Evidence map›Paper›PMID 40549224›Full record

ArticleDiscover oncology2025

Machine learning-based detoxification enzymes-related genes prognosis model in breast cancer: immune landscape and clinical significance.

Jingdi Zhang, Wendi Zhan, Haihong Hu, Hongxia Zhu, Bo Hao, Siyu Wang, Zhuo Li, Zhiming Zhang, Taolan Zhang

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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.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jingdi Zhang *School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, 421001, Hunan, China.
Wendi Zhan *School of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, 421001, Hunan, China.
Haihong HuSchool of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, 421001, Hunan, China.
Hongxia ZhuSchool of Pharmacy, Hengyang Medical College, University of South China, 28 Western Changsheng Road, Hengyang, 421001, Hunan, China.
Bo HaoDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Siyu WangDepartment of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Zhuo LiDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. zhuoli71@126.com.
Zhiming ZhangHealth School of Nuclear Industry, 336 South Dongfeng Road, Hengyang, 421001, Hunan, China. 404974038@qq.com.
Taolan ZhangDepartment of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. TaolZhan@usc.edu.cn.

Funding

Science and technology innovation Program of Hengyang City 202250045223the Clinical Research 4310 Program of the First Affiliated Hospital of the University of South China 20224310NHYCG04the National Natural Science Foundation of China 82473965the Natural Science Foundation of Hunan Province 2023JJ50156
6 · The paper itself

Abstract

backgroundBreast cancer is one of the most common malignant tumors, threatening women's health and life globally. Despite significant treatment advances, its prognosis still faces great challenges. With the rapid development of molecular biology and genomics, the role of detoxification enzymes in breast cancer occurrence, development, and prognosis has gained increasing attention. This paper aims to establish a prognostic model based on detoxification enzymes-related genes to predict breast cancer patient survival.

methodsUnsupervised clustering was used to analyze breast cancer samples based on detoxification enzymes-related genes expression. Lasso cox regression analysis and univariate and multivariate Cox analysis were used to process the data, and machine learning algorithm was used to construct breast cancer prognosis model. The effect of detoxification enzymes-related genes on breast cancer was analyzed by single cell analysis.

resultsThe samples were classified into two subtypes, and a breast cancer prognosis model based on detoxification enzymes-related genes was constructed and validated using TCGA and GEO cohorts. Significant differences in pathways, immune infiltration, immunotherapy response, and drug sensitivity were observed between high- and low-risk groups. Single-cell analysis revealed that SQLE, a detoxification enzymes-related gene, was highly expressed in breast cancer epithelial cells (cancer cells), where SQLE + epithelial cells primarily influenced exhausted CD8 + T cells via the MIF signaling pathway.

conclusionIn summary, the detoxification enzymes-related genes-based prognostic model developed in this study provides an effective tool for predicting breast cancer prognosis and offers new insights for diagnosis and treatment.

Indexed as

Breast cancerDetoxification enzymesPrognosis modelSingle-cell analysis

Identifiers

PMID40549224
PMCPMC12185839

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