ArticleEBioMedicine2014
Prediction of Clinical Outcome for All Stages and Multiple Cell Types of Non-small Cell Lung Cancer in Five Countries Using Lung Cancer Prognostic Index.
Article in EBioMedicine, 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 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.
- Defining the surgical curative time window: identifying patients with an absence of recurrence for 5 years following surgical resection of stage I invasive non-small cell lung cancer.Translational lung cancer research · 2025Article
- Identification of circulating miRNA as early diagnostic molecular markers in malignant glioblastoma base on decision tree joint scoring algorithm.Journal of cancer research and clinical oncology · 2023Article
- Chaperone-mediated autophagy receptor modulates tumor growth and chemoresistance in non-small cell lung cancer.Cancer science · 2020Article
- Gene Expression Signatures Predicting Survival and Chemotherapy Benefit in Patients with Resected Non-small-Cell Lung Cancer.EBioMedicine · 2018Article
- A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease.GigaScience · 2018Article
- Prognostic Impact of TTF-1 Expression in Non-Squamous Non-Small-Cell Lung Cancer: A Meta-Analysis.Journal of Cancer · 2018Article
- Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection.Oncotarget · 2017Article
- Expression Profiles and Clinicopathologic Features in Early Resected Non-small-cell Lung Cancer.EBioMedicine · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is a commonly diagnosed cancer. In this era of personalized medicine, genetic predictive models are becoming increasingly important. However, many current predictive models fail verification tests due to small sample sizes and institutional biases. We collected 17 gene expression datasets from public databases to generate our largest training and testing cohorts. After successfully eliminating institutional variations and merging multiple datasets, we generated a training cohort of 1073 and a testing cohort of 659. Using Siggenes, univariate and multivariate analyses, we identified seven gene signatures, and combined them with the clinical parameter age and stage to design the lung cancer prognostic index (LCPI). Using LCPI, we could differentiate lung cancer patients into three risk groups and predict patient survival probabilities at 10 and 15 year post-surgical resection. We extensively verified the predictive ability of LCPI for overall and recurrence free survival using 6 other datasets from five different countries.
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Registered trials
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