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

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 mi...

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Vydáno v:Journal of physiology and biochemistry Ročník 81; číslo 2; s. 441 - 457
Hlavní autoři: Zhou, Yudong, Yu, Shibo, Zhu, Lizhe, Wang, Yalong, Duan, Chenglong, Li, Danni, Du, Jinsui, Zhang, Jiaqi, Zhang, Jianing, Ma, Ruichao, He, Jianjun, Ren, Yu, Wang, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.05.2025
Springer Nature B.V
Témata:
Algorithms
Amino acids
Amino Acids - metabolism
Animal Physiology
Bioinformatics
Biomarkers
Biomarkers, Tumor - genetics
Biomarkers, Tumor - metabolism
Biomedical and Life Sciences
Biomedicine
Breast cancer
Breast Neoplasms - diagnosis
Breast Neoplasms - genetics
Breast Neoplasms - immunology
Breast Neoplasms - metabolism
Breast Neoplasms - mortality
Breast Neoplasms - pathology
Cancer therapies
Cell proliferation
Chemotherapy
Cluster analysis
Computational Biology
Customization
Data analysis
Female
Fibroblasts
Gene expression
Gene Expression Regulation, Neoplastic
Genes
Human Physiology
Humans
Immune status
Impact analysis
Learning algorithms
Machine Learning
Medical prognosis
Metabolism
Methionine
Middle Aged
Nomograms
Penicillin
Prognosis
Regression analysis
Risk
Survival
Therapeutic targets
Tumor Microenvironment
Immune microenvironment
Breast cancer
Amino acid metabolism-related genes
Prognosis
Machine learning
ISSN:1138-7548, 1877-8755, 1877-8755
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Shrnutí: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. Key points The development of precise molecular biomarkers for the prognosis of patients with breast cancer holds immense potential in enhancing the outcomes of breast cancer treatment. This study employed machine learning algorithms and bioinformatics analysis techniques to conduct a comprehensive examination of AAMRGs and their impact on the immune status and overall survival of patients with breast cancer. A risk model predicated on AAMRGs was developed to assess their prognostic significance and could markedly bolster the personalized treatment approach for patients with breast cancer.
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ISSN:1138-7548
1877-8755
1877-8755
DOI:10.1007/s13105-025-01088-5