPA) induced significant decrease in peripheral M-MDSC (-5.41%) in TCGA 2-MSI-High as compared to TCGA-category 4 tumors (p=0.004). In accordance, both treatments induced M-MDSCs (+5.34%) in MSS patients as compared to MSI-High patients (p=0.001). Moreover, in a subgroup of patients, </span><span>primary tumors were highly infiltrated by M-MDSCs in MSS as compared to MSI-high ECs. </span><span>A post hoc analysis displayed higher frequeny of M-MDSCs (p=0.020) and lower frequency of CD4+ (p<0.005) at pretreatment in EC patients as compared to healthy donors. </span><span>In conclusion, </span><span>the</span><span> peripheral evaluation of MDSCs and Tregs correlated with molecular features in EC treated with CP/CPA and may add insights in identifying EC patients responder to first line chemo/chemo-immunotherapy. </span></p>
tRNA-derived fragment tRF-Glu49 inhibits cell proliferation, migration and invasion in cervical cancer by targeting FGL1
<p>A transfer RNA (tRNA)-derived fragment was found to be a new possible biological marker and target in carcinoma therapy. However, the effect exerted by tRFs on cervical carcinoma is still unclear. We identify the potential tumor suppressor gene tRF-Glu49 in cervical carcinoma through tRF and ti-RNA microarray investigation. We then demonstrated that tRF-Glu49 showed downregulation within the cervical carcinoma tissue and was associated with less aggressive clinical features and a better prognosis. Phenotypic studies revealed that tRF-Glu49 inhibited cervical cell proliferation, migration, and invasion processes. Mechanistic investigation revealed that tRF-Glu49 directly regulated the oncogene, fibrinogen-like protein-1 (FGL1). In general, according to the result achieved in this study, tRF-Glu49 can modulate cervical cell proliferation, migration, and invasion processes through the target process for FGL1, and tRF-Glu49 is likely to be a possible prognostic biological marker in patients with cervical carcinoma.</p>
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>
Ecological interactions in breast cancer: Cell facilitation promotes growth and survival under drug pressure
<p>Spheroids of different composition (100% sensitive, 50% sensitive – 50% resistant, 100% resistant) were initiated from Venus-labeled CAMA-1 and mCherry-labeled CAMA-1_ribociclib_resistant cells and were subjected to 1 uM ribociclib treatment. After 11 days, speroids were harvested, washed and cell suspensions were viably frozen for single cell RNA-seq analysis. </p>
Data for for Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing
<p>Datasets accompanying the paper https://doi.org/10.1101/2021.10.18.464684</p>
Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization
<p>To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca).</p> <p>The following files are included:<br> 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered.<br> 2) int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables.<br> 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses.</p> <p>4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels.<br> <br> This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.</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>
RNA sequencing analysis of CAF treated by colon cancer cell-derived exosomes
<p>In this study, we analyzed differentially expressed genes by obtaining gene expression values through transcriptome sequencing of Homo sapiens, and performed functional classification and gene annotation for significant genes based on gene ontology and pathway information. After the pre-processed trimmed reads were mapped to a known reference genome using the HISAT2 program, transcript assembly was performed through the StringTie program. As a result, expression profile values were obtained for each sample for the known transcript, and read count, based on transcript/gene Fragment per Kilobase of transcript per Million mapped reads (FPKM), Transcripts per Kilobase (TPM) Million) values have been summarized. This value was subjected to DEG (Differentially Expressed Genes) analysis using edgeR for comparison combinations (HT-29_Exo-CAF vs. CTL_PBS-CAF, LoVo_Exo-CAF vs. CTL_PBS-CAF, and SW480_Exo-CAF vs. CTL_PBS-CAF), and genes that satisfies the condition |fc|>=2 & exactTest raw p-value<0.05 in at least one comparison combination Dogs were extracted. Transcriptome resequencing data was used to compare expression profiles between comparable samples. Gene Ontology Enrichment analysis was performed using the g:Profiler tool (https://biit.cs.ut.ee/gprofiler/) for a list of genes with significant expression level differences. GO_stat is the result of organizing the associated gene and test stat based on term_id. GO_genes is the result of arranging the associated term_id and DEG analysis result stat based on the gene.</p>
NanoString dataset for study: Dynamic changes in the NK-, Neutrophil-, and B-cell immunophenotypes relevant in high metastatic risk post neoadjuvant chemotherapy–resistant early breast cancers
<p>Pre-processed DSP and mRNA abundance datasets used in this study.</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>
CLEC-1 is a death sensor that limits antigen cross-presentation by dendritic cells and represents a target for cancer immunotherapy
<p>Tumors exploit numerous immune checkpoints including those deployed by myeloid cells to curtail anti-tumor immunity. Here, we show that the C-type lectin receptor CLEC-1 expressed by myeloid cells senses dead cells killed by programmed necrosis. Moreover, we identified TRIM21 as an endogenous ligand over-expressed in various cancers. Interestingly, we observed that in mice CLEC-1 blockade combined with chemotherapy to prolong survival in tumor models. Loss of CLEC-1 reduced the accumulation of immunosuppressive myeloid cells in tumors and invigorated the activation state of dendritic cells (DCs), thereby increasing T cell responses. Mechanistically, we found that the absence of CLEC-1 increased the cross-presentation of dead-cell associated antigens by conventional type-1 DCs. Importantly, we identified anti-human CLEC-1 antagonist antibodies able to enhance anti-tumor immunity in CLEC-1 humanized mice. Altogether, our results demonstrate that CLEC-1 acts as an immune checkpoint in myeloid cells and support CLEC-1 as a novel target for cancer immunotherapy.</p>
Proteomic and Metabolomic Profiling of Plasma Predict Immune-related Adverse Events in Older Patients with Advanced Non-small Cell Lung Cancer
Open the record for dataset details and reuse information.
Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"
<p>Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"</p>
Cytotoxic activity of betulinic acid and ursolic acid againts T47D breast cancer cells
<p><em><span>Uncaria nervosa</span></em><span> Elmer is an Indonesian herbal plant that is traditionally used for breast cancer. The results of phytochemical screening contained alkaloids, flavonoids, and terpenoids in the ethanol extract of this plant. Based on literature searches, reports regarding the bioactive compounds responsible for breast cancer have not been found. </span><span>This study aims to determine the metabolite profiling of ethanol extract, the isolation, characterization of bioactive compounds, and their bioactivity in T47D breast cancer cells. The ethanol extract of <em>Uncaria nervosa</em> Elmer leaves contains nine compounds consisting of alkaloids, terpenoids, and fatty acid. The bioactive compounds that were successfully isolated were betulinic acid, and ursolic acid, with IC50 values of ˃100 and 14,70±4,50 µg/ml, respectively.</span></p>
Distant metastases of breast cancer resemble primary tumors in tumor cell composition but differ in immune cell phenotypes
Open the record for dataset details and reuse information.
NSD3-Short Promotes Migration of A549 Lung Cancer Cells
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp025 Objective:</strong> In a previous experiment (exp024), we observed an increase in E-cadherin expression in response to knockdown of the short isoform of NSD3. To determine if this change is functionally relevant, we performed wound healing assays to measure any corresponding alteration in the migratory potential of A549 lung epithelial cancer cells.</p>
NSD3-Short Represses E-cadherin Expression in A549 Lung Epithelial Cancer Cells
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp024 Objective: </strong>In a previous experiment (exp023), we show that siRNA-mediated knockdown of NSD3 increases expression of E-cadherin, a marker of epithelial cell identity. To determine which isoform of NSD3 is involved in promoting epithelial to mesenchymal transition, I have designed siRNA that targets either the long or short isoform and again use E-cadherin expression as a marker. Additionally, I have started using A549 lung epithelial cancer cells (https://www.atcc.org/Products/All/CCL-185) as my primary model. This cell line proliferates faster and is more amenable to <em>in vitro</em> experimentation than the H1299 cell line I was using previously.</p>
Pan-cancer atlas of endothelial cells
<p>Datasets used to generate a pan-cancer atlas of endothelial cells and additional datasets generated with sorted endothelial cells used for validation.</p>
StarDist_BF_cancer_cell_dataset_20x
<p>This repository contains a StarDist deep learning model and its training and validation datasets designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 20x magnification. Using computational methods, the initial dataset of 20 manually annotated images was augmented to 160 paired images. The model was trained over 400 epochs and achieved an average F1 Score of 0.921, demonstrating high accuracy in cell segmentation tasks.</p> <h3>Specifications</h3> <ul> <li> <p>Model: StarDist for cancer cell segmentation on endothelial cells (20x magnification)</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Number of Original Images: 20 paired brightfield microscopy images and label masks</p> </li> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images with manually segmented masks</p> </li> <li> <p>File Format: TIFF (.tif)</p> </li> <ul> <li> <p>Brightfield Images: 16-bit</p> </li> <li> <p>Masks: 8-bit</p> </li> </ul> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 400</p> </li> <li> <p>Patch Size: 992 x 992 pixels</p> </li> <li> <p>Batch Size: 2</p> </li> </ul> <li> <p>Performance:</p> </li> <ul> <li> <p>Average F1 Score: 0.921</p> </li> <li> <p>Average IoU: 0.793</p> </li> </ul> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <p> </p> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
StarDist_BF_cancer_cell_dataset_10x
<p>This repository includes a StarDist deep learning model and its training dataset designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 10x magnification. The model was trained on 77 manually annotated images, with the dataset being computationally augmented during training by a factor of 8. The model was trained for 500 epochs and achieved an average F1 Score of 0.968, indicating high accuracy in segmenting cancer cells on endothelial cells.</p> <h3>Specifications</h3> <ul> <li> <p>Model: StarDist for cancer cell segmentation on endothelial cells (10x magnification)</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Number of Images: 77 paired brightfield microscopy images and label masks</p> </li> <li> <p>Augmented Dataset: Computational augmentation by a factor of 8 during training</p> </li> <li> <p>Microscope: Nikon Eclipse Ti2-E, 10x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images with manually segmented masks</p> </li> <li> <p>File Format: TIFF (.tif)</p> </li> <ul> <li> <p>Brightfield Images: 16-bit</p> </li> <li> <p>Masks: 8-bit or 16-bit</p> </li> </ul> <li> <p>Image Size: 1024 x 1022 pixels (pixel size: 1.3148 μm)</p> </li> </ul> <li> <p>Training Parameters:</p> </li> <ul> <li> <p>Epochs: 500</p> </li> <li> <p>Patch Size: 992 x 992 pixels</p> </li> <li> <p>Batch Size: 2</p> </li> </ul> <li> <p>Performance:</p> </li> <ul> <li> <p>Average F1 Score: 0.968</p> </li> <li> <p>Average IoU: 0.882</p> </li> </ul> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
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.