Dysregulated gastric microbial communities and functional shifts in chronic atrophic versus non-atrophic gastritis: a Helicobacter pylori-Negative observational study

Abstract

Background

Helicobacter pylori (H. pylori) infection was identified as a substantial risk factor for gastric cancer development, but the eradication of H. pylori did not necessarily lead to a reduction in the incidence of gastric cancer. Non-Helicobacter pylori (non-H. pylori) bacteria in the stomach are involved in the transformation of gastritis carcinoma. The aim of this study was to characterize the microbiome composition of the gastric mucosa and its functions in non-H. pylori (H. pylori-negative) patients with chronic atrophic gastritis (CAG) and chronic non-atrophic gastritis (CNAG).

Methods

Fourteen CNAG samples and twenty-three CAG samples were collected. The composition of the gastric microbiome was analyzed using 16 S rDNA gene sequencing. The bioinformatic analysis was performed using alpha and beta diversity analyses, PICRUSt2, and linear discriminant analysis effect size (LEfSe).

Results

The two groups shared the same most abundant bacterial phyla (Pseudomonadota, Bacillota, Actinomycetota, and Bacteroidota). The top 5 most abundant bacterial genera in the CAG group were Sphingomonas, Ralstonia, Brevundimonas, Methyloversatilis, and Pseudomonas. In the CNAG group, the top genera were Brevundimonas, Ralstonia, Sphingomonas, Methyloversatilis, and Acinetobacter. Differential analysis revealed distinct genera between groups: the CAG group showed enrichment in Sphingomonas, Ralstonia, Bradyrhizobium, Roseateles, and Acidithiobacillus, while the CNAG group was enriched in Brevundimonas, Rhodococcus, Hydrogenophaga, Bacteroides, and Leifsonia (p < 0.05). Sphingomonas exhibited a positive correlation with Acidithiobacillus but negative correlations with Brevundimonas, Hydrogenophaga, and Leifsonia. Pathways related to xenobiotic biodegradation, metabolism, signal transduction, cofactor/vitamin metabolism, cancer, infectious diseases, and digestive system were enriched in the CAG group. In contrast, the CNAG group showed enrichment in amino acid metabolism, translation, replication/repair, and terpenoid/polyketide metabolism.

Conclusion

Gastric mucosal microbiota dysbiosis and functional shifts are significantly associated with chronic atrophic gastritis. Taxa such as Sphingomonas and Ralstonia, enriched in CAG patients, may indicate microbial signatures associated with early atrophic transition and provide candidates for further mechanistic validation.

Introduction

The term chronic gastritis refers to inflammatory lesions of the gastric mucosa caused by various etiologies, primarily classified based on the presence or absence of gastric mucosal atrophy into chronic non-atrophic gastritis or chronic atrophic gastritis [1, 2]. The Correa cascade, describing the progression of gastric adenocarcinoma (CNAG → CAG → intestinal metaplasia → dysplasia → gastric cancer), is widely accepted [3]. As CAG with intestinal metaplasia represents a precancerous state, current research prioritizes intercepting this progression [4, 5]. While Helicobacter pylori (H. pylori) infection poses a substantial risk for developing gastric cancer; nevertheless, the occurrence rate remains below 3% in infected individuals, and eradicating H. pylori does not hinder the progression of gastric cancer [6, 7]. Studies have demonstrated the significant contribution of non-H. pylori bacteria in the stomach to the maintenance of the gastric environment and overall human health [8]. The prevailing consensus is that the gastric microbiota exerts a significant influence on the process of carcinogenesis [9]. The presence of chronic atrophic gastritis plays a pivotal role in the progression toward intestinal-type gastric cancer [3], and the presence of non-H. pylori microbiota in the gastric environment might significantly contribute to the pathogenesis of chronic gastritis.

16 S rDNA sequencing enables high-throughput sequencing of bacterial communities in specific environmental or host-derived samples, facilitating the study of microbial community composition, interpretation of diversity, richness, and population structure, and exploration of the relationships between microorganisms and their environment or host. Currently, it has been extensively employed in research to analyze the structural organization and abundance of gastric microorganisms [10, 11].

In our study, gastric biopsies were collected from patients who required upper endoscopy, and the patients were categorized into two groups based on the results of histologic examination and breath tests. Utilizing 16 S rDNA sequencing to investigate the gastric microbiome composition in patients with CNAG and CAG may enhance our understanding of the pathogenesis of gastritis, and offer novel insights and approaches for disease diagnosis and treatment.

Materials and methods

Patients

This cross-sectional study included patients with chronic gastritis who underwent upper gastrointestinal endoscopy at Foshan Hospital of Traditional Chinese Medicine between August 2022 and August 2023. The diagnostic criteria were established according to the 2022 edition of the Chinese Guidelines for Chronic Gastritis [12], and the diagnosis was confirmed based on the results of gastroscopy and histopathological examination. The inclusion criteria were: (a) patients who were older than 18 years and younger than 80 years; (b) patients who did not take antibiotics, proton-pump inhibitors, H2 receptor antagonists, bismuth agents, or mucosal protective agents within the 4 weeks prior to enrollment; (c) patients who did not undergo gastrectomy; and (d) patients without gastric cancer (GC) or other malignancies. The exclusion criteria were as follows: (a) patients who refused to receive upper gastrointestinal endoscopy; (b) patients who were not suitable for upper gastrointestinal endoscopy, including those with high blood pressure, abdominal pain, and/or asthma; and (c) patients with a positive 13–14 C breath test results. Consecutive patients meeting the eligibility criteria were enrolled during the study period. Patients were consecutively enrolled without stratification to reflect real-world clinical practice. The sample size was determined based on the availability of eligible patients during the study period, consistent with similar microbiome studies [13].

A total of 37 participants were included in this research. The individuals were divided into two groups based on the histological characteristics of their gastric mucosa and their H.pylori infection status.

Sample collection

Two pieces of gastric antrum mucosal tissue were obtained at the same location by endoscopic biopsy from each enrolled patient. One sample was processed for histological examination, and the other sample was stored in sterile cryovials at − 80 °C for bacterial DNA extraction.

Bacterial DNA extraction

As directed by the manufacturer, CTAB was used to extract the DNA from the samples. It has been demonstrated that this reagent, which is used to extract DNA from minute amounts of material, works well for most bacteria. Nuclease-free water served as the blank. After the whole DNA was eluted in 50 µL of elution buffer, it was kept at -80 °C until LC-Bio Technology Co., Ltd (Hangzhou, China), was detected via PCR.

PCR amplification and 16 S rDNA sequencing

After amplifying the variable 16S rDNA V3-V4 regions [14], a fragment gene library was created. The forward primer 341F (5’-CCTACGGGNGGCWGCAG-3’) and the reverse primer 805R (5’-GACTACHVGGGTATCTAATCC-3’) were utilized. For each sample and the sequencing universal primers, unique barcodes were applied to the 5’ ends of the primers. A total of 25 µL of reaction mixture which included 2.5 µL of each primer, 12.5 µL of PCR Premix, and PCR-grade water to regulate the volume was used for PCR amplification. Prokaryotic 16 S fragments were amplified using PCR under the following conditions: 30 s of initial denaturation at 98 °C, 32 cycles of denaturation at 98 °C for 10 s, annealing at 54 °C for 30 s, and extension at 72 °C for 45 s, with a final extension at 72 °C for 10 min. Electrophoresis on a 2% agarose gel was used to validate the PCR results. Throughout the DNA extraction procedure, ultrapure water was utilized as a negative control in place of a sample solution to reduce the risk of false-positive PCR results. Qubit^® (Invitrogen, USA) was used to quantify the PCR products after they had been purified using AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA). The amplified pools were prepared for sequencing, and the size and quantity of the amplicon library were evaluated using an Agilent 2100 Bioanalyzer (Agilent, USA) and an Illumina Library Quantification Kit (Kapa Biosciences, Woburn, MA, USA), respectively. Libraries were sequenced on an Illumina NovaSeq PE250 platform.

Bioinformatic processing

The samples were sequenced on an Illumina NovaSeq platform following the manufacturer’s recommendations provided by LC-Bio. Paired-end reads were assigned based on the unique barcode of each sample and then truncated by removing the barcode and primer sequence. Subsequently, FLASH was used to merge paired-end reads. The raw reads were subjected to quality filtering using fqtrim v0.94 under specific conditions to eliminate low-quality sequences and obtain high-quality clean tags. Vsearch software (v2.3.4) was used for further screening of chimeric sequences, while DADA2 [15] performed dereplication to generate feature lists and sequences. The sequence numbers were randomly normalized to ensure consistency across all samples. Samples with missing sequencing data or failed PCR amplification were excluded from the analysis. Bioinformatic processing (QIIME2 [16]) included amplicon sequence variant (ASV) generation and diversity calculations, followed by taxonomic annotation of ASVs via SILVA database alignment (BLAST). Subsequently, feature abundance was normalized based on the relative abundance of each sample.

Statistical analysis

For continuous variables, data normality was assessed using the Shapiro–Wilk test. Normally distributed variables are presented as mean ± standard deviation (SD) and analyzed via the independent samples t-test. Non-normally distributed variables are summarized as median (interquartile range, IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages. Group comparisons were performed using the chi-square test, with a two-tailed p-value < 0.05 considered statistically significant. Bacterial abundance and diversity were assessed using alpha diversity analysis, with results described by the Shannon index, Chao1 index, and Observed Species. The similarity of bacterial composition was evaluated through beta diversity analysis. Principal component analysis (PCA), nonmetric multidimensional scaling analysis, and ANOSIM were employed to demonstrate differences among the samples. The statistical analysis was performed using IBM SPSS Statistics v29.0 and R v4.1.0. The biomarkers were categorized into two groups using linear discriminant analysis (LDA) effect size (LEfSe). The correlation analysis of bacterial genera was performed using a Spearman network and corrplot, with a threshold for the correlation coefficient |ρ| > 0.4. Functional pathway predictions were performed using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) with the KEGG database. Differential abundance of functional pathways was statistically assessed using STAMP v2.1.3 (Statistical Analysis of Metagenomic Profiles) with two-tailed Welch’s t-test (p < 0.05). Nonparametric ANCOVA (Quade’s test) was applied to adjust for age effects on alpha diversity and ASV abundance after rank transformation of age. For beta diversity, PERMANOVA with rank-transformed age as a covariate was implemented using 999 permutations (vegan package in R).

RESULTS

Patient characteristics

During the study period, based on diagnostic and inclusion criteria, 27 patients were initially enrolled in the chronic atrophic gastritis (CAG) group. Among them, 3 patients were excluded due to insufficient gastric mucosal tissue for DNA extraction, and 1 patient was excluded owing to highly divergent sequencing data. Similarly, 27 patients were initially enrolled in the chronic non-atrophic gastritis (CNAG) group, with 12 patients excluded due to inadequate DNA extraction from gastric mucosal samples and 1 patient excluded because of anomalous sequencing data. Consequently, a total of 37 patients were ultimately included in the analysis: 23 in the CAG group and 14 in the CNAG group. All patients with chronic atrophic gastritis were pathologically confirmed to exhibit intestinal metaplasia. Complete clinical data were available for all 37 participants; no missing values were observed for age, sex, or histologic diagnosis. The characteristics of the patients are shown in Table 1. Both chronic non-atrophic gastritis (CNAG) and chronic atrophic gastritis (CAG) groups exhibited non-normal age distributions (Shapiro-Wilk test: CNAG, W = 0.935, p < 0.001; CAG, W = 0.910, p < 0.001), necessitating the use of non-parametric statistical analyses. There were no significant sex-related differences between the two groups, but there were significant age-related differences. This suggested a correlation between the progression of chronic gastritis and age.

Table 1 Clinical characteristics of patients who underwent CNAG or CAG (n = 37), ***: p ≤ 0.001

Microbiome abundance and diversity

Microbial community abundance and diversity were assessed using the Shannon index, Chao1 index, and Observed Species. While the Shannon index differed significantly between CAG and CNAG groups (p = 0.049) (Fig. 1A), no statistical significance was observed for the Chao1 index (p = 0.057) or Observed Species (p = 0.062), likely due to the limited sample size reducing statistical power to detect richness differences. Principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) of Bray-Curtis distances were used to compare bacterial community structures between groups (Fig. 1B, C). Analysis of Similarity (ANOSIM) further confirmed significant differences in microbial composition (R = 0.339, p = 0.001) (Fig. 1D).

Despite the significant age difference between CAG and CNAG groups (p < 0.001, Table 1), age-adjusted analyses revealed no confounding effects of age on alpha diversity (Chao1: p = 0.128; Observed Species: p = 0.135; Shannon: p = 0.149) or ASV-level microbial abundance (p = 0.126). In contrast, age explained 17.19% of beta diversity variation (p = 0.004, PERMANOVA), though the primary group distinction (CAG vs. CNAG) remained robust after adjustment (p = 0.001). This suggests that microbiome structural divergence between groups is driven by pathological progression rather than age-related factors.

Fig. 1

Analysis of alpha and beta diversity. (A) Shannon index determined through a violin plot with the Wilcoxon signed-rank test (p = 0.049). (B) Principal component analysis of ASV abundance. (C) Non-metric multidimensional scaling of the Bray-Curtis distance. (D) ANOSIM analysis indicated a statistically significant difference (ANOSIM R = 0.339 p = 0.001). *: p ≤ 0.05,**: p ≤ 0.01, ***: P ≤ 0.001

Bacterial compositions

At the phylum level, the two groups had the same most abundant bacterial phyla (Pseudomonadota, Bacillota, Actinomycetota, and Bacteroidota), although there was a difference in richness between the two groups (Fig. 2A). At the genus level, the top 5 most abundant bacterial genera in the CAG group were Sphingomonas, Ralstonia, Brevundimonas, Methyloversatilis, and Pseudomonas, while in the CNAG group, they were Brevundimonas, Ralstonia, Sphingomonas, Methyloversatilis, and Acinetobacter (Fig. 2B). The two groups had 27 common bacterial phyla and 476 common bacterial genera (Fig. 2C, D).

Fig. 2

Bacterial compositions of CAG and CNAG. (A) Bacterial phylum-level composition. (B) Bacterial genus-level compositions. (C) Venn diagram of the proportion of bacterial phyla. (D) Venn diagram of the distribution of bacterial genera

Comparative analysis of significant microbial differences between two groups

LEfSe analysis identified distinct microbial biomarkers between the CAG and CNAG groups through a two-step statistical approach. First, taxa with significant abundance variations (Wilcoxon rank-sum test, α = 0.05) were filtered, followed by linear discriminant analysis (LDA) to quantify group-specific effect sizes. Taxonomic hierarchies of these discriminative biomarkers were visualized in a cladogram (Fig. 3A), where yellow and green nodes represent lineages enriched in the CAG and CNAG groups, respectively. Node sizes reflect the log10-transformed LDA scores, emphasizing taxa with stronger associations (LDA > 3.0, Kruskal-Wallis test p < 0.05). The cladogram highlights divergent evolutionary branches dominated by CAG-associated taxa (e.g., Sphingomonas, Ralstonia, Roseateles) and CNAG-enriched genera (e.g., Brevundimonas, Bacteroides). At the genus level (Fig. 3B), 10 taxa showed significant enrichment in the CAG group (LDA scores > 3.0, yellow bars), while 11 genera were overrepresented in the CNAG group (green bars)..

Fig. 3

(A) The cladogram illustrates taxonomic hierarchies of microbial biomarkers significantly associated with the CAG (yellow nodes) and CNAG (green nodes) groups. Node size corresponds to the LDA effect size (log10-transformed), with larger nodes indicating stronger group-specific associations. Colored branches highlight taxonomic lineages enriched in each group (yellow: CAG; green: CNAG). Only taxa meeting a significance threshold of p < 0.05 (Kruskal-Wallis test) and LDA score > 3.0 are displayed. (B) Horizontal bars represent the LDA scores (log10-transformed) of bacterial genera significantly enriched in the CAG (yellow) or CNAG (green) groups. Statistical thresholds: p < 0.05

Inter-Species correlation analysis of bacterial taxa

Microbial co-occurrence patterns were analyzed through correlation heatmaps and network analysis (|ρ| > 0.4 threshold) (Fig. 4A, B). Key interactions included: Sphingomonas exhibited positive correlations with Acidithiobacillus and Roseateles, while showing negative associations with Brevundimonas, Hydrogenophaga, and Leifsonia. Brevundimonas demonstrated contrasting relationships: Positive correlations with Hydrogenophaga, Leifsonia, Aquabacterium, and Acinetobacter; Negative correlations with Sphingomonas and Acidithiobacillus.

Fig. 4

(A. Correlation network of the top 30 differentially abundant bacterial genera. Nodes: Color indicates bacterial phylum; size reflects degree centrality (number of connections). Edges: Significant correlations (Spearman’s|ρ| > 0.4, p < 0.05, FDR-adjusted); thickness indicates strength; solid lines = positive (ρ > 0.4), dashed lines = negative (ρ < −0.4). B. Pairwise Spearman correlations between genera. Color intensity and hue denote correlation strength and direction (red: positive, blue: negative;|ρ| > 0.4, p < 0.05). Coefficients are annotated numerically. Lower left: Statistical significance of correlations (p-values). Significance levels: * p ≤ 0.05, ** p ≤ 0. 01, *** p ≤ 0.001.)

Functional features of the gastric microbiota

As shown in Fig. 5, PICRUSt2 suggested potential functional alterations. Pathways related to xenobiotic biodegradation, metabolism, cofactor/vitamin metabolism, cancer, infectious diseases, and digestive system-related pathways were enriched in the CAG group. In contrast, the CNAG group showed enrichment in amino acid metabolism, translation, replication/repair, and terpenoid/polyketide metabolism (Fig. 5).

Fig. 5

Functional pathway prediction analysis of gastric microbiota (KEGG Level 2). Left panel: Relative abundance of KEGG pathways with significant differences (p < 0.05, Wilcoxon rank-sum test) between CNAG (yellow bars) and CAG (blue bars). The y-axis lists pathway categories; the x-axis represents the relative proportion of each pathway in the two groups. Right panel: Effect size and 95% confidence intervals of differentially abundant pathways. The x-axis shows the difference in pathway proportions (CAG vs. CNAG); the y-axis displays the corresponding p-values. Dots represent individual pathways; horizontal lines indicate 95% confidence intervals

Discussion

Based on the Corrêa cascade, H. pylori infection was identified as a substantial risk factor for gastric cancer development. Nevertheless, after undergoing H. pylori eradication treatment, the risk of gastric cancer decreased by less than 50% [17]. As demonstrated in the current study, solely employing H. pylori eradication therapy did not prove to be an effective method for preventing the progression of gastric precancerous lesions [18, 19]. Studies demonstrate that in the Insulin-Gastrin (INS-GAS) transgenic mouse model, mice carrying gastric-specific pathogen-free microbiota developed more severe gastric precancerous lesions compared to H. pylori mono-infected controls [20]. Furthermore, transplantation of gastric microbiota and fluids from human patients with intestinal metaplasia or gastric cancer successfully induced precancerous features in germ-free mice, whereas H. pylori mono-infection alone failed to replicate this carcinogenic progression [21,22,23]. Collectively, these findings suggest that non-H. pylori gastric microbiota may participate in the pathological progression from chronic gastritis to gastric cancer. Given that chronic atrophic gastritis (CAG) and intestinal metaplasia (IM) are obligatory precursors of intestinal-type gastric cancer, elucidating microbial taxa and functional pathways associated with these precancerous states is critical.

In our study, we confirmed the microbiome composition in the gastric mucosal tissues of individuals with chronic non-atrophic gastritis and chronic atrophic gastritis. Our findings revealed a notable decrease in microbial diversity among patients with intestinal metaplasia and chronic atrophic gastritis compared to those with chronic non-atrophic gastritis [24, 25]. Similarly, the abundance of gastric microorganisms from CAG-PLGC-GC (Chronic Atrophic Gastritis-Precancerous Lesions-Gastric Cancer) gradually decreased. Similarly, other studies also indicated that compared with that in non-atrophic gastritis, the microbial richness of intestinal metaplasia and gastric cancer patients was significantly reduced [25,26,27,28].

At the taxonomic level, Pseudomonadota, Bacillota, Actinomycetota, and Bacteroidota were the four predominant phyla shared by both groups, consistent with previous studies [28]. The abundance of Pseudomonadota increased from chronic non-atrophic gastritis to chronic atrophic gastritis, while the relative abundance of Bacillota, Actinomycetota, and Bacteroidota decreased during this transition. This was in line with previous research findings [29].

At the genus level, in the atrophic gastritis group, the five bacterial genera with the greatest abundance were identified as Sphingomonas, Ralstonia, Brevundimonas, Methyloversatilis, and Pseudomonas. Conversely, in the non-atrophic gastritis group, Brevundimonas, Ralstonia, Sphingomonas, Methyloversatilis, and Acinetobacter were found to be the top five bacterial genera. The prevalence of Sphingomonas, Ralstonia, Bradyrhizobium, Roseateles and Acidithiobacillus increased from chronic non-atrophic gastritis to chronic atrophic gastritis; conversely, the relative prevalence of Brevundimonas, Rhodococcus, Hydrogenophaga, Bacteroides and Leifsonia decreased during this transition. There were discernible disparities among these bacterial genera. One study [30] also revealed an enrichment of Sphingomonas, Ralstonia, and Roseateles in bladder cancer tissue. Furthermore, these bacteria might play a role in the degradation of polycyclic aromatic hydrocarbons, which are a class of organic pollutants that exert detrimental effects on both human health and the environment [31]. We identified the genus Acinetobacter (phylum Pseudomonadota), a group of Gram-negative opportunistic pathogens frequently implicated in nosocomial infections. Notably, recent studies have reported elevated abundance of Acinetobacter in mucosal dysplastic lesions compared to adjacent healthy tissues [13, 32]. Furthermore, studies have indicated that Acinetobacter lwoffii and Streptococcus anginosus were associated with persistent gastric inflammation [32,33,34].

We identified significant correlations and associations using Spearman network analysis of the gastric microbiota. Sphingomonas exhibited positive correlations with Acidithiobacillus and Roseateles, while showing negative associations with Brevundimonas, Hydrogenophaga, and Leifsonia. Functional pathway predictions via PICRUSt2 suggested potential alterations in the metabolic potential of gastric microbial communities in chronic atrophic gastritis. These included a relative enrichment in pathways linked to cofactor and vitamin metabolism and xenobiotic biodegradation, alongside a relative decrease in amino acid and nucleotide metabolism (see Fig. 5). Notably, these predictions reflect inferred functional capacities based on 16 S rRNA gene data and require further validation through direct metabolic profiling or multi-omics approaches. Changes in gastric microorganisms during the progression from chronic nonatrophic gastritis to chronic atrophic gastritis might weaken the synthesis of amino acids and nucleotides, as well as the regulation and expression of proteins. Signal transduction might also be adjusted to increase the demand for exogenous biodegradation and cofactors and vitamins. This indicated that the microorganisms in the stomach might play a role in promoting chronic inflammation through these mechanisms. Alterations in the gastric microbiota might lead to the depletion of parietal cells and an increase in inflammatory foci [21].

Some limitations of our study should be considered. First, The modest cohort size (n = 37) likely constrained our ability to detect subtle intergroup differences in bacterial genus abundances between chronic atrophic gastritis (CAG) and chronic non-atrophic gastritis (CNAG) patients. This limitation is further supported by the non-significant trends observed in alpha diversity metrics quantifying species richness, including the Chao1 index (p = 0.128) and Observed Species (p = 0.135), despite significant differences in Shannon diversity (p = 0.049). To definitively characterize microbial richness variations during atrophic transition, validation through large-scale multicenter studies is imperative. However, 24 bacterial genera were identified in our study, although some bacterial genera have been reported in other studies. While age contributed to beta diversity variation, its confounding effect on alpha diversity and differentially abundant ASVs was negligible. This underscores that the observed microbiome differences between CAG and CNAG groups are independent of age. Nevertheless, future studies should explore age-microbiome interactions in larger cohorts to validate these findings. Second, we did not evaluate the changes of significantly different bacterial genera during the progression from chronic nonatrophic gastritis to chronic atrophic gastritis, intestinal metaplasia, dysplasia or gastric cancer, which might further provide valuable insights into the identification of different bacterial genera at different pathological stages of gastric cancer transformation. Third, while we did not evaluate novel insights into microbial drivers of gastric inflammation, our findings are derived from a single-center cohort. Future multi-center studies with larger sample sizes are warranted to validate these results, particularly given the multifactorial influences on the human microbiome. And while PICRUSt2 provides a cost-effective approach to hypothesize functional shifts, future studies integrating metagenomic sequencing and metabolomic profiling are essential to validate these predicted metabolic alterations.

In general, the objective of this study was to examine the changes in microbiome composition in patients with CNAG and CAG with a specific focus on non-H. pylori individuals. The differentially enriched bacterial genera between CAG and CNAG groups, particularly Sphingomonas and Ralstonia, may indicate microbial signatures associated with early atrophic transition and provide candidates for further mechanistic validation Additionally, microbes might be involved in biodegradation, metabolism, signal transduction, and inflammation and participate in the modulation of persistent inflammation within neoplastic lesions.