Background:Growing evidence suggests that the imbalance of liquid-liquid phase separation (LLPS) can alter the spatiotemporal coordination ability of biomolecular condensates, thereby playing an important role in carcinogenesis and cachexia. Gastric cancer (GC), ranking as the fifth most prevalent malignancy globally, remains lacking in systematic analysis at the GC-LLPS level within current research. This study aims to identify differentially expressed LLPS-related genes (LLPSGs) in GC and elucidate the role of LLPS in the initiation and progression of GC. Identifying the role of LLPS in carcinogenesis facilitates the development of personalized treatment strategies. Methods:The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) dataset was employed as the training cohort, encompassing RNA sequencing data from 375 GC samples and 32 normal samples, along with comprehensive clinical information from 443 GC patients. Differentially expressed genes associated with GC were identified, and LLPS genes correlated with overall survival (OS) in GC patients were determined using the LLPS database. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct LLPS-based prognostic models, validated using the GEO15459 dataset containing clinical and gene expression data from 192 GC patients. Model accuracy was assessed via area under the curve (AUC) values. Multiple algorithms were employed to calculate immune cell infiltration scores for high- and low-risk groups. Finally, gene set enrichment analysis (GSEA) enrichment analysis was performed on the selected genes to explore biological processes and pathways. Results:Through univariate Cox analysis and the LASSO Cox penalized regression analysis, six genes (VCAN, APOD, MYB, SNCG, F5, BRI3BP) associated with the OS of GC patients were found and an LLPSG prognostic model was constructed. In our LLPS-related prognostic model, GC patients in the high-risk group had a poorer OS rate than those in the low-risk group. For 1-, 3-, and 5-year survival rates, the AUC predictive values of the LLPSG nomogram were 0.63, 0.63, and 0.70, respectively. The GSE15459 cohort confirmed the favorable prognostic effect of our model. The predicted survival rates at 1-, 3-, and 5-year are 0.61, 0.64, and 0.66, respectively. There were also significant differences in immune cell infiltration between the high- and low-risk groups of the model. GSEA analysis showed that the six genes were differentially enriched in various cancer-related pathways. Conclusions:Our research establishes and validates an LLPS-associated risk model centered on the genes VCAN, APOD, MYB, SNCG, F5, and BRI3BP. These genes are poised to be considered as potential therapeutic targets in the treatment of GC.

Gastric cancer (GC) ranks as the fifth most common cancer worldwide and is the third main cause of cancer-related mortality, posing a substantial burden to global public health. Research suggests that targeted therapy and immunotherapy may become more effective treatment options for advanced, unresectable, or metastatic gastric cancer. Ras-related C3 botulinum toxin substrate 1 (Rac1), a small GTP-binding protein within the Rac subfamily of the Rho GTPase family, is a critical molecule that promotes cancer cell invasion and metastasis by regulating signal transmission and promoting cell polarity. It has emerged as a key driver of tumor development and metastasis in several malignancies, including breast, lung, prostate, ovarian, gastric, and pancreatic cancers. This review summarizes the structure, regulatory dynamics, and signaling mechanisms of Rac1 in gastric cancer growth, epithelial-to-mesenchymal transition (EMT), and metastasis, as well as the roles of factors such as hypoxia, oxidative stress, and H. pylori infection. Additionally, it highlights small-molecule inhibitors targeting Rac1, miRNAs capable of suppressing Rac1, and ongoing research on Rac1-related immunotherapy. The potential of Rac1 as a therapeutic biomarker in gastric cancer and the remaining challenges in this area are also discussed. This review advances the understanding of Rac1’s role in gastric cancer, provides a theoretical foundation for further studies, and supports the development of precision medicine for this disease.

The effect of actively targeting nanomaterials on deep tumor penetration is poor, limiting their efficacy. Increasing drug penetration by improving tumor vascular permeability is a promising solution. Herein, we developed nanoparticles consisting of HER2 monoclonal antibody conjugated to poly(l-glutamic acid)-7-ethyl-10-hydroxycamptothecin (HER2-PLG-SN38) and combined them with the vascular disrupting agent combretastatin A4 phosphate (CA4P) to target HER2-expressing gastric cancer cells of the National Cancer Institute-N87 (NCI-N87) cell line. The nanoparticles were synthesized by using click chemistry. In vitro assays showed that HER2-PLG-SN38 significantly increased tumor cell death compared to IgG-PLG-SN38. The amount of HER2-PLG-SN38 infiltrating from the tumor vasculature into the tumor tissue was increased after CA4P treatment. HER2-PLG-SN38 combined with CA4P achieved a tumor suppression rate of 90.8% in vivo compared to that (70.0%) achieved with HER2-PLG-SN38 alone. Our findings suggest that combining HER2-PLG-SN38 with CA4P enhances tumor targeting and penetration, offering a potential strategy for improving the efficacy of nanomaterial-based cancer therapies. This research inspires further exploration of synergistic approaches to overcome the penetration limitations of nanomedicine in cancer treatment.

Colorectal adenomas are responsible for the origin of most colorectal cancers. Early detection together with active intervention of colorectal adenomas plays a crucial role in the prevention of colorectal cancer. This study aimed to construct and validate a new nomogram for the forecasting of the risk of colorectal adenomas based on lifestyle risk factors that could offer potential benefits for colorectal cancer prevention. Colonoscopy reports, pathology reports, physical factors, family history, personal history of disease, diet, and lifestyle habits were collected from 1,133 subjects who underwent complete colonoscopy. All subjects were divided into the training cohort (n = 792) and the validation cohort (n = 341). A nomogram predicting the risk of colorectal adenoma development was constructed using the training cohort, and the C-index was calculated. The predictive accuracy and clinical applicability of the nomogram were verified in the validation cohort. The nomogram was constructed by six statistically significant variables selected from 18 health factors, including advanced age, male, smoking, drinking, pickles, and irregular defecation. The C-index of the training cohort was 0.778, and the C-index of the validation cohort was 0.754. The calibration curve and decision curve analysis also confirmed that the model has good predictive ability and high profit. The nomogram constructed in this study was validated and can be applied to predicting the occurrence risk of colorectal adenoma. The model can guide the identification of patients with nonsymptomatic colorectal adenomas and the recognition of high-risk individuals for whom colonoscopy is advisable. Prevention Relevance: Colorectal adenomas are the origin of most colorectal cancers. In this research, we explored the risk factors of colorectal adenomas and constructed a colorectal adenoma risk prediction nomogram in the expectation of early detection of patients with nonsymptomatic colorectal adenoma and advocated for their aggressive treatment to achieve colorectal cancer prevention.

This paper presents the design of an ultra-wide-angle multispectral narrow-band absorber for reconstructing infrared spectra. The absorber offers several advantages, including polarization sensitivity, robustness against structural wear, wide azimuthal angle coverage, high narrow-band absorption, and adjustable working wavelength. To accomplish infrared spectrum reconstruction, an absorber is employed as a spectral sampling channel, eliminating the influence of slits or complex optical splitting elements in spectral imaging technology. Additionally, we propose using a truncation regularization algorithm based on the design matrix singular value ratio, namely IReg, which can enable high-precision spectral reconstruction under largely disturbed environments. The results demonstrate that, even when the number of absorption spectrum curve is reduced to a range of 1/2 to 1/3, high-precision spectral reconstruction is achievable for both flat and high-energy steep mid- and long-infrared spectral targets, while effectively accomplishing data dimension reduction.

To enhance the performance of the optical payload, a novel computational imaging spectrometer using a board-bandpass film-filter line array (IS-BFLA) was developed. Since there is an obvious difference between the IS-BFLA and the one utilizing dispersion, conventional calibration and evaluation methods are not suitable. In this paper, we present a calibration method for the IS-BFLA. The calibration objects are determined by the mathematical model. We develop an automated calibration system and design a set of data processing procedures. Two evaluation methods: self-evaluation of multi-measurement based on Euclidean distance and multi-pixel inter-evaluation based on spectral angle, are developed to evaluate the precision and accuracy of the calibration. Furthermore, comparing the calibration value with the design value and spectral reconstruction are also utilized. The accuracy of spectral reconstruction: R2 is greater than 0.99, which determines the feasibility of the calibrated data. We hope that the work in this paper will assist in subsequent related studies.

Hyperspectral Imagery (IISI), based on its high resolution spatial and spectral information has important applications in military acrospace, civil, and other remote sensing fields, which has great research significance, Deep learning has the advantages of strong learning ability, wide coverage, and strong portability, which has become a hot spot in the research of high precision hyperspectral image classification, Convolutional Neural Networks (CNN) are widely used in the research of hyperspectral image classification because of their powerful feature extraction ability and have achieved effective research results, Still, such methods are usually based on 2D-CNN or 3D-CNN alone. For the single feature of hyperspectral image, the complete feature information of hyperspectral data cannot be fully utilized. Secondly, the local feature optimization of the corresponding extraction network is good, but the overall generalization ability is insufficient. There are limitations in the deep mining of spatial and spectral information of IISL. Because of this, this paper proposes a Hybrid Spectral Convolutional Neural Network Attention Mechanism (HybridSN AM) based on attention mechanism. The principal component analysis method is used to reduce the dimension of hyperspectral images, and the convolutional neural network is used as the main body of the classification model to screen out more distinguishable features through the attention mechanism so that the model can extract more accurate and more core joint space spectral information, and realize high precision classification of hyperspectral images. The proposed method was applied to three datasets, Indian Pines (IP), the University of Pavia (UP), and Salinas (SA). The experimental results show that the overall classification accuracy, average classification accuracy, and kappa coefficient of target images based on this model are higher than 98. 14%, 97.17%, and 97.87%. Compared with the conventional HybridSN model, the classification accuracy of the HybridSN AM model on the three data sets increased by 0.89%, 0.07%, and 0.73%, respectively. It effectively solves the problem of hyperspectral image joint space spectral feature extraction and fusion, improves the accuracy of 11ST classification, and has strong generalization ability. It fully verifies the effectiveness and feasibility of the attention mechanism combined with a hybrid convolutional neural network in hyperspectral image classification, which has important theoretical value for developing and applying hyperspectral image classification technology.

Disulfidptosis is a novel discovered form of programmed cell death (PCD) that diverges from apoptosis, necroptosis, ferroptosis, and cuproptosis, stemming from disulfide stress-induced cytoskeletal collapse. In cancer cells exhibiting heightened expression of the solute carrier family 7 member 11 (SLC7A11), excessive cystine importation and reduction will deplete nicotinamide adenine dinucleotide phosphate (NADPH) under glucose deprivation, followed by an increase in intracellular disulfide stress and aberrant disulfide bond formation within actin networks, ultimately culminating in cytoskeletal collapse and disulfidptosis. Disulfidptosis involves crucial physiological processes in eukaryotic cells, such as cystine and glucose uptake, NADPH metabolism, and actin dynamics. The Rac1-WRC pathway-mediated actin polymerization is also implicated in this cell death due to its contribution to disulfide bond formation. However, the precise mechanisms underlying disulfidptosis and its role in tumors are not well understood. This is probably due to the multifaceted functionalities of SLC7A11 within cells and the complexities of the downstream pathways driving disulfidptosis. This review describes the critical roles of SLC7A11 in cells and summarizes recent research advancements in the potential pathways of disulfidptosis. Moreover, the less-studied aspects of this newly discovered cell death process are highlighted to stimulate further investigations in this field.

In this study, a broadband mid-infrared photonic filter is designed. It has the advantages of polarization insensitivity, ultrawide angle, high manufacturability, and easy integration. For infrared spectrum reconstruction, the use of filters as spectral sampling channels can eliminate the influence of slits or complex optical splitting elements in spectral imaging technology. Further, we propose a low-rank matrix logarithm regularization algorithm based on a three-segment filter function (IReg algorithm) combined with the designed filter for a variety of mid-infrared detector front ends, which can significantly suppress noise interference. Here, we demonstrate that the stability of the spectral reconstruction accuracy under the large disturbance environment obtained by the IReg algorithm is much higher than those of the L2 and L1 algorithms. In the low-dimensional matrix state, the accuracy and stability of the ill-conditioned linear equations are guaranteed, the anti-noise ability is enhanced, the effect of data dimensionality reduction is realized, and the accuracy of mid-infrared spectral reconstruction is improved.

Aircraft targets, as high-value subjects, are a focal point in Synthetic Aperture Radar (SAR) image interpretation. To tackle challenges like limited SAR aircraft datasets and shortcomings in existing detection algorithms (complexity, poor performance, weak generalization), we present the Feature Enhancement and Multi-Scales Fusion Network (FEMSFNet) for SAR aircraft detection. FEMSFNet employs diverse image augmentation and integrates optimized Squeeze-and-Excitation Networks (SE) with residual network (ResNet) in a SdE-Resblock structure for a lightweight yet accurate model. It introduces ssppf-CSP module, an improved pyramid pooling model, to prevent receptive field deviation in deep network training. Tailored for SAR aircraft detection, FEMSFNet optimizes loss functions, emphasizing both speed and accuracy. Evaluation on the SAR Aircraft Detection Dataset (SADD) demonstrates significant improvements compared to the contrasted algorithms: precision rate (92%), recall rate (96%), and F1 score (94%), with a maximum increase of 12.2% in precision, 12.9% in recall, and 13.3% in F1 score.

A micro-nano structure spectral filter facilitates bandpass imaging with a large optical flux and strong spectral curve controllability, which reduces the difficulty of processing and complexity. Therefore, considering the thin film transport theory and the finite element method, a visible-near infrared (400-900 nm) wide-band spectral filter satisfying the target spectral reconstruction requirements was fabricated. Employing lithography, coating, and lift-off processes, 24 groups of composite grating array broadband filter film lines were fabricated on the same substrate. The filter and optical microscope were packaged at the front of the GSENSE5130 detector of a large surface array in a dust-free environment to ensure the parallel placement and utilization rate of the membrane system and detector image element. Finally, a three-segment log-regularization constrained convex optimization algorithm (IReg-Cvx algorithm) based on a low-dimensional spectral curve was employed to spectrally reconstruct the multi-group ground target spectral curve. The detector errors in the gray values of the same set of transmission curves were +/- 2%. The proposed IReg-Cvx algorithm was confirmed to outperform the standard regularization algorithm in terms of mean square error and relative reconstruction errors. Moreover, the spectral reconstruction accuracy was higher upon the application of the IReg-Cvx algorithm and the target spectral features were stably captured. The low-dimensional compound grating array broadband filter facilitated high-precision spectral reconstruction of the visible-near infrared spectrum and is applicable for object-oriented data acquisition for identifying ground objects and targets. Therefore, it can aid in the design and development of compact and lightweight star-borne imaging spectrometers.

Recently, micro-spectrometer based on filter array has received extensive attention in terms of cost and size. Yet, the spectrometer will produce large noise in the work, which has a great impact on the spectral reconstruction. In this paper, a low-dimensional filter array is selected based on the K-means-PSO（Particle Swarm Optimization） method to achieve the purpose of data dimensionality reduction, which further reduces the cost and processing difficulty of the micro-spectrometer. To address the redundancy and poor accuracy of spectral reconstruction data obtained by micro-spectrometers, a convex optimization algorithm constrained by three-segment regularization of a low-rank-matrix (IReg-Cvx algorithm) was proposed for spectral reconstruction in this study. In order to test algorithm universality and stability better, we selected 120 kinds of ground spectral curves, and the low-dimensional filter array is fused with the IReg-Cvx algorithm. Apply the corresponding constraints according to the different slopes of the curve, and the high-quality spectral reconstruction of the ground object target spectrum can be stably realized under the noise environment of 30, 25, and 20 dB.

Gastric cancer (GC) has emerged as a significant issue in public health all worldwide as a result of its high mortality rate and dismal prognosis. AT-rich interactive domain 1 A (ARID1A) is a vital component of the switch/sucrose-non-fermentable (SWI/SNF) chromatin remodeling complex, and ARID1A mutations occur in various tumors, leading to protein loss and decreased expression; it then affects the tumor biological behavior or prognosis. More significantly, ARID1A mutations will likely be biological markers for immune checkpoint blockade (ICB) treatment and selective targeted therapy. To provide theoretical support for future research on the stratification of individuals with gastric cancer with ARID1A as a biomarker to achieve precision therapy, we have focused on the clinical significance, predictive value, underlying mechanisms, and possible treatment strategies for ARID1A mutations in gastric cancer in this review.

Abstract Gastric cancer (GC) is a frequent malignant disease and the main cause of cancer‐related death in the world. Podoplanin (PDPN) has been proved to be involved in the progression of various cancers. However, the role and biological mechanism of PDPN in GC are still vague. In our study, we detected the expression of PDPN in GC tissues and cell lines using RT‐qPCR, western blot and datasets. The overall survival of GC patients was analysed with a Kaplan–Meier plot. The effects of PDPN overexpression and silencing on GC cell progression were assessed by Cell Counting Kit‐8, flow cytometry and a wound healing assay. Besides, the modulation of PDPN on ezrin activation was investigated. We further explored the role of PDPN in the crosstalk between GC cells and cancer associated fibroblasts (CAFs). Results showed that PDPN was upregulated in GC tissues and cell lines. High expression of PDPN was correlated with poor prognosis of GC patients. PDPN positively regulated the viability, migration and invasion, but inhibited apoptosis, of GC cells by mediating the activation of ezrin. Meanwhile, the change in PDPN in GC cells activated CAFs and promoted the production of cytokines secreted by CAFs, which induced the progression of GC cells. These findings may provide a novel target for GC therapy.

Currently, the engineering of miniature spectrometers mainly faces three problems: the mismatch between the number of filters at the front end of the detector and the spectral reconstruction accuracy; the lack of a stable spectral reconstruction algorithm; and the lack of a spectral reconstruction evaluation method suitable for engineering. Therefore, based on 20 sets of filters, this paper classifies and optimizes the filter array by the K-means algorithm and particle swarm algorithm, and obtains the optimal filter combination under different matrix dimensions. Then, the truncated singular value decomposition-convex optimization algorithm is used for high-precision spectral reconstruction.In terms of spectral evaluation, due to the strong randomness of the target detected during the working process of the spectrometer, the standard value of the target spectrum cannot be obtained. Therefore, we adopt the method of joint cross-validation of multiple sets of data for spectral evaluation. The results show that when the random error of +/− 2 code values is applied multiple times for reconstruction, the spectral angle cosine value between the reconstructed curves becomes more than 0.995, which proves that the spectral reconstruction under this algorithm has high stability. At the same time, the spectral angle cosine value of the spectral reconstruction curve and the standard curve can reach above 0.99, meaning that it realizes a high-precision spectral reconstruction effect. A high-precision spectral reconstruction algorithm based on truncated singular value-convex optimization, is established in this paper, providing important scientific research value for the engineering application of micro-spectrometers.

Background The Mitogen-activated protein kinase 1 (MAPK1) has both independent functions of phosphorylating histones as a kinase and directly binding the promoter regions of genes to regulate gene expression as a transcription factor. Previous studies have identified elevated expression of MAPK1 in human gastric cancer, which is associated with its role as a kinase, facilitating the migration and invasion of gastric cancer cells. However, how MAPK1 binds to its target genes as a transcription factor and whether it modulates related gene expressions in gastric cancer remains unclear. Results Here, we integrated biochemical assays (protein interactions and chromatin immunoprecipitation (ChIP)), cellular analysis assays (cell proliferation and migration), RNA sequencing, ChIP sequencing, and clinical analysis to investigate the potential genomic recognition patterns of MAPK1 in a human gastric adenocarcinoma cell-line (AGS) and to uncover its regulatory effect on gastric cancer progression. We confirmed that MAPK1 promotes AGS cells invasion and migration by regulating the target genes in different directions, up-regulating seven target genes ( KRT13 , KRT6A , KRT81 , MYH15 , STARD4 , SYTL4 , and TMEM267 ) and down-regulating one gene ( FGG ). Among them, five genes ( FGG , MYH15 , STARD4 , SYTL4 , and TMEM267 ) were first associated with cancer procession, while the other three ( KRT81 , KRT6A , and KRT13 ) have previously been confirmed to be related to cancer metastasis and migration. Conclusion Our data showed that MAPK1 can bind to the promoter regions of these target genes to control their transcription as a bidirectional transcription factor, promoting AGS cell motility and invasion. Our research has expanded the understanding of the regulatory roles of MAPK1, enriched our knowledge of transcription factors, and provided novel candidates for cancer therapeutics.

Methanol gasoline because of its high octane number, low cost advantage to become the new fossil fuel alternatives, the methanol content of accurate detection is an important link in determine its quality, the quantitative analysis of methanol gasoline components is of great practical significance for alleviating the shortage of traditional petroleum resources but increasing demand in China. The conventional methods of methanol detection in methanol gasoline, such as alcohol analyzer determination, quick test box determination, etc., are complicated in operation and low in accuracy and quality, the conventional methods of methanol detection in methanol gasoline, such as alcohol analyzer determination, quick test box determination, etc., are complicated in operation and low in accuracy and quality. Near infrared analysis method is widely used in qualitative or quantitative analysis of components in many industries due to its detection speed and accuracy, Methanol gasoline near infrared spectrum are studied non-destructive detecting method, made up of 0.5%similar to 30% methanol gasoline, nearinfrared spectrum acquisition system is designed and detect 60 components of methanol gasoline spectral data, Moving average smoothing method, S-G convolution smoothing and multiple scattering correction(MSC) were used to establish a prediction model after comparative analysis of spectral data, BP Artificial Neural Network(ANN) and Principal Component regression (PCR) were used to predict the determination coefficient and root mean square error of the mode, comparing the results and prediction effects of the two algorithms. The results show that the root mean square error of each model is less than 1%, and the fitting degree of SG smooth-principal component regression prediction model is the best, and the determination coefficient is 0. 998 98, the model based on SG convolution smoothing algorithm and neural network algorithm has the smallest deviation between the predicted value and the true value, and its root mean square error (RMSEP) is 0. 322 84%. This study shows that the performance of SG smooth-neural network prediction model in the application of near infrared spectroscopy detection and analysis technology to detect methanol content in methanol gasoline is good, and meets the application requirements, this study provides a theoretical basis for the practical detection and application of methanol gasoline components, and provides technical support for the effective development and utilization of methanol gasoline.

This paper proposes a direct parametric design method for the attitude tracking control of a rigid spacecraft to address the problem of satellite attitude control performance affected by external disturbance. Firstly, the fully-actuated system model for attitude tracking control of a rigid spacecraft is established using quaternions. An extended state observer (ESO) is then developed to handle the influence caused by external disturbances. Then, a controller is designed using the direct parametric method to achieve high-accuracy attitude tracking control of the rigid spacecraft and reach the desired position. Finally, a numerical example involving a rigid spacecraft is presented to demonstrate the effectiveness of the proposed approach in achieving the design objective and exhibiting good control performance.

Nonparallel ground track imaging of optical remote sensing satellite is an efficient imaging mode which could realize one-time transit imaging of complex observation tasks due to its fast and flexible imaging capability, and has attracted more attentions in the earth observation field recently. In this paper, an off-line attitude trajectory planning and on-line moving horizon attitude tracking control scheme based on optimal control idea is designed to satisfy the imaging requirement of high-performance attitude control oriented to nonparallel ground track imaging. Considering the entire satellite attitude adjustment of the drift angle, based on the geometric relationship of sapce vectors and the coordinate transformation principle, the three-axis attitude calculation of the precise pointing of key feature targets in the interested ground curve strip is conducted. Taking the attitude orientation as the constraint, the pseudospectral method is used following the established agile satellite control model to design the optimal trajectory of the attitude manever and the feedforward control torque of actuator for nonparallel ground track imaging. A moving horizon tracking control law based on the nonlinear error control model is designed to realize the high-precision attitude tracking control. The feasibility and effectiveness of the proposed control scheme are illustrated by numerical and imaging simulations.

Gastric cancer (GC) is the fifth most commonly diagnosed malignant disease and the third leading cause of cancer‑related deaths worldwide. Recently, numerous microRNAs (miRNAs) have been determined to contribute to GC initiation and progression, suggesting that miRNAs may be developed as effective diagnostic and prognostic molecular biomarkers and can be investigated as therapeutic targets for patients with this disease. Therefore, further investigation of the miRNAs involved in GC development represents an opportunity to improve the prognosis of GC patients. miRNA‑454 (miR‑454) is abnormally expressed in multiple types of human cancer. However, the expression pattern, biological roles and underlying mechanism of miR‑454 in GC remain unclear and require further investigation. In the present study, we assessed miR‑454 expression in GC tissues and cell lines. We also explored the effects of miR‑454 on the biological behaviours of tumour cells and the underlying molecular mechanisms of miR‑454. The results revealed that miR‑454 was significantly downregulated in GC tissues and cell lines. Low miR‑454 expression was positively associated with lymph node metastasis, invasive depth and TNM stage. Additionally, upregulation of miR‑454 inhibited cell proliferation and invasion and induced the apoptosis of the GC cells. Subsequently, mitogen‑activated protein kinase 1 (MAPK1) was identified as a direct target of miR‑454. MAPK1 was upregulated in GC tissues and was found to be negatively correlated with the miR‑454 expression level. Downregulation of MAPK1 also suppressed GC cell proliferation and invasion and increased apoptosis, thereby resembling the suppressive effects of miR‑454 overexpression in GC. Moreover, upregulation of MAPK1 reversed the tumour‑suppressive effects of miR‑454 in GC. Collectively, our data demonstrated that miR‑454 may play tumour‑suppressing roles in GC through the regulation of MAPK1, suggesting that miR‑454 may be a novel biomarker and therapeutic target for patients with this disease.