Gastric cancer (GC) is an aggressive and heterogeneous disease with poor survival outcomes. The progression of GC involves complex, multi-step processes. Endothelial cells (ECs) play a crucial role in tumor angiogenesis, proliferation, invasion, and metastasis, particularly through the process of endothelial-to-mesenchymal transition (EndoMT). However, the specific role and mechanisms of EndoMT in gastric cancer remain unclear. Based on 6 GC single-cell RNA-sequencing (scRNA-seq) cohorts (samples = 97), we established an EndoMT-related gene signature, termed EdMTS. Leveraging this gene signature, ssGSEA was applied to calculate sample scores across multiple bulk RNA-seq datasets, which include information on immunotherapy, metastasis, GC progression, and survival. Moreover, we applied the Monocle2 method to calculate cell pseudotime and used CellChat to analyze interactions between malignant and EC cells. We verified the molecular mechanism by multiple immunofluorescence and cell function experiments. Findings In this study, we established a single-cell atlas of ECs in GC and identified a subpopulation of COL1A1+ ECs that play a critical role in tumor progression and metastasis. These COL1A1+ ECs were significantly associated with worse clinical outcomes in GC patients. Further analysis revealed that COL1A1+ ECs originated from lymphatic ECs and underwent EndoMT through the upregulation of CEBPB, driving tumor invasiveness. Moreover, COL1A1+ ECs interacted with malignant cells via ANGPTL4-SDC4 axis, enhancing invasion and migration. These findings provide a deeper understanding of the role of COL1A1+ ECs in GC progression and highlight potential therapeutic targets for disrupting the EndoMT process in these cells to provide a benefit for GC patients.

Gastric cancer (GC), one of the most prevalent malignancies worldwide, is distinguished by extensive genetic and phenotypic heterogeneity, posing persistent challenges to conventional diagnostic and therapeutic strategies. The significant global burden of GC highlights an urgent need to unravel its complex underlying mechanisms, discover novel diagnostic and prognostic biomarkers, and develop more effective therapeutic interventions. In this context, this review comprehensively examines the transformative roles of cutting-edge technologies, including radiomics, pathomics, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, in advancing precision diagnosis and treatment for GC. Multiomics data analysis not only deepens our understanding of GC pathogenesis and molecular subtypes but also identifies promising biomarkers, facilitating the creation of tailored therapeutic approaches. Additionally, integrating multiomics approaches holds immense potential for elucidating drug resistance mechanisms, predicting patient outcomes, and uncovering novel therapeutic targets, thereby laying a robust foundation for precision medicine in the comprehensive management of GC.

Molecular subtyping of small-cell lung cancer (SCLC) has major implications for prognostic relevance and treatment guidance. This study aimed to explore the feasibility of a novel tracer targeting C-X-C-chemokine-receptor-type-4 (CXCR4) for distinguishing different SCLC subtypes.

Thirty-five patients with pathologically confirmed SCLC were enrolled in this prospective study. Immunohistochemical staining was performed to classify the molecular subtypes into SCLC-A, SCLC-N, SCLC-P, and SCLC-I. [¹⁸F]AlF-NOTA-QHY-04 PET/CT parameters were obtained, including the maximum, mean, and peak standard uptake values (SUVmax, SUVmean, and SUVpeak, respectively) and the ratios of tumors (T) and normal tissues (NT) based on the SUVmax (T/NT). These parameters were compared among the molecular subtypes. A receiver operating characteristic (ROC) curve was used to analyze the performance of the parameters for distinguishing SCLC-N from other subtypes and neuroendocrine (NE) subtypes (SCLC-A and SCLC-N) from non-NE subtypes (SCLC-P and SCLC-I).

The molecular subtypes were SCLC-A (n = 17), SCLC-N (n = 6), SCLC-P (n = 7), and SCLC-I (n = 5). The SCLC-N subtype exhibited significantly higher uptake in both primary tumors and lymph node metastases than the other three subtypes (P < 0.05). When SCLC-N was compared with the other three subtypes combined (referred to as "other SCLCs"), all parameters were significantly higher in the SCLC-N group (P < 0.05). ROC analysis showed that these parameters had high accuracy in distinguishing SCLC-N from other SCLCs (area under ROC curve: 0.868-0.948 for primary tumors and 0.783-0.888 for lymph node metastases). Compared with the non-NE group, the SUVmax, SUVmean, and T/NTlung were significantly higher in the NE group for primary tumors. ROC analysis showed moderate accuracy in distinguishing between the NE and non-NE groups (ROC area: 0.692-0.786 for primary tumors and 0.692-0.815 for lymph node metastases).

Our preliminary findings indicate that CXCR4-directed PET/CT imaging using [¹⁸F]AlF-NOTA-QHY-04 may differentiate between SCLC-N and other molecular subtypes and between NE and non-NE subtypes of SCLC.

The tumor microenvironment (TME) significantly influences cancer prognosis and therapeutic outcomes, yet its composition remains highly heterogeneous, and currently, no highly accessible, high-throughput method exists to define it. To address this complexity, the TMEclassifier, a machine-learning tool that classifies cancers into three distinct subtypes: immune Exclusive (IE), immune Suppressive (IS), and immune Activated (IA), is developed. Bulk RNA sequencing categorizes patient samples by TME subtype, and in vivo mouse model validates TME subtype differences and differential responses to immunotherapy. The IE subtype is marked by high stromal cell abundance, associated with aggressive cancer phenotypes. The IS subtype features myeloid-derived suppressor cell infiltration, intensifying immunosuppression. In contrast, the IA subtype, often linked to EBV/MSI, exhibits robust T-cell presence and improved immunotherapy response. Single-cell RNA sequencing is applied to explore TME cellular heterogeneity, and in vivo experiments demonstrate that targeting IL-1 counteracts immunosuppression of IS subtype and markedly improves its responsiveness to immunotherapy. TMEclassifier predictions are validated in this prospective gastric cancer cohort (TIMES-001) and other diverse cohorts. This classifier could effectively stratify patients, guiding personalized immunotherapeutic strategies to enhance precision and overcome resistance.

Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. Additionally, an independent saliva-derived model was created and evaluated using sensitivity, specificity, accuracy, area under the curve (AUC), and likelihood ratio (LR) metrics. Tissue-derived models demonstrated excellent performance, with AUC values exceeding 0.9, but did not translate effectively to saliva, suggesting distinct molecular landscapes between tissue and saliva in GC. The saliva-specific model using support vector machine (SVM) achieved the highest performance, with an AUC of 0.87 (95% CI 0.72-0.97), a sensitivity of 0.79 (95% CI 0.58-0.95) and a specificity of 0.70 (95% CI 0.40-0.90). While saliva may not mirror tissue gene expression profile, it represents a promising non-invasive predictive tool for the early detection of GC. Further research is warranted to optimize saliva-derived molecular signatures, increasing their sensitivity and specificity for early cancer detection and advance the use of liquid biopsies in personalized medicine for improved screening, diagnostic and prognostic capabilities.

Gastric cancer arises from complex carcinogenic factor interactions, with limited treatment options due to the lack of targetable driver gene mutations and significant tumor heterogeneity. Recent studies have provided promising novel approaches to improve our understanding of gastric cancer heterogeneity through integrated characterization, combining genomics with emerging technologies. Delineating the molecular changes and targeting specific molecular subtypes will enhance the efficacy of gastric cancer treatment and improve clinical outcomes. This review provides a comprehensive overview of current technologies used in gastric cancer research, highlighting key discoveries and treatment strategies driven by these innovations. Finally, we discuss the emerging technology-guided directions and potential breakthroughs that could enhance the understanding of gastric cancer tumor heterogeneity, ultimately improving clinical outcomes.

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune checkpoint inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEER's tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (MSI), Epstein-Barr virus (EBV)-associated, genomically stable (GS), and chromosomally instable (CIN). However, an association between molecular subtypes and clinical outcomes has not been clearly demonstrated. Given few actionable biomarkers, we investigated the clinical relevance of TCGA classification system.

We identified all patients with esophagogastric adenocarcinoma whose tumors underwent prospective next-generation sequencing using the Memorial Sloan Kettering-IMPACT assay from 2014 to 2023. We classified all tumors in accordance with TCGA methodology and correlated molecular subtypes with high-quality clinicopathologic data.

Among 1,438 included patients, 941 had CIN, 344 had GS, 103 had MSI, and 50 had EBV tumors. Accounting for the clinical stage and tumor grade, molecular classification was independently associated with overall cancer-specific survival (P < 0.001) on Cox multivariable analysis. Furthermore, genomic signatures, patient demographics, pathologic responses to neoadjuvant therapy, patterns of recurrence, and metastatic organotropism differed significantly by molecular subtype. Although most distal esophageal and gastroesophageal junction tumors were CIN, up to 25% of these included GS, MSI, or EBV subtypes in contrast to TCGA. Random forest machine learning demonstrated that the molecular subtype is more influential in predicting response to treatment than tumor location.

Molecular classification is independently prognostic and may warrant inclusion in future staging and treatment guidelines. Routine molecular profiling is clinically feasible and may play a role in the management of patients to help guide appropriate treatment selection and clinical trial enrollment in the place of tumor location.

Advanced esophageal (EAC), gastroesophageal junction (GEJAC) and gastric (GAC) adenocarcinomas with HER2 amplification or overexpression (HER2+) are routinely treated with trastuzumab. However, it remains unclear if HER2+ is associated with superior overall survival (OS).

The cohort included recurrent or de novo metastatic GAC, GEJAC and EAC from Kaiser Permanente Northern California. We used Cox regression modelling to examine association between HER2+ and OS, adjusting for demographics, performance status, CCI, receipt of chemotherapy and p53 (mutp53), KRAS (mutKRAS), CDKN2A, PIK3CA co-mutations and MYC amplification.

Of 875 total eligible patients, 173 had EAC, 276 had GEJAC and 426 had GAC. HER2+ was associated with better OS among the full cohort (HR = 0.74, 95% CI [0.60-0.93]), among EAC (HR = 0.62; [95% CI, 0.40-0.96]) and GEJAC (HR = 0.59; [95% CI, 0.38-0.87]), but not among GAC (HR = 0.89; [95% CI, 0.59-1.35]) patients. GEJAC had better OS than EAC (HR = 0.68, [95% CI, 0.54-0.86]). Trastuzumab treatment was associated with better OS (HR = 0.40, 95% CI [0.21-0.77]). In addition, HER2+ was associated with better OS across the molecular subgroups except that of KRAS mutation (mutKRAS). Our data also show that GEJAC, EAC and GAC were differentially associated with mutp53, mutKRAS and MYC amplification.

HER2+ and treatment with trastuzumab in HER2+ patients were associated with superior OS in upper gastrointestinal adenocarcinomas across molecular subgroups except that of mutKRAS. These results reaffirm the importance of anti-HER2 treatment in HER2+ patients and provide insight on the prognostic and biological divergence among these anatomically linked upper gastrointestinal adenocarcinomas.

Diffuse-type gastric cancer (DGC), characterized by poorly cohesive cells within fibrotic stroma, is associated with advanced disease and poor prognosis. Here, to identify distinct biomarkers for DGC compared with intestinal-type gastric cancer, we constructed a comprehensive large-scale signaling network using RNA-sequencing data from three genomic databases (The Cancer Genome Atlas, GSE62254 and GSE26253), developed a mathematical model and conducted simulation analyses. For validation, we used tissue microarray blocks of gastric cancers with immunohistochemical staining, single-cell RNA sequencing, primary cultures of cancer-associated fibroblasts (CAFs) and organoids, and a co-culture system involving CAFs and cancer cells. Signaling network analysis identified six differentially activated signaling components across the database, including BIRC5, TTK, NEK2, FHL1, NR2F1 and FBLN5. Among the differentially activated signaling components, high tumoral expression of fibulin-5 protein encoded by FBLN5 correlated with poor overall and disease-specific survival rates in patients with DGC, even after adjusting for the tumor, node, metastases (TNM) stage. Fibulin-5, derived from CAFs within DGC stroma, promoted organoid growth and epithelial-mesenchymal transition (EMT) in DGC cell lines via the cAMP response element-binding protein (CREB) pathway in a CAF co-culture system. FBLN5 knockdown in CAFs reduced the aggressive phenotype of co-cultured DGC cells, while CREB inhibitors reversed EMT. Furthermore, levels of secreted FBLN5 in patient blood samples correlated with its expression in primary tumors. In summary, fibulin-5 secreted by CAFs and interacted with DGC cells promotes EMT and is clinically associated with poor patient outcomes. These findings suggest fibulin-5 as a potential prognostic marker and therapeutic target in patients with DGC.

There is a clinical need to identify early predictors for response to neoadjuvant chemotherapy (NAC) in patients with gastric and gastroesophageal junction cancer (GC and GEJC). Radiomics involves extracting quantitative features from medical images. This study aimed to apply radiomics to build prediction models for the response to NAC.

All consecutive patients with non-metastatic GC and GEJC undergoing NAC and surgical resection in an Italian high-volume referral center between 2005 and 2021 were considered eligible. In patients selected, the CT scans performed upon staging were reviewed to segment the tumor and extract radiomic features using MODDICOM. The primary endpoint was to develop and validate radiomic-based predictive models to identify major responders (MR: tumor regression grade TRG 1-2) and non-responders (NR: TRG 4-5) to NAC. Following an initial feature selection, radiomic and combined radiomic-clinicopathologic prediction models were built for the MR or NR status based on logistic regressions. Internal validation was performed for each model. Radiomic models (in the entire case series and according to NAC regimens) were evaluated using the receiver operating characteristic area under the curve (AUC), sensitivity, and negative predictive value (NPV).

The study included 77 patients undergoing NAC and subsequent tumor resection. The MR prediction model after all types of NAC (AUC of 0.876, CI 95% 0.786 - 0.966, sensitivity 83%, and NPV 96%) was based on a statistical feature. The models predicting NR among patients undergoing epirubicin with cisplatin and fluorouracil (ECF), epirubicin with oxaliplatin and capecitabin (EOX), or fluorouracil with oxaliplatin and docetaxel (FLOT) (AUC 0.760, CI 95% 0.639-0.882), oxaliplatin-based chemotherapy (AUC 0.810, CI 95% 0.692-0.928), and FLOT (AUC 0.907, CI 95% 0.818 - 0.995) were based on statistical, morphological and textural features.

The developed radiomic models resulted promising in predicting the response to different neoadjuvant chemotherapy strategies. Once further implemented on larger datasets, they could be valuable and cost-effective instruments to target multimodal treatment in patients with GC.

Peritoneal dissemination portends a dismal prognosis in patients with gastric adenocarcinoma in the context of limited effective treatments. The underlying cellular processes that drive gastric peritoneal carcinomatosis remain unclear, limiting the application of novel targeted therapies. In this comprehensive review, we aimed to identify and summarize all existing context-dependent molecular mechanisms that have been implicated in peritoneal dissemination and peritoneal carcinomatosis establishment from primary gastric adenocarcinoma. We applied a multilevel examination including data from in vivo murine models using human gastric cancer cell lines, in vitro technique-based studies, ex vivo models, and genomic/proteomic and molecular profiling analyses to report on various aspects of gastric cancer peritoneal metastasis biology. Mechanisms promoting peritoneal dissemination were grouped into three main functional categories: (1) intrinsic cancer cell biology, (2) cancer cell-peritoneal surface adhesion, and (3) peritoneal tumor microenvironment. We identified significant overlap among the three categories, indicating a complex interplay between multiple molecular mechanisms. By interrupting these pathways, peritoneal-directed therapies have the potential to improve quality and length of life in patients with high-risk primary gastric cancer.

Transcriptome-based tumor classification has enhanced the molecular characterization of muscle-invasive bladder cancer (MIBC) subtypes. However, the degraded nature of formalin-fixed paraffin-embedded (FFPE) material and the expensive sequencing costs for routine use have limited the use of subtypes in clinical and trial settings. Here, we present an optimized analysis workflow for MIBC molecular subtype prediction from FFPE samples. FFPE material from 240 MIBC samples was sequenced using QuantSeq 3' mRNA sequencing with unique molecular identifiers (UMIs) and analyzed via a customized RNA-Seq pipeline. The association of consensus subtypes with histology and immunohistochemical expression of core basal/luminal protein markers was assessed. In addition, subtype robustness was explored by simulating scenarios at lower sequencing depths and without UMIs. Five MIBC consensus subtypes were identified in the cohort. The basal/squamous group showed higher expression of KRT14, KRT5, and CD44, and was mainly divergent squamous. Vice versa, luminal, and stroma-rich subtypes had conventional urothelial or urothelial subtype histology, with higher expression of KRT20, FOXA1, and GATA3. The neuroendocrine-like samples had small cell neuroendocrine histology and were negative for luminal/basal markers. Subtype calling from 24 matched fresh-frozen samples analyzed with full-length RNA-Seq showed 87.5% agreement. Furthermore, the subtypes were robust to decreasing sequencing depths and to the absence of UMIs. Taken together, we provide a robust and cost-effective workflow for MIBC consensus molecular subtyping from FFPE-derived RNA. This workflow can be easily implemented as a molecular pathological assay for patient care, clinical trials, and translational research.

Diffuse-type gastric cancer (GC) is closely associated with genetic abnormalities; however, its exact pathological mechanisms are still not understood. It manifests without symptoms before advanced stages and is often difficult to diagnose using routine imaging tests. Therefore, specific targeted therapies for diffuse GC are currently unavailable. In this study, the extract of Pseudolysimachion pusanensis, which is endemic to Korea, inhibited the proliferation of GC cells, MKN1 and SNU668, while the other extracts of the two endemic Pseudolysimachion species did not show any activity. This led to the molecular networking analysis of Pseudolysimachion species to identify the molecules mostly observed only in P. pusanensis. Thirteen new (1-13) and eight known (14-21) compounds were obtained and structurally characterized. Of these, cucurbitacin derivative compounds 1 and 14-16 showed activity, and particularly, the IC50 value of compound 1 was 0.65 and 0.21 μM. Ki-67 expression analysis, single-cell originated cell proliferation assay, and western blot analysis of apoptotic cell death-related molecules revealed that compound 1 mediated both cytostasis and cellular death via apoptosis. Particularly, this compound exhibited an anti-tumorigenic effect on the invasion of diffuse-type GC cells by perturbing the epithelial-to-mesenchymal transition (EMT) pathway, as demonstrated by invasion assays using both 2D models and in vivo-like 3D spheroid co-culture models, as well as western blot analysis of EMT markers. In addition, some functional groups that may or may not be necessary for the activity of cucurbitacin derivatives were identified, and some clues that the presence of metal ions may affect the activity were provided.

Alpha-fetoprotein-producing gastric carcinoma (AFPGC) is a rare and aggressive subtype of gastric cancer (GC). A comprehensive analysis of clinicopathological features, immunophenotypes, molecular characteristics, and survival in AFPGC contributes to identifying potential therapeutic targets and developing new strategies to manage this disease. A retrospective cohort study was conducted at Shanxi Cancer Hospital from January 2018 to December 2020, involving patients diagnosed with GC and elevated AFP serum levels. Among these, 91 patients underwent immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and next-generation sequencing (NGS) to reveal the immunophenotypic and molecular characteristics of AFPGC. We found that AFPGC is more common in males and primarily occurs in the cardia and antrum of the stomach. A panel of IHC markers including AFP, GPC3, SALL4, CD10, CDX-2, and ATBF1 can be used for the diagnosis and differentiating AFPGC. NGS analysis revealed that TP53 hypermutation was the most frequent molecular event associated with AFPGC. The altered signaling pathways included disease signal transduction, receptor tyrosine kinase signaling and intracellular second messenger signaling pathways. The cumulative incidence of 21 genes, based on the evidence of clinical actionability in the OncoKB, was found to be 59.34%. Among these genes, CCNE1, ERBB2, and EGFR were the most frequently observed. This underscores the potential benefit of targeted therapy for patients with AFPGC. Furthermore, LRP1B and ARID1A have been identified as prognostic factors associated with overall survival (OS) and disease-free survival (DFS), respectively. Our results aim to improve AFPGC diagnosis by identifying potential therapeutic targets and prognostic factors, which could help facilitate the development of new treatment strategies.

Findings from previous gastric cancer microbiome studies have been conflicting, potentially due to patient and/or tumor heterogeneity. The intratumoral gastric cancer microbiome and its relationship with clinicopathological variables have not yet been characterized in detail. We hypothesized that variation in gastric cancer microbial abundance, alpha diversity, and composition is related to clinicopathological characteristics.

Metagenomic analysis of 529 GC samples was performed, including whole exome sequencing data from The Cancer Genome Atlas (TCGA) and whole genome sequencing data from the 100,000 Genomes Project. Microbial abundance, alpha diversity, and composition were compared across patient age, sex, tumor location, geographic origin, pathological depth of invasion, pathological lymph node status, histological phenotype, microsatellite instability status, and TCGA molecular subtype.

Gastric cancer microbiomes resembled previous results, with Prevotella, Selenomonas, Stomatobaculum, Streptococcus, Lactobacillus, and Lachnospiraceae commonly seen across both cohorts. Within the TCGA cohort, microbial abundance and alpha diversity were greater in gastric cancers with microsatellite instability, lower pathological depth of invasion, intestinal-type histology, and those originating from Asia. Microsatellite instability status was associated with microbiome composition in both cohorts. Sex and pathological depth of invasion were associated with microbiome composition in the TCGA cohort.

The intratumoral gastric cancer microbiome appears to differ according to clinicopathological factors. Certain clinicopathological factors associated with favourable outcomes in gastric cancer were observed to be associated with greater microbial abundance and diversity. This highlights the need for further work to understand the underlying biological mechanisms behind the observed microbiome differences and their potential clinical and therapeutic impact.

Fibroblast growth factor receptor (FGFR) 4 is overexpressed in gastric cancer (GC) and is a potential therapeutic target for GC. Since the FGF/FGFR signaling is involved in tumor microenvironment inducing the formation of an immunosuppression, lenvatinib is expected to inhibit FGFR4 leading to reduced tumor PD-L1 levels and regulatory T cell (Treg) infiltration, improving pembrolizumab efficacy. This study explored the background of the molecular mechanisms underlying the therapeutic efficacy of lenvatinib plus pembrolizumab.

Expression of FGFR4 and its specific ligand FGF19 was assessed by immunohistochemical staining and clinicopathological relevance was also examined. The effect of lenvatinib on FGF19-FGFR4 signaling was evaluated using cellular experiments. Lastly, the expression of FGFR4 on Treg cells was evaluated by immunostaining and flow cytometry. The Cancer Genome Atlas cBioPortal and Gene Expression Omnibus microarray databases were accessed to support these results.

High FGFR4 expression was associated with histological type and venous invasion and predominantly detected in human epidermal growth factor receptor 2 and Epstein-Barr virus-positive GC. Bioinformatics data suggested that FGF19-FGFR4 signaling was activated in GC, and cellular experiments showed that lenvatinib reduced FGFR4 and PD-L1 expression in GC cells. Results of integrating various analyses suggested that FGFR4 did not seem to be enough expressed on Treg cells in GC.

The FGF19-FGFR4 signaling has a pivotal role in gastric tumorigenesis and may be involved in immunosuppression through PD-L1 modification. But, lenvatinib may not regulate immune editing by directly inhibiting FGFR4 on Treg cells.

Gastric cancer remains a major health challenge worldwide, with nearly 1 million new cases annually contributing to more than 650 000 deaths. Epidemiologically, gastric cancer shows substantial geographical variation in incidence, with higher rates in Asia, South America, and eastern Europe, and a rapid increase in early-onset cases among people younger than 50 years. Key risk factors for gastric cancer include Helicobacter pylori infection, diet, obesity, smoking, and genetic predisposition. Early detection through comprehensive diagnostic procedures is crucial for optimising treatment outcomes. Standard treatment approaches for locally advanced gastric cancer include surgical resection, particularly D2 lymphadenectomy, complemented by chemotherapy and radiotherapy. There is increasing implementation of minimally invasive surgical techniques for operable disease and integration of immune checkpoint inhibitors and targeted therapies for advanced stages. Emerging therapies, such as novel targeted treatments and next-generation immunotherapies, show promise in improving survival and quality of life. Future directions in the management of gastric cancer focus on precision medicine, continued advancement in immunotherapy, novel early detection methods, and a multidisciplinary approach to care. These strategies aim to enhance the overall effectiveness of treatment and prognosis worldwide.

Gastric cancer (GC) commonly presents in advanced stages with metastatic spread to the peritoneal cavity, and outcomes associated with gastric cancer with peritoneal metastasis (GCPM) continue to carry a dismal prognosis. Persistent challenges in the detection of peritoneal metastasis (PM) have resulted in a relative paucity of high-quality data to inform management decisions. Several consensus groups have published recommendations to guide management, including most recently the National Comprehensive Cancer Network guidelines, which now include cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) as a potential treatment modality in select patients with GCPM. Multiple clinical trials have investigated the use of CRS/HIPEC and other peritoneal-directed therapies, such as intraperitoneal chemotherapy (IPC) and pressurized intraperitoneal aerosolized chemotherapy (PIPAC). As high-volume centers work to incorporate such therapies into their practice, ongoing clinical trials are aimed at understanding their efficacy. Recent findings have improved understanding of the mechanisms and pathophysiology underlying GCPM while the discovery of novel targets offers potential for drug development and therapeutic strategies to overcome treatment resistance. This review highlights recent advancements and addresses the persistent challenges in managing GCPM while also offering a comprehensive summary of current guidelines and treatment strategies.

Helicobacter pylori (H. pylori) infection contributes significantly to gastric cancer (GC) progression. The intrinsic mechanisms of H. pylori-host interactions and their role in promoting GC progression need further investigation. In this study, we explored the potential role of fat mass and obesity-associated protein (FTO) in mediating Cytotoxin-associated gene A (CagA)-induced GC progression.

The effects of H. pylori infection on N6-methyladenosine (m6A) modification were evaluated in both human samples and GC cell lines. The function of FTO in the progression of GC was elucidated through in vitro and in vivo studies. A series of techniques, including methylated RNA immunoprecipitation sequencing, RNA sequencing, RNA binding protein immunoprecipitation, and chromatin immunoprecipitation assays, were utilized to investigate the mechanism by which FTO mediates the capacity of cagA-positive H. pylori to promote GC progression. Furthermore, the therapeutic potential of the FTO inhibitor meclofenamic acid (MA) in impeding GC progression was evaluated across GC cells, animal models, and human GC organoids.

Infection with cagA-positive H. pylori upregulated the expression of FTO, which was essential for CagA-mediated GC metastasis and significantly associated with a poor prognosis in GC patients. Mechanistically, CagA delivered by H. pylori enhanced FTO transcription via Jun proto-oncogene. Elevated FTO induced demethylation of m6A and inhibited the degradation of heparin-binding EGF-like growth factor (HBEGF), thereby facilitating the epithelial-mesenchymal transition (EMT) process in GC cells. Interestingly, eradication of H. pylori did not fully reverse the increases in FTO and HBEGF levels induced by cagA-positive H. pylori. However, treatment with a combination of antibiotics and MA substantially inhibited cagA-positive H. pylori-induced EMT and prevented GC metastasis.

Our study revealed that FTO mediates the "hit-and-run" mechanism of CagA-induced GC progression, which suggests that the therapeutic targeting of FTO could offer a promising approach to the prevention of CagA-induced cancer progression.

Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most important predictors and unravel their interactions to classify patients, which might guide prioritized treatment decisions.

A total of 140 patients with histopathological confirmed GC who underwent surgery between 2011 and 2016 were enrolled in the study. The inspired modification of the partial least squares (SIMPLS)-based model was used to identify the most significant predictors and interactions between variables. Predictive partition analysis was employed to establish the decision tree model to prioritize markers for clinical use. ML models have also been developed to predict TNM stage and different subtypes of GC. Latent class analysis (LCA) and principal component analysis (PCA) were carried out to cluster the GC patients and to find a subgroup of survivors who tended to die.

The findings revealed that the SIMPLS method was able to predict the mortality of GC patients with high predictabilities (Q2 = 0.45-0.70). The analysis identified MMP-7, P53, Ki67, and vimentin as the top predictors. Correlation analysis revealed different patterns of prognostic markers in the non-survivor and survivor cohorts and different GC subtypes. The main prediction models were verified via other ML-based analyses, with a high area under the curve (AUC) (0.84-0.99), specificity (0.82-0.99) and sensitivity (0.87-0.99). Patients were classified into three clusters of mortality risk, which highlighted the most significant mortality predictors. Partition analysis prioritizes the most significant predictors P53 ≥ 6, COX-2 > 2, vimentin > 2, Ki67 ≥ 13 in mortality of patients (AUC = 0.85-0.90).

The present study highlights the importance of considering multiple variables and their interactions to predict the prognosis of mortality and stage in GC patients through ML-based techniques. These findings suggest that the incorporation of molecular biomarkers may enhance patient prognosis compared to relying solely on clinical factors. Furthermore, they demonstrate the potential for personalized medicine in GC treatment by identifying high-risk patients for early intervention and optimizing therapeutic strategies. The partition analysis technique offers a practical tool for identifying cutoffs and prioritizing markers for clinical application. Additionally, providing Clinical Decision Support systems with predictive tools can assist clinicians and pathologists in identifying aggressive cases, thereby improving patient outcomes while minimizing unnecessary treatments. Overall, this study contributes to the ongoing efforts to improve patient outcomes by advancing our comprehension of the intricate nature of GC.

Gastric cancer is the third most deadly cancer worldwide. Helicobacter pylori (H. pylori) infection and specific diets are key risk factors for this illness, which is more frequent in various nations. Nearly half of the world's population, 4.4 billion, had H. pylori in 2015. East has a higher incidence rate than West. GC may spread to the liver, lungs, and bones. The majority of cases are adenocarcinomas (90%). In 2022, stomach cancer caused 968,784 new cases and 660,175 deaths worldwide. GC accounts for 7% of cancer diagnoses and 9% of deaths. The high death rate of gastric cancer highlights the need for preventative methods to improve prognosis. Early identification via biomarker screening, especially in high-risk groups, may improve outcomes and treatments.

Gastric cancer peritoneal metastasis (GCPM) typically indicates a poor clinical prognosis and is frequently observed in diffuse gastric cancer (GC) patients with CDH1 loss of function. GCPM characterized for its aggressiveness and resistance to chemotherapy, most notably paclitaxel (PTX), poses significant treatment challenges. Previously, no mouse gastric adenocarcinoma (MGA) cell lines with Trp53 (encoding mouse p53) and Cdh1 (encoding mouse E-cadherin) mutations and a high potential for peritoneal metastasis in mice have been established. Here, we derived a mouse GC cell line, called MTC, from subcutaneously transplanted mouse Trp53-/-Cdh1-/- GC organoids. Through matching the short tandem repeat profile of MTC with those in current cell banks, we verified the uniqueness of MTC. Furtherly, we confirmed the features of MTC by detecting the expression of p53, E-cadherin, and pan-CK. After long-term exposure of the original MTC line to PTX, we developed a more aggressive, PTX-resistant cell line, termed MTC-R. Compared with MTC, MTC-R demonstrated enhanced tumorigenicity and high potential for peritoneal metastasis in subcutaneous and intraperitoneal tumour models both in BALB/c nude mice and C57BL/6 J mice. Transcriptome analysis revealed the ECM‒receptor interaction pathway activation during the development of PTX resistance, and dasatinib (DASA) was identified as a potential drug targeting this pathway. DASA showed promise in ameliorating disease progression and improving overall survival in MTC-R GCPM model in C57BL/6 J mice. Overall, we established a novel MGA cell line with Trp53 and Cdh1 mutations and its PTX-resistant variant and demonstrated the efficacy of DASA in treating PTX-resistant GCPM.

More than 90% of advanced gastric cancers (GC) are microsatellite-stable (MSS). Compared to the high response rate of immune checkpoint inhibitors (ICI) in microsatellite-instability-high (MSI-H) GCs, only 10% of unstratified MSS GCs respond to ICIs. In this study, we apply semi-supervised learning to stratify potential ICI responders in MSS GCs, achieving high accuracy, quantified by an area under the curve of 0.924. Spatial analysis of the tumor microenvironment of ICI-sensitive GCs reveals a high level of T-bet+ CD8 + T cell infiltration in their tumor compartments. T-bet+ CD8 + T cells exhibit superior anti-tumor activity due to their increased ability to infiltrate tumors and secrete cytotoxic molecules. Adoptive transfer of T-bet+ CD8 + T cells boosts anti-tumor immunity and confers susceptibility to ICIs in immune-ignorant MSS GCs in a humanized mouse model. Spatial RNA sequencing suggests a positive-feedback loop between T-bet+ T cells and PD-L1+ tumor cells, which eventually drives T cell exhaustion and can therefore be leveraged for ICI therapy. In summary, our research provides insights into the underlying mechanism of anti-tumor immunity and deepens our understanding of varied ICI responses in MSS GCs.

SMAD4 mutation and homozygous deletion represent the most prevalent genomic events driving aggressive biological behavior in gastric cancer (GC). However, clinical outcome and therapeutic response in GC patients with Smad4-loss remains obscure.

This study included 990 GC patients from four independent clinical centers including the Zhongshan Hospital (ZSHS) cohort, the Cancer Genomic Atlas (TCGA) cohort, the Samsung Medical Center (SMC) cohort and the Memorial Sloan Kettering Cancer Center (MSKCC) cohort.

In ZSHS cohort, 60/454 GC patients harbored Smad4-loss are characterized by lower pN stage, well histology differentiation, lower EBV infection, null p53 staining and lower tumor proliferation. Smad4-loss GC patients exhibit miserable overall survival across ZSHS cohort and TCGA cohort. Moreover, Smad4-loss GC patients yield no impact on adjuvant chemotherapy, poor outcome upon anti-PD-1 immunotherapy or anti-HER-2 therapy. Interestingly, Smad4-loss GC show more well and intermediate differentiation and lower Ki67 staining. Furthermore, Smad4-loss GC exhibit tumor immunosuppressive contexture characterized with enriched CXCL13+CD8+T cells, reduced IFN-γ+ cells and GZMB+ cells infiltration.

Smad4 loss yields poor clinical outcome, immunotherapy and anti-HER-2 treatment resistance and tumor immunosuppressive contexture in GC patients. Our findings provide clues for further detailed biological investigation and aggressive clinical management in Smad4-loss GC patients.

Gastric cancer (GC) remains a leading cause of cancer-related deaths and exhibits considerable heterogeneity among patients. Thus, accurate classifications are essential for predicting prognosis and developing personalized therapeutic strategies. To address this, we retrospectively analyzed multi-omics data from 359 GC samples, incorporating transcriptomic RNA (mRNA), DNA methylation, mutation data, and clinical parameters. Using ten clustering algorithms, we integrated these datasets to classify GC into molecular subtypes. The robustness of our clustering approach was externally validated using an independent cohort generated from different sequencing technologies, and we characterized the heterogeneity of each subtype. Our analysis identified three distinct molecular subtypes of GC, designated CS1, CS2, and CS3. These subtypes exhibited significant differences in survival outcomes, activation of cancer-related pathways, immune microenvironment composition, genomic alterations, and responses to immunotherapy and chemotherapy. Notably, Cathepsin V (CTSV) was significantly downregulated in the immunologically active and highly responsive CS3 subtype, while it was upregulated in the immunologically exhausted CS2 subtype. These findings suggest that CTSV could serve as both a prognostic marker and a molecular classifier. Furthermore, this study provides the first evidence of CTSV's high expression in GC and its potential role in tumor progression. The novel clustering approach, based on ten clustering algorithms and comprehensive analysis of multi-omics data in gastric cancer, can guide prognosis, characterize different clinical and biological features, and elucidate the tumor immune microenvironment, providing insights into the intratumor heterogeneity of GC and potential novel therapeutic strategies. Additionally, CTSV emerges as a prognostic marker linked to tumor immunity and disease progression, which lays the foundation for improved stratification strategies and the development of targeted therapeutic approaches in GC.

Microsatellite stable (MSS) gastric cancer (GC) is largely unresponsive to immunotherapy, presenting a persistent and formidable challenge in the field. Patients with advanced GC and Helicobacter pylori (H. pylori) infection have shown benefits from immunotherapy. However, it remains unreported whether neoadjuvant immunotherapy is beneficial for H. pylori-positive MSS GC patients.

This retrospective cohort study analyzed data from GC patients treated at three medical centers in China between January 1, 2014, and July 1, 2024. Patients with gastric adenocarcinoma or adenocarcinoma of the gastroesophageal junction underwent testing for H. pylori infection prior to receiving neoadjuvant therapy.

In this retrospective analysis, those positive for H. pylori had a higher objective response rate of 63.77% (95% CI, 51.98-74.11%) compared to 47.73% (95% CI, 39.39-56.19%) in H. pylori-negative patients. Pathological complete remission was higher in H. pylori-positive patients at 17.39% (95% CI, 10.24-27.98%) versus 15.91% (95% CI, 10.65-23.10%). Logistic regression analysis revealed a strong correlation between H. pylori positivity and increased objective remission rate (P = 0.031, OR = 1.928, 95% CI 1.06-3.51). In H. pylori-positive MSS GC patients receiving neoadjuvant immunotherapy pCR rates can reach 27.27% (95% CI, 15.07-44.21%), much higher than the 8.33% (95% CI, 2.87-21.82%) in neoadjuvant chemotherapy patients. Survival analysis showed a 3-year OS rate of 74.2% (95% CI, 56.75-86.30%) in the H. pylori-positive group and 64.3% (95% CI, 51.20-75.55%) in the H. pylori-negative group, and the hazard ratio (HR) of these two groups was 0.50 (95% CI, 0.28-0.87; P <.001). Multivariable analysis for OS further showed the survival benefit of H. pylori, with HRs of 0.51 (95% CI, 0.29-0.91; P = 0.02).

H. pylori infection has emerged as a favorable factor for neoadjuvant immunotherapy in MSS GC, underscoring the importance of considering H. pylori status in preoperative treatment strategies.

Claudin18.2 (CLDN18.2)-specific chimeric antigen receptor (CAR)-T cell treatment holds promise for advanced gastric cancer (GC) but has variable efficacy. This study investigates the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) in CAR-T cell treatment and elucidates the molecular mechanisms of treatment resistance.

GC patients treated with CLDN18.2-specific CAR-T cell treatment were analyzed. Outcomes included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS). Survival analyses utilized Kaplan-Meier methods, log-rank tests, and Cox regression. Single-cell RNA sequencing was performed on peripheral blood samples to investigate the mechanisms of pro-tumor circulating neutrophils.

Elevated NLR was significantly associated with lower ORR (34.2% vs. 55.9%, P < 0.001), shorter median PFS (3.6 vs. 8.0 months, P < 0.001), and OS (5.6 vs. 13.8 months, P < 0.001). Single-cell sequencing identified a circulating neutrophil subcluster (NE-3) linked to disease progression. NE-3 expressed pro-tumoral factors (MMP-9), and was enriched in the IL-17 signaling pathway. The cellular interactions between neutrophils and T cells were more prominent in progression disease (PD) group than in partial response (PR) group.

This study highlights NLR as a significant prognostic factor in advanced GC patients receiving CLDN18.2-specific CAR-T cell treatment and provides insights into neutrophil-mediated treatment resistance. Further validation and exploration of strategies to mitigate neutrophil-induced immunosuppression are needed.

NCT03874897.

Cisplatin-based chemotherapy resistance is a common issue for cancer clinical efficacy. Metformin is being studied for its possible anticancer effect. The present study aimed to investigate whether metformin affects the chemosensitivity of gastric cancer to cisplatin and reveal the molecular mechanism. In this study, the effects of combination therapy with metformin and cisplatin on cell viability, cell apoptosis, malondialdehyde, superoxide dismutase, reactive oxygen species level, glucose uptake, lactate production, protein level, and xenograft tumor formation were analyzed in gastric cancer cells. Immunohistochemical staining was performed to detect Ki67 expression in matched tumor samples. The results showed that NCI-N87 and SNU-16 cells were most resistant and sensitive to cisplatin, respectively. Metformin treatment increased the cisplatin sensitivity of gastric cancer by inhibiting cell viability and metabolic reprogramming and promoting cell apoptosis and oxidative stress. Furthermore, overexpression of nuclear factor erythroid 2-related factor 2 (Nrf2) reversed the effects of metformin in the cisplatin sensitivity of gastric cancer by inhibiting cell viability and metabolic reprogramming and promoting cell apoptosis and oxidative stress. Metformin activated p53 and AMPK pathways in cisplatin-induced NCI-N87 cells, which were reversed by upregulating Nrf2. BAY-3827 (AMPK inhibitor) or p-nitro-Pifithrin-α (p53 inhibitor) treatments also reversed the effects of metformin increased the cisplatin sensitivity of gastric cancer by inhibiting cell viability and metabolic reprogramming and promoting cell apoptosis and oxidative stress. These results suggest that metformin significantly increases chemosensitivity of gastric cancer to cisplatin by inhibiting Nrf2 expression and metabolic reprogramming and activating oxidative stress and the pathway of p53 and AMPK.

The purpose of this research is to evaluate the predictive capabilities of a radiomics model for the Ki67 index and its correlation with prognosis in advanced gastric tubular adenocarcinoma patients.

Clinical data from 213 patients were analyzed, categorizing patients into high and low Ki67 index groups. The radiomic features of 192 patients were selected by lasso method and the model was constructed, which was validated using the TCIA dataset. Univariate and multivariate Cox regression analyses were used to further analyze clinical features associated with the prognosis of gastric cancer, radiomic models are also used to assess patient outcomes.

The radiomics model demonstrated moderate accuracy, with AUC values of 0.634, 0.666, and 0.602 for the training, validation 1, and validation 2 sets, respectively. Additionally, a significant correlation was found between the Ki67 index and radiomics scores, a higher Ki67 index was associated with improved outcomes. Kaplan-Meier analysis showed distinct survival differences between patients with high and low radiomics scores, indicating that higher scores predict better prognosis.

The radiomics model accurately predicts the Ki67 index and correlates with prognosis in advanced gastric tubular adenocarcinoma, offering valuable insights for clinical decision-making and personalized treatment strategies.

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide, with limited therapeutic options and a poor prognosis, particularly in advanced stages. Glycometabolism, a hallmark of cancer, plays a critical role in tumor progression, immune evasion, and response to therapy. However, the specific roles of glycometabolism-related genes and their prognostic and therapeutic implications in GC remain inadequately understood.

Transcriptomic and clinical data from GC patients were retrieved from TCGA and GEO databases. Glycometabolism-related genes were identified and analyzed using machine learning algorithms to construct a prognostic model. Functional assays, immune profiling, and pathway enrichment analyses were performed to explore the roles of these genes in tumor progression, immune-modulatory effects, and drug resistance. PLOD2, the gene with the highest prognostic significance, was further investigated to uncover its underlying regulatory mechanisms, roles in immune modulation, and contribution to therapeutic resistance.

A glycometabolism-related prognostic model consisting of four genes (PLOD2, CHSY3, SLC2A3 and SLC5A1) was developed and validated, effectively stratifying GC patients into high- and low-risk subgroups with distinct survival outcomes. Among these, PLOD2 emerged as the most significant gene, exhibiting strong associations with tumor progression and poor survival. Functional analyses revealed that PLOD2 promotes glycolysis and tumor progression through activation of the PI3K/AKT/mTOR pathway. Immune profiling revealed that PLOD2 overexpression is associated with an immunosuppressive tumor microenvironment, characterized by increased M2 macrophage infiltration and reduced immune activity. Moreover, treatment with rapamycin, an mTOR inhibitor, significantly suppressed PLOD2-mediated proliferation and anchorage-independent growth in GC cells, highlighting the central role of the PI3K/AKT/mTOR pathway in PLOD2-driven oncogenic behaviors.

This study identifies PLOD2 as a key prognostic biomarker and therapeutic target in gastric cancer. As a central component in a glycometabolism-related model, PLOD2 promotes glycolysis, tumor progression, and immune evasion via the PI3K/AKT/mTOR pathway. The model effectively stratifies patient risk, offering both prognostic utility and therapeutic insight. Targeting PLOD2-mediated pathways may represent a promising strategy for precision therapy and improved clinical outcomes in gastric cancer.

Adding pembrolizumab to first-line fluoropyrimidine (5-FU)/platinum chemotherapy plus trastuzumab improves outcomes in advanced HER2+ gastroesophageal adenocarcinomas, but the benefit is largely confined to dual HER2+ and PD-L1+ patients. To assess the contributions of components, we conducted a phase II trial evaluating 5-FU/platinum/trastuzumab and added pembrolizumab in cycle 2 in patients with metastatic HER2+ disease.

Treatment-naïve patients with advanced HER2+ gastroesophageal cancer underwent a baseline biopsy and received a single dose of 5-FU/platinum with trastuzumab followed by repeat biopsy. Pembrolizumab was added, and a third biopsy was performed after six cycles. The primary endpoint was the objective response rate. Secondary endpoints included progression-free and overall survival. Exploratory biomarker analysis and dynamic changes in HER2 and PD-L1 were prespecified.

Sixteen patients were enrolled. The objective response rate was 69%, and the median progression-free survival was 11.9 months. Serial whole-exome, single-cell RNA, T-cell receptor sequencing, and spatial transcriptomics from pretreatment and on-treatment samples revealed early trastuzumab-induced NK cell infiltration in HER2+ tumor beds and an increase in Fc receptor gamma III expression in macrophages, suggesting that trastuzumab directs Fc receptor-mediated antibody-dependent cytotoxicity. This favorable remodeling was enhanced by the addition of pembrolizumab, primarily in PD-L1+ samples. We observed TGF-β signaling in HER2-negative tumor regions, which was associated with nonresponder status.

These data highlight the biology of intratumoral heterogeneity and the impact of tumor and immune cell features on clinical outcomes and may partly explain the lesser magnitude of pembrolizumab benefit in HER2+ and PD-L1-negative subgroups.