Identification of a prognostic signature based on expression of post-translational modification-related genes in acute myeloid leukemia.
BACKGROUND Acute myeloid leukemia (AML) features high biological heterogeneity and unfavorable prognoses, demanding reliable prognostic biomarkers. Dysregulated post-translational modifications (PTMs) drive AML progression by disrupting protein function and cellular signaling. This work constructed a PTM-based risk signature to predict AML survival and dissect the tumor immune microenvironment. METHODS RNA-seq and clinical data of TCGA-Acute Myeloid Leukemia (LAML) AML patients and normal genotype-tissue expression samples were analyzed. Limma identified differentially expressed genes (DEGs), and overlapping PTM-related DEGs were screened. Unsupervised consensus clustering stratified patients into molecular subgroups, whose overall survival (OS) was compared via Kaplan-Meier curves. A prognostic score model was built through univariate Cox screening, least absolute shrinkage and selection operator dimension reduction and multivariate Cox regression, validated by time-dependent receiver operating characteristic curves. Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and single-sample gene set enrichment analysis quantified immune infiltration, while tumor mutation burden (TMB) was calculated to characterize genomic features. The GSE71014 cohort served as external validation. RESULTS A four-gene signature (ITGAX, DOCK1, CPNE8, and GABRE) was established and validated. High-risk patients had markedly shorter OS, with 1-, 3-, and 5-year AUC values of 0.79, 0.79, and 0.90, consistent with external cohort results. The two risk groups displayed divergent immune landscapes; high-risk patients overexpressed multiple immune checkpoints. Though TMB was similar across groups, high-risk patients with low TMB had the worst survival. Low-risk patients showed greater cytarabine susceptibility, confirming the model's clinical value. CONCLUSIONS This PTM-associated signature accurately stratifies AML patients and reveals immune microenvironment disparities. It enables precise personalized prognosis and identifies PTM-related genes as promising therapeutic targets for AML immunotherapy.