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World J Clin Oncol. Apr 24, 2026; 17(4): 117705
Published online Apr 24, 2026. doi: 10.5306/wjco.v17.i4.117705
Published online Apr 24, 2026. doi: 10.5306/wjco.v17.i4.117705
Breast cancer and metabolic comorbidities: From epidemiology and molecular mechanisms to precision interventions
Shen-Hao Zhang, Yuan Zhang, Medical College, Yanbian University, Yanji 133002, Jilin Province, China
Yang Yang, Key Laboratory of Pathobiology of High Frequency Oncology in Ethnic Minority Areas, Yanbian University, Yanji 133002, Jilin Province, China
Author contributions: Zhang SH wrote the original manuscript; Zhang Y and Yang Y supervised and made important revisions; and all authors prepared drafts and approved the submitted versions.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Yuan Zhang, DM, PhD, Medical College, Yanbian University, No. 977 Gongyuan Road, Yanji 133002, Jilin Province, China. 0000008641@ybu.edu.cn
Received: December 15, 2025
Revised: January 5, 2026
Accepted: February 11, 2026
Published online: April 24, 2026
Processing time: 128 Days and 20.6 Hours
Revised: January 5, 2026
Accepted: February 11, 2026
Published online: April 24, 2026
Processing time: 128 Days and 20.6 Hours
Core Tip
Core Tip: Reframes metabolic comorbidities as active, modifiable drivers of breast cancer. Integrates epidemiology, tumour microenvironment, signalling and gut microbiota. Maps obesity, type 2 diabetes mellitus and metabolic syndrome pathways to pharmacologic and lifestyle interventions. Highlights glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter 2 inhibitors and bariatric surgery as precision tools. Proposes a metabolic oncology model with artificial intelligence-enabled multimodal risk prediction.