Question: You are an expert researcher providing comprehensive, well-cited information.

Provide detailed information focusing on: 1. Key concepts and definitions with current understanding 2. Recent developments and latest research (prioritize 2023-2024 sources) 3. Current applications and real-world implementations 4. Expert opinions and analysis from authoritative sources 5. Relevant statistics and data from recent studies

Format as a comprehensive research report with proper citations. Include URLs and publication dates where available. Always prioritize recent, authoritative sources and provide specific citations for all major claims.

Disease Characteristics Research Template

Target Disease

• Disease Name: EBV-Associated Gastric Cancer
• MONDO ID: (if available)
• Category:

Research Objectives

Please provide a comprehensive research report on EBV-Associated Gastric Cancer covering all of the disease characteristics listed below. This report will be used to populate a disease knowledge base entry. Be thorough and cite primary literature (PMID preferred) for all claims.

For each section, suggested databases/resources are listed. These are the first places you should search for information on each topic.

1. Disease Information

Search first: OMIM, Orphanet, ICD-10/ICD-11, MeSH, PubMed

• What is the disease? Provide a concise overview.
• What are the key identifiers? (OMIM, Orphanet, ICD-10/ICD-11, MeSH, Mondo)
• What are the common synonyms and alternative names?
• Is the information derived from individual patients (e.g., EHR) or aggregated disease-level resources?

2. Etiology

• Disease Causal Factors: What are the primary causes? (genetic, environmental, infectious, mechanistic)
• Risk Factors:

  Search first: PubMed, Cochrane Library, UpToDate, clinical guidelines, ClinVar, ClinGen, GWAS Catalog, PheGenI, CTD, CDC, WHO, epidemiological databases

• Genetic risk factors (causal variants, susceptibility loci, modifier genes)
• Environmental risk factors (toxins, lifestyle, occupational exposures, age, sex, family history)
• Protective Factors:

  Search first: PubMed, Cochrane Library, clinical trial databases, GWAS Catalog, gnomAD, WHO, CDC, nutrition databases

• Genetic protective factors (protective variants, modifier alleles)
• Environmental protective factors (diet, lifestyle, exposures that reduce risk)
• Gene-Environment Interactions: How do genetic and environmental factors interact to influence disease?

  Search first: CTD, PubMed, PheGenI, GxE databases

3. Phenotypes

Search first: HPO (Human Phenotype Ontology), OMIM, Orphanet, PubMed, clinicaltrials.gov, MedDRA, SNOMED CT, DECIPHER, LOINC

For each phenotype, provide: - Phenotype type: symptoms, clinical signs, physical manifestations, behavioral changes, or laboratory abnormalities

For symptoms/signs: HPO, OMIM, Orphanet, PubMed For behavioral changes: HPO, DSM, RDoC (Research Domain Criteria), PubMed For laboratory abnormalities: LOINC, SNOMED CT, LabTests Online, PubMed - Phenotype characteristics: Search first: OMIM, Orphanet, HPO, PubMed - Age of symptom onset (neonatal, childhood, adult-onset, late-onset) - Symptom severity (mild, moderate, severe, variable) - Symptom progression (stable, progressive, episodic, fluctuating) - Frequency among affected individuals (percentage or qualitative) - Quality of life impact: Effects on daily functioning and well-being (per-phenotype when possible) Search first: EQ-5D database, SF-36, WHO QOL databases, PubMed - Suggest HPO (Human Phenotype Ontology) terms for each phenotype

4. Genetic/Molecular Information

• Causal Genes: Gene mutations or chromosomal abnormalities responsible for disease (gene symbols, OMIM IDs)

  Search first: OMIM, ClinVar, HGMD, Ensembl, NCBI Gene

• Pathogenic Variants:
• Affected genes (gene symbols, HGNC IDs) > Search first: OMIM, NCBI Gene, Ensembl, HGNC, UniProt, GeneCards
• Variant classification (pathogenic, likely pathogenic, VUS per ACMG/AMP guidelines) > Search first: ClinVar, ClinGen, ACMG/AMP guidelines, VarSome
• Variant type/class (missense, frameshift, nonsense, splice-site, structural)
• Allele frequency in population databases > Search first: gnomAD, 1000 Genomes, ExAC, TOPMed, dbSNP
• Somatic vs germline origin > Search first: COSMIC (somatic), ClinVar, ICGC, TCGA
• Functional consequences (loss of function, gain of function, dominant negative)
• Modifier Genes: Genes that modify disease severity or expression
• Epigenetic Information: DNA methylation, histone modifications, chromatin changes affecting disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Chromosomal Abnormalities: Large-scale genetic changes (aneuploidy, translocations, inversions)

  Search first: DECIPHER, ClinVar, ECARUCA, UCSC Genome Browser

5. Environmental Information

• Environmental Factors: Non-genetic contributing factors (toxins, radiation, pollution, occupational exposure)

  Search first: CTD (Comparative Toxicogenomics Database), TOXNET, PubMed, EPA databases

• Lifestyle Factors: Behavioral factors (smoking, diet, exercise, alcohol consumption)

  Search first: CDC databases, WHO, PubMed, NHANES

• Infectious Agents: If applicable, pathogens causing or triggering disease (bacteria, viruses, fungi, parasites)

  Search first: NCBI Taxonomy, ViPR, BV-BRC, MicrobeDB, GIDEON

6. Mechanism / Pathophysiology

• Molecular Pathways: Specific signaling cascades or biochemical pathways involved (Wnt, MAPK, mTOR, PI3K-AKT, etc.)

  Search first: KEGG, Reactome, WikiPathways, PathBank, BioCyc

• Cellular Processes: Cell-level mechanisms (apoptosis, autophagy, cell cycle dysregulation, inflammation, etc.)

  Search first: Gene Ontology (GO), Reactome, KEGG, PubMed

• Protein Dysfunction: How protein structure or function is altered (misfolding, aggregation, loss of function, gain of function)

  Search first: UniProt, PDB (Protein Data Bank), InterPro, Pfam, AlphaFold

• Metabolic Changes: Alterations in metabolic processes (energy metabolism, lipid metabolism, amino acid metabolism)

  Search first: KEGG, BioCyc, HMDB (Human Metabolome Database), BRENDA

• Immune System Involvement: Role of immune response (autoimmunity, immunodeficiency, chronic inflammation)

  Search first: ImmPort, Immunome Database, IEDB, Gene Ontology

• Tissue Damage Mechanisms: How tissues/ are injured (oxidative stress, ischemia, fibrosis, necrosis)

  Search first: PubMed, Gene Ontology, Reactome

• Biochemical Abnormalities: Specific molecular defects (enzyme deficiencies, receptor dysfunction, ion channel defects)

  Search first: BRENDA, UniProt, KEGG, OMIM, PubMed

• Epigenetic Changes: DNA methylation, histone modifications affecting gene expression in disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Molecular Profiling (if available):
• Transcriptomics/gene expression changes > Search first: GEO (Gene Expression Omnibus), ArrayExpress, GTEx, Human Cell Atlas, SRA
• Proteomics findings > Search first: PRIDE, ProteomeXchange, Human Protein Atlas, STRING, BioGRID
• Metabolomics signatures > Search first: MetaboLights, Metabolomics Workbench, HMDB, METLIN
• Lipidomics alterations > Search first: LIPID MAPS, SwissLipids, LipidHome, Metabolomics Workbench
• Genomic structural features > Search first: UCSC Genome Browser, Ensembl, NCBI, dbVar, DGV
• Advanced Technologies (if applicable):
• Single-cell analysis findings (cell-type specific mechanisms, cellular heterogeneity) > Search first: Human Cell Atlas, Single Cell Portal, GEO, CELLxGENE
• Spatial transcriptomics findings > Search first: GEO, Spatial Research, Vizgen, 10x Genomics data
• Multi-omics integration results > Search first: TCGA, ICGC, cBioPortal, LinkedOmics, PubMed
• Functional genomics screens (CRISPR, RNAi) > Search first: DepMap, GenomeRNAi, PubMed, BioGRID ORCS

For each mechanism, describe: - The causal chain from initial trigger to clinical manifestation - Which mechanisms are upstream vs downstream - What cell types and biological processes are involved - Suggest GO terms for biological processes and CL terms for cell types

7. Anatomical Structures Affected

• Organ Level:
• Primary organs directly affected
• Secondary organ involvement (complications, secondary effects)
• Body systems involved (cardiovascular, nervous, digestive, respiratory, endocrine, etc.)

  Search first: Uberon, FMA (Foundational Model of Anatomy), OMIM, HPO, ICD-11, MeSH, SNOMED CT

• Tissue and Cell Level:
• Specific tissue types affected (epithelial, connective, muscle, nervous)
• Specific cell populations targeted (with Cell Ontology terms)

  Search first: Uberon, Human Protein Atlas, Cell Ontology, Human Cell Atlas, CellMarker, PanglaoDB

• Subcellular Level:
• Cellular compartments involved (mitochondria, nucleus, ER, lysosomes) (with GO Cellular Component terms)

  Search first: Gene Ontology (Cellular Component), UniProt, Human Protein Atlas

• Localization:
• Specific anatomical sites (with UBERON terms) > Search first: FMA, Uberon, NeuroNames (for brain), SNOMED CT
• Lateralization (unilateral, bilateral, asymmetric) > Search first: HPO, clinical literature, imaging databases

8. Temporal Development

• Onset:
• Typical age of onset (congenital, pediatric, adult, geriatric)
• Onset pattern (acute, subacute, chronic, insidious)

  Search first: OMIM, Orphanet, HPO, PubMed

• Progression:
• Disease stages (early, intermediate, advanced, end-stage) > Search first: Cancer Staging Manual (AJCC), WHO classifications, PubMed
• Progression rate (rapid, slow, variable)
• Disease course pattern (episodic, relapsing-remitting, progressive, stable)
• Disease duration (self-limited, chronic lifelong)

  Search first: Disease registries, longitudinal cohort databases, natural history studies, PubMed, Orphanet, OMIM

• Patterns:
• Remission patterns (spontaneous, treatment-induced) > Search first: Clinical trial databases, disease registries, PubMed
• Critical periods (time windows of vulnerability or opportunity for intervention) > Search first: PubMed, developmental biology databases, clinical guidelines

9. Inheritance and Population

• Epidemiology:
• Prevalence (cases per 100,000 at given time)
• Incidence (new cases per 100,000 per year)

  Search first: Orphanet, CDC, WHO, GBD (Global Burden of Disease), national registries, SEER, disease registries

• For Genetic Etiology:
• Inheritance pattern (AD, AR, X-linked, mitochondrial, multifactorial, polygenic) > Search first: OMIM, Orphanet, ClinVar, GTR (Genetic Testing Registry)
• Penetrance (complete, incomplete, age-dependent) > Search first: ClinVar, OMIM, PubMed, ClinGen
• Expressivity (variable, consistent) > Search first: OMIM, ClinVar, PubMed
• Genetic anticipation (increasing severity in successive generations) > Search first: OMIM, PubMed (especially for repeat expansion disorders)
• Germline mosaicism > Search first: ClinVar, OMIM, genetic counseling literature, PubMed
• Founder effects (population-specific mutations) > Search first: gnomAD, population genetics databases, PubMed
• Consanguinity role > Search first: OMIM, population studies, genetic counseling resources
• Carrier frequency > Search first: gnomAD, carrier screening databases, GeneReviews, GTR
• Population Demographics:
• Affected populations (ethnic or demographic groups with higher prevalence) > Search first: gnomAD, 1000 Genomes, PAGE Study, PubMed, population registries
• Geographic distribution (endemic areas, regional variation) > Search first: WHO, CDC, GBD, Orphanet, geographic epidemiology databases
• Geographic distribution of specific variants
• Sex ratio (male:female) > Search first: Disease registries, OMIM, PubMed, epidemiological databases
• Age distribution of affected individuals > Search first: CDC, disease registries, SEER, Orphanet

10. Diagnostics

• Clinical Tests:
• Laboratory tests (blood, urine, tissue chemistry, specific enzyme assays) > Search first: LOINC, LabTests Online, PubMed
• Biomarkers (proteins, metabolites, genetic markers, circulating biomarkers) > Search first: FDA Biomarker List, BEST (Biomarkers, EndpointS, and other Tools), PubMed
• Imaging studies (X-ray, CT, MRI, PET, ultrasound) > Search first: RadLex, DICOM, Radiopaedia, imaging databases
• Functional tests (pulmonary function, cardiac stress tests) > Search first: LOINC, clinical guidelines, PubMed
• Electrophysiology (EEG, EMG, ECG, nerve conduction studies) > Search first: LOINC, clinical neurophysiology databases, PubMed
• Biopsy findings (histopathology, immunohistochemistry) > Search first: SNOMED CT, College of American Pathologists resources, PubMed
• Pathology findings (microscopic examination) > Search first: SNOMED CT, Digital Pathology databases, PubMed
• Genetic Testing:

  Search first: GTR (Genetic Testing Registry), GeneReviews, ClinGen

• Overview of recommended genetic testing approach
• Whole genome sequencing (WGS) utility > Search first: GTR, ClinVar, GEL (Genomics England), gnomAD
• Whole exome sequencing (WES) utility > Search first: GTR, ClinVar, OMIM, GeneMatcher
• Gene panels (which panels, which genes) > Search first: GTR, ClinVar, laboratory-specific databases
• Single gene testing > Search first: GTR, ClinVar, OMIM, GeneReviews
• Chromosomal microarray (CMA) > Search first: DECIPHER, ClinVar, dbVar, ECARUCA
• Karyotyping > Search first: Chromosome Abnormality Database, ClinVar, cytogenetics resources
• FISH > Search first: ClinVar, cytogenetics databases, PubMed
• Mitochondrial DNA testing > Search first: MITOMAP, MSeqDR, ClinVar, GTR
• Repeat expansion testing > Search first: GTR, ClinVar, repeat expansion databases, PubMed
• Omics-Based Diagnostics (if applicable):
• RNA sequencing / transcriptomics > Search first: GEO, ArrayExpress, GTEx, RNA-seq databases
• Proteomics > Search first: PRIDE, ProteomeXchange, FDA Biomarker database
• Metabolomics > Search first: MetaboLights, Metabolomics Workbench, HMDB
• Epigenomics > Search first: GEO, ENCODE, Roadmap Epigenomics, MethBase
• Liquid biopsy > Search first: COSMIC, ClinVar, liquid biopsy databases, PubMed
• Clinical Criteria:
• Standardized diagnostic criteria (DSM, ICD, society guidelines) > Search first: DSM-5, ICD-11, clinical society guidelines, UpToDate
• Differential diagnosis (other conditions to rule out, with distinguishing features) > Search first: DynaMed, UpToDate, clinical decision support systems
• Screening:
• Screening methods for asymptomatic individuals (newborn screening, carrier screening, cascade screening) > Search first: ACMG recommendations, CDC newborn screening, GTR

11. Outcome/Prognosis

• Survival and Mortality:
• Survival rate (5-year, 10-year, overall) > Search first: SEER, cancer registries, disease-specific registries, PubMed
• Life expectancy (with and without treatment if applicable) > Search first: Orphanet, disease registries, actuarial databases, PubMed
• Mortality rate > Search first: CDC, WHO, GBD, national mortality databases
• Disease-specific mortality (deaths directly attributable to disease) > Search first: Disease registries, CDC Wonder, GBD, PubMed
• Morbidity and Function:
• Morbidity (disease-related disability and health impacts) > Search first: GBD, WHO, disability databases, PubMed
• Disability outcomes (long-term functional impairments) > Search first: ICF (International Classification of Functioning), disability registries
• Quality of life measures (EQ-5D, SF-36, PROMIS, disease-specific tools) > Search first: EQ-5D database, SF-36, PROMIS, PubMed
• Disease Course:
• Complications (secondary problems: infections, organ failure, etc.) > Search first: ICD codes, disease registries, clinical databases, PubMed
• Recovery potential (likelihood and extent of recovery, with vs without treatment) > Search first: Natural history studies, rehabilitation databases, PubMed
• Prediction:
• Prognostic factors (age, disease severity, biomarkers, treatment response) > Search first: Prognostic models databases, clinical calculators, PubMed
• Prognostic biomarkers (molecular markers predicting disease course) > Search first: FDA Biomarker database, PubMed, cancer prognostic databases

12. Treatment

• Pharmacotherapy:
• Pharmacological treatments (drug names, drug classes, mechanisms of action) > Search first: DrugBank, RxNorm, ATC classification, DailyMed, FDA databases
• Pharmacogenomics (how genetic variants affect drug metabolism, efficacy, toxicity) > Search first: PharmGKB, CPIC (Clinical Pharmacogenetics), FDA Table of PGx Biomarkers
• Advanced Therapeutics:
• Gene therapy (viral vectors, CRISPR, gene replacement, gene editing) > Search first: ClinicalTrials.gov, FDA gene therapy database, ASGCT resources
• Cell therapy (stem cell transplant, CAR-T, cellular therapeutics) > Search first: ClinicalTrials.gov, FDA cell therapy database, FACT standards
• RNA-based therapies (ASOs, siRNA, mRNA therapies) > Search first: ClinicalTrials.gov, FDA approvals, PubMed
• Targeted therapies (treatments directed at specific molecular targets) > Search first: My Cancer Genome, OncoKB, ClinicalTrials.gov, FDA approvals
• Immunotherapies (checkpoint inhibitors, monoclonal antibodies) > Search first: Cancer Immunotherapy Database, FDA approvals, ClinicalTrials.gov
• Surgical and Interventional:
• Surgical interventions (types of surgery, timing, outcomes) > Search first: CPT codes, surgical registries, clinical guidelines, PubMed
• Supportive and Rehabilitative:
• Supportive care (symptom management, pain control, nutrition) > Search first: Clinical guidelines, Cochrane Library, PubMed
• Rehabilitation (physical therapy, occupational therapy, speech therapy) > Search first: Rehabilitation medicine databases, clinical guidelines, PubMed
• Experimental:
• Experimental treatments in clinical trials (with NCT identifiers if available) > Search first: ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
• Treatment Outcomes:
• Treatment response rates > Search first: Clinical trial databases, FDA reviews, systematic reviews, PubMed
• Side effects and adverse events > Search first: FDA Adverse Event Reporting System (FAERS), MedWatch, PubMed
• Treatment Strategy:
• Treatment algorithms (clinical pathways, decision trees) > Search first: Clinical practice guidelines, NCCN Guidelines, UpToDate
• Combination therapies > Search first: ClinicalTrials.gov, treatment guidelines, PubMed
• Personalized medicine approaches (genotype-guided treatment) > Search first: My Cancer Genome, CIViC, PharmGKB, precision medicine databases

For each treatment, suggest MAXO (Medical Action Ontology) terms where applicable.

13. Prevention

• Prevention Levels:
• Primary prevention (preventing disease occurrence: vaccination, risk factor modification) > Search first: CDC, WHO, USPSTF recommendations, Cochrane Library
• Secondary prevention (early detection and treatment: screening programs, early intervention) > Search first: USPSTF, CDC screening guidelines, WHO
• Tertiary prevention (preventing complications in those with disease) > Search first: Clinical guidelines, disease management protocols, PubMed
• Immunization: Vaccine strategies (if applicable)

  Search first: CDC vaccine schedules, WHO immunization, FDA vaccine database

• Screening and Early Detection:
• Screening programs (population-based: newborn screening, cancer screening) > Search first: CDC screening programs, USPSTF, cancer screening databases
• Genetic screening (carrier screening, preimplantation genetic diagnosis, prenatal testing) > Search first: ACMG recommendations, ACOG guidelines, GTR
• Risk stratification (identifying high-risk individuals for targeted prevention) > Search first: Risk prediction models, clinical calculators, PubMed
• Behavioral Interventions: Lifestyle modifications to reduce risk

  Search first: CDC, WHO, behavioral intervention databases, Cochrane Library

• Counseling: Genetic counseling (risk assessment, family planning guidance)

  Search first: NSGC resources, ACMG guidelines, GeneReviews

• Public Health:
• Public health interventions (sanitation, vector control, health education) > Search first: CDC, WHO, public health databases, PubMed
• Environmental interventions (reducing environmental risk factors) > Search first: EPA databases, WHO environmental health, PubMed
• Prophylaxis: Preventive medications or procedures

  Search first: Clinical guidelines, FDA approvals, PubMed

14. Other Species / Natural Disease

• Taxonomy: Species affected (with NCBI Taxon identifiers)

  Search first: NCBI Taxonomy

• Breed: Specific breeds affected (with VBO identifiers if applicable)

  Search first: VBO (Vertebrate Breed Ontology)

• Gene: Orthologous genes in other species (with NCBI Gene IDs)

  Search first: NCBI Gene

• Natural Disease:
• Naturally occurring disease in other species (companion animals, wildlife) > Search first: OMIA (Online Mendelian Inheritance in Animals), VetCompass, PubMed
• Veterinary relevance and importance in animal health > Search first: OMIA, veterinary databases, PubMed
• Comparative Biology:
• Comparative pathology (similarities and differences across species) > Search first: OMIA, comparative pathology databases, PubMed
• Evolutionary conservation of disease mechanisms > Search first: HomoloGene, OrthoMCL, Alliance of Genome Resources
• Transmission (if applicable):
• Zoonotic potential > Search first: CDC zoonotic diseases, WHO zoonoses, GIDEON
• Cross-species susceptibility > Search first: NCBI Taxonomy, veterinary databases, PubMed

15. Model Organisms

• Model Types:
• Model organism type (mammalian, invertebrate, cellular, in vitro) > Search first: Alliance of Genome Resources, model organism databases
• Specific model systems (mouse, rat, zebrafish, Drosophila, C. elegans, yeast, cell lines, organoids, iPSCs) > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, SGD, ATCC, Cellosaurus
• Induced models (drug treatment, surgical intervention, environmental manipulation) > Search first: MGI, model organism databases, PubMed
• Genetic Models:
• Types available (knockout, knock-in, transgenic, conditional, humanized) > Search first: MGI, IMPC, KOMP, EuMMCR, IMSR
• Model Characteristics:
• Phenotype recapitulation (how well model reproduces human disease features) > Search first: Model organism databases, comparative studies, PubMed
• Model limitations (aspects of human disease not captured) > Search first: Model organism databases, PubMed, review articles
• Applications:
• Research applications (what aspects of disease can be studied) > Search first: Model organism databases, PubMed
• Resources:
• Model databases > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, IMSR, EMMA, MMRRC

Citation Requirements

• Cite primary literature (PMID preferred) for all mechanistic and clinical claims
• Prioritize recent reviews and landmark papers
• Include direct quotes from abstracts where possible to support key statements
• Distinguish evidence source types: human clinical, model organism, in vitro, computational

Output Format

Structure your response as a comprehensive narrative organized by the sections above. For each section, provide: - Factual content with specific details (numbers, percentages, gene names, variant nomenclature) - Ontology term suggestions (HPO, GO, CL, UBERON, CHEBI, MAXO, MONDO) where applicable - Evidence citations with PMIDs - Direct quotes from abstracts to support key claims - Clear indication when information is not available or not applicable for this disease

This report will be used to populate a disease knowledge base entry with: - Pathophysiology descriptions with causal chains - Gene/protein annotations (HGNC, GO terms) - Phenotype associations (HP terms) with frequencies - Cell type involvement (CL terms) - Anatomical locations (UBERON terms) - Chemical entities (CHEBI terms) - Treatment annotations (MAXO terms) - Evidence items with PMIDs and exact abstract quotes - Epidemiology, prognosis, diagnostic, and prevention information - Animal model descriptions with phenotype recapitulation details

EBV-Associated Gastric Cancer (EBVaGC) — Disease Characteristics Research Report (2023–2024 prioritized)

Target Disease

• Disease name: Epstein–Barr virus (EBV)-associated gastric cancer (EBVaGC) / EBV-positive gastric carcinoma
• Category: Infection-associated epithelial malignancy; molecular subtype of gastric adenocarcinoma
• MONDO ID: Not identified from the retrieved evidence in this run (additional ontology lookup required).

1. Disease information

Overview (what is the disease?)

EBV-associated gastric cancer (EBVaGC) is a molecularly and clinicopathologically distinct subset of gastric cancer defined by the presence of EBV infection within the malignant epithelial (tumor) cells, typically demonstrated by in situ hybridization for EBV-encoded small RNAs (EBER) (saito2021landscapeofebvpositive pages 1-2, li2023assessingepstein–barrvirus pages 1-2). It is recognized as one of the major molecular subtypes in The Cancer Genome Atlas (TCGA) gastric cancer classification (saito2021landscapeofebvpositive pages 1-2, lee2024associationofpdl1 pages 1-2).

Synonyms / alternative names

• EBV-positive gastric cancer (EBV(+) GC) (saito2021landscapeofebvpositive pages 1-2)
• EBV-associated gastric carcinoma (EBVaGC) (bai2022efficacyandpredictive pages 1-2)
• Gastric carcinoma with lymphoid stroma (GCLS) / carcinoma with lymphoid stroma (CLS) when presenting with prominent lymphoid infiltration (park2023geneticlandscapeand pages 1-2, lee2024associationofpdl1 pages 1-2)
• Lymphoepithelioma-like carcinoma (LELC) pattern in stomach (often overlaps with GCLS conceptually) (corallo2024unlockingthepotential pages 6-8, saito2021landscapeofebvpositive pages 1-2)

Key identifiers (status)

• ICD/MeSH/OMIM/Orphanet/MONDO: Not reliably extractable from the retrieved primary/full-text evidence in this run; this report therefore focuses on primary literature definitions and diagnostic criteria.

Evidence provenance

The information in this report is derived from aggregated disease-level primary literature (cohort studies, meta-analyses, and mechanistic studies), plus a small number of prospective/observational immunotherapy cohorts and clinical trial registry entries (xie2020positivestatusof pages 1-2, bai2022efficacyandpredictive pages 1-2, pyo2023prognosticimplicationof pages 1-2).

2. Etiology

Primary causal factor

EBVaGC is causally linked to EBV infection of gastric epithelial cells with maintenance of EBV genomes/latency-associated gene expression in tumor cells; EBVaGC is considered an “infection-associated” cancer subtype with distinct molecular/immune features (salnikov2024theviraletiology pages 1-2).

Risk factors

• Infectious: EBV infection in tumor cells is the defining etiologic factor; EBV positivity is observed in ~5–10% of gastric cancers in many series (mcmiller2024immunemicroenvironmentof pages 1-2).
• Anatomic/clinical context: EBV positivity appears enriched in gastric remnant cancers (post-distal gastrectomy), with EBV positivity reported as 18.7% in remnant cancers vs 6.0% in proximal non-remnant controls in a Norwegian population-based study (Bringeland et al. 2024; DOI: 10.3390/cancers16112000; retrieved in this run but not yet evidence-extracted beyond summary in artifact) (artifact-00).

Protective factors

A meta-analysis suggests EBV-positive status is associated with better overall survival compared to EBV-negative gastric cancer (HR < 1), which is prognostic rather than strictly protective against disease occurrence (pyo2023prognosticimplicationof pages 1-2).

Gene–environment / microbe–environment interactions

Direct gene–environment interaction statistics were not extractable from the retrieved texts. However, EBV-associated tumors show characteristic host epigenetic reprogramming (hypermethylation) and immune pathway activation, consistent with pathogen-driven remodeling of host gene regulation (corallo2024unlockingthepotential pages 3-5, salnikov2024theviraletiology pages 1-2).

3. Phenotypes (clinical and pathological presentation)

Clinicopathologic phenotype (human)

Commonly reported features include: - Male predominance (e.g., 72.2% male in one 2023 cohort; and EBVaGC more common in males in a 420-patient cohort) (li2023assessingepstein–barrvirus pages 1-2, park2023geneticlandscapeand pages 1-2). - Tumor location: preferential involvement of upper-to-middle stomach (saito2021landscapeofebvpositive pages 1-2, park2023geneticlandscapeand pages 1-2). - Histology: frequent association with gastric carcinoma with lymphoid stroma (GCLS) characterized by dense intra/peritumoral lymphocytic infiltration (park2023geneticlandscapeand pages 1-2, lee2024associationofpdl1 pages 1-2). - Stage associations: one cohort found association with early T stage and early TNM stage (li2023assessingepstein–barrvirus pages 1-2).

Suggested HPO terms (examples; mapping-level suggestions)

Because EBVaGC is a cancer subtype, phenotypes overlap with gastric cancer generally; the literature extracted here emphasized histology and immune infiltration. - Gastric adenocarcinoma: HP:0006753 (Malignant neoplasm of stomach; verify exact HPO term naming in curation pipeline) - Gastrointestinal bleeding (particularly in remnant cancers): HP:0002239 (artifact-00; Bringeland 2024 study context) - Weight loss/anorexia (common in GC but not quantified here): HP:0001824 (Weight loss), HP:0004396 (Anorexia) - Lymphocyte-rich tumor stroma (not a standard HPO clinical term; better captured via pathology ontologies)

Frequency data

• EBV positivity in GCLS: 85% (181/214) in one large 2024 cohort (lee2024associationofpdl1 pages 1-2).

4. Genetic / molecular information

“Causal genes”

EBVaGC is not a Mendelian disorder and does not have a single causal germline gene. Its defining cause is EBV infection in tumor cells, with recurrent somatic alterations.

Key recurrent somatic alterations (current understanding)

Across genomic profiling and reviews, EBVaGC is characterized by: - PIK3CA and ARID1A recurrent mutations, and relatively fewer TP53 mutations (saito2021landscapeofebvpositive pages 1-2, guo2024developmentofa pages 1-2). - 9p24.1 amplifications including JAK2, CD274 (PD-L1), and PDCD1LG2 (PD-L2), contributing to immune evasion (corallo2024unlockingthepotential pages 3-5, mcmiller2024immunemicroenvironmentof pages 1-2). - Extensive DNA hypermethylation (including CDKN2A promoter hypermethylation noted in TCGA summaries; MLH1 promoter hypermethylation is not a typical feature in EBV subtype as summarized in one review) (corallo2024unlockingthepotential pages 3-5).

One 2024 review summarized reported frequencies from prior genomic studies: PIK3CA 80%, ARID1A 55%, BCOR 23%, and JAK2/CD274/PDCD1LG2 amplifications 15% (corallo2024unlockingthepotential pages 3-5).

Epigenetic information

EBVaGC is notable for DNA hypermethylation compared to other gastric cancer subtypes (corallo2024unlockingthepotential pages 3-5).

5. Mechanism / pathophysiology

Core mechanistic themes

EBVaGC’s pathophysiology is shaped by combined viral oncogenic programs, host epigenetic remodeling, and immune microenvironment remodeling.

Causal chain (conceptual): 1) EBV infects gastric epithelial cells and establishes a latency program in tumor cells (definition-level) → 2) EBV products and host responses drive epigenetic reprogramming (hypermethylation) and select for particular somatic alterations (e.g., PIK3CA/ARID1A) → 3) Tumors develop an “immune-hot” yet immunoregulatory environment, often with strong lymphocyte infiltration and immune checkpoint activation/amplification (PD-L1/PD-L2; JAK2) → 4) This immune contexture can be associated with higher responsiveness to checkpoint blockade in some cohorts, but responses are heterogeneous (corallo2024unlockingthepotential pages 6-8, bai2022efficacyandpredictive pages 1-2, mcmiller2024immunemicroenvironmentof pages 1-2).

Immune microenvironment (2024 primary study)

A 2024 JITC study comparing EBV+ vs EBV− primary gastric cancers reported: - EBVaGC background prevalence “~5–10% of GCs,” and EBV+ tumors often harbor PD-L1/PD-L2 amplifications with robust CD8+ infiltrates (mcmiller2024immunemicroenvironmentof pages 1-2). - In a treatment-naïve set (11 EBV+, 14 EBV−), CD8+ T-cell densities were higher in EBV+ tumors (p=0.044) (mcmiller2024immunemicroenvironmentof pages 1-2). - PD-L1+ tumor cells were observed in 3/25 tumors (all EBV+) (mcmiller2024immunemicroenvironmentof pages 1-2). - EBV− tumors overexpressed inflammatory/immunosuppressive pathways including COX-2/PGE2 (PTGS2/COX-2 up 32-fold, p=0.005) (mcmiller2024immunemicroenvironmentof pages 1-2).

Recent developments (2023–2024)

• Single-cell immunology in EBV(+) GC under therapy: A 2023 study used longitudinal scRNA-seq/scTCR/BCR-seq in EBV(+) GC treated with immunochemotherapy and reported a baseline intratumoral ISG-15+ CD8+ T-cell population associated with immunotherapy responsiveness, and suggested potential benefit of anti-LAG-3 in refractory EBV(+) GC (bai2022efficacyandpredictive pages 1-2).
• Operationalizing TCGA subtyping in practice: A 2024 NGS-based TCGA-surrogate classification study reported EBV subtype prevalence 5.2% in a real-world cohort and found 100% disease control rate for ICI therapy in EBV and MSI subtypes in a small validation cohort (guo2024developmentofa pages 1-2).

Suggested GO terms (mechanism-level; mapping suggestions)

• GO:0006955 immune response
• GO:0002682 regulation of immune system process
• GO:0006325 chromatin organization (epigenetic remodeling)
• GO:0006306 DNA methylation
• GO:0008283 cell population proliferation

Suggested CL terms (cell types)

• CD8+ T cell (CL:0000625)
• Macrophage (CL:0000235); M2-like macrophage signatures noted for immunosuppressive environments (mcmiller2024immunemicroenvironmentof pages 1-2)
• Gastric epithelial cell (use appropriate CL term for gastric mucosal epithelial lineage in curation)

6. Epidemiology, population, and prognosis

Prevalence / proportion among gastric cancers

Estimates vary by region and detection methods. - Meta-analysis (2023): EBV infection in 10.4% of gastric carcinomas (95% CI 8.2%–13.1%) (pyo2023prognosticimplicationof pages 1-2). - TCGA-surrogate NGS cohort (2024): EBV subtype 5.2% (guo2024developmentofa pages 1-2). - Individual cohorts can range widely (e.g., 12.62% in one 420-patient cohort) (li2023assessingepstein–barrvirus pages 1-2).

Prognosis (statistics)

A 2023 systematic review/meta-analysis (57 studies; 22,943 patients) found EBV positivity associated with improved overall survival: HR 0.890 (95% CI 0.816–0.970) (pyo2023prognosticimplicationof pages 1-2). In diffuse-type Lauren classification, EBV positivity was associated with stronger favorable prognosis (HR 0.400, 95% CI 0.300–0.534) (pyo2023prognosticimplicationof pages 1-2).

7. Diagnostics

Core diagnostic definition

EBER in situ hybridization (EBER-ISH) is repeatedly described as the reference method for defining EBV-positive tumor status. - In a clinical cohort paper: “EBV-encoded RNA (EBER) in situ hybridization method was used to evaluate the EBV status in GC” (li2023assessingepstein–barrvirus pages 1-2). - A 2022 immunotherapy biomarker study states EBER-ISH is “regarded as the gold standard” but has limitations for multiplex biomarker assessment (bai2022efficacyandpredictive pages 1-2).

Biomarkers used alongside EBV status

• PD-L1 IHC (CPS): EBV+ GCLS shows high PD-L1 positivity; CPS ≥1/≥5/≥10 were 81.8%, 70.2%, 55.3% in EBV+ GCLS (lee2024associationofpdl1 pages 1-2).
• MSI/MMR status: EBV+ GCLS rarely MSI-high (0.6%), while EBV− GCLS often MSI-high (54.5%) (lee2024associationofpdl1 pages 1-2).

Omics-based diagnostics / recent development

An NGS-based EBV detection algorithm (validated against EBER-ISH) achieved sensitivity 95.7% and specificity 100%, and enabled simultaneous evaluation of clinically relevant biomarkers (TMB, MSI, HER2, fusions) (bai2022efficacyandpredictive pages 1-2).

Differential diagnosis

Differential considerations include MSI-high gastric cancers (which can also have prominent immune infiltration and PD-L1 positivity) and other histologic subtypes; in lymphoid-stroma–rich tumors, MSI-H is a key alternate subtype (lee2024associationofpdl1 pages 1-2).

8. Treatment and real-world implementation

Standard-of-care context

EBVaGC is treated within standard gastric cancer algorithms (surgery/endoscopic resection for early disease; chemotherapy ± targeted therapy; immunotherapy in selected advanced settings). EBV positivity is increasingly used as a biomarker relevant to immunotherapy selection/stratification (bai2022efficacyandpredictive pages 1-2, mcmiller2024immunemicroenvironmentof pages 1-2).

Immunotherapy (checkpoint blockade) — response statistics

Evidence suggests EBVaGC can show high response rates to ICI in some cohorts, but responses vary. - In an EBV+/pMMR cohort receiving immunotherapy, ORR 54.5% (12/22) was reported (bai2022efficacyandpredictive pages 1-2). - A prospective observational cohort of 9 stage-IV EBVaGC treated with ICIs reported: “Three patients … showed partial response, 5 stable disease” and the “longest duration of response was 18 months” (xie2020positivestatusof pages 1-2). - A 2024 review summarized heterogeneous reports with ORRs “from 0 to 100%,” including a small pembrolizumab series reported as ORR 100% (n=6; PFS 8.5 months) and a small camrelizumab series with ORR 0% (n=6; DCR 67%; PFS 2.2 months; OS 6.8 months) (corallo2024unlockingthepotential pages 6-8).

Translational/precision treatment implications

• EBV subtype tumors frequently have PD-L1/PD-L2 amplification and inflamed immune phenotype, which supports immunotherapy candidacy (mcmiller2024immunemicroenvironmentof pages 1-2).
• Practical molecular subtyping by NGS may support immunotherapy selection: in one validation cohort, ICI disease control rate was 100% in MSI and EBV subtypes (guo2024developmentofa pages 1-2).

Clinical trials (real-world implementations; ClinicalTrials.gov)

Trials explicitly incorporating EBV status in gastric cancer include: - NCT05970627 (Phase 2): perioperative chemotherapy + toripalimab for EBV-associated locally advanced gastric/EGJ adenocarcinoma (NOT_YET_RECRUITING) (trial retrieved in this run). - NCT03257163 (Phase 2): pembrolizumab + capecitabine + radiation in mismatch-repair deficient and EBV-positive gastric cancer (ACTIVE_NOT_RECRUITING) (trial retrieved in this run). - NCT05535569 (Phase Ib/II): nivolumab + paclitaxel in EBV-related/MSI-H/PD-L1+ advanced gastric cancer (COMPLETED) (trial retrieved in this run).

Suggested MAXO terms (treatment action ontology; mapping suggestions)

• Immune checkpoint inhibitor therapy (e.g., anti-PD-1): MAXO term for immune checkpoint inhibitor therapy (exact ID to be selected during ontology curation)
• Gastric cancer surgical resection / gastrectomy
• Endoscopic submucosal dissection (for select early lesions)
• Radiotherapy / chemoradiation

9. Model organisms and experimental models

Mechanistic and translational models

Direct model-system inventories (specific EBV+ gastric cancer organoids, cell lines, or mouse models) were not comprehensively extractable from the retrieved evidence set. However, EBV-associated gastric cancer is frequently studied using EBV-positive gastric cancer cell lines and xenografts in mechanistic viral-miRNA studies; for example, EBV-encoded miR-BART11-3p was shown to modulate the DUSP6–MAPK axis and promote proliferation/metastasis-related phenotypes in vitro and in vivo (xenograft context) (paper retrieved but not evidence-extracted in this run).

10. Summary table artifact

Key quantitative facts from recent literature are consolidated below.

Table: This table consolidates key quantitative findings for EBV-associated gastric cancer across prevalence, molecular features, biomarkers, immune microenvironment, prognosis, and immunotherapy response. It is useful as a quick-reference evidence summary for knowledge base curation and clinical/research interpretation.

11. Expert opinion / authoritative synthesis (2024 reviews)

A 2024 Frontiers in Immunology review emphasizes that EBVaGC is “immunologically, molecularly and pathologically distinct” and describes an overall “immune-hot” phenotype with improved prognosis relative to EBV-negative gastric cancers (salnikov2024theviraletiology pages 1-2). A 2024 Pathogens review highlights EBV status as a potential predictive biomarker for response to chemotherapy and immune checkpoint inhibitors, while emphasizing heterogeneity and the need for prospective validation (corallo2024unlockingthepotential pages 6-8).

Data gaps and limitations (important for knowledge base curation)

• Ontology identifiers (MONDO/MeSH/ICD) were not obtainable from the retrieved full-text evidence in this run; dedicated ontology/database queries are needed.
• Several key statements in recent reviews summarize TCGA-era findings; where possible, this report relied on cohort studies/meta-analyses for quantitative estimates (pyo2023prognosticimplicationof pages 1-2, guo2024developmentofa pages 1-2, mcmiller2024immunemicroenvironmentof pages 1-2).
• Immunotherapy response estimates remain largely based on small cohorts and heterogeneous designs; even recent syntheses report ORRs ranging from 0–100% across small series (corallo2024unlockingthepotential pages 6-8, bai2022efficacyandpredictive pages 1-2).

References

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2. (li2023assessingepstein–barrvirus pages 1-2): Guanghua Li, Zhihao Zhou, Zhixiong Wang, and Zhao Wang. Assessing epstein–barr virus in gastric cancer: clinicopathological features and prognostic implications. Infectious Agents and Cancer, Feb 2023. URL: https://doi.org/10.1186/s13027-023-00489-9, doi:10.1186/s13027-023-00489-9. This article has 12 citations and is from a peer-reviewed journal.

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