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Identification of FERMT1 and SGCD as key marker in acute aortic dissection from the perspective of predictive, preventive, and personalized medicine

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Abstract  

Acute aortic dissection (AAD) is a severe aortic injury disease, which is often life-threatening at the onset. However, its early prevention remains a challenge. Therefore, in the context of predictive, preventive, and personalized medicine (PPPM), it is particularly important to identify novel and powerful biomarkers. This study aimed to identify the key markers that may contribute to the predictive early risk of AAD and analyze their role in immune infiltration. Three datasets, including a total of 23 AAD and 20 healthy control aortic samples, were retrieved from the Gene Expression Omnibus (GEO) database, and a total of 519 differentially expressed genes (DEGs) were screened in the training set. Using the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) algorithm, FERMT1 (AUC = 0.886) and SGCD (AUC = 0.876) were identified as key markers of AAD. A novel AAD risk prediction model was constructed using an artificial neural network (ANN), and in the validation set, the AUC = 0.920. Immune infiltration analysis indicated differential gene expression in regulatory T cells, monocytes, γδ T cells, quiescent NK cells, and mast cells in the patients with AAD and the healthy controls. Correlation and ssGSEA analysis showed that two key markers’ expression in patients with AAD was correlated with many inflammatory mediators and pathways. In addition, the drug-gene interaction network identified motesanib and pyrazoloacridine as potential therapeutic agents for two key markers, which may provide personalized medical services for AAD patients. These findings highlight FERMT1 and SGCD as key biological targets for AAD and reveal the inflammation-related potential molecular mechanism of AAD, which is helpful for early risk prediction and targeted prevention of AAD. In conclusion, our study provides a new perspective for developing a PPPM method for managing AAD patients.

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Code availability

The code that supports the findings of this study is available from the corresponding author upon request.

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Acknowledgements

We would like to thank the Gene Expression Omnibus (GEO) database for the precious data used for free in scientific research.

Funding

This work was supported by the Construction of key laboratories in Xinjiang Uygur Autonomous Region (NO.2019D04017) and the Tianshan cedar Project Fund of Xinjiang (2020XS13).

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  1. Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830000, China

    Mierxiati Ainiwan, Qi Wang, Gulinazi Yesitayi & Xiang Ma

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  1. Mierxiati Ainiwan
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Contributions

Mierxiati: study design, data collection, statistical analysis, visualization, writing, and revised original draft. Wang Qi and Gulinazi revised the manuscript and performed the experiments. Ma Xiang led the study and provided scientific supervision. The final draft was verified by all authors before the submission.

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Correspondence to Xiang Ma.

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Data retrieved from the GEO database was collected from patients who provided informed consent based on guidelines laid out by the GEO Ethics, Law, and Policy Group. The procedures used in this study adhere to the tenets of the Declaration of Helsinki and approval was obtained from the ethics committee of Xinjiang Medical University.

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Ainiwan, M., Wang, Q., Yesitayi, G. et al. Identification of FERMT1 and SGCD as key marker in acute aortic dissection from the perspective of predictive, preventive, and personalized medicine. EPMA Journal 13, 597–614 (2022). https://doi.org/10.1007/s13167-022-00302-4

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