ArticleOncotarget2016
Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multiomics assessment of lung adenocarcinoma subtypes defined through tumor purity-adjusted DNA methylation.Genome medicine · 2026Article
- Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways.Cell reports. Medicine · 2022Article
- SRIQ clustering: A fusion of Random Forest, QT clustering, and KNN concepts.Computational and structural biotechnology journal · 2022Article
- Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor.Briefings in bioinformatics · 2021Article
- Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.Cancers · 2021Review
- A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis.International journal of cancer · 2021Article
- Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer.Briefings in bioinformatics · 2020Article
- Simplified molecular classification of lung adenocarcinomas based on EGFR, KRAS, and TP53 mutations.BMC cancer · 2020Article
- A Meta-Analysis of the Performance of a Blood-Based Exposure Response Gene Signature Across Clinical Studies on the Tobacco Heating System 2.2 (THS 2.2).Frontiers in pharmacology · 2019Article
- Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model.Scientific reports · 2018Article
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2 authors.
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Abstract
Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.
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