Acute myeloid leukemia (AML) is a hematological cancer prevalent worldwide. Anoikis-related genes (ARGs) are crucial in the progression of cancer and metastasis of tumors. However, their role in AML needs to be clarified. In the present study, differential analysis was performed on data from The Cancer Genome Atlas database to identify differentially expressed ARGs (DE-ARGs). Subsequently, a prognostic model for patients with AML was constructed using univariate Cox, Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses. This model was based on four key DE-ARGs [lectin galactoside-binding soluble 1 (LGALS1), integrin subunit α 4 (ITGA4), hepatocyte growth factor (HGF) and Ras homolog gene family member C (RHOC)]. Independent prognostic factors for AML included prior treatment, age, risk scores and diagnosis. A nomogram was constructed based on these factors to aid clinical decision-making. Furthermore, bone marrow samples were collected from individuals diagnosed with AML and healthy donors to validate the expression of the identified ARGs using reverse transcription-quantitative PCR. The mRNA levels of LGALS1 and RHOC were significantly higher, while those of ITGA4 and HGF were significantly lower in patients with AML than in healthy donors (all P<0.05). The results of the present study expands the understanding of the function of ARGs in AML, providing a new theoretical basis for the treatment of AML.
Keywords: acute myeloid leukemia; anoikis-related genes; bioinformatics; drug prediction; prognostic model.
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