Metabolomic profiling identifies biomarkers and metabolic impacts of surgery for colorectal cancer - Zhuang, Feng; Bai, Xuesong; Shi, Yang; Chang, Le; Ai, Wanchao; Du, Juan; Liu, Wei; Liu, Humin; Zhou, Xukun; Wang, Zhong; Hong, Tao (Frontiers in Surgery, 2022)
Background: Colorectal cancer (CRC) is one of the most common malignant tumors with recurrence and metastasis after surgical resection. This study aimed to identify the physiological changes after surgery and explore metabolites and metabolic pathways with potential prognostic value for CRC. Methods: Ultra-high performance liquid chromatography Q-Exactive mass spectrometry was used to profile serum metabolites from 67 CRC patients and 50 healthy volunteers. Principal component analysis and orthogonal projections to latent structures-discriminant analysis were used to distinguish the internal characteristics of data in different groups. Multivariate statistics was conducted to screen the significant metabolites and metabolic pathways. Result: 180 metabolites were detected totally. Under the conditions of variable importance in projection >1 and p value < 0.05, 46 differently expressed metabolites were screened for further pathway enrichment analysis. Based on the Kyoto Encyclopedia of Genes and Genomes database and Small Molecule Pathway Database, 3 metabolic pathways including Arginine and proline metabolism, Ascorbate and aldarate metabolism and Phenylalanine metabolism were significantly altered after surgery resection and identified associated with the removal of CRC. Notably, gamma-linolenic acid was upregulated in the CRC pre-operative patients compared to healthy volunteers, but returned to healthy levels after surgery. Conclusion: Through serum-based metabolomics, our study demonstrated the differential metabolic characteristics in CRC patients after surgery compared with those before surgery. Our results suggest that metabonomics analysis may be a powerful method for exploring physiological alterations in CRC patients after surgery, as well as a useful tool for identifying candidate biomarkers and monitoring disease recurrence.