A novel immune-related gene signature correlated with serum IL33 expression in acute myeloid leukemia prognosis.

PURPOSE

To identify and validate the immune-related gene signature in patients with acute myeloid leukemia (AML).

METHODS

Differentially expressed genes (DEGs) profiles and survival data were obtained from The Cancer Genome Atlas (TCGA), following screened immune-associated genes from the InnateDB database. Subsequently, the weighted gene co-expression network analysis (WGCNA) was used to detect functional modules, and survival analysis was performed. The least absolute shrinkage and selection operator (LASSO) regression model combined with a partial likelihood-based Cox proportional hazard regression model was applied to select prognostic genes, and the ESTIMATE algorithm was used to construct an immune score-based risk assessment model. Finally, two independent datasets from the Gene Expression Omnibus (GEO) and our clinical data were used for external validation. Moreover, a subpopulation of the immune microenvironment cells was analyzed by the CIBERSORT algorithm, and its related serum indicator was identified by the enzyme-linked immunosorbent assay (ELISA) in clinical samples.

RESULTS

Finally, and were identified as the immune-related gene signature, and the risk stratification model was validated in both the GSE12417 database and our clinical cohort. Furthermore, the fraction of activated mast cells was identified. CIBERSORT algorithm showed that these cells have a positive association with prognosis. In addition, mast cell stimulator IL-33 was markedly decreased in AML patients with poor prognoses.

CONCLUSION

A novel immune-related gene signature () and its associated plasma indicator (mast cells activator, IL-33) were found to have prognostic value in AML patients.