Immune Remodeling and Dysbiosis May Distinguish the Microenvironments of Gastric Adenocarcinoma and Peritumoral Tissue

Immune Remodeling and Dysbiosis May Distinguish the Microenvironments of Gastric Adenocarcinoma and Peritumoral Tissue | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Immune Remodeling and Dysbiosis May Distinguish the Microenvironments of Gastric Adenocarcinoma and Peritumoral Tissue View ORCID Profile Ronald Matheus da Silva Mourão , View ORCID Profile Juliana Barreto Albuquerque Pinto , View ORCID Profile Jéssica Manoelli Costa da Silva , Daniel de Souza Avelar da Costa , View ORCID Profile Valéria Cristiane Santos da Silva , View ORCID Profile Ana Karyssa Mendes Anaissi , Samia Demachki , View ORCID Profile Williams Fernandes Barra , View ORCID Profile Fabiano Cordeiro Moreira , View ORCID Profile Paulo Pimentel de Assumpção doi: https://doi.org/10.1101/2025.08.27.672687 Ronald Matheus da Silva Mourão 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ronald Matheus da Silva Mourão For correspondence: ronald.mourao{at}icb.ufpa.br Juliana Barreto Albuquerque Pinto 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Juliana Barreto Albuquerque Pinto Jéssica Manoelli Costa da Silva 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jéssica Manoelli Costa da Silva Daniel de Souza Avelar da Costa 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valéria Cristiane Santos da Silva 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valéria Cristiane Santos da Silva Ana Karyssa Mendes Anaissi 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ana Karyssa Mendes Anaissi Samia Demachki 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Williams Fernandes Barra 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Williams Fernandes Barra Fabiano Cordeiro Moreira 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fabiano Cordeiro Moreira Paulo Pimentel de Assumpção 1 Núcleo de Pesquisas em Oncologia, Federal University of Pará , Belém, PA, Brazil 2 Graduate Program in Genetics and Molecular Biology, Federal University of Pará , Belém, PA, Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paulo Pimentel de Assumpção Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The gastric tumor microenvironment is dynamically shaped by the interactions between the local microbiota and the host immune system, although the functional integration of these elements remains incompletely understood. In this study, we characterized microbial diversity, immune cell composition, and immune-related gene expression profiles in samples of gastric adenocarcinoma (GAC) and adjacent peritumoral tissue (PTT), aiming to elucidate their functional organization. A total of 106 samples of 75 patients were analyzed using bulk RNA-Seq expression profiling, immune deconvolution, and bacterial taxonomic reconstruction. While alpha diversity remained preserved between GAC and PTT, distinct compositional differences emerged: GAC was enriched with Pseudomonadota, Enterobacteriaceae , and Escherichia , whereas PTT exhibited a predominance of Helicobacteraceae and Helicobacter . Immune deconvolution revealed an expansion of cancer-associated fibroblasts (CAFs) and mast cells in GAC, correlated with higher expression levels of TGFB1 and FOXP3 , while neutrophils and B cells predominated in PTT. Integrated analysis demonstrated that GAC formed dense and cohesive networks connecting pro-inflammatory bacteria, activated immune cells, and inflammatory genes such as IL1B, CXCL8 , and IFNG . In contrast, PTT exhibited dispersed networks and negative correlations, suggesting a less structured, tolerogenic environment. Our findings indicate that gastric cancer progression involves not only compositional shifts in microbiota and immune cells but also the active construction of functionally integrated inflammatory networks, providing new insights into potential therapeutic targets at the microbiome-immune interface. 1 Introduction Gastric adenocarcinoma (GAC) is a multifactorial epithelial malignancy whose progression involves not only intrinsic genetic alterations within tumor cells but also progressive reprogramming of the surrounding tissue microenvironment[ 1 – 3 ]. Within this context, the immune-inflammatory axis and the influence of the microbiome have emerged as central elements in the transition from inflamed mucosa to established tumor states[ 4 , 5 ]. Chronic activation of the immune system, phenotypic remodeling of fibroblasts, and the presence of specialized bacterial consortia collectively contribute to the creation of a permissive environment for carcinogenesis and immune evasion[ 6 ]. Functional compartmentalization of the gastric microenvironment - segregating inflammatory responses, adaptive immunity, and microbial stimuli - is a critical feature of tissue homeostasis[ 7 ]. As tumor progression advances, this compartmentalized architecture tends to collapse, fostering the overlap of chronic inflammation, immunosuppression, and bacterial dysbiosis[ 2 ]. Previous studies have demonstrated that immune infiltration in GAC is marked by signs of functional exhaustion and a predominance of tolerogenic inflammatory profiles, in contrast to the more balanced environment observed in peritumoral tissues[ 8 ]. However, the spatial and functional dynamics of microbiome, immunity, and gene expression interactions during gastric tumor progression remain poorly understood. The gastric microbiome, traditionally associated with Helicobacter pylori , is now recognized as a broader and more dynamic ecosystem capable of modulating inflammatory pathways, altering local cellular profiles, and influencing tumor evolution[ 9 – 11 ]. Certain microbial communities promote immunosuppressive environments, whereas others drive pro-inflammatory activation, directly reshaping the functional architecture of the microenvironment[ 12 ]. The tripartite interaction between epithelium, immunity, and microbiota thus emerges as a key axis in configuring the functional heterogeneity of gastric tissues. Understanding how microbial and immune networks organize - or become disorganized - during GAC progression is critical for identifying therapeutic intervention points[ 13 ]. This study aims to delineate the functional integration among the microbiome, cellular composition, and gene expression profiles in gastric adenocarcinoma and adjacent peritumoral tissue, characterizing the structural transitions of the microenvironment associated with tumor progression. 2 Material and Methods 2.1 Sample Characterization and Ethical Considerations In this study, tumor and adjacent peritumoral tissue (PTT) samples were collected from patients diagnosed with GAC, the most common type of gastric cancer. A total of 75 patients were analyzed, comprising 62 GACs tissues and 44 PTT samples. The cohort included 29 female and 45 male patients and 1 patient with unreported gender. GAC samples were staged according to the ypTNM classification: Of the patients for whom staging information was available, 6 were classified as stage I, 18 as stage II, 32 as stage III, and 3 as stage IV. Recruitment and sample collection were conducted between July 2, 2022, and July 6, 2023, at the João de Barros Barreto University Hospital in Belém, Brazil. The study objectives were clearly explained to all participants, who provided written informed consent. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the João de Barros Barreto University Hospital (approval number: 47580121.9.0000.5634). 2.2 RNA Extraction and Quality Assessment Approximately 50-100 mg of tissue from each sample were macerated, followed by the addition of 1 mL of TRIZOL® reagent to facilitate RNA extraction. The integrity and concentration of total RNA were evaluated using Qubit 4.0 (Thermo Fisher Scientific) and NanoDrop ND-1000 (Thermo Fisher Scientific) fluorometers. Optimal criteria for total RNA integrity were considered met when samples exhibited an A260/A280 ratio between 1.8 and 2.2, an A260/A230 ratio greater than 1.8, and an RNA Integrity Number (RIN) ≥ 5. This threshold was selected to accommodate the inherent variability in RNA quality from clinical tissue samples, ensuring the inclusion of a representative cohort while maintaining data reliability. 2.3 cDNA Library Construction and Sequencing The TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold (Illumina) was used to remove cytoplasmic and mitochondrial rRNA. Libraries were processed using the NextSeq® 500 High Output V2 kit - 150 cycles (Illumina), following the manufacturer’s specifications. After library construction, a new assessment of RNA integrity was performed using the 2200 TapeStation System (Agilent). The cDNA libraries were then loaded onto the Illumina NextSeq sequencing platform and sequenced in paired-end mode. 2.4 Quality, Alignment, Quantification and Transcriptome Expression Read quality was assessed using FastQC (v0.11.9), and low-quality reads and adapter sequences were removed with Trimommatic, applying a minimum Phred quality threshold of QV15. QV15 was selected as a pragmatic threshold, given that Salmon’s k-mer-based pseudoalignment is robust to moderate base quality variation. Filtered reads were quantified at the transcript level using Salmon (v1.5.2)[ 14 ] against the human transcriptome reference (hg38). Transcript abundances were imported using the Tximport[ 3 ], and a DESeq2[ 15 ] object was created to normalize gene expression levels, accounting for tissue type (GAC or PTT) and sequencing batch effects. Variance-stabilized (VST) and batch-corrected expression values were used for subsequent analyses. 2.5 Selection of Immune-Related Genes A curated panel of immune-related genes was assembled to investigate key processes within the tumor microenvironment and host-microbiome interactions. The selection was informed by comprehensive immunological databases, such as MSigDB, and was further refined based on our group’s previous unpublished study. The panel included classical immune checkpoints ( PDCD1, CD274, CTLA4, LAG3, HAVCR2, CD47 ), pro-inflammatory cytokines and mediators ( IFNG, TNF, IL6, IL1B, CXCL8, CCL2, CCL5 ), regulatory and immunosuppressive markers ( IL10, TGFB1, FOXP3 ), macrophage and myeloid cell markers ( CD163, CD68, CD86, CD83 ), signaling molecules involved in immune activation and regulation ( STAT3, MYD88, NFKB1 ), as well as genes associated with antigen presentation ( B2M, HLA . A ), epithelial plasticity ( SOX9 ), angiogenesis ( VEGFA ), and mucosal immune defense ( PIGR ). This focused selection allowed a comprehensive assessment of inflammatory activation, immune regulation, stromal remodeling, and adaptive responses. 2.6 Microbiome Microbiome characterization was performed by taxonomic classification of RNA-Seq reads using Kraken2 (v2.1.4) [ 16 ] against the comprehensive PlusPF database. The primary focus of this study was to quantify bacterial microbiome expressions associated with GAC. To achieve this, relevant bacterial genomes were obtained from the RefSeq database, and Salmon (v1.10.1) was used to quantify expression by aligning reads against these genomes. The resulting expression counts were used to estimate bacterial abundance. To prioritize the most representative bacterial genera across samples, we computed an abundance score that integrates both dominance and prevalence ( equation 1 ) [ 17 ]. For each sample, genera were ranked in descending order based on their absolute abundance. The mean rank of each genus across all samples in which it was detected was then multiplied by a frequency-based penalty factor (1.1 - f g ), where f g represents the proportion of samples in which that genus was present. The constant 1.1 was introduced to avoid disproportionately penalizing highly prevalent genera, ensuring that those consistently detected and highly ranked retained meaningful scores. Based on this criterion, the 15 most abundant genera were selected to effectively represent the most relevant taxa for downstream analyses. Additionally, genera with recognized roles in microbiome-immune interactions and tumor biology - such as Parvimonas, Peptostreptococcus, Campylobacter, Actinomyces, Escherichia, Klebsiella, Streptococcus, Helicobacter, Prevotella, Halomonas, Pseudomonas, Sphingomonas, Lactobacillus, Shewanella, Acinetobacter, Corynebacterium, Bacillus, Neisseria, Leptotrichia, Veillonella, Bacteroides, Faecalibacterium, Bifidobacterium, Chryseobacterium, Oscillospira, Haemophilus, Actinobacillus, Staphylococcus, Lactococcus, Porphyromonas, Propionibacterium , and Fusobacterium - were also included, based both on published evidence and prior findings from our research group. Alpha diversity analysis was conducted using the Shannon, Chao1, and Observed indices, with group comparisons performed using the Wilcoxon rank-sum test and p ≤ 0.05. The microbiome package was used to estimate sample diversity between GAC and PTT groups. 2.7 Cellular Deconvolution Cellular deconvolution was performed using three computational tools: CIBERSORT[ 18 ], quanTIseq[ 19 ], and EPIC[ 20 ]. The LM22 immune cell signature file was loaded, and gene expression data were normalized to generate a TPM (Transcripts Per Million) matrix. The CIBERSORT function was employed to estimate cell composition, and the run_quantiseq function was applied for additional cellular composition estimation in tumor samples. The EPIC package was used to calculate cellular fractions across the samples. The results from each tool were integrated to generate a comprehensive deconvolution table of immune cell fractions. Differences in cell proportions between GAC and PTT groups were assessed using the Wilcoxon test, with Benjamini-Hochberg correction for multiple testing (FDR ≤ 0.05). 2.8 Hierarchical Clustering The dataset - comprising immune-related genes, inferred immune cell fractions, and relative abundances of bacterial genera - was transposed so that variables became rows, enabling the analysis of their similarities. To enable meaningful comparisons across variables with different scales and units, the data was first log2-transformed and subsequently standardized using z-scores. The distance matrix between variables was calculated using standard Euclidean distance, and hierarchical clustering was performed using the Ward.D2 method, which minimizes the total variance within clusters. Cluster structure visualization was performed with the fviz_dend function from the factoextra package, using the “rectangle” type combined with the “layout.gem” radial layout. Interpretation of the dendrogram focused exclusively on tree topology, considering the visual proximity of elements as indicative of relative functional similarity, as reflected in the original distance matrix. Closely clustered groups were interpreted as functionally related modules, whereas distant branches suggested differentiation among cellular, genetic, or microbial profiles within the GAC and PTT microenvironments. 2.9 Statistical Analyses Correlations among bacterial abundance, gene expression, and immune cell fractions were evaluated using Spearman’s correlation. A threshold of |rho| > 0.3 and p ≤ 0.05 was retained in line with exploratory objectives and biomedical literature precedent. Additional statistical tests, such as the Wilcoxon test, were conducted to compare differences between experimental groups. Result visualizations, including boxplots, bar graphs, and significant correlations, were generated using the ggplot2, ggcorrplot , and cowplot packages. 3 Results 3.1 Microbiome Alpha diversity analysis revealed no significant differences in Shannon indices between GAC and PTT (p = 0.6; Figure 1A ), suggesting no notable variation in ecological heterogeneity between the two tissue types. Similarly, species richness measures did not differ between GAC and PTT ( Figure 1B ), supporting the notion of a global stability in microbial complexity. Download figure Open in new tab Figure 1. Bacterial microbiome analyses: (A) Shannon diversity index; (B) Species richness estimates (Observed and Chao1); (C) Relative abundance of major bacterial phyla; (D) Relative abundance of predominant bacterial genera; (E) Mean difference in genus abundance between GAC and PTT samples. (F) Correlation between bacterial abundance and the expression of immune response genes across all samples (GAC and PTT combined). Regarding taxonomic composition, the phylum Pseudomonadota was the most dominant, ac-counting for 41% of the total, followed by Bacillota (18.6%), Campylobacterota (12.4%), and Bacteroidota (10.6%) ( Figure 1C ). At the family level, Enterobacteriaceae was the most abundant, representing 17.8% of the total microbiota and associated with GAC (66.4% of its fraction). In contrast, Helicobacteraceae was markedly more prevalent in PTT (76.3% of its relative abun-dance). Lactobacillaceae (4.4%) and Streptococcaceae (2.0%) were also more prominent in GAC (88.8% and 80.5%, respectively) ( Figure 4A , see appendix I). Among genera, Escherichia emerged as the most abundant (12.7% of the total), with 68% of its representation in GAC. Conversely, Helicobacter concentrated 76.3% of its abundance in PTT. Genera such as Prevotella (68.2% GAC) and Lactobacillus (88.7% GAC) were also more associated with the tumor environment, whereas Cutibacterium (39.4% GAC, 60.6% PTT) and Rhizobium (44.7% GAC, 55.3% PTT) displayed a more balanced distribution ( Figure 1D ). Differential abundance analysis between genera highlighted several relevant disparities ( Figure 1E ). Helicobacter showed a highly significant difference (adjusted p < 0.001), confirming its greater prevalence in PTT. Other genera, including Staphylococcus, Propionibacterium, Faecalibacterium, Chryseobacterium, Campylobacter, Bradyrhizobium, Bacteroides, Alistipes , and Actinoplanes , were more abundant in GAC (adjusted p < 0.05). These differences remained significant after multiple-testing correction. Correlation analysis between bacterial abundance and gene expression across all samples (GAC and PTT combined) revealed patterns characterized by negative associations ( Figure 1F ). Several genera exhibited inverse correlations with key genes involved in inflammatory responses and antigen presentation, including Bacteroides with B2M, HLA . A , and VEGFA ; Vibrio with NFKB1 and HLA . A ; and Bradyrhizobium with IFNG, IL6, CXCL8 , and STAT3 . Some genera, such as Acidovorax, Alistipes , and Faecalibacterium , demonstrated mixed patterns, positively correlating with the immunoregulatory marker HAVCR2 while negatively correlating with pro-inflammatory and angiogenic genes. 3.2 Immune Microenvironment Estimation Cellular deconvolution revealed substantial quantitative differences between GAC and PTT ( Figure 2A ; Figure 5 , see appendix). In GAC samples, epic_CAFs (p = 6.83 × 10 −9 ), ep-ic_Macrophages (p = 1.53 × 10 −3 ), quantiseq_Macrophages.M1 (p = 6.40 × 10 □ □), cibersort_Dendritic cells resting (p = 4.92 × 10 −2 ), cibersort_Mast cells resting (p = 1.45 × 10 −2 ), and epic_NKcells (p = 8.09 × 10 −3 ) were significantly more abundant, delineating a tumor microenvironment enriched in stromal, myeloid, mast cell, and NK cell populations. In PTT samples, the most abundant populations were epic_Bcells (p = 1.63 × 10 −2 ), quantiseq_B.cells (p = 1.63 × 10 −2 ), epic_CD8_Tcells (p = 4.92 × 10 −2 ), and cibersort_Neutrophils (p = 1.63 × 10 −2 ), composing a more effector and inflammatory immune profile in this tissue. Download figure Open in new tab Figure 2. Immune deconvolution analyses GAC PTT: (A) Mean differences in immune cell proportions between GAC and PTT groups; (B) Correlations between immune cell proportions and immune response gene expression in GAC samples; (C) Correlations between immune cell proportions and immune response gene expression in PTT samples. Global analysis of cellular proportions, considering all samples together, showed that cibersort_Mast cells resting comprised the largest fraction (30%), followed by quantiseq_B.cells (19.5%), epic_CAFs (13%), quantiseq_Macrophages.M1 (11.4%), and epic_CD8_Tcells (8.1%) ( Figure 5 , see appendix). These results indicate that in GAC and its PTT, the cellular landscape is dominated by stromal components, B lymphocytes, and myeloid cells. Comparative analysis between GAC and PTT revealed notable structural contrasts. In GAC, a predominance of epic_CAFs (88.4%), cibersort_Dendritic cells resting (77.5%), epic_NKcells (78.1%), cibersort_Mast cells resting (68.7%), epic_Macrophages (72.8%), and quantiseq_Macrophages.M1 (75.8%) was observed, configuring a microenvironment dominated by stromal, myeloid, and mast cell populations. Although the overall proportion of epic_CD8_Tcells was lower in GAC compared to PTT, some tumor samples exhibited significant infiltration of cytotoxic T lymphocytes, suggesting intratumoral heterogeneity. In PTT, the most representative populations included cibersort_Neutrophils (83.2%), epic_Bcells (56.1%), and epic_CD8_Tcells (59.1%) ( Figure 5 , see appendix), reflecting a more effector-dominant microenvironment, characterized by greater infiltration of adaptive immune cells and localized inflammatory responses. Correlation analysis between cellular composition and gene expression corroborated the structur-al patterns observed ( Figures 2B-2C ). In GAC samples, epic_CAFs showed positive correlations with immunosuppressive and extracellular matrix-modulating genes, notably TGFB1 (ρ = 0.56) and IL6 (ρ = 0.46). Quantiseq_Macrophages.M1 were strongly associated with inflammatory genes, including CXCL8 (ρ = 0.62), IL1B (ρ = 0.44), TNF (ρ = 0.42), and NFKB1 (ρ = 0.51), as well as with antigen-regulatory genes such as CD86 and CD83 . Epic_Bcells positively correlated with adaptive immune genes, particularly MS4A1 (ρ = 0.69) and PIGR (ρ = 0.48), while also displaying negative correlations with inflammatory markers such as CXCL8 (ρ = −0.36) and VEGFA (ρ = −0.48). Cibersort_Mast cells resting exhibited mixed patterns, positively associating with IFNG (ρ = 0.31) and negatively with MS4A1 (ρ = −0.32). In PTT samples, epic_Bcells maintained a strong positive correlation with MS4A1 (ρ = 0.80) and correlated positively with inflammatory genes such as IL6 (ρ = 0.36) and CXCL8 (ρ = 0.36). Quantiseq_Macrophages.M1 showed positive associations with pro-inflammatory genes, notably IL1B (ρ = 0.58) and CXCL8 (ρ = 0.37), as well as with regulatory genes such as TGFB1 (ρ = 0.33) and NFKB1 (ρ = 0.41). Cibersort_Dendritic cells resting demonstrated a positive correlation with HAVCR2 (ρ = 0.36), while cibersort_Mast cells resting positively correlated with TGFB1 (ρ = 0.38), STAT3 (ρ = 0.46), and VEGFA (ρ = 0.42), and negatively with IL6 (ρ = −0.44) and CD274 (ρ = −0.35). Overall, quantiseq_Macrophages.M1 and epic_Bcells were the cellular subsets that exhibited the highest number and intensity of correlations in both GAC and PTT. In GAC, associations were positive and related to inflammatory axes. In PTT, a combined pattern of positive correlations with both inflammatory ( IL1B, CXCL8 ) and immunoregulatory ( TGFB1, HAVCR2 ) genes were observed, reflecting a functionally more heterogeneous environment. Negative correlations, particularly involving cibersort_Dendritic cells resting and cibersort_Mast cells resting, were more frequent in PTT, suggesting localized patterns of immune modulation. 3.3 Integration of the Microbial, Immune and Genetic axis Integrated analysis of the gene expression, immune cells, and microbiota in GAC and PTT revealed highly organized patterns of interaction, supported by robust correlations. In GAC, the formation of an immunoregulatory cluster composed of IDO1, FOXP3, HAVCR2, IL10 , and LAG3 stood out, reflecting the activation of immune suppression programs within the tumor microenvi-ronment ( Figure 3A ). The correlation between IDO1 and FOXP3 (r = 0.57) and the coexpression of PIGR and MS4A1 (r = 0.35) ( Figure 3B ) further reinforce the robustness of this regulatory signature. Methodological convergence in the detection of B cells, as evidenced by the strong correlation between quantiseq_B . cells and epic_Bcells (r = 0.74), adds additional consistency to these observations. In parallel, an inflammatory cluster consolidated the activation of effector immune response pathways, with associations between IFNG and CD274 (r = 0.50), CD86 and CTLA4 (r = 0.63), and CXCL8 and IL1B (r = 0.80), outlining an acute inflammatory environment associated with immune checkpoint activation. Download figure Open in new tab Figure 3. Integrated analysis of microbiome, immune cells, and gene expression in GAC and PTT: (A) Hierarchical clustering of bacterial genera, immune cell fractions, and immune-related genes in GAC samples; (B) Correlation matrix illustrating significant associations among bacterial abundance, immune cell fractions, and gene expression in GAC; (C) Hierarchical clustering of bacterial genera, immune cell fractions, and immune-related genes in PTT samples; (D) Correlation matrix illustrating significant associations among bacterial abundance, immune cell fractions, and gene expression in PTT. Microbiome structuring revealed two distinct bacterial axes. A cluster of pathogenic oral bacteria, including Fusobacterium, Prevotella, Porphyromonas, Haemophilus , and Veillonella , exhibited strong co-occurrences, suggesting the formation of biofilms associated with tumor progression. Another cluster, composed of commensal and environmental bacteria such as Blautia, Faecalibacterium, Bacteroides, Pseudomonas , and Escherichia , indicated the coexistence of diverse ecological communities within the tumor microenvironment. Helicobacter showed relevant integration into both bacterial networks, linking to oral species ( Veillonella, Streptococcus ) and environmental species ( Pseudomonas, Escherichia ), suggesting its participation in complex microbial consortia within GAC. In PTT analysis, the preservation of functional clusters was evident ( Figure 3C ). The potential adaptive immune cluster involving quantiseq_B . cells, epic_Bcells , and MS4A1 stood out, indicating the persistence of adaptive responses in adjacent tissue, supported by strong correlations (r = 0.80 and r = 0.72) ( Figure 3D ). Regarding the PTT microbiome, clusters of oral and environmental bacteria were evidenced by strong co-occurrences between Alistipes and Faecalibacterium (r = 0.87), Fusobacterium and Leptotrichia (r = 0.83), and Prevotella and Neisseria (r = 0.74). The integration of Helicobacter into PTT bacterial networks further supports the hypothesis of complex microbial adaptations occurring not only within tumors but also in adjacent tissues. Although topological analysis revealed clustering in both tissue types, statistical validation indicated that not all observed proximities corresponded to robust associations, particularly in PTT; therefore, only functionally and statistically supported cores were emphasized in the results. 4 Discussion This study revealed that although global bacterial microbiome diversity and richness are pre-served between GAC and PTT, taxonomic composition and functional organization of the microenvironments diverge substantially[ 21 ]. These findings suggest that gastric tumor progression may involve not only a preservation of microbial heterogeneity, but also compositional shifts in bacterial consortia potentially driven by tumor-associated environmental pressures. The preservation of Shannon, Observed, and Chao1 indices between GAC and PTT indicates that global ecological complexity is maintained during tumor progression. However, the redistribution of relative abundances - with enrichment of Pseudomonadota and Bacillota in GAC and Campylobacterota and Bacteroidota in PTT - points to selective ecological reprogramming. Conditions such as hypoxia, acidification, and nutritional imbalances in GAC likely function as selective pressures, favoring bacterial phyla more adapted to inflammatory and metabolically hostile environments[ 22 ]. At a finer taxonomic resolution, the greater abundance of Enterobacteriaceae and Escherichia in GAC, along with positive correlations with inflammatory genes such as IFNG and CD86 , suggests that these bacteria may contribute to maintaining a chronic inflammatory state permissive to tumorigenesis[ 23 ]. In contrast, the predominance of Helicobacteraceae and Helicobacter in PTT, along with negative correlations with IFNG and IL6 , may reflect a more regulatory environment, potentially characteristic of an early stage of immune escape[ 24 ]. We propose that the replacement of Helicobacter by pro-inflammatory bacterial consortia represents a critical transition in the remodeling of the gastric microenvironment. Immune composition analysis further reinforced this interpretation. In GAC, an enrichment of epic_CAFs and cibersort_Mast cells resting was observed, whereas in PTT, quantiseq_B.cells, epic_Bcells, and epic_CD8_Tcells predominated[ 25 – 27 ]. The strong correlation between ep-ic_CAFs and TGFB1 and FOXP3 suggests that stromal fibrosis is integrated into immune suppression circuits within the tumor[ 28 ]. Conversely, the presence of adaptive B cells in PTT was corroborated by the strong correlation between quantiseq_B.cells and epic_Bcells (r = 0.74), indicating methodological consistency in the detection of this population. Importantly, this correlation reflects the identification of the same B cell population by distinct deconvolution methods (quanTIseq and EPIC) Functional integration of the microbiome, cellular composition, and gene expression revealed the formation of highly organized pro-inflammatory axes in GAC[ 29 ]. The topological proximity of Fusobacterium, Escherichia , activated macrophages (quantiseq_Macrophages.M1), and genes such as IL1B[ 30 ], CXCL8[ 31 ], IFNG[ 31 ] , and TNF [ 32 ] outlines a dense functional architecture, indicating that tumor-associated inflammation may be an orchestrated rather than a random process[ 33 – 35 ]. We propose that these axes represent critical maintenance hubs, where microbiota and immunity cooperate to perpetuate chronic inflammation. Conversely, in PTT, functional organization was more diffuse. The association of Helicobacter , resting dendritic cells (cibersort_Dendritic cells resting), and regulatory genes such as TGFB1 and IL10 suggests an immunomodulated microenvironment capable of containing inflammation at subclinical levels[ 36 ]. The preservation of functional compartmentalization in PTT contrasts with the collapse observed in GAC, suggesting that tumor progression may involve the gradual dissolution of these regulatory barriers[ 37 ]. These observations are reinforced by topological analyses showing spatial overlap of activated B cells, immunosuppressive macrophages, inflammatory myeloid cells, and oral bacteria in GAC, versus organized segregation between adaptive responses and commensal microbiota in PTT[ 38 , 39 ]. This structural opposition suggests that gastric cancer progression may be driven not only by inflammatory expansion but also by the loss of functional compartmentalization among immunity, inflammation, and microbial stimuli. These findings have relevant clinical implications. The identification of microbiome-immune consortia organized around inflammatory genes in GAC points to potential therapeutic strategies targeting the disruption of these networks - for instance, through microbiota modulation or stromal reprogramming - aiming to restore local immune surveillance. Future capabilities to map inflammatory hotspots in the gastric microenvironment may guide more precise local or systemic therapies. However, certain limitations must be acknowledged. Bulk RNA-based approaches do not permit single-cell spatial resolution, and inferences drawn from deconvolution and correlation analyses, while robust, require additional experimental validation. Moreover, the lack of longitudinal data limits the evaluation of the temporal dynamics of the observed networks. Despite these limitations, this study provides a new perspective on the functional interaction among the microbiome, immunity, and gastric cancer progression. By demonstrating that gastric carcinogenesis is associated not merely with compositional changes but with the active formation of organized inflammatory networks, our findings propose new paradigms for the understanding and therapeutic targeting of the disease. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Disclosure of Interests The authors report no conflicts of interest related to this study. Acknowledgments The authors express their gratitude to CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for providing a doctoral fellowship to R.M. da S. Mourão. We are also grateful to the High-Performance Computing Center (CCAD) at the Federal University of Pará for their support in computational resources. Furthermore, we acknowledge the Fundação Amazônia de Amparo a Estudos e Pesquisas (Fapespa) for the financial support that made this research possible. Appendix I Figure 4. Relative proportion of families and genera. (A) Relative abundance of predominant bacterial families; (B) Relative abundance of predominant bacterial genera. Each color represents a families or genera. Legends indicate the proportion of families or genera in GAC and PTT and Total Figure 5. Relative proportion of cell. Composition of immune cell fractions across individual samples. 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Share Immune Remodeling and Dysbiosis May Distinguish the Microenvironments of Gastric Adenocarcinoma and Peritumoral Tissue Ronald Matheus da Silva Mourão , Juliana Barreto Albuquerque Pinto , Jéssica Manoelli Costa da Silva , Daniel de Souza Avelar da Costa , Valéria Cristiane Santos da Silva , Ana Karyssa Mendes Anaissi , Samia Demachki , Williams Fernandes Barra , Fabiano Cordeiro Moreira , Paulo Pimentel de Assumpção bioRxiv 2025.08.27.672687; doi: https://doi.org/10.1101/2025.08.27.672687 Share This Article: Copy Citation Tools Immune Remodeling and Dysbiosis May Distinguish the Microenvironments of Gastric Adenocarcinoma and Peritumoral Tissue Ronald Matheus da Silva Mourão , Juliana Barreto Albuquerque Pinto , Jéssica Manoelli Costa da Silva , Daniel de Souza Avelar da Costa , Valéria Cristiane Santos da Silva , Ana Karyssa Mendes Anaissi , Samia Demachki , Williams Fernandes Barra , Fabiano Cordeiro Moreira , Paulo Pimentel de Assumpção bioRxiv 2025.08.27.672687; doi: https://doi.org/10.1101/2025.08.27.672687 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetics Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41936) Biophysics (21451) Cancer Biology (18588) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88603) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)