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Background <p>Breast cancer (BRCA) is the most common malignant tumor in women, with prognoses differing markedly by molecular subtype. Exercise is known to be related to tumor occurrence and progression, yet its role in BRCA remains understudied. This research seeks to explore the prognostic and immunomodulatory functions of exercise-related genes (ERGs) in BRCA, and their links to molecular subtypes, so as to inform targeted and precision treatment.</p> Methods <p>We obtained BRCA gene expression profiles and clinical data from the TCGA-BRCA project. Using LASSO, random forest, gradient boosting machine (GBM), stepwise Cox regression (Stepcox), and extreme gradient boosting (XGBOOST) algorithms, we screened prognostic signature genes from the intersecting ERGs, determined by differential expression analysis and weighted gene co-expression network analysis (WGCNA), established and validated a novel risk prediction model. We further analyzed biological functions, mutation profiles, immune traits, and molecular subtype associations across different risk groups. Additionally, the prognostic, biological and immunological roles of the core risk biomarker in the exercise-related signature was evaluated.</p> Results <p>WGCNA identified 345 differentially expressed ERGs in BRCA, and five machine learning algorithms further screened 6 prognostic genes (SNX22, SLC52A2, TNFRSF18, NFE2, EZR, and EMP1). The low-risk group had significantly higher overall survival rate than the high-risk group, a difference driven by the functional roles of the two groups’ differentially expressed genes, which regulated the cell cycle and epithelial-mesenchymal transition (EMT). Additionally, the high- and low-risk groups showed distinct mutation and immunomodulatory features. Consensus clustering of prognosis between the groups linked these differences to BRCA molecular subtypes. As a core candidate factor, SLC52A2 contributed to BRCA progression and poor prognosis in patients by participating in multiple signaling pathways and regulating immune cells.</p> Conclusion <p>ERGs-based prognostic signatures could distinguish clinical outcomes of BRCA patients and were correlated with immune regulation and mutation patterns. The ERGs-constructed risk scoring system and molecular subtypes have the potential to serve as preclinical biological and immunological markers, paving the way for clinical management and treatment of BRCA.</p>

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Exercise-related genes predicts overall survival and tumor immune microenvironment, and identifies the biological role of SLC52A2 in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Caiping Chen,
  • Chao Han,
  • Li Xue,
  • Xiang Lu

摘要

Background

Breast cancer (BRCA) is the most common malignant tumor in women, with prognoses differing markedly by molecular subtype. Exercise is known to be related to tumor occurrence and progression, yet its role in BRCA remains understudied. This research seeks to explore the prognostic and immunomodulatory functions of exercise-related genes (ERGs) in BRCA, and their links to molecular subtypes, so as to inform targeted and precision treatment.

Methods

We obtained BRCA gene expression profiles and clinical data from the TCGA-BRCA project. Using LASSO, random forest, gradient boosting machine (GBM), stepwise Cox regression (Stepcox), and extreme gradient boosting (XGBOOST) algorithms, we screened prognostic signature genes from the intersecting ERGs, determined by differential expression analysis and weighted gene co-expression network analysis (WGCNA), established and validated a novel risk prediction model. We further analyzed biological functions, mutation profiles, immune traits, and molecular subtype associations across different risk groups. Additionally, the prognostic, biological and immunological roles of the core risk biomarker in the exercise-related signature was evaluated.

Results

WGCNA identified 345 differentially expressed ERGs in BRCA, and five machine learning algorithms further screened 6 prognostic genes (SNX22, SLC52A2, TNFRSF18, NFE2, EZR, and EMP1). The low-risk group had significantly higher overall survival rate than the high-risk group, a difference driven by the functional roles of the two groups’ differentially expressed genes, which regulated the cell cycle and epithelial-mesenchymal transition (EMT). Additionally, the high- and low-risk groups showed distinct mutation and immunomodulatory features. Consensus clustering of prognosis between the groups linked these differences to BRCA molecular subtypes. As a core candidate factor, SLC52A2 contributed to BRCA progression and poor prognosis in patients by participating in multiple signaling pathways and regulating immune cells.

Conclusion

ERGs-based prognostic signatures could distinguish clinical outcomes of BRCA patients and were correlated with immune regulation and mutation patterns. The ERGs-constructed risk scoring system and molecular subtypes have the potential to serve as preclinical biological and immunological markers, paving the way for clinical management and treatment of BRCA.