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

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.

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.

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.

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.