Lung adenocarcinoma cell line Wnt responsive signature
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE14107 | GEO | 2009/07/10
SECONDARY ACCESSION(S): PRJNA111197
REPOSITORIES: GEO
Similar Datasets
Project description:Identify Wnt3A responsive signature in lung adenocarcinoma cells Experiment Overall Design: 4 different cell lines +/- Wnt3A; replicate samples
2009-07-19 | E-GEOD-14107 | biostudies-arrayexpress
Project description:Gene signature driving invasive mucinous adenocarcinoma of the lung
| PRJNA343081 | ENA
Project description:<p>Cross-talk among downstream regulatory networks of genome mutations has been the major hypothetical mechanism underlying poor prognosis and low-responsive rates for targeted therapy of pancreatic ductal adenocarcinoma (PDAC). By applying a causal inference-based mutation-upstream-of-the-metabolomic-signature (MUMS), we demonstrated a potential application of prognostic relevant serum metabolomic signature as an indicator to aggregate crosstalk among respective regulatory network downstream of genome mutations, and their collective interference on tumor progression. Moreover, a feature selection of 9-serum metabolites panel also highlights the clinical potential for predicting survival outcomes across multiple PDAC cohorts. Notably, integrating causal inference-based MUMS analysis identified and functional verified GRPEL1 as a novel tumour promoting gene, which shares a common down-stream metabolic signature with mTOR/PI3K/Akt signaling, sensitizing PDAC cells to mTOR inhibition-induced proliferation arrest. Our MUMS based functional cross-talk analysis provided proof-of-concept evidence that serum metabolic signatures reflect crosstalk among tumour mutational landscape, and such co-regulations would provide a novel aspect for identifying neo-targets for targeted therapy and rational based combined therapy design.</p>
2025-10-13 | MTBLS13116 | MetaboLights
Project description:<p>Cross-talk among downstream regulatory networks of genome mutations has been the major hypothetical mechanism underlying poor prognosis and low-responsive rates for targeted therapy of pancreatic ductal adenocarcinoma (PDAC). By applying a causal inference-based mutation-upstream-of-the-metabolomic-signature (MUMS), we demonstrated a potential application of prognostic relevant serum metabolomic signature as an indicator to aggregate crosstalk among respective regulatory network downstream of genome mutations, and their collective interference on tumor progression. Moreover, a feature selection of 9-serum metabolites panel also highlights the clinical potential for predicting survival outcomes across multiple PDAC cohorts. Notably, integrating causal inference-based MUMS analysis identified and functional verified GRPEL1 as a novel tumour promoting gene, which shares a common down-stream metabolic signature with mTOR/PI3K/Akt signaling, sensitizing PDAC cells to mTOR inhibition-induced proliferation arrest. Our MUMS based functional cross-talk analysis provided proof-of-concept evidence that serum metabolic signatures reflect crosstalk among tumour mutational landscape, and such co-regulations would provide a novel aspect for identifying neo-targets for targeted therapy and rational based combined therapy design.</p>
2025-10-13 | MTBLS13127 | MetaboLights
Project description:<p>Cross-talk among downstream regulatory networks of genome mutations has been the major hypothetical mechanism underlying poor prognosis and low-responsive rates for targeted therapy of pancreatic ductal adenocarcinoma (PDAC). By applying a causal inference-based mutation-upstream-of-the-metabolomic-signature (MUMS), we demonstrated a potential application of prognostic relevant serum metabolomic signature as an indicator to aggregate crosstalk among respective regulatory network downstream of genome mutations, and their collective interference on tumor progression. Moreover, a feature selection of 9-serum metabolites panel also highlights the clinical potential for predicting survival outcomes across multiple PDAC cohorts. Notably, integrating causal inference-based MUMS analysis identified and functional verified GRPEL1 as a novel tumour promoting gene, which shares a common down-stream metabolic signature with mTOR/PI3K/Akt signaling, sensitizing PDAC cells to mTOR inhibition-induced proliferation arrest. Our MUMS based functional cross-talk analysis provided proof-of-concept evidence that serum metabolic signatures reflect crosstalk among tumour mutational landscape, and such co-regulations would provide a novel aspect for identifying neo-targets for targeted therapy and rational based combined therapy design.</p>
2025-10-13 | MTBLS13111 | MetaboLights
Project description: Not available
| S-ECPF-GEOD-14107 | biostudies-other
Project description:Lung cancer remains the leading cause of cancer death worldwide. Overall 5-year survival is about 10-15% and despite curative intent surgery, treatment failure is primarily due to recurrent disease. Conventional prognostic markers are unable to determine which patients with completely resected disease within each stage group are likely to relapse. To identify a gene signature associated with recurrent adenocarcinoma (AC) of lung, we analyzed primary tumour gene expression for a total of 48 stage I ACs on 22,323 element microarrays, comparing expression profiles for individuals who remained disease-free for a minimum of 36 months with those from individuals whose disease recurred within 18 months of complete resection. Genome-wide profiling has revealed a distinct gene expression profile for recurrent lung AC which may be clinically useful as a prognostic tool. Keywords: non-small cell lung carcinoma, squamous cell, tumor recurrence, expression profiling
2007-05-01 | GSE5843 | GEO
Project description:Lung adenocarcinoma (LUAD) exhibits substantial intra-tumor heterogeneity (ITH), contributing to disease progression and therapeutic challenges. This study utilized single-cell RNA sequencing (scRNA-seq) to profile cells from stage I and III LUAD tumors and matched normal tissues, aiming to delineate malignant cell states and identify tumor-propagating subpopulations. Unsupervised clustering revealed eight major cell lineages, with epithelial cells dominating in tumors and a shift toward immunosuppressive niches in advanced stages, including elevated T cell exhaustion and pro-angiogenic macrophages. Malignant cells formed 11 subclusters, with stage III tumors enriched for invasive and immunosuppressive states. Pseudotime analysis suggested evolutionary trajectories from alveolar-like to squamous and stress-responsive lineages. Using CytoTRACE to infer stemness without predefined markers, we identified sub-cancer stem cells (sub-CSCs) predominantly in stage III, characterized by high transcriptional diversity and a core signature involving cytoskeleton motility, immune modulation, metabolic reprogramming, and protein homeostasis. A four-gene panel demonstrated strong prognostic value in independent cohorts. These findings provide insights into stemness-driven progression in LUAD and nominate potential targets for addressing heterogeneity.
2026-07-31 | GSE333596 | GEO
Project description:Primary dermal fibroblasts from patients with dSSc and healthy controls were treated with TGF beta for up to 24h and the genome-wide patterns of gene expression measured on DNA microarrays. 894 genes were identified as TGF beta-responsive in 4 independent cultures of dermal fibroblasts (2 healthy control and 2 dSSc patients). The 894 genes in the TGF beta-responsive signature are associated with induction of growth factor signaling, collagen synthesis and extracellular matrix deposition.
2009-09-15 | GSE12493 | GEO
Project description:Background: The occurrence of lung adenocarcinoma (LUAD) is a complicated process, involving the genetic and epigenetic changes of proto-oncogenes and oncogenes. The objective of this study was to establish new predictive signatures of lung adenocarcinoma based on copy number variation (CNVs) and gene expression data. Methods: Next-generation sequencing was implemented to obtain gene expression and CNV information. According to univariate, multivariate survival Cox regression analysis and Lasso analysis, the expression profiles of lung adenocarcinoma patients were screened and a risk score formula was established and experimentally validated in a local cohort. The model was evaluated by three independent cohorts, and then validated by clinical samples from LUAD patients. Results: A total of 844 CNV-related differential expressed genes (CNV related DEGs) were identified. An CNV associated-six gene signature was dramatically linked to overall survival in lung adenocarcinoma samples from both training and validation groups. Functional enrichment analysis further revealed involvement of genes in p53 signaling pathway and cell cycle, as well as the mismatch repair pathway. Risk score is an independent marker considering clinical parameters and had better prediction in clinical sub-population. The same signature also classified tumors tissues of clinical patients those with CNV detected from their corresponding non-tumorous tissues with an accuracy of 0.92. Conclusions: In conclusion, we identified a new class of 6 CNV-related gene markers that may act as efficient prognostic predictors of lung adenocarcinoma, thus contributing to individualized treatment decisions in patients.
2022-10-26 | GSE197346 | GEO