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2,248 results for “squamous cell carcinoma”
Nab-Paclitaxel and Cisplatin or Nab-paclitaxel as Induction Therapy for Locally Advanced Squamous Cell Carcinoma of the Head and Neck (HNSCC)
ClinicalTrials.gov study NCT02573493. IPD Sharing: NO. Countries: 1. Publications: 1.
Cilengitide in Recurrent and/or Metastatic Squamous Cell Carcinoma of the Head and Neck (SCCHN)
ClinicalTrials.gov study NCT00705016. IPD Sharing: Not stated. Countries: 9. Publications: 1.
Erbitux Combined With Chemo-radiotherapy in Esophageal Squamous Cell Carcinoma
ClinicalTrials.gov study NCT00815308. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Dose-finding Study of Metformin With Chemoradiation in Locally Advanced Head and Neck Squamous Cell Carcinoma
ClinicalTrials.gov study NCT02325401. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Defining metabolic flexibility in hair follicle stem cell induced squamous cell carcinoma
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Hormone receptors AR, ER, PR and growth factor receptor Her-2 expression in oral squamous cell carcinoma: Correlation with overall survival, disease-free survival and 10-year survival in a high-risk population
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Opportunities for targeted therapies: trametinib as a therapeutic approach to canine oral squamous cell carcinomas
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Raw IMC files for Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.
<p>Raw MCD files for the Stanford cohort of oral squamous cell carcinoma patients in this publication:</p> <p>Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data (DOI:<span> <a href="https://doi.org/10.1016/j.isci.2023.108486" target="_blank" rel="noopener">10.1016/j.isci.2023.108486</a>).</span></p>
Radiomics metrics combined with clinical data in the surgical management of early-stage (cT1-T2 N0) of tongue squamous cell carcinomas: a preliminary study
<p>We uploaded the clinical and the hematological parameters of enrolled patients in the study "Radiomics metrics combined with clinical data in the surgical management of early-stage (cT1-T2 N0) of tongue squamous cell carcinomas: a preliminary study" accepted on Biology journal.</p> <p>Clinical and hematological parameters include: age; gender; DOI, NLR; PLR; LMR; SIRI; SII; T stage; grading; metastatic lymph nodes; perineural infiltration; vascular infiltration.</p> <p> </p>
CLIC1 plasma concentration is associated with lymph node metastases in oral squamous cell carcinoma - Supplementary Material (Statistical Analysis)
<p>Supplementary file containing detailed statistical analysis of results of the article <em>CLIC1 plasma concentration is associated with lymph node metastases in oral squamous cell carcinoma.</em></p>
A Riskscore Model for Predicting Survival, Tumor Microenvironment, Immunotherapy and drug sensitivity of Lung Squamous Cell Carcinoma Based on PI3K/AKT/MTOR Pathway-Related Genes
<p>Firstly, the data we provide is the raw data downloaded from the TCGA database. Secondly, we provide the following explanations for the raw data, taking Figure 1 as an example:</p> <p>First, we selected a LUSC dataset from the TCGA database that includes RNA-seq data (FPKM values) and clear clinical information, consisting of 51 normal samples and 501 LUSC samples. The raw data <span>can be found in</span> the "mRNA" document in the "Fig.1" file<span>, and t</span>he data includes TCGA <span>id</span> information.</p> <p>Next, the data was imported into "mRNA_edgeR" to analyze whether the RNA-seq data from these 552 samples meet the criteria of |logFC| > 0.585 and FDR < 0.05, <span>and the results are shown in</span> the "diffSig" table. Subsequently, the genes in the "diffSig" table were intersected with 105 PAGs, <span>and</span> 44 key PAGs <span>were identified, </span>as shown in Figure 1.</p> <p>The <span>other</span> figures can <span>also </span>be reproduced sequentially based on the methods described <span>above</span>.</p> <p>Furthermore, due to the limited number of LUSC patients, the clinical information in the database is relatively incomplete. Hence, the clinical information that we provide constitutes the entire content available in the database.</p>
Validation and Test Datasets for "High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma"
<p>This deposition contains the validation and test dataset for our study "High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma".</p> <p>The training dataset for this study can be found at the following DOI: [<strong>10.5281/zenodo.12636426</strong>].<br><br></p>
Genetic alterations and expression programs of oral squamous cell carcinoma associated with betel quid chewing
<p><span>Betel quid (BQ) chewing is a profound risk for</span><span> oral squamous cell carcinoma (OSCC) <span>in Southeast Asia.</span> To decipher contributory genomic abnormalities and transcriptional reprogramming in these malignancies, we conducted a multi-omics survey, including exome sequencing of tumor-normal pairs from <span>261 male </span>patients with OSCC (129 habitual BQ chewers and 132 non-BQ users), alone with integrated single-cell and spatial transcriptomics of a set of tumors. Comparative analyses of the mutational catalog identified enrichment of significantly altered genes (e.g mutations of <em>TP53</em> and <em>CHUK</em>, copy gains of <em>MAP3K13</em> and <em>FADD</em>, copy losses of <em>CDKN2A</em>) and mutational signatures associated with BQ chewing. Assessment of oncogenic and co-occurring actionable alterations demonstrated frequently altered oncogenic pathways (Hippo and p53 signaling) and potential combination therapy opportunities linked to BQ use. In addition, evaluation of epithelial, immune, stromal expression programs in the corresponding tissue compartments revealed a shift of tumor microenvironment in BQ-related OSCC, characterized by induced hypoxia of tumor epithelium, altered immunosuppression of dendritic cells, and raised sprouting angiogenesis of tumor endothelium. Quantitative predictions of intercellular communications inferred a more heterogeneous cell-cell crosstalk among BQ-related OSCC, highlighted by extensive interactions of fibroblasts and dendritic cells with other non-epithelial cell types via mostly extracellular matrix-receptor signaling pathways. Collectively, these differences in genomic landscape and tumor niche suggest that OSCC caused by BQ chewing could be an etiological subtype different from their BQ-negative counterparts.</span></p>
Single cell RNA-seq data from: Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma
<p>Seurat object for single cell RNA-seq of 10 head and neck squamous cell carcinoma patients, epithelial cells only. From "Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma ". <span><span>Two APOBEC (apolipoprotein-B mRNA editing enzyme catalytic polypeptide-like) </span><span>DNA </span><span>cytosine deaminase enzymes (APOBEC3A and APOBEC3B) generate somatic mutations in cancer, driving tumour development and drug resistance. Here we used single cell RNA sequencing to study </span></span><span><span>APOBEC3A</span></span><span><span> and </span></span><span><span>AP</span><span>OB</span><span>EC3B</span></span><span><span> expression in healthy and malignant mucosal epithelia, </span><span>validating</span> <span>key</span><span> observations </span><span>with</span><span> immunohistochemistry, spatial </span><span>transcriptomics</span><span> and functional experiments. Wh</span><span>ereas</span> </span><span><span>APOBEC3B</span></span><span><span> is expressed in keratinocytes entering mitosis, we show that </span></span><span><span>APOBEC3A</span></span><span><span> expression is confined</span> <span>largely</span><span> to</span><span> terminally differentiating cells</span><span> and </span><span>requires </span><span>Grainyhead</span><span>-like transcription factor 3 (GRHL3). T</span><span>hus</span><span>, in normal tissue,</span><span> neither </span><span>deaminase</span> <span>appears to be</span><span> expressed at </span><span>high levels</span><span> during DNA replication, the c</span><span>ell cycle stage</span> <span>associated with</span><span> APOBEC-mediated mutagenesis. </span><span>In</span><span> contrast, we show that in squamous cell carcinoma, there is expansion of </span></span><span><span>GRHL3</span></span><span> <span>expression and </span><span>activity to a subset of cells undergoing DNA replication and concomitant extension of </span></span><span><span>APOBEC3A</span></span><span><span> expression to proliferating cells. </span></span><span><span>These findings </span><span>suggest</span><span> that</span> <span>APOBEC3A</span><span> may play a functional role during keratinocyte differentiation</span><span> and offer</span> </span><span><span>a mechanism for acquisition of APOBEC3A mutagenic activity in tumour</span><span>s</span><span>.</span></span><span> </span></p>
Utilizing DNA pooling to predict cancer eye, ocular squamous cell carcinoma, in Hereford cattle
<p>Files include genotypes and dye intensity data from BovineHD bead array from a small closed population of Hereford cattle. 42 animals are individually genotyped and 10 pools of 50 animals are genotyped. Random regression of individual dye intensity data were regressed on pool data to estimate genetic connections between animals and pools. Covariances among animals for pool contributions were also estimated.</p>
Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma
<p>Data supporting the findings of the paper "<a href="https://doi.org/10.1038/s41698-022-00253-z">Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma</a>." Files include output of multiplex immunohistochemistry computational image processing workflow for each tumor region and survival data for each patient. The code used to produce the results of this study is available at: <a href="https://github.com/kblise/HNSCC_mIHC_paper">https://github.com/kblise/HNSCC_mIHC_paper</a>.</p> <p>Notes about data files:</p> <ul> <li>clinicalData.csv = Contains the following columns for each tumor region: <ul> <li>sample = tumor region ID</li> <li>dtr = Progression free survival (days to recurrence)</li> <li>tnm = TNM stage</li> <li>anatomy = anatomic site of resection</li> <li>tx = therapy administered</li> <li>area = area in mm<sup>2</sup> of tissue region</li> </ul> </li> <li>pt .csv files = Matrix of single cells (rows) and marker expression (columns). One file per tumor region (n=47). Other columns include: <ul> <li>class = Cell phenotype assigned via hierarchical gating strategy (see below for classes)</li> <li>Location_Center_X and Location_Center_Y = Cartesian coordinates of cell center</li> <li>Cellsp_PD1p = PD-1 expression; 1 = PD-1<sup>+</sup>, 0 = PD-1<sup>-</sup></li> <li>Cellsp_PDL1p = PD-L1 expression; 1 = PD-L1<sup>+</sup>, 0 = PD-L1<sup>-</sup></li> <li>Cellsp_KI67p = Ki-67 expression; 1 = Ki-67<sup>+</sup>, 0 = Ki-67<sup>-</sup></li> </ul> </li> </ul> <p>Classes, corresponding cell phenotype, and gating strategy used:</p> <ul> <li>A = CD8<sup>+</sup> T Cell (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>+</sup> CD8<sup>+</sup>)</li> <li>B = CD4<sup>+</sup> T Helper (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>+</sup> CD8<sup>-</sup> FOXP3<sup>-</sup>)</li> <li>C = B Cell (CD45<sup>+</sup> CD20<sup>+</sup>)</li> <li>D = Macrophage (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>-</sup> CD68<sup>+</sup>)</li> <li>E = Other Immune (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>-</sup> CD68<sup>-</sup> MHCII<sup>- </sup>CD8<sup>-</sup> FOXP3<sup>-</sup>)</li> <li>F = Other Non-Immune (CD45<sup>-</sup> PANCK<sup>- </sup>αSMA<sup>-</sup>) - excluded from analysis</li> <li>G = Noise - excluded from analysis</li> <li>H = Neoplastic Tumor (CD45<sup>-</sup> PANCK<sup>+</sup>)</li> <li>J = Granulocyte (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>+</sup>)</li> <li>K = CD4<sup>+</sup> Regulatory T Cell (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>+</sup> CD8<sup>-</sup> FOXP3<sup>+</sup>)</li> <li>N = αSMA<sup>+</sup> Mesenchymal (CD45<sup>-</sup> PANCK<sup>- </sup>αSMA<sup>+</sup>)</li> <li>X = Antigen Presenting Cell (CD45<sup>+</sup> CD20<sup>-</sup> CD3<sup>-</sup> CD66B<sup>-</sup> CD68<sup>-</sup> MHCII<sup>+</sup>)</li> </ul>
Master chart- Oral Squamous Cell Carcinoma cases
<p>Details of OSCC patients demographic data , clinical and histopathological diagnosis</p>
Immune Checkpoint Inhibitor, Nivolumab, Combined with Chemotherapy Improved the Survival of Unresectable Ad-vanced and Metastatic Esophageal Squamous Cell Carcinoma: a real world experience
<p><strong>Figure S1 the detail of treatments.</strong> Blue arrow means this patient was still alive at the latest date of follow up. Hollow circle means this patient die. The others were lose follow up at the latest date of follow up.</p> <p><strong>Figure S2 progression free survival (PFS) of patients received immunotherapy, including 5 nivolumab and chemotherapy and 1 dual immune check point inhibitor, on different PD-L1 tumor cells (TC) expression.</strong> (A) divided by PD-L1 TC <1% or ≧1%. (B)divided by PD-L1 TC <10% or ≧10%.</p>
A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma.
<p>Raw Data for PeerJ</p>
Salivary chemical barrier proteins in Oral Squamous Cell Carcinoma – Alterations in the defense mechanism of the oral cavity
<p>Oral squamous cell carcinoma (OSCC) is one of the most frequent type of head and neck cancers. Despite the genetic and environmental risk factors, OSCC is also associated with microbial infections and/or dysbiosis. The secreted saliva serves as the chemical barrier of the oral cavity and since OSCC can alter the protein composition of saliva, our aim was to analyze the effect of OSCC on the salivary chemical barrier proteins. Publicly available datasets regarding the analysis of salivary proteins from patients with OSCC and controls were collected and examined in order to identify differentially expressed chemical barrier proteins. The network analysis and gene onthology (GO) classification of the differentially expressed chemical barrier proteins were performed, as well. 127 proteins showing different expression pattern between the OSCC and control groups were found. The protein-protein interaction network of up- and down-regulated proteins were constructed and analyzed. The main hub proteins (IL-6, IL-1B, IL-8, TNF, APOA1, APOA2, APOB, APOC3, APOE, and HP) were identified and the enriched GO terms were examined. Our study highlighted the importance of the chemical barrier of saliva in the development of OSCC.</p>
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.