ArticleBMC cancer2020
Identification of a four-gene panel predicting overall survival for lung adenocarcinoma.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma.Functional & integrative genomics · 2026Article
- Development and validation of a 5mC-based prognostic model for lung adenocarcinoma survival.Cell reports. Medicine · 2026Article
- Integrative analysis of scRNA-seq and RNA-seq to investigate the prognostic value of lactylation and fibroblast-related genes in intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Exploring the regulatory mechanisms of paraptosis-related prognostic genes in gastric cancer using single-cell sequencing and transcriptome analysis.Scientific reports · 2025Article
- Prognostic value of circadian rhythm-associated genes in breast cancer.World journal of surgical oncology · 2025Article
- Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs.Frontiers in immunology · 2025Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
- Construction of a prognostic model based on memory CD4+ T cell-associated genes for lung adenocarcinoma and its applications in immunotherapy.CPT: pharmacometrics & systems pharmacology · 2024Article
- The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma.Biochemical genetics · 2023Article
- Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity.BMC cancer · 2023Article
- A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.Medical & biological engineering & computing · 2023Article
- Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes.Annals of medicine · 2022Article
- A novel metabolic-immune related signature predicts prognosis and immunotherapy response in lung adenocarcinoma.Heliyon · 2022Article
- Identification of molecular subtypes in lung adenocarcinoma based on DNA methylation and gene expression profiling-a bioinformatic analysis.Annals of translational medicine · 2022Article
- Comprehensive Analysis of Programmed Cell Death Signature in the Prognosis, Tumor Microenvironment and Drug Sensitivity in Lung Adenocarcinoma.Frontiers in genetics · 2022Article
- Comprehensive analysis to identify GNG7 as a prognostic biomarker in lung adenocarcinoma correlating with immune infiltrates.Frontiers in genetics · 2022Article
- Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma.Frontiers in immunology · 2022Article
- Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma.Bioengineered · 2021Article
- Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer.World journal of surgical oncology · 2021Article
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5 authors.
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Abstract
backgroundLung cancer is the most frequently diagnosed carcinoma and the leading cause of cancer-related mortality. Although molecular targeted therapy and immunotherapy have made great progress, the overall survival (OS) is still poor due to a lack of accurate and available prognostic biomarkers. Therefore, in this study we aimed to establish a multiple-gene panel predicting OS for lung adenocarcinoma.
methodsWe obtained the mRNA expression and clinical data of lung adenocarcinoma (LUAD) from TCGA database for further integrated bioinformatic analysis. Lasso regression and Cox regression were performed to establish a prognosis model based on a multi-gene panel. A nomogram based on this model was constructed. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. The prognosis value of the multi-gene panel was further validated in TCGA-LUAD patients with EGFR, KRAS and TP53 mutation and a dataset from GEO. Gene set enrichment analysis (GSEA) was performed to explore potential biological mechanisms of a novel prognostic gene signature.
resultsA four-gene panel (including DKK1, GNG7, LDHA, MELTF) was established for LUAD prognostic indicator. The ROC curve revealed good predicted performance in both test cohort (AUC = 0.740) and validation cohort (AUC = 0.752). Each patient was calculated a risk score according to the model based on the four-gene panel. The results showed that the risk score was an independent prognostic factor, and the high-risk group had a worse OS compared with the low-risk group. The nomogram based on this model showed good prediction performance. The four-gene panel was still good predictors for OS in LUAD patients with TP53 and KRAS mutations. GSEA revealed that the four genes may be significantly related to the metabolism of genetic material, especially the regulation of cell cycle pathway.
conclusionOur study proposed a novel four-gene panel to predict the OS of LUAD, which may contribute to predicting prognosis accurately and making the clinical decisions of individual therapy for LUAD patients.
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