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Updated: Sep 15, 2025
A Doxorubicin-Induced Murine Model of Dilated Cardiomyopathy In Vivo
Published on: May 16, 2020
Integrating Microarray Analysis, Machine Learning, and Molecular Docking to Explore the Mechanism of
Yidong Zhu1, Jun He2, Rong Wei2
1Department of Traditional Chinese Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Introduction:
Doxorubicin (DOX) is a chemotherapeutic agent widely used for the treatment of various cancers; however, its clinical use is limited by its cardiotoxicity. However, the underlying molecular mechanisms remain poorly understood, hindering the development of effective preventive and treatment strategies. This study aimed to identify core target genes and explore the mechanisms involved in DOX-induced cardiotoxicity by integrating microarray analysis, machine learning, and molecular docking.
Materials And Methods:
Differential expression analysis was performed using microarray data from DOX-induced cardiotoxic samples and healthy controls. Multiple machine learning algorithms were applied to identify core target genes. The predictive performance of these genes was evaluated using receiver operating characteristic (ROC) curves. Molecular docking was conducted to evaluate the binding affinity of DOX to the target genes. Functional analysis was performed to investigate potential toxic mechanisms.
Results:
In total, 276 differentially expressed genes were identified between DOX-induced cardiotoxicity samples and controls. The support vector machine algorithm demonstrated the best performance, leading to the identification of five core target genes: RAP1A, CTLA4, OR2M1P, TRIM53, and LOC149837. The ROC curves confirmed the strong predictive power of these genes, with area under the curve values greater than 0.85. Molecular docking showed stable binding between DOX and the target genes. Functional analysis suggested that the Rap1 signaling pathway and immune system regulation may be involved in DOX-induced cardiotoxicity.
Discussion:
Traditional toxicological studies often rely on limited experimental approaches that do not fully capture the complexity of disease mechanisms. The integration of microarray analysis, machine learning, and molecular docking in this study offers a comprehensive framework for investigating the toxicological pathways of DOXinduced cardiotoxicity, thereby providing insights into therapeutic development and safety regulations.
Conclusion:
By combining microarray analysis, machine learning, and molecular docking, we identified five key target genes associated with DOX-induced cardiotoxicity. Functional analysis further suggested the involvement of the Rap1 signaling pathway and immune system regulation in DOX-induced cardiotoxicity. These findings offer insights into the molecular mechanisms underlying DOX-induced cardiotoxicity and have implications for the development of protective strategies and therapeutic interventions.
Insights
Doxorubicin (DOX) chemotherapy causes cardiotoxicity due to poorly understood mechanisms. This study identified five key genes and pathways, including Rap1 signaling, offering insights for safer cancer treatments.
Area of Science:
- Cardiotoxicity research
- Chemotherapeutic drug safety
- Molecular toxicology
Background:
- Doxorubicin (DOX) is a vital chemotherapy drug, but its use is limited by cardiotoxicity.
- The molecular basis of DOX-induced cardiotoxicity is not fully understood, hindering effective interventions.
- Identifying key molecular targets is crucial for developing strategies to mitigate DOX cardiotoxicity.
Purpose of the Study:
- To identify core target genes implicated in Doxorubicin-induced cardiotoxicity.
- To explore the molecular mechanisms underlying Doxorubicin-induced cardiotoxicity.
- To integrate multi-omics and computational approaches for toxicological pathway discovery.
Main Methods:
- Differential gene expression analysis of microarray data from DOX-treated samples.
- Application of machine learning algorithms (e.g., Support Vector Machine) to identify critical genes.
- Molecular docking simulations to assess drug-target interactions and functional pathway analysis.
Main Results:
- Identified 276 differentially expressed genes in DOX cardiotoxicity.
- Discovered five core target genes (RAP1A, CTLA4, OR2M1P, TRIM53, LOC149837) with high predictive power (AUC > 0.85).
- Revealed stable binding of DOX to target genes and suggested involvement of Rap1 signaling and immune regulation.
Conclusions:
- Integrated bioinformatics approaches provide a robust framework for understanding complex toxicological mechanisms.
- The identified genes and pathways offer novel insights into DOX-induced cardiotoxicity.
- Findings support the development of targeted protective strategies against Doxorubicin cardiotoxicity.
