Prediction of prognosis and immunotherapy response of amino acid metabolism genes in acute myeloid leukemia - Zhou, Hui; Wang, Fengjuan; Niu, Ting (Frontiers in Nutrition, 2022)
Background: Amino acid (AA) metabolism plays a crucial role in cancer. However, its role in acute myeloid leukemia (AML) is still unavailable. We screened out AA metabolic genes, which related to prognosis, and analyzed their correlation with tumor immune microenvironment in AML. Methods: We evaluated 472 amino acid metabolism-related genes in 132 AML patients. The predictive risk model was developed according to differentially expressed genes, univariate Cox and LASSO analyses. We validated the risk signature by survival analysis and independence tests. Single-sample gene set enrichment analysis (ssGSEA), tumor immune microenvironment (TME), tumor mutation burden (TMB), functional enrichment, and the IC50 of drugs were assessed to explore the correlations among the risk model, immunity, and drug sensitivity of AML. Results: Six amino acid metabolism-related genes were confirmed to develop the risk model, including TRH, HNMT, TFEB, SDSL, SLC43A2, and SFXN3. The high-risk subgroup had an immune “hot” phenotype and was related to a poor prognosis. The high-risk group was also associated with more activity of immune cells, such as Tregs, had higher expression of some immune checkpoints, including PD1 and CTLA4, and might be more susceptible to immunotherapy. JAK/STAT3, complement, the inflammatory response, the reactive oxygen species (ROS) pathway, and fatty acid metabolism were active in the high-risk subgroup. Furthermore, the high-risk subgroup was sensitive to sorafenib, selumetinib, and entospletinib. ssGSEA discovered that the processes of glutamine, arginine, tryptophan, cysteine, histidine, L-serine, isoleucine, threonine, tyrosine, and L-phenylalanine metabolism were more active in the high-risk subgroup. Conclusion: This study revealed that AA metabolism-related genes were correlated with the immune microenvironment of AML patients and could predict the prognosis and immunotherapy response of AML patients.