ArticleScientific reports2023
An eleven-gene risk model associated with lymph node metastasis predicts overall survival in lung adenocarcinoma.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Multiomics identifies a prognostic signature and SPIB as a potential regulator of gastric cancer lymph node metastasis.iScience · 2026Article
- Integrating Single-Cell, Bulk, and Spatial Transcriptomics Unveils a Novel Ribosome Biogenesis-Related Prognostic Model and DefinesInternational journal of molecular sciences · 2026Article
- Identification of Prognosis Signature Based on cGAS-STING Pathway and Its Immunotherapeutic Significance in Lung Adenocarcinoma.Molecular biotechnology · 2026Article
- A Novel Microfluidic Platform for Circulating Tumor Cell Identification in Non-Small-Cell Lung Cancer.Micromachines · 2025Article
- Significance ofFrontiers in pharmacology · 2025Article
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Abstract
Lung adenocarcinoma (LUAD) occupies major causes of tumor death. Identifying potential prognostic risk genes is crucial to predict the overall survival of patients with LUAD. In this study, we constructed and proved an 11-gene risk signature. This prognostic signature divided LUAD patients into low- and high-risk groups. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.699, 5 years: 0.713, and 7 years: 0.716). Two GEO datasets also indicate the great accuracy of the risk signature (AUC = 782 and 771, respectively). Multivariate analysis identified 4 independent risk factors including stage N (HR 1.320, 95% CI 1.102-1.581, P = 0.003), stage T (HR 3.159, 95% CI 1.920-3.959, P < 0.001), tumor status (HR 5.688, 95% CI 3.883-8.334, P < 0.001), and the 11-gene risk model (HR 2.823, 95% CI 1.928-4.133, P < 0.001). The performance of the nomogram was good in the TCGA database (AUC = 0.806, 0.798, and 0.818 for 3-, 5- and 7-year survival). The subgroup analysis in different age, gender, tumor status, clinical stage, and recurrence stratifications indicated that the accuracy was high in different subgroups (all P < 0.05). Briefly, our work established an 11-gene risk model and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of LUAD patients for clinicians.
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