Aging
Navigate
Submit an Article
Research Paper|Volume 15, Issue 21|pp 12388—12399

A neural network model was constructed by screening the potential biomarkers of aortic dissection based on genes associated with pyroptosis

Cheng Chen1, Lulu Gao2, Hongwei Ge1, Weibin Huang1, Rong Zhao3, Renjun Gu4,5, Ziyun Li6, Xin Wang1
  • 1Department of Vascular Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213000, China
  • 2Department of Anesthesiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213000, China
  • 3Department of Cardiology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213000, China
  • 4School of Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
  • 5Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
  • 6School of Acupuncture and Tuina, School of Regimen and Rehabilitation, Nanjing University of Chinese Medicine, Nanjing, China
* Share first authorship
Received: August 4, 2023Accepted: October 8, 2023Published: November 7, 2023
https://doi.org/10.18632/aging.205187

Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Aortic dissection (AD) is one of the crucial and common cardiovascular diseases, and pyroptosis is a novel cell delivery mechanism that is probably involved in the pathogenesis of various cardiovascular diseases. However, no study has investigated the role of pyroptosis in AD.

Methods: We obtained two AD datasets, GSE153434 and GSE190635, from the Gene Expression Omnibus database. The differential expression of AD-related genes was determined by differential analysis, and their enrichment analysis was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Additionally, a protein–protein interaction network was established. Next, potential biomarkers were screened by Lasso regression analysis, and a neural network model was constructed. Finally, the potential biomarkers were validated by constructing a mouse model of AD.

Results: A total of 1033 differentially expressed related genes were distinguished and these genes were mainly associated with the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) and mitogen-activated protein kinase signaling pathways. The Lasso regression results showed five potential biomarkers, namely platelet endothelial cell adhesion molecule-1 (PECAM1), caspase 4 (CASP4), mixed lineage kinase domain-like pseudokinase (MLKL), APAF1-interacting protein (APIP), and histone deacetylase 6 (HDAC6) and successfully constructed a neural network model to predict AD occurrence. The results showed that CASP4 and MLKL were highly expressed, whereas PECAM1 and HDAC6 were lowly expressed in AD samples, and no statistically significant difference was observed in APIP expression in AD samples.

Conclusion: Pyroptosis plays a crucial role in AD occurrence and development. Moreover, the five potential biomarkers identified in the present study can act as targets for the early diagnosis of AD in patients.