Frontiers in pharmacology · 2026
Integration of tumor microenvironment and metabolic signatures reveals TMPI-defined prognostic and immunotherapy-relevant phenotypes in breast cancer.
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Huang Zhong, Lin Shuhui, Liang Ying, Khan Muhammad, Chen Weirui, Li Xuefeng, Liu Xiaolan, Wu Yunyu, Ye Jiacai
Аннотация
BACKGROUND: Breast cancer heterogeneity presents a significant challenge for effective diagnosis and treatment. A deeper understanding of the tumor microenvironment (TME) and its metabolic dynamics is essential for addressing this complexity. This study explored the interplay between TME components and metabolic profiles in breast cancer using RNA sequencing data from both bulk and single-cell analyses.
METHODS: Transcriptomic data and corresponding clinical information for breast cancer patients were obtained from TCGA and GEO databases. qPCR and immunohistochemistry (IHC) were performed for experimental validation. Data analyses were conducted using R software.
RESULTS: A high-risk phenotype was identified based on 36 interconnected genes primarily associated with the TME and metabolic processes. This phenotype demonstrated enrichment of nicotinamide adenine dinucleotide (NAD+) metabolism and was associated with reduced T- and NK-cell activity within the TME, contributing to poor prognosis in HER2 and Luminal B breast cancer subtypes. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, a 23-gene risk model (TMPI) was established, which further refined prognostic stratification and highlighted elevated risk in HER2 and Luminal B contexts. Immune and stromal analyses, along with single-cell validation, revealed that key NAD metabolism genes (NMNAT2 and QPRT) and the adenosine pathway enzyme NT5E were predominantly expressed in stromal and malignant cell populations, including endothelial cells and fibroblasts. qPCR validation confirmed subtype-specific expression patterns, with NMNAT2 and QPRT upregulated in ER-positive cells and NT5E enriched in triple-negative breast cancer (TNBC) cells. IHC analysis further validated their increased expression in tumor tissues compared to adjacent normal tissues. Importantly, TMPI was consistently associated with immune-desert and immune-excluded tumor microenvironment states across immunotherapy cohorts and demonstrated therapy-dependent associations with anti-PD-1 and anti-PD-L1 treatment response patterns, suggesting that NAD metabolism-associated immune states may influence immunotherapy sensitivity in a context-dependent manner.
CONCLUSION: These findings provide insights into the interplay between metabolism and the tumor microenvironment in breast cancer. TMPI captures the prognostically relevant dimension of TME-metabolism crosstalk and may serve as a useful framework for patient stratification, characterization of tumor immune phenotypes, and therapeutic targeting, particularly in HER2+ and Luminal B breast cancers.
METHODS: Transcriptomic data and corresponding clinical information for breast cancer patients were obtained from TCGA and GEO databases. qPCR and immunohistochemistry (IHC) were performed for experimental validation. Data analyses were conducted using R software.
RESULTS: A high-risk phenotype was identified based on 36 interconnected genes primarily associated with the TME and metabolic processes. This phenotype demonstrated enrichment of nicotinamide adenine dinucleotide (NAD+) metabolism and was associated with reduced T- and NK-cell activity within the TME, contributing to poor prognosis in HER2 and Luminal B breast cancer subtypes. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, a 23-gene risk model (TMPI) was established, which further refined prognostic stratification and highlighted elevated risk in HER2 and Luminal B contexts. Immune and stromal analyses, along with single-cell validation, revealed that key NAD metabolism genes (NMNAT2 and QPRT) and the adenosine pathway enzyme NT5E were predominantly expressed in stromal and malignant cell populations, including endothelial cells and fibroblasts. qPCR validation confirmed subtype-specific expression patterns, with NMNAT2 and QPRT upregulated in ER-positive cells and NT5E enriched in triple-negative breast cancer (TNBC) cells. IHC analysis further validated their increased expression in tumor tissues compared to adjacent normal tissues. Importantly, TMPI was consistently associated with immune-desert and immune-excluded tumor microenvironment states across immunotherapy cohorts and demonstrated therapy-dependent associations with anti-PD-1 and anti-PD-L1 treatment response patterns, suggesting that NAD metabolism-associated immune states may influence immunotherapy sensitivity in a context-dependent manner.
CONCLUSION: These findings provide insights into the interplay between metabolism and the tumor microenvironment in breast cancer. TMPI captures the prognostically relevant dimension of TME-metabolism crosstalk and may serve as a useful framework for patient stratification, characterization of tumor immune phenotypes, and therapeutic targeting, particularly in HER2+ and Luminal B breast cancers.