ArticleMedical science monitor : international medical journal of experimental and clinical research2020
A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 4 citations in OpenAlex.
- Construction and validation of a lung adenocarcinoma prognostic model based on neutrophil extracellular traps and oxidative stress-related genes.European journal of medical research · 2025Article
- The bioinformatics analysis and experimental validation of the carcinogenic role of EXO1 in lung adenocarcinoma.Frontiers in oncology · 2024Article
- Establishment of a Prognostic Model of Lung Adenocarcinoma Based on Tumor Heterogeneity.Frontiers in molecular biosciences · 2022Article
- A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?Annals of translational medicine · 2021Review
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4 authors at 1 institution in 1 country.
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Abstract
BACKGROUND Lung adenocarcinoma is the most life-threatening malignancy with high incidence and poor long-term survival. The crucial role of tumor immunity reveals the significance of exploring immune-related prognostic predictors in lung adenocarcinoma. MATERIAL AND METHODS Immune-related genes were screened out applying the ESTIMATE algorithm. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset was trained for the construction of Cox proportional hazard model. Univariate Cox and Lasso regression analysis was conducted to reduce the overfitting of model. Nomogram integrated the signature and clinicopathological characteristics was established for prognosis prediction of LUAD. The GSE30219, GSE41271, and GSE42127 datasets were analyzed for external validation. LUAD patients were separated into low-risk and high-risk subgroups based on the optimum cutoff threshold of calculated risk score. The predictive value of the signature was evaluated using Kaplan-Meier survival analysis, Harrell's C-index, and receiver operating characteristic (ROC) curve analysis and calibration curve. Clinical- and immune-correlation of the signature was further performed. Gene Set Enrichment Analysis (GSEA) was performed for functional exploration. RESULTS An immune-related signature containing 7 genes was identified. The signature exhibited reliable performance in the prediction of overall survival for LUAD with the C-index being 0.72. The areas under the curve (AUCs) of the model in 1-year risk prediction were 0.781, 0.797, 0.659, and 0.822 for TCGA-LUAD, GSE30219, GSE41271, and GSE42127 datasets, respectively. In all datasets, the signature proved to an independent risk factor for LUAD. Correlation analyses and GSEA further revealed the close relationship between the predictive biomarker and tumor immunity. CONCLUSIONS A Cox proportional hazard model consisting of 7 genes was identified for prognostic prediction of LUAD. The signature was highly correlated with immunity and deserves further exploration.
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