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Bladder cancer is a common malignant tumour worldwide. Epithelial–mesenchymal transition (EMT)-related biomarkers can be used for early diagnosis and prognosis of cancer patients. To explore, accurate prediction models are essential to the diagnosis and treatment for bladder cancer. In the present study, an EMT-related long noncoding RNA (lncRNA) model was developed to predict the prognosis of patients with bladder cancer. Firstly, the EMT-related lncRNAs were identified by Pearson correlation analysis, and a prognostic EMT-related lncRNA signature was constructed through univariate and multivariate Cox regression analyses. Then, the diagnostic efficacy and the clinically predictive capacity of the signature were assessed. Finally, Gene set enrichment analysis (GSEA) and functional enrichment analysis were carried out with bioinformatics. An EMT-related lncRNA signature consisting of TTC28-AS1, LINC02446, AL662844.4, AC105942.1, AL049840.3, SNHG26, USP30-AS1, PSMB8-AS1, AL031775.1, AC073534.1, U62317.2, C5orf56, AJ271736.1, and AL139385.1 was constructed. The diagnostic efficacy of the signature was evaluated by the time-dependent receiver-operating characteristic (ROC) curves, in which all the values of the area under the ROC (AUC) were more than 0.73. A nomogram established by integrating clinical variables and the risk score confirmed that the signature had a good clinically predict capacity. GSEA analysis revealed that some cancer-related and EMT-related pathways were enriched in high-risk groups, while immune-related pathways were enriched in low-risk groups. Functional enrichment analysis showed that EMT was associated with abundant GO terms or signaling pathways. In short, our research showed that the 14 EMT-related lncRNA signature may predict the prognosis and progression of patients with bladder cancer.

No abstract

Sensitive gas sensors are becoming increasingly important in toxic gas detection and environmental monitoring. The applications of conventional gas sensors are limited due to their low sensitivity or high operating temperature. MXenes with high conductivity are conducive to the rapid transmission of electrons and are suitable as highly sensitive NH3 gas sensors. Considering the limited research on the experimental details and sensing mechanism of MXene-based NH3 gas sensors, our research focuses on precisely controlling the atomic structure of MXenes to improve the performance of NH3 gas sensors. The atomic structures of a typical monolayer Ti3C2O2 MXene and its Ti-deficient counterpart as the NH3 gas sensor are systematically studied through first-principles calculations and the nonequilibrium Green’s function method. The Ti-deficient Ti3C2O2 MXene has a relatively stronger physical interaction with NH3 and is comparatively more suitable as a highly sensitive NH3 gas sensor. Atomic-level device simulations show that the current has a greater change when NH3 is adsorbed on the surface of Ti-deficient Ti3C2O2. These detailed calculations provide substantial theoretical support and a useful design scheme to improve the sensitivity of MXene-based gas sensors by deliberately introducing Ti vacancies in the MXene.