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Background <p>Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by poor prognosis and limited treatment options, which underscores the urgency of the discovery of new biomarkers. Metabolic reprogramming is a hallmark of cancer and is expected to serve as a strong predictive biomarker for breast cancer.</p> Methods <p>We integrated RNA expression data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the associations between metabolism-related gene expression and TNBC prognosis. Our comprehensive approach included differential expression analysis, enrichment analysis, Cox regression analysis, machine learning, and in vitro experimental validation.</p> Results <p>We identified five pivotal genes—SDS, RDH12, IDO1, GLDC, and ALOX12B—that were significantly correlated with the prognosis of TNBC patients. A prognostic model incorporating these genes was developed and validated in an independent patient cohort. The model demonstrated predictive validity, as TNBC patients classified into the high-risk group exhibited significantly poorer prognoses. Additionally, utilizing the risk model, we evaluated the mutational landscape, immune infiltration, immunotherapy response, and drug sensitivity in TNBC, providing insights into potential therapeutic strategies.</p> Conclusions <p>This study established a robust prognostic model capable of accurately predicting clinical outcomes and metastasis, which could aid in personalized clinical decision-making.</p>

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Comprehensive analysis of metabolism-related gene biomarkers reveals their impact on the diagnosis and prognosis of triple-negative breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月11日

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  • Weibin Ren,
  • Yuyun Yu,
  • Tao Wang,
  • Xueyao Wang,
  • Kunkai Su,
  • Yanbo Wang,
  • Wenjie Tang,
  • Miaomiao Liu,
  • Yanhui Zhang,
  • Long Yang,
  • Hongyan Diao

摘要

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by poor prognosis and limited treatment options, which underscores the urgency of the discovery of new biomarkers. Metabolic reprogramming is a hallmark of cancer and is expected to serve as a strong predictive biomarker for breast cancer.

Methods

We integrated RNA expression data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the associations between metabolism-related gene expression and TNBC prognosis. Our comprehensive approach included differential expression analysis, enrichment analysis, Cox regression analysis, machine learning, and in vitro experimental validation.

Results

We identified five pivotal genes—SDS, RDH12, IDO1, GLDC, and ALOX12B—that were significantly correlated with the prognosis of TNBC patients. A prognostic model incorporating these genes was developed and validated in an independent patient cohort. The model demonstrated predictive validity, as TNBC patients classified into the high-risk group exhibited significantly poorer prognoses. Additionally, utilizing the risk model, we evaluated the mutational landscape, immune infiltration, immunotherapy response, and drug sensitivity in TNBC, providing insights into potential therapeutic strategies.

Conclusions

This study established a robust prognostic model capable of accurately predicting clinical outcomes and metastasis, which could aid in personalized clinical decision-making.