LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer

• Published in: Methods (San Diego, Calif.) Vol. 222; pp. 100 - 111
• Main Authors: Gong, Sisi, Wang, Qingshui, Huang, Jiewei, Huang, Rongfu, Chen, Shanshan, Cheng, Xiaojuan, Liu, Lei, Dai, Xiaofang, Zhong, Yameng, Fan, Chunmei, Liao, Zhijun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2024
• Subjects: Biomarker; Biomarkers; Biomarkers, Tumor; blood serum; Breast cancer; breast neoplasms; Chromatography, Liquid - methods; Early Detection of Cancer; Female; Humans; Liquid Chromatography-Mass Spectrometry; Machine learning; mass spectrometry; metabolic diseases; metabolites; Metabolomics; Metabolomics - methods; pipecolic acid; regression analysis; Tandem Mass Spectrometry; Triple Negative Breast Neoplasms - diagnosis; Triple-negative breast cancer; ultra-performance liquid chromatography; NAT; Metabolomics; BC; Biomarker; Triple-negative breast cancer; AUC; TNM; QC; RSDs; CA15-3; ESI; TNBC; CEA; ML; AA; TCA cycle; OPLS-DA; ROC; UHPLC-MS; 7-MG; Breast cancer; PCA; BPC; 2-AG; Machine learning; HC; VIP; FC

•Serum profile based on LC-MS was utilized to metabolomic analysis of breast cancer (BC).•The use of machine learning methods allowed for the identification of significant serum metabolic differences between TNBC and non-TNBC groups.•Two metabolic panels were expected to predict BC and TNBC, respect...