Long noncoding RNAs (lncRNAs) play crucial roles during cancer occurrence and progression. The pseudogene-expressed lncRNA is one major type of lncRNA family. However, their association with cancers is largely unknown. In this study, we focused on small ubiquitin-like modifier (SUMO) 1 pseudogene 3, SUMO1P3. Gastric cancer tissues and adjacent nontumor tissues were collected from 96 patients with gastric cancer. The SUMO1P3 levels were detected by quantitative reverse transcription-polymerase chain reaction. Then, the association between the level of SUMO1P3 in gastric cancer tissues and the clinicopathological features of patients with gastric cancer was further analyzed. A receiver operating characteristic curve was constructed for differentiating patients with gastric cancer from patients with benign gastric diseases. The results showed that SUMO1P3 was significantly up-regulated in gastric cancer tissues compared with paired-adjacent nontumorous tissues (p < 0.01). Its expression level was significantly correlated with tumor size (p = 0.003), differentiation (p = 0.002), lymphatic metastasis (p = 0.001), and invasion (p = 0.039). The area under the ROC curve of SUMO1P3 was up to 0.666. These results indicated, for the first time, that pseudogene-expressed lncRNA SUMO1P3 may be a potential biomarker in the diagnosis of gastric cancer.

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This paper proposes a neural network-based nonsingular terminal sliding mode controller with prescribed performances for the target tracking problem of underactuated underwater robots. Firstly, the mathematical formulation of the target tracking problem is presented with an underactuated underwater robot model and the corresponding control objectives. Then, the target tracking errors from the line-of-sight guidance law are transformed using the prescribed performance technique to achieve good dynamic performance and steady-state performance that meet the pre-set conditions. Meanwhile, considering the model’s uncertainties and the external disturbances to the underwater robots, a target tracking controller is proposed based on the radial basis function (RBF) neural network and the non-singular terminal sliding mode control. Lyapunov stability analysis and homogeneity theory prove the tracking errors can converge on a small region that contains the origin with prescribed performance in finite time. In the simulation comparison, the controller proposed in this paper had better dynamic performance, steady-state performance and chattering supression. In particular, the steady-state error of the tracking error was lower, and the convergence time of the tracking error in the vertical distance was reduced by 19.1%.

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