Journal
HEMATOLOGY
Volume 28, Issue 1, Pages -Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/16078454.2023.2231760
Keywords
Acute myeloid leukemia(1); NAD(+) metabolism(2); prognostic signature(3); immune landscape(4); lncRNA(5); >
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This study investigates the potential of a NAD(+) metabolism-related lncRNA signature as a prognostic biomarker for acute myeloid leukemia (AML). Transcriptome profiles and clinical data of AML patients were obtained from The Cancer Genome Atlas (TCGA) database. NAD(+) metabolism-related lncRNAs were identified and a predictive model was constructed. The model showed good predictive power and outperformed other markers in terms of prognosis.
Background This study aimed to investigate the potential of a NAD(+) metabolism-related lncRNA signature as a reliable prognostic biomarker for acute myeloid leukemia (AML). Methods Transcriptome profiles and clinical data of AML patients were obtained from The Cancer Genome Atlas (TCGA) database. NAD+ metabolism-related genes (NMRGs) were identified from the KEGG and Reactome databases. Coexpression analysis was used to screen NAD(+) metabolism-related lncRNAs. The NAD(+) metabolismrelated lncRNA signature was constructed using univariate analysis, LASSO regression, and multivariate analysis. High- and low-risk groups were compared for survival, tumor mutation burden, immune cell infiltration, and response to immunotherapy. Enrichment analysis explored the biological functions. Results LINC01679, AC079922.2, TRAF3IP2-AS1, and LINC02465 were identified to construct the risk model. The model exhibited good predictive power and outperformed age and gender as an independent prognostic marker. High-risk patients showed poorer survival, distinct TP53 mutations, and altered immune cell infiltration compared to low-risk patients. Additionally, low-risk patients exhibited greater sensitivity to immunotherapy. Enriched biological functions included leukocyte migration and positive regulation of cytokine production. Conclusions The NAD(+) metabolism-related lncRNA signature shows promise in predicting clinical outcomes for AML patients.
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