The potential novel immune-related prognostic factors for acute myeloid leukemia

Abstract Background The pathological progression in acute myeloid leukemia (AML) was significantly affected by the immune microenvironment of bone marrow, where the immune-related genes (IRGs) and immune cells are involved in the prognosis of the disease. Studying immune-related components provide new ideas for treatment. Methods The transcriptome data and clinical information of 151 TCGA-LAML and 337 GTEx-whole-blood cohorts were downloaded from the UCSC Xena database. The IRGs were obtained from ImmPort database. Differentially expressed IRGs (DEIGs) were obtained from differentially expressed genes (DEGs). A prognostic model was constructed by COX regression analysis and verified by Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves in the training and validation cohorts (GEO37642 cohort). The relationship between gene expression level and prognosis was analyzed by K-M method. The independent prognostic factors were screened by univariate and multivariate Cox regression analysis. Relative immune cell composition of AML and healthy samples was calculated using the CIBERSORT algorithm. Results Enrichment analysis revealed that the immune cells and immune-related biology functions participated in AML progression. A prognostic model containing eight genes was constructed. In the training and validation cohorts, the survival rate of the low-risk group was significantly higher than that of the high-risk group. The area under the curve (AUC) values of ROC curves were ≥ 0.7. Among the model, high expression of CANX (P = 0.012), CLEC11A (P = 0.016), and TRH (P = 1.256E-04) was associated with a higher survival rate, while high expression of IL3RA (P = 0.038), KIR2DS4 (P = 0.016), APOBEC3G (P = 1.426E-04), and CKLF (P = 0.015) was associated with lower survival rate. The CANX expression level may affect the sensitivity of some drugs. Age, karyotype, and risk score are independent prognostic factors for AML. The differential level of 17 immune cells was observed between the AML and healthy samples. The levels of Macrophages M1, T cells follicular helper, and T cells CD8 were positively correlated with survival rate. Discussions The prognostic model may be helpful in predicting patient outcomes and 7 IRGs and 3 immune cells may be potential biomarkers and immunotherapy targets for AML in the future.