ArticleAnnals of translational medicine2021
Identification and validation of a metabolism-related model and associated with tumor-infiltrating lymphocytes in p53 mutant lung adenocarcinoma patients.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 8 citations in OpenAlex.
- Natural Compound Melatonin Suppresses Breast Cancer Development by Regulating Circadian Rhythm.Nutrients · 2025Article
- Establishing a new-onset diabetes-related metabolism signature for predicting the prognosis and immune landscape in pancreatic cancer.Carcinogenesis · 2025Article
- Review
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- A novel immune-related prognostic model with surgical status to predict tumor immune cell infiltration and drug sensitivity in head and neck squamous cell carcinoma.Biochemistry and biophysics reports · 2023Article
- Hypoxia-induced ALDH3A1 promotes the proliferation of non-small-cell lung cancer by regulating energy metabolism reprogramming.Cell death & disease · 2023Article
- Mutant p53 achieved Gain-of-Function by promoting tumor growth and immune escape through PHLPP2/AKT/PD-L1 pathway.International journal of biological sciences · 2022Article
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4 authors at 1 institution in 1 country.
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Abstract
backgroundThe immunosuppressive tumor microenvironment produced by cancer cells is a key mechanisms of cancer immune escape. In this study, we investigated the relationship between the metabolic patterns and tumor immune environment in the TME of lung adenocarcinoma (LUAD) with the p53 mutation.
methodsThe clinical data of 495 LUAD patients was obtained from The Cancer Genome Atlas as transcriptomic and somatic mutation data. Using differential analysis, survival analysis, and a LASSO regression model based on metabolic unigenes from KEGG pathways, a tumor metabolic model was constructed to predict the prognosis of LUAD patients. Subsequently, nomogram, receiver operating characteristic, and decision curve analyses were conducted to assess the predictive ability of the model. In addition, the ESTIMATE and CIBERSORT algorithms were used to detect tumor purity and estimate the fractions of 22 immune cell types in each patient, respectively. We found a correlation between the composition of immune cells and the tumor metabolic model. The results were validated using an independent GSE72094 dataset with 442 patients, as well as an immunohistochemistry assay, RT-qPCR, and western blot.
resultsThe tumor metabolic model reassigned the risk score of every patient, and a tumor metabolic risk score (TMRS) was generated to show the predictive ability for patient prognoses (hazard ratio =0.39; 95% confidence interval: 0.18-0.85). Using a combination of TMRS and clinical features, a nomogram was produced with a predictive accuracy of 0.72. Further analysis showed that CD4 memory resting T cells and M1 macrophages may by correlated with the TMRS, which corresponded to immunoediting in p53 mutant patients. Additionally, the similar expression of ALDH3A1 and MGAT5B were also verified by wetlab experiments.
conclusionsBased on the identified tumor metabolism-immune landscape, we were able to predict a metabolism risk score for patient prognosis and identify a correlation with two types of infiltrating lymphocytes in the TME of p53-mutated LUAD. This landscape provides insights that will help identify the molecular mechanisms of immune-editing tumor metabolism.
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