⚡ Quick Summary
This study utilized integrated multiomics analysis and machine learning to refine neutrophil extracellular trap (NET)-related molecular subtypes and prognostic models for acute myeloid leukemia (AML). The findings revealed that patients with a high NET score had a significantly worse prognosis, highlighting the potential for personalized treatment strategies.
🔍 Key Details
- 📊 Dataset: Multiomics data from 9 multicenter AML cohorts
- 🧩 Methods used: Gene set variation analysis (GSVA), consensus clustering, weighted gene coexpression network analysis (WGCNA), and 10 machine learning algorithms
- 🏆 Key findings: Identification of two molecular subtypes with high and low NET scores
- 📈 Prognostic model: Developed using C-index and validated through survival analysis and ROC curve
🔑 Key Takeaways
- 🧬 NETs play a crucial role in the immune response and are impaired in AML.
- 🔍 Two molecular subtypes were identified based on NET scores: high-NET and low-NET.
- 💔 High-NET score subtype showed a pronounced immunosuppressive effect and worse prognosis.
- 📉 Patients with high-risk scores had significantly poorer prognoses compared to those with lower scores.
- 🧪 Drug sensitivity analysis revealed specific drugs that may be more effective for high-risk patients.
- 💉 High-risk patients may benefit more from anti-PD-1 therapy, indicating potential for immunotherapy.
- 📊 The nomogram integrating risk scores and clinicopathological factors demonstrated high accuracy in predicting overall survival.
📚 Background
Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by the rapid proliferation of abnormal myeloid cells. The role of neutrophil extracellular traps (NETs) in AML is particularly intriguing, as their formation is often impaired, leading to immunodeficiency and increased susceptibility to infections. Understanding the molecular subtypes associated with NETs can provide insights into the disease’s progression and treatment responses.
🗒️ Study
The study employed a comprehensive approach by integrating multiomics data and advanced machine learning techniques to analyze the role of NETs in AML. Using the gene set variation analysis (GSVA) algorithm, researchers calculated NET scores and identified distinct molecular subtypes through consensus clustering. The weighted gene coexpression network analysis (WGCNA) further elucidated potential genes and biological pathways linked to NETs.
📈 Results
The analysis revealed two distinct molecular subtypes based on NET scores. The low-NET score subgroup exhibited increased infiltration of immune effector cells, while the high-NET score subtype was characterized by an abundance of monocytes and M2 macrophages, along with elevated expression of immune checkpoint genes. Notably, patients with high-risk scores demonstrated significantly poorer prognoses, as confirmed by survival analysis and ROC curve evaluations.
🌍 Impact and Implications
The findings from this study have significant implications for the management of AML. By refining prognostic models through the integration of multiomics data and machine learning, healthcare professionals can better predict patient outcomes and tailor treatment strategies. The identification of drug sensitivities in high-risk patients also opens avenues for personalized medicine, potentially improving therapeutic efficacy and patient survival rates.
🔮 Conclusion
This research highlights the promising potential of a NET-related signature as a valuable tool for prognostic prediction and personalized medicine in AML. The integration of diverse machine learning algorithms with multiomics data not only enhances our understanding of AML but also paves the way for innovative treatment approaches. Continued exploration in this area is essential for advancing patient care and outcomes in AML.
💬 Your comments
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Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia.
Abstract
BACKGROUND: Neutrophil extracellular traps (NETs) play pivotal roles in various pathological processes. The formation of NETs is impaired in acute myeloid leukemia (AML), which can result in immunodeficiency and increased susceptibility to infection.
METHODS: The gene set variation analysis (GSVA) algorithm was employed for the calculation of NET score, while the consensus clustering algorithm was utilized to identify molecular subtypes. Weighted gene coexpression network analysis (WGCNA) revealed potential genes and biological pathways associated with NETs, and a total of 10 machine learning algorithms were applied to construct the optimal prognostic model.
RESULTS: Through the analysis of multiomics data, we identified two molecular subtypes with high and low NET scores. The low-NET score subgroup exhibited increased infiltration of immune effector cells. Conversely, the high-NET score subtype presented an abundance of monocytes and M2 macrophages, accompanied by elevated expression levels of immune checkpoint genes. These findings suggest that a pronounced immunosuppressive effect is associated with a significantly worse prognosis for this subtype. The optimal risk score model was selected by employing the C-index as the criterion on the basis of training 10 machine learning algorithms on 9 multicenter AML cohorts. Survival analysis confirmed that patients with high-risk scores had considerably poorer prognoses than those with lower scores. Receiver operating characteristic (ROC) curve and Cox regression analyses further validated the strong independent prognostic value of the risk score model. The nomogram, which was constructed by integrating the risk score model and clinicopathological factors, demonstrated high accuracy in predicting the overall survival of AML patients. Moreover, patients with refractory or chemotherapy-unresponsive AML had significantly higher risk scores. By analyzing drug therapy data from in vitro AML cells, we identified a subset of drugs that demonstrated increased sensitivity in the high-risk score group. Additionally, patients with a high risk score were also predicted to exhibit a favorable response to anti-PD-1 therapy, suggesting that these individuals may derive greater benefits from immunotherapy.
CONCLUSION: The NET-related signature, derived from a combination of diverse machine learning algorithms, has promising potential as a valuable tool for prognostic prediction, preventive measures, and personalized medicine in patients with AML.
Author: [‘Zhong F’, ‘Yao F’, ‘Wang Z’, ‘Liu J’, ‘Huang B’, ‘Wang X’]
Journal: Front Immunol
Citation: Zhong F, et al. Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia. Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia. 2025; 16:1558496. doi: 10.3389/fimmu.2025.1558496