Objectives: Acute myeloid leukemia (AML) is one of the common hematological diseases with low survival rates. Studies have highlighted the dysregulated expression of immune-related and exosome-related genes (ERGs) in cancers. Nevertheless, it remains to be determined whether combining these genes have a prognostic significance in AML. Methods: Immune-ERG profiles for 151 AML patients from TCGA were analyzed. A risk model was constructed and optimized through the combination of univariate Cox regression and LASSO regression analysis. GEO datasets were utilized as the external validation for the robustness of the risk model. In addition, we performed KEGG and GO enrichment analyses to investigate the role played by these genes in AML. The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq. Results: A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. The risk model based on five immune-ERGs (CD37, NUCB2, LSP1, MGST1, and PLXNB1) demonstrated a correlation with the clinical outcomes. Additionally, age, FAB classification, cytogenetics risk, and risk score were identified as independent prognostic factors. The five immune-ERGs exhibited correlations with cytokine- cytokine receptor interaction, and antigen processing and presentation. Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints. Conclusions: An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.
Obiettivi La leucemia mieloide acuta (AML) è una delle malattie ematologiche comuni con bassi tassi di sopravvivenza. Gli studi hanno evidenziato l'espressione disregolata di geni correlati al sistema immunitario e agli esosomi (ERG) nei tumori. Tuttavia, resta da determinare se la combinazione di questi geni abbia un significato prognostico nella LMA. Metodi Sono stati analizzati i profili immuno-ERG per 151 pazienti con AML di TCGA. Un modello di rischio è stato costruito e ottimizzato attraverso la combinazione di regressione Cox univariata e analisi di regressione LASSO. I set di dati GEO sono stati utilizzati come convalida esterna per la robustezza del modello di rischio. Inoltre, abbiamo eseguito analisi di arricchimento KEGG e GO per indagare sul ruolo svolto da questi geni nella LMA. Le variazioni nelle infiltrazioni delle cellule immunitarie tra i gruppi a rischio sono state valutate attraverso quattro algoritmi. L'espressione del gene hub in una cellula specifica è stata analizzata da RNA seq a singola cellula. Risultati Sono stati identificati un totale di 85 ERG immunitari associati alla prognosi, consentendo la costruzione di un modello di rischio per la LML. Il modello di rischio basato su cinque ERG immunitari (CD37, NUCB2, LSP1, MGST1 e PLXNB1) ha dimostrato una correlazione con gli esiti clinici. Inoltre, l'età, la classificazione FAB, il rischio citogenetico e il punteggio di rischio sono stati identificati come fattori prognostici indipendenti. I cinque ERG immunitari hanno mostrato correlazioni con l'interazione del recettore citochina-citochina e l'elaborazione e la presentazione dell'antigene. In particolare, il modello di rischio ha dimostrato associazioni significative con le risposte immunitarie e l'espressione di checkpoint immunitari. Conclusioni È stato sviluppato un modello di rischio basato sull'ERG immunitario per prevedere efficacemente i risultati prognostici per i pazienti con AML. C'è un potenziale per la terapia immunitaria nell'AML che prende di mira i cinque geni hub.
Esosomi e il loro potenziale come biomarcatori prognostici nella leucemia mieloide acuta
IACOPONI, VIRGINIA
2023/2024
Abstract
Objectives: Acute myeloid leukemia (AML) is one of the common hematological diseases with low survival rates. Studies have highlighted the dysregulated expression of immune-related and exosome-related genes (ERGs) in cancers. Nevertheless, it remains to be determined whether combining these genes have a prognostic significance in AML. Methods: Immune-ERG profiles for 151 AML patients from TCGA were analyzed. A risk model was constructed and optimized through the combination of univariate Cox regression and LASSO regression analysis. GEO datasets were utilized as the external validation for the robustness of the risk model. In addition, we performed KEGG and GO enrichment analyses to investigate the role played by these genes in AML. The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq. Results: A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. The risk model based on five immune-ERGs (CD37, NUCB2, LSP1, MGST1, and PLXNB1) demonstrated a correlation with the clinical outcomes. Additionally, age, FAB classification, cytogenetics risk, and risk score were identified as independent prognostic factors. The five immune-ERGs exhibited correlations with cytokine- cytokine receptor interaction, and antigen processing and presentation. Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints. Conclusions: An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12075/20777