Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.946468/full |
| _version_ | 1818190702349123584 |
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| author | Mengping Jiang Mengping Jiang Xiangyan Wu Shengnan Bao Shengnan Bao Xi Wang Xi Wang Fei Qu Fei Qu Qian Liu Qian Liu Xiang Huang Xiang Huang Wei Li Wei Li Jinhai Tang Yongmei Yin Yongmei Yin |
| author_facet | Mengping Jiang Mengping Jiang Xiangyan Wu Shengnan Bao Shengnan Bao Xi Wang Xi Wang Fei Qu Fei Qu Qian Liu Qian Liu Xiang Huang Xiang Huang Wei Li Wei Li Jinhai Tang Yongmei Yin Yongmei Yin |
| author_sort | Mengping Jiang |
| collection | DOAJ |
| description | TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC. |
| first_indexed | 2024-12-12T00:02:55Z |
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| id | doaj.art-7466bb37ec1143078e12ab3b0d8b0c66 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-12T00:02:55Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-7466bb37ec1143078e12ab3b0d8b0c662022-12-22T00:45:11ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-07-011310.3389/fimmu.2022.946468946468Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancerMengping Jiang0Mengping Jiang1Xiangyan Wu2Shengnan Bao3Shengnan Bao4Xi Wang5Xi Wang6Fei Qu7Fei Qu8Qian Liu9Qian Liu10Xiang Huang11Xiang Huang12Wei Li13Wei Li14Jinhai Tang15Yongmei Yin16Yongmei Yin17Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaSchool of Electro-mechanical Engineering, Guangdong University of Technology, Guangzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Clinical College of Nanjing Medical University, Nanjing, ChinaDepartment of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaJiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, ChinaTP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC.https://www.frontiersin.org/articles/10.3389/fimmu.2022.946468/fullTP53prognostic modelimmune heterogeneitymetabolic heterogeneitybreast cancer |
| spellingShingle | Mengping Jiang Mengping Jiang Xiangyan Wu Shengnan Bao Shengnan Bao Xi Wang Xi Wang Fei Qu Fei Qu Qian Liu Qian Liu Xiang Huang Xiang Huang Wei Li Wei Li Jinhai Tang Yongmei Yin Yongmei Yin Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer Frontiers in Immunology TP53 prognostic model immune heterogeneity metabolic heterogeneity breast cancer |
| title | Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer |
| title_full | Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer |
| title_fullStr | Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer |
| title_full_unstemmed | Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer |
| title_short | Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer |
| title_sort | immunometabolism characteristics and a potential prognostic risk model associated with tp53 mutations in breast cancer |
| topic | TP53 prognostic model immune heterogeneity metabolic heterogeneity breast cancer |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.946468/full |
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