Researchers have discovered two promising biomarkers – LRRK2 and ANKRD13A – that could help diagnose a common complication of heart disease called in-stent restenosis (ISR). The study, which used advanced bioinformatics and machine learning techniques, also provides insights into how these biomarkers are linked to the immune system and inflammation. This research could pave the way for improved early detection and personalized treatment strategies for ISR, a serious public health issue. Coronary artery disease and percutaneous coronary intervention are major focus areas in this study.
Uncovering the Role of Mitophagy in In-Stent Restenosis
Coronary artery disease is a leading cause of death worldwide, and one of the most effective treatments is percutaneous coronary intervention (PCI) with stent implantation. However, a common complication of this procedure is in-stent restenosis (ISR), where the treated artery becomes narrowed again. ISR can severely impact the long-term effectiveness of PCI, posing a significant public health and economic burden.
The researchers in this study aimed to identify new biomarkers related to mitophagy – a process where cells selectively degrade damaged mitochondria – and explore its role in the development of ISR. Mitophagy is crucial for maintaining vascular health and has been linked to various cardiovascular diseases, but its specific involvement in ISR was not well understood.
Integrating Bioinformatics and Machine Learning
The researchers used a combination of bioinformatics and machine learning techniques to analyze gene expression data from two ISR-related datasets. They first identified 23 differentially expressed mitophagy-related genes (DEMRGs) between ISR and control samples. Gene ontology analysis revealed that these DEMRGs were significantly enriched in biological processes and molecular functions related to mitophagy, such as ubiquitination and phosphorylation.
Next, the researchers used Weighted Gene Co-expression Network Analysis (WGCNA) and three machine learning algorithms (Logistic-LASSO, Random Forest, and SVM-RFE) to identify the most promising biomarkers among the DEMRGs. This approach pinpointed LRRK2 and ANKRD13A as the two key hub genes that could serve as diagnostic biomarkers for ISR.
Uncovering the Mechanisms Behind LRRK2 and ANKRD13A
To further understand the roles of LRRK2 and ANKRD13A in ISR, the researchers conducted additional analyses. They found that these two genes were closely associated with immune and inflammatory pathways, suggesting their involvement in regulating the immune response during ISR development.
