Bioinformatics analysis of the endoplasmic reticulum stress-related prognostic model and immune cell infiltration in acute myeloid leukemia

Background: Acute myeloid leukemia (AML) is the most common malignant hematological disease originating from hematopoietic stem cells. Endoplasmic reticulum stress (ERs) has been reported to be involved in multiple tumor-related biological processes. However, the prognostic role of ERs-related genes in AML has not been fully investigated. Methods: The TCGA-LAML RNA-seq dataset was downloaded from the UCSC Xena website as the training cohort. Univariate Cox regression analysis was used to identify 42 ER stress-related genes associated with prognosis. Then, a ERs risk score prognostic model was established through LASSO regression analysis. AML patients were divided into high- and low-risk groups according to the median risk score. The Kaplan-Meier survival curve, time ROC curve analysis and univariate and multivariate independent prognostic analyses were presented for the high- and low-risk groups. Moreover, we verified the ERs risk model in the TARGET-AML and GSE37642 datasets. Next, we performed immune cell infiltration analysis, immune checkpoint gene expression analysis and drug sensitivity analysis. Results: We found 42 ER stress-related genes with prognostic significance, and a prognostic model consisting of 13 genes was constructed and verified. The survival rate of AML patients in the low-risk group was better than that in the high-risk group. The tumor microenvironment and immune cell infiltration results showed that immune cell infiltration was correlated with the survival status of patients. Conclusion: This research identified a ERs risk model with significant prognostic value. These genes are expected to be potential prognostic biomarkers in AML, providing a new theoretical basis for disease management.