ArticleCurrent genomics2024
A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma.
Article in Current genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Multi-cell death gene signature predicts lung adenocarcinoma prognosis and reveals FURIN regulates ferroptosis via NRF2-xCT-GPX4 axis.Respiratory research · 2026Article
- DNMT1 is associated with QDPR downregulation and chemotherapy response in lung adenocarcinoma.Medical oncology (Northwood, London, England) · 2026Article
- Pattools‑implemented methylation vector analysis reveals aberrant subtype‑specific methylation in lung cancer across tissue and plasma cfDNA.Clinical and translational medicine · 2026Article
- Molecular differences between poorly and well/moderately differentiated lung adenocarcinoma and their clinical implications.Journal of thoracic disease · 2026Article
- Article
- Glycosylation-related gene risk model and functional validation of OSTC and TUBA1C in lung adenocarcinoma.Respiratory research · 2026Article
- COG8 serves as a predictive and prognostic biomarker in lung adenocarcinoma and other human malignancies.Discover oncology · 2026Article
- The prognostic and immunological role of NADPH oxidase in early and locally advanced lung adenocarcinoma.Discover oncology · 2026Article
- Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data.Discover oncology · 2026Article
- miR-542-3p targets TTF1 to regulate proliferation, invasion, and migration of lung adenocarcinoma via the MAPK signaling pathway.Scientific reports · 2026Article
- Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms.BMC gastroenterology · 2026Article
- A nomogram with online dynamic calculator for predicting osteoporosis: development and validation based on NHANES.BMC musculoskeletal disorders · 2026Article
- Prognostic biomarkers for lung adenocarcinoma based on Mendelian randomization analysis.The Journal of international medical research · 2026Article
- ABTB1 modulates EGFR signaling and confers icotinib sensitivity in lung adenocarcinoma.Respiratory research · 2026Article
- Development of a risk model for outcome prediction and treatment guidance in lung adenocarcinoma based on hypoxia, glycolysis, and lymph node metastasis-related genes.Discover oncology · 2026Article
- The TMPO-AS1/hsa-let-7b-5p regulatory axis shapes transcriptional heterogeneity and immune landscape in lung adenocarcinoma.Cell division · 2026Article
- Multifaceted bioinformatic analysis uncover links m5C-related ferroptosis gene SLC2A1 to prognosis and immune infiltration in lung adenocarcinoma.Discover oncology · 2026Article
- Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning.iScience · 2026Article
- Downregulation of GNPNAT1 impaired proliferation and correlates with immune infiltration in lung adenocarcinoma.Discover oncology · 2026Article
- Multiomics analysis identifies the prognostic significance and biological roles of the HNRNP family in lung adenocarcinoma.Discover oncology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Specific methylation sites have shown promise in the early diagnosis of lung adenocarcinoma (LUAD). However, their utility in predicting LUAD prognosis remains unclear. This study aimed to construct a reliable methylation-based predictor for accurately predicting the prognosis of LUAD patients. Methods: DNA methylation data and survival data from LUAD patients were obtained from the TCGA and a GEO series. A DNA methylation-based signature was developed using univariate least absolute shrinkage and selection operators and multivariate Cox regression models. Results: Eight CpG sites were identified and validated as optimal prognostic signatures for the overall survival of LUAD patients. Receiver operating characteristic analysis demonstrated the high predictive ability of the eight-site methylation signature combined with clinical factors for overall survival. Conclusion: This research successfully identified a novel eight-site methylation signature for predicting the overall survival of LUAD patients through bioinformatic integrated analysis of gene methylation markers used in the early diagnosis of lung cancer.
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