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A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer
Objective: Tumor suppressor genes (TSGs) play critical roles in the cell cycle checkpoints and in modulating genomic stability. Here, we aimed to develop a TSG-based prognostic classifier for breast cancer. Methods: Gene expression profiles and clinical information of breast cancer were curated from...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850650/ https://www.ncbi.nlm.nih.gov/pubmed/35186006 http://dx.doi.org/10.3389/fgene.2021.783026 |
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author | Jiang, Suxiao Bu, Xiangjing Tang, Desheng Yan, Changsheng Huang, Yan Fang, Kun |
author_facet | Jiang, Suxiao Bu, Xiangjing Tang, Desheng Yan, Changsheng Huang, Yan Fang, Kun |
author_sort | Jiang, Suxiao |
collection | PubMed |
description | Objective: Tumor suppressor genes (TSGs) play critical roles in the cell cycle checkpoints and in modulating genomic stability. Here, we aimed to develop a TSG-based prognostic classifier for breast cancer. Methods: Gene expression profiles and clinical information of breast cancer were curated from TCGA (discovery set) and Gene Expression Omnibus (GEO) repository (GSE12093 and GSE17705 datasets as testing sets). Univariate cox regression analysis and random forest machine learning method were presented for screening characteristic TSGs. After multivariate cox regression analyses, a TSG-based prognostic classifier was constructed. The predictive efficacy was verified by C-index and receiver operating characteristic (ROC) curves. Meanwhile, the predictive independency was assessed through uni- and multivariate cox regression analyses and stratified analyses. Tumor immune infiltration was estimated via ESTIMATE and CIBERSORT algorithms. Small molecule agents were predicted through CMap method. Molecular subtypes were clustered based on the top 100 TSGs with the most variance. Results: A prognostic classifier including nine TSGs was established. High-risk patients were predictive of undesirable prognosis. C-index and ROC curves demonstrated its excellent predictive performance in prognosis. Also, this prognostic classifier was independent of conventional clinicopathological parameters. Low-risk patients exhibited increased infiltration levels of immune cells like T cells CD8. Totally, 48 small molecule compounds were predicted to potentially treat breast cancer. Five TSG-based molecular subtypes were finally constructed, with distinct prognosis and clinicopathological features. Conclusion: Collectively, this study provided a TSG-based prognostic classifier with the potential to predict clinical outcomes and immune infiltration in breast cancer and identified potential small molecule agents against breast cancer. |
format | Online Article Text |
id | pubmed-8850650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88506502022-02-18 A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer Jiang, Suxiao Bu, Xiangjing Tang, Desheng Yan, Changsheng Huang, Yan Fang, Kun Front Genet Genetics Objective: Tumor suppressor genes (TSGs) play critical roles in the cell cycle checkpoints and in modulating genomic stability. Here, we aimed to develop a TSG-based prognostic classifier for breast cancer. Methods: Gene expression profiles and clinical information of breast cancer were curated from TCGA (discovery set) and Gene Expression Omnibus (GEO) repository (GSE12093 and GSE17705 datasets as testing sets). Univariate cox regression analysis and random forest machine learning method were presented for screening characteristic TSGs. After multivariate cox regression analyses, a TSG-based prognostic classifier was constructed. The predictive efficacy was verified by C-index and receiver operating characteristic (ROC) curves. Meanwhile, the predictive independency was assessed through uni- and multivariate cox regression analyses and stratified analyses. Tumor immune infiltration was estimated via ESTIMATE and CIBERSORT algorithms. Small molecule agents were predicted through CMap method. Molecular subtypes were clustered based on the top 100 TSGs with the most variance. Results: A prognostic classifier including nine TSGs was established. High-risk patients were predictive of undesirable prognosis. C-index and ROC curves demonstrated its excellent predictive performance in prognosis. Also, this prognostic classifier was independent of conventional clinicopathological parameters. Low-risk patients exhibited increased infiltration levels of immune cells like T cells CD8. Totally, 48 small molecule compounds were predicted to potentially treat breast cancer. Five TSG-based molecular subtypes were finally constructed, with distinct prognosis and clinicopathological features. Conclusion: Collectively, this study provided a TSG-based prognostic classifier with the potential to predict clinical outcomes and immune infiltration in breast cancer and identified potential small molecule agents against breast cancer. Frontiers Media S.A. 2022-02-03 /pmc/articles/PMC8850650/ /pubmed/35186006 http://dx.doi.org/10.3389/fgene.2021.783026 Text en Copyright © 2022 Jiang, Bu, Tang, Yan, Huang and Fang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Jiang, Suxiao Bu, Xiangjing Tang, Desheng Yan, Changsheng Huang, Yan Fang, Kun A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer |
title | A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer |
title_full | A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer |
title_fullStr | A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer |
title_full_unstemmed | A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer |
title_short | A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer |
title_sort | tumor suppressor gene-based prognostic classifier predicts prognosis, tumor immune infiltration, and small molecule compounds in breast cancer |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850650/ https://www.ncbi.nlm.nih.gov/pubmed/35186006 http://dx.doi.org/10.3389/fgene.2021.783026 |
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