A lncRNA signature associated with endoplasmic reticulum stress supports prognostication and prediction of drug resistance in acute myelogenous leukemia.

Background: Acute myelogenous leukemia (AML) is a type of blood cancer that is characterized by the accumulation of young and undeveloped myeloid cells in the bone marrow. It is considered a heterogeneous disease due to its diverse nature. Endoplasmic reticulum (ER) stress has emerged as a critical regulator of tumor development and drug resistance in various cancers. Long non-coding RNAs (lncRNAs) have been found to play a role in the development and prognosis of AML. Nonetheless, there is still limited understanding regarding the involvement of ER stress-related lncRNAs in AML prognosis and their predictive ability for drug resistance. The objective of this study was to examine the potential prognostic and predictive significance of an ER stress-related lncRNA signature in patients diagnosed with AML. Methods: Based on the bulk RNA sequence data, we constructed an ER stress-related lncRNA signature using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. We established nomograms and calibration curves to assess the clinical value of the signature by analyzing overall survival (OS) rates between different risk groups. We also conducted tumor mutation burden (TMB) analysis, predicted immune responses, performed functional and biological enrichment analysis, and evaluated drug sensitivity to investigate the impact of the prognostic signature. Additionally, we built a consensus cluster to explore the need for personalized immunotherapy approaches in treating patients with AML. Results: A prognostic signature was constructed using 227 ER stress-related lncRNAs that showed differential expression. Patients in the high-risk category demonstrated decreased OS rates in comparison to individuals in the low-risk category. The findings from the nomogram and receiver operating characteristic (ROC) curve analysis suggest a notable disparity in age between the different categories. Among the group at high risk, we noticed a considerably greater TMB in comparison to the low-risk group. Furthermore, individuals with both an elevated risk score and high TMB demonstrated the most unfavorable survival rates. Significant differences were observed in the immune responses between the groups classified as high- and low-risk. We then systematically evaluated three different clusters to assess immune responses and drug responses. Through analyzing the association between the risk score and various medications, we have discovered 18 potential drug contenders capable of effectively addressing AML. Furthermore, we conducted pathway analyses to determine the targeted pathways of these drugs. Conclusions: Our data serve as a valuable resource for decoding the immune responses, somatic mutational landscape, drug resistance, and potential biological functions in AML patients. Additionally, our findings offer valuable insights into the association between related lncRNAs and the immune microenvironment of AML. It provides us with promising insights that can help in the development of precise therapeutic strategies.