Streptococcus anginosus induces macrophage pyroptosis and drives gastric "inflammation-to-cancer" transition by activating the NLRP3/IL-1β signaling pathway

Streptococcus anginosus induces macrophage pyroptosis and drives gastric "inflammation-to-cancer" transition by activating the NLRP3/IL-1β signaling pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Streptococcus anginosus induces macrophage pyroptosis and drives gastric "inflammation-to-cancer" transition by activating the NLRP3/IL-1β signaling pathway xiaodong han, Dandan Cao, Lanping Zhu, Zhu Liu, Yangyang Hui, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9269296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Streptococcus anginosus (SA) is enriched in the gastric cancer (GC) microenvironment and correlates with GC risk, but its mechanism in the "inflammation-to-cancer" transition is unclear. This study investigates whether SA induces macrophage pyroptosis through the NLRP3/IL-1β signaling pathway, promoting this transition. Methods Tissue samples from patients (non-H. pylori infected) with chronic superficial gastritis (CSG), intestinal metaplasia (IM), and GC were analyzed for SA infection, pyroptosis-related proteins, and macrophage markers. In vitro, the effects of SA supernatant on GC cell proliferation, migration, and macrophage pyroptosis were examined, with NLRP3 inhibitor MCC950 used for intervention. Metabolomics identified 15(S)-HPETE as a key metabolite. Results Results showed that SA infection load, NLRP3 activation, and macrophage pyroptosis were significantly higher in IM and GC groups than in CSG, with a positive correlation. SA supernatant promoted GC cell proliferation, migration, and macrophage pyroptosis, further driving epithelial-mesenchymal transition (EMT) in GC cells and intestinal metaplasia in gastric cells. These effects were reversed by MCC950. 15(S)-HPETE was found to promote malignant phenotypes in GC cells. Conclusions The study suggests SA induces macrophage pyroptosis via NLRP3/IL-1β signaling, contributing to a pro-inflammatory microenvironment that drives gastric "inflammation-to-cancer" transition, with 15(S)-HPETE as a key mediator. Streptococcus anginosus gastric "inflammation-to-cancer" transition macrophages pyroptosis NLRP3/IL-1β 15(S)-HPETE Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Gastric cancer (GC) is one of the most common malignant tumors globally, characterized by insidious onset. Most patients are diagnosed at an advanced stage, and despite diverse treatment options, the median survival time remains less than one year, with a 5-year survival rate of only 20% to 25% [ 1 – 4 ]. Chronic inflammation is a significant driving factor in GC development. Based on Virchow's hypothesis and Colotta's concept of "inflammation-cancer transition," prolonged inflammation can promote malignant transformation through cytokine release, DNA damage, and epigenetic changes [ 5 – 9 ]. According to the Correa model, gastric mucosal lesions progress through stages of inflammation, atrophy, intestinal metaplasia, dysplasia, and finally GC, during which immune cell infiltration increases, particularly macrophages, which play a critical role in the tumor microenvironment [ 10 – 13 ]. In addition to Helicobacter pylori (HP), gastric microbiome dysbiosis has also been linked to GC. Phylum Firmicutes and Bacteroidetes are enriched in GC tissues, and animal experiments have confirmed that microbiota can accelerate tumor progression [ 14 – 21 ]. Streptococcus anginosus (SA), as a potential carcinogenic bacterium, secretes virulence factors and inflammatory cytokines. Its surface protein TMPC can activate the mitogen-activated protein kinase (MAPK) pathway via the ANXA2 receptor, contributing to GC onset [ 22 – 26 ]. Pyroptosis, an inflammatory form of programmed cell death mediated by gasdermins, plays a dual role in tumors: it can both suppress and promote tumor progression by disrupting anti-tumor immunity [ 27 – 31 ]. NLRP3 inflammasome activation induces pyroptosis, and its activation relies on NF-κB signaling, reactive oxygen species (ROS), and other secondary signals, participating in the inflammation-to-cancer transition of gastric precancerous lesions [ 28 , 32 , 33 ]. Metabolomics, by analyzing endogenous and exogenous metabolites, systematically reveals the metabolic responses of organisms in pathological states, providing a more phenotype-based view compared to other omics approaches [ 34 – 37 ]. Untargeted metabolomics is unbiased and high-throughput, making it suitable for discovering potential metabolic biomarkers and pathways [ 38 – 40 ]. Based on the above background, this study aims to investigate the role and mechanisms of SA in the gastric "inflammation-to-cancer" transition. Through clinical sample analysis, in vitro experiments, and metabolomics validation, we systematically explore the potential mechanisms by which SA drives GC development. Materials and Methods Human gastric adenocarcinoma cell lines AGS (CL-0022), MKN-45 (CL-0292), human monocytic leukemia cell line THP-1 (CL-0233), and human gastric mucosal cell line GES-1 (CL-0563) were purchased from Wuhan Puno Sai Life Science Technology Co., Ltd. SA strain was generously provided by the Institute of Gastroenterology, Tianjin Medical University General Hospital. RPMI 1640 medium (PM150110), RPMI 1640 complete medium (PM150110B), Ham's F-12 medium (PM150810), Ham's F-12 complete medium (PM150810B), fetal bovine serum (164210-50), and premium fetal bovine serum (164210) were obtained from Wuhan Puno Sai Life Science Technology Co., Ltd. Trypsin (25200056) was purchased from Gibco. THB broth (LA1860-250g) was obtained from Beijing Soleibao Technology Co., Ltd. Cell Counting Kit-8 (CCK-8) reagent (GK10001-5), LDH assay kit (GK10003), and Trizol (GK20008-100) were obtained from Glpbio. ROS assay kit (E-BC-K138-F-96T9) was purchased from Elabscience. IL-1β (sbj-h0417-48T) and IL-18 (sbj-h0423) ELISA kits were obtained from Nanjing Senbega Biotechnology Co., Ltd. Chemical reagents and inducers: dimethyl sulfoxide (DMSO, D8371-50ml), RIPA lysis buffer (R0010-100ml) were purchased from Beijing Soleibao Technology Co., Ltd. Phorbol 12-myristate 13-acetate (PMA) (HY-18739), N-Acetylcysteine (NAC) (HY-B0215), and MCC950 (HY-12815) were obtained from MedChemExpress (MCE), USA. Snail (AF6032), Vimentin (AF7013), and NLRP3 (DF7438) primary antibodies were purchased from Affinity Biosciences. Caspase-1 (342947), Gasdermin D (GSDMD) (R24514), CDX2 (R380757), MUC2 (R381746), β-actin (R380624) primary antibodies, and HRP-conjugated goat anti-rabbit secondary antibody were obtained from Chengdu Zhengneng Biotechnology Co., Ltd. IL-1β (PAB45925) was obtained from Wuhan Beinyile Biotechnology Co., Ltd. HRP-conjugated goat anti-rabbit IgG (Ab205718) and Anti-CD68 antibody (ab303565) were purchased from Abcam. The polyclonal GSDMD antibody (PAB45820) was obtained from Bio-Swamp. Immunohistochemistry (IHC) kit (PV-9000) was from Beijing Zhongshu Jinqiao Biotechnology Co., Ltd.; DAPI staining solution (C1002/G1012) was from Shanghai Biyuntian or Wuhan Saiweier Biotechnology Co., Ltd.; Anti-fluorescence quenching mounting medium (G1401/0100-01), recombinant proteinase K (G1234), and citrate antigen repair solution (G1202) were purchased from Wuhan Saiweier Biotechnology Co., Ltd. or Southernbiotech; concentrated normal goat serum (AR1009) was obtained from Wuhan Boster Biological Engineering Co., Ltd.; FITC-labeled probe (5' to 3':AGTTAAACAGTTTCCAAAGCCTAC) was synthesized by Wuhan Double Helix Biotech Co., Ltd. Clinical Sample Collection We selected six cases each of chronic superficial gastritis (CSG), intestinal metaplasia (IM), and GC patients diagnosed via gastroscopy and pathology from the Department of Gastroenterology, Tianjin Medical University General Hospital, between April 2023 and May 2024. Gastric mucosal tissue samples from all patients were prepared by our hospital's pathology department. The inclusion criteria were as follows: (1) age between 18 and 75 years, with no gender restriction; (2) HP-negative as confirmed by the 13 C urea breath test; (3) no use of antibiotics, probiotics, or immunosuppressants within the past month; (4) no primary biliary reflux; (5) newly diagnosed GC with no prior treatment. The exclusion criteria included: (1) other organic diseases of the upper digestive tract, such as esophageal cancer or peptic ulcers; (2) uncontrolled primary or secondary biliary reflux; (3) previous gastric surgery, such as partial gastrectomy or fundoplication; (4) HP-positive as confirmed by the 13 C urea breath test or recent HP eradication therapy; (5) use of proton pump inhibitors for more than 4 weeks in the past 3 months, or long-term use of NSAIDs or immunosuppressants; (6) GC patients who have received neoadjuvant chemotherapy, radiotherapy, or palliative treatment; (7) history of other organ malignancies or hematological cancers; (8) autoimmune diseases, such as systemic lupus erythematosus or rheumatoid arthritis. Fluorescence In Situ Hybridization (FISH) for Detection of SA Infection in Paraffin Sections The paraffin sections were dewaxed and rehydrated, followed by antigen retrieval and digestion. Pre-hybridization, hybridization, and post-hybridization washing were performed. Nuclei were counterstained with DAPI. Fluorescence microscopy was used to observe bacterial clusters or scattered signals exhibiting specific green fluorescence (SA probe) in each high-power field. The wavelengths for fluorescence detection were: DAPI (UV excitation 330-380 nm, emission 420 nm, blue color); FAM(488)-labeled probe (excitation 465-495 nm, emission 515-555 nm, green); CY3-labeled probe (excitation 510-560 nm, emission 590 nm, red).Fluorescence microscopy was used to observe and count bacterial clusters or scattered signals exhibiting specific green fluorescence (SA probe) in each high-power field. Immunohistochemistry (IHC) for Detection of Pyroptosis-Related Protein Expression After dewaxing the paraffin sections, antigen retrieval was performed, followed by blocking endogenous peroxidase activity. The sections were then incubated sequentially with primary antibodies targeting NLRP3, IL-1β, or Caspase-1, polymer enhancers, and HRP-conjugated secondary antibodies. After color development, sections were counterstained with hematoxylin, dehydrated, and mounted. Results were analyzed by scanning software, with 200x magnification images taken of the target tissue area. Using Image-Pro Plus 6.0 software, the positive staining pixel area in three randomly selected fields per section was measured and the percentage of positive area (positive pixel area/total tissue pixel area × 100%) was calculated. Immunofluorescence (IF) for Detection of CD68 and GSDMD Protein Expression After dewaxing, antigen retrieval, and serum blocking of the paraffin sections, sequential immunofluorescence staining was performed. First, GSDMD primary antibody (dilution 1:200) was incubated, followed by the corresponding fluorescent-labeled secondary antibody. After antibody elution, serum blocking was repeated, then CD68 primary antibody (dilution 1:1000) and another fluorescent-labeled secondary antibody (dilution 1:2000) were incubated. Finally, the nuclei were counterstained with DAPI and mounted. The target tissue areas were imaged at 200x magnification using scanning software. Using Image-Pro Plus 6.0 software, green (GSDMD) or red (CD68) fluorescence channel images were converted to black-and-white images, with a uniform grayscale threshold set as the positive criteria. The positive signal pixel area and total tissue pixel area in three randomly selected fields per section were measured, and the percentage of positive area (positive pixel area/total tissue pixel area × 100%) was calculated. Preparation of SA Supernatant The SA seed stock stored at -80°C was thawed in a 37°C water bath, and 1 mL was inoculated into 29 mL of THB broth. The culture was incubated at 37°C with 200 rpm shaking for 24 hours. After a 1:30 subculture, the optical density (OD) was measured every 4 hours. After 24 hours, the bacterial culture was diluted and the OD was measured to calculate the bacterial concentration. The bacterial culture was centrifuged at 4°C and 4000 rpm for 20 minutes, and the supernatant was collected. The supernatant was then filtered twice through a 0.22 µm filter to sterilize it. The resulting filtrate was the SA supernatant, which was aliquoted and stored at -80°C for later use. CCK-8 Assay for Evaluation of SA's Effect on GC Cell Viability AGS and MKN45 cells in the logarithmic growth phase were collected and seeded at a density of 1 × 10⁴ cells/mL in a 96-well plate (100 µL per well). After cell attachment, the experimental groups were treated with various concentrations of SA supernatant (1/128, 1/64, 1/32, 1/16, 1/8, 1/4, 1/2 volume ratios), and each group was performed in triplicates. THB medium was added to each well to bring the final volume to 200 µL. The control group received an equal volume of THB medium. The cells were incubated at 37°C with 5% CO₂ for 24 hours. After incubation, fresh medium was added and 10 µL of CCK-8 solution was added to each well. The plates were incubated for an additional 4 hours. Absorbance at 450 nm was measured using a microplate reader to assess cell viability, and the optimal concentration for subsequent experiments was selected. Wound Healing Assay for Evaluation of SA's Effect on GC Cell Migration AGS and MKN45 cells in the logarithmic growth phase were seeded at a density of 1 × 10⁵ cells/mL in a 24-well plate (500 µL per well). The cells were cultured until they reached approximately 70% confluence. A sterile pipette tip was used to create a straight-line scratch along the bottom of each well. The wells were gently washed three times with PBS to remove cell debris. The experimental group was treated with the optimal concentration of SA supernatant, and the control group was treated with an equal volume of THB medium. Images of the scratched area were immediately captured under an inverted microscope at 200x magnification. After 24 hours of incubation, the same conditions were used to capture additional images. The number of cells that migrated into the scratch area was counted to assess the cell migration ability. THP-1 Cell Culture, Differentiation, and SA Intervention Concentration Screening THP-1 cells in the logarithmic growth phase were adjusted to a density of 5 × 10⁵ cells/mL and seeded into a 6-well plate, with 2 mL per well. Phorbol 12-myristate 13-acetate (PMA, final concentration 100 ng/mL) was added to induce differentiation into M0 macrophages, and the morphological changes of the cells were observed under a microscope. When the M0 cells reached approximately 60% confluence, they were treated with different concentrations of SA supernatant. The concentrations used were 0 (control group), 1/64, 1/32, and 1/16, resulting in four groups. After 24 hours of intervention, cell culture supernatants were collected, and the levels of IL-1β and IL-18 were measured by ELISA. The optimal concentration of SA supernatant for further research was selected based on the amount of IL-1β released. Cell Toxicity (LDH Release) Assay Based on the screened SA intervention concentration (1/64), four groups were set up for the experiment: Control, SA intervention (Ms), SA + NLRP3 inhibitor MCC950 pre-treatment (MsM, MCC950 concentration 20 μM, pre-treated for 1 hour), and SA + reactive oxygen species (ROS) inhibitor NAC pre-treatment (MsN, NAC concentration 1 mM, pre-treated for 1 hour). When the M0 cells reached approximately 60% confluence, the interventions were applied according to the groups. After 24 hours, cell culture supernatants were collected, and lactate dehydrogenase (LDH) release was measured by ELISA. Real-Time Quantitative PCR (RT-qPCR) for Pyroptosis-Related Gene Transcription The experimental groups were the same as for the "Cell Toxicity (LDH Release) Assay," i.e., Control, Ms, MsM, and MsN. After 24 hours of intervention, total RNA was extracted from the cells using the Trizol method. Then, mRNA was reverse transcribed into cDNA using a reverse transcription kit from Novogene Biotechnology Co., Ltd. (Reaction conditions: 37°C for 15 minutes, followed by 85°C for 5 seconds). The primers for amplifying NLRP3, GSDMD, Caspase-1, and the housekeeping gene β-actin were synthesized by GeneWiz Biotech Co., Ltd. RT-qPCR was performed using synthesized cDNA templates and specific primers. The relative expression of target genes was calculated using the 2^(-ΔΔCt) method, and data were analyzed with GraphPad Prism 8 software. The following primers (5' to 3') were used: NLRP3: Forward: 5’-GGACTGAAGCACCTGTTGTGCA-3’; Reverse: 5’-TCCTGAGTCTCCCAAGGCATTC-3’ GSDMD: Forward: 5’-GTGTGTCAACCTGTCTATCAAGG-3’; Reverse: 5’-CATGGCATCGTAGAAGTGGAAG-3’ Caspase-1: Forward: 5’-GCTGAGGTTGACATCACAGGCA-3’; Reverse: 5’-TGCTGTCAGAGGTCTTGTGCTC-3’ β-actin: Forward: 5’-CACCATTGGCAATGAGCGGTTC-3’; Reverse: 5’-AGGTCTTTGCGGATGTCCACGT-3’ Western Blot (WB) for Pyroptosis-Related Protein Expression The experimental groups were the same as for the "Cell Toxicity (LDH Release) Assay," i.e., Control, Ms, MsM, and MsN. After the intervention, cells were lysed using high-efficiency RIPA lysis buffer, and the supernatant was collected after centrifugation. Protein quantification was performed using a protein assay kit. Denatured protein samples were subjected to SDS-PAGE electrophoresis and transferred to a membrane. The membrane was incubated overnight at 4°C with primary antibodies targeting NLRP3, GSDMD, Caspase-1, and the housekeeping protein β-actin. After washing, the membrane was incubated at room temperature for 1 hour with HRP-conjugated secondary antibody. Chemiluminescence (ECL) detection was performed, and the membrane was exposed using a Bio-Rad imaging system. Image J software was used to analyze the grayscale values of the target protein bands, and β-actin was used as an internal reference for normalization. CCK-8 Assay for GC Cell Viability To assess the effect of SA supernatant and macrophage supernatant after intervention on GC cell viability, a CCK-8 assay was performed. AGS and MKN45 cells in the logarithmic growth phase were seeded at a density of 1 × 10⁴ cells/mL into a 96-well plate (200 µL per well). After cell attachment, the following experimental groups were set up for intervention: 1) Control group (Blank), 2) SA supernatant group (SA 1/64), 3) M0 macrophage supernatant group (M0 1/32), 4) SA intervention on M0 macrophage supernatant group (Ms 1/32), and 5) SA + MCC950 inhibitor-treated M0 macrophage supernatant group (MsM 1/32). After 24 hours of incubation at 37°C with 5% CO₂, the medium was replaced with fresh medium, and 10 µL of CCK-8 solution was added to each well, followed by another 4 hours of incubation. The absorbance at 450 nm was measured using a microplate reader to assess cell viability. Wound Healing Assay for G C Cell Migration A wound healing assay was used to evaluate the effect of different supernatants on the migration ability of GC cells. AGS and MKN45 cells were seeded at a density of 1 × 10⁵ cells/mL into a 24-well plate (500 µL per well). When the cells reached approximately 70% confluence, a straight-line scratch was made with a sterile pipette tip. The wells were gently washed three times with PBS to remove any cell debris. The experimental groups followed the same setup as for the "GC Cell Viability Assay." After incubation at 37°C with 5% CO₂ for 24 hours, images were captured under an inverted microscope at 200x magnification. The number of cells that migrated into the scratched area was counted to assess changes in cell migration ability. RT-qPCR for Epithelial-to-Mesenchymal Transition (EMT)-Related Gene Transcription in G C Cells The experimental groups followed the same setup as for the "GC Cell Viability Assay." After 24 hours of intervention with the respective supernatants, RNA was extracted from AGS and MKN45 cells, reverse transcribed into cDNA, and subjected to RT-qPCR to measure the transcriptional levels of vimentin and Snail. β-actin was used as the internal reference gene. The following primers (5' to 3') were used: β-actin: Forward: 5’-CACCATTGGCAATGAGCGGTTC-3’; Reverse: 5’-AGGTCTTTGCGGATGTCCACGT-3’ Vimentin: Forward: 5’-AGGCAAAGCAGGAGTCCACTGA-3’; Reverse: 5’-ATCTGGCGTTCCAGGGACTCAT-3’ Snail: Forward: 5’-TGCCCTCAAGATGCACATCCGA-3’; Reverse: 5’-GGGACAGGAGAAGGGCTTCTC-3’ WB for EMT-Related Protein Expression in GC Cells The experimental groups followed the same setup as for the "GC Cell Viability Assay." After 24 hours of intervention with the respective supernatants, cells were lysed, proteins extracted, quantified, subjected to SDS-PAGE electrophoresis, transferred to a membrane, and immunoblotted. The protein expression levels of Vimentin and Snail were analyzed, with β-actin used as the internal reference. RT-qPCR for Intestinal Metaplasia-Related Gene Transcription Human gastric mucosal epithelial cells (GES-1) were revived and cultured. When the cells reached approximately 40% confluence in a 6-well plate, they were treated with the respective supernatants according to the "GC Cell Viability Assay" grouping (Control, SA 1/64, M0 1/32, Ms 1/32, MsM 1/32) for 72 hours. After the intervention, RNA was extracted, reverse transcribed into cDNA, and subjected to RT-qPCR to analyze the transcriptional levels of MUC2, Krüppel-like factor 4 (KLF4), and caudal-related homeobox transcription factor 2 (CDX2). The following primers (5' to 3') were used: MUC2: Forward: 5’-ACTCTCCACACCCAGCATCATC-3’; Reverse: 5’-GTGTCTCCGTATGTGCCGTTGT-3’ KLF4: Forward: 5’-CATCTCAAGGCACACCTGCGAA-3’; Reverse: 5’-TCGGTCGCATTTTTGGCACTGG-3’ CDX2: Forward: 5’-ACAGTCGCTACATCACCATCCG-3’; Reverse: 5’-CCTCTCCTTTGCTCTGCGGTTC-3’ β-actin: Forward: 5’-CACCATTGGCAATGAGCGGTTC-3’; Reverse: 5’-AGGTCTTTGCGGATGTCCACGT-3’ Western Blot (WB) for Intestinal Metaplasia-Related Protein Expression The experimental groups and cell intervention methods followed the same setup as for the "GC Cell Viability Assay." After 72 hours of intervention, Western blot analysis was performed to assess the protein expression levels of MUC2, KLF4, and CDX2. β-actin was used as the internal reference. Untargeted Metabolomics Analysis Based on Liquid Chromatography-Mass Spectrometry (LC-MS) Sample Preparation: The samples were thawed on ice. A 50 µL sample was mixed with 150 µL internal standard extraction solution containing 20% acetonitrile-methanol, vortexed, and then centrifuged at 12,000 rpm for 10 minutes at 4°C. The supernatant was incubated at -20°C for 30 minutes, followed by a second centrifugation. The resulting 120 µL supernatant was transferred to an injection vial for analysis. LC-MS Acquisition: LC analysis was performed using a Waters HSS T3 column (1.8 µm, 2.1 × 100 mm) at a column temperature of 40°C. The mobile phase consisted of 0.1% formic acid aqueous solution and 0.1% formic acid acetonitrile, with a flow rate of 0.4 mL/min using a gradient elution. The mass spectrometry analysis was performed on an AB TripleTOF 6600 system, acquiring data in both positive (ESI+) and negative (ESI-) ion modes. Data Processing and Quality Control: After converting the raw data, peak extraction, alignment, and correction were performed using XCMS. Metabolites were identified based on a self-built library and public databases, such as HMDB and KEGG. Stringent quality control included checking the overlap of total ion chromatograms (TIC) of quality control (QC) samples, assessing the Pearson correlation between QC samples, and calculating the coefficient of variation (CV) of metabolites in QC samples to ensure experimental stability and data reliability. Statistical Analysis: Unsupervised principal component analysis (PCA) was first performed to assess overall group differences and intra-group variability. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was used to maximize group separation, and permutation testing was performed to validate the model's effectiveness. Differential metabolites were selected based on variable importance in projection (VIP > 1) and t-test (p < 0.05). Bioinformatics analysis included hierarchical clustering, correlation analysis, KEGG pathway enrichment analysis, and differential abundance scoring (DA Score) to reveal significantly enriched metabolic pathways and overall trends. CCK-8 Assay for the Effect of 15-Hydroperoxy-5,8,11,13-eicosatetraenoic Acid (15(S)-HPETE) on AGS G C Cell Viability To assess the effect of 15(S)-HPETE on AGS GC cell viability, a CCK-8 assay was performed. AGS cells in the logarithmic growth phase were seeded at a density of 1 × 10⁴ cells/mL into a 96-well plate (200 µL per well). After cell attachment, the following experimental groups were set up: 0 µM, 0.2 µM, 0.4 µM, 0.8 µM, 1.6 µM, 3.2 µM, and 6.4 µM 15(S)-HPETE. The cells were incubated at 37°C with 5% CO₂ for 24 hours. After replacing the medium, 10 µL of CCK-8 solution was added, mixed gently, and incubated for an additional 4 hours. The absorbance at 450 nm was measured using a microplate reader to evaluate cell viability and determine the optimal concentration for subsequent experiments. Wound Healing Assay for the Effect of 15(S)-HPETE on AGS Cell Migration A wound healing assay was performed to assess the effect of 15(S)-HPETE on AGS cell migration. AGS cells in the logarithmic growth phase were seeded at a density of 1 × 10⁵ cells/mL into a 24-well plate (500 µL per well). When the cells reached approximately 70% confluence, a straight-line scratch was made using a 10 µL pipette tip. The wells were then gently washed three times with PBS to remove any debris. The experimental groups included 0 µM (Control), 0.2 µM, and 0.4 µM 15(S)-HPETE. Images were taken under an inverted microscope at 200x magnification. After incubating for 24 hours at 37°C with 5% CO₂, the migration of cells into the scratched area was observed and quantified. RT-qPCR for the Effect of 15(S)-HPETE on EMT-Related Gene Transcription in AGS Cells When the AGS cells in the 6-well plate reached approximately 60% confluence, they were treated with the same experimental groups as in the "Wound Healing Assay." After 24 hours of intervention, total RNA was extracted, reverse transcribed into cDNA, and subjected to RT-qPCR to evaluate the transcription levels of vimentin and snail genes. The primers for vimentin and snail genes are as follows: Vimentin: Forward: 5’-AGGCAAAGCAGGAGTCCACTGA-3’; Reverse: 5’-ATCTGGCGTTCCAGGGACTCAT-3’ Snail: Forward: 5’-TGCCCTCAAGATGCACATCCGA-3’; Reverse: 5’-GGGACAGGAGAAGGGCTTCTC-3’ WB for the Effect of 15(S)-HPETE on EMT-Related Protein Expression in AGS Cells When AGS cells in the 6-well plate reached approximately 60% confluence, they were treated with the same experimental groups as in the "Wound Healing Assay." After 24 hours of treatment, cells were lysed, proteins extracted, quantified, subjected to SDS-PAGE, and transferred to membranes for immunoblotting. The expression levels of vimentin and snail proteins were analyzed, with β-actin as the internal reference. RT-qPCR for the Effect of 15(S)-HPETE on Intestinal Metaplasia-Related Gene Transcription in GES-1 Cells GES-1 cells were cultured until they reached approximately 40% confluence in a 6-well plate. Cells were treated with the experimental groups as in the "Wound Healing Assay" for 72 hours. After the treatment, total RNA was extracted and reverse transcribed into cDNA for RT-qPCR analysis to evaluate the transcription levels of MUC2, KLF4, and CDX2 genes. The primers (5' to 3') used are as follows: MUC2: Forward: 5’-ACTCTCCACACCCAGCATCATC-3’; Reverse: 5’-GTGTCTCCGTATGTGCCGTTGT-3’ KLF4: Forward: 5’-CATCTCAAGGCACACCTGCGAA-3’; Reverse: 5’-TCGGTCGCATTTTTGGCACTGG-3’ CDX2: Forward: 5’-ACAGTCGCTACATCACCATCCG-3’; Reverse: 5’-CCTCTCCTTTGCTCTGCGGTTC-3’ β-actin: Forward: 5’-CACCATTGGCAATGAGCGGTTC-3’; Reverse: 5’-AGGTCTTTGCGGATGTCCACGT-3’ WB for the Effect of 15(S)-HPETE on Intestinal Metaplasia-Related Protein Expression in GES-1 Cells When GES-1 cells in the 6-well plate reached approximately 40% confluence, they were treated with the same experimental groups as in the "Wound Healing Assay" for 72 hours. After the treatment, Western blot analysis was performed to assess the expression levels of MUC2, KLF4, and CDX2 proteins, using β-actin as the internal reference. Statistical Methods All data were analyzed using GraphPad Prism 9.5 software. Data with a normal distribution are presented as Mean ± SD. The comparison between two groups was conducted using an independent samples t-test, and multiple group comparisons were performed using one-way ANOVA. Pearson correlation coefficient analysis was used for correlation analysis. A p-value < 0.05 was considered statistically significant. Results SA infection load exhibited an increasing trend in CSG, IM, and GC, and this trend was positively correlated with macrophage infiltration and pyroptosis. Meanwhile, macrophage infiltration and pyroptosis were also positively correlated. To explore SA infection in CSG, IM, and GC in non-HP infected patients, 18 paraffin-embedded samples (6 samples per group) were analyzed. FISH detection using SA fluorescence probes was performed. The results revealed that SA infection load increased progressively from CSG to IM to GC, with significant differences observed (P < 0.05) (Figure 1A). To further investigate pyroptosis in CSG, IM, and GC, IHC was performed on the aforementioned samples. The results showed that the expression of NLRP3 (Figure 1B), Caspase-1 (Figure 1C), and IL-1β (Figure 1D) proteins in the IM and GC groups was significantly higher compared to the CSG group (P < 0.05).To investigate the relationship between macrophage infiltration and pyroptosis in CSG, IM, and GC, we performed IF detection of CD68 and GSDMD proteins. Dual fluorescence detection with Cy3 and 488 channels showed that in the GC group (Figure 1G), CD68 (red) and GSDMD (green) expression were significantly higher compared to the IM group (Figure 1F) and CSG group (Figure 1E), with DAPI staining marking the cell nuclei in blue. These results indicated increased macrophage infiltration and pyroptosis in the GC group compared to IM and CSG groups, with significant differences (P < 0.05) (Figure 1H). No significant differences were observed between IM and CSG groups. To elucidate the correlation between SA infection, macrophage infiltration, and pyroptosis in CSG, IM, and GC, as well as the relationship between macrophage infiltration and pyroptosis, Pearson correlation analysis was performed (Figure 1I). The results showed a positive correlation between CD68 expression and SA infection (P < 0.05), a positive correlation between GSDMD, NLRP3, and IL-1β expression and SA infection (P < 0.05), and a positive correlation between CD68 and GSDMD expression (P < 0.001). SA Growth Curve and Its Promotion of Malignant Phenotype in G C Cells SA was cultured, and bacterial turbidity was measured every 4 hours to plot the growth curve (*P < 0.05, **P < 0.01, Figure 2A). The SA growth curve followed the general pattern of microbial growth, which included four phases: 0-4 hours (adjustment phase), 4-16 hours (log phase), 16-24 hours (stationary phase), and post-24 hours (decline phase). The SA concentration after 24 hours of cultivation was approximately 9.72 × 10⁸ CFU/mL. The SA bacterial solution and sterile THB medium were centrifuged at 4°C and 4000 rpm for 20 minutes. The supernatant was filtered twice using a 0.22 µm filter and stored at -80°C for further use. CCK-8 assay revealed that after 24 hours of intervention with different concentrations of SA supernatant, AGS (Figure 1B) and MKN45 (Figure 1C) GC cell viability was enhanced. The most significant proliferative effect was observed at a 1/64 volume ratio of SA supernatant to reaction system, and this effect gradually weakened as the concentration increased. Significant differences were found compared to the control group (P < 0.05, **P < 0.01, ****P < 0.0001). SA-Induced Pyroptosis in M0 Macrophages After 24 hours of PMA treatment, THP-1 cells differentiated into M0 macrophages, transitioning from a suspended state to a fully adherent form, changing from a round to an irregular shape, with an increase in size. The cytoplasm became more loose, and the nucleus enlarged significantly. Numerous organelles became visible, and a few protrusions appeared around the cell membrane (Figure 3A). ELISA detection showed that after 24 hours of intervention with different concentrations of SA supernatant, IL-1β (Figure 3B) and IL-18 (Figure 3C) levels in the supernatant were elevated, with the most significant increase at a concentration of 1/64, which showed a statistically significant difference compared to the control group (P < 0.05). Therefore, this concentration was chosen for subsequent experiments. Further ELISA analysis of the effect of SA on LDH levels in M0 macrophages revealed that after 24 hours of SA supernatant (Ms group) intervention, LDH levels in the supernatant increased significantly, showing a statistically significant difference compared to the control group (P < 0.001). When the NLRP3 inhibitor MCC950 (MsM group) and ROS inhibitor NAC (MsN group) were applied, this effect was significantly reversed (P < 0.001) (Figure 3D). RT-qPCR results showed that, compared to the control group, the Ms group exhibited a significant increase in mRNA expression levels of NLRP3, GSDMD, Caspase-1, and IL-1β after 24 hours of intervention, while the MsM and MsN groups showed a significant decrease in expression (P < 0.0001) (Figure 3E). Western blot analysis showed that after 24 hours of intervention with SA supernatant (Ms group), the expression of NLRP3, GSDMD, Caspase-1, and IL-1β proteins in M0 macrophages increased. However, when the NLRP3 inhibitor MCC950 (MsM group) and ROS inhibitor NAC (MsN group) were added, these effects were attenuated, and the differences compared to the control group were statistically significant (P < 0.01) (Figure 3F). SA and M0 Macrophage Co-culture Induces a "Gastric Inflammation-Cancer Transformation" Microenvironment CCK-8 assay showed that after 24 hours of intervention, SA, M0, and Ms groups enhanced AGS (Figure 4A) and MKN45 (Figure 4B) cell viability, with statistically significant differences compared to the control group (P < 0.001). The MsM group exhibited decreased cell viability, suggesting that inhibition of NLRP3 reversed the proliferative effect of the co-culture supernatant on GC cells, with significant differences. Scratch assay (Figure 4C) showed that when the SA supernatant concentration was 1/64, it promoted cell migration in AGS and MKN45 GC cells after 24 hours of intervention. When the co-culture supernatant (Ms group, 1/32 concentration) was used for intervention, it further enhanced cell migration. Statistically significant differences were observed between these two groups and compared to the control group (P < 0.001). The MsM group (1/32 concentration) weakened the migratory ability of cells compared to the Ms group, and the difference was statistically significant (P < 0.001). RT-qPCR results showed that compared to the control group, after 24 hours of intervention with SA (1/64 concentration) and Ms (1/32 concentration) supernatants, the mRNA expression levels of Snail and Vimentin in AGS (Figure 4D) and MKN45 (Figure 4E) cells were significantly increased (P < 0.0001). In contrast, the MsM group showed significant downregulation of Snail and Vimentin gene mRNA expression levels compared to the Ms group (P < 0.0001). Western blot analysis showed that after 24 hours of intervention with SA (1/64 concentration) and Ms (1/32 concentration) supernatants, Snail protein expression in AGS (Figure 4F) and MKN45 (Figure 4G) cells was significantly upregulated compared to the control group (P < 0.01). However, only the Ms group showed significant upregulation of Vimentin protein in both cell types (P < 0.001). In both cell types, the MsM group showed downregulation of Vimentin and Snail protein expression compared to the Ms group (P < 0.01). RT-qPCR results showed that, compared to the control group, after 72 hours of intervention with SA (1/64 concentration) and Ms (1/32 concentration) supernatants, the mRNA expression levels of MUC2, KLF4, and CDX2 in GES-1 cells were significantly increased (P < 0.05). However, in the group treated with the NLRP3 inhibitor MCC950 (MsM group, 1/32 concentration), the mRNA expression levels of MUC2, KLF4, and CDX2 were significantly decreased compared to the Ms group (P < 0.001) (Figure 4I). Western blot analysis showed that after 72 hours of intervention with SA (1/64 concentration), M0, and Ms (1/32 concentration) supernatants, MUC2, KLF4, and CDX2 protein expression levels in AGS and MKN45 cells were significantly upregulated compared to the control group, with statistically significant differences (P < 0.05). However, in the MsM group (1/32 concentration), MUC2 and CDX2 protein expression was downregulated compared to the Ms group (P < 0.001), while no significant difference was observed in KLF4 protein expression (Figure 4I). Untargeted Metabolomics Analysis The untargeted metabolomics analysis results showed that the TIC of the QC samples had high overlap (Figure 5A), indicating stable instrument performance. No significant internal standard peaks were detected in the blank samples (Figure 5B), indicating that cross-contamination was controlled. The QC samples exhibited very high correlation (Pearson coefficient ≥ 0.9997) (Figure 5C), demonstrating good repeatability of the measurements. The overall clustering of the samples is shown in Figure 5D, with samples along the x-axis and metabolite information along the y-axis. The "Group" represents the sample groups, with different colors corresponding to relative content values normalized after processing; red indicates high content, while green represents low content. PCA results indicate metabolic differences between the groups (Figure 5E). The univariate control chart shows that the PC1 scores of all QC samples were within ±3 standard deviations (Figure 5F), suggesting that the analysis was stable and under controlled conditions. OPLS-DA score plots (Figure 5G) indicate clear separation between the groups. The OPLS-DA model was validated as effective (R²Y = 1, Q² = 0.928, P < 0.005) (Figure 5H). The S-plot from OPLS-DA (Figure 5I) highlights the metabolites contributing most to the separation between the groups. The volcano plot of differential metabolites is shown in Figure 5J, where each point represents a metabolite. Green points represent downregulated metabolites (193 metabolites), red points represent upregulated metabolites (233 metabolites), and gray points represent metabolites with no significant difference (1292 metabolites). The x-axis shows the log2 fold change (FC), and the y-axis represents the significance level. The point size corresponds to the Variable Importance in Projection (VIP) value. The figure highlights 15(S)-HPETE and indoleacetic acid (IAA).The heatmap for differential metabolites clustering is shown in Figure 5K, where the x-axis represents the sample names, and the y-axis shows the differential metabolites. "Group" represents the sample groups, with red indicating high content and blue indicating low content. The "heatmap_class" indicates heatmap classification by substance type, with "Class" representing the primary substance categories.The correlation analysis of differential metabolites is shown in Figure 5L, where each point represents a differential metabolite. The point size is related to the connectivity (degree), with larger points representing higher connectivity. Red lines represent positive correlations, and blue lines represent negative correlations. The thickness of the lines represents the absolute value of the Pearson correlation coefficient, with thicker lines indicating stronger correlations. This plot shows the top 50 differential metabolites based on VIP values.The Z-values for differential metabolites are presented in Figure 5M, where the x-axis shows the Z-value and the y-axis shows the metabolites. Different colors represent different groups of samples. The top 50 differential metabolites with the highest VIP values are displayed. The pathway classification of differential metabolites is shown in Figure 5N, with the y-axis representing metabolic pathways and the x-axis showing the number of differential metabolites annotated to each pathway, as well as the proportion of these metabolites relative to the total number of annotated differential metabolites.KEGG enrichment analysis of differential metabolites is shown in Figure 5O, where the x-axis represents the Rich Factor for each pathway, and the y-axis shows the pathway names sorted by P-value. The color of the points reflects the P-value, with red indicating more significant enrichment. The point size represents the number of differential metabolites enriched in the pathway. 15(S)-HPETE Promotes Malignant Progression of G C Cells and Induces Intestinal Metaplasia in Gastric Mucosa CCK-8 assay results showed that after 24 hours of intervention with different concentrations of 15(S)-HPETE, the viability of AGS GC cells increased. At concentrations of 0.2 μM and 0.4 μM, significant differences were observed compared to the control group (P 0.05) (Figure 6A).Scratch assay results demonstrated that after 24 hours of intervention with 0.2 μM and 0.4 μM 15(S)-HPETE, the migration ability of AGS cells was enhanced, with significant differences compared to the control group (P < 0.05) (Figure 6B).RT-qPCR analysis showed that after 24 hours of 0.2 μM and 0.4 μM 15(S)-HPETE intervention, Vimentin and Snail gene transcription levels were upregulated in AGS cells, with significant differences compared to the control group (P < 0.01) (Figure 6C).Western blot analysis revealed that after 24 hours of 0.2 μM and 0.4 μM 15(S)-HPETE intervention, the protein expression of Vimentin and Snail was upregulated in AGS cells, with significant differences compared to the control group (P < 0.01) (Figure 6D).RT-qPCR results showed that after 72 hours of 0.2 μM and 0.4 μM 15(S)-HPETE intervention, MUC2, KLF4, and CDX2 gene transcription levels were upregulated in GES-1 cells, with significant differences compared to the control group (P < 0.05) (Figure 6E).Western blot analysis showed that after 72 hours of 0.2 μM and 0.4 μM 15(S)-HPETE intervention, MUC2, KLF4, and CDX2 protein expression levels were upregulated in GES-1 cells, with significant differences compared to the control group (P < 0.001) (Figure 6F). These results suggest that 15(S)-HPETE promotes intestinal metaplasia in GES-1 cells after 72 hours of intervention. Discussion Reduced microbial diversity has been widely recognized as one of the hallmarks of inflammatory diseases and cancer [ 41 – 43 ]. Compared to CSG, the gastric mucosal microbiome richness in IM and GC patients was significantly reduced, and the microbial interactions between GC and CSG, as well as between chronic atrophic gastritis (CAG) and GC, showed differences, suggesting that the development of GC is closely related to changes in the microbiome structure [ 44 ]. At the phylum level, the dominant bacterial groups in the stomach primarily include Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria [ 45 – 47 ]. Among these, genera such as Streptococcus and Lactobacillus are enriched in GC patients, possibly promoting carcinogenesis through mechanisms such as increased exogenous lactic acid, ROS, and N-nitroso compound supply, which may facilitate EMT and induce immune tolerance [ 48 ]. In this study, by analyzing clinical samples from CSG, IM, and GC patients with non-HP infection (using the same pathological case numbers for FISH,IHC, and IF for consistency), we found that the infection levels of SA in the IM and GC groups were significantly higher than in the CSG group. This result further supports and enriches the theory that microbiota communities are clearly associated with GC suggesting that SA likely contributes to the occurrence and development of IM and GC. Chronic inflammation is an important driver of cancer initiation and progression, and its impact is increasingly being recognized [ 49 – 51 ]. Pyroptosis, a form of programmed cell death mediated by gasdermin proteins, is characterized by membrane pore formation, swelling, rupture, and the release of pro-inflammatory cytokines like IL-1β and IL-18, which can trigger strong inflammatory and immune responses [ 52 , 53 ]. This inflammatory cell death can be triggered by various pathological stimuli, including infection and cancer [ 54 ]. IL-1β and IL-18 are both pro-inflammatory cytokines from the IL-1 family, with diverse functions, and they play crucial roles in inflammatory disease processes [ 55 , 56 ]. These cytokines are mainly produced by monocytes/macrophages and macrophages/dendritic cells/epithelial cells [ 57 ] and act as key inflammatory mediators in infection and cancer. In this study, immunohistochemical analysis revealed that in non-HP infected samples, the expression levels of pyroptosis-related proteins NLRP3, Caspase-1, and IL-1β in the IM and GC groups were significantly higher than those in the CSG group, suggesting more pronounced inflammation in the IM and GC stages, which likely promotes disease progression. Macrophages play a central role in tumor development and are critical regulators of tumor biology [ 58 ]. Tumor-associated macrophage (TAM) infiltration is positively correlated with poor prognosis in GC and is crucial for promoting GC invasion and metastasis [ 59 ]. GSDMD, a key executor of pyroptosis, is cleaved by caspases (caspase-1/4/5/11), and its N-terminal fragment forms pores in the cell membrane, causing cell lysis and the release of inflammatory factors [ 60 , 61 ]. In this study, using immunofluorescence co-staining, we found that in the IM and GC groups with non-HP infection, the co-expression levels of GSDMD and the macrophage marker CD68 were significantly higher than those in the CSG group, indicating that macrophage pyroptosis is involved in the progression of IM and GC. Further correlation analysis showed that the degree of SA infection was significantly positively correlated with the expression of NLRP3, IL-1β, GSDMD, and CD68 (P < 0.05), and there was also a positive correlation between GSDMD and CD68 expression (P < 0.05). Combined with the fact that SA infection is more pronounced in IM and GC, we conclude that SA infection drives the gastric "inflammation-cancer conversion" process by inducing macrophage pyroptosis. However, as a retrospective analysis, this study has limitations, including a small sample size, potential bias, and the lack of simultaneous detection of HP infection status. With the development of omics technologies, the complex microbial community and its functions within the gastric mucosal microenvironment are being continuously revealed. Besides HP, microorganisms such as Epstein-Barr virus, Fusobacterium nucleatum, Streptococcus species, Escherichia coli, and Candida albicans have been linked to GC [ 62 ]. In this study, we found that SA supernatant can dose-dependently (with the most significant effect at a concentration of 1/64) directly promote the proliferation and migration of AGS and MKN45 GC cells. Mechanistically, SA induces macrophage pyroptosis through the ROS/NLRP3/Caspase-1/GSDMD signaling axis, manifested by increased LDH release, upregulation of IL-1β and IL-18 secretion, and enhanced expression of related markers. This effect peaks at a concentration of 1/64, suggesting that SA may disrupt lysosomal stability via specific virulence factors (such as streptolysin S), leading to the release of cathepsin B and activation of NLRP3 [ 63 ]. The use of MCC950 (an NLRP3 inhibitor) and NAC (an ROS inhibitor) reversed this pyroptotic phenotype, confirming the central role of the ROS/NLRP3 axis. We hypothesize that SA might trigger massive ROS production by disrupting mitochondrial functions, activating the NLRP3 inflammasome, which is one of the key mechanisms for inducing macrophage pyroptosis. Moreover, IL-1β released during pyroptosis may form a positive feedback loop through autocrine or paracrine signaling, exacerbating the inflammatory "storm" [ 64 ]. Future studies should explore whether SA-induced pyroptosis interacts with other forms of programmed cell death. To further simulate the tumor microenvironment, this study investigated the impact of SA-induced macrophage pyroptosis (SA + M0 supernatant) on GC and gastric epithelial cells. The results showed that this microenvironment significantly enhanced the migration ability and EMT process of GC cells, and this effect could be blocked by MCC950. This indicates that pyroptosis-related inflammatory factors, such as IL-1β, play a crucial role in reshaping the tumor microenvironment. IL-1β activates the NF-κB pathway through the IL-1R/MyD88 signaling pathway, inducing Snail and inhibiting E-cadherin, thus promoting EMT [ 65 ]. This study is the first to link SA-related macrophage pyroptosis with EMT, supporting the "bacteria-inflammation-EMT-metastasis" cascade hypothesis [ 66 ]. Additionally, SA + M0 supernatant significantly upregulated intestinal metaplasia markers MUC2, KLF4, and CDX2 in GES-1 cells, and this process was dependent on the NLRP3 pathway. CDX2, as a core transcription factor for intestinal epithelial differentiation, is often abnormally expressed in response to chronic inflammation [ 67 ]. This study associates SA infection with gastric mucosal intestinal metaplasia and provides new evidence for the "bacteria-inflammation-metaplasia-carcinogenesis" Correa cascade theory [ 68 ]. In summary, this study reveals a key molecular pathway by which SA drives gastric "inflammation-cancer conversion": SA activates the NLRP3 inflammasome to induce macrophage pyroptosis, and the released inflammatory factors promote GC cell EMT and gastric epithelial cell intestinal metaplasia. The use of the NLRP3 inhibitor MCC950 can block these effects. To further explore the molecular basis of SA's action, we employed untargeted metabolomics to analyze the metabolic features of SA supernatant. Through LC-MS/MS combined with multivariate statistical analysis, we identified 426 differential metabolites (233 upregulated, 193 downregulated). KEGG pathway enrichment analysis revealed that these metabolites were significantly enriched in amino acid metabolism, lipid metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid cycle pathways, suggesting that SA may affect gastric mucosal cells through metabolic reprogramming. Notably, polyunsaturated fatty acids such as arachidonic acid and their lipid peroxidation products were significantly upregulated. Based on this, we focused on and validated the role of the upregulated differential metabolite 15(S)-HPETE. 15(S)-HPETE, an oxidized lipid mediator derived from arachidonic acid via lipoxygenase metabolism, plays a key role in inflammation. Studies show that it can increase intracellular ROS levels by enhancing NADPH oxidase activity [ 69 ], and ROS can activate the NF-κB pathway and upregulate pro-IL-1β transcription [ 70 ]. At the same time, 15(S)-HPETE may induce potassium ion efflux by affecting ion channels [ 71 ], or disrupt lysosomal membrane stability to release cathepsin B [ 72 ], thereby activating the NLRP3 inflammasome. Functional experiments confirmed that 15(S)-HPETE not only promotes the proliferation, migration, and EMT of AGS GC cells but also induces the upregulation of intestinal metaplasia markers MUC2, KLF4, and CDX2 in GES-1 cells. This suggests that 15(S)-HPETE is an important effector molecule driving the gastric "inflammation-cancer conversion" process. Future studies should combine in vivo models to further validate its function and elucidate its direct molecular mechanisms of interaction with the NLRP3/IL-1β pathway. In conclusion, this study systematically elucidates the molecular mechanisms by which SA drives gastric "inflammation-cancer conversion." Clinical and experimental evidence indicates that SA accumulates in precancerous lesions and induces macrophage pyroptosis through the ROS/NLRP3/Caspase-1/GSDMD signaling axis, remodeling the pro-inflammatory, pro-tumor microenvironment and accelerating GC cell EMT and gastric mucosal intestinal metaplasia. At the same time, metabolomics research identified key metabolite 15(S)-HPETE, confirming its direct promotion of malignant phenotypes and metaplastic progression. This study provides important theoretical evidence for early prevention and targeted intervention of SA-related GC. Abbreviations SA : Streptococcus anginosus GC : Gastric cancer CSG : Chronic superficial gastritis IM: Intestinal metaplasia EMT: Epithelial-mesenchymal transition HP: Helicobacter pylori MAPK: Mitogen-activated protein kinase ROS: Reactive oxygen species FISH: Fluorescence In Situ Hybridization IHC: Immunohistochemistry IF: Immunofluorescence OD: optical density PMA: Phorbol 12-myristate 13-acetate LDH: lactate dehydrogenase RT-qPCR : Real-Time Quantitative PCR ECL: Chemiluminescence WB: Western Blot KLF4 : Krüppel-like factor 4 CDX2 : Caudal-related homeobox transcription factor 2 LC-MS : Liquid Chromatography-Mass Spectrometry TIC : total ion chromatograms QC : Quality control CV: Coefficient of variation PCA : Principal component analysis OPLS-DA : Orthogonal partial least squares discriminant analysis CAG: chronic atrophic gastritis TAM: Tumor-associated macrophage Declarations Ethics approval and consent to participate This study was followed by the Declaration of Helsinki Principles and approved by the ethical committee of the Medical Ethics Committee of Tianjin Medical University. (Ethics Approval Number: IRB2025-YX-015-01). Consent for publication Not applicable. Data Availability The datasets used and/or analysed during the current study are available from the corresponding authors on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by the Research project on Traditional Chinese Medicine and Integrative Medicine funded by the Tianjin Municipal Health Commission (2025185), Tianjin Education Commission's Research Program(2025KJ081). Author Contributions XC, QZZ, and WLZ conceptualized and designed the study. XDH drafted the manuscript. XDH, DDC and LPZ performed the analyses and experiments. ZL, YYH, MY and LL conducted data analysis and processing. ZHY, JJY, and QYY reviewed and revised the study content and manuscript. All authors reviewed and approved the final manuscript. Acknowledgements Not applicable. References Rawla P, Barsouk A. Epidemiology of gastric cancer: global trends, risk factors and prevention. Prz Gastroenterol. 2019;14(1):26-38. doi:10.5114/pg.2018.80001 Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264-279. doi:10.3322/caac.21657 Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635-648. doi:10.1016/S0140-6736(20)31288-5 Hironaka S. Anti-angiogenic therapies for gastric cancer. Asia Pac J Clin Oncol. 2019;15(4):208-217. doi:10.1111/ajco.13174 Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25(12):1822-1832. doi:10.1038/s41591-019-0675-0 Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27-41. doi:10.1016/j.immuni.2019.06.025 Manjili SH, Isbell M, Ghochaghi N, Perkinson T, Manjili MH. Multifaceted functions of chronic inflammation in regulating tumor dormancy and relapse. Semin Cancer Biol. 2022;78:17-22. doi:10.1016/j.semcancer.2021.03.023 Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow?. Lancet. 2001;357(9255):539-545. doi:10.1016/S0140-6736(00)04046-0 Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow?. Lancet. 2001;357(9255):539-545. doi:10.1016/S0140-6736(00)04046-0 Correa P, Piazuelo MB. The gastric precancerous cascade. J Dig Dis. 2012;13(1):2-9. doi:10.1111/j.1751-2980.2011.00550.x Capparelli R, Iannelli D. Epigenetics and Helicobacter pylori. Int J Mol Sci. 2022;23(3):1759. Published 2022 Feb 3. doi:10.3390/ijms23031759 Zhang H, Wang X, Shen Z, Xu J, Qin J, Sun Y. Infiltration of diametrically polarized macrophages predicts overall survival of patients with gastric cancer after surgical resection. Gastric Cancer. 2015;18(4):740-750. doi:10.1007/s10120-014-0422-7 Zhang P, Yang M, Zhang Y, Xiao S, Lai X, Tan A, et al. Dissecting the Single-Cell Transcriptome Network Underlying Gastric Premalignant Lesions and Early Gastric Cancer. Cell Rep. 2020;30(12):4317. doi:10.1016/j.celrep.2020.03.020 Compare D, Rocco A, Nardone G. Risk factors in gastric cancer. Eur Rev Med Pharmacol Sci. 2010;14(4):302-308. Nardone G, Compare D. The human gastric microbiota: Is it time to rethink the pathogenesis of stomach diseases?. United European Gastroenterol J. 2015;3(3):255-260. doi:10.1177/2050640614566846 Aviles-Jimenez F, Vazquez-Jimenez F, Medrano-Guzman R, Mantilla A, Torres J. Stomach microbiota composition varies between patients with non-atrophic gastritis and patients with intestinal type of gastric cancer. Sci Rep. 2014;4:4202. Published 2014 Feb 26. doi:10.1038/srep04202 Eun CS, Kim BK, Han DS, Kim SY, Kim KM, Choi BY, et al. Differences in gastric mucosal microbiota profiling in patients with chronic gastritis, intestinal metaplasia, and gastric cancer using pyrosequencing methods. Helicobacter. 2014;19(6):407-416. doi:10.1111/hel.12145 Dicksved J, Lindberg M, Rosenquist M, Enroth H, Jansson JK, Engstrand L. Molecular characterization of the stomach microbiota in patients with gastric cancer and in controls. J Med Microbiol. 2009;58(Pt 4):509-516. doi:10.1099/jmm.0.007302-0 Peek RM Jr, Crabtree JE. Helicobacter infection and gastric neoplasia. J Pathol. 2006;208(2):233-248. doi:10.1002/path.1868 Lofgren JL, Whary MT, Ge Z, Muthupalani S, Taylor NS, Mobley M, et al. Lack of commensal flora in Helicobacter pylori-infected INS-GAS mice reduces gastritis and delays intraepithelial neoplasia. Gastroenterology. 2011;140(1):210-220. doi:10.1053/j.gastro.2010.09.048 Lee CW, Rickman B, Rogers AB, Muthupalani S, Takaishi S, Yang P, et al. Combination of sulindac and antimicrobial eradication of Helicobacter pylori prevents progression of gastric cancer in hypergastrinemic INS-GAS mice. Cancer Res. 2009;69(20):8166-8174. doi:10.1158/0008-5472.CAN-08-3856 Asam D, Spellerberg B. Molecular pathogenicity of Streptococcus anginosus. Mol Oral Microbiol. 2014;29(4):145-155. doi:10.1111/omi.12056 Kuryłek A, Stasiak M, Kern-Zdanowicz I. Virulence factors of Streptococcus anginosus - a molecular perspective. Front Microbiol. 2022;13:1025136. Published 2022 Oct 26. doi:10.3389/fmicb.2022.1025136 Liu X, Shao L, Liu X, Ji F, Mei Y, Cheng Y, et al. Alterations of gastric mucosal microbiota across different stomach microhabitats in a cohort of 276 patients with gastric cancer. EBioMedicine. 2019;40:336-348. doi:10.1016/j.ebiom.2018.12.034 Zhou CB, Pan SY, Jin P, Deng JW, Xue JH, Ma XY, et al. Fecal Signatures of Streptococcus anginosus and Streptococcus constellatus for Noninvasive Screening and Early Warning of Gastric Cancer. Gastroenterology. 2022;162(7):1933-1947.e18. doi:10.1053/j.gastro.2022.02.015 Fu K, Cheung AHK, Wong CC, Liu W, Zhou Y, Wang F, et al. Streptococcus anginosus promotes gastric inflammation, atrophy, and tumorigenesis in mice. Cell. 2024;187(23):6783. doi:10.1016/j.cell.2024.10.036 Shi J, Zhao Y, Wang K, Shi X, Wang Y, Huang H, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature. 2015;526(7575):660-665. doi:10.1038/nature15514 Ding J, Wang K, Liu W, She Y, Sun Q, Shi J, et al. Pore-forming activity and structural autoinhibition of the gasdermin family. Nature. 2016;535(7610):111-116. doi:10.1038/nature18590 Fang Y, Tian S, Pan Y, Li W, Wang Q, Tang Y, et al. Pyroptosis: A new frontier in cancer. Biomed Pharmacother. 2020;121:109595. doi:10.1016/j.biopha.2019.109595 Al Mamun A, Mimi AA, Aziz MA, Zaeem M, Ahmed T, Munir F, et al. Role of pyroptosis in cancer and its therapeutic regulation. Eur J Pharmacol. 2021;910:174444. doi:10.1016/j.ejphar.2021.174444 Lin W, Chen Y, Wu B, Chen Y, Li Z. Identification of the pyroptosis‑related prognostic gene signature and the associated regulation axis in lung adenocarcinoma. Cell Death Discov. 2021;7(1):161. Published 2021 Jun 25. doi:10.1038/s41420-021-00557-2 He Y, Hara H, Núñez G. Mechanism and Regulation of NLRP3 Inflammasome Activation. Trends Biochem Sci. 2016;41(12):1012-1021. doi:10.1016/j.tibs.2016.09.002 Sun X, Cai H, Li Z, Li S, Yin W, Dong G, et al. Association between IL-1β polymorphisms and gastritis risk: A meta-analysis. Medicine (Baltimore). 2017;96(5):e6001. doi:10.1097/MD.0000000000006001 Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181-1189. doi:10.1080/004982599238047 Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016;15(7):473-484. doi:10.1038/nrd.2016.32 Clish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud. 2015;1(1):a000588. doi:10.1101/mcs.a000588 Griffin JL, Shockcor JP. Metabolic profiles of cancer cells. Nat Rev Cancer. 2004;4(7):551-561. doi:10.1038/nrc1390 Dunn WB. Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys Biol. 2008;5(1):011001. Published 2008 Feb 20. doi:10.1088/1478-3975/5/1/011001 Beger RD, Dunn WB, Bandukwala A, Bethan B, Broadhurst D, Clish CB, et al. Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics. 2019;15(1):4. Published 2019 Jan 3. doi:10.1007/s11306-018-1460-7 Gika H, Virgiliou C, Theodoridis G, Plumb RS, Wilson ID. Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art. J Chromatogr B Analyt Technol Biomed Life Sci. 2019;1117:136-147. doi:10.1016/j.jchromb.2019.04.009 Gevers D, Kugathasan S, Denson LA, Vázquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe. 2014;15(3):382-392. doi:10.1016/j.chom.2014.02.005 Lepage P, Häsler R, Spehlmann ME, Rehman A, Zvirbliene A, Begun A, et al. Twin study indicates loss of interaction between microbiota and mucosa of patients with ulcerative colitis. Gastroenterology. 2011;141(1):227-236. doi:10.1053/j.gastro.2011.04.011 Ahn J, Sinha R, Pei Z, Dominianni C, Wu J, Shi J, et al. Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst. 2013;105(24):1907-1911. doi:10.1093/jnci/djt300 Coker OO, Dai Z, Nie Y, Zhao G, Cao L, Nakatsu G, et al. Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut. 2018;67(6):1024-1032. doi:10.1136/gutjnl-2017-314281 Bik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, Francois F, et al. Molecular analysis of the bacterial microbiota in the human stomach. Proc Natl Acad Sci U S A. 2006;103(3):732-737. doi:10.1073/pnas.0506655103 Delgado S, Cabrera-Rubio R, Mira A, Suárez A, Mayo B. Microbiological survey of the human gastric ecosystem using culturing and pyrosequencing methods. Microb Ecol. 2013;65(3):763-772. doi:10.1007/s00248-013-0192-5 Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyrén P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One. 2008;3(7):e2836. Published 2008 Jul 30. doi:10.1371/journal.pone.0002836 Vinasco K, Mitchell HM, Kaakoush NO, Castaño-Rodríguez N. Microbial carcinogenesis: Lactic acid bacteria in gastric cancer. Biochim Biophys Acta Rev Cancer. 2019;1872(2):188309. doi:10.1016/j.bbcan.2019.07.004 FFurman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25(12):1822-1832. doi:10.1038/s41591-019-0675-0 Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27-41. doi:10.1016/j.immuni.2019.06.025 Manjili SH, Isbell M, Ghochaghi N, Perkinson T, Manjili MH. Multifaceted functions of chronic inflammation in regulating tumor dormancy and relapse. Semin Cancer Biol. 2022;78:17-22. doi:10.1016/j.semcancer.2021.03.023 Xia X, Wang X, Cheng Z, Qin W, Lei L, Jiang J, et al. The role of pyroptosis in cancer: pro-cancer or pro-"host"?. Cell Death Dis. 2019;10(9):650. Published 2019 Sep 9. doi:10.1038/s41419-019-1883-8 Broz P, Pelegrín P, Shao F. The gasdermins, a protein family executing cell death and inflammation. Nat Rev Immunol. 2020;20(3):143-157. doi:10.1038/s41577-019-0228-2 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(8):2930-2940. doi:10.1172/JCI91190 Dinarello CA. The IL-1 family and inflammatory diseases. Clin Exp Rheumatol. 2002;20(5 Suppl 27):S1-S13. Man SM, Kanneganti TD. Converging roles of caspases in inflammasome activation, cell death and innate immunity. Nat Rev Immunol. 2016;16(1):7-21. doi:10.1038/nri.2015.7 Dinarello CA, Novick D, Kim S, Kaplanski G. Interleukin-18 and IL-18 binding protein. Front Immunol. 2013;4:289. Published 2013 Oct 8. doi:10.3389/fimmu.2013.00289 Kloosterman DJ, Akkari L. Macrophages at the interface of the co-evolving cancer ecosystem. Cell. 2023;186(8):1627-1651. doi:10.1016/j.cell.2023.02.020 Li D, Xia L, Huang P, Wang Z, Guo Q, Huang C, et al. Cancer-associated fibroblast-secreted IGFBP7 promotes gastric cancer by enhancing tumor associated macrophage infiltration via FGF2/FGFR1/PI3K/AKT axis. Cell Death Discov. 2023;9(1):17. Published 2023 Jan 21. doi:10.1038/s41420-023-01336-x Shi J, Zhao Y, Wang K, Shi X, Wang Y, Huang H, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature. 2015;526(7575):660-665. doi:10.1038/nature15514 Kayagaki N, Stowe IB, Lee BL, O'Rourke K, Anderson K, Warming S, et al. Caspase-11 cleaves gasdermin D for non-canonical inflammasome signalling. Nature. 2015;526(7575):666-671. doi:10.1038/nature15541 Coker OO, Dai Z, Nie Y, Zhao G, Cao L, Nakatsu G, et al. Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut. 2018;67(6):1024-1032. doi:10.1136/gutjnl-2017-314281 Harder J, Franchi L, Muñoz-Planillo R, Park JH, Reimer T, Núñez G. Activation of the Nlrp3 inflammasome by Streptococcus pyogenes requires streptolysin O and NF-kappa B activation but proceeds independently of TLR signaling and P2X7 receptor. J Immunol. 2009;183(9):5823-5829. doi:10.4049/jimmunol.0900444 Guo H, Callaway JB, Ting JP. Inflammasomes: mechanism of action, role in disease, and therapeutics. Nat Med. 2015;21(7):677-687. doi:10.1038/nm.3893 Sullivan NJ, Sasser AK, Axel AE, Vesuna F, Raman V, Ramirez N, et al. Interleukin-6 induces an epithelial-mesenchymal transition phenotype in human breast cancer cells. Oncogene. 2009;28(33):2940-2947. doi:10.1038/onc.2009.180 Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883-899. doi:10.1016/j.cell.2010.01.025 Silberg DG, Furth EE, Taylor JK, Schuck T, Chiou T, Traber PG. CDX1 protein expression in normal, metaplastic, and neoplastic human alimentary tract epithelium. Gastroenterology. 1997;113(2):478-486. doi:10.1053/gast.1997.v113.pm9247467 Correa P. A human model of gastric carcinogenesis. Cancer Res. 1988;48(13):3554-3560. Mahipal SV, Subhashini J, Reddy MC, Reddy MM, Anilkumar K, Roy KR, et al. Effect of 15-lipoxygenase metabolites, 15-(S)-HPETE and 15-(S)-HETE on chronic myelogenous leukemia cell line K-562: reactive oxygen species (ROS) mediate caspase-dependent apoptosis. Biochem Pharmacol. 2007;74(2):202-214. doi:10.1016/j.bcp.2007.04.005 Zhou R, Yazdi AS, Menu P, Tschopp J. A role for mitochondria in NLRP3 inflammasome activation. Nature. 2011;469(7329):221-225. doi:10.1038/nature09663 Muñoz-Planillo R, Kuffa P, Martínez-Colón G, Smith BL, Rajendiran TM, Núñez G. K⁺ efflux is the common trigger of NLRP3 inflammasome activation by bacterial toxins and particulate matter. Immunity. 2013;38(6):1142-1153. doi:10.1016/j.immuni.2013.05.016 Duewell P, Kono H, Rayner KJ, Sirois CM, Vladimer G, Bauernfeind FG, et al. NLRP3 inflammasomes are required for atherogenesis and activated by cholesterol crystals. Nature. 2010;464(7293):1357-1361. doi:10.1038/nature08938 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 07 Apr, 2026 First submitted to journal 06 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9269296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622472298,"identity":"bc103f30-6caa-47f5-85eb-b903ebd5b57e","order_by":0,"name":"xiaodong han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACAyBiZvhhI8cP4TMTqYWxJ81Yso0kLQxshxM3HCNWi7lE8jbpAh5mY+P73WkSDBXWiQ3sZw/g1WLZc6zYeIYFm5zZMd5tEgxn0hMbePIS8DvseI/hYx4eHmOwFsa2w4kNEjwG+LUc5gEiNonEzW0gLf+I0QK2hc0gcQMbSEsDMVrOAP3C25NgLHEsd7NFwrF04zaeHAJabgBDjOfHfzn+5rMbb3yosZbtZz+DXwsqSABiNhLUj4JRMApGwSjAAQBrB0AkjZxxoAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-2559-6942","institution":"Tianjin Fourth Hospital: Tianjin 4th Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"xiaodong","middleName":"","lastName":"han","suffix":""},{"id":622472299,"identity":"eccd46ca-36ef-460f-9032-da7b33557be5","order_by":1,"name":"Dandan Cao","email":"","orcid":"","institution":"tian jin shi di si zhong xin yi yuan: Tianjin Fourth Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Cao","suffix":""},{"id":622472300,"identity":"274e84df-b441-4210-820d-65218a96e870","order_by":2,"name":"Lanping Zhu","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lanping","middleName":"","lastName":"Zhu","suffix":""},{"id":622472301,"identity":"a8a56a20-c795-4474-b4de-fd1bfdf3a862","order_by":3,"name":"Zhu Liu","email":"","orcid":"","institution":"Maternal and Child Health Bureau","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Liu","suffix":""},{"id":622472302,"identity":"7383cc49-ae09-4c12-ac59-3ddbd560f1fa","order_by":4,"name":"Yangyang Hui","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yangyang","middleName":"","lastName":"Hui","suffix":""},{"id":622472303,"identity":"5055b155-6121-4328-a607-977e422beff7","order_by":5,"name":"Mo Yang","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mo","middleName":"","lastName":"Yang","suffix":""},{"id":622472304,"identity":"2ba53348-b985-425f-a98a-a11f42d4f716","order_by":6,"name":"LIn Lin","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"LIn","middleName":"","lastName":"Lin","suffix":""},{"id":622472305,"identity":"eed81d16-983a-4d07-ae1f-d85b07fb718e","order_by":7,"name":"Zihan Yu","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Yu","suffix":""},{"id":622472306,"identity":"3838c756-0a68-4a3d-8631-1ce56918c38b","order_by":8,"name":"Junjie Yuan","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Yuan","suffix":""},{"id":622472307,"identity":"82dae5b1-f517-4075-aa9f-75b52861d805","order_by":9,"name":"Qinyan Yao","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qinyan","middleName":"","lastName":"Yao","suffix":""},{"id":622472308,"identity":"4cd2a2ef-47f8-4531-aa07-0365abdd1c89","order_by":10,"name":"weilong Zhong","email":"","orcid":"","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"weilong","middleName":"","lastName":"Zhong","suffix":""},{"id":622472309,"identity":"3d68f68a-48a1-4429-98ab-bb09fd8e3cdb","order_by":11,"name":"Qiuzan Zhang","email":"","orcid":"","institution":"Tianjin Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiuzan","middleName":"","lastName":"Zhang","suffix":""},{"id":622472310,"identity":"18743e9f-aed8-4c0f-b28d-a1b29634f7ee","order_by":12,"name":"Xin Chen","email":"","orcid":"https://orcid.org/0000-0003-3024-9053","institution":"Tianjin Medical University First Clinical College: Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-30 16:00:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9269296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9269296/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707156,"identity":"86747e44-fa0c-4fbd-bf4b-ad79c33a5e0e","added_by":"auto","created_at":"2026-04-24 09:19:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4215198,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical Sample Analysis Results\u003c/strong\u003e A: FISH detection of SA infection (50×). The infection load of SA in CSG, IM, and GC showed a progressive increase from CSG \u0026lt; IM \u0026lt; GC, with significant differences. B, C, D: IHC detection of pyroptosis-related protein expression (200×). The expression of NLRP3 (B), Caspase-1 (C), and IL-1β (D) in the IM and GC groups was significantly higher compared to the CSG group. E, F, G, H: IF detection of CD68 and GSDMD expression. In the GC group (G), the expression of CD68 (red) and GSDMD (green) was significantly higher compared to the IM (F) and CSG (E) groups, with blue fluorescence indicating DAPI-labeled cells. The differences were significant, and no significant differences were observed between the IM and CSG groups (H). I: Correlation analysis. Pearson correlation analysis showed a positive correlation between CD68 expression and SA infection, between GSDMD, NLRP3, and IL-1β expression and SA infection, and between CD68 and GSDMD expression. *ns P \u0026gt; 0.5, * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/9ff1d93550266c1b10623e95.png"},{"id":107614219,"identity":"943e43bc-b622-4193-95e4-2335e21045b9","added_by":"auto","created_at":"2026-04-23 09:07:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6329908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSA Growth Curve and Its Impact on the Malignant Phenotype of GC Cells \u003c/strong\u003eA: SA growth curve. B, C: CCK-8 cell viability assay shows that SA supernatant enhances AGS (B) and MKN45 (C) cell viability. D: Scratch assay shows that when the SA supernatant concentration is 1/64 or 1/32, after 24 hours of intervention, there is a significant promotion of AGS and MKN45 GC cell migration, with a more pronounced effect at the 1/64 concentration compared to 1/32, and all differences are statistically significant. ns P \u0026gt; 0.05, *P \u0026lt; 0.05, **P \u0026lt; 0.01, **** P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/ac58d9eb02c37e5131cb63c7.png"},{"id":107614221,"identity":"df14a58f-2462-4ef2-a50c-ae6d65d46acd","added_by":"auto","created_at":"2026-04-23 09:07:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5236015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of SA on Pyroptosis in M0 Macrophages\u003c/strong\u003e A: THP-1 cells differentiated into M0 macrophages after 24 hours of PMA treatment. B, C, D: ELISA results show that SA supernatant promoted the secretion of IL-1β (B) and IL-18 (C), and the release of LDH (D) in M0 macrophages. E: RT-qPCR results show that after 24 hours of intervention, the mRNA expression levels of NLRP3, GSDMD, Caspase-1, and IL-1β in the Ms group significantly increased compared to the control group, while the expression in the MsM and MsN groups significantly decreased. F: Western blot results show that after 24 hours of intervention with SA supernatant (Ms group), the expression of NLRP3, GSDMD, Caspase-1, and IL-1β proteins in M0 macrophages increased. When NLRP3 inhibitor MCC950 (MsM group) and ROS inhibitor NAC (MsN group) were used, the expression of these proteins decreased, and the differences compared to the control group were statistically significant. ns P \u0026gt; 0.05, * P \u0026lt; 0.05, ** P \u0026lt; 0.01, **** P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/ae15fd6c5f986de8ac49a306.png"},{"id":107614222,"identity":"b8b0f7be-8b17-4408-8eac-1526627479f8","added_by":"auto","created_at":"2026-04-23 09:07:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14630462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSA + M0 Co-culture Creates a Microenvironment Driving Gastric \"Inflammation-Cancer Transformation\"\u003c/strong\u003e A, B: CCK-8 assay shows that after 24 hours of intervention, the cell viability of AGS (A) and MKN45 (B) cells was significantly enhanced in the SA, M0, and Ms groups compared to the control group (P \u0026lt; 0.001). The MsM group exhibited suppressed cell viability, with significant differences observed. C: Scratch assay shows that, after 24 hours of intervention, SA and Ms groups significantly enhanced the migration ability of AGS and MKN45 cells compared to the control group, with the Ms group showing a more pronounced effect. The MsM group demonstrated reduced migration ability compared to the Ms group, and this difference was statistically significant (P \u0026lt; 0.001). D, E: RT-qPCR analysis shows that, after 24 hours of intervention, Snail and Vimentin mRNA expression levels in AGS (D) and MKN45 (E) cells were significantly increased in the SA and Ms groups compared to the control group (P \u0026lt; 0.0001). However, the MsM group exhibited significantly reduced expression levels compared to the Ms group (P \u0026lt; 0.0001). F, G: Western blot analysis shows that after 24 hours of intervention with SA and Ms supernatants, Snail protein expression in AGS (F) and MKN45 (G) cells was significantly upregulated compared to the control group. Vimentin protein expression was only significantly upregulated in the Ms group in both cell types. The MsM group showed significantly reduced expression of both Vimentin and Snail proteins compared to the Ms group (P \u0026lt; 0.01).\u003cbr\u003e\nH: RT-qPCR results show that, after 72 hours of intervention with SA and Ms supernatants, MUC2, KLF4, and CDX2 mRNA expression levels in GES-1 cells were significantly increased compared to the control group (P \u0026lt; 0.05). In contrast, the MsM group showed significantly reduced expression of MUC2, KLF4, and CDX2 mRNA compared to the Ms group (P \u0026lt; 0.001).\u003cbr\u003e\nI: Western blot analysis shows that after 72 hours of intervention with SA, M0, and Ms supernatants, MUC2, KLF4, and CDX2 protein expression levels in AGS and MKN45 cells were significantly upregulated compared to the control group (P \u0026lt; 0.05). However, the MsM group showed significantly reduced expression of MUC2 and CDX2 proteins compared to the Ms group (P \u0026lt; 0.001), while there was no significant difference in KLF4 protein expression. ns P \u0026gt; 0.05, * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, **** P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/267d141233f3ab3153e8e0cf.png"},{"id":107707141,"identity":"ac6a7878-9c19-4755-8302-ca3be5f97b31","added_by":"auto","created_at":"2026-04-24 09:19:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3367167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUntargeted Metabolomics Analysis \u003c/strong\u003eA: TIC overlap plot of the mass spectrometry detection of QC samples. B: EIC plot of the mass spectrometry detection of blank samples. C: Correlation analysis of QC samples. D: Overall clustering of samples. E: PCA score plot of the samples and QC samples’ mass spectrometry data. F: PC1 control chart for the overall samples.\u003cbr\u003e\nG: OPLS-DA score plot. H: Validation of the OPLS-DA model. I: OPLS-DA S-plot. J: Volcano plot of differential metabolites. K: Heatmap clustering of differential metabolites. L: Correlation analysis of differential metabolites. M: Z-value plot of differential metabolites. N: Pathway classification of differential metabolites. O: Pathway enrichment analysis of differential metabolites.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/901a3e5ac376cfacd5cccae0.png"},{"id":107614224,"identity":"49cea5f9-3204-45e3-b396-91c60e050033","added_by":"auto","created_at":"2026-04-23 09:07:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14029156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e15(S)-HPETE Promotes GC Progression and Intestinal Metaplasia in Gastric Mucosa \u003c/strong\u003eA: CCK-8 assay shows that 15(S)-HPETE promotes AGS GC cell proliferation. B: Scratch assay shows the effect of 15(S)-HPETE on AGS cell migration (40× magnification). C: RT-qPCR shows that 15(S)-HPETE promotes Vimentin and Snail gene transcription in AGS cells. D: Western blot shows that 15(S)-HPETE promotes Vimentin and Snail protein expression in AGS cells. E: RT-qPCR shows that 15(S)-HPETE promotes the transcription of intestinal metaplasia-related genes in GES-1 cells. F: Western blot shows that 15(S)-HPETE promotes MUC2, KLF4, and CDX2 protein expression in GES-1 cells. ns P \u0026gt; 0.5, * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, **** P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/33a802ae473f11a17e949a32.png"},{"id":107709230,"identity":"a7e193b7-a874-4d88-ac07-1bfd8417ce48","added_by":"auto","created_at":"2026-04-24 09:35:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":43762284,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9269296/v1/622fc47d-f0d3-43d2-b0dd-bf0318626c46.pdf"}],"financialInterests":"","formattedTitle":"Streptococcus anginosus induces macrophage pyroptosis and drives gastric \"inflammation-to-cancer\" transition by activating the NLRP3/IL-1β signaling pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is one of the most common malignant tumors globally, characterized by insidious onset. Most patients are diagnosed at an advanced stage, and despite diverse treatment options, the median survival time remains less than one year, with a 5-year survival rate of only 20% to 25% [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic inflammation is a significant driving factor in GC development. Based on Virchow's hypothesis and Colotta's concept of \"inflammation-cancer transition,\" prolonged inflammation can promote malignant transformation through cytokine release, DNA damage, and epigenetic changes [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to the Correa model, gastric mucosal lesions progress through stages of inflammation, atrophy, intestinal metaplasia, dysplasia, and finally GC, during which immune cell infiltration increases, particularly macrophages, which play a critical role in the tumor microenvironment [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to Helicobacter pylori (HP), gastric microbiome dysbiosis has also been linked to GC. Phylum Firmicutes and Bacteroidetes are enriched in GC tissues, and animal experiments have confirmed that microbiota can accelerate tumor progression [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Streptococcus anginosus (SA), as a potential carcinogenic bacterium, secretes virulence factors and inflammatory cytokines. Its surface protein TMPC can activate the mitogen-activated protein kinase (MAPK) pathway via the ANXA2 receptor, contributing to GC onset [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePyroptosis, an inflammatory form of programmed cell death mediated by gasdermins, plays a dual role in tumors: it can both suppress and promote tumor progression by disrupting anti-tumor immunity [\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. NLRP3 inflammasome activation induces pyroptosis, and its activation relies on NF-κB signaling, reactive oxygen species (ROS), and other secondary signals, participating in the inflammation-to-cancer transition of gastric precancerous lesions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolomics, by analyzing endogenous and exogenous metabolites, systematically reveals the metabolic responses of organisms in pathological states, providing a more phenotype-based view compared to other omics approaches [\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Untargeted metabolomics is unbiased and high-throughput, making it suitable for discovering potential metabolic biomarkers and pathways [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the above background, this study aims to investigate the role and mechanisms of SA in the gastric \"inflammation-to-cancer\" transition. Through clinical sample analysis, in vitro experiments, and metabolomics validation, we systematically explore the potential mechanisms by which SA drives GC development.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eHuman gastric adenocarcinoma cell lines AGS (CL-0022), MKN-45 (CL-0292), human monocytic leukemia cell line THP-1 (CL-0233), and human gastric mucosal cell line GES-1 (CL-0563) were purchased from Wuhan Puno Sai Life Science Technology Co., Ltd. SA strain was generously provided by the Institute of Gastroenterology, Tianjin Medical University General Hospital. RPMI 1640 medium (PM150110), RPMI 1640 complete medium (PM150110B), Ham\u0026apos;s F-12 medium (PM150810), Ham\u0026apos;s F-12 complete medium (PM150810B), fetal bovine serum (164210-50), and premium fetal bovine serum (164210) were obtained from Wuhan Puno Sai Life Science Technology Co., Ltd. Trypsin (25200056) was purchased from Gibco. THB broth (LA1860-250g) was obtained from Beijing Soleibao Technology Co., Ltd. Cell Counting Kit-8 (CCK-8) reagent (GK10001-5), LDH assay kit (GK10003), and Trizol (GK20008-100) were obtained from Glpbio. ROS assay kit (E-BC-K138-F-96T9) was purchased from Elabscience. IL-1\u0026beta; (sbj-h0417-48T) and IL-18 (sbj-h0423) ELISA kits were obtained from Nanjing Senbega Biotechnology Co., Ltd. Chemical reagents and inducers: dimethyl sulfoxide (DMSO, D8371-50ml), RIPA lysis buffer (R0010-100ml) were purchased from Beijing Soleibao Technology Co., Ltd. Phorbol 12-myristate 13-acetate (PMA) (HY-18739), N-Acetylcysteine (NAC) (HY-B0215), and MCC950 (HY-12815) were obtained from MedChemExpress (MCE), USA. Snail (AF6032), Vimentin (AF7013), and NLRP3 (DF7438) primary antibodies were purchased from Affinity Biosciences. Caspase-1 (342947), Gasdermin D (GSDMD) (R24514), CDX2 (R380757), MUC2 (R381746), \u0026beta;-actin (R380624) primary antibodies, and HRP-conjugated goat anti-rabbit secondary antibody were obtained from Chengdu Zhengneng Biotechnology Co., Ltd. IL-1\u0026beta; (PAB45925) was obtained from Wuhan Beinyile Biotechnology Co., Ltd. HRP-conjugated goat anti-rabbit IgG (Ab205718) and Anti-CD68 antibody (ab303565) were purchased from Abcam. The polyclonal GSDMD antibody (PAB45820) was obtained from Bio-Swamp. Immunohistochemistry (IHC) kit (PV-9000) was from Beijing Zhongshu Jinqiao Biotechnology Co., Ltd.; DAPI staining solution (C1002/G1012) was from Shanghai Biyuntian or Wuhan Saiweier Biotechnology Co., Ltd.; Anti-fluorescence quenching mounting medium (G1401/0100-01), recombinant proteinase K (G1234), and citrate antigen repair solution (G1202) were purchased from Wuhan Saiweier Biotechnology Co., Ltd. or Southernbiotech; concentrated normal goat serum (AR1009) was obtained from Wuhan Boster Biological Engineering Co., Ltd.; FITC-labeled probe (5\u0026apos; to 3\u0026apos;:AGTTAAACAGTTTCCAAAGCCTAC) was synthesized by Wuhan Double Helix Biotech Co., Ltd.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eClinical Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected six cases each of chronic superficial gastritis (CSG), intestinal metaplasia (IM), and GC patients diagnosed via gastroscopy and pathology from the Department of Gastroenterology, Tianjin Medical University General Hospital, between April 2023 and May 2024. Gastric mucosal tissue samples from all patients were prepared by our hospital\u0026apos;s pathology department. The inclusion criteria were as follows: (1) age between 18 and 75 years, with no gender restriction; (2) HP-negative as confirmed by the \u003csup\u003e13\u003c/sup\u003eC urea breath test; (3) no use of antibiotics, probiotics, or immunosuppressants within the past month; (4) no primary biliary reflux; (5) newly diagnosed GC with no prior treatment. The exclusion criteria included: (1) other organic diseases of the upper digestive tract, such as esophageal cancer or peptic ulcers; (2) uncontrolled primary or secondary biliary reflux; (3) previous gastric surgery, such as partial gastrectomy or fundoplication; (4) HP-positive as confirmed by the \u003csup\u003e13\u003c/sup\u003eC urea breath test or recent HP eradication therapy; (5) use of proton pump inhibitors for more than 4 weeks in the past 3 months, or long-term use of NSAIDs or immunosuppressants; (6) GC patients who have received neoadjuvant chemotherapy, radiotherapy, or palliative treatment; (7) history of other organ malignancies or hematological cancers; (8) autoimmune diseases, such as systemic lupus erythematosus or rheumatoid arthritis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluorescence In Situ Hybridization (FISH) for Detection of SA Infection in Paraffin Sections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe paraffin sections were dewaxed and rehydrated, followed by antigen retrieval and digestion. Pre-hybridization, hybridization, and post-hybridization washing were performed. Nuclei were counterstained with DAPI. Fluorescence microscopy was used to observe bacterial clusters or scattered signals exhibiting specific green fluorescence (SA probe) in each high-power field. The wavelengths for fluorescence detection were: DAPI (UV excitation 330-380 nm, emission 420 nm, blue color); FAM(488)-labeled probe (excitation 465-495 nm, emission 515-555 nm, green); CY3-labeled probe (excitation 510-560 nm, emission 590 nm, red).Fluorescence microscopy was used to observe and count bacterial clusters or scattered signals exhibiting specific green fluorescence (SA probe) in each high-power field.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry (IHC) for Detection of Pyroptosis-Related Protein Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter dewaxing the paraffin sections, antigen retrieval was performed, followed by blocking endogenous peroxidase activity. The sections were then incubated sequentially with primary antibodies targeting NLRP3, IL-1\u0026beta;, or Caspase-1, polymer enhancers, and HRP-conjugated secondary antibodies. After color development, sections were counterstained with hematoxylin, dehydrated, and mounted. Results were analyzed by scanning software, with 200x magnification images taken of the target tissue area. Using Image-Pro Plus 6.0 software, the positive staining pixel area in three randomly selected fields per section was measured and the percentage of positive area (positive pixel area/total tissue pixel area \u0026times; 100%) was calculated.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence (IF) for Detection of CD68 and GSDMD Protein Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter dewaxing, antigen retrieval, and serum blocking of the paraffin sections, sequential immunofluorescence staining was performed. First, GSDMD primary antibody (dilution 1:200) was incubated, followed by the corresponding fluorescent-labeled secondary antibody. After antibody elution, serum blocking was repeated, then CD68 primary antibody (dilution 1:1000) and another fluorescent-labeled secondary antibody (dilution 1:2000) were incubated. Finally, the nuclei were counterstained with DAPI and mounted. The target tissue areas were imaged at 200x magnification using scanning software. Using Image-Pro Plus 6.0 software, green (GSDMD) or red (CD68) fluorescence channel images were converted to black-and-white images, with a uniform grayscale threshold set as the positive criteria. The positive signal pixel area and total tissue pixel area in three randomly selected fields per section were measured, and the percentage of positive area (positive pixel area/total tissue pixel area \u0026times; 100%) was calculated.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePreparation of SA Supernatant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SA seed stock stored at -80\u0026deg;C was thawed in a 37\u0026deg;C water bath, and 1 mL was inoculated into 29 mL of THB broth. The culture was incubated at 37\u0026deg;C with 200 rpm shaking for 24 hours. After a 1:30 subculture, the optical density (OD) was measured every 4 hours. After 24 hours, the bacterial culture was diluted and the OD was measured to calculate the bacterial concentration. The bacterial culture was centrifuged at 4\u0026deg;C and 4000 rpm for 20 minutes, and the supernatant was collected. The supernatant was then filtered twice through a 0.22 \u0026micro;m filter to sterilize it. The resulting filtrate was the SA supernatant, which was aliquoted and stored at -80\u0026deg;C for later use.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCCK-8 Assay for Evaluation of SA\u0026apos;s Effect on\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cell Viability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAGS and MKN45 cells in the logarithmic growth phase were collected and seeded at a density of 1 \u0026times; 10⁴ cells/mL in a 96-well plate (100 \u0026micro;L per well). After cell attachment, the experimental groups were treated with various concentrations of SA supernatant (1/128, 1/64, 1/32, 1/16, 1/8, 1/4, 1/2 volume ratios), and each group was performed in triplicates. THB medium was added to each well to bring the final volume to 200 \u0026micro;L. The control group received an equal volume of THB medium. The cells were incubated at 37\u0026deg;C with 5% CO₂ for 24 hours. After incubation, fresh medium was added and 10 \u0026micro;L of CCK-8 solution was added to each well. The plates were incubated for an additional 4 hours. Absorbance at 450 nm was measured using a microplate reader to assess cell viability, and the optimal concentration for subsequent experiments was selected.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWound Healing Assay for Evaluation of SA\u0026apos;s Effect on GC Cell Migration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAGS and MKN45 cells in the logarithmic growth phase were seeded at a density of 1 \u0026times; 10⁵ cells/mL in a 24-well plate (500 \u0026micro;L per well). The cells were cultured until they reached approximately 70% confluence. A sterile pipette tip was used to create a straight-line scratch along the bottom of each well. The wells were gently washed three times with PBS to remove cell debris. The experimental group was treated with the optimal concentration of SA supernatant, and the control group was treated with an equal volume of THB medium. Images of the scratched area were immediately captured under an inverted microscope at 200x magnification. After 24 hours of incubation, the same conditions were used to capture additional images. The number of cells that migrated into the scratch area was counted to assess the cell migration ability.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTHP-1 Cell Culture, Differentiation, and SA Intervention Concentration Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTHP-1 cells in the logarithmic growth phase were adjusted to a density of 5 \u0026times; 10⁵ cells/mL and seeded into a 6-well plate, with 2 mL per well. Phorbol 12-myristate 13-acetate (PMA, final concentration 100 ng/mL) was added to induce differentiation into M0 macrophages, and the morphological changes of the cells were observed under a microscope. When the M0 cells reached approximately 60% confluence, they were treated with different concentrations of SA supernatant. The concentrations used were 0 (control group), 1/64, 1/32, and 1/16, resulting in four groups. After 24 hours of intervention, cell culture supernatants were collected, and the levels of IL-1\u0026beta; and IL-18 were measured by ELISA. The optimal concentration of SA supernatant for further research was selected based on the amount of IL-1\u0026beta; released.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCell Toxicity (LDH Release) Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the screened SA intervention concentration (1/64), four groups were set up for the experiment: Control, SA intervention (Ms), SA + NLRP3 inhibitor MCC950 pre-treatment (MsM, MCC950 concentration 20 \u0026mu;M, pre-treated for 1 hour), and SA + reactive oxygen species (ROS) inhibitor NAC pre-treatment (MsN, NAC concentration 1 mM, pre-treated for 1 hour). When the M0 cells reached approximately 60% confluence, the interventions were applied according to the groups. After 24 hours, cell culture supernatants were collected, and lactate dehydrogenase (LDH) release was measured by ELISA.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eReal-Time Quantitative PCR (RT-qPCR) for Pyroptosis-Related Gene Transcription\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental groups were the same as for the \u0026quot;Cell Toxicity (LDH Release) Assay,\u0026quot; i.e., Control, Ms, MsM, and MsN. After 24 hours of intervention, total RNA was extracted from the cells using the Trizol method. Then, mRNA was reverse transcribed into cDNA using a reverse transcription kit from Novogene Biotechnology Co., Ltd. (Reaction conditions: 37\u0026deg;C for 15 minutes, followed by 85\u0026deg;C for 5 seconds). The primers for amplifying NLRP3, GSDMD, Caspase-1, and the housekeeping gene \u0026beta;-actin were synthesized by GeneWiz Biotech Co., Ltd. RT-qPCR was performed using synthesized cDNA templates and specific primers. The relative expression of target genes was calculated using the 2^(-\u0026Delta;\u0026Delta;Ct) method, and data were analyzed with GraphPad Prism 8 software. The following primers (5\u0026apos; to 3\u0026apos;) were used:\u003c/p\u003e\n\u003cp\u003eNLRP3: Forward: 5\u0026rsquo;-GGACTGAAGCACCTGTTGTGCA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-TCCTGAGTCTCCCAAGGCATTC-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eGSDMD: Forward: 5\u0026rsquo;-GTGTGTCAACCTGTCTATCAAGG-3\u0026rsquo;; Reverse: 5\u0026rsquo;-CATGGCATCGTAGAAGTGGAAG-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eCaspase-1: Forward: 5\u0026rsquo;-GCTGAGGTTGACATCACAGGCA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-TGCTGTCAGAGGTCTTGTGCTC-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003e\u0026beta;-actin: Forward: 5\u0026rsquo;-CACCATTGGCAATGAGCGGTTC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-AGGTCTTTGCGGATGTCCACGT-3\u0026rsquo;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWestern Blot (WB) for Pyroptosis-Related Protein Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental groups were the same as for the \u0026quot;Cell Toxicity (LDH Release) Assay,\u0026quot; i.e., Control, Ms, MsM, and MsN. After the intervention, cells were lysed using high-efficiency RIPA lysis buffer, and the supernatant was collected after centrifugation. Protein quantification was performed using a protein assay kit. Denatured protein samples were subjected to SDS-PAGE electrophoresis and transferred to a membrane. The membrane was incubated overnight at 4\u0026deg;C with primary antibodies targeting NLRP3, GSDMD, Caspase-1, and the housekeeping protein \u0026beta;-actin. After washing, the membrane was incubated at room temperature for 1 hour with HRP-conjugated secondary antibody. Chemiluminescence (ECL) detection was performed, and the membrane was exposed using a Bio-Rad imaging system. Image J software was used to analyze the grayscale values of the target protein bands, and \u0026beta;-actin was used as an internal reference for normalization.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCCK-8 Assay for\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cell Viability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the effect of SA supernatant and macrophage supernatant after intervention on GC cell viability, a CCK-8 assay was performed. AGS and MKN45 cells in the logarithmic growth phase were seeded at a density of 1 \u0026times; 10⁴ cells/mL into a 96-well plate (200 \u0026micro;L per well). After cell attachment, the following experimental groups were set up for intervention: 1) Control group (Blank), 2) SA supernatant group (SA 1/64), 3) M0 macrophage supernatant group (M0 1/32), 4) SA intervention on M0 macrophage supernatant group (Ms 1/32), and 5) SA + MCC950 inhibitor-treated M0 macrophage supernatant group (MsM 1/32). After 24 hours of incubation at 37\u0026deg;C with 5% CO₂, the medium was replaced with fresh medium, and 10 \u0026micro;L of CCK-8 solution was added to each well, followed by another 4 hours of incubation. The absorbance at 450 nm was measured using a microplate reader to assess cell viability.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWound Healing Assay for G\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cell Migration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA wound healing assay was used to evaluate the effect of different supernatants on the migration ability of GC cells. AGS and MKN45 cells were seeded at a density of 1 \u0026times; 10⁵ cells/mL into a 24-well plate (500 \u0026micro;L per well). When the cells reached approximately 70% confluence, a straight-line scratch was made with a sterile pipette tip. The wells were gently washed three times with PBS to remove any cell debris. The experimental groups followed the same setup as for the \u0026quot;GC Cell Viability Assay.\u0026quot; After incubation at 37\u0026deg;C with 5% CO₂ for 24 hours, images were captured under an inverted microscope at 200x magnification. The number of cells that migrated into the scratched area was counted to assess changes in cell migration ability.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRT-qPCR for Epithelial-to-Mesenchymal Transition (EMT)-Related Gene Transcription in G\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental groups followed the same setup as for the \u0026quot;GC Cell Viability Assay.\u0026quot; After 24 hours of intervention with the respective supernatants, RNA was extracted from AGS and MKN45 cells, reverse transcribed into cDNA, and subjected to RT-qPCR to measure the transcriptional levels of vimentin and Snail. \u0026beta;-actin was used as the internal reference gene. The following primers (5\u0026apos; to 3\u0026apos;) were used:\u003c/p\u003e\n\u003cp\u003e\u0026beta;-actin: Forward: 5\u0026rsquo;-CACCATTGGCAATGAGCGGTTC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-AGGTCTTTGCGGATGTCCACGT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eVimentin: Forward: 5\u0026rsquo;-AGGCAAAGCAGGAGTCCACTGA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-ATCTGGCGTTCCAGGGACTCAT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eSnail: Forward: 5\u0026rsquo;-TGCCCTCAAGATGCACATCCGA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-GGGACAGGAGAAGGGCTTCTC-3\u0026rsquo;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWB for EMT-Related Protein Expression in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental groups followed the same setup as for the \u0026quot;GC Cell Viability Assay.\u0026quot; After 24 hours of intervention with the respective supernatants, cells were lysed, proteins extracted, quantified, subjected to SDS-PAGE electrophoresis, transferred to a membrane, and immunoblotted. The protein expression levels of Vimentin and Snail were analyzed, with \u0026beta;-actin used as the internal reference.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRT-qPCR for Intestinal Metaplasia-Related Gene Transcription\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman gastric mucosal epithelial cells (GES-1) were revived and cultured. When the cells reached approximately 40% confluence in a 6-well plate, they were treated with the respective supernatants according to the \u0026quot;GC Cell Viability Assay\u0026quot; grouping (Control, SA 1/64, M0 1/32, Ms 1/32, MsM 1/32) for 72 hours. After the intervention, RNA was extracted, reverse transcribed into cDNA, and subjected to RT-qPCR to analyze the transcriptional levels of MUC2, Kr\u0026uuml;ppel-like factor 4 (KLF4), and caudal-related homeobox transcription factor 2 (CDX2). The following primers (5\u0026apos; to 3\u0026apos;) were used:\u003c/p\u003e\n\u003cp\u003eMUC2: Forward: 5\u0026rsquo;-ACTCTCCACACCCAGCATCATC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-GTGTCTCCGTATGTGCCGTTGT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eKLF4: Forward: 5\u0026rsquo;-CATCTCAAGGCACACCTGCGAA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-TCGGTCGCATTTTTGGCACTGG-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eCDX2: Forward: 5\u0026rsquo;-ACAGTCGCTACATCACCATCCG-3\u0026rsquo;; Reverse: 5\u0026rsquo;-CCTCTCCTTTGCTCTGCGGTTC-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003e\u0026beta;-actin: Forward: 5\u0026rsquo;-CACCATTGGCAATGAGCGGTTC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-AGGTCTTTGCGGATGTCCACGT-3\u0026rsquo;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWestern Blot (WB) for Intestinal Metaplasia-Related Protein Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental groups and cell intervention methods followed the same setup as for the \u0026quot;GC Cell Viability Assay.\u0026quot; After 72 hours of intervention, Western blot analysis was performed to assess the protein expression levels of MUC2, KLF4, and CDX2. \u0026beta;-actin was used as the internal reference.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eUntargeted Metabolomics Analysis Based on Liquid Chromatography-Mass Spectrometry (LC-MS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample Preparation: The samples were thawed on ice. A 50 \u0026micro;L sample was mixed with 150 \u0026micro;L internal standard extraction solution containing 20% acetonitrile-methanol, vortexed, and then centrifuged at 12,000 rpm for 10 minutes at 4\u0026deg;C. The supernatant was incubated at -20\u0026deg;C for 30 minutes, followed by a second centrifugation. The resulting 120 \u0026micro;L supernatant was transferred to an injection vial for analysis.\u003c/p\u003e\n\u003cp\u003eLC-MS Acquisition: LC analysis was performed using a Waters HSS T3 column (1.8 \u0026micro;m, 2.1 \u0026times; 100 mm) at a column temperature of 40\u0026deg;C. The mobile phase consisted of 0.1% formic acid aqueous solution and 0.1% formic acid acetonitrile, with a flow rate of 0.4 mL/min using a gradient elution. The mass spectrometry analysis was performed on an AB TripleTOF 6600 system, acquiring data in both positive (ESI+) and negative (ESI-) ion modes.\u003c/p\u003e\n\u003cp\u003eData Processing and Quality Control: After converting the raw data, peak extraction, alignment, and correction were performed using XCMS. Metabolites were identified based on a self-built library and public databases, such as HMDB and KEGG. Stringent quality control included checking the overlap of total ion chromatograms (TIC) of quality control (QC) samples, assessing the Pearson correlation between QC samples, and calculating the coefficient of variation (CV) of metabolites in QC samples to ensure experimental stability and data reliability.\u003c/p\u003e\n\u003cp\u003eStatistical Analysis: Unsupervised principal component analysis (PCA) was first performed to assess overall group differences and intra-group variability. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was used to maximize group separation, and permutation testing was performed to validate the model\u0026apos;s effectiveness. Differential metabolites were selected based on variable importance in projection (VIP \u0026gt; 1) and t-test (p \u0026lt; 0.05). Bioinformatics analysis included hierarchical clustering, correlation analysis, KEGG pathway enrichment analysis, and differential abundance scoring (DA Score) to reveal significantly enriched metabolic pathways and overall trends.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCCK-8 Assay for the Effect of 15-Hydroperoxy-5,8,11,13-eicosatetraenoic Acid (15(S)-HPETE) on AGS G\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cell Viability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the effect of 15(S)-HPETE on AGS GC cell viability, a CCK-8 assay was performed. AGS cells in the logarithmic growth phase were seeded at a density of 1 \u0026times; 10⁴ cells/mL into a 96-well plate (200 \u0026micro;L per well). After cell attachment, the following experimental groups were set up: 0 \u0026micro;M, 0.2 \u0026micro;M, 0.4 \u0026micro;M, 0.8 \u0026micro;M, 1.6 \u0026micro;M, 3.2 \u0026micro;M, and 6.4 \u0026micro;M 15(S)-HPETE. The cells were incubated at 37\u0026deg;C with 5% CO₂ for 24 hours. After replacing the medium, 10 \u0026micro;L of CCK-8 solution was added, mixed gently, and incubated for an additional 4 hours. The absorbance at 450 nm was measured using a microplate reader to evaluate cell viability and determine the optimal concentration for subsequent experiments.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWound Healing Assay for the Effect of 15(S)-HPETE on AGS Cell Migration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA wound healing assay was performed to assess the effect of 15(S)-HPETE on AGS cell migration. AGS cells in the logarithmic growth phase were seeded at a density of 1 \u0026times; 10⁵ cells/mL into a 24-well plate (500 \u0026micro;L per well). When the cells reached approximately 70% confluence, a straight-line scratch was made using a 10 \u0026micro;L pipette tip. The wells were then gently washed three times with PBS to remove any debris. The experimental groups included 0 \u0026micro;M (Control), 0.2 \u0026micro;M, and 0.4 \u0026micro;M 15(S)-HPETE. Images were taken under an inverted microscope at 200x magnification. After incubating for 24 hours at 37\u0026deg;C with 5% CO₂, the migration of cells into the scratched area was observed and quantified.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRT-qPCR for the Effect of 15(S)-HPETE on EMT-Related Gene Transcription in AGS Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen the AGS cells in the 6-well plate reached approximately 60% confluence, they were treated with the same experimental groups as in the \u0026quot;Wound Healing Assay.\u0026quot; After 24 hours of intervention, total RNA was extracted, reverse transcribed into cDNA, and subjected to RT-qPCR to evaluate the transcription levels of vimentin and snail genes. The primers for vimentin and snail genes are as follows:\u003c/p\u003e\n\u003cp\u003eVimentin: Forward: 5\u0026rsquo;-AGGCAAAGCAGGAGTCCACTGA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-ATCTGGCGTTCCAGGGACTCAT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eSnail: Forward: 5\u0026rsquo;-TGCCCTCAAGATGCACATCCGA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-GGGACAGGAGAAGGGCTTCTC-3\u0026rsquo;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWB for the Effect of 15(S)-HPETE on EMT-Related Protein Expression in AGS Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen AGS cells in the 6-well plate reached approximately 60% confluence, they were treated with the same experimental groups as in the \u0026quot;Wound Healing Assay.\u0026quot; After 24 hours of treatment, cells were lysed, proteins extracted, quantified, subjected to SDS-PAGE, and transferred to membranes for immunoblotting. The expression levels of vimentin and snail proteins were analyzed, with \u0026beta;-actin as the internal reference.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eRT-qPCR for the Effect of 15(S)-HPETE on Intestinal Metaplasia-Related Gene Transcription in GES-1 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGES-1 cells were cultured until they reached approximately 40% confluence in a 6-well plate. Cells were treated with the experimental groups as in the \u0026quot;Wound Healing Assay\u0026quot; for 72 hours. After the treatment, total RNA was extracted and reverse transcribed into cDNA for RT-qPCR analysis to evaluate the transcription levels of MUC2, KLF4, and CDX2 genes. The primers (5\u0026apos; to 3\u0026apos;) used are as follows:\u003c/p\u003e\n\u003cp\u003eMUC2: Forward: 5\u0026rsquo;-ACTCTCCACACCCAGCATCATC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-GTGTCTCCGTATGTGCCGTTGT-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eKLF4: Forward: 5\u0026rsquo;-CATCTCAAGGCACACCTGCGAA-3\u0026rsquo;; Reverse: 5\u0026rsquo;-TCGGTCGCATTTTTGGCACTGG-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eCDX2: Forward: 5\u0026rsquo;-ACAGTCGCTACATCACCATCCG-3\u0026rsquo;; Reverse: 5\u0026rsquo;-CCTCTCCTTTGCTCTGCGGTTC-3\u0026rsquo;\u003c/p\u003e\n\u003cp\u003e\u0026beta;-actin: Forward: 5\u0026rsquo;-CACCATTGGCAATGAGCGGTTC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-AGGTCTTTGCGGATGTCCACGT-3\u0026rsquo;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eWB for the Effect of 15(S)-HPETE on Intestinal Metaplasia-Related Protein Expression in GES-1 Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen GES-1 cells in the 6-well plate reached approximately 40% confluence, they were treated with the same experimental groups as in the \u0026quot;Wound Healing Assay\u0026quot; for 72 hours. After the treatment, Western blot analysis was performed to assess the expression levels of MUC2, KLF4, and CDX2 proteins, using \u0026beta;-actin as the internal reference.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were analyzed using GraphPad Prism 9.5 software. Data with a normal distribution are presented as Mean \u0026plusmn; SD. The comparison between two groups was conducted using an independent samples t-test, and multiple group comparisons were performed using one-way ANOVA. Pearson correlation coefficient analysis was used for correlation analysis. A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSA infection load exhibited an increasing trend in CSG, IM, and GC, and this trend was positively correlated with macrophage infiltration and pyroptosis. Meanwhile, macrophage infiltration and pyroptosis were also positively correlated.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore SA infection in CSG, IM, and GC in non-HP infected patients, 18 paraffin-embedded samples (6 samples per group) were analyzed. FISH detection using SA fluorescence probes was performed. The results revealed that SA infection load increased progressively from CSG to IM to GC, with significant differences observed (P \u0026lt; 0.05) (Figure 1A). To further investigate pyroptosis in CSG, IM, and GC, IHC was performed on the aforementioned samples. The results showed that the expression of NLRP3 (Figure 1B), Caspase-1 (Figure 1C), and IL-1\u0026beta; (Figure 1D) proteins in the IM and GC groups was significantly higher compared to the CSG group (P \u0026lt; 0.05).To investigate the relationship between macrophage infiltration and pyroptosis in CSG, IM, and GC, we performed IF detection of CD68 and GSDMD proteins. Dual fluorescence detection with Cy3 and 488 channels showed that in the GC group (Figure 1G), CD68 (red) and GSDMD (green) expression were significantly higher compared to the IM group (Figure 1F) and CSG group (Figure 1E), with DAPI staining marking the cell nuclei in blue. These results indicated increased macrophage infiltration and pyroptosis in the GC group compared to IM and CSG groups, with significant differences (P \u0026lt; 0.05) (Figure 1H). No significant differences were observed between IM and CSG groups. To elucidate the correlation between SA infection, macrophage infiltration, and pyroptosis in CSG, IM, and GC, as well as the relationship between macrophage infiltration and pyroptosis, Pearson correlation analysis was performed (Figure 1I). The results showed a positive correlation between CD68 expression and SA infection (P \u0026lt; 0.05), a positive correlation between GSDMD, NLRP3, and IL-1\u0026beta; expression and SA infection (P \u0026lt; 0.05), and a positive correlation between CD68 and GSDMD expression (P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSA Growth Curve and Its Promotion of Malignant Phenotype in G\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSA was cultured, and bacterial turbidity was measured every 4 hours to plot the growth curve (*P \u0026lt; 0.05, **P \u0026lt; 0.01, Figure 2A). The SA growth curve followed the general pattern of microbial growth, which included four phases: 0-4 hours (adjustment phase), 4-16 hours (log phase), 16-24 hours (stationary phase), and post-24 hours (decline phase). The SA concentration after 24 hours of cultivation was approximately 9.72 \u0026times; 10⁸ CFU/mL. The SA bacterial solution and sterile THB medium were centrifuged at 4\u0026deg;C and 4000 rpm for 20 minutes. The supernatant was filtered twice using a 0.22 \u0026micro;m filter and stored at -80\u0026deg;C for further use.\u003c/p\u003e\n\u003cp\u003eCCK-8 assay revealed that after 24 hours of intervention with different concentrations of SA supernatant, AGS (Figure 1B) and MKN45 (Figure 1C) GC cell viability was enhanced. The most significant proliferative effect was observed at a 1/64 volume ratio of SA supernatant to reaction system, and this effect gradually weakened as the concentration increased. Significant differences were found compared to the control group (P \u0026lt; 0.05, **P \u0026lt; 0.01, ****P \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSA-Induced Pyroptosis in M0 Macrophages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter 24 hours of PMA treatment, THP-1 cells differentiated into M0 macrophages, transitioning from a suspended state to a fully adherent form, changing from a round to an irregular shape, with an increase in size. The cytoplasm became more loose, and the nucleus enlarged significantly. Numerous organelles became visible, and a few protrusions appeared around the cell membrane (Figure 3A). ELISA detection showed that after 24 hours of intervention with different concentrations of SA supernatant, IL-1\u0026beta; (Figure 3B) and IL-18 (Figure 3C) levels in the supernatant were elevated, with the most significant increase at a concentration of 1/64, which showed a statistically significant difference compared to the control group (P \u0026lt; 0.05). Therefore, this concentration was chosen for subsequent experiments. Further ELISA analysis of the effect of SA on LDH levels in M0 macrophages revealed that after 24 hours of SA supernatant (Ms group) intervention, LDH levels in the supernatant increased significantly, showing a statistically significant difference compared to the control group (P \u0026lt; 0.001). When the NLRP3 inhibitor MCC950 (MsM group) and ROS inhibitor NAC (MsN group) were applied, this effect was significantly reversed (P \u0026lt; 0.001) (Figure 3D). RT-qPCR results showed that, compared to the control group, the Ms group exhibited a significant increase in mRNA expression levels of NLRP3, GSDMD, Caspase-1, and IL-1\u0026beta; after 24 hours of intervention, while the MsM and MsN groups showed a significant decrease in expression (P \u0026lt; 0.0001) (Figure 3E). Western blot analysis showed that after 24 hours of intervention with SA supernatant (Ms group), the expression of NLRP3, GSDMD, Caspase-1, and IL-1\u0026beta; proteins in M0 macrophages increased. However, when the NLRP3 inhibitor MCC950 (MsM group) and ROS inhibitor NAC (MsN group) were added, these effects were attenuated, and the differences compared to the control group were statistically significant (P \u0026lt; 0.01) (Figure 3F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSA and M0 Macrophage Co-culture Induces a \u0026quot;Gastric Inflammation-Cancer Transformation\u0026quot; Microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCCK-8 assay showed that after 24 hours of intervention, SA, M0, and Ms groups enhanced AGS (Figure 4A) and MKN45 (Figure 4B) cell viability, with statistically significant differences compared to the control group (P \u0026lt; 0.001). The MsM group exhibited decreased cell viability, suggesting that inhibition of NLRP3 reversed the proliferative effect of the co-culture supernatant on GC cells, with significant differences. Scratch assay (Figure 4C) showed that when the SA supernatant concentration was 1/64, it promoted cell migration in AGS and MKN45 GC cells after 24 hours of intervention. When the co-culture supernatant (Ms group, 1/32 concentration) was used for intervention, it further enhanced cell migration. Statistically significant differences were observed between these two groups and compared to the control group (P \u0026lt; 0.001). The MsM group (1/32 concentration) weakened the migratory ability of cells compared to the Ms group, and the difference was statistically significant (P \u0026lt; 0.001). RT-qPCR results showed that compared to the control group, after 24 hours of intervention with SA (1/64 concentration) and Ms (1/32 concentration) supernatants, the mRNA expression levels of Snail and Vimentin in AGS (Figure 4D) and MKN45 (Figure 4E) cells were significantly increased (P \u0026lt; 0.0001). In contrast, the MsM group showed significant downregulation of Snail and Vimentin gene mRNA expression levels compared to the Ms group (P \u0026lt; 0.0001). Western blot analysis showed that after 24 hours of intervention with SA (1/64 concentration) and Ms (1/32 concentration) supernatants, Snail protein expression in AGS (Figure 4F) and MKN45 (Figure 4G) cells was significantly upregulated compared to the control group (P \u0026lt; 0.01). However, only the Ms group showed significant upregulation of Vimentin protein in both cell types (P \u0026lt; 0.001). In both cell types, the MsM group showed downregulation of Vimentin and Snail protein expression compared to the Ms group (P \u0026lt; 0.01). RT-qPCR results showed that, compared to the control group, after 72 hours of intervention with SA (1/64 concentration) and Ms (1/32 concentration) supernatants, the mRNA expression levels of MUC2, KLF4, and CDX2 in GES-1 cells were significantly increased (P \u0026lt; 0.05). However, in the group treated with the NLRP3 inhibitor MCC950 (MsM group, 1/32 concentration), the mRNA expression levels of MUC2, KLF4, and CDX2 were significantly decreased compared to the Ms group (P \u0026lt; 0.001) (Figure 4I). Western blot analysis showed that after 72 hours of intervention with SA (1/64 concentration), M0, and Ms (1/32 concentration) supernatants, MUC2, KLF4, and CDX2 protein expression levels in AGS and MKN45 cells were significantly upregulated compared to the control group, with statistically significant differences (P \u0026lt; 0.05). However, in the MsM group (1/32 concentration), MUC2 and CDX2 protein expression was downregulated compared to the Ms group (P \u0026lt; 0.001), while no significant difference was observed in KLF4 protein expression (Figure 4I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUntargeted Metabolomics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe untargeted metabolomics analysis results showed that the TIC of the QC samples had high overlap (Figure 5A), indicating stable instrument performance. No significant internal standard peaks were detected in the blank samples (Figure 5B), indicating that cross-contamination was controlled. The QC samples exhibited very high correlation (Pearson coefficient \u0026ge; 0.9997) (Figure 5C), demonstrating good repeatability of the measurements. The overall clustering of the samples is shown in Figure 5D, with samples along the x-axis and metabolite information along the y-axis. The \u0026quot;Group\u0026quot; represents the sample groups, with different colors corresponding to relative content values normalized after processing; red indicates high content, while green represents low content. PCA results indicate metabolic differences between the groups (Figure 5E). The univariate control chart shows that the PC1 scores of all QC samples were within \u0026plusmn;3 standard deviations (Figure 5F), suggesting that the analysis was stable and under controlled conditions. OPLS-DA score plots (Figure 5G) indicate clear separation between the groups. The OPLS-DA model was validated as effective (R\u0026sup2;Y = 1, Q\u0026sup2; = 0.928, P \u0026lt; 0.005) (Figure 5H). The S-plot from OPLS-DA (Figure 5I) highlights the metabolites contributing most to the separation between the groups. The volcano plot of differential metabolites is shown in Figure 5J, where each point represents a metabolite. Green points represent downregulated metabolites (193 metabolites), red points represent upregulated metabolites (233 metabolites), and gray points represent metabolites with no significant difference (1292 metabolites). The x-axis shows the log2 fold change (FC), and the y-axis represents the significance level. The point size corresponds to the Variable Importance in Projection (VIP) value. The figure highlights 15(S)-HPETE and indoleacetic acid (IAA).The heatmap for differential metabolites clustering is shown in Figure 5K, where the x-axis represents the sample names, and the y-axis shows the differential metabolites. \u0026quot;Group\u0026quot; represents the sample groups, with red indicating high content and blue indicating low content. The \u0026quot;heatmap_class\u0026quot; indicates heatmap classification by substance type, with \u0026quot;Class\u0026quot; representing the primary substance categories.The correlation analysis of differential metabolites is shown in Figure 5L, where each point represents a differential metabolite. The point size is related to the connectivity (degree), with larger points representing higher connectivity. Red lines represent positive correlations, and blue lines represent negative correlations. The thickness of the lines represents the absolute value of the Pearson correlation coefficient, with thicker lines indicating stronger correlations. This plot shows the top 50 differential metabolites based on VIP values.The Z-values for differential metabolites are presented in Figure 5M, where the x-axis shows the Z-value and the y-axis shows the metabolites. Different colors represent different groups of samples. The top 50 differential metabolites with the highest VIP values are displayed.\u003cbr\u003e\u0026nbsp;The pathway classification of differential metabolites is shown in Figure 5N, with the y-axis representing metabolic pathways and the x-axis showing the number of differential metabolites annotated to each pathway, as well as the proportion of these metabolites relative to the total number of annotated differential metabolites.KEGG enrichment analysis of differential metabolites is shown in Figure 5O, where the x-axis represents the Rich Factor for each pathway, and the y-axis shows the pathway names sorted by P-value. The color of the points reflects the P-value, with red indicating more significant enrichment. The point size represents the number of differential metabolites enriched in the pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e15(S)-HPETE Promotes Malignant Progression of G\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cells and Induces Intestinal Metaplasia in Gastric Mucosa\u003cbr\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCCK-8 assay results showed that after 24 hours of intervention with different concentrations of 15(S)-HPETE, the viability of AGS GC cells increased. At concentrations of 0.2 \u0026mu;M and 0.4 \u0026mu;M, significant differences were observed compared to the control group (P \u0026lt; 0.0001). However, as the concentration increased, the effect diminished, showing no significant difference compared to the control group (P \u0026gt; 0.05) (Figure 6A).Scratch assay results demonstrated that after 24 hours of intervention with 0.2 \u0026mu;M and 0.4 \u0026mu;M 15(S)-HPETE, the migration ability of AGS cells was enhanced, with significant differences compared to the control group (P \u0026lt; 0.05) (Figure 6B).RT-qPCR analysis showed that after 24 hours of 0.2 \u0026mu;M and 0.4 \u0026mu;M 15(S)-HPETE intervention, Vimentin and Snail gene transcription levels were upregulated in AGS cells, with significant differences compared to the control group (P \u0026lt; 0.01) (Figure 6C).Western blot analysis revealed that after 24 hours of 0.2 \u0026mu;M and 0.4 \u0026mu;M 15(S)-HPETE intervention, the protein expression of Vimentin and Snail was upregulated in AGS cells, with significant differences compared to the control group (P \u0026lt; 0.01) (Figure 6D).RT-qPCR results showed that after 72 hours of 0.2 \u0026mu;M and 0.4 \u0026mu;M 15(S)-HPETE intervention, MUC2, KLF4, and CDX2 gene transcription levels were upregulated in GES-1 cells, with significant differences compared to the control group (P \u0026lt; 0.05) (Figure 6E).Western blot analysis showed that after 72 hours of 0.2 \u0026mu;M and 0.4 \u0026mu;M 15(S)-HPETE intervention, MUC2, KLF4, and CDX2 protein expression levels were upregulated in GES-1 cells, with significant differences compared to the control group (P \u0026lt; 0.001) (Figure 6F). These results suggest that 15(S)-HPETE promotes intestinal metaplasia in GES-1 cells after 72 hours of intervention.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eReduced microbial diversity has been widely recognized as one of the hallmarks of inflammatory diseases and cancer [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Compared to CSG, the gastric mucosal microbiome richness in IM and GC patients was significantly reduced, and the microbial interactions between GC and CSG, as well as between chronic atrophic gastritis (CAG) and GC, showed differences, suggesting that the development of GC is closely related to changes in the microbiome structure [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. At the phylum level, the dominant bacterial groups in the stomach primarily include Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Among these, genera such as Streptococcus and Lactobacillus are enriched in GC patients, possibly promoting carcinogenesis through mechanisms such as increased exogenous lactic acid, ROS, and N-nitroso compound supply, which may facilitate EMT and induce immune tolerance [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, by analyzing clinical samples from CSG, IM, and GC patients with non-HP infection (using the same pathological case numbers for FISH,IHC, and IF for consistency), we found that the infection levels of SA in the IM and GC groups were significantly higher than in the CSG group. This result further supports and enriches the theory that microbiota communities are clearly associated with GC suggesting that SA likely contributes to the occurrence and development of IM and GC.\u003c/p\u003e \u003cp\u003eChronic inflammation is an important driver of cancer initiation and progression, and its impact is increasingly being recognized [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Pyroptosis, a form of programmed cell death mediated by gasdermin proteins, is characterized by membrane pore formation, swelling, rupture, and the release of pro-inflammatory cytokines like IL-1β and IL-18, which can trigger strong inflammatory and immune responses [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This inflammatory cell death can be triggered by various pathological stimuli, including infection and cancer [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. IL-1β and IL-18 are both pro-inflammatory cytokines from the IL-1 family, with diverse functions, and they play crucial roles in inflammatory disease processes [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These cytokines are mainly produced by monocytes/macrophages and macrophages/dendritic cells/epithelial cells [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and act as key inflammatory mediators in infection and cancer. In this study, immunohistochemical analysis revealed that in non-HP infected samples, the expression levels of pyroptosis-related proteins NLRP3, Caspase-1, and IL-1β in the IM and GC groups were significantly higher than those in the CSG group, suggesting more pronounced inflammation in the IM and GC stages, which likely promotes disease progression.\u003c/p\u003e \u003cp\u003eMacrophages play a central role in tumor development and are critical regulators of tumor biology [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Tumor-associated macrophage (TAM) infiltration is positively correlated with poor prognosis in GC and is crucial for promoting GC invasion and metastasis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. GSDMD, a key executor of pyroptosis, is cleaved by caspases (caspase-1/4/5/11), and its N-terminal fragment forms pores in the cell membrane, causing cell lysis and the release of inflammatory factors [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In this study, using immunofluorescence co-staining, we found that in the IM and GC groups with non-HP infection, the co-expression levels of GSDMD and the macrophage marker CD68 were significantly higher than those in the CSG group, indicating that macrophage pyroptosis is involved in the progression of IM and GC. Further correlation analysis showed that the degree of SA infection was significantly positively correlated with the expression of NLRP3, IL-1β, GSDMD, and CD68 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and there was also a positive correlation between GSDMD and CD68 expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Combined with the fact that SA infection is more pronounced in IM and GC, we conclude that SA infection drives the gastric \"inflammation-cancer conversion\" process by inducing macrophage pyroptosis. However, as a retrospective analysis, this study has limitations, including a small sample size, potential bias, and the lack of simultaneous detection of HP infection status.\u003c/p\u003e \u003cp\u003eWith the development of omics technologies, the complex microbial community and its functions within the gastric mucosal microenvironment are being continuously revealed. Besides HP, microorganisms such as Epstein-Barr virus, Fusobacterium nucleatum, Streptococcus species, Escherichia coli, and Candida albicans have been linked to GC [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In this study, we found that SA supernatant can dose-dependently (with the most significant effect at a concentration of 1/64) directly promote the proliferation and migration of AGS and MKN45 GC cells. Mechanistically, SA induces macrophage pyroptosis through the ROS/NLRP3/Caspase-1/GSDMD signaling axis, manifested by increased LDH release, upregulation of IL-1β and IL-18 secretion, and enhanced expression of related markers. This effect peaks at a concentration of 1/64, suggesting that SA may disrupt lysosomal stability via specific virulence factors (such as streptolysin S), leading to the release of cathepsin B and activation of NLRP3 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The use of MCC950 (an NLRP3 inhibitor) and NAC (an ROS inhibitor) reversed this pyroptotic phenotype, confirming the central role of the ROS/NLRP3 axis. We hypothesize that SA might trigger massive ROS production by disrupting mitochondrial functions, activating the NLRP3 inflammasome, which is one of the key mechanisms for inducing macrophage pyroptosis. Moreover, IL-1β released during pyroptosis may form a positive feedback loop through autocrine or paracrine signaling, exacerbating the inflammatory \"storm\" [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Future studies should explore whether SA-induced pyroptosis interacts with other forms of programmed cell death.\u003c/p\u003e \u003cp\u003eTo further simulate the tumor microenvironment, this study investigated the impact of SA-induced macrophage pyroptosis (SA\u0026thinsp;+\u0026thinsp;M0 supernatant) on GC and gastric epithelial cells. The results showed that this microenvironment significantly enhanced the migration ability and EMT process of GC cells, and this effect could be blocked by MCC950. This indicates that pyroptosis-related inflammatory factors, such as IL-1β, play a crucial role in reshaping the tumor microenvironment. IL-1β activates the NF-κB pathway through the IL-1R/MyD88 signaling pathway, inducing Snail and inhibiting E-cadherin, thus promoting EMT [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This study is the first to link SA-related macrophage pyroptosis with EMT, supporting the \"bacteria-inflammation-EMT-metastasis\" cascade hypothesis [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Additionally, SA\u0026thinsp;+\u0026thinsp;M0 supernatant significantly upregulated intestinal metaplasia markers MUC2, KLF4, and CDX2 in GES-1 cells, and this process was dependent on the NLRP3 pathway. CDX2, as a core transcription factor for intestinal epithelial differentiation, is often abnormally expressed in response to chronic inflammation [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This study associates SA infection with gastric mucosal intestinal metaplasia and provides new evidence for the \"bacteria-inflammation-metaplasia-carcinogenesis\" Correa cascade theory [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, this study reveals a key molecular pathway by which SA drives gastric \"inflammation-cancer conversion\": SA activates the NLRP3 inflammasome to induce macrophage pyroptosis, and the released inflammatory factors promote GC cell EMT and gastric epithelial cell intestinal metaplasia. The use of the NLRP3 inhibitor MCC950 can block these effects. To further explore the molecular basis of SA's action, we employed untargeted metabolomics to analyze the metabolic features of SA supernatant. Through LC-MS/MS combined with multivariate statistical analysis, we identified 426 differential metabolites (233 upregulated, 193 downregulated). KEGG pathway enrichment analysis revealed that these metabolites were significantly enriched in amino acid metabolism, lipid metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid cycle pathways, suggesting that SA may affect gastric mucosal cells through metabolic reprogramming. Notably, polyunsaturated fatty acids such as arachidonic acid and their lipid peroxidation products were significantly upregulated. Based on this, we focused on and validated the role of the upregulated differential metabolite 15(S)-HPETE. 15(S)-HPETE, an oxidized lipid mediator derived from arachidonic acid via lipoxygenase metabolism, plays a key role in inflammation. Studies show that it can increase intracellular ROS levels by enhancing NADPH oxidase activity [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and ROS can activate the NF-κB pathway and upregulate pro-IL-1β transcription [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. At the same time, 15(S)-HPETE may induce potassium ion efflux by affecting ion channels [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], or disrupt lysosomal membrane stability to release cathepsin B [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], thereby activating the NLRP3 inflammasome. Functional experiments confirmed that 15(S)-HPETE not only promotes the proliferation, migration, and EMT of AGS GC cells but also induces the upregulation of intestinal metaplasia markers MUC2, KLF4, and CDX2 in GES-1 cells. This suggests that 15(S)-HPETE is an important effector molecule driving the gastric \"inflammation-cancer conversion\" process. Future studies should combine in vivo models to further validate its function and elucidate its direct molecular mechanisms of interaction with the NLRP3/IL-1β pathway.\u003c/p\u003e \u003cp\u003eIn conclusion, this study systematically elucidates the molecular mechanisms by which SA drives gastric \"inflammation-cancer conversion.\" Clinical and experimental evidence indicates that SA accumulates in precancerous lesions and induces macrophage pyroptosis through the ROS/NLRP3/Caspase-1/GSDMD signaling axis, remodeling the pro-inflammatory, pro-tumor microenvironment and accelerating GC cell EMT and gastric mucosal intestinal metaplasia. At the same time, metabolomics research identified key metabolite 15(S)-HPETE, confirming its direct promotion of malignant phenotypes and metaplastic progression. This study provides important theoretical evidence for early prevention and targeted intervention of SA-related GC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eSA\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Streptococcus anginosus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGC\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Gastric cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCSG\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Chronic superficial gastritis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIM:\u003c/strong\u003e Intestinal metaplasia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEMT:\u003c/strong\u003e Epithelial-mesenchymal transition\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHP:\u003c/strong\u003e Helicobacter pylori\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAPK:\u003c/strong\u003e Mitogen-activated protein kinase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS:\u003c/strong\u003e Reactive oxygen species\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFISH:\u003c/strong\u003e Fluorescence In Situ Hybridization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIHC:\u003c/strong\u003e Immunohistochemistry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIF:\u0026nbsp;\u003c/strong\u003eImmunofluorescence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOD:\u0026nbsp;\u003c/strong\u003eoptical density\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePMA:\u003c/strong\u003e Phorbol 12-myristate 13-acetate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDH:\u003c/strong\u003e lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRT-qPCR\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eReal-Time Quantitative PCR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECL:\u003c/strong\u003e Chemiluminescence\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWB:\u0026nbsp;\u003c/strong\u003eWestern Blot\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKLF4\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Kr\u0026uuml;ppel-like factor 4\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCDX2\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eCaudal-related homeobox transcription factor 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eLiquid Chromatography-Mass Spectrometry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTIC\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003etotal ion chromatograms\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQC\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Quality control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCV:\u003c/strong\u003e Coefficient of variation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOPLS-DA\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Orthogonal partial least squares discriminant analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAG:\u0026nbsp;\u003c/strong\u003echronic atrophic gastritis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTAM:\u003c/strong\u003e Tumor-associated macrophage\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was followed by the Declaration of Helsinki Principles and approved by the ethical committee of the Medical Ethics Committee of Tianjin Medical University. (Ethics Approval Number: IRB2025-YX-015-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Research project on Traditional Chinese Medicine and Integrative Medicine funded by the Tianjin Municipal Health Commission (2025185), Tianjin Education Commission\u0026apos;s Research Program(2025KJ081).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXC, QZZ, and WLZ conceptualized and designed the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXDH drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eXDH, DDC and LPZ performed the analyses and experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZL, YYH, MY and LL conducted data analysis and processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZHY, JJY, and QYY reviewed and revised the study content and manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRawla P, Barsouk A. Epidemiology of gastric cancer: global trends, risk factors and prevention. Prz Gastroenterol. 2019;14(1):26-38. doi:10.5114/pg.2018.80001\u003c/li\u003e\n\u003cli\u003eJoshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264-279. doi:10.3322/caac.21657\u003c/li\u003e\n\u003cli\u003eSmyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635-648. doi:10.1016/S0140-6736(20)31288-5\u003c/li\u003e\n\u003cli\u003eHironaka S. Anti-angiogenic therapies for gastric cancer. Asia Pac J Clin Oncol. 2019;15(4):208-217. doi:10.1111/ajco.13174\u003c/li\u003e\n\u003cli\u003eFurman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25(12):1822-1832. doi:10.1038/s41591-019-0675-0\u003c/li\u003e\n\u003cli\u003eGreten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27-41. doi:10.1016/j.immuni.2019.06.025\u003c/li\u003e\n\u003cli\u003eManjili SH, Isbell M, Ghochaghi N, Perkinson T, Manjili MH. Multifaceted functions of chronic inflammation in regulating tumor dormancy and relapse. Semin Cancer Biol. 2022;78:17-22. doi:10.1016/j.semcancer.2021.03.023\u003c/li\u003e\n\u003cli\u003eBalkwill F, Mantovani A. Inflammation and cancer: back to Virchow?. Lancet. 2001;357(9255):539-545. doi:10.1016/S0140-6736(00)04046-0\u003c/li\u003e\n\u003cli\u003eBalkwill F, Mantovani A. Inflammation and cancer: back to Virchow?. Lancet. 2001;357(9255):539-545. doi:10.1016/S0140-6736(00)04046-0\u003c/li\u003e\n\u003cli\u003eCorrea P, Piazuelo MB. The gastric precancerous cascade. J Dig Dis. 2012;13(1):2-9. doi:10.1111/j.1751-2980.2011.00550.x \u003c/li\u003e\n\u003cli\u003eCapparelli R, Iannelli D. Epigenetics and Helicobacter pylori. Int J Mol Sci. 2022;23(3):1759. Published 2022 Feb 3. doi:10.3390/ijms23031759 \u003c/li\u003e\n\u003cli\u003eZhang H, Wang X, Shen Z, Xu J, Qin J, Sun Y. Infiltration of diametrically polarized macrophages predicts overall survival of patients with gastric cancer after surgical resection. Gastric Cancer. 2015;18(4):740-750. doi:10.1007/s10120-014-0422-7\u003c/li\u003e\n\u003cli\u003eZhang P, Yang M, Zhang Y, Xiao S, Lai X, Tan A, et al. Dissecting the Single-Cell Transcriptome Network Underlying Gastric Premalignant Lesions and Early Gastric Cancer. Cell Rep. 2020;30(12):4317. doi:10.1016/j.celrep.2020.03.020\u003c/li\u003e\n\u003cli\u003eCompare D, Rocco A, Nardone G. Risk factors in gastric cancer. Eur Rev Med Pharmacol Sci. 2010;14(4):302-308.\u003c/li\u003e\n\u003cli\u003eNardone G, Compare D. The human gastric microbiota: Is it time to rethink the pathogenesis of stomach diseases?. United European Gastroenterol J. 2015;3(3):255-260. doi:10.1177/2050640614566846 \u003c/li\u003e\n\u003cli\u003eAviles-Jimenez F, Vazquez-Jimenez F, Medrano-Guzman R, Mantilla A, Torres J. Stomach microbiota composition varies between patients with non-atrophic gastritis and patients with intestinal type of gastric cancer. Sci Rep. 2014;4:4202. Published 2014 Feb 26. doi:10.1038/srep04202\u003c/li\u003e\n\u003cli\u003eEun CS, Kim BK, Han DS, Kim SY, Kim KM, Choi BY, et al. Differences in gastric mucosal microbiota profiling in patients with chronic gastritis, intestinal metaplasia, and gastric cancer using pyrosequencing methods. Helicobacter. 2014;19(6):407-416. doi:10.1111/hel.12145\u003c/li\u003e\n\u003cli\u003eDicksved J, Lindberg M, Rosenquist M, Enroth H, Jansson JK, Engstrand L. Molecular characterization of the stomach microbiota in patients with gastric cancer and in controls. J Med Microbiol. 2009;58(Pt 4):509-516. doi:10.1099/jmm.0.007302-0 \u003c/li\u003e\n\u003cli\u003ePeek RM Jr, Crabtree JE. Helicobacter infection and gastric neoplasia. J Pathol. 2006;208(2):233-248. doi:10.1002/path.1868\u003c/li\u003e\n\u003cli\u003eLofgren JL, Whary MT, Ge Z, Muthupalani S, Taylor NS, Mobley M, et al. Lack of commensal flora in Helicobacter pylori-infected INS-GAS mice reduces gastritis and delays intraepithelial neoplasia. Gastroenterology. 2011;140(1):210-220. doi:10.1053/j.gastro.2010.09.048\u003c/li\u003e\n\u003cli\u003eLee CW, Rickman B, Rogers AB, Muthupalani S, Takaishi S, Yang P, et al. Combination of sulindac and antimicrobial eradication of Helicobacter pylori prevents progression of gastric cancer in hypergastrinemic INS-GAS mice. Cancer Res. 2009;69(20):8166-8174. doi:10.1158/0008-5472.CAN-08-3856\u003c/li\u003e\n\u003cli\u003eAsam D, Spellerberg B. Molecular pathogenicity of Streptococcus anginosus. Mol Oral Microbiol. 2014;29(4):145-155. doi:10.1111/omi.12056\u003c/li\u003e\n\u003cli\u003eKuryłek A, Stasiak M, Kern-Zdanowicz I. Virulence factors of Streptococcus anginosus - a molecular perspective. Front Microbiol. 2022;13:1025136. Published 2022 Oct 26. doi:10.3389/fmicb.2022.1025136\u003c/li\u003e\n\u003cli\u003eLiu X, Shao L, Liu X, Ji F, Mei Y, Cheng Y, et al. Alterations of gastric mucosal microbiota across different stomach microhabitats in a cohort of 276 patients with gastric cancer. EBioMedicine. 2019;40:336-348. doi:10.1016/j.ebiom.2018.12.034\u003c/li\u003e\n\u003cli\u003eZhou CB, Pan SY, Jin P, Deng JW, Xue JH, Ma XY, et al. Fecal Signatures of Streptococcus anginosus and Streptococcus constellatus for Noninvasive Screening and Early Warning of Gastric Cancer. Gastroenterology. 2022;162(7):1933-1947.e18. doi:10.1053/j.gastro.2022.02.015\u003c/li\u003e\n\u003cli\u003eFu K, Cheung AHK, Wong CC, Liu W, Zhou Y, Wang F, et al. Streptococcus anginosus promotes gastric inflammation, atrophy, and tumorigenesis in mice. Cell. 2024;187(23):6783. doi:10.1016/j.cell.2024.10.036\u003c/li\u003e\n\u003cli\u003eShi J, Zhao Y, Wang K, Shi X, Wang Y, Huang H, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature. 2015;526(7575):660-665. doi:10.1038/nature15514\u003c/li\u003e\n\u003cli\u003eDing J, Wang K, Liu W, She Y, Sun Q, Shi J, et al. Pore-forming activity and structural autoinhibition of the gasdermin family. Nature. 2016;535(7610):111-116. doi:10.1038/nature18590 \u003c/li\u003e\n\u003cli\u003eFang Y, Tian S, Pan Y, Li W, Wang Q, Tang Y, et al. Pyroptosis: A new frontier in cancer. Biomed Pharmacother. 2020;121:109595. doi:10.1016/j.biopha.2019.109595\u003c/li\u003e\n\u003cli\u003eAl Mamun A, Mimi AA, Aziz MA, Zaeem M, Ahmed T, Munir F, et al. Role of pyroptosis in cancer and its therapeutic regulation. Eur J Pharmacol. 2021;910:174444. doi:10.1016/j.ejphar.2021.174444\u003c/li\u003e\n\u003cli\u003eLin W, Chen Y, Wu B, Chen Y, Li Z. Identification of the pyroptosis‑related prognostic gene signature and the associated regulation axis in lung adenocarcinoma. Cell Death Discov. 2021;7(1):161. Published 2021 Jun 25. doi:10.1038/s41420-021-00557-2\u003c/li\u003e\n\u003cli\u003eHe Y, Hara H, N\u0026uacute;\u0026ntilde;ez G. Mechanism and Regulation of NLRP3 Inflammasome Activation. Trends Biochem Sci. 2016;41(12):1012-1021. doi:10.1016/j.tibs.2016.09.002 \u003c/li\u003e\n\u003cli\u003eSun X, Cai H, Li Z, Li S, Yin W, Dong G, et al. Association between IL-1\u0026beta; polymorphisms and gastritis risk: A meta-analysis. Medicine (Baltimore). 2017;96(5):e6001. doi:10.1097/MD.0000000000006001\u003c/li\u003e\n\u003cli\u003eNicholson JK, Lindon JC, Holmes E. \u0026apos;Metabonomics\u0026apos;: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29(11):1181-1189. doi:10.1080/004982599238047\u003c/li\u003e\n\u003cli\u003eWishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016;15(7):473-484. doi:10.1038/nrd.2016.32\u003c/li\u003e\n\u003cli\u003eClish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud. 2015;1(1):a000588. doi:10.1101/mcs.a000588\u003c/li\u003e\n\u003cli\u003eGriffin JL, Shockcor JP. Metabolic profiles of cancer cells. Nat Rev Cancer. 2004;4(7):551-561. doi:10.1038/nrc1390\u003c/li\u003e\n\u003cli\u003eDunn WB. Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys Biol. 2008;5(1):011001. Published 2008 Feb 20. doi:10.1088/1478-3975/5/1/011001\u003c/li\u003e\n\u003cli\u003eBeger RD, Dunn WB, Bandukwala A, Bethan B, Broadhurst D, Clish CB, et al. Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics. 2019;15(1):4. Published 2019 Jan 3. doi:10.1007/s11306-018-1460-7\u003c/li\u003e\n\u003cli\u003eGika H, Virgiliou C, Theodoridis G, Plumb RS, Wilson ID. Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art. J Chromatogr B Analyt Technol Biomed Life Sci. 2019;1117:136-147. doi:10.1016/j.jchromb.2019.04.009\u003c/li\u003e\n\u003cli\u003eGevers D, Kugathasan S, Denson LA, V\u0026aacute;zquez-Baeza Y, Van Treuren W, Ren B, et al. The treatment-naive microbiome in new-onset Crohn\u0026apos;s disease. Cell Host Microbe. 2014;15(3):382-392. doi:10.1016/j.chom.2014.02.005 \u003c/li\u003e\n\u003cli\u003eLepage P, H\u0026auml;sler R, Spehlmann ME, Rehman A, Zvirbliene A, Begun A, et al. Twin study indicates loss of interaction between microbiota and mucosa of patients with ulcerative colitis. Gastroenterology. 2011;141(1):227-236. doi:10.1053/j.gastro.2011.04.011 \u003c/li\u003e\n\u003cli\u003eAhn J, Sinha R, Pei Z, Dominianni C, Wu J, Shi J, et al. Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst. 2013;105(24):1907-1911. doi:10.1093/jnci/djt300 \u003c/li\u003e\n\u003cli\u003eCoker OO, Dai Z, Nie Y, Zhao G, Cao L, Nakatsu G, et al. Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut. 2018;67(6):1024-1032. doi:10.1136/gutjnl-2017-314281 \u003c/li\u003e\n\u003cli\u003eBik EM, Eckburg PB, Gill SR, Nelson KE, Purdom EA, Francois F, et al. Molecular analysis of the bacterial microbiota in the human stomach. Proc Natl Acad Sci U S A. 2006;103(3):732-737. doi:10.1073/pnas.0506655103 \u003c/li\u003e\n\u003cli\u003eDelgado S, Cabrera-Rubio R, Mira A, Su\u0026aacute;rez A, Mayo B. Microbiological survey of the human gastric ecosystem using culturing and pyrosequencing methods. Microb Ecol. 2013;65(3):763-772. doi:10.1007/s00248-013-0192-5 \u003c/li\u003e\n\u003cli\u003eAndersson AF, Lindberg M, Jakobsson H, B\u0026auml;ckhed F, Nyr\u0026eacute;n P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One. 2008;3(7):e2836. Published 2008 Jul 30. doi:10.1371/journal.pone.0002836 \u003c/li\u003e\n\u003cli\u003eVinasco K, Mitchell HM, Kaakoush NO, Casta\u0026ntilde;o-Rodr\u0026iacute;guez N. Microbial carcinogenesis: Lactic acid bacteria in gastric cancer. Biochim Biophys Acta Rev Cancer. 2019;1872(2):188309. doi:10.1016/j.bbcan.2019.07.004\u003c/li\u003e\n\u003cli\u003eFFurman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25(12):1822-1832. doi:10.1038/s41591-019-0675-0 \u003c/li\u003e\n\u003cli\u003eGreten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity. 2019;51(1):27-41. doi:10.1016/j.immuni.2019.06.025\u003c/li\u003e\n\u003cli\u003eManjili SH, Isbell M, Ghochaghi N, Perkinson T, Manjili MH. Multifaceted functions of chronic inflammation in regulating tumor dormancy and relapse. Semin Cancer Biol. 2022;78:17-22. doi:10.1016/j.semcancer.2021.03.023\u003c/li\u003e\n\u003cli\u003eXia X, Wang X, Cheng Z, Qin W, Lei L, Jiang J, et al. The role of pyroptosis in cancer: pro-cancer or pro-\u0026quot;host\u0026quot;?. Cell Death Dis. 2019;10(9):650. Published 2019 Sep 9. doi:10.1038/s41419-019-1883-8 \u003c/li\u003e\n\u003cli\u003eBroz P, Pelegr\u0026iacute;n P, Shao F. The gasdermins, a protein family executing cell death and inflammation. Nat Rev Immunol. 2020;20(3):143-157. doi:10.1038/s41577-019-0228-2 \u003c/li\u003e\n\u003cli\u003eAyers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-\u0026gamma;-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127(8):2930-2940. doi:10.1172/JCI91190\u003c/li\u003e\n\u003cli\u003eDinarello CA. The IL-1 family and inflammatory diseases. Clin Exp Rheumatol. 2002;20(5 Suppl 27):S1-S13. \u003c/li\u003e\n\u003cli\u003eMan SM, Kanneganti TD. Converging roles of caspases in inflammasome activation, cell death and innate immunity. Nat Rev Immunol. 2016;16(1):7-21. doi:10.1038/nri.2015.7 \u003c/li\u003e\n\u003cli\u003eDinarello CA, Novick D, Kim S, Kaplanski G. Interleukin-18 and IL-18 binding protein. Front Immunol. 2013;4:289. Published 2013 Oct 8. doi:10.3389/fimmu.2013.00289 \u003c/li\u003e\n\u003cli\u003eKloosterman DJ, Akkari L. Macrophages at the interface of the co-evolving cancer ecosystem. Cell. 2023;186(8):1627-1651. doi:10.1016/j.cell.2023.02.020 \u003c/li\u003e\n\u003cli\u003eLi D, Xia L, Huang P, Wang Z, Guo Q, Huang C, et al. Cancer-associated fibroblast-secreted IGFBP7 promotes gastric cancer by enhancing tumor associated macrophage infiltration via FGF2/FGFR1/PI3K/AKT axis. Cell Death Discov. 2023;9(1):17. Published 2023 Jan 21. doi:10.1038/s41420-023-01336-x \u003c/li\u003e\n\u003cli\u003eShi J, Zhao Y, Wang K, Shi X, Wang Y, Huang H, et al. Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. Nature. 2015;526(7575):660-665. doi:10.1038/nature15514 \u003c/li\u003e\n\u003cli\u003eKayagaki N, Stowe IB, Lee BL, O\u0026apos;Rourke K, Anderson K, Warming S, et al. Caspase-11 cleaves gasdermin D for non-canonical inflammasome signalling. Nature. 2015;526(7575):666-671. doi:10.1038/nature15541 \u003c/li\u003e\n\u003cli\u003eCoker OO, Dai Z, Nie Y, Zhao G, Cao L, Nakatsu G, et al. Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut. 2018;67(6):1024-1032. doi:10.1136/gutjnl-2017-314281 \u003c/li\u003e\n\u003cli\u003eHarder J, Franchi L, Mu\u0026ntilde;oz-Planillo R, Park JH, Reimer T, N\u0026uacute;\u0026ntilde;ez G. Activation of the Nlrp3 inflammasome by Streptococcus pyogenes requires streptolysin O and NF-kappa B activation but proceeds independently of TLR signaling and P2X7 receptor. J Immunol. 2009;183(9):5823-5829. doi:10.4049/jimmunol.0900444 \u003c/li\u003e\n\u003cli\u003eGuo H, Callaway JB, Ting JP. Inflammasomes: mechanism of action, role in disease, and therapeutics. Nat Med. 2015;21(7):677-687. doi:10.1038/nm.3893 \u003c/li\u003e\n\u003cli\u003eSullivan NJ, Sasser AK, Axel AE, Vesuna F, Raman V, Ramirez N, et al. Interleukin-6 induces an epithelial-mesenchymal transition phenotype in human breast cancer cells. Oncogene. 2009;28(33):2940-2947. doi:10.1038/onc.2009.180 \u003c/li\u003e\n\u003cli\u003eGrivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883-899. doi:10.1016/j.cell.2010.01.025 \u003c/li\u003e\n\u003cli\u003eSilberg DG, Furth EE, Taylor JK, Schuck T, Chiou T, Traber PG. CDX1 protein expression in normal, metaplastic, and neoplastic human alimentary tract epithelium. Gastroenterology. 1997;113(2):478-486. doi:10.1053/gast.1997.v113.pm9247467 \u003c/li\u003e\n\u003cli\u003eCorrea P. A human model of gastric carcinogenesis. Cancer Res. 1988;48(13):3554-3560.\u003c/li\u003e\n\u003cli\u003eMahipal SV, Subhashini J, Reddy MC, Reddy MM, Anilkumar K, Roy KR, et al. Effect of 15-lipoxygenase metabolites, 15-(S)-HPETE and 15-(S)-HETE on chronic myelogenous leukemia cell line K-562: reactive oxygen species (ROS) mediate caspase-dependent apoptosis. Biochem Pharmacol. 2007;74(2):202-214. doi:10.1016/j.bcp.2007.04.005 \u003c/li\u003e\n\u003cli\u003eZhou R, Yazdi AS, Menu P, Tschopp J. A role for mitochondria in NLRP3 inflammasome activation. Nature. 2011;469(7329):221-225. doi:10.1038/nature09663 \u003c/li\u003e\n\u003cli\u003eMu\u0026ntilde;oz-Planillo R, Kuffa P, Mart\u0026iacute;nez-Col\u0026oacute;n G, Smith BL, Rajendiran TM, N\u0026uacute;\u0026ntilde;ez G. K⁺ efflux is the common trigger of NLRP3 inflammasome activation by bacterial toxins and particulate matter. Immunity. 2013;38(6):1142-1153. doi:10.1016/j.immuni.2013.05.016 \u003c/li\u003e\n\u003cli\u003eDuewell P, Kono H, Rayner KJ, Sirois CM, Vladimer G, Bauernfeind FG, et al. NLRP3 inflammasomes are required for atherogenesis and activated by cholesterol crystals. Nature. 2010;464(7293):1357-1361. doi:10.1038/nature08938 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Streptococcus anginosus, gastric \"inflammation-to-cancer\" transition, macrophages, pyroptosis, NLRP3/IL-1β, 15(S)-HPETE","lastPublishedDoi":"10.21203/rs.3.rs-9269296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9269296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStreptococcus anginosus (SA) is enriched in the gastric cancer (GC) microenvironment and correlates with GC risk, but its mechanism in the \"inflammation-to-cancer\" transition is unclear. This study investigates whether SA induces macrophage pyroptosis through the NLRP3/IL-1β signaling pathway, promoting this transition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue samples from patients (non-H. pylori infected) with chronic superficial gastritis (CSG), intestinal metaplasia (IM), and GC were analyzed for SA infection, pyroptosis-related proteins, and macrophage markers. In vitro, the effects of SA supernatant on GC cell proliferation, migration, and macrophage pyroptosis were examined, with NLRP3 inhibitor MCC950 used for intervention. Metabolomics identified 15(S)-HPETE as a key metabolite.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults showed that SA infection load, NLRP3 activation, and macrophage pyroptosis were significantly higher in IM and GC groups than in CSG, with a positive correlation. SA supernatant promoted GC cell proliferation, migration, and macrophage pyroptosis, further driving epithelial-mesenchymal transition (EMT) in GC cells and intestinal metaplasia in gastric cells. These effects were reversed by MCC950. 15(S)-HPETE was found to promote malignant phenotypes in GC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study suggests SA induces macrophage pyroptosis via NLRP3/IL-1β signaling, contributing to a pro-inflammatory microenvironment that drives gastric \"inflammation-to-cancer\" transition, with 15(S)-HPETE as a key mediator.\u003c/p\u003e","manuscriptTitle":"Streptococcus anginosus induces macrophage pyroptosis and drives gastric \"inflammation-to-cancer\" transition by activating the NLRP3/IL-1β signaling pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:07:21","doi":"10.21203/rs.3.rs-9269296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-13T18:15:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T16:55:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T13:12:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-04-06T11:37:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8af4defa-1326-4f2e-8dc1-ead73f7dd074","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:07:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:07:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9269296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9269296","identity":"rs-9269296","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}