Evidence map›Paper›PMID 41021173›Full record

ArticleDiscover oncology2025

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

Dan Lu, Jun Yang, Reng-Min Wu, Zi-Lu Xie, Shu-Hang Cao, Huan-Huan Shi, Shaofeng Jiang, Chen Yi, Dong-Juan Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Dan Lu *Department of Laboratory Medicine, Maternal and Child Health Hospital of Hubei Province, Tongji Medical college, Huazhong University of Science and Technology, Wuhan, 430070, China.
Jun Yang *Department of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China.
Reng-Min WuDepartment of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China.
Zi-Lu XieDepartment of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China.
Shu-Hang CaoDepartment of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China.
Huan-Huan ShiDepartment of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China.
Shaofeng JiangDepartment of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China.
Chen YiDepartment of Biomedical Engineering, Nanchang Hang Kong University, Jiangxi, 330063, China. 470835297@qq.com.
Dong-Juan ChenDepartment of Laboratory Medicine, Maternal and Child Health Hospital of Hubei Province, Tongji Medical college, Huazhong University of Science and Technology, Wuhan, 430070, China. chendj@alumni.hust.edu.cn.

Funding

Natural Science Foundation of Hubei Province 2019CFB391the PhD fellowship of Nanchang Hangkong University EA202008259
6 · The paper itself

Abstract

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

methodsThis 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.

resultsThe 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.

conclusionThis 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.

Indexed as

ChemotherapyEarly diagnosisPrecision medicineTumor immune microenvironmentTyrosine metabolism

Identifiers

PMID41021173
PMCPMC12480192

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.