Proteomic and metabolomic characterization of bone, liver, and lung metastases in plasma of breast cancer patients
- Hui Ye 1,2, Xiabo Shen 2,3, Yaohan Li 4, Weibin Zou 2,3, Syed Shams Ul Hassan 2, Yue Feng 2, Xiaojia Wang 2,3, Jingkui Tian 2, Xiying Shao 2,3, Yi Tao 1, Wei Zhu 2
- Hui Ye 1,2, Xiabo Shen 2,3, Yaohan Li 4
- 1College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China.
- 2Key Laboratory for Molecular Medicine and Chinese Medicine Preparations, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- 3Department of Breast Medical Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.
- 4College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
- 0College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies novel plasma biomarkers for detecting breast cancer (BC) bone, liver, and lung metastases. These biomarkers, validated using machine learning, offer high accuracy for early metastasis detection and improved patient outcomes.
Area Of Science
- Oncology
- Proteomics
- Metabolomics
Background
- Breast cancer (BC) is a leading cause of cancer death in women, with metastasis being the primary driver of mortality.
- Identifying reliable biomarkers for early detection of BC metastasis is crucial for improving patient survival rates.
Purpose Of The Study
- To identify and validate plasma-based proteomic and metabolomic biomarkers for detecting bone, liver, and lung metastases in breast cancer patients.
- To develop accurate diagnostic models for different types of breast cancer metastasis.
Main Methods
- Comprehensive plasma proteomics and metabolomics analysis in 51 BC patients.
- Biomarker screening using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest algorithms.
- Validation of prognostic biomarkers using enzyme-linked immunosorbent assay (ELISA) kits and untargeted metabolomics in an independent cohort.
Main Results
- Extracellular matrix (ECM)-related functional enrichments were observed in breast cancer with bone metastases.
- Proteins involved in retinol metabolism (liver) and leukocyte transendothelial migration (lung) were identified as key in metastasis.
- Machine learning models achieved high diagnostic accuracy (AUC > 0.94) for bone, liver, and lung metastases.
Conclusions
- Validated biomarkers for bone metastasis include leucyl-tryptophan, LysoPC(P-16:0/0:0), FN1, and HSPG2.
- Biomarkers dUDP, LPE(18:1/0:0), and aspartylphenylalanine were confirmed for liver metastasis.
- Established biomarkers for lung metastasis are dUDP, testosterone sulfate, and PE(14:0/20:5).
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