Journal of inflammation research · 2025
Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling.
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Zhao Zihe, Chen Taicai, Liu Qingyuan, Hu Jianhang, Ling Tong, Tong Yuanhao, Han Yuexue, Zhu Zhengyang, Duan Jianfeng, Jin Yi, Fu Dongsheng, Wang Yuzhu, Pan Chaohui, Keyoumu Reyaguli, Sun Lili, Li Wendong, Gao Xia, Shi Yinghuan, Dou Huan, Liu Zhao
Аннотация
PURPOSE: Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.
PATIENTS AND METHODS: Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.
RESULTS: Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.
CONCLUSION: Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.
PATIENTS AND METHODS: Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.
RESULTS: Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.
CONCLUSION: Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.