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<p>Triple-negative breast cancer (TNBC) presents significant therapeutic challenges due to its aggressive nature and lack of targeted treatment options. Emerging evidence highlights the critical role of immune infiltration patterns in TNBC progression and prognosis, yet a comprehensive classification system integrating immune and transcriptomic features remains elusive. In this study, we employed the xCell algorithm to characterize immune infiltration across TNBC samples and utilized the Similarity Network Fusion and Consensus Clustering (SNF-CC) method to identify molecular subtypes. Our analysis revealed three distinct TNBC subtypes with marked heterogeneity in transcriptomic profiles and immune microenvironment composition. Subtype 1 exhibited high immune infiltration, while Subtype 3 demonstrated immunosuppressive characteristics. Functional enrichment analysis linked subtype-specific differentially expressed genes (DEGs) to pathways such as T-cell activation and cytokine signaling. Protein-protein interaction networks identified key hub genes (PTPRC, CD4, and UBE2C) showing elevated expression in TNBC tissues. Experimental validation in breast cancer cell lines confirmed that knockout of PTPRC, CD4, or UBE2C significantly impaired proliferation, migration, and invasion, while rescue experiments restored these oncogenic phenotypes. These findings establish an immune-based molecular subtyping framework for TNBC and uncover pivotal genes driving tumor progression. Our work provides novel insights into TNBC heterogeneity and identifies potential therapeutic targets for precision immunotherapy, advancing strategies to improve clinical outcomes in this recalcitrant malignancy.</p>

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Immune-based molecular subtyping of triple-negative breast cancer via SNF-CC and functional validation of key immune-associated genes

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Pengwei Guo,
  • Fengfei Yan,
  • Hui Chang,
  • Fuchun Li,
  • Jinpeng Hu,
  • Meizhe Liu,
  • Aidong Li

摘要

Triple-negative breast cancer (TNBC) presents significant therapeutic challenges due to its aggressive nature and lack of targeted treatment options. Emerging evidence highlights the critical role of immune infiltration patterns in TNBC progression and prognosis, yet a comprehensive classification system integrating immune and transcriptomic features remains elusive. In this study, we employed the xCell algorithm to characterize immune infiltration across TNBC samples and utilized the Similarity Network Fusion and Consensus Clustering (SNF-CC) method to identify molecular subtypes. Our analysis revealed three distinct TNBC subtypes with marked heterogeneity in transcriptomic profiles and immune microenvironment composition. Subtype 1 exhibited high immune infiltration, while Subtype 3 demonstrated immunosuppressive characteristics. Functional enrichment analysis linked subtype-specific differentially expressed genes (DEGs) to pathways such as T-cell activation and cytokine signaling. Protein-protein interaction networks identified key hub genes (PTPRC, CD4, and UBE2C) showing elevated expression in TNBC tissues. Experimental validation in breast cancer cell lines confirmed that knockout of PTPRC, CD4, or UBE2C significantly impaired proliferation, migration, and invasion, while rescue experiments restored these oncogenic phenotypes. These findings establish an immune-based molecular subtyping framework for TNBC and uncover pivotal genes driving tumor progression. Our work provides novel insights into TNBC heterogeneity and identifies potential therapeutic targets for precision immunotherapy, advancing strategies to improve clinical outcomes in this recalcitrant malignancy.