Objective. Non-small-cell lung cancer (NSCLC) is one of the most lethal cancers. Although cisplatin-based chemotherapies have been regarded as a promising treatment approach, cisplatin resistance still remains one of the major clinical challenges. Curcumin, a naturally occurring polyphenol, has been proved to increase chemotherapeutic efficiency of NSCLC cells. However, the role of curcumin in cisplatin-resistant NSCLC cells has been rarely investigated. This study aims to investigate whether curcumin enhances cisplatin sensitivity of human NSCLC cells and its underlying mechanisms. Method. A549/DDP and H1299/DDP cells were treated by DDP or/and curcumin before cell viability, and apoptosis were determined by using a CCK-8 assay and flow cytometer. The expressions of apoptosis and ER stress-related proteins, including cleaved caspase-3, cleaved PARP, CHOP, GRP78, XBP-1, ATF6, and caspase-4, were measured by the qPCR and western blotting. After cotreatment by DDP and curcumin, A549/DDP and H1299/DDP cells were further treated by the ER stress inhibitor, salubrinal (20 μm), after which the cell apoptosis and viability were detected. Result. Treatment by DDP and curcumin can substantially decrease cell viability, while can increase the cell apoptosis rate, elevate mRNA and protein expressions of apoptosis and ER stress-related proteins, compared with cells treated by DDP or curcumin alone. Salubrinal treatment can counteract the suppressive effect of DDP and curcumin on cell viability and decrease the cell apoptosis of A549/DDP and H1299/DDP cells. Conclusion. Curcumin can increase the sensitivity of NSCLC to cisplatin through an ER stress pathway and thus can be served as one of the molecular targets for overcoming the cisplatin resistance.
Background. Endothelium-mesenchymal transition (EndMT) is a process of phenotypic and functional transition from activated endothelial cells to mesenchymal cells. Recently, EndMT has been proved to be one of the main pathological mechanisms of pulmonary artery hypertension (PAH). However, the molecular mechanism is not clear. Methods. Primary rat pulmonary arterial endothelial cells (rPAECs) were isolated from Sprague-Dawley rats and verified by CD31 immunofluorescence staining. rPAECs were exposed to hypoxic conditions to induce EndMT. RNA and protein levels in cells were detected by RT-qPCR and Western blot. The migration ability was verified by the transwell assay. The RIP experiment was used to test the m6A modification of TRPC6 mRNA and the binding relationship between TRPC6 and METTL3. Calcineurin/NFAT signaling was measured by using commercial kits. Results. METTL3 was found to be highly expressed by hypoxia treatment in a time-dependent manner. Knockdown of METTL3 significantly suppressed cell migration, downregulated the levels of interstitial cell-related markers like α-SMA and vimentin, and increased the levels of endothelial cell markers including CD31 and VE-cadherin. Mechanistically, METTL3 increased TRPC6 expression by enhancing the m6A modification of TRPC6 mRNA, thus activating calcineurin/NFAT signaling. Our experiments showed that METTL3 silencing mediated the inhibitory roles in the hypoxia-mediated EndMT process, which were significantly reversed by TRPC6/calcineurin/NFAT signaling activation. Conclusion. Our results elucidated that METTL3 knockdown inhibited the hypoxia-mediated EndMT process by inactivating TRPC6/calcineurin/NFAT signaling.
Objective. To establish a prediction model of pneumonia risk in SARS-CoV-2-infected patients to reduce unnecessary chest CT scans. Materials and Methods. The model was constructed based on a retrospective cohort study. We selected SARS-CoV-2 test-positive patients and collected their clinical data and chest CT images from the outpatient and emergency departments of Hunan Provincial People’s Hospital, China. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were utilized to identify predictors of pneumonia risk for patients infected with SARS-CoV-2. These predictors were then incorporated into a nomogram to establish the model. To ensure its performance, the model was evaluated from the aspects of discrimination, calibration, and clinical validity. In addition, a smoothed curve was fitted using a generalized additive model (GAM) to explore the association between the pneumonia grade and the model’s predicted probability of pneumonia. Results. We selected 299 SARS-CoV-2 test-positive patients, of whom 205 cases were in the training cohort and 94 cases were in the validation cohort. Age, CRP natural log-transformed value (InCRP), and monocyte percentage (%Mon) were found to be valid predictors of pneumonia risk. This predictive model achieved good discrimination of AUC in the training and validation cohorts which was 0.7820 (95% CI: 0.7254–0.8439) and 0.8432 (95% CI: 0.7588–0.9151), respectively. At the cut-off value of 0.5, it had a sensitivity and specificity of 70.75% and 66.33% in the training cohort and 76.09% and 73.91% in the validation cohort, respectively. With suitable calibration accuracy shown in calibration curves, decision curve analysis indicated high clinical value in predicting pneumonia probability in SARS-CoV-2-infected patients. The probability of pneumonia predicted by the model was positively correlated with the actual pneumonia classification. Conclusion. This study has developed a pneumonia risk prediction model that can be utilized for diagnostic purposes in predicting the probability of pneumonia in patients infected with SARS-CoV-2.
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