ArticleFrontiers in immunology2023
A novel prognostic classification integrating lipid metabolism and immune co-related genes in acute myeloid leukemia.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Identification of MTMR2 as an AML-associated candidate biomarker derived from lipid metabolism-related transcriptomic analysis.Frontiers in oncology · 2026Article
- Plasma lipid levels predict chemotherapy response and survival in acute myeloid leukemia.Blood · 2025Article
- Literature-Guided 6-Gene Signature for the Stratification of High-Risk Acute Myeloid Leukemia.Cancer research and treatment · 2025Article
- miR-584-5p Regulates MSMO1 to Modulate the AKT/PI3K Pathway and Inhibit Breast Cancer Progression.Protein and peptide letters · 2025Article
- Targeting lipid metabolic vulnerabilities with small-molecule inhibitors in acute myeloid leukemia: Emerging insights and therapeutic opportunities.Tzu chi medical journalReview
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
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Abstract
Background: As a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML. Methods: First, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored. Results: Eight genes were identified from the analysis and verified in the clinical samples, including Conclusions: This study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.
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