Identifying a prognostic model and screening of potential natural compounds for acute myeloid leukemia.

BACKGROUND

Acute myeloid leukemia (AML) is one of the most common hematologic malignancies with a poor prognosis and high recurrence rate. The discovery of new predictive models and therapeutic agents plays a crucial role.

METHODS

The differentially expressed gene that was explicitly highly expressed in The Cancer Genome Atlas (TCGA) and GSE9476 transcriptome databases were screened and included in the least absolute shrinkage and selection operator (LASSO) regression model to derive risk coefficients and build a risk score model. Functional enrichment analysis was conducted on the screened hub genes to explore the potential mechanisms. Subsequently, critical genes were incorporated into a nomogram model based on risk scores to analyze prognostic value. Finally, this study combined network pharmacology to find potential natural compounds for hub genes and used molecular docking to verify the binding ability of molecular structures to natural compounds to explore drug development for possible efficacy in AML.

RESULTS

A total of 33 highly expressed genes may be associated with poor prognosis of AML patients. After LASSO and multivariate Cox regression analysis of 33 critical genes, Rho-related BTB domain containing 2 (), phospholipase A2 (), interleukin-2 receptor-α (), cysteine and glycine-rich protein 1 (), and olfactomedin-like 2A () were found to played a significant role in the prognosis of AML patients. and were independent prognostic factors of AML. The predictive power of these 5 hub genes in combination with clinical features was better than clinical data alone in predicting AML in the column line graphs and had better predictive value at 1, 3, and 5 years. Finally, through network pharmacology and molecular docking, this study found that diosgenin in Guadi docked well with , beta-sitosterol in Fangji docked well with , and docked well with 3,4-di-O-caffeoylquinic acid in Beiliujinu.

CONCLUSIONS

The predictive model of , , , , and combined with clinical features can better guide the prognosis of AML. In addition, the stable docking of , , and with natural compounds may provide new options for treating AML.