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<p>Immune-related genes (IRGs) play a pivotal role in the tumor microenvironment of triple-negative breast cancer (TNBC). This study aimed to uncover novel prognostic markers for TNBC based on IRGs. Differential expression analysis, univariate Cox regression, and machine learning algorithms identified BPI, GPHA2, KIR3DL3, MASP1, RASGRP1, and SEMA4A as potential prognostic genes. A random survival forest (RSF) model was developed and subsequently validated, alongside the creation of a prognostic nomogram to assess gene diagnostic performance. Further, epithelial cells, fibroblasts, and T cells were recognized as the primary cell types through which RASGRP1 exerts its effects. Finally, an analysis of the immune microenvironment and drug predictions provided fresh insights for the treatment of TNBC.</p>

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Immune microenvironment and prognostic genes of triple-negative breast cancer in the context of transcriptome and single-cell sequencing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月13日

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  • Xiangqin Huang,
  • Wei Chen,
  • Zhiwu Lin,
  • Hui Zhang,
  • Ruijuan Wang,
  • Cailing Lin

摘要

Immune-related genes (IRGs) play a pivotal role in the tumor microenvironment of triple-negative breast cancer (TNBC). This study aimed to uncover novel prognostic markers for TNBC based on IRGs. Differential expression analysis, univariate Cox regression, and machine learning algorithms identified BPI, GPHA2, KIR3DL3, MASP1, RASGRP1, and SEMA4A as potential prognostic genes. A random survival forest (RSF) model was developed and subsequently validated, alongside the creation of a prognostic nomogram to assess gene diagnostic performance. Further, epithelial cells, fibroblasts, and T cells were recognized as the primary cell types through which RASGRP1 exerts its effects. Finally, an analysis of the immune microenvironment and drug predictions provided fresh insights for the treatment of TNBC.