Identification and validation of immune-associated gene signatures for prognostic prediction in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with poor prognosis, and reliable prognostic biomarkers are essential for improving risk stratification and personalized treatment strategies. In the present study, our objective was to identify immune-associated differentially expressed genes (DEGs) that were correlated with overall survival of AML patients. Transcriptome data from multiple cohorts, including the cancer genome atlas, genotype-tissue expression, and gene expression omnibus were integrated to identify AML-specific biomarkers. DEGs were identified between AML and healthy samples, which were mainly involved in immune-associated processes. Based on the immune-associated DEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression. A 9-gene signature ( CAPZB , TFEB , CAP1 , ITGAX , ATP6V0D1 , NCR1 , LILRB3 , LST1 , and PAK1 ) model was constructed and validated in multiple independent datasets, showing robust predictive accuracy for overall survival, with high area under the curve values corresponding to 1-, 3-, and 5-year survival. Additionally, qRT-PCR experiment verified the differential expression of crucial genes in clinical AML samples. This study provides a promising immune-based prognostic model for AML, contributing to better patient stratification and personalized treatment approaches.