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Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis
BACKGROUND: The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114340/ https://www.ncbi.nlm.nih.gov/pubmed/37072703 http://dx.doi.org/10.1186/s12859-023-05268-2 |
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author | Li, He Yang, Lei Wang, Yuanyuan Wang, Lingchan Chen, Gang Zhang, Li Wang, Dongchang |
author_facet | Li, He Yang, Lei Wang, Yuanyuan Wang, Lingchan Chen, Gang Zhang, Li Wang, Dongchang |
author_sort | Li, He |
collection | PubMed |
description | BACKGROUND: The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD. METHODS: Genomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein–protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD. RESULTS: TP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R(2) < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates. CONCLUSIONS: TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05268-2. |
format | Online Article Text |
id | pubmed-10114340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101143402023-04-20 Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis Li, He Yang, Lei Wang, Yuanyuan Wang, Lingchan Chen, Gang Zhang, Li Wang, Dongchang BMC Bioinformatics Research BACKGROUND: The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD. METHODS: Genomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein–protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD. RESULTS: TP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R(2) < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates. CONCLUSIONS: TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05268-2. BioMed Central 2023-04-18 /pmc/articles/PMC10114340/ /pubmed/37072703 http://dx.doi.org/10.1186/s12859-023-05268-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, He Yang, Lei Wang, Yuanyuan Wang, Lingchan Chen, Gang Zhang, Li Wang, Dongchang Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
title | Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
title_full | Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
title_fullStr | Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
title_full_unstemmed | Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
title_short | Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
title_sort | integrative analysis of tp53 mutations in lung adenocarcinoma for immunotherapies and prognosis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114340/ https://www.ncbi.nlm.nih.gov/pubmed/37072703 http://dx.doi.org/10.1186/s12859-023-05268-2 |
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