⚡ Quick Summary

This study investigates the role of disulfidptosis related genes (DiGs) in pulmonary hypertension (PH), identifying 12 significant DiGs and utilizing an XGBoost model that achieved an impressive AUC of 0.958 for diagnostic accuracy. The findings suggest potential therapeutic avenues, including the use of folic acid.

🔍 Key Details

• 📊 Dataset: GSE11726, with validation from GSE57345 and GSE48166
• 🧩 Features used: Disulfidptosis related genes and immune characteristics
• ⚙️ Technology: Machine learning models including RF, SVM, GLM, and XGB
• 🏆 Performance: XGB model: AUC 0.958

🔑 Key Takeaways

• 🔬 Disulfidptosis is a newly recognized cell death mechanism potentially linked to PH.
• 📈 Twelve DiGs were identified as significantly associated with PH.
• 🤖 The XGB model outperformed other machine learning models in diagnostic accuracy.
• 🌐 A CeRNA network was constructed involving core genes, 40 miRNAs, and 115 lncRNAs.
• 💊 Drug prediction indicated therapeutic potential for folic acid.
• 🐀 Experimental validation was conducted using a hypoxia-induced PH rat model.
• 🔍 Core genes identified: DSTN, NDUFS1, RPN1, TLN1, and MYH10.
• 📊 Validation datasets confirmed the effectiveness of the diagnostic model.

📚 Background

Pulmonary hypertension (PH) is a serious condition characterized by elevated pressures in the pulmonary arteries, which can lead to right heart dysfunction and significantly impact survival rates. Understanding the underlying mechanisms and identifying reliable biomarkers for PH is crucial for improving diagnosis and treatment strategies. The emerging concept of disulfidptosis as a cell death mechanism offers new insights into the pathology of PH, warranting further exploration of its related genes.

🗒️ Study

This study utilized the GSE11726 dataset to analyze the role of DiGs in PH. Researchers employed various machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Generalized Linear Model (GLM), and XGBoost (XGB), to identify core genes that influence the progression of PH. The study also established a CeRNA network and validated findings through a hypoxia-induced PH rat model, employing Western blot analysis for experimental confirmation.

📈 Results

The analysis revealed 12 DiGs significantly associated with PH. The XGB model demonstrated exceptional diagnostic accuracy, achieving an AUC of 0.958. Core genes identified included DSTN, NDUFS1, RPN1, TLN1, and MYH10. The constructed CeRNA network provided a comprehensive view of the interactions among these genes, miRNAs, and lncRNAs. Additionally, drug prediction analyses suggested that folic acid could be a promising therapeutic option, supported by robust molecular docking results.

🌍 Impact and Implications

The findings of this study have significant implications for the diagnosis and treatment of pulmonary hypertension. By uncovering the distinct expression patterns of DiGs and their potential roles in PH, researchers can pave the way for new diagnostic tools and therapeutic strategies. The identification of core genes and their interactions within a CeRNA network enhances our understanding of the molecular mechanisms underlying PH, potentially leading to improved patient outcomes and targeted therapies.

🔮 Conclusion

This research highlights the importance of disulfidptosis related genes in the context of pulmonary hypertension. The successful application of the XGB machine-learning model for diagnostic purposes and the identification of potential therapeutic agents like folic acid mark significant advancements in the field. Continued exploration of DiGs and their roles in PH could lead to transformative changes in how this condition is diagnosed and treated, ultimately benefiting patient care.

💬 Your comments

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