The Database of Human Gastric Cancer (DBGC) is a comprehensive database that integrates various human gastric cancer-related data resources. Human gastric cancer-related transcriptomics projects, proteomics projects, mutations, biomarkers and drug-sensitive genes from different sources were collected and unified in this database. Moreover, epidemiological statistics of gastric cancer patients in China and clinicopathological information annotated with gastric cancer cases were also integrated into the DBGC. We believe that this database will greatly facilitate research regarding human gastric cancer in many fields. DBGC is freely available at http://bminfor.tongji.edu.cn/dbgc/index.do

Early detection is crucial for increasing the survival rate of gastric cancer (GC). We aimed to identify a methylated cell-free DNA (cfDNA) marker panel for detecting GC. The differentially methylated CpGs (DMCs) were selected from datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The selected DMCs were validated and further selected in tissue samples (40 gastric cancer and 36 healthy white blood cell samples) and in a quarter sample volume of plasma samples (37 gastric cancer, 12 benign gastric disease, and 43 healthy individuals). The marker combination selected was then evaluated in a normal sample volume of plasma samples (35 gastric cancer, 39 control diseases, and 40 healthy individuals) using real-time methylation-specific PCR (MSP). The analysis of the results compared methods based on 2−ΔΔCt values and Ct values. In the results, 30 DMCs were selected through bioinformatics methods, and then 5 were selected for biological validation. The marker combination of two fragments of IRF4 (IRF4–1 and IRF4–2) and one of ZEB2 was selected due to its good performance. The Ct-based method was selected for its good results and practical advantages. The assay, IRF4–1 and IRF4–2 in one fluorescence channel and ZEB2 in another, obtained 74.3% sensitivity for the GC group at any stage, at 92.4% specificity. In conclusion, the panel of IRF4 and ZEB2 in plasma cfDNA demonstrates good diagnostic performance and application potential in clinical settings.

This is a dataset of pathological lymph nodes for gastric cancer. It can be used to test a gastric cancer lymph node metastasis detection model proposed in "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning".

The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the CIP TCGA Radiology Initiative.

This dataset represents a collection of tissue types in histological images of human gastric cancer, containing 31,096 non-overlapping images of 224x224 pixels each, extracted from H&E-stained pathological slides at Harbin Medical University Cancer Hospital. The dataset was generated by predicting tissue components in gastric cancer using annotations from a publicly available colorectal cancer dataset to create tissue heatmaps. Professional pathologists then selected 300 whole slide images with high prediction accuracy. Finally, a substantial number of images, each belonging to one of eight tissue categories (Adipose (ADI), Background (BACK), Debris (DEB), Lymphocytes (LYM), Mucus (MUC), Smooth Muscle (MUS), Normal Colon Mucosa (NORM), Cancer-associated Stroma (STR), Tumor (TUM)), were extracted from these slides.

This dataset includes three clinical cohorts of gastric cancer (GC) patients from Fujian Medical University Union Hospital and Zhangzhou Affiliated Hospital.

The UnionH-1 cohort (50 NACI-treated GC patients) is used for bulk gene expression profiling.

The UnionH-2 cohort (9 treatment-naïve GC patients) is employed for spatial transcriptome analysis.

The UnionH-3 cohort (10 GC patients) includes gastroscopy samples analyzed via IMC. Patient data include clinicopathological features, TNM staging, and follow-up information.

A large histological images dataset of gastric cancer with tumour microenvironment

Yearly citation counts for the publication titled "Treatment Patterns, Costs, and Survival among Medicare-Enrolled Elderly Patients Diagnosed with Advanced Stage Gastric Cancer: Analysis of a Linked Population-Based Cancer Registry and Administrative Claims Database".

Raw data for the publication "Volatilomic profiles of gastric juice in gastric cancer patients". Measured with Agilent 8890/7079B GC-MS (Agilent, USA).

RNASeq data generated from organoid cultures established from gastric cancers and normal mucosae, paired tumor frozen tissues, and cultured fibroblast.

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4+ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

Objective: To explore the expression of the CXCR4 gene in the tumor microenvironment of gastric cancer tissues and its clinical significance.Methods: The transcriptome data and clinical data of gastric cancer from the The Cancer Genome Atlas (TCGA) database were downloaded, and the expression difference of the CXCR4 gene was analyzed using the ESTIMATES algorithm and DESeq2, and the clinicopathological parameters of the patients were also analyzed. Surgical tissue specimens of patients diagnosed with gastric cancer by surgical treatment and pathological examination at Hefei First People's Hospital from August 2022 to August 2023 were retrospectively collected for reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry to verify its expression. The CIBERSORT algorithm was used to evaluate the correlation between CXCR4 and immune cell infiltration.Results: The immune score might be more suitable for indicating the prognosis of STAD patients. TCGA data showed that the expression level of the CXCR4 gene in gastric cancer tissues was significantly higher than that in adjacent tissues (P < 0.001), and the expression of CXCR4 had significant differences in tumor invasion depth and distant metastasis (all P < 0.05). The experimental results showed that the expression of CXCR4 was positively correlated with tumor distant metastasis and differentiation degree (all P < 0.05). Kaplan - Meier survival analysis of both TCGA data and clinical data showed that the survival time of gastric cancer patients in the high - expression group was significantly shortened (P = 0.003, P < 0.001). Immune cell infiltration analysis: two types of TICs, such as B cell memory and CD8 + T cells, were positively correlated with the expression of CXCR4; six types of TICs, such as resting CD4 memory T cells, activated dendritic cells, plasma cells, activated NK killer cells, macrophages M0, and activated mast cells, were negatively correlated with the expression of CXCR4.Conclusion: The high expression of CXCR4 in gastric cancer indicates a poor prognosis, which is closely related to the progression and metastasis of the tumor. And it is related to immune cell infiltration and may become a potential target for immunotherapy.

Dataset concerning the systematic review of locally advanced gastric cancer.

BackgroundMetabolic reprogramming is involved in different stages of tumorigenesis. There are six widely recognized tumor-associated metabolic pathways, including cholesterol catabolism process, fatty acid metabolism, glutamine metabolic process, glycolysis, one carbon metabolic process, and pentose phosphate process. This study aimed to classify gastric cancer patients into different metabolic bio-similar clusters.MethodWe analyzed six tumor-associated metabolic pathways and calculated the metabolic pathway score through RNA-seq data using single sample gene set enrichment analysis. The consensus clustering analysis was performed to classify patients into different bio-similar clusters by multi-dimensional scaling. Kaplan–Meier curves were presented between different metabolic bio-similar groups for OS analysis.ResultsA training set of 370 patients from the Cancer Genome Atlas database with primary gastric cancer was chosen. Patients were classified into four metabolic bio-similar clusters, which were identified as metabolic non-specificity, metabolic-active, cholesterol-silence, and metabolic-silence clusters. Survival analysis showed that patients in metabolic-active cluster and metabolic-silence cluster have significantly poor prognosis than other patients (p=0.031). Patients in metabolic-active cluster and metabolic-silence cluster had significantly higher intra-tumor heterogeneity than other patients (p=0.032). Further analysis was performed in metabolic-active cluster and cholesterol-silence cluster. Three cell-cycle-related pathways, including G2M checkpoints, E2F targets, and MYC targets, were significantly upregulated in metabolic-active cluster than in cholesterol-silence cluster. A validation set of 192 gastric cancer patients from the Gene Expression Omnibus data portal verified that metabolic bio-similar cluster can predict prognosis in gastric cancer.ConclusionOur study established a multi-dimension metabolic prognostic model in gastric cancer, which may be feasible for predicting clinical outcome.

BackgroundAlthough breakthroughs have been achieved in gastric cancer (GC) therapy with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1), the acquisition of high response rate remains a huge challenge for clinicians. It is imperative to identify novel biomarkers for predicting response to immunotherapy and explore alternative therapeutic strategy for GC.MethodsThe transcriptomic profiles and clinical information of GC patients from The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) database was used to screen differentially expressed lncRNAs between the tumor specimens and the paracancerous tissues. The TargetScan, miRDB and miRcode database were then utilized to construct competing endogenous RNA (ceRNA) networks and identify pivotal lncRNAs. An independent dataset from GEO (GSE70880) and 23 pairs of GC specimens of our cohort were subsequently performed for external validity. The relationship between clinical variables and gene expression were evaluated by Kruskal–wallis test and Wilcoxon signed-rank. The prognostic value of the candidate genes was assessed using Kaplan-Meier analysis and Cox regression models. CIBERSORT and Gene set enrichment analysis (GSEA) were used to determine immune cell infiltration. Gastric adenocarcinoma AGS cells and human embryonic kidney 293T (HEK293T) cells with knockdown of LINC01094 were generated by siRNA transfection, followed by detecting the alteration of the target miRNA and PD-L1/PD-L2 by RT-qPCR. Besides, the interaction between lncRNA and the miRNA–PD-L1/PD-L2 axis were verified by dual luciferase reporter assay.ResultsTwenty-two intersecting lncRNAs were identified to be PD-L1/PD-L2-related lncRNAs and LINC01094–miR-17-5p–PD-L1/PD-L2 was constructed as a potential ceRNA network. LINC01094 was increased in tumor specimens than adjacent normal samples and was positively associated with advanced tumor stages and EBV and MSI status. Furthermore, LINC01094 expression was an independent risk factor for poor overall survival (OS) in GC patients. CD8+ T cell exhaustion-related genes were enriched in high-LINC01094 tissues and high-PD-L2 group. A strong positive association of LINC01094 expression was established with M2 macrophages, IL-10+ TAM, as well as PD-L1 and PD-L2 levels, therefore a LINC01094–miR-17-5p–IL-10 network was proposed in macrophages. Using the exoRBase database, LINC01094 was assumed in blood exosomes of GC patients The results of knockdown experiments and luciferase reporter assays revealed that LINC01094 interacted with miR-17-5p and served as a miRNA sponge to regulate the expression of PD-L1 and PD-L2.ConclusionLINC01094 dually regulates the expression of PD-L1 and PD-L2 and shapes the immunosuppressive tumor microenvironment via sponging miR-17-5p. LINC01094 may serve as a potential prognostic predictor and therapeutic target in GC.

Gastric Cancer Detection

## Overview

Gastric Cancer Detection is a dataset for object detection tasks - it contains Cancer annotations for 610 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The extracellular matrix (ECM) is a major component of the tumor microenvironment and can influence tumor initiation, proliferation, invasion, and angiogenesis. However, published research on the relationship between ECM and gastric cancer (GC) prognosis is limited. There is currently no ECM-related prognostic risk model to predict the prognosis of GC patients. We screened the differentially expressed genes (DEGs) between normal and GC tissues based on The Cancer Genome Atlas (TCGA) database. ECM-related DEGs were selected and LASSO Cox regression analysis was performed for these DEGs. We established a prognostic risk model based on five ECM-related genes. A nomogram for clinical diagnosis was constructed based on Riskscore and clinical characteristics. The results showed that GC patients with lower RiskScore had better survival outcomes than those with higher RiskScore. The receiver operating characteristic (ROC) curve confirmed the accuracy of the prognostic risk signatures. The performance of the prognostic risk model was further validated in two external datasets.

Clinical data of gastric cancer patients whose genomic data was aggregrated for integrated analysis.

Legacy unique identifier: P00672

Whole exome sequencing data generated from organoid cultures established from gastric cancers, paired gastric tumor frozen tissues and blood leukocyte DNA.