TUC338 Promotes Diffuse Large B Cell Lymphoma Growth via Regulating EGFR/PI3K/AKT Signaling Pathway

Li, Yan; Jia, Zhenwei; Zhao, Hongbo; Liu, Xiaoyan; Luo, Jianmin; Cui, Gui-Rong; Kong, Xiaoyang

doi:10.1155/2021/5593720

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…U2932 is a unique B cell line established from a patient suffering from Hodgkin’s lymphoma followed by non-Hodgkin’s lymphoma. It is a commonly used DLBCL cell line and has been successfully used to establish in vivo DLBCL xenograft models [ 63–65 ]. As a mature B-cell neoplasm, DLBCL cell implant into mice models demonstrated CSC properties.…”

Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: Knockdown of zinc finger protein 267 suppresses diffuse large B-cell lymphoma progression, metastasis, and cancer stem cell properties

et al. 2022

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Zinc finger protein 267 (ZNF267) is a member of the Kruppel-like transcription factor family, which regulates various biological processes such as cell proliferation and differentiation. However, the biological significance of ZNF267 and its potential role in diffuse large B-cell lymphoma (DLBCL) remain to be documented. Experiments were herein conducted to study the role of ZNF267 in DLBCL. real-time quantitative reverse transcription PCR and Western blotting assays were conducted to detect the expression of ZNF267 in tissues and cells. Tissue microarray and bioinformatics analyses of public data were also done to detect the expression status and clinical significance of ZNF267. Functional cell experiments including CCK8 assay, colony formation assay, 5-ethynyl-2ʹ-deoxyuridine (EDU) assay, terminal deoxynucleotidyl transferase biotin-dUTP nick end labeling (TUNEL) assay, transwell assay, and wound healing assay were conducted to study the effects of ZNF267 knockdown and overexpression on cell proliferation and mobility. Xenograft assay was also conducted to confirm the effects of ZNF267 knockdown in vivo . In the present study, we found ZNF267 was significantly upregulated in DLBCL and predicted a poor survival outcome based on the bioinformatics analysis. Functionally, the knockdown of ZNF267 resulted in less cell proliferation and mobility, whereas the overexpression led to enhanced cell proliferation and mobility. Animal experiments also confirmed that ZNF267 silence contributed to less tumor growth and less lung metastasis. Further analysis showed that ZFN267 knockdown resulted in decreased epithelial-mesenchymal transition (EMT) and cancer stem cell (CSC) properties. Our results suggest that ZNF267 is an oncogene in DLBCL and its silence could compromise the aggression of DLBCL, which makes ZNF267 a promising therapeutic target.

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Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: Knockdown of zinc finger protein 267 suppresses diffuse large B-cell lymphoma progression, metastasis, and cancer stem cell properties

et al. 2022

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“…The drug Erlotinib has been proved to be effective in the treatment of breast cancer and lymphoma [28,29]. Erlotinib has been substantiated as a potent inhibitor of EGFR [30], and its gene expression has also been demonstrated to be correlated with several cancers [31,32]. This finding is also consistent with the results obtained in DeepCDR [2].…”

Section: Deepeag Predicts Cancer-related Genessupporting

confidence: 76%

DeepEAG: An edge-powered graph neural network using SMILES augmentation for cancer drug response prediction

Lao,

Zheng,

Chen

et al. 2023

Preprint

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Motivation:: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. Results:: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepEAG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing Simplified Molecular Input Line Entry Specification (SMILES) data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepEAG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepEAG in guiding the design of specific cancer treatment regimens.

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“…The drug Erlotinib has been proved to be effective in the treatment of breast cancer and lymphoma [ 35 , 36 ]. Erlotinib has been substantiated as a potent inhibitor of EGFR [ 37 ], and its gene expression has also been demonstrated to be correlated with several cancers [ 38 , 39 ]. This finding is also consistent with the results obtained in DeepCDR [ 5 ].…”

Section: Results and Conclusionmentioning

confidence: 99%

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

Lao,

Zheng,

Chen

et al. 2024

BMC Bioinformatics

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Motivation The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. Results Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

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TUC338 Promotes Diffuse Large B Cell Lymphoma Growth via Regulating EGFR/PI3K/AKT Signaling Pathway

Cited by 8 publications

References 31 publications

RETRACTED ARTICLE: Knockdown of zinc finger protein 267 suppresses diffuse large B-cell lymphoma progression, metastasis, and cancer stem cell properties

RETRACTED ARTICLE: Knockdown of zinc finger protein 267 suppresses diffuse large B-cell lymphoma progression, metastasis, and cancer stem cell properties

DeepEAG: An edge-powered graph neural network using SMILES augmentation for cancer drug response prediction

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

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