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8,187 results for “Lung cancer”
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>
Supplementary tables for the paper: "Comprehensive computational analysis via Adverse Outcome Pathways and Aggregate Exposure Pathways in exploring synergistic effects from radon and tobacco smoke on lung cancer."
<p><strong>Authors</strong>:<br>Thomas Jaylet, Vinita Chauhan, Laura Mezquita, <em>Nadia Boroumand</em><em>, </em>Olivier Laurent, <em>Karine Elihn</em><em>, Lovisa Lundholm</em><em>, </em>Olivier Armant, Karine Audouze</p>
Immunotherapy-mediated thyroid dysfunction: genetic risk and impact on outcomes with PD-1 blockade in non-small cell lung cancer
<p>Polygenic risk score weights derived using LDpred for hypothyroidism and thyroid medication use.</p>
CarcSeq measurement of lung cancer driver mutations predicts mouse strain- and sex-related incidence of spontaneous lung neoplasia
<p>The data contained herein are the fastq files collected for individual mouse lung DNA samples. Ten samples each of male and female, B6C3F1 and CD-1 mouse lung DNA were analyzed using the CarcSeq method for error-corrected next-generation sequencing as previously described [Harris, KL, Walia, V, Gong, B, et al. Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq. <i>Environ Mol Mutagen</i>. 2020; 61: 872– 889. <a href="https://doi.org/10.1002/em.22409">https://doi.org/10.1002/em.22409</a> and Karen L McKim, Meagan B Myers, Kelly L Harris, Binsheng Gong, Joshua Xu, Barbara L Parsons, CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia, <em>Toxicological Sciences</em>, 2021;, kfab040, <a href="https://doi.org/10.1093/toxsci/kfab040">https://doi.org/10.1093/toxsci/kfab040</a>]. A readme text file provides the key to which sample numbers correspond with which mouse strain and sex. All the fastq files within a given folder were used to construct single strand consensus sequences, which were then subject to downstream analyses as reported in the associated publication.</p>
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade
<p>Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949–0.978, <em>P</em> < 0.001; AUC =0.965, 95% CI: 0.940–0.989, <em>P</em> < 0.001; AUC = 0.959, 95% CI: 0.942–0.976, <em>P</em> < 0.001, respectively). Patients with CNN-high had longer progression-free survival (PFS) and overall survival (OS) than patients with CNN-low in the three cohorts. Subgroup analysis confirmed the efficient predictive ability of CNN. Combining three cML methods (CNN, SVM, and RF) yielded a robust comprehensive nomogram for predicting PFS and OS in the three cohorts (each <em>P</em> < 0.001). The proposed deep-learning method based on mutational genes revealed the potential value of clinical benefit prediction in patients with NSCLC and provides novel insights for combined machine learning in PD-1/PD-L1 blockade.</p>
CONCORDANCE OF BRONCHOSCOPIC AND RADIOLOGICAL FINDINGS IN SUSPECTED LUNG CANCER AND ITS OUTCOME- A CROSS SECTIONAL STUDY IN A TERTIARY CARE HOSPITAL
<p>Background</p> <p>Bronchoscopy and CT thorax are the most common investigations utilized to screen and diagnose lung cancer. Their individual utility in diagnosing lung cancer has been described affirmatively in existing literature. Studies correlating the airway characteristics of the two modalities in lung cancer are few.</p> <p>Objectives</p> <p>To analyze and characterize lung lesions bronchoscopically and correlate the same radiologically by CT of the thorax and thus assess its positive predictive value.</p> <p>Methods</p> <p>56 consecutive adults who presented to Respiratory Medicine Department at a tertiary care hospital in South India from November 2018 to June 2020, having Clinico-radiological suspicion of malignancy and fulfilling study criteria were recruited. They were subjected to CECT of thorax and bronchoscopy. All bronchoscopic procedures were performed using a protocolized number of passes for biopsy. The baseline demographic and clinical data, findings of bronchoscopy and CT and biopsy reports were recorded. Cohens kappa coefficient was used to estimate the agreement of findings on the two modalities. Statistical analysis was performed using SPSS software.</p> <p>Results</p> <p>Bronchoscopy was normal in 22.14 % of cases, the corresponding CTs also revealed normal airway. Bronchoscopy revealed airway lesions in 78.6 % cases; the corresponding CT revealed airway abnormalities in only 46.41 % of cases, among these 52.1 % of cases revealed an exophytic growth. There was fair strength of agreement for the two modalities in the detection of airway lesions of lung cancer. [k= 0.38, p = <0.001]. CECT thorax has a negative predictive value of 44 %, a sensitivity of 59.1 % and specificity of 100 % at 95 % CI in the detection of airway lesions.</p> <p>Conclusion</p> <p>CT is not definitive in the evaluation of lung cancer, especially in those with central disease. Combination of the two modalities improves the diagnostic outcome in patients with lung cancer.</p> <p>Keywords- Thoracic Oncology, Bronchogenic Carcinoma, Adenocarcinoma of lung, Bronchoscopy, Diagnostic Imaging, Multidetector Computed Tomography.</p>
Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients
<p>This dataset contains the processed scRNA-seq data and code used to investigate the association between lung cancers and COVID-19. Please refer to the article 'Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients' for the detailed data and method description.</p> <p>covid_cancer.rds: the processed scRNA-seq data saved as a Seurat object.</p> <p>notebooks.zip: Jupyter notebooks containing code for data analysis.</p>
Fig. 1 in An overview on the role of plant-derived tannins for the treatment of lung cancer
Fig. 1. Molecular targets modulated by tannins in lung cancer. Tannins Abbreviations: CASU - casuarinin; EGCG - epigallocatechin-3-gallate; FPTF - fructus phyllanthi tannin fraction; GA – gallic acid; GERA - geraniin; GRA – granatin A; GRB – granatin B; GSPs - grape seed proanthocyanidins; GSPC - grape seed procyanidins; OEB - oenothein B; PAC – proanthocyanidin rich cranberry fraction; PARE – procyanidin rich extract from sorghum bran; PBOG - prodelphinidin B-2 3′-Ogallate; PCC – procyanidins from cinnamomi cortex; PCCC – procyanidin C1 from cinnamomi cortex; PRFR – proanthocyanidin rich fraction from red rice; TA - tannic acid; TT – total tannins. Abbreviations for molecules: AP-1 - Activator protein 1; Apaf-1 - Apoptotic protease activating factor 1; BAX - BCL2 Associated X; BCL2 - Bcell lymphoma 2; BCL-XL - B-cell lymphoma-extra large; CD31 - cluster of differentiation 31; CDK - cyclin dependent kinase; CDKN1A - cyclin-dependent kinase inhibitor; c-FLIP - FLICE-like inhibitory protein; Cip1/p21 - cyclin-dependent kinase inhibitor 1; COX-2 – cyclooxygenase-2; Cyt C - cytochrome C; E-Cad – E-cadherin; EMT – epithelial-to-mesenchymal transition; Fas - apoptosis antigen 1; FasL - Fas ligand; FN - fibronectin; GR – glutathione reductase; GSH – reduced glutathione; GST - glutathione S-transferase; 15-HETE - 15-hydroxyeicosatetraenoic acid; IGFBP-3 - insulin like factor binding protein 3; IGF-2R - insulin-like growth factor 2 receptor; Kip1/p27 - cyclin-dependent kinase inhibitor 1B; MAPK - mitogen-activated protein kinase; MDM2 - mouse double minute 2 homolog; mFasL - membrane-bound FasL; miR – microRNA; NANOG - transcriptional factor; N-cad - N-cadherin; NOX - NADPH oxidase; NF-kB - nuclear factor kappa-light-chainenhancer of activated B cells; NRF2 - nuclear factor erythroid 2 (NFE2)-related factor 2; NQO1 - NAD(P)H:quinone oxidoreductase; OCT-4 - octamer-binding transcription factor 4; PARP - poly-ADP ribose polymerase; PCNA - proliferating cell nuclear antigen; PGE2 - prostaglandin E2; 6-keto-PGF1α - 6-keto-prostaglandin F1α; PTEN - phosphatase and tensin homolog; PTGIS - prostacyclin synthase; pMDM2 – phospho mouse double minute 2 homolog; pAKT – phosphorylated protein kinase B; pERK1/2 – phosphorylated extracellular signal-regulated kinase 1/2; pJNK1/2 – phosphorylated c-Jun N-terminal kinase 1/2; pNF-kB - phosphorylated nuclear factor kappa-light-chain-enhancer of activated B cells; pPI3K – phosphorylated phosphatidylinositol 3-kinase; α-SMA – alpha-smooth muscle actin; pSmad2 – phosphorylated SMAD family member 2; pSmad3 – phosphorylated SMAD family member 3; p21/WAF1 - cyclin-dependent kinase inhibitor 1; p22phox - human neutrophil cytochrome b light chain; p47phox – neutrophil cytosol factor 1; pp38 – phosphorylated p38; Rb - retinoblastoma protein; sFasL - soluble FasL; Smac/ DIABLO - Second mitochondria-derived activator of caspase/direct inhibitor of apoptosis-binding protein with low pI; SNAIL1 - Zinc finger protein SNAI1; SOX2 - SRY (sex determining region Y)-box 2; pEGFR – phosphorylated epidermal growth factor receptor; TGF-βR1 - transforming growth factor beta receptor; UGT - uridine diphosphate glucuronosyl transferase; VIM - vimentin; VEGF - vascular endothelial growth factor; XIAP - X-linked inhibitor of apoptosis protein; ZO1 - Zonula occludens1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in An overview on the role of plant-derived tannins for the treatment of lung cancer
Fig. 2. Schematic representation of the mechanism of action of tannins as apoptosis inducers, by targeting multiple cellular signal transduction pathways involved in cancer. Abbreviations: AA - arachidonic acid; Akt - protein kinase B; Axl - AXL receptor tyrosine kinase; BAX - BCL2 Associated X; BCL2 - B-cell lymphoma 2; BCL-XL - B-cell lymphoma-extra large; cAMP - adenosine 3′,5′-cyclic monophosphate; CASU - casuarinin; COX2 – cyclooxygenase 2; Cyt C - cytochrome c; EGCG - epigallocatechin-3-gallate; EGFR - epidermal growth factor receptor; EP receptor - E prostanoid receptor; FADD - Fas-associated protein with death domain; Fas - apoptosis antigen 1; FasL - Fas ligand; FPTF - fructus phyllanthi tannin fraction; GA – gallic acid; GERA - geraniin; GRA - granatin A; GRB - granatin B; GSP - grape seed proanthocyanidins; GSPC - grape seed procyanidins; 15-HETE - 15-Hydroxyeicosatetraenoic acid; IP receptor - prostacyclin receptor; 15-LOX-2 - 15-lipoxygenase-2; MAPK - mitogen-activated protein kinase; MMP - mitochondrial membrane potential; OEB - oenothein B; PAC – proanthocyandin-rich cranberry fraction; PARP - poly-ADP ribose polymerase; PBOG - prodelphinidin B-2 3′-O-gallate; PGE - prostaglandin E2; PGI - prostaglandin I2; PKA - protein kinase A; PI3K - 2 2 phosphatidylinositol 3-kinase; PLA2 - phospholipase A2; PTGIS - prostacyclin synthase; ROS - reactive oxygen species; TA – tannic acid; TAM receptors - receptor tyrosine kinases; Tyro3 - TYRO3 protein tyrosine kinase. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Symptom Burden and Unmet Supportive Care Needs in Lung Cancer Patients Undergoing First or Second Line Immunotherapy
ClinicalTrials.gov study NCT03741868. IPD Sharing: NO. Countries: 1. Publications: 2.
Development of a Fluorescent Visualization System for Non-visible Lung Cancer Nodules
ClinicalTrials.gov study NCT06101394. IPD Sharing: NO. Countries: 1. Publications: 1.
Elimination of PTV Margins Based on MRI-guided Adaptive Stereotactic Radiotherapy for Non-small Cell Lung Cancer With Brain Metastasis
ClinicalTrials.gov study NCT06582940. IPD Sharing: NO. Countries: 1. Publications: 8.
Tempus Sculptor Study: Small Cell Lung Cancer (SCLC) Observational Study
ClinicalTrials.gov study NCT05257551. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Methylprednisolone After Split-course Chemoradiotherapy For Bulky Local Advanced None-small Cell Lung Cancer
ClinicalTrials.gov study NCT03661567. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.
Early Specialized Cardiovascular Intervention Based on Impedance Cardiography in Locally Advanced Non-small Cell Lung Cancer Patients
ClinicalTrials.gov study NCT04980716. IPD Sharing: Not stated. Countries: 1. Publications: 25.
Combination Chemotherapy With or Without Bevacizumab in Treating Patients With Advanced, Metastatic, or Recurrent Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00021060. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Pembrolizumab (MK-3475) vs Placebo for Participants With Non-small Cell Lung Cancer After Resection With or Without Standard Adjuvant Therapy (MK-3475-091/KEYNOTE-091)
ClinicalTrials.gov study NCT02504372. IPD Sharing: YES. Countries: 0. Publications: 1.
Early Versus Delayed Rehabilitation Intervention in Patients With Lung Cancer
ClinicalTrials.gov study NCT06051136. IPD Sharing: NO. Countries: 1. Publications: 1.
Topotecan and Vinorelbine in Treating Patients With Recurrent Lung Cancer
ClinicalTrials.gov study NCT00287963. IPD Sharing: Not stated. Countries: 1. Publications: 1.
HepaSphere Interventional Therapy Using Digital Subtraction Angiography（DSA）for Lung Cancer
ClinicalTrials.gov study NCT02523404. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.