ArticleFrontiers in oncology2022
Establishment of a risk model correlated with metabolism based on RNA-binding proteins associated with cell pyroptosis in acute myeloid leukemia.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Decoding the molecular mechanisms of pyroptosis and its therapeutic development prospects.Frontiers in cell and developmental biology · 2026Review
- Multi-omics integration of molecular genetics, cytogenetics and immunophenotyping: a novel prognostic model for immune landscape characterization and outcome prediction in Chinese patients with acute myeloid leukemia.Frontiers in immunology · 2026Article
- The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients.Frontiers in medicine · 2023Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: RNA-binding protein (RBP) regulates acute myeloid leukemia (AML) by participating in mRNA editing and modification. Pyroptosis also plays an immunomodulatory function in AML. Therefore, this study aimed to identify pyroptosis-related RBP genes that could predict the prognosis of AML patients. Methods: AML related expression data were downloaded from the UCSC website and Gene Expression Omnibus (GEO) database. Pyroptosis-RPB-related differentially expressed genes (PRBP-DEGs) were conducted with a protein-protein interactions (PPI) network to screen out the key PRBP-DEGs, based on which a risk model was constructed by Cox analysis, and evaluated by plotting Receiver operating characteristic (ROC) curves and survival curves. Independent prognostic analysis was performed and a nomogram was constructed. Finally, enrichment analysis was performed for high and low risk groups. Reuslts: A total of 71 PRBP-DEGs were obtained and a pyroptosis-RPB-related risk model was constructed based on IFIT5, MRPL14, MRPL21, MRPL39, MVP, and PUSL1 acquired from Cox analysis. RiskScore, age, and cytogenetics risk category were identified as independent prognostic factors, and the nomogram based on these independent prognostic factors could accurately predict 1-, 3- and 5-year survival of AML patients. Gene set enrichment analysis (GSEA) showed that the high-risk and low-risk groups were mainly enriched in metabolic- and immune-related processes and pathways. Conclusion: In this study, a risk score model correlated with metabolism based on RNA-binding proteins associated with cell pyroptosis in acute myeloid leukemia was established, which provided a theoretical basis and reference value for therapeutic studies and prognosis of AML.
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