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.
10,694
datasets available to search
ShareScore release 0.9.0
Dataset results
10,694 results for “carcinoma,”
Figure 4 from: Marinov L, Georgieva A, Toshkova R, Kostadinova I, Mangarov I, Toshkova-Yotova T, Nikolova I (2024) The effects of meloxicam, lornoxicam, ketoprofen, and dexketoprofen on human cervical, colorectal, and mammary carcinoma cell lines. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e113677
Figure 4 Effects of the NSAIDs meloxicam, lornoxicam, ketoprofen, and dexketoprofen on the migration capacity of the human tumor cell lines HeLa, HT-29, MCF-7.
Figure 3 from: Marinov L, Georgieva A, Toshkova R, Kostadinova I, Mangarov I, Toshkova-Yotova T, Nikolova I (2024) The effects of meloxicam, lornoxicam, ketoprofen, and dexketoprofen on human cervical, colorectal, and mammary carcinoma cell lines. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e113677
Figure 3 Fluorescence microscopy of human tumor cells HeLa, HT-29, and MCF-7 treated with the NSAIDs meloxicam, lornoxicam, ketoprofen, and dexketoprofen. Control (a–c); HeLa cells (a, d, g, j, m); HT-29 cells (b, e, h, k, n); MCF-7 cells (c, f, i, l, o); Meloxicam (d–f); Lornoxicam (g–i); Ketoprofen (j–l); Dexketoprofen (m–o); Lens 40X; DAPI staining.
Figure 1 from: Marinov L, Georgieva A, Toshkova R, Kostadinova I, Mangarov I, Toshkova-Yotova T, Nikolova I (2024) The effects of meloxicam, lornoxicam, ketoprofen, and dexketoprofen on human cervical, colorectal, and mammary carcinoma cell lines. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e113677
Figure 1 Effects of the NSAIDs meloxicam, lornoxicam, ketoprofen, and dexketoprofen on the cell viability and proliferation of the human tumor cell lines HeLa, HT-29, and MCF-7 and the nontumor cell line BALB/3T3.
Figure 2 from: Marinov L, Georgieva A, Toshkova R, Kostadinova I, Mangarov I, Toshkova-Yotova T, Nikolova I (2024) The effects of meloxicam, lornoxicam, ketoprofen, and dexketoprofen on human cervical, colorectal, and mammary carcinoma cell lines. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e113677
Figure 2 Fluorescence microscopy of human tumor cells HeLa, HT-29, and MCF-7 treated with the NSAIDs meloxicam, lornoxicam, ketoprofen, and dexketoprofen. Control (a–c); HeLa cells (a, d, g, j, m); HT-29 cells (b, e, h, k, n); MCF-7 cells (c, f, i, l, o); Meloxicam (d–f); Lornoxicam (g–i); Ketoprofen (j–l); Dexketoprofen (m–o); Lens 40X; AO/EtBr staining.
Characterization of the tumor-immune microenvironment in hepatocellular carcinoma patients undergoing immune checkpoint inhibitor therapy by highly multiplexed imaging mass cytometry
<p>Imaging mass cytometry data of 42 HCC patients that received immune checkpoint inhibtor therapy after tumor biopsy or resection. </p> <ul> <li>ICI_img_normalized: Preprocessed and normalized multistack .tiff images. Each stack represents one channel. Channel annotations are stored in the ICICohort_panel.csv file.</li> <li>ICI_cellmasks: Masks identifying individual cells on the images.</li> <li>ICI_stromamasks: Masks identifying stromal and parenchymal regions on the image.</li> <li>ICICohort_panel.csv: table containing channel information (metal tag and marker).</li> </ul> <p>Patient etadata may be found in the supplementary table 3 of DOI <a href="https://doi.org/10.1136/gutjnl-2024-332837" target="_blank" rel="noopener noreferrer">10.1136/gutjnl-2024-332837</a>.</p>
Quantitative assessment of the lipiodol staining after transarterial chemoembolization for hepatocellular carcinoma: can it predict tumor recurrence?
Open the record for dataset details and reuse information.
Figure 3 from: Rumahorbo CGP, Ilyas S, Hutahaean S, Fatimah Zuhra C (2024) Bischofia javanica and Phaleria macrocarpa nano herbal combination on blood and liver-kidney biochemistry in Oral Squamous Cell Carcinoma-induced rats. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e117398
Figure 3 The serum lipid profile. Panels a, b, c, and d represent statistically significant group differences. K0: Normal, K1: OSCC, P1: OSCC treated with nano herbal Bischofia javanica, P2: OSCC treated with nano herbal Phaleria macrocarpa, P3: OSCC treated with a combination of nano herbal Bischofia javanica and Phaleria macrocarpa, P4: OSCC treated with Vitamin C.
Figure 4 from: Rumahorbo CGP, Ilyas S, Hutahaean S, Fatimah Zuhra C (2024) Bischofia javanica and Phaleria macrocarpa nano herbal combination on blood and liver-kidney biochemistry in Oral Squamous Cell Carcinoma-induced rats. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e117398
Figure 4 Blood Biochemical Values for Assessing Liver, Kidney, and Pancreas Functions. Panels a, b, c, and d denote statistically significant group differences. K0: Normal, K1: OSCC, P1: OSCC treated with nano herbal Bischofia javanica, P2: OSCC treated with nano herbal Phaleria macrocarpa, P3: OSCC treated with a combination of nano herbal Bischofia javanica and Phaleria macrocarpa, P4: OSCC treated with Vitamin C.
Figure 2 from: Rumahorbo CGP, Ilyas S, Hutahaean S, Fatimah Zuhra C (2024) Bischofia javanica and Phaleria macrocarpa nano herbal combination on blood and liver-kidney biochemistry in Oral Squamous Cell Carcinoma-induced rats. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e117398
Figure 2 Hematological parameter values. a, b, c, d represent significant differences between groups. ns = p > 0.05/Not substantial. K0: Normal, K1: OSCC, P1: OSCC treated with nano herbal Bischofia javanica, P2: OSCC treated with nano herbal Phaleria macrocarpa, P3: OSCC treated with a combination of nano herbal Bischofia javanica and Phaleria macrocarpa, P4: OSCC treated with Vitamin C.
Figure 1 from: Rumahorbo CGP, Ilyas S, Hutahaean S, Fatimah Zuhra C (2024) Bischofia javanica and Phaleria macrocarpa nano herbal combination on blood and liver-kidney biochemistry in Oral Squamous Cell Carcinoma-induced rats. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e117398
Figure 1 Pap Smear results of the oral mucosa of OSCC-Induced Rats; A. Grade I from K0; B. Grade II from P1; C. Grade III from P2; D. Grade IV from P3, E. Grade V from K1, and F. Grade I from P4. K0: Normal, K1: OSCC, P1: OSCC treated with nano herbal Bischofia javanica, P2: OSCC treated with nano herbal Phaleria macrocarpa, P3: OSCC treated with a combination of nano herbal Bischofia javanica and Phaleria macrocarpa, P4: OSCC treated with Vitamin C.
Antioxidant and anti-inflammatory function of walnut green husk aqueous extract (WNGH-AE) on human hepatocellular carcinoma cells (HepG2) treated with t-BHP
Open the record for dataset details and reuse information.
External RNA-seq dataset for manuscript entitled "Downregulated dual-specificity protein phosphatase 1 in ovarian carcinoma: a comprehensive study with multiple methods"
<p>RNA-seq dataset of ovarian cancer from TCGA and normal ovary from GTEx database</p>
External microarray datasets for manuscript entitled "Downregulated dual-specificity protein phosphatase 1 in ovarian carcinoma: a comprehensive study with multiple methods"
<p>Microarrays related to ovarian carcinoma from GEO database</p>
An AAV Gene Therapy Computes Over Multiple Cellular Inputs to Enable Precise Targeting of Multifocal Hepatocellular Carcinoma in Mice
<p>Data underlying the figures in the publication “An AAV gene therapy computes over multiple cellular inputs to enable precise targeting of multifocal hepatocellular carcinoma in mice”, published in <em>Sci. Transl. Med.</em>, <strong>2021</strong>, 13, eabh4456.</p> <p><a href="https://doi.org/10.1126/scitranslmed.abh4456">DOI: 10.1126/scitranslmed.abh4456</a></p> <p>Table of contents:</p> <p><strong>1. Supplementary Material</strong>: File containing Materials and Methods, <em>Fig S1-S8</em>, <em>Table S1</em>, Legend for <em>Table S2</em>, Legends for data files <em>S1</em> and <em>S2</em> and References (63-65).</p> <p><strong>2. Dataset 1.xlsx</strong>: Raw data points used to create the main figures of the paper (<em>1C, 1D, 2B, 2C, 2E, 2G, 2H, 2I, 2K, 3A, 3B, 3C, 4B, 4C, 51, 6A, 7A, 7B, 8B, 8C </em>and<em> 8F</em>) arranged in worksheets panel by panel. The processing steps are described in the methods section of the manuscript and in the supplementary material.</p> <p><strong>3. Dataset 2.xlsx:</strong> Raw data points used to create the supplementary figures of the paper (<em>S1A, S1B, S1C, S3D, S3E, S4A, S4B, S5B, S8A, S8B </em>and <em>S8C</em>) arranged in worksheets panel by panel. The processing steps are described in the methods section of the manuscript and in the supplementary material.</p> <p><strong>4. Table S2</strong>: Experimental data for <em>Table S2.</em></p>
Identification and verification of potential biomarkers in esophageal squamous cell carcinoma and adenocarcinoma
<p>This zip file includes five pdf files.</p>
CircRNAs sequencing database of Gastric carcinoma cells (SGC-7901) and 5-fluorouracil-resistant cells (SGC-7901-5-fu)
<p>The circRNAs of two different gastric cancer cell lines were sequenced in this database. The sequencing results were compared and annotated with the database as analysis background data, and the screening conditions for differential expression of circRNA in the two cells line were defined as fold changes (FC) ≥ 2 and P <0.05.</p>
Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma
<p>Here, we performed an integrative analysis of scRNA-seq and scATAC-seq data from four ccRCC patients and aimed to identify the key regulatory molecules that mediate tumor development and manipulate the function of immune cells.</p>
Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment - Validation mIF
<p>mIF object from <em>spatialTIME</em> R package, calculating univariate Ripley's K on multiplex immunofluorescence images stained with FOXP3, CD8, CD68, ITGAV, COL4, VIM, SMA, and PCK. Data was processed with InForm and HALO. </p> <p>Clear cell renal cell carcinoma and papillary renal cell carcinoma were profiled before and after exposure to immunotherapy, with and without sarcomatoid features in clear cell tumors. Each tumor had a field of view in the stromal compartment and field of view in the tumor compartment.</p> <p>For appropriate clinical information associated with this study, please contact Dr. Brandon Manley.</p>
Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma
Open the record for dataset details and reuse information.
Data from: Analysis of head and neck carcinoma progression reveals novel and relevant stage-specific changes associated with immortalisation and malignancy
Head and neck squamous cell carcinoma (HNSCC) is a widely prevalent cancer globally with high mortality and morbidity. We report here changes in the genomic landscape in the development of these tumours from potentially premalignant lesions (PPOLS) to malignancy and lymph node metastases. Frequent likely pathological mutations are restricted to a relatively small set of genes including TP53, CDKN2A, FBXW7, FAT1, NOTCH1 and KMT2D; these arise early in tumour progression and are present in PPOLs with NOTCH1 mutations restricted to cell lines from lesions that subsequently progressed to HNSCC. The most frequent genetic changes are of consistent somatic copy number alterations (SCNA). The earliest SCNAs involved deletions of CSMD1 (8p23.2), FHIT (3p14.2) and CDKN2A (9p21.3) together with gains of chromosome 20. CSMD1 deletions or promoter hypermethylation were present in all of the immortal PPOLs and occurred at high frequency in the immortal HNSCC cell lines (promoter hypermethylation ~63%, hemizygous deletions ~75%, homozygous deletions ~18%). Forced expression of CSMD1 in the HNSCC cell line H103 showed significant suppression of proliferation (p=0.0053) and invasion in vitro (p=5.98X10-5) supporting a role for CSMD1 inactivation in early head and neck carcinogenesis. In addition, knockdown of CSMD1 in the CSMD1-expressing BICR16 cell line showed significant stimulation of invasion in vitro (p=1.82 x 10-5) but not cell proliferation (p=0.239). HNSCC with and without nodal metastases showed some clear differences including high copy number gains of CCND1, hsa-miR-548k and TP63 in the metastases group. GISTIC peak SCNA regions showed significant enrichment (adj P<0.01) of genes in multiple KEGG cancer pathways at all stages with disruption of an increasing number of these involved in the progression to lymph node metastases. Sixty-seven genes from regions with statistically significant differences in SCNA/LOH frequency between immortal PPOL and HNSCC cell lines showed correlation with expression including 5 known cancer drivers.
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.