Scholarlink

<p>The development of precise molecular biomarkers for breast cancer prognosis holds immense potential to improve treatment outcomes. This study aimed to investigate the role of amino acid metabolism genes as predictive markers for breast cancer prognosis and their association with the immune-tumour microenvironment. By employing advanced machine learning algorithms and bioinformatics analysis techniques, the impact of amino acid metabolism-related genes (AAMRGs) on the immune status and overall survival of patients with breast cancer was examined. An AAMRG-based risk model was established to assess the prognostic significance. Validated risk models (AIMP2, IYD, and QARS1) accurately predicted patient outcomes [1 y: 0.87 (0.96–0.78); 3 y: 0.82 (0.87–0.76); 5 y: 0.80 (0.86–0.75)]. Furthermore, this study revealed evidence suggesting that QARS1 may influence breast cancer cell proliferation through methionine metabolism. This analysis provides valuable insights into the mechanisms of breast cancer, emphasizing the significance of AAMRGs as prognostic biomarkers and potential therapeutic targets for optimizing personalized treatment strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Molecular biomarkers for the prognosis of breast cancer: role of amino acid metabolism genes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月1日

查看全文
  • Yudong Zhou,
  • Shibo Yu,
  • Lizhe Zhu,
  • Yalong Wang,
  • Chenglong Duan,
  • Danni Li,
  • Jinsui Du,
  • Jiaqi Zhang,
  • Jianing Zhang,
  • Ruichao Ma,
  • Jianjun He,
  • Yu Ren,
  • Bin Wang

摘要

The development of precise molecular biomarkers for breast cancer prognosis holds immense potential to improve treatment outcomes. This study aimed to investigate the role of amino acid metabolism genes as predictive markers for breast cancer prognosis and their association with the immune-tumour microenvironment. By employing advanced machine learning algorithms and bioinformatics analysis techniques, the impact of amino acid metabolism-related genes (AAMRGs) on the immune status and overall survival of patients with breast cancer was examined. An AAMRG-based risk model was established to assess the prognostic significance. Validated risk models (AIMP2, IYD, and QARS1) accurately predicted patient outcomes [1 y: 0.87 (0.96–0.78); 3 y: 0.82 (0.87–0.76); 5 y: 0.80 (0.86–0.75)]. Furthermore, this study revealed evidence suggesting that QARS1 may influence breast cancer cell proliferation through methionine metabolism. This analysis provides valuable insights into the mechanisms of breast cancer, emphasizing the significance of AAMRGs as prognostic biomarkers and potential therapeutic targets for optimizing personalized treatment strategies.