Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer.

Objective: The metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC. Methods: This study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation. Results: We successfully constructed prognostic models and Nomograms based on key MAAD-related molecules. There was a notable survival benefit observed for low-risk patients when contrasted with their high-risk counterparts. In addition, the high-risk group had a poorer response to immunotherapy and stronger tumor heterogeneity compared with the low-risk group. Further research found that by knocking down the MAAD-related gene. LSM8, the malignant characteristics of colorectal cancer cell lines were significantly alleviated, suggesting that LSM8 may become a potential therapeutic target. Conclusion: The MAAD-related gene LSM8 is likely involved in the progression of CRC and could be a hopeful target for therapeutic intervention.