Unveiling the role of PANoptosis-related genes in breast cancer: an integrated study by multi-omics analysis and machine learning algorithms.

Journal: Breast cancer research and treatment

PMID: 39870964

Abstract

BACKGROUND: The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-related genes (PRGs) and construct a robust prognostic model to guide individualized treatment strategies.

Authors

Gang Liu

Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
Liang-Zhi Pan

Party Committee Office, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China.
Jie Chen

School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
Jianying Ma

Department of Breast Surgery, Thyroid Surgery, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, No.141, Tianjin Road, Huangshi, 435000, Hubei, China. majianying2015@sina.com.

Keywords

Algorithms Biomarkers, Tumor Breast Neoplasms Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Multiomics Prognosis Transcriptome Tumor Microenvironment

External Resources

View on PubMed Access via DOI PubMed (39870964)

Unveiling the role of PANoptosis-related genes in breast cancer: an integrated study by multi-omics analysis and machine learning algorithms.

Abstract

Authors

Keywords

External Resources

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