Abstract
Acute myeloid leukemia (AML) is a malignant blood disorder and the most common type of acute leukemia in adults. Notwithstanding the plethora of therapeutic modalities, a significant cohort of patients fail to respond to treatment and experience relapse. Anoikis, a distinct modality of programmed cell death, has been linked to cancer progression. However, the prognostic significance of anoikis in AML remains unclear. In this study, a non-negative matrix factorization algorithm was utilized to efficiently reduce the dimensions of merged datasets. We used differential analysis, weighted gene co-expression network analysis (WGCNA), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression to identify genes associated with prognosis and develop a risk scoring model. Immunohistochemistry was conducted to assess the expression levels of key genes in clinical samples. The association between risk score and the tumor microenvironment (TME), stemness, clinical characteristics, and immunotherapy was evaluated. We identified 41 AML anoikis-related genes (ANRGs) related to survival, and seven genes were chosen to develop prognostic models. The prognostic risk score combined with the clinical and pathological features of AML was used to develop a nomogram, and decision curve analysis demonstrated the net clinical benefit of the model. Furthermore, analysis of ANRGs revealed that PDGFRB inhibition significantly reduced the proliferation of AML cells, promoted apoptosis, and inhibited AML progression both in vitro and in vivo, indicating that PDGFRB plays a crucial role in AML development.