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Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations
Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502304/ https://www.ncbi.nlm.nih.gov/pubmed/37719859 http://dx.doi.org/10.3389/fphar.2023.1224828 |
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author | Li, Lijuan Wu, Nan Zhuang, Gaojian Geng, Lin Zeng, Yu Wang, Xuan Wang, Shuang Ruan, Xianhui Zheng, Xiangqian Liu, Juntian Gao, Ming |
author_facet | Li, Lijuan Wu, Nan Zhuang, Gaojian Geng, Lin Zeng, Yu Wang, Xuan Wang, Shuang Ruan, Xianhui Zheng, Xiangqian Liu, Juntian Gao, Ming |
author_sort | Li, Lijuan |
collection | PubMed |
description | Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies. |
format | Online Article Text |
id | pubmed-10502304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105023042023-09-16 Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations Li, Lijuan Wu, Nan Zhuang, Gaojian Geng, Lin Zeng, Yu Wang, Xuan Wang, Shuang Ruan, Xianhui Zheng, Xiangqian Liu, Juntian Gao, Ming Front Pharmacol Pharmacology Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies. Frontiers Media S.A. 2023-09-01 /pmc/articles/PMC10502304/ /pubmed/37719859 http://dx.doi.org/10.3389/fphar.2023.1224828 Text en Copyright © 2023 Li, Wu, Zhuang, Geng, Zeng, Wang, Wang, Ruan, Zheng, Liu and Gao. 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 | Pharmacology Li, Lijuan Wu, Nan Zhuang, Gaojian Geng, Lin Zeng, Yu Wang, Xuan Wang, Shuang Ruan, Xianhui Zheng, Xiangqian Liu, Juntian Gao, Ming Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
title | Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
title_full | Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
title_fullStr | Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
title_full_unstemmed | Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
title_short | Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
title_sort | heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502304/ https://www.ncbi.nlm.nih.gov/pubmed/37719859 http://dx.doi.org/10.3389/fphar.2023.1224828 |
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