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Backgrounds <p>Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by 2050. Current treatment modalities, encompassing surgical intervention, chemotherapy, and immunotherapy, are markedly hindered by treatment resistance and the inherent complexity of BRCA, thereby impeding effective disease management. Consequently, this study is designed to elucidate cellular heterogeneity within the tumor microenvironment (TME) and to identify prospective therapeutic targets, thus enabling individualized treatment approaches for individuals with BRCA, which remains crucial.</p> Methods <p>GEO and TCGA databases were the sources of all analytical data employed in this investigation. Single-cell RNA sequencing data were employed to identify macrophage-associated genes in BRCA, followed by the development of a machine-learning-based prognostic model (PM) integrating data from TCGA and GEO databases. This model stratifies individuals into cohorts of either low or high risk, enabling a disparity evaluation in survival outcomes and tumor immune microenvironment characteristics. Gene co-expression patterns were examined through an analysis of gene co-expression network weighted by high dimension for screening pivotal central genes implicated in tumor immunity. Clinical PMs were subsequently constructed utilizing machine learning algorithms, with validation performed on training and test sets. Furthermore, XGBoost and LightGBM machine learning algorithms were implemented to pinpoint potential biomarkers. Ultimately, validation of these biomarkers was conducted through ELISA, CCK-8 assay, flow cytometry, Western blotting, and immunoprecipitation assays.</p> Results <p>Findings indicated a pronounced elevation of TAMs in BRCA patients, with their phenotypic attributes exhibiting a strong correlation with prognosis. The developed clinical PM demonstrated high accuracy and robust predictive capability concerning survival rates of 1, 3, or 5&#xa0;years. The comprehensive evaluation determined that high-risk cohort’s immune microenvironment exhibited a greater inclination toward immune suppression, whereas the low-risk cohort was markedly predisposed to anti-tumor immune responses. Notably, the trifolium factor 1 (TFF1) gene was identified as a key determinant, with its overexpression being markedly associated with tumor invasiveness and immune evasion.</p> Conclusions <p>The study underscores cellular heterogeneity inherent in BRCA TME, while PM, predicated on macrophage subpopulation-related genes, offers a novel approach for risk stratification and personalized therapeutic interventions for BRCA patients. Moreover, TFF1 has been established as a prospective therapeutic target, yielding important directions for developing specific treatment interventions.</p>

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Macrophage heterogeneity in breast cancer microenvironment and development of a prognostic model: exploring TFF1 as a novel therapeutic target

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Lin Chen,
  • Zhiqiang Chen,
  • Bailu Shi,
  • Lingli Chen,
  • Guoren Zhou

摘要

Backgrounds

Breast cancer (BRCA) represents the most prevalent malignancy globally, with projections indicating 3.2 million new cases anticipated by 2050. Current treatment modalities, encompassing surgical intervention, chemotherapy, and immunotherapy, are markedly hindered by treatment resistance and the inherent complexity of BRCA, thereby impeding effective disease management. Consequently, this study is designed to elucidate cellular heterogeneity within the tumor microenvironment (TME) and to identify prospective therapeutic targets, thus enabling individualized treatment approaches for individuals with BRCA, which remains crucial.

Methods

GEO and TCGA databases were the sources of all analytical data employed in this investigation. Single-cell RNA sequencing data were employed to identify macrophage-associated genes in BRCA, followed by the development of a machine-learning-based prognostic model (PM) integrating data from TCGA and GEO databases. This model stratifies individuals into cohorts of either low or high risk, enabling a disparity evaluation in survival outcomes and tumor immune microenvironment characteristics. Gene co-expression patterns were examined through an analysis of gene co-expression network weighted by high dimension for screening pivotal central genes implicated in tumor immunity. Clinical PMs were subsequently constructed utilizing machine learning algorithms, with validation performed on training and test sets. Furthermore, XGBoost and LightGBM machine learning algorithms were implemented to pinpoint potential biomarkers. Ultimately, validation of these biomarkers was conducted through ELISA, CCK-8 assay, flow cytometry, Western blotting, and immunoprecipitation assays.

Results

Findings indicated a pronounced elevation of TAMs in BRCA patients, with their phenotypic attributes exhibiting a strong correlation with prognosis. The developed clinical PM demonstrated high accuracy and robust predictive capability concerning survival rates of 1, 3, or 5 years. The comprehensive evaluation determined that high-risk cohort’s immune microenvironment exhibited a greater inclination toward immune suppression, whereas the low-risk cohort was markedly predisposed to anti-tumor immune responses. Notably, the trifolium factor 1 (TFF1) gene was identified as a key determinant, with its overexpression being markedly associated with tumor invasiveness and immune evasion.

Conclusions

The study underscores cellular heterogeneity inherent in BRCA TME, while PM, predicated on macrophage subpopulation-related genes, offers a novel approach for risk stratification and personalized therapeutic interventions for BRCA patients. Moreover, TFF1 has been established as a prospective therapeutic target, yielding important directions for developing specific treatment interventions.