Establishment of a Basement Membrane-Related Prognosis Model and Characterization of Tumor Microenvironment Infiltration in Acute Myeloid Leukemia.

Background: Basement membrane is a special component of extracellular matrix of epithelial and endothelial tissues, which can maintain their normal morphologies and functions. It can also participate in tumor progression and affect tumor treatment. However, the roles of basement membrane-related genes (BMGs) in acute myeloid leukemia (AML) remain unknown. Material and methods: We downloaded the data of AML and normal samples from TCGA, GTEx, and GEO. Then, we performed bioinformatics analysis to identify differential BMGs. We calculated the risk score of the training cohort and divided it into two risk groups. In addition, we also introduced external cohorts, serving as validation cohorts, to estimate the accuracy of risk score. A nomogram was established based on the risk score and clinicopathological characteristics to predict the prognosis. Based on BMGs, AML patients of TCGA were clustered into 2 subtypes. To investigate the biological features and the association between immune cells and TME, we utilized GSVA to assess pathway enrichment and ssGSEA to quantify the levels of immune cell infiltration across samples. Results: We obtained 3 differential BMGs between AML and normal samples. The training cohort was divided into high- and low-risk groups based on the risk score. The Kaplan-Meier survival analysis indicated that the two groups had significant differences. The nomogram could be used to predict the survival outcomes of AML patients. Based on the clustering result, we found significant differences between the two gene clusters. Sankey's diagram suggested that cluster B was associated with the high-risk group and poor prognosis. GSVA analysis showed that cluster B was also related to the upregulation of intercellular and intracellular signal transduction pathways. In TME, resting mast cells, follicular helper T cells, and plasma cells decreased while monocytes increased in the high-risk group. In addition, the high-risk group was more sensitive to BTK and AKT inhibitors. Conclusion: Our study indicated that the nomogram model of BMGs could predict the prognosis of AML patients. Meanwhile, BMGs were correlated with immune TME in AML. A correct and comprehensive assessment of the mechanisms of BMGs in individuals will help guide more effective treatment.