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A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer

BACKGROUND: Breast cancer (BC) is one of the most common cancers worldwide and patients with lymph node metastasis always suffer from a worse prognosis. Tumor mutation burden (TMB) has been reported as a potential predictor for tumor behaviors. However, the correlation between TMB and lymph node met...

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Autores principales: Wang, Cenzhu, Xu, Kun, Deng, Fei, Liu, Yiqiu, Huang, Jinyi, Wang, Runtian, Guan, Xiaoxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798002/
https://www.ncbi.nlm.nih.gov/pubmed/35116541
http://dx.doi.org/10.21037/tcr-20-3471
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author Wang, Cenzhu
Xu, Kun
Deng, Fei
Liu, Yiqiu
Huang, Jinyi
Wang, Runtian
Guan, Xiaoxiang
author_facet Wang, Cenzhu
Xu, Kun
Deng, Fei
Liu, Yiqiu
Huang, Jinyi
Wang, Runtian
Guan, Xiaoxiang
author_sort Wang, Cenzhu
collection PubMed
description BACKGROUND: Breast cancer (BC) is one of the most common cancers worldwide and patients with lymph node metastasis always suffer from a worse prognosis. Tumor mutation burden (TMB) has been reported as a potential predictor for tumor behaviors. However, the correlation between TMB and lymph node metastasis of BC remains unclear. This study aimed to explore TMB-related biomarkers to predict the lymph node metastasis in BC patients. METHODS: A total of 949 BC patients with RNA-seq data, mutation data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We visualized mutation data by “maftools” package. We calculated TMB of each patient and investigated its association with lymph node metastasis. BC patients were divided into lymph node positive and negative groups and we respectively identified TMB-related and lymph node-related differentially expressed genes (DEGs) to figure out intersected genes. Functional enrichment analysis and protein-protein interaction (PPI) network were performed to observe relevant biological functions. We constructed a TMB-related signature for predicting lymph node metastasis through Logistic regression analysis. A validation database (GSE102484) from the Gene Expression Omnibus (GEO) database was downloaded to verify the accuracy. RESULTS: Single nucleotide polymorphism (SNP) occupied the highest proportion in variant types while C>T appeared most frequently in single nucleotide variant (SNV). TMB was regarded as negatively correlated with lymph node metastasis in BC (P=0.003). We identified 125 common DEGs through venn diagram, which were enriched in vesicle localization, calcium signaling pathway and salmonella infection. A TMB-related signature based on six genes (BAHD1, PPM1A, PQLC3, SMPD3, EEF1A1 and S100B) had reliable efficacy for predicting lymph node metastasis in BC and was proven as an independent predictive factor. The accuracy of this signature was further validated by GSE102484 database. CONCLUSIONS: Our results indicated that TMB was associated with lymph node metastasis of BC. We built a TMB-related signature consisting of six genes which might function as a novel biomarker for predicting lymph node metastasis in BC.
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spelling pubmed-87980022022-02-02 A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer Wang, Cenzhu Xu, Kun Deng, Fei Liu, Yiqiu Huang, Jinyi Wang, Runtian Guan, Xiaoxiang Transl Cancer Res Original Article BACKGROUND: Breast cancer (BC) is one of the most common cancers worldwide and patients with lymph node metastasis always suffer from a worse prognosis. Tumor mutation burden (TMB) has been reported as a potential predictor for tumor behaviors. However, the correlation between TMB and lymph node metastasis of BC remains unclear. This study aimed to explore TMB-related biomarkers to predict the lymph node metastasis in BC patients. METHODS: A total of 949 BC patients with RNA-seq data, mutation data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We visualized mutation data by “maftools” package. We calculated TMB of each patient and investigated its association with lymph node metastasis. BC patients were divided into lymph node positive and negative groups and we respectively identified TMB-related and lymph node-related differentially expressed genes (DEGs) to figure out intersected genes. Functional enrichment analysis and protein-protein interaction (PPI) network were performed to observe relevant biological functions. We constructed a TMB-related signature for predicting lymph node metastasis through Logistic regression analysis. A validation database (GSE102484) from the Gene Expression Omnibus (GEO) database was downloaded to verify the accuracy. RESULTS: Single nucleotide polymorphism (SNP) occupied the highest proportion in variant types while C>T appeared most frequently in single nucleotide variant (SNV). TMB was regarded as negatively correlated with lymph node metastasis in BC (P=0.003). We identified 125 common DEGs through venn diagram, which were enriched in vesicle localization, calcium signaling pathway and salmonella infection. A TMB-related signature based on six genes (BAHD1, PPM1A, PQLC3, SMPD3, EEF1A1 and S100B) had reliable efficacy for predicting lymph node metastasis in BC and was proven as an independent predictive factor. The accuracy of this signature was further validated by GSE102484 database. CONCLUSIONS: Our results indicated that TMB was associated with lymph node metastasis of BC. We built a TMB-related signature consisting of six genes which might function as a novel biomarker for predicting lymph node metastasis in BC. AME Publishing Company 2021-05 /pmc/articles/PMC8798002/ /pubmed/35116541 http://dx.doi.org/10.21037/tcr-20-3471 Text en 2021 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
spellingShingle Original Article
Wang, Cenzhu
Xu, Kun
Deng, Fei
Liu, Yiqiu
Huang, Jinyi
Wang, Runtian
Guan, Xiaoxiang
A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
title A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
title_full A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
title_fullStr A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
title_full_unstemmed A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
title_short A six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
title_sort six-gene signature related with tumor mutation burden for predicting lymph node metastasis in breast cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798002/
https://www.ncbi.nlm.nih.gov/pubmed/35116541
http://dx.doi.org/10.21037/tcr-20-3471
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