Page 161 - EJMO-9-2

Eurasian Journal of
Medicine and Oncology Genetic insights into CAD drug targets

1. Introduction false positives; (ii) they typically focus on gene-disease
associations without assessing druggability; and (iii) they
Coronary atherosclerosis (CA) is one of the leading rarely account for intermediate biological process (BPs),
causes of cardiovascular disease, characterized by the such as metabolic pathways that could serve as alternative
gradual formation of lipid plaques in the coronary arteries, therapeutic targets.
1,2
obstructing the normal flow of blood to the heart. As the
disease progresses, CA can lead to severe cardiac events, To address these gaps, our study integrated multiple
such as angina and myocardial infarction, and may even complementary approaches to improve target prioritization
3,4
result in death. With the global aging population, the and druggability assessment. First, we combined MR with
incidence of CA has risen significantly, especially among colocalization analysis to ensure that the identified genetic
the elderly, making CA a major threat to public health, signals for CA and gene expression share a common causal
posing immense pressure on health-care systems and variant, thereby improving confidence in target selection.
socioeconomic structures worldwide. 5 Next, we incorporated summary data-based MR (SMR)
and heterogeneity in dependent instruments (HEIDI)
At present, CA treatment primarily relies on
pharmacological control and interventional procedures, testing to further validate the robustness of our findings
and exclude spurious associations. Unlike previous studies
including lipid-lowering drugs, antiplatelet agents, and that primarily identify genetic associations, we focused on
stenting interventions. While these treatment strategies prioritizing druggable genes – those that encode proteins
6,7
have improved patients’ quality of life to some extent and amenable to pharmacological modulation. We performed
slowed disease progression, a considerable proportion of metabolite-mediated analysis to investigate whether the
patients still exhibit poor responses to existing therapies, identified target genes exert their effects through metabolic
and plaque progression remains difficult to effectively pathways, providing novel mechanistic insights that
control. In addition, many drugs are associated with
significant side effects, such as increased bleeding risk extend beyond direct gene-disease associations. Through
or liver impairment. Although the concepts of precision this multi-step approach, our study surpassed previous
medicine and personalized treatment are increasingly being GWAS-based drug discovery efforts by not only identifying
applied to CA management, further research is needed to genetic risk factors for CA but also refining the selection of
identify new therapeutic targets to enhance efficacy and therapeutic targets with higher translational potential.
reduce side effects. Overall, effectively managing CA, In this study, we conducted MR analysis to integrate
8,9
particularly in the context of refractory and recurrent cases, the druggable genome with three CA GWAS datasets to
remains a critical challenge in the medical field. identify potential therapeutic targets. By intersecting the
Genome-wide association studies (GWAS) have results, we screened key drug targets associated with CA.
successfully identified numerous genetic loci associated We then performed colocalization analysis to determine
with CA, providing valuable insights into its genetic whether these potential targets and CA risk were driven
architecture. However, most of these loci reside in non- by shared genetic variants, thereby ruling out potential
coding or intergenic regions, making it difficult to pinpoint “confounding” effects and helping to identify the true
the causal genes or translate these findings into actionable causal loci underlying the association, improving the
therapeutic targets. 10,11 In addition, many GWAS-identified accuracy of predictions. To ensure the robustness of the
loci merely indicate statistical associations rather than findings, we further conducted SMR analysis and HEIDI
functional relevance, limiting their direct applicability in testing to verify the reliability of the results. In addition,
drug discovery. As a result, further analytical approaches we found that some candidate target genes influence
are required to establish causal links between genetic the disease by regulating metabolite expression. Finally,
variants and CA pathogenesis. through drug prediction and molecular docking studies,
we validated the pharmacological activity of four potential
Mendelian randomization (MR) has emerged as a CA drug targets. By evaluating the binding affinity and
powerful causal inference method that leverages genetic interaction patterns between these targets and drugs, we
variants as instrumental variables (IVs) to assess the effect assessed their feasibility and potential as candidate drug
of gene expression on disease risk. By mitigating the targets. The overall study design is presented in Figure 1.
12
influence of confounding factors and reverse causation,
MR improves upon traditional GWAS by prioritizing genes 2. Methods
that have a direct causal role in CA. However, standard
MR approaches also face notable limitations: (i) they often 2.1. Exposure data
lack robust validation through complementary methods, The druggable genes were obtained from the Drug-Gene
such as colocalization analysis, leading to potential Interaction Database (DGIdb), which provides detailed

Volume 9 Issue 2 (2025) 153 doi: 10.36922/ejmo.7387