โก Quick Summary
This study identified disulfidptosis-related genes (DRGs) in ischemic stroke (IS) using a combination of single-cell sequencing, machine learning algorithms, and in vitro experiments. The findings highlight the potential of these genes in understanding the pathogenesis of IS and developing novel therapeutic strategies.
๐ Key Details
- ๐ Dataset: Expression profiles from human IS patients retrieved from the GEO database
- ๐งฉ Analytical Tools: Machine learning algorithms including LASSO, random forest, and SVM-RFE
- โ๏ธ Predictive Model: Developed a nomogram model for IS risk assessment
- ๐ฌ Experimental Model: Oxygen-glucose deprivation (OGD) model to study microglial polarization
๐ Key Takeaways
- ๐งฌ Identification of DRGs provides insights into the mechanisms of ischemic stroke.
- ๐ Machine learning was effectively utilized to predict IS risk through a nomogram model.
- ๐ก Strong correlation between disulfidptosis levels and immune cell infiltration was observed.
- ๐ Intercellular communication networks were characterized using “CellChat” analysis.
- ๐ TNF signaling pathway was closely linked with the disulfidptosis signature in IS.
- ๐งช SLC7A11 overexpression was shown to suppress M1 polarization while promoting M2 polarization in microglia.
- ๐ Potential for novel therapies targeting DRGs to mitigate ischemic stroke effects.
๐ Background
Ischemic stroke (IS) is a critical neurological condition that can lead to significant morbidity and mortality. Despite advancements in medical science, the underlying mechanisms of IS remain poorly understood. Recently, the concept of disulfidptosis, a novel form of cell death, has emerged as a potential area of interest in stroke research, prompting investigations into its related genes and their roles in the disease.
๐๏ธ Study
The study aimed to explore the mechanistic roles of DRGs in ischemic stroke by leveraging single-cell sequencing data and various machine learning algorithms. Researchers retrieved expression profiles from the GEO database and employed advanced analytical tools to identify key genes associated with disulfidptosis. Additionally, they developed a predictive nomogram model to assess the risk of IS.
๐ Results
The research successfully identified several key genes linked to disulfidptosis and established a reliable nomogram model for predicting ischemic stroke risk. The analysis of single-cell sequencing data revealed a significant correlation between disulfidptosis levels and immune cell infiltration. Furthermore, the study highlighted the intricate intercellular communication networks and the critical role of the TNF signaling pathway in the context of IS.
๐ Impact and Implications
The findings from this study have the potential to transform our understanding of ischemic stroke pathogenesis. By elucidating the role of DRGs, researchers can pave the way for innovative therapeutic strategies aimed at targeting these genes. This could lead to improved outcomes for patients suffering from ischemic stroke and enhance the overall management of this debilitating condition.
๐ฎ Conclusion
This study underscores the importance of disulfidptosis-related genes in ischemic stroke, revealing their potential as targets for future therapies. The integration of machine learning and single-cell sequencing represents a significant advancement in stroke research, offering new avenues for exploration and intervention. Continued research in this area is essential for developing effective treatments and improving patient care.
๐ฌ Your comments
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Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments.
Abstract
BACKGROUND: Ischemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains incompletely understood. Recently, a novel form of cell death known as disulfidptosis has garnered significant attention in the field of ischemic stroke research. This study aims to investigate the mechanistic roles of disulfidptosis-related genes (DRGs) in the context of IS and to examine their correlation with immunopathological features.
METHODS: To enhance our understanding of the mechanistic underpinnings of disulfidptosis in IS, we initially retrieved the expression profile of peripheral blood from human IS patients from the GEO database. We then utilized a suite of machine learning algorithms, including LASSO, random forest, and SVM-RFE, to identify and validate pivotal genes. Furthermore, we developed a predictive nomogram model, integrating multifactorial logistic regression analysis and calibration curves, to evaluate the risk of IS. For the analysis of single-cell sequencing data, we employed a range of analytical tools, such as “Monocle” and “CellChat,” to assess the status of immune cell infiltration and to characterize intercellular communication networks. Additionally, we utilized an oxygen-glucose deprivation (OGD) model to investigate the effects of SLC7A11 overexpression on microglial polarization.
RESULTS: This study successfully identified key genes associated with disulfidptosis and developed a reliable nomogram model using machine learning algorithms to predict the risk of ischemic stroke. Examination of single-cell sequencing data showed a robust correlation between disulfidptosis levels and the infiltration of immune cells. Furthermore, “CellChat” analysis elucidated the intricate characteristics of intercellular communication networks. Notably, the TNF signaling pathway was found to be intimately linked with the disulfidptosis signature in ischemic stroke. In an intriguing finding, the OGD model demonstrated that SLC7A11 expression suppresses M1 polarization while promoting M2 polarization in microglia.
CONCLUSION: The significance of our findings lies in their potential to shed light on the pathogenesis of ischemic stroke, particularly by underscoring the pivotal role of disulfidptosis-related genes (DRGs). These insights could pave the way for novel therapeutic strategies targeting DRGs to mitigate the impact of ischemic stroke.
Author: [‘Zhao S’, ‘Zhuang H’, ‘Ji W’, ‘Cheng C’, ‘Liu Y’]
Journal: Neuromolecular Med
Citation: Zhao S, et al. Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments. Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments. 2024; 26:39. doi: 10.1007/s12017-024-08804-2