Effects of TRAF3 on the proliferation and migration of lung adenocarcinoma depend partly on pyroptosis.

BACKGROUND: Tumor necrosis factor receptor-associated factor 3 (TRAF3) has specific regulatory effects on a wide range of diseases, including tumors. However, the effect and mechanism of TRAF3 on lung adenocarcinoma (LUAD) are still unknown. The aim of the present study was to make clear the role and potential mechanism of TRAF3 in LUAD. METHODS: TIMER2.0 database and western blot were applied to detect the expression of TRAF3 in lung adenocarcinoma tissue. Kaplan-Meier Plotter database was utilized to explore the effect of TRAF3 on the clinical prognosis of lung adenocarcinoma patients. Specific siRNA was used to inhibit the expression of TRAF3 in LUAD cells (A549 and H1299). CCK-8 and EdU assays were performed for assessing LUAD cells proliferation. Wound healing assay and transwell assay were performed for determining cells migration. CCK-8 assay was used to assess the response of the LUAD cells to paclitaxel. TIMER2.0 bioinformatics and western blot were employed to detect the effects of TRAF3 on pyroptosis in LUAD. RESULTS: TRAF3 was highly expressed in lung adenocarcinoma tissues and cell lines. Patients with TRAF3 hyperexpression had a good prognosis compared to those with lower expression. TRAF3 inhibition notably induced proliferation and migration of LUAD cells. Inhibition of TRAF3 also weakened the sensitivity of LUAD cells to paclitaxel. Moreover, bioinformatics results showed that TRAF3 was positively correlated with the expression of pyroptosis-related genes in LUAD. Western blot assays showed that TRAF3 inhibition visibly decreased the expression of apoptosis-associated speck-like protein (ASC), cleaved caspase-1 and matured- IL-1ß. CONCLUSIONS: Inhibition of TRAF3 promotes the proliferation and migration of LUAD cells, and reduces the sensitivity of LUAD cells to paclitaxel. The effects of TRAF3 on LUAD cells were mediated in part by caspase-1-dependent pyroptosis.

Development and validation of a predictive model for pulmonary hemorrhage in computed tomography-guided percutaneous lung biopsy.

PURPOSE: This study aimed to identify risk factors for pulmonary hemorrhage (PH) and higher-grade PH that complicate computed tomography (CT)-guided percutaneous lung biopsy (CT-PNLB) and establish predictive models to quantify the risk. METHODS: A total of 2653 cases of CT-PNLB were enrolled. Multivariate logistic regression was used to identify independent risk factors to develop a nomogram prediction model. The model was assessed using the area under the curve (AUC) of the receiver operator characteristic (ROC) and calibration curves and validated in the validation group. RESULTS: PH occurred in 23.52% (624/2653) of cases, and higher-grade PH occurred in 7.09% (188/2653) of cases. The parameters of lesion size, puncture depth, and contact to pleura were identified as risk factors of PH and higher-grade PH in the logistic regression model, besides the position as a risk factor for PH. The AUC of the PH prediction model was 0.776 [95% confidence interval (CI): 0.752-0.800], whereas that of the validation group was 0.743 (95% CI: 0.706-0.780). The AUC of the higher-grade PH prediction model was 0.782 (95% CI: 0.742-0.832), whereas that of the validation group was 0.769 (95% CI: 0.716-0.822). The calibration curves of the model showed good agreement between the predicted and actual probability in the development and validation groups. CONCLUSION: We identified risk factors associated with PH and higher-grade PH after PNLBs. Furthermore, we developed and validated two risk prediction models for PNLB-related PH and higher-grade PH risk prediction and clinical decision support. Key messages What is already known on this topic Pulmonary hemorrhage (PH) and other hemorrhagic complications are the most common complication in CT-guided percutaneous lung biopsy (CT-PNLB), except pneumothorax. However, the risk factors associated with PH remain controversial, and research on models of PH and higher-grade PH is also limited. What this study adds The parameters of lesion size, puncture depth, and contact to pleura were identified as risk factors of PH and higher-grade PH in the logistic regression model, besides the position as a risk factor for PH. In addition, we developed and validated two risk prediction models for PNLB-related PH and higher-grade PH risk prediction and clinical decision support. How this study might affect research, practice, or policy Of all the predictors, the position is the key factor to be considered by the operator. Moreover, two risk prediction models show good discrimination and calibration characteristics to identify patients at high risk of hemorrhage and higher-grade PH after PNLB, so these could assist clinicians in avoiding risk factors in advance.

Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine.

Although PNLB is generally considered safe, it is still invasive and risky. Pneumothorax, the most common complication of lung puncture, can cause shortness of breath, chest pain, and even life-threatening. Therefore, the auxiliary diagnosis for pneumothorax is of great clinical interest. This paper proposes an ant colony optimizer with slime mould foraging behavior and collaborative hunting, called SCACO, in which slime mould foraging behavior is combined to improve the convergence accuracy and solution quality of ACOR. Then the ability of ACO to jump out of the local optimum is optimized by an adaptive collaborative hunting strategy when trapped in the local optimum. As a first step toward Pneumothorax diagnostic prediction, we suggested an SVM classifier based on bSCACO (bSCACO-SVM), which uses the proposed SCACO's binary version as the basis for its feature selection algorithms. To demonstrate the SCACO performance, we first used the slime mould foraging behavior and adaptive cooperative hunting strategy, then compared SCACO with nine basic algorithms and nine variants, respectively. Finally, we verified bSCACO-SVM on various widely used public datasets and applied it to the Pneumothorax prediction issue, showing that it has robust classification prediction capacity and can be successfully employed for tuberculous pleural effusion diagnostic prediction.

Autophagy regulates the effects of ADSC-derived small extracellular vesicles on acute lung injury.

Small extracellular vesicles (sEVs) have been recognized to be more effective than direct stem cell differentiation into functional target cells in preventing tissue injury and promoting tissue repair. Our previous study demonstrated the protective effect of adipose-derived stem cells (ADSCs) on lipopolysaccharide (LPS)-induced acute lung injury and the effect of autophagy on ADSC functions, but the role of ADSC-derived sEVs (ADSC-sEVs) and autophagy-mediated regulation of ADSC-sEVs in LPS-induced pulmonary microvascular barrier damage remains unclear. After treatment with sEVs from ADSCs with or without autophagy inhibition, LPS-induced human pulmonary microvascular endothelial cell (HPMVECs) barrier damage was detected. LPS-induced acute lung injury in mice was assessed in vivo after intravenous administration of sEVs from ADSCs with or without autophagy inhibition. The effects of autophagy on the bioactive miRNA components of ADSC-sEVs were assessed after prior inhibition of cell autophagy. We found that ADSC-sEV effectively alleviated LPS-induced apoptosis, tight junction damage and high permeability of PMVECs. Moreover, in vivo administration of ADSC-sEV markedly inhibited LPS-triggered lung injury. However, autophagy inhibition, markedly weakened the therapeutic effect of ADSC-sEVs on LPS-induced PMVECs barrier damage and acute lung injury. In addition, autophagy inhibition, prohibited the expression of five specific miRNAs in ADSC-sEVs -under LPS-induced inflammatory conditions. Our results indicate that ADSC-sEVs protect against LPS-induced pulmonary microvascular barrier damage and acute lung injury. Autophagy is a positive mediator of sEVs function, at least in part through controlling the expression of bioactive miRNAs in sEVs.