Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia.

Abstract

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.

• Authors: Yidong Zhu, Jun He, Zihua Li, Wenzhong Yang
• Journal: BMC bioinformatics (BMC Bioinformatics) Vol. 24 Issue 1 Pg. 37 (Feb 03 2023) ISSN: 1471-2105 [Electronic] England
• PMID: 36737692 (Publication Type: Journal Article)
• Copyright: © 2023. The Author(s).

Chemical References

• RNA, Long Noncoding
• Copper

Topics

• Humans
• Algorithms
• Leukemia, Myeloid, Acute (genetics)
• Nomograms
• Prognosis
• RNA, Long Noncoding (genetics)
• Copper
• Apoptosis