Application of cross-channel multiscale permutation entropy in measuring multichannel data complexity.

Entropy is a pivotal concept in nonlinear dynamics, revealing chaos, self-organization, and information transmission in complex systems. Permutation entropy, due to its computational efficiency and lower data length requirements, has found widespread use in various fields. However, in the age of multi-channel data, existing permutation entropy methods are limited in capturing cross-channel information. This paper presents cross-channel multiscale permutation entropy algorithm, and the proposed algorithm can effectively capture the cross-channel information of multi-channel dataset. The major modification lies in the concurrent frequency counting of specific events during the calculation steps. The algorithm improves phase space reconstruction and mapping, enhancing the capability of multi-channel permutation entropy methods to extract cross-channel information. Simulation and real-world multi-channel data analysis demonstrate the superiority of the proposed algorithm in distinguishing different types of data. The improvement is not limited to one specific algorithm and can be applied to various multi-channel permutation entropy variants, making them more effective in uncovering information across different channels.

Improved multivariate multiscale sample entropy and its application in multi-channel data.

Entropy, as a nonlinear feature in information science, has drawn much attention for time series analysis. Entropy features have been used to measure the complexity behavior of time series. However, traditional entropy methods mainly focus on one-dimensional time series originating from single-channel transducers and are incapable of handling the multidimensional time series from multi-channel transducers. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although MMSE generalizes multiscale sample entropy and provides a new method for multidimensional data analysis, it lacks necessary theoretical support and has shortcomings, such as missing cross-channel correlation information and having biased estimation results. This paper proposes an improved multivariate multiscale sample entropy (IMMSE) algorithm to overcome these shortcomings. This paper highlights the existing shortcomings in MMSE under the generalized algorithm. The rationality of IMMSE is theoretically proven using probability theory. Simulations and real-world data analysis have shown that IMMSE is capable of effectively extracting cross-channel correlation information and demonstrating robustness in practical applications. Moreover, it provides theoretical support for generalizing single-channel entropy methods to multi-channel situations.

Remote passive acoustic signal detection using multi-scale correlation networks and network spectrum distance in marine environment.

Detecting acoustic signals in the ocean is crucial for port and coastal security, but existing methods often require informative priors. This paper introduces a new approach that transforms acoustic signal detection into network characterization using a MCN construction method. The method constructs a network representation of the acoustic signal by measuring pairwise correlations at different time scales. It proposes a network spectrum distance method that combines information geometry and graph signal processing theory to characterize these complex networks. By comparing the spectra of two networks, the method quantifies their similarity or dissimilarity, enabling comparisons of multi-scale correlation networks constructed from different time series data and tracking changes in nonlinear dynamics over time. The effectiveness of these methods is substantiated through comprehensive simulations and real-world data collected from the South China Sea. The results illustrate that the proposed approach attains a significant detection probability of over 90% when the signal-to-noise ratio exceeds -18 dB, whereas existing methods require a signal-to-noise ratio of at least -15 dB to achieve a comparable detection probability. This innovative approach holds promising applications in bolstering port security, facilitating coastal operations, and optimizing offshore activities by enabling more efficient detection of weak acoustic signals.

Apolipoprotein E2 inhibits mitochondrial apoptosis in pancreatic cancer cells through ERK1/2/CREB/BCL-2 signaling.

BACKGROUND: Apolipoprotein E2 (ApoE2) is a pleiotropic protein that influences several aspects of cancer metabolism and development. Evading apoptosis is a vital factor for facilitating cancer cell growth. However, the role and mechanism of ApoE2 in regulating cell apoptosis of pancreatic cancer remain unclear. METHODS: In this study, we firstly detected the mRNA and protein expressions of ApoE2 in PANC-1 and Capan-2 cells by real-time polymerase chain reaction and Western blotting. We then performed TUNEL and flow cytometric analyses to explore the role of recombinant human ApoE2, pCMV6-ApoE2 and siApoE2 in the apoptosis of PANC-1 and Capan-2 cells. Furthermore, we investigated the molecular mechanism through which ApoE2 affected apoptosis in PANC-1 cells using immunofluorescence, immunoprecipitation, Western blotting and co-immunoprecipitation analysis. RESULTS: ApoE2 phosphorylated ERK1/2 and inhibited pancreatic cancer cell apoptosis. In addition, our data showed that ApoE2/ERK1/2 altered the expression and mitochondrial localization of BCL-2 via activating CREB. ApoE2/ERK1/2/CREB also increased the total BCL-2/BAX ratio, inhibited the opening of the mitochondrial permeability transition pore and the depolarization of mitochondrial transmembrane potential, blocked the leakage of cytochrome-c and the formation of the apoptosome, and consequently, suppressed mitochondrial apoptosis. CONCLUSIONS: ApoE2 regulates the mitochondrial localization and expression of BCL-2 through the activation of the ERK1/2/CREB signaling cascade to evade the mitochondrial apoptosis of pancreatic cancer cells. ApoE2 may be a distinct prognostic marker and a potential therapeutic target for pancreatic cancer.

The sustained PGE2 release matrix improves neovascularization and skeletal muscle regeneration in a hindlimb ischemia model.

BACKGROUND: The promising therapeutic strategy for the treatment of peripheral artery disease (PAD) is to restore blood supply and promote regeneration of skeletal muscle regeneration. Increasing evidence revealed that prostaglandin E2 (PGE2), a lipid signaling molecule, has significant therapeutic potential for tissue repair and regeneration. Though PGE2 has been well reported in tissue regeneration, the application of PGE2 is hampered by its short half-life in vivo and the lack of a viable system for sustained release of PGE2. RESULTS: In this study, we designed and synthesized a new PGE2 release matrix by chemically bonding PGE2 to collagen. Our results revealed that the PGE2 matrix effectively extends the half-life of PGE2 in vitro and in vivo. Moreover, the PGE2 matrix markedly improved neovascularization by increasing angiogenesis, as confirmed by bioluminescence imaging (BLI). Furthermore, the PGE2 matrix exhibits superior therapeutic efficacy in the hindlimb ischemia model through the activation of MyoD1-mediated muscle stem cells, which is consistent with accelerated structural recovery of skeletal muscle, as evidenced by histological analysis. CONCLUSIONS: Our findings highlight the chemical bonding strategy of chemical bonding PGE2 to collagen for sustained release and may facilitate the development of PGE2-based therapies to significantly improve tissue regeneration.

Research on Area of Uncertainty of Underwater Moving Target Based on Stochastic Maneuvering Motion Model.

Considering the influence of measurement error on target state estimation, there is an uncertain dispersion region for target position estimate, that is, the area of uncertainty (AOU, area of uncertainty). In underwater target tracking, the state estimation is point estimation without AOU estimation and its accuracy is poor in the early stage because of large measurement errors. Fast tracking with higher accuracy and AOU estimation are of great significance to time-sensitive target tracking. To improve the state estimation accuracy in the early stage, and estimate the AOU, a method of AOU estimation of underwater moving target is presented based on a stochastic maneuvering motion (SMM, stochastic maneuvering motion) model. The stochastic maneuvering motion model is established based on the Langevin equation to reflect the movement characteristics of an underwater moving target. Then, the target state is estimated with a noise adaptive Kalman filter by constructing the measurement equation and state equation according to measurement error characteristic and stochastic maneuvering model. Based on the physical significance of the error covariance matrix from the Kalman filter, the parameters of AOU are deduced. Simulation results of underwater target tracking and AOU estimation are presented to demonstrate the relative performance of the proposed algorithm compared with the adaptive Kalman filter. It is clearly shown from the results that SMM tracking algorithm achieves higher accuracy of state estimation in the initial stage of tracking, and the predicted AOU is consistent with the actual distribution of underwater moving targets while yielding more concentrated distribution, which reveals that estimated AOU can be precisely represented by the confidence ellipses. The presented approach and obtained results may be useful in time-sensitive target threat analysis and weapon strike applications.

LAMC2 modulates the acidity of microenvironments to promote invasion and migration of pancreatic cancer cells via regulating AKT-dependent NHE1 activity.

LAMC2, as a unique chain in the Laminin 5 molecule, has been found to be associated with malignant metastases in some cancers. However, the roles and mechanisms by which LAMC2 affects the migration and invasion of pancreatic cancer cells remain unclear. First, we found that laminin 5/LAMC2 and its receptors were highly expressed in pancreatic cancer tissues and cells. Then, we investigated the effects of LAMC2 on pancreatic cancer cell migration/invasion and extracellular (pHe). We also demonstrated that LAMC2 phosphorylated Akt-Ser473 to promote the expression, activity and cell membrane accumulation of NHE1 within pancreatic cancer cells. So we speculated that LAMC2 modulated the pHe to promote migration and invasion of pancreatic cancer cells. Additionally, our data also showed that LAMC2/NHE1 resulted in altered cell morphology and aberrant expression of mesenchymal markers. The function of actin-binding proteins (ABPs) were affected by LAMC2/NHE1 signaling. LAMC2/NHE1 signaling generated extracellular acidification to induce dynamic actin-dependent pseudopodial formation and EMT programs that promote tumor cell invasion in pancreatic cancer cells. Therefore, we found that LAMC2 was responsible for generating the extracellular acidic conditions that mediated invasion of pancreatic cancer cells by activating Akt/NHE1 signaling. LAMC2 is a characteristic prognostic and therapeutic agent of PDCA.

Apolipoprotein E2 modulates cell cycle function to promote proliferation in pancreatic cancer cells via regulation of the c-Myc-p21Waf1 signalling pathway.

Apolipoprotein E2 (ApoE2) is reportedly critical for cell proliferation and survival, and has been identified as a potential tumour-associated marker in many kinds of cancer. However, studies of the function and mechanisms of ApoE2 in pancreatic cancer proliferation and development are rare. In this study, we performed an analysis to determine the modulatory effects of ApoE2-LRP8 (lipoprotein receptor-related protein 8) pathway on cell cycle and cell proliferation, and explored its mechanisms in pancreatic cancer. High expression levels of ApoE2-LRP8/c-Myc were detected in tumour tissues and cell lines by immunohistochemistry and Western blotting. It was also shown that ApoE2-LRP8 induced phosphorylation of ERK1/2 to activate c-Myc and contribute to cell-cycle-related protein expression. ApoE2 conditions induced c-Myc binding to target gene sequences in the p21Waf1 promoter, resulting in decreased transcription. ERK/c-Myc contributes to the promotion of the expression levels of cyclin D1, cdc2, and cyclin B1, and reduces p21Waf1 activity, thereby promoting cell cycle distribution. We demonstrated the function of ApoE2-LRP8 in the activation of the ERK-c-Myc-p21Waf1 signalling cascade and the modulation of G1/S and G2/M transition, indicating ApoE2-LRP8's important role in the cancer cell proliferation. ApoE2 could serve as a diagnostic marker and chemotherapeutic target in pancreatic cancer.

Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels.

Orthogonal Chirp Division Multiplexing (OCDM) is a modulation scheme which outperforms the conventional Orthogonal Frequency Division Multiplexing (OFDM) under frequency selective channels by using chirp subcarriers. However, low complexity equalization algorithms for OCDM based systems under doubly selective channels have not been investigated yet. Moreover, in OCDM, the usage of different phase matrices in modulation will lead to extra storage overhead. In this paper, we investigate an OCDM based modulation scheme termed uniform phase-Orthogonal Chirp Division Multiplexing (UP-OCDM) for high-speed communication over doubly selective channels. With uniform phase matrices equipped, UP-OCDM can reduce the storage requirement of modulation. We also prove that like OCDM, the transform matrix of UP-OCDM is circulant. Based on the circulant transform matrix, we show that the channel matrices in UP-OCDM system over doubly selective channels have special structures that (1) the equivalent frequency-domain channel matrix can be approximated as a band matrix, and (2) the transform domain channel matrix in the framework of the basis expansion model (BEM) is a sum of the product of diagonal and circulant matrices. Based on these special channel structures, two low-complexity equalization algorithms are proposed for UP-OCDM in this paper. The equalization algorithms are based on block LDL H factorization and iterative matrix inversion, respectively. Numerical simulations are finally proposed to show the performance of UP-OCDM and the validity of the proposed low complexity equalization algorithms. It is shown that when the channel is doubly selective, UP-OCDM and OCDM have similar BER performance, and both of them outperform OFDM. Moreover, the proposed low complexity equalizers for UP-OCDM both show better BER performance than their OFDM counterparts.

An Efficient Topology Discovery Protocol with Node ID Assignment Based on Layered Model for Underwater Acoustic Networks.

Underwater acoustic networks are widely used in survey missions and environmental monitoring. When an underwater acoustic network (UAN) is deployed in a marine region or two UANs merge, each node hardly knows the entire network and may not have a unique node ID. Therefore, a network topology discovery protocol that can complete node discovery, link discovery, and node ID assignment are necessary and important. Considering the limited node energy and long propagation delay in UANs, it is challenging to obtain the network topology with reduced overheads and a short delay in this initial network state. In this paper, an efficient topology discovery protocol (ETDP) is proposed to achieve adaptive node ID assignment and topology discovery simultaneously. To avoiding packet collision in this initial network state, ETDP controls the transmission of topology discovery (TD) packets, based on a local timer, and divides the network into different layers to make nodes transmit TD packets orderly. Exploiting the received TD packets, each node could obtain the network topology and assign its node ID independently. Simulation results show that ETDP completes network topology discovery for all nodes in the network with significantly reduced energy consumption and short delay; meanwhile, it assigns the shortest unique IDs to all nodes with reduced overheads.

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars.

Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter a o p t = 1 . As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to H 1 (periodic signal with noise) and H 0 (pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD).

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution.

Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.

Artemin regulates CXCR4 expression to induce migration and invasion in pancreatic cancer cells through activation of NF-κB signaling.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal human malignant tumor because of the early onset of local invasion and distant metastasis. Perineural invasion is a prominent characteristic of pancreatic adenocarcinoma, which is a multifactorial process that involves various signaling molecules from different signaling pathways. The glial cell line-derived neurotrophic factor family of ligands was reported to be involved in perineural invasion in pancreatic cancer. Artemin is one member of the glial cell line-derived neurotrophic factor family of ligands. Although Artemin has previously been demonstrated to promote invasiveness of pancreatic cancer, the mechanisms remain poorly understood. In this study, we performed an analysis to determine the effects of Artemin on modulating tumor cell metastatic potential and invasion activity and explored its mechanisms in pancreatic cancer. We indicated that Artemin and CXCR4 were overexpressed in cancer tissues and widely expressed in pancreatic cancer cell lines. We observed that activation of ERK1/2 and Akt in Artemin-treated cells led to enhanced nuclear accumulation of NF-κB, which then induced CXCR4 expression. Through regulation of the expression of CXCR4, Artemin functionally promoted the migration and invasion in pancreatic cancer cells. The present study indicated that Artemin induced CXCR4 expression by activating Akt and ERK 1/2/NF-κB signaling, thereby modulating tumor cell metastatic potential and invasion activity in pancreatic cancer by regulating SDF-1α/CXCR4 axis. Artemin might be an effective and potent therapeutic target for pancreatic cancer metastasis, especially in perineural invasion.

Dynamic Reference Selection-Based Self-Localization Algorithm for Drifted Underwater Acoustic Networks.

Self-localization has become one of the major areas of research in drifted underwater acoustic networks (DUANs) since many applications are based on the knowledge of nodes' positions. However, self-localization for DUANs faces two main challenges: the insufficient anchors and the varying network topology. Both affect the localization performance seriously. In this paper, we focus on these two challenges and propose a dynamic reference selection-based self-localization algorithm for DUANs (DRSL) to improve the localization performance. First, an optimal reference selection scheme is presented to solve the insufficient anchors' problem. The selected optimal reference node can not only assist the insufficient anchors in accomplishing the localization procedure, but also obviously increase the localization accuracy. Based on the proposed optimal reference selection scheme, a dynamic reference selection-based self-localization algorithm is proposed to solve the topology changing problem. The proposed algorithm can improve the localization performance for DUANs significantly by selecting the reference node dynamically according to the predicted network topology, which is more suitable for DUANs with mobile sensor nodes. Simulation results show that the proposed DRSL algorithm can increase the localization accuracy greatly with insufficient anchor nodes and varying network topology. In addition, DRSL algorithm also has a lower communication cost than other anchor-free approaches, which distinctly demonstrates the advantages of the proposed DRSL algorithm.

Hierarchy Graph Based Barrier Coverage Strategy with a Minimum Number of Sensors for Underwater Sensor Networks.

Underwater sensor networks ( UWSNs ) based barrier coverage is increasingly important for intrusion detection due to the scarcity of underwater sensor resource. To improve UWSNs' detection performance and prolong their lifetime, an efficient barrier coverage strategy is very important. In this paper, a novel concept: hierarchy graph is proposed. Hierarchy graph can make the network's topology more clarity. In accordance with the hierarchy graph, 1-barrier coverage algorithm and k-barrier coverage algorithm are presented to construct the barrier with less sensors for higher energy efficiency. Both analytical and simulation studies demonstrate that the proposed algorithms can provide high detection probability and long lifetime for UWSNs.

A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise.

The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.

Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks.

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.

DOA Estimation for Underwater Target by Active Detection on Virtual Time Reversal Using a Uniform Linear Array.

Aiming at addressing the problem caused by multipath effects in direction of arrival (DOA) estimation for underwater targets, a method based on the active detection on virtual time reversal (ADVTR) Capon algorithm is proposed. Unlike the conventional passive target estimation method ignoring the multipath effects but only considering the direct wave, the proposed method is closer to the actual situation in that the multipath signal propagation model is fully taken into account; in addition, active detection (AD) and virtual time reversal (VTR) processes are added, which use active detection to estimate channels, and virtual time reversal to realize focusing in a computer after the source-receive array (SRA) receives the reflected signal of the target. The combination of the two methods can greatly improve the energy of SRA and the precision of target direction estimation. With the popular acoustic field simulation tool Bellhop, the model proposed in this paper is verified. Compared with the conventional Capon method without time reversal, the simulation results show that the ADVTR Capon estimation method is far better, in terms of resolution and suppressing the sidelobes. It is suitable for the target DOA estimation under low signal-to-noise ratio (SNR) conditions. Further, we also show the ADVTR Capon estimation method works well in a real tank experiment.

Instantaneous brain dynamics mapped to a continuous state space.

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting-state and task-active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. Upon observing the local neighborhood of brain-states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task-active brain states. As task-active brain states often populate a local neighborhood, back-projection of segments of the dynamical state space onto the brain's surface reveals the patterns of brain activity that support many experimentally-defined states.

Gastrin regulates ABCG2 to promote the migration, invasion and side populations in pancreatic cancer cells via activation of NF-κB signaling.

Gastrin is absent in most normal adult pancreatic tissues but is highly expressed in pancreatic cancer tissues. Although Gastrin expression was reported to be associated with tumor proliferation in human pancreatic cancer, studies on the relationship between Gastrin and tumor metastasis in pancreatic cancer are rare. In this study, we performed an analysis to determine the effects of Gastrin on modulating the side populations, cell proportion and tumor cell metastatic potential and invasion activity and explored its mechanisms in pancreatic cancer. We indicated that Gastrin and ABCG2 were widely expressed in pancreatic cancer cell lines and overexpressed in cancer tissues. Gastrin induced ABCG2 expression, and this effect was mediated by NF-κB activation. Gastrin regulated the SP proportion of BxPC-3 cells via modulating ABCG2 expression. Through the regulation of the functions of NF-κB/ABCG2, Gastrin functionally promoted the migration and invasion in pancreatic cancer cell. The present study indicated that Gastrin induced ABCG2 expression by activating NF-κB and thereby modulated the SP proportion, tumor cell metastatic potential and invasion activity in pancreatic cancer. Gastrin could serve as an effective therapeutic target for the metastasis of pancreatic cancer.