Affiliations: [a] Key Laboratory of Cognition and Personality (SWU), Faculty of Psychology, Ministry of Education, Southwest University, Chongqing, China | [b] Tianjin Centers for Disease Control and Prevention, Tianjin, China | [c] Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing, China | [d] Vaccine Research Center, School of Public Health, Peking University, Beijing, China | [e] Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
Correspondence:
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Corresponding authors: Lailai Yan, Department of Laboratorial Science and Technology, School of Public Health, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China. Tel.: +86 13810621102; E-mail: [email protected]. Nan Li, Research Center of Clinical Epidemiology, Peking University Third Hospital, 49 North Garden Road, Haidian District Beijing 100191, China. Tel.: +86 13810366704; E-mail: [email protected].
Note: [1] This article received a correction notice (Erratum) with the reference: 10.3233/CBM-239003, available at [link to erratum].
Note: [2] The two authors contributed equally to this work.
Abstract: BACKGROUND: Breast cancer is the most worldwide commonly found malignancy among women. The evidence for lipidomic studies of breast cancer in the Chinese population is relatively limited. OBJECTIVE: Our current study aimed to identify peripheral lipids capable of distinguishing adults with and without malignant breast cancer in a Chinese population and to explore the potential lipid metabolism pathways implicated in breast cancer. METHODS: Lipidomics was performed with an Ultimate 3000 UHPLC system coupled with a Q-Exactive HF MS platform by using the serum of 71 female patients with malignant breast cancer and 92 age-matched (± 2 years) healthy women. The data were uploaded to and processed by the specialized online software Metaboanalyst 5.0. Both univariate and multivariate analyses were carried out for potential biomarker screening. Areas under the receiver-operating characteristic (ROC) curves (AUCs) of identified differential lipids were obtained for evaluating their classification capacity. RESULTS: A total of 47 significantly different lipids were identified by applying the following criteria: false discovery rate-adjusted P < 0.05, variable importance in projection ⩾ 1.0, and fold change ⩾ 2.0 or ⩽ 0.5. Among them, 13 lipids were identified as diagnostic biomarkers with the area under curve (AUC) greater than 0.7. Multivariate ROC curves indicated that AUCs greater than 0.8 could be achieved with 2–47 lipids. CONCLUSIONS: Using an untargeted LC-MS-based metabolic profiling approach, our study provides preliminary evidence that extensive dysregulations of OxPCs, PCs, SMs and TAGs were involved in the pathological processes of breast cancer. We provided clues for furtherly investigating the role of lipid alterations in the pathoetiology of breast cancer.
Keywords: Lipidomics, breast cancer, biomarker, LC-MS, metabolism
