Insights of Crosstalk between p53 Protein and the MKK3/MKK6/p38 MAPK Signaling Pathway in Cancer

Stramucci, Lorenzo; Pranteda, Angelina; Bossi, Gianluca

doi:10.3390/cancers10050131

Cited by 87 publications

(82 citation statements)

References 75 publications

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“…We next present results from 290 TCGA cutaneous melanomas (SKCM) ( Figure 3). We use this as an exemplar dataset as SKCM is a heterogeneous cancer type with some known driver combinations [28,29,30,31]. We used as input, 20 SMGs from dNdScv, 34 SCNV deletions and 20 SCNV amplifications from GISTIC2 narrow peak calls.…”

Section: Application To Tcga Melanoma Datamentioning

confidence: 99%

“…The 5 expected rules involve combinations of either BRAF or NRAS with well-studied tumor suppressors: 1) NRAS-M+TP53-M, 2) BRAF-M+TP53-M, 3) NRAS-M+CDKN2A-MD, 4) BRAF-M+CDKN2A-MD, and 5) BRAF-M+PTEN-MD. The first 4 of these rules are instances of cooccurring MAPK3 pathway activation with P53 inactivation-a synergy that is known promote carcinogenesis [30,31]. Evidence in multiple cancer types supports the cooperation between activating mutations in KRAS and loss of the G1/S checkpoint by inactivation of CDKN2A or TP53 [7,8,9].…”

Section: Expected Melanoma Combinations Detected By Crsomentioning

confidence: 99%

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Identifying Modules of Cooperating Cancer Drivers

Klein

Cannataro

Townsend

et al. 2020

Preprint

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AbstractIdentifying cooperating modules of driver alterations can provide biological insights to cancer causation and would advance the development of effective personalized treatments. We present Cancer Rule-Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types found a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination, and accounting for a mean of 70% of samples per cancer. CRSO departs from methods based on statistical cooccurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.

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Section: Application To Tcga Melanoma Datamentioning

confidence: 99%

Section: Expected Melanoma Combinations Detected By Crsomentioning

confidence: 99%

Identifying Modules of Cooperating Cancer Drivers

Klein

Cannataro

Townsend

et al. 2020

Preprint

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“…Secondly, the kinase AKT promotes cell-survival and can for example supress the stress response by phosphorylating and inhibiting kinases upstream of JNK and p38 3,10,11 . Thirdly, the transcription factor p53 maintains genomic stability and supresses cancer formation by activating DNA repair proteins, arresting cell growth by pausing the cell cycle, initiating cell-death, and promoting senescence by shortening telomeres 5,7 . Most of these p53 functions require the activation of p53, which is usually achieved by phosphorylation of p53 at sites in its transcriptional domain.…”

Section: Introductionmentioning

confidence: 99%

Model-based identification of the crosstalks and feedbacks that determine the doxorubicin response dynamics of the JNK-p38-p53 network

Tuffery

Halász

Fey

2020

Preprint

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Cellular responses to perturbations and drugs are determined by interconnected networks, rather than linear pathways. Individually, the JNK, p38 and p53 stress and DNA-damage response networks are well understood and regulate critical cell-fate decisions, such as apoptosis, in response to many chemotherapeutical agents, such as doxorubicin. To better understand how interactions between these pathways determine the dynamic behaviour of the entire network, we constructed a data-driven mathematical model. This model contains mechanistic details about the kinase cascades that activate JNK, p38, AKT and p53, and free parameters that describe possible interactions between these pathways. Fitting this model to experimental time-course perturbation data (five time-courses with six time-points under five different conditions), identified specific network interactions that can explain the observed network responses. JNK emerged as an important control node. JNK exhibited a positive feedback loop, was tightly controlled by negative feedback and crosstalk from p38 and AKT, respectively, and was the strongest activator of p53. Compared to static network reconstruction methods, such as modular response analysis, the model-based approach identifies biochemical mechanisms and explains the dynamic control of cell signalling.

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“…Apoptosis signal‐regulating kinase 1 (ASK1) is a MAPK‐upstream kinase that is activated in response to many cellular stresses including oxidative stress . Upon activation, ASK1 promotes apoptotic cell death via P38–P53 activation . Therefore, ROS scavenging with subsequent inactivation of the ASK1–P38–P53 pathway provides a possible mechanism for protection against HCC.…”

Section: Introductionmentioning

confidence: 99%

“…19 Upon activation, ASK1 promotes apoptotic cell death via P38-P53 activation. 20,21 Therefore, ROS scavenging with subsequent inactivation of the ASK1-P38-P53 pathway provides a possible mechanism for protection against HCC.…”

Section: Introductionmentioning

confidence: 99%

A novel chemoprotective effect of tiopronin against diethylnitrosamine‐induced hepatocellular carcinoma in rats: Role of ASK1/P38 MAPK‐P53 signalling cascade

Mo’men

Hussein

Kandeil

2019

Clin Exp Pharma Physio

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Hepatocellular carcinoma (HCC) is the second leading cause of cancer‐related death worldwide. Oxidative stress contributes significantly to HCC pathogenesis. In this study, we investigated the possible chemoprotective effect of the thiol group‐containing compound, tiopronin, against HCC induced chemically by diethylnitrosamine (DENA) in rats. In addition, we elucidated the possible underlying molecular mechanism. Adult male Wistar rats were divided into: Control group, DENA‐treated group and tiopronin + DENA‐treated group. Liver function tests (ALT, AST, ALP, albumin, total and direct bilirubin) as well as alpha fetoprotein (AFP) concentration were measured in the sera of samples. Oxidative stress biomarkers such as malondialdehyde, nitric oxide, catalase and glutathione peroxidase were measured in the liver tissue homogenates. Determination of the phosphorylated apoptosis signal‐regulating kinase 1 (phospho‐ASK1), phospho‐P38 and phospho‐P53 proteins by western blotting, caspase 3 by immunofluorescence in addition to histopathological examination of the liver tissues were performed. Our results showed that tiopronin prevented the DENA‐induced elevation of the liver function enzymes and AFP. It also preserved the activities of antioxidant enzymes as well as providing protection from the appearance of HCC histopathological features. Interestingly, tiopronin significantly decreased the expression level of phospho‐ASK1, phospho‐P38 and phospho‐P53, caspase 3 in the liver tissues. These novel findings suggested that tiopronin is an antioxidant drug with a chemoprotective effect against DENA‐induced HCC through maintaining the normal activity of ASK1/ P38 MAPK/ P53 signalling pathway.

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Insights of Crosstalk between p53 Protein and the MKK3/MKK6/p38 MAPK Signaling Pathway in Cancer

Cited by 87 publications

References 75 publications

Identifying Modules of Cooperating Cancer Drivers

Identifying Modules of Cooperating Cancer Drivers

Model-based identification of the crosstalks and feedbacks that determine the doxorubicin response dynamics of the JNK-p38-p53 network

A novel chemoprotective effect of tiopronin against diethylnitrosamine‐induced hepatocellular carcinoma in rats: Role of ASK1/P38 MAPK‐P53 signalling cascade

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