ArticleCancer biomarkers : section A of Disease markers2020
Eight-gene signature predicts recurrence in lung adenocarcinoma.
Article in Cancer biomarkers : section A of Disease markers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular-Based Risk Prediction Models for Recurrence After Curative Treatment of Early-Stage Lung Cancer: A Systematic Review and Meta-Analysis.Thoracic cancer · 2026Pooled it
- Article
- Identification of a Novel Signature Based on Ferritinophagy-Related Genes to Predict Prognosis in Lung Adenocarcinoma: Focus on AHNAK2.Bioengineering (Basel, Switzerland) · 2024Article
- Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage I-III lung adenocarcinoma.Genes & diseases · 2023Article
- Construction of a hypoxia-derived gene model to predict the prognosis and therapeutic response of head and neck squamous cell carcinoma.Scientific reports · 2022Article
- High myosin binding protein H expression predicts poor prognosis in glioma patients.Scientific reports · 2022Article
- Susceptibility Loci in SLC15A1, UGT1A3, and CWC27 Genes Associated with Bladder Cancer in the Northeast Chinese Population.BioMed research international · 2022Article
- Identification of Novel Subtypes in Lung Adenocarcinoma: Evidence from Gene Set Variation Analysis in Tumor and Adjacent Nontumor Samples.Disease markers · 2022Article
- Feature screening for survival trait with application to TCGA high-dimensional genomic data.PeerJ · 2022Article
- Tumor stemness and immune infiltration synergistically predict response of radiotherapy or immunotherapy and relapse in lung adenocarcinoma.Cancer medicine · 2021Article
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4 authors.
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Abstract
backgroundRecurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD.
objectiveWe performed this study to construct a model to predict risk of recurrence in LUAD.
methodsRNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213). Differentially expressed genes (DEGs) between recurrent and non-recurrent tumors in the training set were identified. Recurrence-associated DEGs were selected using multivariate Cox regression analysis. The recurrence risk model that identifies patients at low and high risk for recurrence was constructed, followed by the validation of its performance in the validation set and a microarray dataset.
resultsIn total, 378 DEGs, including 20 recurrence-associated DEGs, were identified between the recurrent and non-recurrent tumors in the training set. The signatures of 8 genes (including AZGP1, INPP5J, MYBPH, SPIB, GUCA2A, HTR1B, SLC15A1 and TNFSF11) were used to construct the prognostic model to assess the risk of recurrence. This model indicated that patients with high risk scores had shorter recurrence-free survival time compared with patients with low risk scores. ROC curve analysis of this model showed it had high predictive accuracy (AUC > 0.8) to predict LUAD recurrence in the TCGA cohort (the training and validation sets) and GSE50081 dataset. This prognostic model showed high predictive power and performance in predicting recurrence in LUAD.
conclusionWe concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.
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