PHAP1 (Putative HLA‐DR‐associated protein 1), also termed acidic leucine‐rich nuclear phosphoprotein 32A (ANP32A), Phosphoprotein 32 (pp32) or protein phosphatase 2A inhibitor (I1PP2A), is a multifunctional protein aberrantly expressed in multiple types of human cancers. However, its expression pattern and clinical relevance in human glioma remain unknown. In this study, Western blotting and immunohistochemistry analysis demonstrated PHAP1 protein was highly expressed in glioma patients, especially in those with high‐grade disease. Publicly available data also revealed high levels of PHAP1 were associated with poor prognosis in glioma patients. The functional studies showed that knock‐down of PHAP1 suppressed the proliferation of glioma cells, while overexpression of PHAP1 facilitated it. The iTRAQ proteomic analysis suggested that stathmin might be a potential downstream target of PHAP1. Consistently, PHAP1 knock‐down significantly decreased the expression of stathmin, while overexpression of PHAP1 increased it. Also, the upstream negative regulator, p27, expression levels increased upon PHAP1 knock‐down and decreased when PHAP1 was overexpressed. As a result, the phosphorylated Akt (S473), an upstream regulator of p27, expression levels decreased upon silencing of PHAP1, but elevated after PHAP1 overexpression. Importantly, we demonstrate the p27 down‐regulation, stathmin up‐regulation and cell proliferation acceleration induced by PHAP1 overexpression were dependent on Akt activation. In conclusion, the above results suggest that PHAP1 expression is elevated in glioma patients, which may accelerate the proliferation of glioma cells by regulating the Akt/p27/stathmin pathway.
Background To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. Results In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5–9) was significantly higher than that of the mild group (4, IQR,2–5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889–0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867–1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. Conclusion The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.
Background: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics.Methods: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. 217 patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.Results: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR:2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19.Conclusion: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.
Background: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics.Methods: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. 217 patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.Results: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR:2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19.Conclusion: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.
Background: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. 217 patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.Results: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR:2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19.Conclusion: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.
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