Background: Acute myeloid leukemia (AML) is a hematologic tumor with poor prognosis and significant clinical heterogeneity. By integrating transcriptomic data, single-cell RNA sequencing data and independently collected RNA sequencing data this study aims to identify key genes in AML and establish a prognostic assessment model to improve the accuracy of prognostic prediction.
Materials And Methods: We analyzed RNA-seq data from AML patients and combined it with single-cell RNA sequencing data to identify genes associated with AML prognosis. Key genes were screened by bioinformatics methods, and a prognostic assessment model was established based on these genes to validate their accuracy.
Results: The study identified eight key genes significantly associated with AML prognosis: SPATS2L, SPINK2, AREG, CLEC11A, HGF, IRF8, ARHGAP5, and CD34. The prognostic model constructed on the basis of these genes effectively differentiated between high-risk and low-risk patients and revealed differences in immune function and metabolic pathways of AML cells.
Conclusion: This study provides a new approach to AML prognostic assessment and reveals the role of key genes in AML. These genes may become new biomarkers and therapeutic targets that can help improve prognostic prediction and personalized treatment of AML.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683592 | PMC |
| http://dx.doi.org/10.3389/fimmu.2024.1494106 | DOI Listing |
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