Identification of possible drug treatment targets and related immune cell infiltration properties in acute myeloid leukemia utilizing robust rank aggregation algorithm.

In this study, we aimed to uncover novel biomarkers in acute myeloid leukemia (AML) that could serve as prognostic indicators or therapeutic targets. We analyzed AML microarray datasets from the Gene Expression Omnibus (GEO) repository, identifying key differentially expressed genes (DEGs) through the robust rank aggregation (RRA) approach. The functions of these DEGs were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Additionally, the CIBERSORT algorithm was employed to assess immune cell infiltration in AML. Six hub genes were identified using the cytoHubba plugin, and their clinical significance, survival impact, and meta-analyses were conducted. Through comprehensive bioinformatics and qPCR analyses, we gained new insights into AML pathogenesis and metastasis, identifying FCGR3B, FLT3, EREG, and MMP9 as potential prognostic biomarkers. Antagonists targeting FCGR3B, FLT3, and MMP9, or agonists for EREG, hold promise as therapeutic and preventative strategies for AML.