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<p>Triple-negative breast cancer (TNBC) represents a formidable subtype with a grim prognosis. This study aims to pinpoint the molecular targets of Glycosyltransferases (GTs) in TNBC, with the goal of improving prognostic accuracy and boosting the effectiveness of immune therapies. Using publicly available datasets, we combined differentially expressed and correlated genes based on AUCell scores from single-cell sequencing. These were then subjected to enrichment analysis and utilized for constructing a risk model. A total of 780 genes were identified as being closely associated with GTs. Using 101 algorithm combinations, a 17-gene signature emerged with predictive capabilities for TNBC patient prognosis. At the same time, we examined the role of these model genes in the tumor microenvironment(TME). We identified key transcription factors correlating with GTs, including CREB3L1, which showed significant association with the CERCAM gene. Subsequently, the pro-cancerous effects of GTs were validated through a series of experiments, including CCK-8 cell viability assays, scratch wound healing assays, and Transwell migration and invasion assays. This research lays the foundation for targeted drug therapies, offering new opportunities to enhance clinical outcomes in TNBC.</p>

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Integrative single-cell and bulk RNA-seq analysis identifies glycosyltransferases-related signature in triple negative breast cancer

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Junyi Hu,
  • Ningning Yuan,
  • Zhenglan Huang,
  • Yancheng Liu,
  • Bohan Tu,
  • Xinyue Yu,
  • Tianli Hui,
  • Guowei Zuo

摘要

Triple-negative breast cancer (TNBC) represents a formidable subtype with a grim prognosis. This study aims to pinpoint the molecular targets of Glycosyltransferases (GTs) in TNBC, with the goal of improving prognostic accuracy and boosting the effectiveness of immune therapies. Using publicly available datasets, we combined differentially expressed and correlated genes based on AUCell scores from single-cell sequencing. These were then subjected to enrichment analysis and utilized for constructing a risk model. A total of 780 genes were identified as being closely associated with GTs. Using 101 algorithm combinations, a 17-gene signature emerged with predictive capabilities for TNBC patient prognosis. At the same time, we examined the role of these model genes in the tumor microenvironment(TME). We identified key transcription factors correlating with GTs, including CREB3L1, which showed significant association with the CERCAM gene. Subsequently, the pro-cancerous effects of GTs were validated through a series of experiments, including CCK-8 cell viability assays, scratch wound healing assays, and Transwell migration and invasion assays. This research lays the foundation for targeted drug therapies, offering new opportunities to enhance clinical outcomes in TNBC.