Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14,866
datasets available to search
ShareScore release 0.9.0
Dataset results
14,866 results for “cancer cell”
Circulating myeloid-derived suppressor cells facilitate invasion of thyroid cancer cells by repressing miR-486-3p
<p>Background: Myeloid-derived suppressor cells (MDSCs) have become increasingly recognized as facilitators of tumor development. However, the role of MDSCs in papillary thyroid carcinoma (PTC) progression has not been clearly explored.</p> <p>Objective: We aimed to evaluate the levels and function of circulating MDSCs in PTC.</p> <p>Methods: The proportion of circulating polymorphonuclear (PMN)-MDSCs and mononuclearMDSCs from patients with PTC or benign thyroid nodules and healthy controls was measured using flow cytometry. For immunosuppressive activity analysis, sorted circulating MDSCs were cocultured with CD3/CD28-costimulated T lymphocytes and the proliferation of T cells was determined. PTC cell lines (TPC-1 and BC-PAP) were cocultured with PMN-MDSCs, and the effects on cell migration, invasion, proliferation, and apoptosis were evaluated. The differential expressed microribonucleic acids (RNAs) and messenger RNAs and their function were also explored in TPC-1 cells cocultured with or without PMN-MDSCs.</p> <p>Results: PMN-MDSCs were increased in peripheral blood mononuclear cells of patients with PTC. Circulating PMN-MDSCs displayed strong T cell suppressive activity. PTC cells demonstrated enhanced invasive capabilities in vitro and in vivo when cocultured with sorted PMN-MDSCs. PMN-MDSCs decreased expression of miR-486-3p and activated nuclear factor kappa B2 (NF-κB2), a direct target of miR-486-3p. Rescue of miR-486-3p diminished the cell migration and invasion induced by PMN-MDSCs.</p> <p>Conclusion: Collectively, our work indicates that circulating PMN-MDSCs promote PTC progression. By suppressing miR-486-3p, PMN-MDSCs promote the activity of the NF-κB2 signaling pathway, resulting in accelerated invasion of PTC cells, which may provide new therapeutic strategies for treatment of thyroid cancer. </p>
Raw single cell mass cytometry data from breast cancer patient derived xenografts
<p>Raw single cell mass cytometry data from breast cancer patient derived xenografts.</p> <p>Data is organised in an expressioset R object</p>
Data from: Cancer cell lines show high heritability for motility but not generation time
<p>Tumour evolution depends on heritable differences between cells in traits affecting cell survival or replication. It is well-established that cancer cells are genetically and phenotypically heterogeneous; however, the extent to which this phenotypic variation is heritable is far less well-explored. Here we estimate the broad-sense heritability (H2) of two cell traits related to cancer hallmarks - cell motility and generation time - within populations of four cancer cell lines in vitro, and find that motility is strongly heritable. This heritability is stable across multiple cell generations, with heritability values at the high end of those measured for a range of traits in natural populations of animals or plants. These findings confirm a central assumption of cancer evolution, provide a first quantification of the evolvability of key traits in cancer cells, and indicate that there is ample raw material for experimental evolution in cancer cell lines. Generation time, a trait directly affecting cell fitness, shows substantially lower values of heritability than cell speed, consistent with its having been under directional selection removing heritable variation.</p>
Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
Parthenolide induces ROS-dependent cell death in human gastric cancer cells
<p>Excel 1 - The original data-Parthenolide induces ROS-dependent cell death in human gastric cancer cellanalysis.</p><p>Supplemental Table 1 - Post-hoc Dunn's test for the effect of PN on the cell viability of MGC-803 cell.</p><p>Supplemental Table 2 - Post-hoc Dunn's test for the effect of PN on the cell colonies of MGC-803 cells.</p><p>Supplemental Table 3 - Post-hoc Dunn's test for the effect of PN and catalase on ROS Generation of MGC-803 cells</p><p>Supplemental Table 4 - Post-hoc Dunn's test for the effect of PN and catalase on cell viability of MGC-803 cells</p><p>Excel 2 - gene_sample_count from MGC-803 cells treated with DMSO compared with counterparts treated with PN </p>
Datasets for the study "A Pan-Cancer Single Cell Panorama of Human Natural Killer Cells"
<p>File "comb_CD56_CD16_NK.h5ad" contains processed expression data and raw count data used in most part of the paper and annotations such as "meta_tissue_in_paper" and "cellType".</p><p>File "comb_CD56_CD16_NK_blood.h5ad" contains processed expression data used in the analysis of circulating NK cells and annotations such as "meta_tissue" and "cellType".</p><p>File "Integrated_CD16_NK cells.h5ad" contains processed expression data of CD56dimCD16hi NK cells used in most part of the paper and annotations such as "meta_tissue_in_paper" and "cellType".</p><p>File "Integrated_CD56_NK cells.h5ad" contains processed expression data of CD56brightCD16lo NK cells used in most part of the paper and annotations such as "meta_tissue_in_paper" and "cellType".</p>
Supplementary PyMOL sessions for "Small protein blockers of human IL-6 receptor alpha inhibit proliferation and migration of cancer cells"
<p>PyMOL sessions with summary of NEF variants docking to IL-6R. Supplementary to "Small protein blockers of human IL-6 receptor alpha inhibit proliferation and migration of cancer cells".</p>
SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors
<p>Dataset of the paper: Valera, PS; Plou, J; García, I; Astobiza, I; Viera, C; M. Aransay, A; Martin, JE; Sasselli, IR; Carracedo, A; Liz-Marzán, LM. SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors. Proceedings of the National Academy of Sciences of the United States of America, 2023, 120 - 52, e2311674120, https://doi.org/10.1073/pnas.2311674120</p> <p> </p>
PP2A complex disruptor SET prompts widespread hyper-transcription of growth-essential genes in the pancreatic cancer cells
<p>Hyper-activation of the oncogenic transcription reflects the epigenetic plasticity of the cancer cells. SET was described as a nuclear factor that stimulated transcription from the chromatin template. However, the mechanisms of SET-dependent transcription are unknown. Here, we found that over-expression of SET and CDK9 induced very similar transcriptome signatures in multiple cancer cell lines. SET localized in the transcription start site (TSS)-proximal regions and supported the RNA transcription. SET specifically bound the PP2A-C subunit and induced PP2A-A subunit repulsion from the C subunit, which indicated the role of SET as a PP2A-A/C complex disruptor in the TSS-proximal regions. Through blocking PP2A activity, SET assisted CDK9 to maintain Pol II CTD phosphorylation and activated mRNA transcription. Our findings position SET as a key factor that modulates chromatin PP2A activity, promoting the oncogenic transcription in pancreatic cancer.</p>
An mRNA-encoded, long-lasting Interleukin-2 restores CD8+ T cell neoantigen immunity in MHC class I-deficient cancers
<ul> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_simplified_colorordered.RData </span></strong><span>contains all final result of the workflow (data integration and annotation) without subsetting the cell types into the different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_CD8Tcells_subsetted.RData </span></strong><span>contains combined dataset of all present CD8+ T cell types (final UMAP, all annotated), which are subsetted into the four treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_ProlifMacros_subsetted.RData </span></strong><span>contains only the proliferating CD8+ T cells, which are subsetted into the four different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_forJan_Macors_Gr1to4.RData </span></strong><span>contains only the combined macrophages of the dataset, subsetted into the four treatment groups.</span></li> </ul>
Whole Cancer Cell Vaccine Protects Ovarian Functions in Tumor-bearing Mice through Downregulating CXCL10
<p><span>Young female patients with cancer are likely to become sub-fertile or infertile even if they ultimately overcome cancer through various therapies. Cancer immunotherapy has recently emerged as a promising novel therapy against cancers with high malignancy and lethality, but it is unclear whether cancer immunotherapy affects female fertility. This study employed MCA205 cell-allotransplanted B6 mice as a model to investigate whether two popular immunotherapies—PD-1 monoclonal antibody (PD-1) therapy and whole cancer cell vaccine (WCV) therapy—affect ovarian function. MCA205 allotransplanted (M) mice exhibited decreased follicle numbers at each stage, decreased proliferation, increased apoptosis, and a decreased oocyte maturation rate. WCV treatment significantly reversed these abnormalities, whereas PD-1 did not. RNA sequencing of the ovaries revealed that multiple differentially expressed genes (DEGs) were involved in inflammation pathways. Furthermore, cytokine microarray characterized CXCL10 with both biggest increment in M group and best rescue in WCV group. Next, CXCL10 antibody Immunoprecipitation in ovarian lysate and LC-MS baited the only receptor IL18R1. Furthermore, we found that CXCL10 impaired ovarian function through three pathways: inducing ovarian fibrosis through CXCL10→IL18R1→p-JNK→COL1A1, promoting primordial follicle overactivation through CXCL10→IL18R1→p-AKT, and increasing ovarian inflammation through CXCL10→IL18R1→p-P65. Finally, we rescued the decreased ovarian function in the M group by blocking the CXCL10→IL18R1 pathway with CXCL10 antibody or a CXCL10–IL18R1 interface peptide, CIBB. This study provides mechanical evidence and translational strategies for WCVs to achieve the dual functions of suppressing tumor progression while protecting ovarian function.</span></p> <p><strong><span>Supplementary dataset 1</span></strong></p> <p><span>Related to Fig. 3A and 3B. This file includes two sheets. “All_samples-fpkm” includes FPKM values of all identified genes in ovaries from CTR, M, VM, and PM group (three repeats per group); “heat map” includes all average log2 values (from three repeats per group) of 435 DEGs (</span><span>differentially expressed genes</span><span>) </span><span>at the threshold of |log2(M/CTR)| ≥ 1.</span></p> <p><strong><span>Supplementary dataset 2</span></strong></p> <p><span>Related to Fig. 3C. This file includes 33 sheets. “summary” includes all conc. values (pg/ml) of 31 plasma DECs (</span><span>differentially expressed cytokines</span><span>); “Curves” includes standard curves for all 31 cytokines; each of the other 31 sheets include the detailed assays for each plasma cytokine for CTR, M, VM, and PM group (five repeats per group).</span></p> <p><strong><span>Supplementary dataset 3</span></strong></p> <p><span>Related to Fig. 4A. This file includes two sheets. “peptides” includes all related values of all identified peptides; “Protein Groups” includes the related info. Of all proteins corresponding to the identified peptides</span></p>
Code and Data for "Genome-wide repeat landscapes in cancer and cell-free DNA" (Annapragada et.al., Science Translational Medicine, 2024)
<h1><strong>Code and Data for "Genome-wide repeat landscapes in cancer and cell-free DNA"</strong></h1> <div> <div> <p>Citation: <br>Annapragada, A.V. Niknafs, N. White, J.R. Bruhm, D.C., Cherry, C., Medina, J.E., Adleff, V., Hruban C., Mathios, D., Foda, Z.H., Phallen, J., Scharpf, R.B., Velculescu, V.E. Genome-wide repeat landscapes in cancer and cell-free DNA. <em>Science Translational Medicine</em>. 2024.</p> <p><a>The code to run the ARTEMIS pipeline and reproduce manuscript figures is publicly available at </a><a href="https://github.com/cancer-genomics/artemis2024">https://github.com/cancer-genomics/artemis2024</a></p> <p>This code also depends on c<a>ode that generates DELFI features for fragmentation-based analysis, which may be found at </a><a href="https://github.com/cancer-genomics/reproduce_lucas_wflow">https://github.com/cancer-genomics/reproduce_lucas_wflow</a> </p> <p>These github repositories have also been archived in this Zenodo as they were on 02/06/2024 (DELFI fragmentation) and 03/11/2024 (ARTEMIS).</p> </div> </div>
Fig. 4 in Undescribed isoquinolines from Zanthoxylum nitidum and their antiproliferative effects against human cancer cell lines
Fig. 4. Chiral HPLC-UV and chiral HPLC-CD profiles of 1–4, experimental ECD spectra and the calculated ECD spectra (1–4), respectively.
Fig. 5 in Undescribed isoquinolines from Zanthoxylum nitidum and their antiproliferative effects against human cancer cell lines
Fig. 5. Effects of compound 2b on the cell viability, colony formation and cell cycle distribution in the A549 cells. (A) Cells were treated with compound 2b (0.05, 0.1, or 0.2 μM) for 48 h, and then morphological changes were detected by phase-contrast microscopy (200 × magnification). (B) Cells were treated with 2b (0.05, 0.1, or 0.2 μM) for 14 days. Colony formation was subsequently observed under light microscopy (40 × magnification). (C) Cells were treated with 2b for 24 h, and then the cell cycle distribution was analyzed by flow cytometry. (D) Cells were treated with compound 2b for 24 h, and then the protein expressions of Cyclin A, Cyclin B1, Cdc25C and p21 were detected by western blot analysis. Relative intensity of indicated proteins was quantified using NIH ImageJ software (bottom panel).
Fig. 7 in Undescribed isoquinolines from Zanthoxylum nitidum and their antiproliferative effects against human cancer cell lines
Fig. 7. Effects of compound 2b on the Wnt/β-catenin signaling pathways in the A549 cells. Cells were treated with compound 2b for 24 h, and then the indicated protein expressions were detected using western blot analysis. Relative intensity of indicated proteins was quantified using NIH ImageJ software (bar graph).
Fig. 6 in Undescribed isoquinolines from Zanthoxylum nitidum and their antiproliferative effects against human cancer cell lines
Fig. 6. Effects of 2b on the induction of apoptosis, cell migration, and expressions of EMT biomarker proteins in the A549 cells. (A) Cells were seeded, treated with 2b for 48 h, collected, and stained with annexin V-FITC/PI to detect the apoptotic cell population using flow cytometry. The percentages of each cell population (live cell, early apoptosis, late apoptosis, and necrosis) were quantified and presented as a bar graph. (B) Cells were treated with compound 2b for 48 h, the expressions of caspases and Bcl-2 were analyzed by western blotting. Relative intensity of indicated proteins was quantified using NIH ImageJ software (bar graph, bottom panel). (C) Cells were treated with 2b for 24 h or 48 h, and then the cell migration was detected by wound healing assay and photographed under microscope (40 × magnification). (D) Cells were treated with 2b for 24 h, and then the expressions of EMT biomarkers were analyzed by western blotting. Relative intensity of indicated proteins was quantified using NIH ImageJ software (bar graph, bottom panel).
A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture (IMC data)
<p>This repository contains all <strong>raw imaging mass cytometry (IMC) data</strong> for the breast cancer study from Meyer et al., 2025. The code that was used to process and analyze the data is available at <a href="https://github.com/BodenmillerGroup/TNBC_publication">https://github.com/BodenmillerGroup/TNBC_publication</a>. </p> <p><strong>Structure:</strong><br>ZTMA174_raw.zip - Contains raw IMC data for ZTMA 174.</p> <p>ZTMA249_raw.zip - Contains raw IMC data for ZTMA 249.</p>
Single Cell RNAseq Pancreatic Cancer Atlas
<p>Complete object for scRNAseq PDAC atlas.</p> <p> </p> <p>Please cite: Loveless IM, Kemp SB, Hartway KM, Mitchell JT, Wu Y, Zwernik SD, Salas-Escabillas DJ, Brender S, George M, Makinwa Y, Stockdale T, Gartrelle K, Reddy RG, Long DW, Wombwell A, Clark JM, Levin AM, Kwon D, Huang L, Francescone R, Vendramini-Costa DB, Stanger B, Alessio A, Waters AM, Cui Y, Fertig EJ, Kagohara LT, Theisen B, Crawford HC, Steele NG. Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells. Clin Cancer Res. 2024 Dec 5. doi: 10.1158/1078-0432.CCR-24-2183. Epub ahead of print. PMID: 39636224.. </p>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
The single-cell spatial landscape of stage III colorectal cancers
<p>We added H&E images with pathologist annotations in this version (v2). All other files are available in version 1. </p> <p> </p> <p>The Seurat and Scanpy objects for single-cell data, along with a CSV file containing coordinates and cell types for all single cells, have been deposited. Additionally, the original IMC image data has also been uploaded.</p> <p>The Seurat object is: <em>20220215_COAD_cancer_control_umapped_annotated<strong>.rds</strong></em></p> <p>The Scanpy object is: <em>20220215_COAD_cancer_control_umapped_annotated.<strong>h5ad</strong></em></p> <p> </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.