Untargeted GC-MS-Based Metabolomics for Early Detection of Colorectal Cancer

Background: Colorectal cancer (CRC) is one of the most common malignant gastrointestinal cancers in the world and 5-year survival rate of approximately 68%. Though researchers accumulated many scientific evidence, its pathogenesis remains unclear yet. Detecting and removing these malignant polyps promptly is the most effective methods in CRC prevention. Therefore, the analysis and dispose of malignant polyps is conducive to prevent CRCs. Methods: In the study, metabolic profiling as well as diagnostic biomarkers for cancer tissue and paracarinoma tissue and preoperative and postoperative of two weeks based on untargeted GC-MS-based metabolomics methods to explore the intervention approaches. In order to better characterize variations of tissue and serum metabolic profiles, orthogonal partial least-squares discriminant analysis was carried out to further identify significantly features. The key differences tR-m/z pairs were screened by the S-plot and VIP value from OPLS-DA. Identified potential biomarkers were leading-in the KEGG to find interactions, which was shown the relationships among these signal pathways. Results: Finally, 17 in tissue and 13 in serum candidate ions were selected based on their corresponding retention time, p-value, m/z and VIP value. Simultaneously, the most influential pathways which contributing to CRC were inositol phosphate metabolism, primary bile acid biosynthesis, phosphatidylinositol signaling system and linoleic acid metabolism. Conclusions: The preliminary results suggest that the GC-MS-based method coupled with pattern recognition method and understanding these cancer-specific alterations could make it possible to detect CRC early and aid the development of additional treatments for the disease, leading to improvements in CRC patients’ quality of life.