Although overexpression of the long non-coding RNA (lncRNA) UCA1 has been implicated in several human cancers, its biological function in pancreatic cancer remains to be clarified. In this study, we reported that UCA1 expression was significantly increased in pancreatic cancer tissues and correlated with clinicopathological features, tumor stage, and poorer patient outcome. We further showed that UCA1 promoted cell migration and invasion of pancreatic cancer cells. Importantly, we found that UCA1 overexpression inhibited YAP phosphorylation, and increased YAP expression. Mechanistically, UCA1 interacted with MOB1, Lats1, and YAP, forming shielding composites. Moreover, we demonstrated that UCA1 increased YAP nuclear localization and stabilization, and improved TEAD luciferase activity. In turn, YAP promotes UCA1 expression. Collectively, the present study provides insights into the mechanistic regulation of UCA1 promoting pancreatic cancer progression through the Hippo signaling pathway. UCA1 may serve as a candidate biomarker for poor prognosis and a target for new pancreatic cancer therapies.
Background
: Recent studies have shown that circulating long noncoding RNAs (lncRNAs) could be stably detectable in the blood of cancer patients and play important roles in the diagnosis of many different cancers. However, the value of lncRNAs in the diagnosis of pancreatic cancer (PC) has not been fully explored.
Methods
: Eleven PC-related lncRNAs were selected by analyzing bioinformatics databases. The expression levels of the lncRNAs were further analyzed in a small set of plasma samples from a training group including 30 noncancer samples (15 healthy and 15 chronic pancreatitis (CP) subjects) and 15 PC samples. Then, the candidate lncRNAs were validated with data from 46 healthy controls, 97 CP patients and 114 PC patients. Receiver operating characteristic (ROC) curves were employed to evaluate the diagnostic performance of the identified lncRNAs.
Results
: After selection and validation, three characteristic plasma candidate lncRNAs, ABHD11-AS1, LINC00176 and SNHG11, were identified, and their levels were significantly higher in PC patients than in normal controls. We found that among the three candidate lncRNAs, ABHD11-AS1 showed the best diagnostic performance for the detection of PC. Furthermore, ABHD11-AS1 had a higher area under the ROC curve (AUC) than CEA, CA199 and CA125 for early PC diagnosis, while the combination of ABHD11-AS1 and CA199 was more effective than ABHD11-AS1 alone.
Conclusions
: Plasma ABHD11-AS1 could serve as a potential biomarker for detecting PC, and the combination of ABHD11-AS1 and CA199 was more efficient for the diagnosis of PC than ABHD11-AS1 alone, particularly for early tumor screening.
Building EXODUS software is used to calculate the evacuation times and simulate the evacuation behavior. The results and laws are compared with those from a 2D Cellular Automaton (CA) random evacuation model developed by our group. EXODUS simulation is more reasonable than the CA simulation in the case of evacuation from a simple room, but CA model is more reasonable in the case of evacuation in a long corridor after bottlenecks. As far as the evacuation from a simple room with a single exit is concerned, there is a critical value of exit width. The value of exit width should be bigger than the critical value in order to ensure a dilute pedestrian flow, but the value doesn't need to be too big. The bigger the original occupant density, the longer the evacuation time is. They can be fitted as a linear relationship. The principle of taking the shortest route is not always useful. If the distribution of occupant density is not uniform at each building part, balancing the use efficiency of each exit should be the main principle in order to improve evacuation efficiency. All the above laws can be obtained both from EXODUS and the CA model.
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