Background Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. Methods The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan–Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. Results A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. Conclusion Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Background Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might be a promising therapeutic target for cancer treatment. Nevertheless, the role of cuproptosis-related lncRNAs in AML remains unexplored. Methods AML RNA sequencing data and demographical characteristics were downloaded from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis, the least absolute shrinkage and selection operator (LASSO) regression algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility it for AML prognosis prediction. The performance of the proposed signature was measured via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). Functional analysis was implemented to uncover the potential prognostic mechanisms. Moreover, quantitative real-time PCR (qRT-PCR) was used to validate the expression of the prognostic lncRNAs in clinical samples. Results A signature consisting of seven cuproptosis-related lncRNA (NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was identified. Multivariable cox regression analysis revealed that the proposed lncRNA signature was an independent prognostic factor for AML, the nomogram based on this signature showed excellent accuracy in predicting 1-, 3-, and 5-year survival [Area Under Curve (AUC) = 0.846, 0.801, and 0.895, respectively]. Functional analysis suggested a significant association between the prognostic signature and the immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples, which suggested the robustness of these findings. Conclusion In this study, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may be connected with tumor immunity.