Downregulation of Long Non-coding RNA FALEC Inhibits Gastric Cancer Cell Migration and Invasion Through Impairing ECM1 Expression by Exerting Its Enhancer-Like Function

Long non-coding RNAs (lncRNAs) have been shown to play important roles in many human diseases. However, their functions and mechanisms in tumorigenesis and development remain largely unknown. Here, we demonstrated that focally amplified lncRNA in epithelial cancer (FALEC) was upregulated and significantly correlated with lymph node metastasis, TNM stage in gastric cancer (GC). Further experiments revealed that FALEC knockdown significantly inhibited GC cells migration and invasion in vitro. Mechanistic investigations demonstrated that small interfering RNA-induced silencing of FALEC decreased expression of the nearby gene extracellular matrix protein 1 (ECM1) in cis. Additionally, ECM1 and FALEC expression were positively correlated, and high levels of ECM1 predicted shorter survival time in GC patients. Our results suggest that the downregulation of FALEC significantly inhibited the migration and invasion of GC cells through impairing ECM1 expression by exerting an enhancer-like function. Our work provides valuable information and a novel promising target for developing new therapeutic strategies in GC

Simulation Study on the Coupling Relationship between Traffic Network Model and Traffic Mobility under the Background of Autonomous Driving

Autonomous driving technology will bring revolutionary changes to the development of future cities and transportation. In order to study the impact of autonomous driving on urban transportation networks, this paper first summarizes the development status of autonomous driving technology, and then three space–traffic network coupling models are proposed based on the differences of speed and space, which are the traditional difference type, scale variation type, and slow-guided type. On this basis, a new 4 * 4 km grid city model is constructed. Based on the MATSim multi-agent simulation method, the traffic parameters of the three models are studied. The results show that under the same traffic demand, the service scale and level of the three traffic networks are significantly different. The optimal service level of the traditional differential type is 2.15 times the efficiency of the slow-guided type. Under the same demand and road network mode, the travel speed of the autonomous driving mode is 1.7–2.8 times that of the traditional mode. Under the same lane area ratio, the travel speed of traditional driving is much smaller than that of autonomous driving, which is about 2.6–3.6 times greater than the former. The research conclusion has certain reference significance for formulating urban spatial development strategies and policies under autonomous driving environments and for promoting the sustainable development of urban transportation