Let-7b regulates the adriamycin resistance of chronic myelogenous leukemia by targeting AURKB in K562/ADM cells

Zhou, Xue; Ma, Xiancheng; Sun, Hang; Li, Xue; Cao, Huizhen; Jiang, Youzhang; Wang, Pingyu; Xie, Shengsong; Li, Youjie; Sun, Yang

doi:10.1080/10428194.2020.1811269

Cited by 9 publications

(8 citation statements)

References 32 publications

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“…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%

“…38 Additionally, experiments demonstrating the overexpression of let-7b showcased enhanced apoptosis and reduced DOX resistance in chronic granulocytic leukemia cells. 39 Referring to Table 8, it is evident that 16 out of the top 20 ncRNAs associated with Imatinib have been validated through the PubMed-referenced literature. For example, after treatment with Imatinib in chronic myeloid leukemia (CML) patients, a significant increase in the expression of miR-126 was observed.…”

Section: Case Studymentioning

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%

Section: Case Studymentioning

confidence: 99%

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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“…The analysis results suggested that AURKB, KIF11, and PLK1 are hub targets that Tanshinone IIA binding. AURKB (Aurora kinase B) is a mitotic kinase involved in the mitotic process, especially in various cancers, such as clear cell renal cell carcinoma (Wan et al, 2019), chronic myeloid leukemia (Zhou et al, 2020), acute lymphoblastic leukemia (Moreira-Nunes et al, 2020), and HCC (Sistayanarain et al, 2006). The selective AURKB inhibitor was evaluated as a treatment in human HCC cell lines (Aihara et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Molecular mechanisms of Tanshinone IIA in Hepatocellular carcinoma therapy via WGCNA-based network pharmacology analysis

Zhao¹,

Guo²,

Chi³

et al. 2022

Biocell

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Hepatocellular carcinoma (HCC) is a worldwide malignant tumor that caused irreversible consequences.Tanshinone IIA has been shown to play a notable role in HCC treatment. However, the potential targets and associating mechanism of Tanshinone IIA against HCC remain unknown. We first screened out 105 overlapping genes by integrating the predicted targets of Tanshinone IIA from multiple databases and the differentially expressed genes of HCC from the Cancer Genome Atlas (TCGA) database. Then, we performed weighted gene co-expression network analysis (WGCNA) using the RNA-seq profiles of overlapping genes and HCC-related clinical information.23 genes related to clinical tumor grade in the important module were imported for Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) analysis. Comparing the key genes in the important module from WGCNA with the high connectivity nodes from the PPI network, we identified three hub genes, AURKB, KIF11, and PLK1. For further verification, we tested the binding of Tanshinone IIA to three hub genes. The survival curve, receiver operating characteristic (ROC) curve, mRNA expression, and protein expression were also used to validate the hub genes. In the study, WGCNA revealed gradespecific gene modules, and the following KEGG pathway analysis indicated that Tanshinone IIA probably plays therapeutical effect in the development of HCC, especially in the cell cycle. Our result partially explained the pharmacological mechanism of Tanshinone IIA against HCC.

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“…AURKB (Aurora kinase B) is a mitotic kinase involved in the mitotic process, especially in various cancers, such as clear cell renal cell carcinoma [27], chronic myeloid leukemia [28], acute lymphoblastic leukemia [29], and HCC [30]. The selective AURKB inhibitor was evaluated as a treatment in human HCC cell lines [31].…”

Section: Discussionmentioning

confidence: 99%

Molecular Mechanisms of TanshinoneIIA in Hepatocellular carcinomatherapy via WGCNA-based Network Pharmacology Analysis

Zhao

Guo

Chi

et al. 2021

Preprint

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Background: Hepatocellular carcinoma (HCC) is a worldwide malignant tumor that caused irreversible consequences. The studies of Tanshinone IIA showed that Tanshinone IIA has played a notable role in HCC treatment. However, it is still to be investigated to discover the potential targets and associating mechanism of Tanshinone IIA against HCC. Methods: To analyze the correlation between genes and specific clinical features, we applied weighted gene co-expression network analysis (WGCNA), which can help us identify the targets of Tanshinone IIA related to the clinical features of Hepatocellular carcinoma. Results: We screened out 105 overlapping genes by integrating the predicted targets of Tanshinone IIA and the gene expression profile of HCC from the Cancer Genome Atlas (TCGA) database. For WGCNA, we used the RNA-seq profile of the overlapping genes and the related clinical information of HCC from TCGA. And 23 genes related to clinical tumor grade in the important module (R2 = 0.37) were imported for Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) analysis. Compared to the key genes in the significant module from WGCNA with the high connectivity nodes from the PPI network, we can analyze three hub genes, AURKB, KIF11, and PLK1, for further verification. We tested the binding of Tanshinone IIA to the targets of Hepatocellular carcinoma using Autodock Vina. The survival curve validated that the three hub genes represented a poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that the three hub genes were effective in diagnosis. The mRNA expression of the three hub genes was upregulated in the HCC than the normal. AURKB, KIF11 and PLK1 were further upregulated in advanced tumor stage and grade. Moreover, AURKB, KIF11 and PLK1 also had higher protein expression in HCC tissues. Conclusions: In the study, WGCNA revealed grade-specific gene modules, indicating that Tanshinone IIA probably plays its therapeutical effect in the differentiation process of HCC. And the study partly interpreted the pharmacological mechanism of Tanshinone IIA against HCC.

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Let-7b regulates the adriamycin resistance of chronic myelogenous leukemia by targeting AURKB in K562/ADM cells

Cited by 9 publications

References 32 publications

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

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

Molecular mechanisms of Tanshinone IIA in Hepatocellular carcinoma therapy via WGCNA-based network pharmacology analysis

Molecular Mechanisms of TanshinoneIIA in Hepatocellular carcinomatherapy via WGCNA-based Network Pharmacology Analysis

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