HIF-1-miR-219-SMC4 Regulatory Pathway Promoting Proliferation and Migration of HCC under Hypoxic Condition

Chen, Yang; Huang, Fei; Deng, Liang; Yuan, Xinrui; Tao, Qiang; Wang, Tielong; Dong, Li; Fan, Youwen; Peng, Quanzhou; Tang, Di

doi:10.1155/2019/8983704

Cited by 26 publications

(23 citation statements)

References 25 publications

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“…HIF-1 in fact, has as an important role under hypoxic conditions in regulating cell proliferation and metabolism. Taken together, our findings stress the leading role of HIF-1α in NE, which is involved in both the melatonin and polo-like kinase cascades 27 – 30 and interacts directly with both RGS1 and SMC4, which are downstream targets of HIF-1α 30 , 31 . These results are also consistent with our previous data, comparing healthy infants and NE, which showed an upregulation of HIF1A and MALAT1 in NE 9 .…”

Section: Discussionsupporting

confidence: 62%

“…SMC4 is responsible for regulating chromosome organization and dynamics 32 . Recent studies demonstrated also that SMC4 is associated with tumor progression and invasion and the expression of SMC4 is positively correlated with HIF-1a 30 . RGS1 protein is the most abundant RGS protein in the microglia and is a key gene of the immunomodulatory response to neuroinflammation as well as a key target of different immunological and neurodegenerative diseases such as multiple sclerosis, inflammatory bowel and Parkinson's disease 33,34 .…”

Section: Discussionmentioning

confidence: 95%

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Transcriptomic profile of adverse neurodevelopmental outcomes after neonatal encephalopathy

Montaldo

Cunnington

Oliveira

et al. 2020

Sci Rep

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A rapid and early diagnostic test to identify the encephalopathic babies at risk of adverse outcome may accelerate the development of neuroprotectants. We examined if a whole blood transcriptomic signature measured soon after birth, predicts adverse neurodevelopmental outcome eighteen months after neonatal encephalopathy. We performed next generation sequencing on whole blood ribonucleic acid obtained within six hours of birth from the first 47 encephalopathic babies recruited to the Hypothermia for Encephalopathy in Low and middle-income countries (HELIX) trial. Two infants with blood culture positive sepsis were excluded, and the data from remaining 45 were analysed. A total of 855 genes were significantly differentially expressed between the good and adverse outcome groups, of which RGS1 and SMC4 were the most significant. Biological pathway analysis adjusted for gender, trial randomisation allocation (cooling therapy versus usual care) and estimated blood leukocyte proportions revealed over-representation of genes from pathways related to melatonin and polo-like kinase in babies with adverse outcome. These preliminary data suggest that transcriptomic profiling may be a promising tool for rapid risk stratification in neonatal encephalopathy. It may provide insights into biological mechanisms and identify novel therapeutic targets for neuroprotection. Neonatal encephalopathy (NE) related to perinatal asphyxia is the most common cause of death and neurodisability in term babies with an incidence of 2 to 3 per 1,000 live births in high-income countries, and 10 to 20 per 1,000 livebirths in low and middle-income countries 1,2. In high income countries, therapeutic hypothermia partially improves outcomes, although adverse outcomes still occur in up to 30% of the cooled infants 3. As the underlying brain injury evolves over hours and days after birth, early identification of at-risk encephalopathic infants is challenging and often inaccurate, particularly in cooled infants 4. Furthermore, NE is heterogenous condition resulting from a multitude of aetiologies including acute or sub-acute perinatal hypoxia,

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Section: Discussionsupporting

confidence: 62%

Section: Discussionmentioning

confidence: 95%

Transcriptomic profile of adverse neurodevelopmental outcomes after neonatal encephalopathy

Montaldo

Cunnington

Oliveira

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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“…The ordinate is the actual probability (actual incident rate) of the patient. The red line is the fit line, which represents the actual value corresponding to the predicted value [9]. The decision curve is a useful tool to evaluate the clinical application of the model, which displays estimates of the standardized net benefit by the probability threshold used to categorize observations as 'high risk. '…”

Section: Discussionmentioning

confidence: 99%

Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage

et al. 2020

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Background: Patients with critical illness due to infection with the 2019 coronavirus disease (COVID-19) show rapid disease progression to acute respiratory failure. The study aimed to screen the most useful predictive factor for critical illness caused by COVID-19. Methods: The study prospectively involved 61 patients with COVID-19 infection as a derivation cohort, and 54 patients as a validation cohort. The predictive factor for critical illness was selected using LASSO regression analysis. A nomogram based on non-specific laboratory indicators was built to predict the probability of critical illness. Results: The neutrophil-to-lymphocyte ratio (NLR) was identified as an independent risk factor for critical illness in patients with COVID-19 infection. The NLR had an area under receiver operating characteristic of 0.849 (95% confidence interval [CI], 0.707 to 0.991) in the derivation cohort and 0.867 (95% CI 0.747 to 0.944) in the validation cohort, the calibration curves fitted well, and the decision and clinical impact curves showed that the NLR had high standardized net benefit. In addition, the incidence of critical illness was 9.1% (1/11) for patients aged ≥ 50 and having an NLR < 3.13, and 50% (7/14) patients with age ≥ 50 and NLR ≥ 3.13 were predicted to develop critical illness. Based on the risk stratification of NLR according to age, this study has developed a COVID-19 pneumonia management process. Conclusions: We found that NLR is a predictive factor for early-stage prediction of patients infected with COVID-19 who are likely to develop critical illness. Patients aged ≥ 50 and having an NLR ≥ 3.13 are predicted to develop critical illness, and they should thus have rapid access to an intensive care unit if necessary.

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“…Studies about microRNA found that miR-219 would be downregulated under hypoxic conditions and upregulated by the suppression of HIF-1α. Moreover, miR-219 can affect the proliferation and migration of hepatocellular carcinoma (HCC) cells under hypoxia [47]. Some botanical active substances inhibit hypoxiainduced tumor cell growth by suppressing EMT.…”

Section: Anti-emt Activity Of Botanical Active Substancesmentioning

confidence: 99%

Antiepithelial-Mesenchymal Transition of Herbal Active Substance in Tumor Cells via Different Signaling

Cui

Lin

Huang

et al. 2020

Oxidative Medicine and Cellular Longevity

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Epithelial-mesenchymal transition (EMT) is a biological process through which epithelial cells differentiate into mesenchymal cells. EMT plays an important role in embryonic development and wound healing; however, EMT also contributes to some pathological processes, such as tumor metastasis and fibrosis. EMT mechanisms, including gene mutation and transcription factor regulation, are complicated and not yet well understood. In this review, we introduce some herbal active substances that exert antitumor activity through inhibiting EMT that is induced by hypoxia, high blood glucose level, lipopolysaccharide, or other factors.

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HIF-1-miR-219-SMC4 Regulatory Pathway Promoting Proliferation and Migration of HCC under Hypoxic Condition

Cited by 26 publications

References 25 publications

Transcriptomic profile of adverse neurodevelopmental outcomes after neonatal encephalopathy

Transcriptomic profile of adverse neurodevelopmental outcomes after neonatal encephalopathy

Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage

Antiepithelial-Mesenchymal Transition of Herbal Active Substance in Tumor Cells via Different Signaling

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