Journal of the Formosan Medical Association = Taiwan yi zhi · 2025
Dysregulation of chromatin remodeling genes predicts clinical outcomes in acute myeloid leukemia.
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Liu Lanxiang, Wang Li, Zhang Hongbin, Liu Lin, Yan Xinyu
Аннотация
BACKGROUND: Acute myeloid leukemia (AML) is a malignancy of the blood system. The commonly altered regions in the genome of AML encompass a multitude of gene modifications associated with epigenetic regulation. However, the prognostic significance of chromatin remodeling-related genes (CRRGs) as an overall indicator has yet to be assessed in AML.
METHODS: Univariate Cox regression analysis was performed for CRRGs. Following unsupervised clustering analysis, the differentially expressed genes (DEGs) and immune cells between chromatin remodeling related subtypes in TCGA-AML were measured. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to screen prognostic biomarkers for AML, and a risk model was subsequently constructed. Gene set variation analysis (GSVA) analysis and tumor mutation burden (TMB) analysis was conducted in different risk groups. Using Tumor Immune Dysfunction and Exclusion (TIDE) score to assess patients' differences in sensitivity to immunotherapy. Additionally, the construction and verification of the nomogram were carried out. The expression of biomarkers in healthy and AML patients was analyzed by Quantitative Real-time Polymerase Chain Reaction (RT-qPCR).
RESULTS: A total of 106 prognostic CRRGs were identified. Between the two clusters, 3995 genes, activated CD4 memory T cells and activated Dendritic cells were differentially expressed. A total of 6 prognostic biomarkers were identified (ARF6, ASF1B, CHD5, FLNA, KDM5B, and SPI1I), and a risk model was generated based on these biomarkers. The high-risk group exhibited higher TIDE scores. Moreover, 29 drugs showed lower IC50 values in high-risk group. We also found that risk score was an independent prognostic factor for AML. RT-qPCR results showed significant differences in expression of ARF6, KDM5B and CHD5 between healthy and AML patients.
CONCLUSION: We identified six biomarkers, namely ARF6, ASF1B, CHD5, FLNA, KDM5B, and SPI1, thereby establishing a theoretical foundation for clinical diagnosis of AML.
METHODS: Univariate Cox regression analysis was performed for CRRGs. Following unsupervised clustering analysis, the differentially expressed genes (DEGs) and immune cells between chromatin remodeling related subtypes in TCGA-AML were measured. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to screen prognostic biomarkers for AML, and a risk model was subsequently constructed. Gene set variation analysis (GSVA) analysis and tumor mutation burden (TMB) analysis was conducted in different risk groups. Using Tumor Immune Dysfunction and Exclusion (TIDE) score to assess patients' differences in sensitivity to immunotherapy. Additionally, the construction and verification of the nomogram were carried out. The expression of biomarkers in healthy and AML patients was analyzed by Quantitative Real-time Polymerase Chain Reaction (RT-qPCR).
RESULTS: A total of 106 prognostic CRRGs were identified. Between the two clusters, 3995 genes, activated CD4 memory T cells and activated Dendritic cells were differentially expressed. A total of 6 prognostic biomarkers were identified (ARF6, ASF1B, CHD5, FLNA, KDM5B, and SPI1I), and a risk model was generated based on these biomarkers. The high-risk group exhibited higher TIDE scores. Moreover, 29 drugs showed lower IC50 values in high-risk group. We also found that risk score was an independent prognostic factor for AML. RT-qPCR results showed significant differences in expression of ARF6, KDM5B and CHD5 between healthy and AML patients.
CONCLUSION: We identified six biomarkers, namely ARF6, ASF1B, CHD5, FLNA, KDM5B, and SPI1, thereby establishing a theoretical foundation for clinical diagnosis of AML.