Translational cancer research · 2025
A novel manganese metabolism- and immune-related prognostic risk model for acute myeloid leukemia.
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Guo Lin, Cao Yue, Gu Wei-Ying, Lin Yan
Аннотация
BACKGROUND: Manganese (Mn), an essential trace element, is involved in various biological processes (BPs). The abnormal metabolism of Mn may be related to the occurrence and development of blood disease. In recent years, studies on manganese metabolism-related genes (MRGs) and acute myeloid leukemia (AML) have gradually attracted attention. The interaction between the immune system and AML is complex. The immune system not only participates in the occurrence and development of leukemia, but also affects the treatment response and prognosis of AML patients. This study aimed to establish a prognostic risk model for AML by combining MRGs and immune-related genes (IRGs).
METHODS: We constructed a prognostic risk model through univariate and multivariate Cox regression analyses, and validated the predictive efficacy of this model using external datasets. We analyzed the differences in immune infiltration between the high- and low-risk groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to investigate the roles of five Mn metabolism- and immune-related genes (MIRGs). We also analyzed the immune checkpoint expression levels in AML. The expression levels of the five MIRGs in specific cells were analyzed through single-cell RNA sequencing. All the data analyses were conducted using R software version 4.2.2.
RESULTS: This study developed a prognostic risk model based on five MIRGs (CGA, TGFA, GKN1, S100G, and CCL23) linked to cytokine signaling and viral protein interactions. The model, along with age and cytogenetic risk, independently predicted the outcomes of the AML patients. Additionally, the high- and low-risk groups showed distinct immune infiltration patterns and immune checkpoint expression.
CONCLUSIONS: Our study established an MIRG-based prognostic risk model that can effectively predict the prognosis of AML patients.
METHODS: We constructed a prognostic risk model through univariate and multivariate Cox regression analyses, and validated the predictive efficacy of this model using external datasets. We analyzed the differences in immune infiltration between the high- and low-risk groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to investigate the roles of five Mn metabolism- and immune-related genes (MIRGs). We also analyzed the immune checkpoint expression levels in AML. The expression levels of the five MIRGs in specific cells were analyzed through single-cell RNA sequencing. All the data analyses were conducted using R software version 4.2.2.
RESULTS: This study developed a prognostic risk model based on five MIRGs (CGA, TGFA, GKN1, S100G, and CCL23) linked to cytokine signaling and viral protein interactions. The model, along with age and cytogenetic risk, independently predicted the outcomes of the AML patients. Additionally, the high- and low-risk groups showed distinct immune infiltration patterns and immune checkpoint expression.
CONCLUSIONS: Our study established an MIRG-based prognostic risk model that can effectively predict the prognosis of AML patients.