14 research outputs found

    Depletion of TRIM24 inhibits the process of EMT.

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    <p>5(A). Western blotting analysis of the cell-apoptosis related proteins showed the expression of E-cadherin was increased and Snail, Slug, Vimentin, andβ-catenin were decreased after knockdown TRIM24 in HepG2 cells; 5(B). Reduced migration and invasion ability of HepG2 cells at 48 h post-transfection with siTRIM24 (P<0.05, compared with controls).</p

    Immunohistochemical staining of TRIM24 in tissue sections.

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    <p>A. Negative staining in normal liver tissue. B. Negative staining in benign liver lesions tissues (Hepatic hemangioma). C. Negative TRIM24 staining in an AFP>400 ug/L, well differentiated HCC tissue. D. Positive TRIM24 staining in an AFP<400 ug/L, moderate differentiated HCC tissue. E. Negative TRIM24 staining in an AFP<400 ug/L,well differentiated HCC tissue. F&G. Positive TRIM24 staining in an AFP>400 ug/L, poor differentiated HCC tissue. H. Negative control using antibody diluent.</p

    Relationship between the TRIM24 expression and the clinicopathological factors.

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    <p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085462#pone-0085462-t001" target="_blank">Table 1:</a> a. Missing data for 2 patients. b. Missing data for 3 patients. c. Missing data for 1 patient. d. Missing data for 10 patients; 7 patients were excluded according to the inclusion criteria. *.We defined that those patients whose recurrence time is less than 6 months as intrahepatic metastasis.</p

    Depletion of TRIM24 reduces cell proliferation.

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    <p>4(A). Western blotting analysis of the cell-cycle related proteins showed the expression of Cyclin D1 and CDK4 were decreased but showed no significant change in p21 after knockdown TRIM24 in HepG2 cells; 4(B). Cell cycle analyses showed that the percentage of G1 phase was increased in siTRIM24 group (P<0.05), whereas the percentages of S phase (P<0.05) and G2 phase (P<0.05) were decreased in the TRIM24 knockdown cells compared with control cells; 4(C) CCK-8 assay suggested that cell proliferation of HepG2 after TRIM24 silencing was reduced compared with the control group.</p

    miR-135b Promotes Cancer Progression by Targeting Transforming Growth Factor Beta Receptor II (TGFBR2) in Colorectal Cancer

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    <div><p>The transforming growth factor beta (TGF-β) signaling pathway is a tumor-suppressor pathway that is commonly inactivated in colorectal cancer (CRC). The inactivation of TGFBR2 is the most common genetic event affecting the TGF-β signaling pathway. However, the mechanism by which cancer cells downregulate TGFBR2 is unclear. In this study, we found that the TGFBR2 protein levels were consistently upregulated in CRC tissues, whereas its mRNA levels varied in these tissues, suggesting that a post-transcriptional mechanism is involved in the regulation of TGFBR2. Because microRNAs (miRNAs) are powerful post-transcriptional regulators of gene expression, we performed bioinformatic analyses to search for miRNAs that potentially target TGFBR2. We identified the specific targeting site of miR-135b in the 3’-untranslated region (3’-UTR) of TGFBR2. We further identified an inverse correlation between the levels of miR-135b and TGFBR2 protein, but not mRNA, in CRC tissue samples. By overexpressing or silencing miR-135b in CRC cells, we experimentally validated that miR-135b directly binds to the 3’-UTR of the TGFBR2 transcript and regulates TGFBR2 expression. Furthermore, the biological consequences of the targeting of TGFBR2 by miR-135b were examined using in vitro cell proliferation and apoptosis assays. We demonstrated that miR-135b exerted a tumor-promoting effect by inducing the proliferation and inhibiting the apoptosis of CRC cells via the negative regulation of TGFBR2 expression. Taken together, our findings provide the first evidence supporting the role of miR-135b as an oncogene in CRC via the inhibition of TGFBR2 translation.</p></div

    miR-135b directly regulates TGFBR2 expression at the post-transcriptional level.

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    <p><b>(A)</b> Quantitative RT-PCR analysis of the miR-135b levels in HT-29 and SW-480 cells treated with scrambled pre-miR-control, pre-miR-135b, anti-miR-control or anti-miR-135b. <b>(B and C)</b> Western blot analysis of the TGFBR2 protein levels in HT-29 and SW-480 cells treated with pre-miR-control, pre-miR-135b, anti-miR-control or anti-miR-135b. B: representative image; C: quantitative analysis. <b>(D)</b> Quantitative RT-PCR analysis of the TGFBR2 mRNA levels in HT-29 and SW-480 cells treated with pre-miR-control, pre-miR-135b, anti-miR-control or anti-miR-135b. <b>(E)</b> Direct binding of the TGFBR2 3’-UTR to miR-135b. HT-29 and SW-480 cells were co-transfected with a firefly luciferase reporter containing either wild-type (WT) or mutant (Mut) miR-135b binding sites in the TGFBR2 3’-UTR and either pre-miR-control or pre-miR-135b. Twenty-four hours after transfection, the cells were assessed using a luciferase assay kit. The results are displayed as the ratio of firefly luciferase activity in the miR-135b-transfected cells to that in the control cells. * P < 0.05; ** P < 0.01; *** P < 0.001.</p

    Upregulation of the TGFBR2 protein, but not mRNA, in CRC tissues.

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    <p><b>(A and B)</b> Western blot analysis of the TGFBR2 protein levels in five paired CRC and normal adjacent tissue (NAT) samples. A: representative image; B: quantitative analysis. <b>(C)</b> Quantitative RT-PCR analysis of the TGFBR2 mRNA levels in five paired CRC and NAT tissue samples. * P < 0.05; ** P < 0.01; *** P < 0.001.</p

    Image_2_Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.JPEG

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    ObjectiveThe study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.MethodsRegression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.ResultsIn regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.ConclusionThis study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.</p

    Table_1_Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.docx

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    ObjectiveThe study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.MethodsRegression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.ResultsIn regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.ConclusionThis study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.</p
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