Genomic landscape of gastric cancer: molecular classification and potential targets

C118P Suppresses Gastric Cancer Growth via Promoting Autophagy-Lysosomal Degradation of RAB1A

Background/Objectives: Gastric cancer (GC) is the leading cause of cancer-related deaths worldwide. C118P, a microtubule inhibitor with anti-angiogenic and vascular-disrupting activities, was proven to be cytotoxic to various cancer cell lines. This study aimed to explore the anti-tumor effect of C118P against gastric cancer and identify its potential target. Methods: The MTT assay, colony formation assay, and EdU incorporation assay were used to evaluate the effect of C118P on GC cell proliferation. Cell cycle and cell apoptosis were measured using flow cytometry. Molecular docking, a microscale thermophoresis (MST) analysis, and the cellular thermal shift assay (CETSA) were used to investigate the binding of C118P to RAB1A. Autophagy-related effects were evaluated by using the MDC staining assay, immunofluorescence assay, and immunoblotting assay. The SGC-7901 cell line xenograft mouse model was used to confirm the anti-tumor efficacy of C118P. Results: C118P dramatically inhibited proliferation, induced G2/M cell cycle arrest, and triggered apoptosis in GC cell lines HGC-27 and SGC-7901. Mechanistically, C118P was demonstrated to bind with RAB1A and reduce the RAB1A protein level, accompanied by the inhibition of mTORC1 signaling. Moreover, C118P induced autophagosome formation and promoted RAB1A protein degradation in an autophagy-lysosomal-dependent manner. The in vivo study verified that C118P inhibits GC growth by inhibiting the RAB1A-mTOR axis. Conclusions: Our findings suggested that C118P inhibits GC growth by promoting the autophagy-lysosomal-dependent degradation of RAB1A and modulating mTOR C1 signaling. C118P shows potential as being a small molecule drug effective in the treatment of gastric cancer via targeting RAB1A.

Analysis of immune status and prognostic model incorporating lactic acid metabolism-associated genes

BACKGROUND

Cancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism, which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in gastric adenocarcinoma (STAD) remain unclear. This study introduces a novel methodology to evaluate lactic acid metabolism comprehensively in STAD, aiming to elucidate its prognostic significance and impact on immunotherapy efficacy. Targeted therapies directed at key lactic acid metabolism genes (LMGs) identified within the tumor microenvironment (TME) hold promise for personalized treatment strategies.

METHODS

Lactic acid metabolism patterns were assessed in 415 STAD patients using a panel of 21 LMGs. Cox regression and Lasso regression analyses were employed to develop a predictive risk model based on differentially expressed genes (DEGs). Validation of the model was conducted using independent cohorts from the GEO and TCGA databases, as well as additional datasets focused on immunotherapy responses. Further investigations into TME dynamics of lactic acid metabolism included functional assays targeting SLC16A3, a pivotal gene identified through our analyses.

RESULTS

Patients were stratified into distinct risk groups based on their lactic acid metabolism profiles. Low-risk patients exhibited attenuated lactic acid metabolism, correlating with favorable clinical outcomes characterized by prolonged survival and enhanced responsiveness to immunotherapy. Notably, tumor cells within the TME demonstrated heightened levels of active lactic acid metabolism, particularly impacting tumor-infiltrating lymphocytes such as CD8 + T cells and regulatory T cells. Mechanistically, SLC16A3 emerged as a critical regulator promoting STAD cell proliferation, invasion, and migration while modulating the metabolic landscape.

CONCLUSION

This study underscores the prognostic value of a lactic acid metabolism-based model in STAD, providing insights into its potential as a predictive biomarker for patient stratification and therapeutic targeting. The findings highlight SLC16A3 as a promising candidate for therapeutic intervention aimed at modulating lactic acid metabolism in the TME, thereby advancing personalized treatment strategies in gastric cancer management.

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC

BACKGROUND

Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations.

PATIENTS AND METHODS

The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC.

RESULTS

TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05).

CONCLUSIONS

Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey

BACKGROUND

Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy.

METHODS

An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion.

RESULTS

A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells.

CONCLUSIONS

Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.

Mutations of key driver genes in gastric cancer metastasis risk: a systematic review and meta-analysis

Objective: Associations between gene mutations and metastasis in gastric cancer (GC) remain contradictory, resulting in the inaccurate estimation of the magnitude of the risk associated with specific genotypes.Methods: In this study, we first screened out four key driver genes (TP53, PIK3CA, APC and ARID1A) by jointly analyzing the mutation levels and searching the literature for genes associated with GC metastasis. We then performed a meta-analysis to demonstrate the relationship between these key driver gene mutations and GC metastasis, including lymphatic and distance metastasis.Results: We found out four key driver genes (TP53, PIK3CA, APC and ARID1A), associated with risk of GC metastasis. The results showed that TP53 (OR 1.39, 95% CI 1.12-1.72) and APC mutations (OR 0.58, 95% CI 0.38-0.89) were associated with lymph node metastasis and distant metastasis in GC. And TP53 mutations (OR 1.65, 95% CI 1.25-2.18) were significantly related to GC metastasis in the Asian population. APC mutations (OR 0.54, 95% CI 0.29-1.00) were also related to GC metastasis in the European and American populations. There was no significant association with GC metastasis in PIK3CA or ARID1A mutations.Expert opinion:Mutations of TP53 and APC play important roles in lymph node metastasis and distant metastasis of GC and may be potential important biomarkers of progression and therapeutic targets. These observations should be further prospectively verified.

CD47 expression in gastric cancer clinical correlates and association with macrophage infiltration

BACKGROUND

CD47 has been identified as an innate immune checkpoint and found to be associated with inferior survival in various types of cancer. However, the critical role of CD47 in gastric cancer and its association with tumor associated macrophages remain unclear.

METHODS

Tumor tissues of gastric cancer from Zhongshan Hospital and data from GSE62254, GSE84437 and TCGA datasets were analyzed. Immunohistochemistry was performed to detect the expression of CD47, CD11c, CD163 and CD68 in gastric cancer tissues. Kaplan-Meier curves and Cox model were used for comparing the clinical outcomes of patients belonging to different subgroups.

RESULTS

Gastric cancer patients with high CD47 expression exhibited poor prognosis and inferior therapeutic responsiveness to fluorouracil-based adjuvant chemotherapy (ACT). A positive correlation was found between M1-polarized macrophage infiltration and CD47 expression in gastric cancer; however, the prognostic value of M1-polarized macrophages was attenuated in CD47-high gastric cancer patients. Moreover, we found that CD47 mRNA level was enriched in microsatellite-instable (MSI) subtype of gastric cancer and associated with ARID1A mutation and FGFR2 signaling pathway activation.

CONCLUSIONS

Aberrant CD47 expression represented an independent predictor for adverse survival outcome and ACT resistance in gastric cancer. Targeting CD47 might be a promising strategy for gastric cancer patients.

Gastric Cancer: Advances in Carcinogenesis Research and New Therapeutic Strategies

Gastric cancer’s bad incidence, prognosis, cellular and molecular heterogeneity amongst others make this disease a major health issue worldwide. Understanding this affliction is a priority for proper patients’ management and for the development of efficient therapeutical strategies. This review gives an overview of major scientific advances, made during the past 5-years, to improve the comprehension of gastric adenocarcinoma. A focus was made on the different actors of gastric carcinogenesis, including, Helicobacter pylori cancer stem cells, tumour microenvironment and microbiota. New and recent potential biomarkers were assessed as well as emerging therapeutical strategies involving cancer stem cells targeting as well as immunotherapy. Finally, recent experimental models to study this highly complex disease were discussed, highlighting the importance of gastric cancer understanding in the hard-fought struggle against cancer relapse, metastasis and bad prognosis.

circ_0044516 functions in the progression of gastric cancer by modulating MicroRNA-149-5p/HuR axis

Circular RNAs (circRNAs) have emerged as a multifunctional class of RNAs, while there is limited knowledge on their functions in the development of cancers. Herein, we performed the current study to probe into the regulatory mechanism of circ_0044516 in malignant behaviors of gastric cancer (GC) cells with the involvement of microRNA (miR)-149-5p/human antigen R (HuR) axis. Firstly, the expression levels of circ_0044516 in GC cell lines and normal gastric mucosal epithelial cells were determined by qRT-PCR, and GC cell lines with the highest expression of circ_0044516 were screened for further cell experiments. Subsequently, the biological functions of silenced circ_0044516 in GC were identified by CCK-8, colony formation, and transwell assays. Xenograft mouse models were established for in vivo verification. Furthermore, luciferase reporter, RIP, RNA pull-down assay and rescue experiments were performed to explore the sponge regulatory mechanism of circ_0044516. circ_0044516 was suggested to be highly expressed in GC cell lines, and circ_0044516 could promote GC cell proliferation, migration and invasion, as well as in vivo tumor growth. In addition, silenced circ-0044516 reversed the promotive roles in cell viability caused by overexpressed HuR. Furthermore, circ_0044516 mainly localized in the cytoplasm, which may act as a miR-149-5p sponge to modulate HuR expression, thereby playing an essential role in GC development. This study suggests that circ_0044516 may promote HuR expression through sponging miR-149-5p, thereby playing a part in GC progression.

Magnesium-dependent Phosphatase (MDP) 1 is a Potential Suppressor of Gastric Cancer

BACKGROUND

Recurrence is the leading cause of treatment failure and death in patients with gastric cancer (GC). However, the mechanism underlying GC recurrence remains unclear, and prognostic markers are still lacking.

METHODS

We analyzed DNA methylation profiles in gastric cancer cases with shorter survival (<1 year) or longer survival (> 3 years), and identified candidate genes associated with GC recurrence. Then, the biological effects of these genes on gastric cancer were studied.

RESULTS

A novel gene, magnesium-dependent phosphatase 1 (mdp1), was identified as a candidate gene whose DNA methylation was higher in GC samples from patients with shorter survival and lower in patients with longer survival. MDP1 protein was highly expressed in GC tissues with longer survival time, and also had a tendency to be expressed in highly differentiated GC samples. Forced expression of MDP1 in GC cell line BGC-823 inhibited cell proliferation, whereas the knockdown of MDP1 protein promoted cell growth. Overexpression of MDP1 in BGC-823 cells also enhanced cell senescence and apoptosis. Cytoplasmic kinase protein c-Jun N-terminal kinase (JNK) and signal transducer and activator of transcription 3 (Stat3) were found to mediate the biological function of MDP1.

CONCLUSION

These results suggest that MDP1 protein suppresses the survival of gastric cancer cells and loss of MDP expression may benefit the recurrence of gastric cancer.

Precision medicine for gastrointestinal cancer: Recent progress and future perspective

Gastrointestinal (GI) cancer has a high tumor incidence and mortality rate worldwide. Despite significant improvements in radiotherapy, chemotherapy, and targeted therapy for GI cancer over the last decade, GI cancer is characterized by high recurrence rates and a dismal prognosis. There is an urgent need for new diagnostic and therapeutic approaches. Recent technological advances and the accumulation of clinical data are moving toward the use of precision medicine in GI cancer. Here we review the application and status of precision medicine in GI cancer. Analyses of liquid biopsy specimens provide comprehensive real-time data of the tumor-associated changes in an individual GI cancer patient with malignancy. With the introduction of gene panels including next-generation sequencing, it has become possible to identify a variety of mutations and genetic biomarkers in GI cancer. Although the genomic aberration of GI cancer is apparently less actionable compared to other solid tumors, novel informative analyses derived from comprehensive gene profiling may lead to the discovery of precise molecular targeted drugs. These progressions will make it feasible to incorporate clinical, genome-based, and phenotype-based diagnostic and therapeutic approaches and apply them to individual GI cancer patients for precision medicine.

Five-Feature Model for Developing the Classifier for Synergistic vs. Antagonistic Drug Combinations Built by XGBoost

Combinatorial drug therapy can improve the therapeutic effect and reduce the corresponding adverse events. In silico strategies to classify synergistic vs. antagonistic drug pairs is more efficient than experimental strategies. However, most of the developed methods have been applied only to cancer therapies. In this study, we introduce a novel method, XGBoost, based on five features of drugs and biomolecular networks of their targets, to classify synergistic vs. antagonistic drug combinations from different drug categories. We found that XGBoost outperformed other classifiers in both stratified fivefold cross-validation (CV) and independent validation. For example, XGBoost achieved higher predictive accuracy than other models (0.86, 0.78, 0.78, and 0.83 for XGBoost, logistic regression, naïve Bayesian, and random forest, respectively) for an independent validation set. We also found that the five-feature XGBoost model is much more effective at predicting combinatorial therapies that have synergistic effects than those with antagonistic effects. The five-feature XGBoost model was also validated on TCGA data with accuracy of 0.79 among the 61 tested drug pairs, which is comparable to that of DeepSynergy. Among the 14 main anatomical/pharmacological groups classified according to WHO Anatomic Therapeutic Class, for drugs belonging to five groups, their prediction accuracy was significantly increased (odds ratio < 1) or reduced (odds ratio > 1) (Fisher's exact test, p < 0.05). This study concludes that our five-feature XGBoost model has significant benefits for classifying synergistic vs. antagonistic drug combinations.

AlloDriver: a method for the identification and analysis of cancer driver targets

Identifying the variants that alter protein function is a promising strategy for deciphering the biological consequences of somatic mutations during tumorigenesis, which could provide novel targets for the development of cancer therapies. Here, based on our previously developed method, we present a strategy called AlloDriver that identifies cancer driver genes/proteins as possible targets from mutations. AlloDriver utilizes structural and dynamic features to prioritize potentially functional genes/proteins in individual cancers via mapping mutations generated from clinical cancer samples to allosteric/orthosteric sites derived from three-dimensional protein structures. This strategy exhibits desirable performance in the reemergence of known cancer driver mutations and genes/proteins from clinical samples. Significantly, the practicability of AlloDriver to discover novel cancer driver proteins in head and neck squamous cell carcinoma (HNSC) was tested in a real case of human protein tyrosine phosphatase, receptor type K (PTPRK) through a L1143F driver mutation located at the allosteric site of PTPRK, which was experimentally validated by cell proliferation assay. AlloDriver is expected to help to uncover innovative molecular mechanisms of tumorigenesis by perturbing proteins and to discover novel targets based on cancer driver mutations. The AlloDriver is freely available to all users at http://mdl.shsmu.edu.cn/ALD.

Phenotype and target-based chemical biology investigations in cancers

Chemical biology has been attracting a lot of attention because of the key roles of chemical methods and techniques in helping to decipher and manipulate biological systems. Although chemical biology encompasses a broad field, this review will focus on chemical biology aimed at using exogenous chemical probes to interrogate, modify and manipulate biological processes, at the cellular and organismal levels, in a highly controlled and dynamic manner. In this area, many advances have been achieved for cancer biology and therapeutics, from target identification and validation based on active anticancer compounds (forward approaches) to discoveries of anticancer molecules based on some important targets including protein-protein interaction (reverse approaches). Herein we attempt to summarize some recent progresses mainly from China through applying chemical biology approaches to explore molecular mechanisms of carcinogenesis. Additionally, we also outline several new strategies for chemistry to probe cellular activities such as proximity-dependent labeling methods for identifying protein-protein interactions, genetically encoded sensors, and light activating or repressing gene expression system.

Distinct molecular subtypes of gastric cancer: from Laurén to molecular pathology

In Western countries the majority of gastric cancers (GC) are usually diagnosed in advanced stages reporting a 5-year survival rate of only 26%. The Laurén classification of GC was most widely used in clinical practice since it reflects GC morphology, epidemiology, tumor biology, clinical management and outcome. Despite the initial promise of individualizing antitumor treatment, the management of GC still remains relatively broad and general. Apart from clinical staging, molecular profiling enables targeting of the identified underlying alterations, rather than histology. In contrast to breast carcinoma, molecular classification of GC does not yet imply treatment modality. Molecular classifications of GC and their therapeutic implications are therefore extensively studied. The current proposed molecular divisions of GC come from three different parts of the world where different standard treatment modalities for advanced GC are recommended. Wider use of GC molecular subtyping may solve problems, such as susceptibility to novel systemic therapy regimens or selection of patients for aggressive surgery and targeted adjuvant/conversion therapy. In any case, the rapid entry of novel molecular targeted therapies into routine oncology practice clearly underscores the urgent need for clinicians to be aware of these new possibilities.

MiR-129-5p influences the progression of gastric cancer cells through interacting with SPOCK1

The purpose of our study is to clarify the effect of microRNA-129-5p in the progression of human gastric cancer cells by regulating SPOCK1. The expression of microRNA-129-5p and SPOCK1 was tested by quantitative real-time polymerase chain reaction in tissues and cell lines. We validated the targeted relationship between microRNA-129-5p and SPOCK1 by dual luciferase reporter gene assay. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, colony formation, flow cytometry, transwell, and wound scratch assays were used to analyze the effects of microRNA-129-5p on SGC-7901 cell viability, proliferation, cell cycle and apoptosis, invasiveness, and migration. MicroRNA-129-5p was downregulated while SPOCK1 was upregulated in gastric cancer tissues and cell lines. The result of luciferase reporter gene assay demonstrated that microRNA-129-5p can target SPOCK1 by binding to the 3'untranslated region. The overexpression of microRNA-129-5p or the inhibition of SPOCK1 inhibited SGC-7901 viability, proliferation, migration, and invasion while promoted cell cycle arrest in G0/G1 stage and cell apoptosis. Our results suggested that microRNA-129-5p could directly specifically suppress SPOCK1, which might be one of the potential mechanisms in inhibiting cell processes including viability, proliferation, cell mitosis, migration, and invasiveness of gastric cancer cells.

miR-7 Increases Cisplatin Sensitivity of Gastric Cancer Cells Through Suppressing mTOR

MicroRNAs have been reported to play an important role in diverse biological processes and cancer progression. MicroRNA-7 has been observed to be downregulated in human gastric cancer tissues, but the function of microRNA-7 in gastric cancer has not been well investigated. In this study, we demonstrate that the expression of microRNA-7 was significantly downregulated in 30 pairs of human gastric cancer tissues compared to adjacent normal tissues. Enforced expression of microRNA-7 inhibited cell proliferation and migration abilities of gastric cancer cells, BGC823 and SGC7901. Furthermore, microRNA-7 targeted mTOR in gastric cancer cells. In human clinical specimens, mTOR was higher expressed in gastric cancer tissues compared with adjacent normal tissues. More interestingly, microRNA-7 also sensitizes gastric cancer cells to cisplatin (CDDP) by targeting mTOR. Collectively, our results demonstrate that microRNA-7 is a tumor suppressor microRNA and indicate its potential application for the treatment of human gastric cancer in future.