Multi-Tissue Metabolomics Analysis Reveals Susceptible Factors for Chemotherapy-Induced Hepatotoxicity in Colorectal Cancer Patients

Huilin XU ¹ Mingming Li ² Houshan Yao ³ Guoliang Chen ³ Jiani Chen ² Xinyun Hou ² Hongyang Yang ² Chenghang Yu ⁴ Zeshuai Lin ² Jiawei Zhu ⁵ Rong Wang ⁵ Shi Qiu ⁵ Xuan Liu ⁶ Zhipeng Wang ² Xia Tao ^2* Lei Liu ^1*

• ¹ Institutes of Biomedical Sciences and Intelligent Medicine Institute, Fudan University, Shanghai, China
• ² Department of Pharmacy, Second Affiliated Hospital of Naval Medical University, Shanghai, China
• ³ Department of General Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
• ⁴ National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), Shanghai, China
• ⁵ Traditional Chinese Medicine Resource and Technology Center, Shanghai University of Traditional Chinese Medicine, Shanghai, China
• ⁶ Department of Traditional Chinese Medicine, Changzheng Hospital, Naval Medical University, Shanghai, China

Amis: Chemotherapy-induced hepatotoxicity (CIH) is a significant concern in colorectal cancer (CRC) patients treated with the CAPEOX (capecitabine and oxaliplatin) regimen. Identifying predictive factors for CIH is crucial for clinical management.This study analyzed colorectal tissue (CRT), plasma, and urine samples from CRC patients. Differentially expressed metabolites (DEMs) across these tissues were integrated for multi-omics analysis, and predictive models for CIH susceptibility were developed. An independent set of 75 plasma samples was used for validation.Results: A total of 492 differentially expressed compounds were identified in samples from 63 CRC patients, including 105, 149, and 238 DEMs in CRT, plasma, and urine, respectively. Lipids and lipidlike molecules were predominant in all samples. Among these, urine samples exhibited the highest variability and provided the strongest predictive power for CIH susceptibility. Principal component analysis (PCA) effectively differentiated normal patients from those with CIH. The study revealed steatosis as the primary pathological feature of CIH, with disrupted lipid metabolism emerging as a key characteristic. Predictive models constructed from multi-tissue metabolites profile exhibited high accuracy, with the plasma model achieving an AUC of 0.933 in external validation set. Our study underscores the importance of individual metabolic variations in CIH susceptibility, reflecting the complex interplay of genetic, environmental, and lifestyle factors.This study emphasizes the critical role of alterations in lipid, polyamine, and purine metabolism, as well as impaired tissue repair mechanisms, were identified as key endogenous factors underlying CIH susceptibility. The developed predictive models demonstrate potential for clinical application in assessing CIH risk in CRC patients undergoing CAPEOX chemotherapy.