Identification of Molecular and Immune Prognostic Classifiers of Acute Myeloid Leukemia

Abstract

Background Acute myeloid leukemia (AML) is a clinically and genetically heterogeneous hematological malignancy and relapse is the main reason for the poor therapeutic effect and low survival rate. Bioinformatic technology could screen out relative genes that promote the recurrence of AML, providing a theoretical basis for further improving the precision stratification treatment of AML. Methods In this study, gene expression profiles of Dataset Acute Myeloid Leukemia (OHSU, Nature 2018) and GSE134589 were downloaded from cbioportal and GEO, respectively. R software and limma packages were used to identify the DEGs and then run GO enrichment, KEGG pathway, and PPI network. CIBERSORTx was used to enumerate tumor-infiltrating immune cells. Prognosis-related genes were selected by Univariate and multivariate Cox proportional hazards regression analyses and the expression of them were verified by GEPIA. Kaplan–Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict the value of the selected genes. Results Functional analysis showed that the up-regulated DEGs were strikingly enriched in Cytokine-cytokine receptor interaction and positive regulation of cytokine production, and the down-regulated DEGs in the regulation of cell-cell adhesion, TNF signaling pathway. CIBERSORTx analysis revealed that the immune response of AML acted as an intricate network and proceeded in a tightly regulated way. Cox analysis showed that ALDH1L2, KLK1, and LRRN2 were correlated with AML prognosis. Conclusions ALDH1L2, KLK1, and LRRN2 are prognosis-related genes in AML, which may together with some immune pathways, induce poor prognosis, and can be used as potential biomarkers in AML treatment.