Preliminary metabolomics-based predictive models for myelosuppression in breast cancer patients with different anthracycline chemotherapy

作者：Pan Li, Si xian Lao, Lulu Qin, Yi Guo, Yu Wu, Wen bin Jia, Min Huang, Yan Zhong, Guo ping Zhong, Weiwei Zeng · 发表于：Research Square · 年份：2023 · DOI：10.21203/rs.3.rs-3293254/v1 · 被引用次数：1 · 研究领域：Cancer Treatment and Pharmacology、Neutropenia and Cancer Infections、Cancer-related cognitive impairment studies

Abstract Background: Myelosuppression is a common adverse effect in breast cancer patients receiving anthracyclines combined with cyclophosphamide chemotherapy. Screening potential biomarkers and building predictive models have implications for clinical management of myelosuppression. Methodology: This study collected 103 breast cancer patients in Shenzhen, China, from September 2020 to January 2022, including two different chemotherapy (Epirubicin or Doxorubicin). The plasma samples were collected 48 hours after chemotherapy. Plasma metabolomics were measured using Ultra-High-Performance Liquid Chromatography-Tandem Q-Exactive. After identification of metabolites and screening of potential differential metabolites, we mapped the key pathways. Further, we established a LASSO-logistic predictive model for myelosuppression. Results: In the group of patients treated with doxorubicin, we identified 19 differential metabolites. These metabolites were primarily associated with 6 metabolic pathways, including sphingolipid metabolic pathway, glycine, serine and threonine metabolic pathway, glycerol phospholipid metabolic pathway, tryptophan metabolic pathway, primary bile acid biosynthesis pathway and purine metabolic pathway. From these, a final logistic regression model was developed, incorporating seven variables. This model exhibited an accuracy of 84.91% in predicting chemotherapy-related myelosuppression, with an impressive area under the ROC curve of 0.9571. Similarly, in pat...