Affiliation:
1. College of Pharmaceutical Science Zhejiang University of Technology Hangzhou China
2. Key Laboratory for Molecular Medicine and Chinese Medicine Preparations Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou China
3. Department of Breast Medical Oncology Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Hangzhou China
4. College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University & Shandong Academy of Medical Sciences Jinan China
Abstract
AbstractBackgroundBreast cancer (BC) is the second leading cause of cancer‐related deaths among women, primarily due to metastases to other organs rather than the primary tumor.MethodsIn this study, a comprehensive analysis of plasma proteomics and metabolomics was conducted on a cohort of 51 BC patients. Potential biomarkers were screened by the Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithm. Additionally, enzyme‐linked immunosorbent assay (ELISA) kits and untargeted metabolomics were utilized to validate the prognostic biomarkers in an independent cohort.ResultsIn the study, extracellular matrix (ECM)‐related functional enrichments were observed to be enriched in BC cases with bone metastases. Proteins dysregulated in retinol metabolism in liver metastases and leukocyte transendothelial migration in lung metastases were also identified. Machine learning models identified specific biomarker panels for each metastasis type, achieving high diagnostic accuracy with area under the curve (AUC) of 0.955 for bone, 0.941 for liver, and 0.989 for lung metastases.ConclusionsFor bone metastasis, biomarkers such as leucyl‐tryptophan, LysoPC(P‐16:0/0:0), FN1, and HSPG2 have been validated. dUDP, LPE(18:1/0:0), and aspartylphenylalanine have been confirmed for liver metastasis. For lung metastasis, dUDP, testosterone sulfate, and PE(14:0/20:5) have been established.