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Identification of Key Ferroptosis-Related Genes in the Peripheral Blood of Patients with Relapsing-Remitting Multiple
Xi Song1, Zixuan Wang1, Zixin Tian1
1Department of Cell Biology and Genetics, Institute of Molecular Medicine and Oncology, Chongqing Medical University, Medical School Road 1#, Yuzhong District, Chongqing 400016, China.
Abstract:
Multiple sclerosis (MS) is a neurodegenerative disease with a complex pathogenesis. Re-lapsing-remitting multiple sclerosis (RRMS) is the most common subset of MS, accounting for approximately 85% of cases. Recent studies have shown that ferroptosis may contribute to the progression of RRMS, but the underlying mechanism remains to be elucidated. Herein, this study intended to explore the molecular network of ferroptosis associated with RRMS and establish a predictive model for efficacy diagnosis. Firstly, RRMS-related module genes were identified using weighted gene co-expression network analysis (WGCNA). Secondly, the optimal machine learning model was selected from four options: the generalized linear model (GLM), random forest model (RF), support vector machine model (SVM), and extreme gradient boosting model (XGB). Subsequently, the predictive efficacy of the diagnostic model was evaluated using receiver operator characteristic (ROC) analysis. Finally, a SVM diagnostic model based on five genes (JUN, TXNIP, NCOA4, EIF2AK4, PIK3CA) was established, and it demonstrated good predictive performance in the validation dataset. In summary, our study provides a systematic exploration of the complex relationship between ferroptosis and RRMS, which may contribute to a better understanding of the role of ferroptosis in the pathogenesis of RRMS and provide promising diagnostic strategies for RRMS patients.
Insights
This study reveals ferroptosis
Area of Science:
- Neuroscience
- Immunology
- Genetics
Background:
- Multiple sclerosis (MS) is a complex neurodegenerative disease, with relapsing-remitting MS (RRMS) being the most common form.
- Emerging evidence suggests ferroptosis, a regulated cell death pathway, plays a role in RRMS progression.
- The precise molecular mechanisms linking ferroptosis to RRMS pathogenesis require further investigation.
Purpose of the Study:
- To explore the molecular network of ferroptosis in relation to RRMS.
- To develop a predictive model for diagnosing the efficacy of treatments in RRMS patients.
- To identify key genes involved in the ferroptosis-RRMS pathway.
Main Methods:
- Weighted gene co-expression network analysis (WGCNA) to identify RRMS-related genes.
- Comparison of four machine learning models: GLM, RF, SVM, and XGB.
- Receiver operator characteristic (ROC) analysis to evaluate predictive efficacy.
- Development and validation of a support vector machine (SVM) diagnostic model.
Main Results:
- Identification of a significant molecular network connecting ferroptosis and RRMS.
- Establishment of an SVM-based diagnostic model utilizing five key genes: JUN, TXNIP, NCOA4, EIF2AK4, and PIK3CA.
- The developed SVM model demonstrated robust predictive performance on a validation dataset.
Conclusions:
- This research provides a systematic analysis of the intricate relationship between ferroptosis and RRMS.
- The findings enhance the understanding of ferroptosis's role in RRMS pathogenesis.
- The established predictive model offers potential for improved diagnostic strategies in RRMS management.
