ArticleBMC genomic data2022
Construction and validation of a fatty acid metabolism risk signature for predicting prognosis in acute myeloid leukemia.
Article in BMC genomic data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The effect of 2-Deoxy-D-glucose on glycolytic metabolism in acute myeloblastic leukemic ML-1 cells.Scientific reports · 2025Article
- Monocytic Differentiation in Acute Myeloid Leukemia Cells: Diagnostic Criteria, Biological Heterogeneity, Mitochondrial Metabolism, Resistance to and Induction by Targeted Therapies.International journal of molecular sciences · 2024Review
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Authors and funding
5 authors.
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Abstract
backgroundFatty acid metabolism has been reported to play important roles in the development of acute myeloid leukemia (AML), but there are no prognostic signatures composed of fatty acid metabolism-related genes. As the current prognostic evaluation system has limitations due to the heterogeneity of AML patients, it is necessary to develop a new signature based on fatty acid metabolism to better guide prognosis prediction and treatment selection.
methodsWe analyzed the RNA sequencing and clinical data of The Cancer Genome Atlas (TCGA) and Vizome cohorts. The analyses were performed with GraphPad 7, the R language and SPSS.
resultsWe selected nine significant genes in the fatty acid metabolism gene set through univariate Cox analysis and the log-rank test. Then, a fatty acid metabolism signature was established based on these genes. We found that the signature was as an independent unfavourable prognostic factor and increased the precision of prediction when combined with classic factors in a nomogram. Gene Ontology (GO) and gene set enrichment analysis (GSEA) showed that the risk signature was closely associated with mitochondrial metabolism and that the high-risk group had an enhanced immune response.
conclusionThe fatty acid metabolism signature is a new independent factor for predicting the clinical outcomes of AML patients.
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