Acute myeloid leukemia (AML) is a lethal hematologic malignancy with a variable prognosis that is highly dependent on the bone marrow microenvironment. Consequently, a better understanding of the AML microenvironment is crucial for early diagnosis, risk stratification, and personalized therapy. In recent years, the role of bioinformatics as a powerful tool in clarifying the complexities of cancer has become more prominent. Gene expression profile and clinical data of 173 AML patients were downloaded from the TCGA database, and the xCell algorithm was applied to calculate the microenvironment score (MS). Then, the correlation of MS with FAB classification, and CALGB cytogenetic risk category was investigated. Differentially expressed genes (DEGs) were identified, and the correlation analysis of DEGs with patient survival was done using univariate cox. The prognostic value of candidate prognostic DEGs was confirmed based on the GEO database. In the last step, real-time PCR was used to compare the expression of the top three prognostic genes between patients and the control group. During TCGA data analysis, 716 DEGs were identified, and survival analysis results showed that 152 DEGs had survival-related changes. In addition, the prognostic value of 31 candidate prognostic genes was confirmed by GEO data analysis. Finally, the expression analysis of FLVCR2, SMO, and CREB5 genes, the most related genes to patients' survival, was significantly different between patients and control groups. In summary, we identified key microenvironment-related genes that influence the survival of AML patients and may serve as prognostic and therapeutic targets.
Keywords: Acute myeloid leukemia; Bioinformatics; Bone marrow microenvironment; Prognosis; TCGA; xCell.
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.