ArticleJournal of oncology2020
Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma.
Article in Journal of oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Glutathione Metabolism-Related Transcriptomic Signature for Prognostic Assessment and Biological Characterization of Lung Adenocarcinoma.Cancer informatics · 2026Article
- GNPNAT1 acts as a prognostic biomarker associated with immune infiltration in lung adenocarcinoma and promotes the proliferation and invasion of cancer cells.Discover oncology · 2025Article
- Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis.Current cancer drug targets · 2025Article
- Phospholipid Acyltransferases: Characterization and Involvement of the Enzymes in Metabolic and Cancer Diseases.Cancers · 2024Review
- Article
- The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma.Biochemical genetics · 2023Article
- A risk score model based on lipid metabolism-related genes could predict response to immunotherapy and prognosis of lung adenocarcinoma: a multi-dataset study and cytological validation.Discover oncology · 2023Article
- Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue.Cancer science · 2023Article
- Glucosamine-phosphate N-acetyltransferase 1 and its DNA methylation can be biomarkers for the diagnosis and prognosis of lung cancer.Journal of clinical laboratory analysis · 2022Article
- Identification of Two Subtypes and Prognostic Characteristics of Lung Adenocarcinoma Based on Pentose Phosphate Metabolic Pathway-Related Long Non-coding RNAs.Frontiers in public health · 2022Article
- GNPNAT1 Predicts Poor Prognosis and Cancer Development in Non-Small Cell Lung Cancer.Cancer management and research · 2022Article
- A Metabolic-associated Nomogram Predicts Recurrence Survival of Thyroid Cancer.Current medical science · 2021Article
- 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
- A five-m6A regulatory gene signature is a prognostic biomarker in lung adenocarcinoma patients.Aging · 2021Article
- Potential role of glucosamine-phosphate N-acetyltransferase 1 in the development of lung adenocarcinoma.Aging · 2021Article
- A Novel Prognostic and Predictive Signature for Lung Adenocarcinoma Derived from Combined Hypoxia and Infiltrating Immune Cell-Related Genes in TCGA Patients.International journal of general medicine · 2021Article
- Construction of a Prognostic Model in Lung Adenocarcinoma Based on Ferroptosis-Related Genes.Frontiers in genetics · 2021Article
- Article
- Article
- Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706-0.804) and 0.691 (95% confidence interval: 0.636-0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.
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Registered trials
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