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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...

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Autores principales: Li, Lijuan, Wu, Nan, Zhuang, Gaojian, Geng, Lin, Zeng, Yu, Wang, Xuan, Wang, Shuang, Ruan, Xianhui, Zheng, Xiangqian, Liu, Juntian, Gao, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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.
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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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