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Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis....

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Autores principales: Chen, De-Lun, Cai, Jia-Hua, Wang, Charles C. N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140789/
https://www.ncbi.nlm.nih.gov/pubmed/35627287
http://dx.doi.org/10.3390/genes13050902
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author Chen, De-Lun
Cai, Jia-Hua
Wang, Charles C. N.
author_facet Chen, De-Lun
Cai, Jia-Hua
Wang, Charles C. N.
author_sort Chen, De-Lun
collection PubMed
description Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.
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spelling pubmed-91407892022-05-28 Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis Chen, De-Lun Cai, Jia-Hua Wang, Charles C. N. Genes (Basel) Article Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets. MDPI 2022-05-18 /pmc/articles/PMC9140789/ /pubmed/35627287 http://dx.doi.org/10.3390/genes13050902 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, De-Lun
Cai, Jia-Hua
Wang, Charles C. N.
Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
title Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
title_full Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
title_fullStr Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
title_full_unstemmed Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
title_short Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis
title_sort identification of key prognostic genes of triple negative breast cancer by lasso-based machine learning and bioinformatics analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140789/
https://www.ncbi.nlm.nih.gov/pubmed/35627287
http://dx.doi.org/10.3390/genes13050902
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