Mapping immune trajectories from Helicobacter pylori gastritis to gastric cancer

Helicobacter pylori-associated gastric tumorigenesis is typically staged by histopathology, yet clinical decisions often hinge on immune dynamics that histology alone cannot resolve. This mini review introduces a phase-specific “immune snapshot” framework that integrates three elements—who is present in the microenvironment, how key circuits are engaged, and where these features reside—to track immune trajectories across chronic gastritis, atrophy with intestinal metaplasia, and dysplasia or early cancer. Rather than cataloging markers, we emphasized a practical approach that maps compact tissue and fluid assays to decision points, including when to normalize stroma and vasculature, when to prime antigenicity, and when to deploy checkpoint blockade. The novelty lies in linking stage-aware immune states to actionable sequencing of interventions and to risk-adapted surveillance intervals. By complementing histopathology with dynamic, mechanism-informed readouts that are feasible in routine practice, the framework aims to identify reversible barriers, clarify timing, and improve the effectiveness of prevention and early immunotherapy.

1 Introduction

Helicobacter pylori is one of the most prevalent chronic infections worldwide and a principal driver of the Correa cascade—from chronic active gastritis to atrophy, intestinal metaplasia, dysplasia, and ultimately gastric cancer (1). Although eradication therapy reduces population-level risk, patients who have already reached atrophy or intestinal metaplasia retain substantial residual risk, underscoring the limits of histology-only stratification and the need for immune-informed frameworks (2). Beyond epithelial mutations and epigenetic drift, mounting evidence shows that dynamic remodeling of the gastric immune microenvironment—oscillating among inflamed, immune-excluded, and immune-desert states—critically shapes progression along this continuum.

In addition to histologic and immune changes, the gastric cancer cascade is accompanied by stepwise molecular and genetic alterations, including H. pylori virulence factor-driven signaling, epigenetic drift, and progressive accumulation of oncogenic mutations. Many of these events are closely intertwined with the evolving immune context, shaping antigen presentation, cytokine networks, and the balance between effector and regulatory cells. At disease inception, gastric epithelial and myeloid cells sense microbial patterns via Toll-like receptors (TLRs) and Nucleotide-binding oligomerization domain (NOD)-like receptors, activating the NF-κB/IRF pathways, and inducing IL-1β, IL-6, TNF-α, and CXCL8 that recruit neutrophils and monocytes (3). With chronicity, IL-6/STAT3 and TGF-β/SMAD axes remain engaged, Th1/Th17 polarization intensifies alongside inducible regulatory T cells, and myeloid programs skew toward suppression [tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs)] (4). Antigen-presentation pathways are perturbed—including aberrant epithelial HLA class II—while interferon-γ drives early checkpoint induction such as PD-L1 on epithelial and myeloid compartments, foreshadowing T-cell dysfunction (5). H. pylori virulence further rewires immunity: CagA reprograms epithelial signaling and polarity, whereas VacA dampens T-cell proliferation and impairs dendritic maturation, creating high antigenic stimulation but blunted effector function (6, 7).

Contemporary technologies now enable high-resolution profiling of these transitions. Single-cell and spatial transcriptomics, multiplex immunohistochemistry/fluorescence, imaging mass cytometry, and complementary fluid assays delineate cell states, pathway activity, and spatial relationships within intact tissue architecture (8). In parallel, clinical experience with PD-1/PD-L1 inhibitors has highlighted the relevance of pre-existing T-cell inflammation, checkpoint expression, myeloid suppression, TGF-β-rich stroma, vascular abnormalities, and antigen-presentation integrity in gastrointestinal cancers, including gastric cancer (9).

To integrate these strands, we proposed phase-specific “immune snapshots” that concurrently profile three dimensions: cellular composition and activation, pathway activity (IFN-γ/STAT1, IL-6/STAT3, TGF-β/SMAD, NF-κB, WNT/β-catenin, antigen presentation, immune checkpoints, and VEGF/vasculature), and spatial distribution (intralesional, perilesional, and stromal) (4, 10). This framework tracks immune trajectories across H. pylori-driven gastric tumorigenesis—encompassing chronic gastritis, atrophy with intestinal metaplasia, and dysplasia/early cancer—and links each phase to actionable assays and interventions, such as stromal normalization prior to immunotherapy or antigenicity priming before checkpoint blockade. Our goal is to complement conventional histopathology with dynamic, mechanism-oriented metrics that delineate windows for intervention.

The concept of immune contexture highlights the prognostic value of immune-cell type, density, and location, forming the basis of the Immunoscore. Our immune snapshot builds on this by adding functional pathway readouts and barrier features such as CAF/CXCL12 fences, extracellular matrix (ECM) structure, and vascular dysfunction, now measurable by multiplex and spatial assays. In short, contexture addresses who is present and where, while an immune snapshot also asks what they are doing and why access fails or succeeds. Immune snapshots can be aligned with tumor immune microenvironment (TIME) categories. Stage I lesions resemble an inflamed TIME with emerging PD-L1. Stage II resembles an immune-excluded TIME marked by chemokine-rich but entry-poor profiles and CAF/TGF-β/VEGF activity. Stage III splits into inflamed-exhausted or desert/low-presentation states depending on antigen-presentation integrity (11). This mapping clarifies transitions between TIME classes and suggests mechanism-guided interventions. Figure 1 summarizes the phase-specific immune snapshot framework, aligning who–how–where readouts with stage-specific intervention windows.

Figure 1

Figure 1. Phase-specific immune snapshots in Helicobacter pylori-driven gastric tumorigenesis. Diagram linking three readout layers—who is present, how circuits are engaged, and where they reside—to stage-specific decisions across chronic gastritis, atrophy with intestinal metaplasia, and dysplasia/early cancer, highlighting eradication, stromal/vascular normalization, antigenicity priming, and checkpoint blockade.

2 Stage I—chronic non-atrophic gastritis: inflammatory set-point and immune orientation

At the outset of chronic non-atrophic gastritis, the stomach does not jump directly to immune exclusion. It first establishes an inflamed set-point. Epithelial cells and lamina propria myeloid cells sense H. pylori through TLR and NOD receptors, activate the NF-κB and IRF pathways, and release IL-1β, IL-6, TNF-α, and CXCL8, which recruit neutrophils and monocytes (12). A low trickle of IFN-γ appears as Th1 priming begins. This reinforces antimicrobial tone and remodels antigen presentation (13). With ongoing exposure, neutrophils remain abundant, and monocyte-derived macrophages and dendritic cells accumulate in superficial glands and the lamina propria. Antigen-processing machinery increases, and aberrant epithelial HLA class II may emerge, prolonging local T-cell stimulation and driving tissue injury (14). The adaptive response tilts toward Th1 and Th17, while inducible Tregs expand as counter-regulation. IFN-γ improves presentation yet also induces PD-L1 on epithelial and myeloid cells, creating one of the earliest checkpoint brakes in H. pylori gastritis (15). Virulence factors tune these programs. CagA perturbs epithelial signaling through SHP-2–ERK and weakens junctional integrity, amplifying inflammatory and growth cues. VacA reduces T-cell proliferation and blunts dendritic cell maturation, lowering effector quality despite persistent antigenic stimulation (16). For example, H. pylori CagA and VacA not only activate the epithelial signaling pathways (such as SHP-2/ERK and disruption of cell polarity) but also dampen dendritic cell function and T-cell activation, thereby promoting persistent inflammation and early immune evasion. Even before a CAF-rich stroma develops, chronic injury has frayed tight junctions and the mucus layer, increased leakage of microbial patterns, and repeatedly re-engaged TLR and NOD circuits. Early myofibroblast activation, light extracellular matrix deposition, and uneven perfusion together pre-condition the tissue for later immune exclusion.

A pragmatic phase-specific immune snapshot can be obtained without exhaustive testing. Map CD8⁺, Th17, and Tregs across superficial glands and the lamina propria using multiplex immunohistochemistry (IHC) or immunofluorescence (IF) or imaging mass cytometry, and when needed, pair these with single-cell or spatial transcriptomics to couple cellular state with position (17). Add pathway readouts such as NF-κB targets exemplified by CXCL8, p-STAT1 as an IFN-γ response marker, and antigen-processing genes TAP1 and PSMB measured by RNA- In situ hybridization (ISH) or RNA-seq and phospho-IHC (18). Complement these with checkpoint and presentation metrics such as PD-L1 H-score in epithelial and myeloid compartments and epithelial HLA class II aberrancy assessed by multiplex IHC or IF or by flow cytometry on dissociated biopsies. Non-invasive adjuncts from gastric juice or serum, including IL-6 and CXCL8, together with exploratory exosomal PD-L1, can mirror mucosal activation and restraint.

Clinically, this is the reversible window. Eradication remains the principal disease-modifying step. Coupling routine histology with a minimalist immune snapshot that includes the CD8 to Treg ratio, PD-L1 H-score, p-STAT1, and gastric juice IL-6 and CXCL8 can identify patients drifting toward patterns that later consolidate into exclusionary CAF and TGF-β-rich phenotypes (19). This information can guide surveillance and the timing of microenvironment-normalizing strategies.

3 Stage II—atrophic gastritis and intestinal metaplasia

Atrophy with intestinal metaplasia marks a decisive bend in the H. pylori cascade. Chronic epithelial injury now couples to stromal remodeling and sustained checkpoint engagement, and the mucosa shifts from a chiefly inflamed state toward regulatory and immune-excluded phenotypes (20). Under this pressure, epithelial plasticity, myeloid reprogramming, and barrier dysfunction evolve in step with persistent IL-6 and STAT3 activity together with TGF-β and SMAD signaling. These programs redefine where and when effector lymphocytes can act (21). In clinical practice, this phase often coincides with field-wide epigenetic change and altered acid dynamics that reshape host–microbe interactions and help lock in exclusion circuits even when bacterial burden declines (22). The result is a chemokine-rich microenvironment that becomes progressively less permissive to productive T-cell engagement (23).

Several drivers push tissue toward atrophy and metaplasia. Ongoing H. pylori colonization maintains epithelial pattern-recognition signaling and oxidative stress, while CagA and VacA amplify junctional disruption and antigenic drive that bias lineage programs toward atrophy and reprogramming (24, 25). Parietal-cell loss reduces acid and reshapes the physicochemical niche, which favors overgrowth of oral and intestinal taxa and the accumulation of nitrosative by-products that reinforce epithelial injury and metaplastic commitment. In parallel, chronic IL-6 with STAT3 and TGF-β with SMAD link repair programs to immunoregulatory outputs, dampening cytotoxicity and strengthening stromal crosstalk (26). Eradication often lowers inflammation, yet IL-6 with STAT3 and TGF-β with SMAD may remain partially imprinted, sustaining metaplastic fields that no longer depend on pathogen status.

Beyond the morphologic changes, atrophic gastritis with intestinal metaplasia is characterized by a progressive increase in CpG island methylation (“methylation index”) and promoter hypermethylation of key genes such as CDH1, CDKN2A (p16), and RUNX3. These epigenetic lesions arise in the setting of chronic H. pylori-driven inflammation and DNA methyltransferase activation and are paralleled by dysregulation of microRNAs that sustain IL-6/STAT3, NF-κB, and TGF-β signaling. Together, these changes link epithelial epigenetic drift to the expansion of immunoregulatory circuits and the establishment of an immunosuppressive microenvironment at this stage.

The immune-cell landscape also tilts. Neutrophils wane relative to monocyte-derived macrophages and dendritic cells, which cluster around metaplastic glands and adopt mixed inflammatory and regulatory states with IL-10 and TGF-β output (14). CD8⁺ and Th1 or Th17 effectors collect at lesion margins, while inducible Tregs and checkpoint-positive myeloid cells rise within and around metaplastic epithelium, raising the threshold for productive cytotoxicity. Epithelial and myeloid PD-L1 increase early, often together with VISTA or TIGIT, and this pattern foreshadows later exhaustion despite interferon priming (27). At the same time, cDC1 frequency and maturation fall relative to cDC2, cross-priming weakens, and CD103⁺ tissue-resident T cells remain constrained by TGF-β and nutrient competition (28).

A practical Stage II immune snapshot is feasible and does not require exhaustive testing. First, quantify CAF density marked by α-SMA, the perilesional ring of CD8⁺ T cells, and the number and location of tertiary lymphoid structures. Map immature and mature dendritic cells and checkpoint-positive macrophages with multiplex IHC or IF and imaging mass cytometry, and add spatial transcriptomics when positioning is needed (18, 29). Next, read out the key pathways. Measure p-STAT3 and p-SMAD2 or p-SMAD3 in epithelium and stroma, and align CXCL12 and CXCL9 to CXCL11 transcripts to cell positions using RNA-ISH or RNA-seq with phospho-IHC (30). Add checkpoint and presentation metrics that capture the chemokine-high yet entry-low paradox, including PD-L1 H-score in epithelial and myeloid cells; patterns of VISTA, TIGIT, and LAG-3; and the status of HLA and TAP1 modules. Finally, document stromal and vascular features that explain failed CD8⁺ accrual, including collagen signatures, microvascular metrics, and hypoxia markers, and consider fluid adjuncts such as serum or gastric juice IL-6 and TGF-β together with exploratory exosomal PD-L1 and CXCL12 for longitudinal monitoring (31–33).

From a clinical standpoint, the dominant picture is immune exclusion. Perilesional CD8⁺ accumulation, high CAF and TGF-β with CXCL12, and engaged checkpoints argue that barrier relief should precede or accompany PD-1 or PD-L1 therapy. Anti-TGF-β, anti-VEGF, and selected matrix-targeted strategies are rational entry points, and surveillance intervals may need to be shortened when exclusion signatures coincide with extensive metaplasia (23). To operationalize this stage, we proposed a minimal immune snapshot that aligns core readouts with corresponding actions (Table 1).

Table 1

Table 1. Minimal immune snapshot for atrophic gastritis/intestinal metaplasia.

4 Stage III—dysplasia and early gastric cancer: consolidation of immune escape

As metaplasia progresses to dysplasia and early cancer, the immune terrain hardens into escape. CD8⁺ T cells co-express PD-1, TIM-3, LAG-3, and TIGIT, with lower GZMB and IFN-γ and with poor metabolic flexibility, which together signal entrenched exhaustion (34). Clonotypes narrow and proliferation lags, so interferon-response modules uncouple from effective killing. TOX and NR4A programs and DNA methylation marks consolidate these states, while mitochondrial dysfunction and lipid accumulation limit recovery even when PD-1 is blocked (35, 36).

Myeloid suppression comes to the fore. Tumor-associated macrophages that are CD163⁺ and ARG1⁺, and myeloid-derived suppressor cells expand in dysplastic and early carcinoma niches and release IL-10, TGF-β, and PGE₂, which blunt T-cell activation and antigen presentation (37). Crosstalk between myeloid cells and cancer-associated fibroblasts sustains suppressive polarization and builds an exclusionary stromal layout. Myeloid aggregates cap gland entrances, and fibroblast cords align with vessels, channeling traffic away from epithelial cores (38).

Tumor-intrinsic escape tightens. HLA class I/II fall or are lost, β2-microglobulin can be altered, and TAP1 and PSMB components are impaired, so neoantigens are less visible despite inflammation. Loss of heterozygosity at HLA together with β2M defects often appears with promoter silencing across antigen-processing genes, creating bottlenecks at several nodes (39). Two subsets stand out as partial exceptions. Epstein–Barr virus (EBV)-positive and Microsatellite instability (MSI)-high disease can retain immunogenicity and respond to PD-1 blockade. Exclusion circuits run in parallel (40). WNT and β-catenin activities drive dendritic cell scarcity and weak priming, while VEGF skews vessels and hypoxia deepens, restricting leukocyte trafficking and reinforcing suppression (41, 42).

A Stage III snapshot can be built with a focused panel rather than an exhaustive one. First, map exhausted CD8⁺ cells defined by the co-expression of PD-1, TIM-3, LAG-3, and TIGIT, together with the abundance and location of tumor-associated macrophages, myeloid-derived suppressor cells, and cancer-associated fibroblasts (43). Chart intratumoral and peritumoral distribution with multiplex IHC or IF, imaging mass cytometry, and spatial transcriptomics. Next, align pathways with position. Read exhaustion signatures, IL-10 with TGF-β with PGE₂ programs, and WNT with β-catenin and VEGF modules (33). Add presentation and checkpoint metrics to capture concurrent antigen-presentation loss and checkpoint dominance. Track HLA class I/II, β2M, and TAP1 or PSMB9 together with PD-L1 H-scores. Where tissue sampling is limited, add fluid surrogates. Cytokine panels and exosomal PD-L1 can complement tissue readouts, and circulating tumor DNA (ctDNA) together with T-cell receptor (TCR) or B-cell receptor (BCR) fragments can assist longitudinal tracking (44). In addition to tissue analysis, ctDNA could be explored using both mutation-based and methylation-based markers. Candidate mutations include recurrent gastric cancer driver alterations, while promoter methylation of genes such as CDH1, CDKN2A (p16), and RUNX3 may serve as epigenetic readouts of field cancerization. These ctDNA strategies are currently exploratory but may help to non-invasively monitor genetic and epigenetic escape in advanced stages.

Therapeutically, single-agent PD-1 or PD-L1 is unlikely to be durable without circuit relief. Practical triage follows the dominant barrier (43). An inflamed yet exhausted pattern points to PD-1 combined with TIGIT or LAG-3 plus metabolic support. An excluded pattern calls for anti-TGF-β or anti-VEGF or selected matrix-targeted approaches first, followed by PD-1. A low-presentation pattern benefits from a brief priming step, such as radiation or chemotherapy, or oncolytic vectors, before checkpoint therapy. Pair tissue snapshots with fluid surrogates to time barrier relief or priming cycles and to adjust surveillance between interventions (42).

5 Discussion

This mini-review proposes phase-specific “immune snapshots” that integrate cellular composition and activation, key pathway activity, and spatial distribution across chronic gastritis, atrophy with intestinal metaplasia, and dysplasia or early cancer. The framework links conventional histopathology to actionable mechanistic readouts and aims to identify reversible immune barriers and optimal windows for intervention.

The conceptual advance is the coupling of immune contexture with functional states, including IFN-γ/STAT1, IL-6/STAT3, TGF-β/SMAD, antigen presentation, and checkpoint pathways, together with CAF programs, extracellular matrix and vascular features, and spatial logic of infiltration. In early lesions, preserved antigen presentation can coexist with PD-L1, indicating a brake that may be lifted by timely therapy. With progression, CAF- and TGF-β-associated exclusion, endothelial non-responsiveness, and downregulated antigen presentation consolidate T-cell exhaustion, which helps explain the limited activity of single-agent PD-1 or PD-L1 therapy.

Clinical translation can rely on concise, standardized assays. Multiplex IHC or imaging mass cytometry can enumerate CD8, Treg, TAM, CAF, and Tertiary lymphoid structure (TLS) while preserving compartmental information. RNA-ISH or targeted RNA sequencing can map p-STAT1, p-STAT3, p-SMAD2/3, and chemokine gradients, combined with HLA, β2M, TAP, and PSMB, to assess antigen processing and presentation. When tissue is scarce, gastric juice or serum measures of IL-6, TGF-β, and vesicle-associated PD-L1 and CXCL12 may serve as longitudinal surrogates. Snapshot-guided decisions include stromal and vascular normalization before immunotherapy, and antigenicity priming and dendritic cell activation before checkpoint blockade. The framework also supports individualized risk stratification and surveillance. In the setting of intestinal metaplasia, high Cancer-associated fibroblast (CAF) density, strong TGF-β/SMAD signaling, perilesional CD8 rings, and vascular abnormalities argue for shortened endoscopic intervals. When the inflammatory set-point is restored, IL-6/STAT3 activity is reduced, and PD-L1 is modest, standard intervals may be appropriate.

Limitations include uncertainty from cross-sectional sampling, anatomic heterogeneity within the stomach, non-equivalence among checkpoint and stromal axes, and the unproven tissue specificity of fluid biomarkers. Future work should prospectively validate snapshot–risk associations in eradication and surveillance cohorts, test barrier-first and priming-then-checkpoint sequences, and establish a minimal standardized panel with operational thresholds for routine care. These sequencing strategies are mechanistically derived and warrant prospective validation with spatial pharmacodynamic readouts to confirm benefit.

6 Conclusion

In sum, immune snapshots recast H. pylori-associated gastric tumorigenesis as a sequence of modifiable immune states. By converting cellular, pathway, and spatial information into executable assays and sequencing rules, this approach has the potential to improve stratification and therapeutic efficacy beyond histopathology alone.

Author contributions

YS: Formal Analysis, Data curation, Writing – original draft. JQ: Methodology, Writing – original draft. JW: Writing – original draft, Investigation. WL: Data curation, Writing – original draft. QW: Project administration, Funding acquisition, Resources, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Hooi JKY, Lai WY, Ng WK, Suen MMY, Underwood FE, Tanyingoh D, et al. Global prevalence of helicobacter pylori infection: systematic review and meta-analysis. Gastroenterology. (2017) 153:420–9. doi: 10.1053/j.gastro.2017.04.022

PubMed Abstract | Crossref Full Text | Google Scholar

2. Liou JM, Malfertheiner P, Lee YC, Sheu BS, Sugano K, Cheng HC, et al. Screening and eradication of helicobacter pylori for gastric cancer prevention: the taipei global consensus. Gut. (2020) 69:2093–112. doi: 10.1136/gutjnl-2020-322368

PubMed Abstract | Crossref Full Text | Google Scholar

3. Camilo V, Sugiyama T, and Touati E. Pathogenesis of helicobacter pylori infection. Helicobacter. (2017) 22 Suppl 1:hel.12405. doi: 10.1111/hel.12405

PubMed Abstract | Crossref Full Text | Google Scholar

4. Yu H, Pardoll D, and Jove R. Stats in cancer inflammation and immunity: A leading role for stat3. Nat Rev Cancer. (2009) 9:798–809. doi: 10.1038/nrc2734

PubMed Abstract | Crossref Full Text | Google Scholar

5. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. Ifn-Γ-related mrna profile predicts clinical response to pd-1 blockade. J Clin Invest. (2017) 127:2930–40. doi: 10.1172/jci91190

PubMed Abstract | Crossref Full Text | Google Scholar

6. Hatakeyama M. Structure and function of helicobacter pylori caga, the first-identified bacterial protein involved in human cancer. Proc Japan Acad Ser B Phys Biol Sci. (2017) 93:196–219. doi: 10.2183/pjab.93.013

PubMed Abstract | Crossref Full Text | Google Scholar

7. Cover TL and Blanke SR. Helicobacter pylori vaca, a paradigm for toxin multifunctionality. Nat Rev Microbiol. (2005) 3:320–32. doi: 10.1038/nrmicro1095

PubMed Abstract | Crossref Full Text | Google Scholar

8. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Sci (New York NY). (2016) 353:78–82. doi: 10.1126/science.aaf2403

PubMed Abstract | Crossref Full Text | Google Scholar

9. Paz-Ares L, Ciuleanu TE, Cobo M, Schenker M, Zurawski B, Menezes J, et al. First-line nivolumab plus ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (Checkmate 9la): an international, randomised, open-label, phase 3 trial. Lancet Oncol. (2021) 22:198–211. doi: 10.1016/s1470-2045(20)30641-0

PubMed Abstract | Crossref Full Text | Google Scholar

10. Spranger S, Bao R, and Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. (2015) 523:231–5. doi: 10.1038/nature14404

PubMed Abstract | Crossref Full Text | Google Scholar

11. Joyce JA and Fearon DT. T cell exclusion, immune privilege, and the tumor microenvironment. Sci (New York NY). (2015) 348:74–80. doi: 10.1126/science.aaa6204

PubMed Abstract | Crossref Full Text | Google Scholar

12. Zhang Y, Yan Z, Jiao Y, Feng Y, Zhang S, and Yang A. Innate immunity in helicobacter pylori infection and gastric oncogenesis. Helicobacter. (2025) 30:e70015. doi: 10.1111/hel.70015

PubMed Abstract | Crossref Full Text | Google Scholar

13. Parang K and Paydary K. Inflammation and detection: rethinking the biomarker landscape in gastric cancer. World J Clin Oncol. (2025) 16:109717. doi: 10.5306/wjco.v16.i9.109717

PubMed Abstract | Crossref Full Text | Google Scholar

14. Del Prete A, Salvi V, Soriani A, Laffranchi M, Sozio F, Bosisio D, et al. Dendritic cell subsets in cancer immunity and tumor antigen sensing. Cell Mol Immunol. (2023) 20:432–47. doi: 10.1038/s41423-023-00990-6

PubMed Abstract | Crossref Full Text | Google Scholar

15. Imai Y, Chiba T, Kondo T, Kanzaki H, Kanayama K, Ao J, et al. Interferon-Γ Induced pd-L1 expression and soluble pd-L1 production in gastric cancer. Oncol Lett. (2020) 20:2161–8. doi: 10.3892/ol.2020.11757

PubMed Abstract | Crossref Full Text | Google Scholar

16. Takahashi-Kanemitsu A, Lu M, Knight CT, Yamamoto T, Hayashi T, Mii Y, et al. The helicobacter pylori caga oncoprotein disrupts wnt/pcp signaling and promotes hyperproliferation of pyloric gland base cells. Sci Signaling. (2023) 16:eabp9020. doi: 10.1126/scisignal.abp9020

PubMed Abstract | Crossref Full Text | Google Scholar

17. Li J, Li Z, and Wang K. Targeting angiogenesis in gastrointestinal tumors: strategies from vascular disruption to vascular normalization and promotion strategies angiogenesis strategies in gi tumor therapy. Front Immunol. (2025) 16:1550752. doi: 10.3389/fimmu.2025.1550752

PubMed Abstract | Crossref Full Text | Google Scholar

18. Semba T and Ishimoto T. Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues. Br J Cancer. (2024) 131:1737–47. doi: 10.1038/s41416-024-02882-6

PubMed Abstract | Crossref Full Text | Google Scholar

19. Kumar N, Prakash PG, Wentland C, Kurian SM, Jethva G, Brinkmann V, et al. Decoding spatiotemporal transcriptional dynamics and epithelial fibroblast crosstalk during gastroesophageal junction development through single cell analysis. Nat Commun. (2024) 15:3064. doi: 10.1038/s41467-024-47173-z

PubMed Abstract | Crossref Full Text | Google Scholar

20. Entezam M, Sanaei MJ, Mirzaei Y, Mer AH, Abdollahpour-Alitappeh M, Azadegan-Dehkordi F, et al. Current progress and challenges of immunotherapy in gastric cancer: A focus on car-T cells therapeutic approach. Life Sci. (2023) 318:121459. doi: 10.1016/j.lfs.2023.121459

PubMed Abstract | Crossref Full Text | Google Scholar

21. Reyes ME, Pulgar V, Vivallo C, Ili CG, Mora-Lagos B, and Brebi P. Epigenetic modulation of cytokine expression in gastric cancer: influence on angiogenesis, metastasis and chemoresistance. Front Immunol. (2024) 15:1347530. doi: 10.3389/fimmu.2024.1347530

PubMed Abstract | Crossref Full Text | Google Scholar

22. Rhodes JD, Goldenring JR, and Lee SH. Regulation of metaplasia and dysplasia in the stomach by the stromal microenvironment. Exp Mol Med. (2024) 56:1322–30. doi: 10.1038/s12276-024-01240-z

PubMed Abstract | Crossref Full Text | Google Scholar

23. Bruni S, Mercogliano MF, Mauro FL, Cordo Russo RI, and Schillaci R. Cancer immune exclusion: breaking the barricade for a successful immunotherapy. Front Oncol. (2023) 13:1135456. doi: 10.3389/fonc.2023.1135456

PubMed Abstract | Crossref Full Text | Google Scholar

24. Peng Y, Lei X, Yang Q, Zhang G, He S, Wang M, et al. Helicobacter pylori caga-mediated ether lipid biosynthesis promotes ferroptosis susceptibility in gastric cancer. Exp Mol Med. (2024) 56:441–52. doi: 10.1038/s12276-024-01167-5

PubMed Abstract | Crossref Full Text | Google Scholar

25. Duan Y, Xu Y, Dou Y, and Xu D. Helicobacter pylori and gastric cancer: mechanisms and new perspectives. J Hematol Oncol. (2025) 18:10. doi: 10.1186/s13045-024-01654-2

PubMed Abstract | Crossref Full Text | Google Scholar

26. Thrift AP, Jove AG, Liu Y, Tan MC, and El-Serag HB. Associations of duration, intensity, and quantity of smoking with risk of gastric intestinal metaplasia. J Clin Gastroenterol. (2022) 56:e71–e6. doi: 10.1097/mcg.0000000000001479

PubMed Abstract | Crossref Full Text | Google Scholar

27. Lee YJ, Baek JH, Kim JG, Park KB, Park JY, Kwon OK, et al. Clinical significance of pd-L1, lag3, and vista in patients with poorly cohesive cell gastric cancer. Anticancer Res. (2025) 45:3939–51. doi: 10.21873/anticanres.17752

PubMed Abstract | Crossref Full Text | Google Scholar

28. Liu J, Zhang X, Cheng Y, and Cao X. Dendritic cell migration in inflammation and immunity. Cell Mol Immunol. (2021) 18:2461–71. doi: 10.1038/s41423-021-00726-4

PubMed Abstract | Crossref Full Text | Google Scholar

29. Nunes JB, Ijsselsteijn ME, Abdelaal T, Ursem R, van der Ploeg M, Giera M, et al. Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling. Nat Methods. (2024) 21:1796–800. doi: 10.1038/s41592-024-02392-6

PubMed Abstract | Crossref Full Text | Google Scholar

30. Choi SW, Kim JH, Hong J, and Kwon M. Mapping immunotherapy potential: spatial transcriptomics in the unraveling of tumor-immune microenvironments in head and neck squamous cell carcinoma. Front Immunol. (2025) 16:1568590. doi: 10.3389/fimmu.2025.1568590

PubMed Abstract | Crossref Full Text | Google Scholar

31. Wang Y, Zhang F, Qian Z, Jiang Y, Wu D, Liu L, et al. Targeting collagen to optimize cancer immunotherapy. Exp Hematol Oncol. (2025) 14:101. doi: 10.1186/s40164-025-00691-y

PubMed Abstract | Crossref Full Text | Google Scholar

32. Ma Y, Zhang X, Liu C, and Zhao Y. Extracellular vesicles in cancers: mechanisms, biomarkers, and therapeutic strategies. MedComm. (2024) 5:e70009. doi: 10.1002/mco2.70009

PubMed Abstract | Crossref Full Text | Google Scholar

33. Shin K, Kim J, Park SJ, Lee MA, Park JM, Choi MG, et al. Prognostic value of soluble pd-L1 and exosomal pd-L1 in advanced gastric cancer patients receiving systemic chemotherapy. Sci Rep. (2023) 13:6952. doi: 10.1038/s41598-023-33128-9

PubMed Abstract | Crossref Full Text | Google Scholar

34. Gumber D and Wang LD. Improving car-T immunotherapy: overcoming the challenges of T cell exhaustion. EBioMedicine. (2022) 77:103941. doi: 10.1016/j.ebiom.2022.103941

PubMed Abstract | Crossref Full Text | Google Scholar

35. Nair R, Somasundaram V, Kuriakose A, Krishn SR, Raben D, Salazar R, et al. Deciphering T-cell exhaustion in the tumor microenvironment: paving the way for innovative solid tumor therapies. Front Immunol. (2025) 16:1548234. doi: 10.3389/fimmu.2025.1548234

PubMed Abstract | Crossref Full Text | Google Scholar

36. Peralta RM, Xie B, Lontos K, Nieves-Rosado H, Spahr K, Joshi S, et al. Dysfunction of exhausted T cells is enforced by mct11-mediated lactate metabolism. Nat Immunol. (2024) 25:2297–307. doi: 10.1038/s41590-024-01999-3

PubMed Abstract | Crossref Full Text | Google Scholar

37. Li J, Sun J, Zeng Z, Liu Z, Ma M, Zheng Z, et al. Tumour-associated macrophages in gastric cancer: from function and mechanism to application. Clin Trans Med. (2023) 13:e1386. doi: 10.1002/ctm2.1386

PubMed Abstract | Crossref Full Text | Google Scholar

38. Wang S, Wang J, Chen Z, Luo J, Guo W, Sun L, et al. Targeting M2-like tumor-associated macrophages is a potential therapeutic approach to overcome antitumor drug resistance. NPJ Precis Oncol. (2024) 8:31. doi: 10.1038/s41698-024-00522-z

PubMed Abstract | Crossref Full Text | Google Scholar

39. Tovar Perez JE, Zhang S, Hodgeman W, Kapoor S, Rajendran P, Kobayashi KS, et al. Epigenetic regulation of major histocompatibility complexes in gastrointestinal Malignancies and the potential for clinical interception. Clin Epigenet. (2024) 16:83. doi: 10.1186/s13148-024-01698-8

PubMed Abstract | Crossref Full Text | Google Scholar

40. Lee SM and Oh H. Association of pd-L1 positivity with epstein barr virus infection and microsatellite instability in gastric carcinomas with lymphoid stroma. Sci Rep. (2024) 14:30932. doi: 10.1038/s41598-024-81764-6

PubMed Abstract | Crossref Full Text | Google Scholar

41. Sun BY, Wang ZT, Chen KZ, Song Y, Wu JF, Zhang D, et al. Mobilization and activation of tumor-infiltrating dendritic cells inhibits lymph node metastasis in intrahepatic cholangiocarcinoma. Cell Death Discov. (2024) 10:304. doi: 10.1038/s41420-024-02079-z

PubMed Abstract | Crossref Full Text | Google Scholar

42. Choi Y and Jung K. Normalization of the tumor microenvironment by harnessing vascular and immune modulation to achieve enhanced cancer therapy. Exp Mol Med. (2023) 55:2308–19. doi: 10.1038/s12276-023-01114-w

PubMed Abstract | Crossref Full Text | Google Scholar

43. Cai L, Li Y, Tan J, Xu L, and Li Y. Targeting lag-3, tim-3, and tigit for cancer immunotherapy. J Hematol Oncol. (2023) 16:101. doi: 10.1186/s13045-023-01499-1

PubMed Abstract | Crossref Full Text | Google Scholar

44. Li JH, Zhang DY, Zhu JM, and Dong L. Clinical applications and perspectives of circulating tumor DNA in gastric cancer. Cancer Cell Int. (2024) 24:13. doi: 10.1186/s12935-024-03209-4

PubMed Abstract | Crossref Full Text | Google Scholar