TLR4/NF-κB axis induces fludarabine resistance by suppressing TXNIP expression in acute myeloid leukemia cells

Huy, Hangsak; Kim, Tae-Don; Kim, Won Sam; Kim, Dong Oh; Byun, Jaechul; Kim, Mi Jeong; Park, Young-Jun; Yoon, Suk Ran; Noh, Ji-Yoon; Lee, Jungwoon; Lee, Kyoo‐Hyung; Choi, Inpyo; Jung, Hye Young

doi:10.1016/j.bbrc.2018.10.047

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…Interestingly, a recent study reported miR-7-5p has a role in regulating oxaliplatin, a platinum reagent, resistant in hepatocellular carcinoma (Hu, Yang, Li, & Zeng, 2018). In addition, miR-29a-3p was reported to regulate the response of chronic myeloid leukemia cell to tyrosine kinase inhibitor (Salati et al, 2017 and COL4A1 were associated with chemoresistant in human cancers (Gao et al, 2019;Huang, Gu, Sun, & Gao, 2018;Huy et al, 2018;Ju et al, 2017;Li et al, 2019;Ma et al, 2017;Sasca et al, 2019;Yadav, Kumar, Varshney, & Yadava, 2019).…”

Section: Discussionmentioning

confidence: 99%

Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin‐resistant

Zhao

Tang

Zuo

et al. 2019

Journal Cellular Physiology

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To construct a long noncoding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory network related to epithelial ovarian cancer (EOC) cisplatinresistant, differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) between MDAH and TOV-112D cells lines were identified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. Downstream mRNAs or upstream lncRNAs for miRNAs were analyzed at miRTarBase 7.0 or DIANA-LncBase V2, respectively. A total of 485 significant DEGs, 85 DELs, and 5 DEMs were identified. Protein-protein interaction (PPI) network of DEGs contrains 81 nodes and 141 edges was constructed, and 25 hub genes related to EOC cisplatin-resistant were identified. Subsequently, a lncRNA-miRNA-mRNA regulatory network contains 4 lncRNAs, 4 miRNAs, and 35 mRNAs was established. Taken together, our study provided evidence concerning the alteration genes involved in EOC cisplatin-resistant, which will help to unravel the mechanisms underlying drug resistant. K E Y W O R D S cisplatin-resistant, epithelial ovarian cancer, lncRNA, miRNA, mRNA

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Section: Discussionmentioning

confidence: 99%

Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin‐resistant

Zhao

Tang

Zuo

et al. 2019

Journal Cellular Physiology

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“…In our study, we observed that AKIP1 high expression correlated with decreased CR achievement, EFS, and OS in AML patients, which might due to the following reasons: (a) Patients with AKIP1 high expression presented severer disease condition than those with AKIP1 low expression; thus, AKIP1 high expression patients might have less possibility to achieve CR . (b) AKIP1 might promote cell proliferation (via regulating CXCL1, CXCL2, and CXCL8) or enhance cell invasion to accelerate disease progression and retard treatment efficacy, which further led to worse EFS and OS in AML patients; (c) considering AKIP1 expression negatively correlated with CR achievement and independently predicted survival profiles, we speculated that AKIP1 might cause drug resistance through activating NF‐κB pathway or Akt pathway, which impaired the treatment efficacy, aggravated disease progression, and eventually reduced survival profiles in AML patients . Furthermore, we performed subgroup analysis and observed that AKIP1 high expression correlated with shorter EFS and OS in intermediate‐ and poor‐risk patients but not in better‐risk patients, indicating that AKIP1 could be a biomarker for assisting prognosis prediction in intermediate‐risk and as poor‐risk AML patients.…”

Section: Discussionmentioning

confidence: 83%

Identification of A‐kinase interacting protein 1 as a potential biomarker for risk and prognosis of acute myeloid leukemia

Yan¹,

Li²,

Gao³

2020

Clinical Laboratory Analysis

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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. AbstractBackground: This study aimed to investigate the correlation of A-kinase interacting protein 1 (AKIP1) expression with disease risk, clinical characteristics, and prognosis of acute myeloid leukemia (AML).Methods: 291 de novo AML patients and 97 controls were consecutively recruited, and bone marrow samples were collected to detect AKIP1 expression using quantitative polymerase chain reaction prior to initial treatment. Treatment response, eventfree survival (EFS), and overall survival (OS) in AML patients were evaluated. Results:A-kinase interacting protein 1 expression was higher in AML patients than that in controls; meanwhile, receiver operating characteristic curve displayed that AKIP1 was able to distinguish AML patients from controls (area under the curve:0.772, 95%CI: 0.720-0.823). Among AML patients, AKIP1 high expression was correlated with −7 or 7q−, monosomal karyotype, and worse risk stratification. Moreover, AKIP1 expression was negatively correlated with complete remission achievement, while no correlation of AKIP1 expression with hematopoietic stem cell transplantation achievement was observed. AKIP1 high expression was associated with shorter EFS and OS in total patients, and further subgroup analysis exhibited that AKIP1 high expression correlated with worse EFS and OS in intermediate-risk and poor-risk patients but not in better-risk patients. Besides, subsequent analysis revealed that AKIP1 high expression was an independent factor predicting unfavorable EFS and OS. Conclusion:A-kinase interacting protein 1 has the potential to be a novel marker for assisting AML management. K E Y W O R D S acute myeloid leukemia, A-kinase interacting protein 1, risk stratification, survival 1 | INTRODUC TI ON Acute myeloid leukemia (AML), an aggressive hematological malignancy, is characterized by abnormal cell proliferation and differentiation of the immature myeloid cells. 1,2 It accounts for 20% of all hematological malignancy related deaths, with a 5-year survival rate of 40% for younger patients (18-60 years) and 5-year survival rate of 10% for the elder patients (age above 60 years). 3-5 Despite there are potentially curative treatments for AML patients, including intensive chemotherapy and allogeneic stem cell transplantation, How to cite this article: Yan Y, Li X, Gao J. Identification of A-kinase-interacting protein 1 as a potential biomarker for risk and prognosis of acute myeloid leukemia. J Clin Lab Anal.

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“… TLR4 as well as IL6 and TNFα (tumor necrosis factor α) expression can be increased by in vitro induction of a dendritic AML cell phenotype in primary leukemic blasts after exposure to GM-CSF, IL4, and TNFα [ 29 ]. Studies in AML cell lines suggest that TLR4-NFκB signaling causes resistance to the antileukemic agent fludarabine, possibly by counteracting the fludarabine-induced increase of the tumor suppressor thioredoxin-interacting protein [ 30 ]. The S100A8 and S100A9 proteins are important for AML cell proliferation, cell cycle regulation, differentiation, and chemosensitivity [ 31 , 32 ].…”

Section: Tlr4 In Acute Myeloid Leukemiamentioning

confidence: 99%

Toll-like Receptor 4, Osteoblasts and Leukemogenesis; the Lesson from Acute Myeloid Leukemia

2022

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Toll-like receptor 4 (TLR4) is a pattern-recognizing receptor that can bind exogenous and endogenous ligands. It is expressed by acute myeloid leukemia (AML) cells, several bone marrow stromal cells, and nonleukemic cells involved in inflammation. TLR4 can bind a wide range of endogenous ligands that are present in the bone marrow microenvironment. Furthermore, the TLR4-expressing nonleukemic bone marrow cells include various mesenchymal cells, endothelial cells, differentiated myeloid cells, and inflammatory/immunocompetent cells. Osteoblasts are important stem cell supporting cells localized to the stem cell niches, and they support the proliferation and survival of primary AML cells. These supporting effects are mediated by the bidirectional crosstalk between AML cells and supportive osteoblasts through the local cytokine network. Finally, TLR4 is also important for the defense against complicating infections in neutropenic patients, and it seems to be involved in the regulation of inflammatory and immunological reactions in patients treated with allogeneic stem cell transplantation. Thus, TLR4 has direct effects on primary AML cells, and it has indirect effects on the leukemic cells through modulation of their supporting neighboring bone marrow stromal cells (i.e., modulation of stem cell niches, regulation of angiogenesis). Furthermore, in allotransplant recipients TLR4 can modulate inflammatory and potentially antileukemic immune reactivity. The use of TLR4 targeting as an antileukemic treatment will therefore depend both on the biology of the AML cells, the biological context of the AML cells, aging effects reflected both in the AML and the stromal cells and the additional antileukemic treatment combined with HSP90 inhibition.

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TLR4/NF-κB axis induces fludarabine resistance by suppressing TXNIP expression in acute myeloid leukemia cells

Cited by 17 publications

References 33 publications

Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin‐resistant

Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin‐resistant

Identification of A‐kinase interacting protein 1 as a potential biomarker for risk and prognosis of acute myeloid leukemia

Toll-like Receptor 4, Osteoblasts and Leukemogenesis; the Lesson from Acute Myeloid Leukemia

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