Construction of AML prognostic model with CYP2E1 and GALNT12 biomarkers based on golgi- associated genes

Acute myeloid leukaemia (AML) was originally an aggressive malignancy of the bone marrow and one of the deadliest forms of acute leukaemia. The 5-year mortality benefit for patients with AML was only 28.3%. Moreover, a large proportion of patients experienced frequent relapses even after remission, thus predicting a bleak prognosis. This research employed differential expression analysis of AML and normal samples sourced from the GSE30029 database, as well as weighted gene co-expression network analysis (WGCNA). We discovered differential golgi apparatus-related genes (DGARGs) specifically associated with AML. Via regressivity analysis and machine learning algorithm, the cancer genome atlas-acute myeloid leukemia (TCGA-AML) cohort developed a prognostic model using characteristic prognostic genes. The performance value of risk score was analysed using Kaplan-Meier (KM) curves and Cox regression. A predictive nomogram was developed to assess the outcome. The association between prognostic trait genes and the immune microenvironment was examined. Finally, immunoactivity and drug susceptibilities were evaluated in various risk groups identified by prognostic signature genes. A total of 77 DGARGs were obtained by differential expression analysis with WGCNA analysis. Following univariate Cox regression and LASSO regression, six prognostic signature genes (ARL5B, GALNT12, MANSC1, PDE4DIP, NCALD and CYP2E1) were utilized to develop a prognostic model. This model was calibrated via KM survival and receiver operating characteristic (ROC) curves, which concluded that it had a predictive impact on the prognosis of AML. Further analysis of the tumour microenvironment in AML patients demonstrated notable variances in immune cell APC_co_inhibition, CCR, Parainflammation, Type_I_IFN_Response, and Type_II_IFN_Response between the high-risk and low-risk groups. A prognostic model was devised in this study using six prognostic genes linked to the Golgi apparatus. The exactness of the model in guiding the prognosis of AML was established. As a result of expression validation, CYP2E1 and GALNT12 will be used as biomarkers to offer fresh insights into the prognosis and treatment of AML patients.