6487d66b-e640-439a-b525-5746cdd12503 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001008026 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:N6-methyladenosine (m6A) modification plays a central role in both RNA and tumor biology. Yet comprehensive m6A epitranscriptome profiling of primary tumors remains uncharted. To fill this gap, we profiled the m6A epitranscriptome of 10 non-neoplastic lung (NL) tissues and 51 lung adenocarcinoma (LUAD) tumors, integrating them with corresponding transcriptome, proteome and extensive clinical annotation. A total of 8,030 genes are marked with m6A modification, with variable abundances across tumors. Distinct clusters and genes were found to be linked exclusively to disease progression through m6A, rather than mRNA or protein levels. In comparison with NL tissues, 430 transcripts were hypo-methylated in tumors, and 222 were hyper-methylated. Fully 69% of these had no influence on RNA abundances. Amongst these genes, EML4 emerged as a novel metastatic driver, displaying significant hyper-methylation in tumors. m6A modification promoted EML4 translation and led to widespread EML4 overexpression in primary tumors. Functionally, EML4 modulated cytoskeleton dynamics and cell morphology, triggering cellular protrusions and enhancing cellular motility, local invasion, and metastasis. Clinically, high EML4 protein abundance correlated with features of metastasis. A METTL3 small molecule inhibitor markedly diminished both EML4 m6A and protein abundance, and efficiently suppressed lung metastases in vivo. Our findings unveil a dynamic and functional epitranscriptomic landscape in lung adenocarcinoma, pinpointing the hyper-methylation of EML4 as a key driver of tumor metastasis.
2024-05-31 | GSE263726 | GEO
Project description:N6-methyladenosine (m6A) modification plays a central role in both RNA and tumor biology. Yet comprehensive m6A epitranscriptome profiling of primary tumors remains uncharted. To fill this gap, we profiled the m6A epitranscriptome of 10 non-neoplastic lung (NL) tissues and 51 lung adenocarcinoma (LUAD) tumors, integrating them with corresponding transcriptome, proteome and extensive clinical annotation. A total of 8,030 genes are marked with m6A modification, with variable abundances across tumors. Distinct clusters and genes were found to be linked exclusively to disease progression through m6A, rather than mRNA or protein levels. In comparison with NL tissues, 430 transcripts were hypo-methylated in tumors, and 222 were hyper-methylated. Fully 69% of these had no influence on RNA abundances. Amongst these genes, EML4 emerged as a novel metastatic driver, displaying significant hyper-methylation in tumors. m6A modification promoted EML4 translation and led to widespread EML4 overexpression in primary tumors. Functionally, EML4 modulated cytoskeleton dynamics and cell morphology, triggering cellular protrusions and enhancing cellular motility, local invasion, and metastasis. Clinically, high EML4 protein abundance correlated with features of metastasis. A METTL3 small molecule inhibitor markedly diminished both EML4 m6A and protein abundance, and efficiently suppressed lung metastases in vivo. Our findings unveil a dynamic and functional epitranscriptomic landscape in lung adenocarcinoma, pinpointing the hyper-methylation of EML4 as a key driver of tumor metastasis.
2024-05-31 | GSE263725 | GEO
Project description:RNA sequencing of 53 early stage lung adenocarcinoma
| PRJNA701684 | ENA
Project description:The model is based on publication:
Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplification
Abstract:
Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5–22% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. Limited contribution of ErbB3 in gefitinib resistance by MET amplification is also demonstrated using HCC827-GR cells in culture experiments. Our mathematical model provides a quantitative understanding of the molecular reactions underlying drug resistance.
2024-09-02 | BIOMD0000000827 | BioModels
Project description:The Herbal Formula YYJD inhibit migration and invasion of lung adenocarcinoma cells, we aims to profile a comprehensive alternative polyadenylation (APA) dynamic pattern of Lung Adenocarcinoma Cells under YYJD treatment unsing 3T-seq method.
2019-10-31 | E-MTAB-8151 | biostudies-arrayexpress
Project description:Mutant KRAS lung adenocarcinoma cells, H2009, were depleted of FOSL1 by a specific shRNA and its transcriptome was profiled
2017-06-12 | GSE76290 | GEO
Project description:An investigation into the molecular landscape of 53 Indian lung adenocarcinoma patient samples, using a targeted ODxET panel of 46 cancer-relevant oncogenes
2024-03-31 | PRJEB69688 | EVA
Project description:Lung adenocarcinoma is the most common histological subtype of lung cancer. Although early-stage LUAD (esLUAD) patients have much better prognosis than patients with advanced disease, among early stage patients treated primarily with surgery, some of early stage patients will develop metastasis with overall 5 year survival for stage 1 and 2 non-small cell lung cancer of 70% and 35%, respectively. Within lung adenocarcinoma, histology is heterogenous and associated with tumor invasion and clinical outcomes. Invasiveness is one of cancer hallmarks and is directly related with metastatic potential and clinical outcomes of the tumor. In this study, we characterize invasiveness mechanisms in esLUAD by analyzing gene expression of a novel cohort of 53 histologically heterogenous esLUAD samples.
2022-02-01 | GSE166720 | GEO
Project description:We profiled gene expression in formalin-fixed, paraffin-embedded (FFPE) tumor biopsy sample of the patient with non-small-cell lung adenocarcinoma, ALK rearrangement positive.
2019-07-02 | GSE133605 | GEO
Project description:In cancer cells, the process of epithelial–mesenchymal transition (EMT) confers migratory and invasive capacity, resistance to apoptosis, drug resistance, evasion of host immune surveillance and tumor stem cell traits. Cells undergoing EMT may represent tumor cells with metastatic potential. Characterizing the EMT secretome may identify biomarkers to monitor EMT in tumor progression and provide a prognostic signature to predict patient survival. Utilizing a transforming growth factor-β-induced cell culture model of EMT, we quantitatively profiled differentially secreted proteins, by GeLC-tandem mass spectrometry. Integrating with the corresponding transcriptome, we derived an EMT-associated secretory phenotype (EASP) comprising of proteins that were differentially upregulated both at protein and mRNA levels. Four independent primary tumor-derived gene expression data sets of lung cancers were used for survival analysis by the random survival forests (RSF) method. Analysis of 97-gene EASP expression in human lung adenocarcinoma tumors revealed strong positive correlations with lymph node metastasis, advanced tumor stage and histological grade. RSF analysis built on a training set (n = 442), including age, sex and stage as variables, stratified three independent lung cancer data sets into low-, medium- and high-risk groups with significant differences in overall survival. We further refined EASP to a 20 gene signature (rEASP) based on variable importance scores from RSF analysis. Similar to EASP, rEASP predicted survival of both adenocarcinoma and squamous carcinoma patients. More importantly, it predicted survival in the early-stage cancers. These results demonstrate that integrative analysis of the critical biological process of EMT provides mechanism-based and clinically relevant biomarkers with significant prognostic value.
Research is published, core data not used but project description is relevant:
http://www.jimmunol.org/content/194/12/5789.long
2013-04-03 | ST000010 | MetabolomicsWorkbench