ArticlePloS one2014
In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2024Review
- Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients.Cancer communications (London, England) · 2022Article
- Review
- EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models.Metabolites · 2020Article
- Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2019Review
- Article
- Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review.Oncotarget · 2017Review
- Reconstruction and validation of a constraint-based metabolic network model for bone marrow-derived mesenchymal stem cells.Cell proliferation · 2015Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolism expresses the phenotype of living cells and understanding it is crucial for different applications in biotechnology and health. With the increasing availability of metabolomic, proteomic and, to a larger extent, transcriptomic data, the elucidation of specific metabolic properties in different scenarios and cell types is a key topic in systems biology. Despite the potential of the elementary flux mode (EFM) concept for this purpose, its use has been limited so far, mainly because their computation has been infeasible for genome-scale metabolic networks. In a recent work, we determined a subset of EFMs in human metabolism and proposed a new protocol to integrate gene expression data, spotting key 'characteristic EFMs' in different scenarios. Our approach was successfully applied to identify metabolic differences among several human healthy tissues. In this article, we evaluated the performance of our approach in clinically interesting situation. In particular, we identified key EFMs and metabolites in adenocarcinoma and squamous-cell carcinoma subtypes of non-small cell lung cancers. Results are consistent with previous knowledge of these major subtypes of lung cancer in the medical literature. Therefore, this work constitutes the starting point to establish a new methodology that could lead to distinguish key metabolic processes among different clinical outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.