JDP2 inhibits the epithelial-to-mesenchymal transition in pancreatic cancer BxPC3 cells

Zhe, Liu; Du, Ruixia; Jin, Long; Dong, Anbing; Jianpeng, Fan; Ke, Guo; Xu, Yuanhong

doi:10.1007/s13277-012-0404-5

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Yuanhong et al [30] reported that JDP2 downregulation is associated with metastasis in pancreatic cancer patients. It was also reported that JDP2 inhibits the epithelial-mesenchymal transition of pancreatic cancer BxPC3 cells [31]. By contrast, the present study suggests that JDP2 negatively regulates TRAIL/DR5 signaling and promotes tumor cell survival.…”

Section: Discussioncontrasting

confidence: 41%

JDP2 is directly regulated by ATF4 and modulates TRAIL sensitivity by suppressing the ATF4–DR5 axis

et al. 2020

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Jun dimerization protein 2 (JDP2) is a bZip type transcription factor, which acts as a repressor or activator of several cellular processes, including cell differentiation and chromatin remodeling. Previously, we found that a stress-responsive transcription factor, known as activating transcription factor 4 (ATF4) enhances JDP2 gene expression in human astrocytoma U373MG and cervical cancer HeLa cells; however, the role of JDP2 in the ATF4-mediated stress response remained unclear. Here, we reported that siRNA-mediated JDP2 knockdown enhances the expression of several ATF4 target genes, including ASNS, and death receptors 4 and 5 (DR4 and DR5) in HeLa cells. In addition, the results of a transient reporter assay indicate that JDP2 overexpression represses ER stress-mediated DR5 promoter activation suggesting that JDP2 negatively regulates ATF4-mediated gene expression. Curiously, knockdown of JDP2 increases the sensitivity of cells to TNF-related apoptosis-inducing ligand (TRAIL), which induces apoptosis in cancer cells through DR4 and DR5. These results indicate that JDP2 functions as a negative feedback regulator of the ATF4 pathway and contributes to TRAIL resistance in cancer cells.

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Section: Discussioncontrasting

confidence: 41%

JDP2 is directly regulated by ATF4 and modulates TRAIL sensitivity by suppressing the ATF4–DR5 axis

et al. 2020

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“…Transforming growth factor-β1 (TGF-β1) is one of the main EMT-inducing factors in both physiological and pathological conditions ( 5 ), by activating EMT-associated transcriptional regulators, such as Snail, Twist and Zinc finger E-box binding homeobox 1 (ZEB1) ( 6 ). TGF-β1 exerts profound effects on the growth and EMT-like responses of thyroid epithelial cells, oral squamous cell carcinoma cells and pancreatic cancer cells ( 7 – 9 ). The ability of TGF-β1 to induce EMT plays a critical role in the acquisition of a migratory and invasive phenotype that correlates with an enhanced metastatic potential in tumor cells ( 26 ).…”

Section: Introductionmentioning

confidence: 99%

Transforming growth factor-β1 induces EMT by the transactivation of epidermal growth factor signaling through HA/CD44 in lung and breast cancer cells

Liang

et al. 2015

International Journal of Molecular Medicine

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Epithelial-mesenchymal transition (EMT), a process closely related to tumor development, is regulated by a variety of signaling pathways and growth factors, such as transforming growth factor-β1 (TGF-β1) and epidermal growth factor (EGF). Hyaluronan (HA) has been shown to induce EMT through either TGF-β1 or EGF signaling and to be a regulator of the crosstalk between these two pathways in fibroblasts. In this study, in order to clarify whether HA has the same effect in tumor cells, we utilized the lung cancer cell line, A549, and the breast cancer cell line, MCF-7, and found that the effects of stimulation with TGF-β1 were more potent than those of EGF in regulating the expression of EMT-associated proteins and in enhancing cell migration and invasion. In addition, we observed that TGF-β1 activated EGF receptor (EGFR) and its downstream AKT and extracellular signal-regulated kinase (ERK) pathways. Furthermore, we found that TGF-β1 upregulated the expression of hyaluronan synthases (HAS1, HAS2 and HAS3) and promoted the expression of CD44, a cell surface receptor for HA, which interacts with EGFR, resulting in the activation of the downstream AKT and ERK pathways. Conversely, treatment with 4-methylumbelliferone (4-MU; an inhibitor of HAS) prior to stimulation with TGF-β1, inhibited the expression of CD44 and EGFR, abolished the interaction between CD44 and EGFR. Furthermore, the use of shRNA targeting CD44 impaired the expression of EGFR, deactivated the AKT and ERK pathways, reversed EMT and decreased the migration and invasion ability of cells. In conclusion, our data demonstrate that TGF-β1 induces EMT by the transactivation of EGF signaling through HA/CD44 in lung and breast cancer cells.

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“…For instance, Liu et al reported that JDP2 suppressed the epithelial-to-mesenchymal transition in pancreatic cancer BxPC3 cells. [ 17 ] Song et al demonstrated that UCHL3 promoted pancreatic cancer progression and chemo-resistance via FOXM1 stabilization. [ 18 ] Peluffo et al suggested that EN1 served as a transcriptional dependency in triple-negative breast cancer involved in brain metastasis.…”

Section: Discussionmentioning

confidence: 99%

A 6 transcription factors-associated nomogram predicts the recurrence-free survival of thyroid papillary carcinoma

Wang

Tian²,

Xie³

et al. 2021

Medicine

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Various researches demonstrated that transcription factors (TFs) played a crucial role in the progression and prognosis of cancer. However, few studies indicated that TFs were independent biomarkers for the prognosis of thyroid papillary carcinoma (TPC). Our aim was to establish and validate a novel TF signature for the prediction of TPC patients’ recurrence-free survival (RFS) from The Cancer Genome Atlas (TCGA) database to improve the prediction of survival in TPC patients. The genes expression data and corresponding clinical information for TPC were obtained from TCGA database. In total, 722 TFs and 545 TPC patients with eligible clinical information were determined to build a novel TF signature. All TFs were included in a univariate Cox regression model. Then, the least absolute shrinkage and selection operator Cox regression model was employed to identify candidate TFs relevant to TPC patients’ RFS. Finally, multivariate Cox regression was conducted via the candidate TFs for the selection of the TF signatures in the RFS assessment of TPC patients. We identified 6 TFs that were related to TPC patients’ RFS. Receiver operating characteristic analysis was performed in training, validation, and whole datasets, we verified the high capacity of the 6-TF panel for predicting TPC patients’ RFS (AUC at 1, 3, and 5 years were 0.880, 0.934, and 0.868, respectively, in training dataset; 0.760, 0.737, and 0.726, respectively, in validation dataset; and 0.777, 0.776, and 0.761, respectively, in entire dataset). The result of Kaplan–Meier analysis suggested that the TPC patients with low scores had longer RFS than the TPC patients with high score ( P = .003). A similar outcome was displayed in the validation dataset ( P = .001) and the entire dataset ( P = 2e-05). In addition, a nomogram was conducted through risk score, cancer status, C-index, receiver operating characteristic, and the calibration plots analysis implied good value and clinical utility of the nomogram. We constructed and validated a novel 6-TF signature-based nomogram for predicting the RFS of TPC patients.

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JDP2 inhibits the epithelial-to-mesenchymal transition in pancreatic cancer BxPC3 cells

Cited by 13 publications

References 26 publications

JDP2 is directly regulated by ATF4 and modulates TRAIL sensitivity by suppressing the ATF4–DR5 axis

JDP2 is directly regulated by ATF4 and modulates TRAIL sensitivity by suppressing the ATF4–DR5 axis

Transforming growth factor-β1 induces EMT by the transactivation of epidermal growth factor signaling through HA/CD44 in lung and breast cancer cells

A 6 transcription factors-associated nomogram predicts the recurrence-free survival of thyroid papillary carcinoma

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