ArticleBMC genomics2015
Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Significantly altered peripheral blood cell DNA methylation profile as a result of immediate effect of metformin use in healthy individuals.Clinical epigenetics · 2018Trial
- Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.JCO clinical cancer informatics · 2023Article
- Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma.Genome medicine · 2022Article
- A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis.Journal of biomedical science · 2022Article
- Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer.Frontiers in oncology · 2021Article
- Analysis of copy number alterations reveals the lncRNA ALAL-1 as a regulator of lung cancer immune evasion.The Journal of cell biology · 2020Article
- The Crosstalk between Src and Hippo/YAP Signaling Pathways in Non-Small Cell Lung Cancer (NSCLC).Cancers · 2020Review
- PGAM5 expression and macrophage signatures in non-small cell lung cancer associated with chronic obstructive pulmonary disease (COPD).BMC cancer · 2018Article
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Authors and funding
17 authors.
Funding
Abstract
backgroundThe development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies.
resultsA new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n = 73 for ADC, n = 97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P = 0.008 for ADC and P = 0.019 for SCC) and outperforms both the clinical models (P = 0.060 for ADC and P = 0.121 for SCC) and the genomic models applied separately (P = 0.350 for ADC and P = 0.269 for SCC).
conclusionThe present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
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