Profiling Secreted miRNA Biomarkers of Chemical-Induced Neurodegeneration in Human iPSC-Derived Neurons

You, Dahea; Cohen, Jennifer D.; Pustovalova, Olga; Lewis, Lauren; Shen, Lei

doi:10.1093/toxsci/kfac011

Cited by 2 publications

(2 citation statements)

References 88 publications

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“…In Table , 16 out of the top 20 identified ncRNAs associated with DOX have been confirmed by referenced validations from the PubMed literature. For instance, a study evaluating neurotoxic chemicals in hiPSC-derived neurons observed the downregulation of miR-30d following exposure to DOX . Additionally, experiments demonstrating the overexpression of let-7b showcased enhanced apoptosis and reduced DOX resistance in chronic granulocytic leukemia cells …”

Section: Experiments and Resultsmentioning

confidence: 99%

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Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery

Zhang,

2024

J. Chem. Inf. Model.

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Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability. Addressing these challenges necessitates the development of efficient computational methods for the accurate prediction of potential ncRNA-drug resistance associations (NDRA). However, most existing predictive models primarily focus on known ncRNA-drug resistance associations, often neglecting the critical aspect of similarity information between ncRNAs and drug resistance. This oversight may hinder the accuracy of characterizing these associations. To overcome the limitations of existing computational models, we proposed B-NDRA, a computational framework designed for the discovery of drug resistance-related ncRNA. Initially, we constructed a heterogeneous graph that integrates ncRNA-drug resistance pairs, leveraging both known associations and similarity fusion information between ncRNAs and drug resistance. Subsequently, we employed an attention mechanism to aggregate local features of graph nodes following a dimensionality reduction of node features. Further, a graph neural network (GNN) facilitated the learning of global node embeddings. Notably, the integration of dual adaptive deep adjustment architectures, encompassing intrablock and interblock methodologies, enabled efficient extraction of global features while balancing local and global features. Finally, B-NDRA employed a multilayer perceptron to predict associations between ncRNAs and drug resistance. Through rigorous 5-fold cross-validation, B-NDRA achieved average AUC, AUPR, Accuracy, Precision, Recall, and F1-score values of 92.2%, 91.9%, 84.88%, 86.9%, 82.37%, and 84.44%, respectively. Furthermore, comparative evaluations were conducted on established models, namely, GAEMDA, GRPAMDA, and LRGCPND. The results, obtained through three distinct 5-fold cross-validation strategies, demonstrated a notable performance improvement across almost all metrics for our B-NDRA. Specific case studies targeting Doxorubicin and Imatinib further validated the practicality of our B-NDRA in discovering potential NDRA. These results confirm the potential of our B-NDRA as a valuable tool in advancing cancer research and therapeutic development. The source code and data set of B-NDRA can be found at https://github.com/XuanLi1145/B-NDRA.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…For instance, a study evaluating neurotoxic chemicals in hiPSC-derived neurons observed the downregulation of miR-30d following exposure to DOX. 38 Additionally, experiments demonstrating the overexpression of let-7b showcased enhanced apoptosis and reduced DOX resistance in chronic granulocytic leukemia cells. 39 …”

Section: Experiments and Resultsmentioning

confidence: 99%