# Mitochondria-Associated Endoplasmic Reticulum Membrane Biomarkers in Coronary Heart Disease and Atherosclerosis: A Transcriptomic and Mendelian Randomization Study

## TL;DR

This study identifies two genes linked to mitochondrial-endoplasmic reticulum interactions that may help diagnose and treat coronary heart disease.

## Contribution

The study introduces DHX36 and GPR68 as novel MAM-related biomarkers with causal links to CHD.

## Key findings

- 4174 differentially expressed genes were identified, with 3326 linked to MAMs.
- DHX36 and GPR68 showed strong causal relationships with CHD and high diagnostic accuracy.
- Validation in human blood and mouse aortic tissue confirmed the biomarkers' relevance.

## Abstract

Background: Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide. Mitochondria-associated endoplasmic reticulum membranes (MAMs) have recently emerged as critical mediators in cardiovascular pathophysiology; however, their specific contributions to CHD pathogenesis remain largely unexplored. Objective: This study aimed to identify and validate MAM-related biomarkers in CHD through integrated analysis of transcriptomic sequencing data and Mendelian randomization, and to elucidate their underlying mechanisms. Methods: We analyzed two gene expression microarray datasets (GSE113079 and GSE42148) and one genome-wide association study (GWAS) dataset (ukb-d-I9_CHD) to identify differentially expressed genes (DEGs) associated with CHD. MAM-related DEGs were filtered using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis, Mendelian randomization, and machine learning algorithms were employed to identify biomarkers with direct causal relationships to CHD. A diagnostic model was constructed to evaluate the clinical utility of the identified biomarkers. Additionally, we validated the two hub genes in peripheral blood samples from CHD patients and normal controls, as well as in aortic tissue samples from a low-density lipoprotein receptor-deficient (LDLR−/−) atherosclerosis mouse model. Results: We identified 4174 DEGs, from which 3326 MAM-related DEGs (DE-MRGs) were further filtered. Mendelian randomization analysis coupled with machine learning identified two biomarkers, DHX36 and GPR68, demonstrating direct causal relationships with CHD. These biomarkers exhibited excellent diagnostic performance with areas under the receiver operating characteristic (ROC) curve exceeding 0.9. A molecular interaction network was constructed to reveal the biological pathways and molecular mechanisms involving these biomarkers. Furthermore, validation using peripheral blood from CHD patients and aortic tissues from the Ldlr−/− atherosclerosis mouse model corroborated these findings. Conclusions: This study provides evidence supporting a mechanistic link between MAM dysfunction and CHD pathogenesis, identifying candidate biomarkers that have the potential to serve as diagnostic tools and therapeutic targets for CHD. While the validated biomarkers offer valuable insights into the molecular pathways underlying disease development, additional studies are needed to confirm their clinical relevance and therapeutic potential in larger, independent cohorts.