Identification of Novel Diagnostic Markers for Atherosclerosis Using Machine-Learning Algorithms
1Department of Neurosurgery, The First Hospital of China Medical University, Liaoning, China.
Objective:
To outline immune-cell infiltration and identify diagnostic genes for atherosclerosis (AS) to better understand the potential molecular processes involved in AS development.
Study Design:
Descriptive study. Place and Duration of the Study: Department of Cardiology, The First Hospital of China Medical University, Shenyang, Liaoning, China, from 10th June to 8th October 2024.
Methodology:
Relevant datasets were collected from the Gene Expression Omnibus database. Gene set enrichment analysis was conducted on differentially expressed genes (DEGs). Subsequently, three machine-learning algorithms were used to identify the core genes. Receiver operating characteristic (ROC) curves were used to analyse the clinical diagnostic value of the core genes.
Results:
A Total of 3,307 DEGs, which were found primarily enriched in inflammation-related pathways. Further analysis of the core genes using three machine-learning algorithms revealed four intersecting genes, IBSP, PI16, MYOC, and IGLL5, which are all inflammation-related genes; they also showed good clinical diagnostic abilities, which were verified using ROC curves (area under the curve: 0.959, 0.946, 0.931, and 0.880, respectively).
Conclusion:
IBSP, PI16, MYOC, and IGLL5 participate in AS pathogenesis by regulating inflammatory reactions. These are novel diagnostic markers and are expected to become potential targets for AS-targeted therapies.
Key Words:
Atherosclerosis, Inflammatory reaction, Machine-learning algorithms, Bioinformatic.
