ArticleMolecular therapy oncolytics2020
Prognostic Implication of a Metabolism-Associated Gene Signature in Lung Adenocarcinoma.
Article in Molecular therapy oncolytics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database.Technology in cancer research & treatmentTrial
- Metabolic pathway signatures define prognostic subtypes of lung adenocarcinoma.Discover oncology · 2026Article
- ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.Journal of translational medicine · 2026Article
- Unraveling lung cancer dynamics: a new metabolic signature improving the prediction of recurrence in resected lung adenocarcinoma.Journal of translational medicine · 2026Article
- ScRNA-seq reveals dynamic macrophage heterogeneity in chronic liver disease progression and prognostic biomarkersFrontiers in immunology · 2026Article
- Acetylated Pyrimidine Metabolism Genes as Prognostic Markers and Their Influence on Immune Profiles in Lung Adenocarcinoma.Human mutation · 2026Article
- Integrating Bioinformatics and Experimental Validation Identify CCNA2 as a Novel Prognostic Biomarker and Tumor Promoter via the PI3K/AKT Pathway in Lung Adenocarcinoma.International journal of general medicine · 2026Article
- Article
- Development of a Mitochondrial Permeability Transition-Driven Necrosis-Related Prognostic Signature in Cervical Cancer: Integrating Bulk Transcriptomic and Single-Cell Data.Cancer medicine · 2025Article
- Characteristics of folic acid metabolism-related genes unveil prognosis and treatment strategy in lung adenocarcinoma.BMC pulmonary medicine · 2025Article
- An integrative analysis combining bioinformatics, network pharmacology and experimental methods identified key genes of EGCG targets in Nasopharyngeal Carcinoma.Discover oncology · 2025Article
- Identification of a metabolic-immune signature associated with prognosis in colon cancer and exploration of potential predictive efficacy of immunotherapy response.Clinical and experimental medicine · 2025Article
- Identification of Biomarkers Based on Starvation Response-Related Genes for Assessing the Immune Profile and Prognosis in Lung Adenocarcinoma.International journal of genomics · 2025Article
- A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.NPJ systems biology and applications · 2024Article
- Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types.Biology · 2024Article
- Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma.Discover oncology · 2024Article
- Prognostic correlation between specialized capillary endothelial cells and lung adenocarcinoma.Heliyon · 2024Article
- Article
- Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma.Frontiers in immunology · 2024Article
- Pan-Cancer Profiling of Intron Retention and Its Clinical Significance in Diagnosis and Prognosis.Cancers · 2023Article
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5 authors.
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Abstract
Lung cancer is the most common cancer worldwide, leading to high mortality each year. Metabolic pathways play a vital role in the initiation and progression of lung cancer. We aimed to establish a prognostic prediction model for lung adenocarcinoma (LUAD) patients based on a metabolism-associated gene (MTG) signature. Differentially expressed (DE)-MTGs were screened from The Cancer Genome Atlas (TCGA) LUAD cohorts. Univariate Cox regression analysis was performed on these DE-MTGs to identify genes significantly correlated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression was performed on the resulting genes to establish an optimal risk model. Survival analysis was used to assess the prognostic ability of the model. The prognostic value of the gene signature was further validated in independent Gene Expression Omnibus (GEO) datasets. A gene signature with 13 metabolic genes was identified as an independent prognostic factor. Kaplan-Meier survival analysis demonstrated the good performance of the risk model in both TCGA training and GEO validation cohorts. Finally, a nomogram incorporating clinical parameters and the metabolic gene signature was constructed to help individualize outcome predictions. The calibration curves showed excellent agreement between the actual and predicted survival.
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