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Background <p>The impact of tyrosine metabolism on the early diagnosis and treatment of breast cancer remains unclear. This underlines importance of exploring its mechanisms.</p> Methods <p>This study conducted an integrated analysis of breast cancer transcriptome data from the TCGA and GEO databases, utilizing differential expression analysis, enrichment analysis, immune infiltration analysis, single-cell RNA sequencing analysis, hdWGCNA analysis, and molecular docking to investigate the role of tyrosine metabolism in breast cancer and its relationship with chemotherapy response.</p> Results <p>The co-expression prognosis model of tyrosine metabolism developed in this study demonstrated superior performance in the prognostic assessment of breast cancer, achieving an AUC value of 0.735, surpassing traditional clinical indicators. The identified two key genes(MAOA, MAOB) and their interaction network showed significant value in the diagnosis and prognosis of breast cancer. Moreover, the early diagnosis model “Extra Trees (BO)” developed using machine learning algorithms exhibited excellent stability and generalization capability. These findings not only highlight the critical role of tyrosine metabolism in regulating the tumor immune microenvironment but also mark Monoamine oxidase A (MAOA) and Monoamine oxidase B (MAOB) as important potential biomarkers linking immunotherapy and chemotherapy.</p> Conclusion <p>This research provides an effective model for the prognostic assessment and early diagnosis of breast cancer, opening new avenues for research into the precision treatment of breast cancer and the management of chemotherapy side effects.</p>

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Deciphering key chemotherapeutic drug targets within tyrosine metabolism for breast cancer and advancing a wide-ranging diagnostic strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Dan Lu,
  • Jun Yang,
  • Reng-min Wu,
  • Zi-lu Xie,
  • Shu-hang Cao,
  • Huan-huan Shi,
  • Shaofeng Jiang,
  • Chen Yi,
  • Dong-juan Chen

摘要

Background

The impact of tyrosine metabolism on the early diagnosis and treatment of breast cancer remains unclear. This underlines importance of exploring its mechanisms.

Methods

This study conducted an integrated analysis of breast cancer transcriptome data from the TCGA and GEO databases, utilizing differential expression analysis, enrichment analysis, immune infiltration analysis, single-cell RNA sequencing analysis, hdWGCNA analysis, and molecular docking to investigate the role of tyrosine metabolism in breast cancer and its relationship with chemotherapy response.

Results

The co-expression prognosis model of tyrosine metabolism developed in this study demonstrated superior performance in the prognostic assessment of breast cancer, achieving an AUC value of 0.735, surpassing traditional clinical indicators. The identified two key genes(MAOA, MAOB) and their interaction network showed significant value in the diagnosis and prognosis of breast cancer. Moreover, the early diagnosis model “Extra Trees (BO)” developed using machine learning algorithms exhibited excellent stability and generalization capability. These findings not only highlight the critical role of tyrosine metabolism in regulating the tumor immune microenvironment but also mark Monoamine oxidase A (MAOA) and Monoamine oxidase B (MAOB) as important potential biomarkers linking immunotherapy and chemotherapy.

Conclusion

This research provides an effective model for the prognostic assessment and early diagnosis of breast cancer, opening new avenues for research into the precision treatment of breast cancer and the management of chemotherapy side effects.