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

Abstract

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 ( RHOBTB2 ), phospholipase A2 ( PLA2G4A ), interleukin-2 receptor-α ( IL2RA ), cysteine and glycine-rich protein 1 ( CSRP1 ), and olfactomedin-like 2A ( OLFML2A ) were found to played a significant role in the prognosis of AML patients. CSRP1 and OLFML2A 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 PLA2G4A , beta-sitosterol in Fangji docked well with IL2RA , and OLFML2A docked well with 3,4-di-O-caffeoylquinic acid in Beiliujinu. Conclusions The predictive model of RHOBTB2 , PLA2G4A , IL2RA , CSRP1 , and OLFML2A combined with clinical features can better guide the prognosis of AML. In addition, the stable docking of PLA2G4A , IL2RA , and OLFML2A with natural compounds may provide new options for treating AML.

Data mining in acute myeloid leukemia: identification of disease biomarkers, prognostic factors, novel targets, and potential drugs

“…In a recent issue, Sun et al ( 5 ) reported five hub genes ( RHOBTB2, PLA2G4A, IL2RA, CSRP1, and OLFML2A ) with prognostic relevance using two independent AML cohorts. Furthermore, the authors used chemoinformatics tools to propose natural compounds as potential ligands for proteins resulting from these genes to modulate the biological activity of these potential new targets ( 5 ). Two points deserve to be highlighted, the identification of new targets and the use of natural compounds in AML from data mining and chemoinformatics.…”

“…On the other hand, the role of CSRP1 is still little known in AML, but its role as a potential oncogene was recently described in solid tumors ( 18 ). Given this background, the study conducted by Sun et al ( 5 ) proposes a new biomarker and consolidates some prognostic markers in AML, promoting progress in the field of study.…”

“…The suggestion by Sun et al ( 5 ) for the use of natural products for the treatment of AML, the strategy is interesting and deserves future studies in experimental models of AML. Historically, natural compounds have been used by mankind for centuries as a treatment for a myriad of health issues.…”

“…In summary, the study by Sun et al ( 5 ) identified/consolidated biomarkers and prognostic factors in AML, in addition to suggesting new potential compounds of natural origin as disease modulators for the therapy of this aggressive blood cancer. Together these findings reinforce the integration of data mining, bioinformatics, and chemoinformatics as a promising strategy in oncology.…”