ArticleChest2020
Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects.
Article in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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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.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions.Journal of advanced research · 2025Pooled it
- COVPRIG robustly predicts the overall survival of IDH wild-type glioblastoma and highlights METTL1Journal of translational medicine · 2023Pooled it
- Pooled it
- Development and independent validation of a hypoxemia risk prediction model (HAPPY-12K) for sedated gastrointestinal endoscopy: a multicentre prospective cohort study.EClinicalMedicine · 2026Article
- A trans-omics gene-smoking interaction study of lung cancer based on consortium data.American journal of respiratory and critical care medicine · 2026Article
- Radon-Induced Radiation Biomarkers: A Scoping Review from Exposure Dosimetry to Early Biological Effects on the Lung.International journal of molecular sciences · 2026Article
- Translating tumor epigenetic subtyping into methylome network-based prognostic models in early-stage NSCLC: results from the prospective MOBIT study.Journal of translational medicine · 2026Article
- ITPG: an immune-related transcriptomic predictive model for gastric cancer prognosis.Translational cancer research · 2026Article
- A Machine Learning Approach to Predicting Mortality Risk in Chemotherapy-Treated Lung Cancer: Machine Learning Model Development and Validation.JMIR medical informatics · 2025Article
- Tumor immune microenvironment score predicts efficacy of immune checkpoint inhibitors-based regimens in advanced non-small cell lung cancer.Journal of translational medicine · 2025Article
- Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics.International journal of molecular sciences · 2024Article
- Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.Cell reports methods · 2024Article
- Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.Scientific reports · 2024Article
- Review
- Article
- ATHENA: an independently validated autophagy-related epigenetic prognostic prediction model of head and neck squamous cell carcinoma.Clinical epigenetics · 2023Article
- A trans-omics assessment of gene-gene interaction in early-stage NSCLC.Molecular oncology · 2023Article
- Mutational and Transcriptional Characterization Establishes Prognostic Models for Resectable Lung Squamous Cell Carcinoma.Cancer management and research · 2023Article
- Article
- MiR-30a-3p Suppresses the Growth and Development of Lung Adenocarcinoma Cells Through Modulating GOLM1/JAK-STAT Signaling.Molecular biotechnology · 2022Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
backgroundDNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions. RESEARCH QUESTION: Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis? STUDY DESIGN AND
methodsBiomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.
resultsTwenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10
interpretationThe integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.
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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.