Evidence map›Paper›PMID 40671236›Full record

ArticleCurrent medicinal chemistry2026

Integrating Microarray Analysis, Machine Learning, and Molecular Docking to Explore the Mechanism of Doxorubicin-induced Cardiotoxicity.

Yidong Zhu, Jun He, Rong Wei

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Article in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yidong ZhuDepartment of Traditional Chinese Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.ORCID 0000-0002-8505-8257
Jun HeDepartment of Hematology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Rong WeiDepartment of Hematology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDoxorubicin (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

methodsDifferential 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.

resultsIn 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: 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.

conclusionBy 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.

Indexed as

CardiotoxicityDoxorubicinMachine LearningMicroarray AnalysisMolecular Docking SimulationHumansOligonucleotide Array Sequence AnalysisDoxorubicincardiotoxicityDoxorubicinimmunitymachine learningmicroarray analysismolecular docking

Identifiers

PMID40671236

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.