Bacteria-derived DNA in serum extracellular vesicles as a biomarker for gastric cancer

Bacteria-derived DNA in serum extracellular vesicles as a biomarker for gastric cancer | 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 Bacteria-derived DNA in serum extracellular vesicles as a biomarker for gastric cancer Kaoru Fujikawa, Takuro Saito, Atsunari Kawashima, Kentaro Jingushi, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7281626/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 11 You are reading this latest preprint version Abstract Bacterial flora is present in various parts of the human body, and recent studies have detected bacterial 16S rRNA genes in the bloodstream. Distinct blood microbiomes have been identified in various diseases, including cancer, and are thought to play a role in disease pathogenesis. In this study, we conducted a 16S rRNA metagenomic analysis of serum extracellular vesicles from 89 patients with gastric cancer (GC) and 15 healthy donors and identified lower levels of Bacteroidetes and Actinobacteria and higher levels of Firmicutes in patients with GC than in healthy donors. By integrating this characteristic bacterial DNA profile, we developed a BAF index, defined as the ratio of Bacteroidetes and Actinobacteria to Firmicutes , which exhibited high sensitivity for detecting GC in both the discovery and validation cohorts, suggesting its potential utility as a screening tool. A high BAF index was significantly associated with an advanced tumor stage and poor prognosis. Moreover, a high BAF index was linked to an immunosuppressive tumor microenvironment, which may contribute to the unfavorable outcomes observed in these patients. These findings indicate that circulating bacterial signatures may serve as promising biomarkers for GC. 16S rRNA gene bacterial DNA extracellular vesicles gastric cancer tumor-infiltrating lymphocyte T cell exhaustion Figures Figure 1 Figure 2 Figure 3 Introduction Gastric cancer (GC) accounts for approximately 10% of all malignancies worldwide [ 1 ]. Since early-stage GC often lacks clinical symptoms, there is an urgent need to establish a biomarker that enables the easy and accurate diagnosis of GC. Although carcinoembryonic antigen (CEA) and cancer antigen 19–9 (CA19-9) are commonly used tumor markers, their positivity rates in early-stage GC are below 10%, making them insufficient for screening purposes [ 2 , 3 ]. Consequently, the National Comprehensive Cancer Network (NCCN) guidelines do not recommend their use for diagnostic screening [ 4 ]. Therefore, the development of a simple, sensitive, and noninvasive screening test for the early detection of GC is imperative. Recent studies have demonstrated the presence of bacteria within tumors and their influence on tumorigenesis and antitumor immune responses [ 5 – 9 ]. For example, Lactobacillus reuteri within melanoma tumors promotes interferon-γ production by CD8 + T cells via the secretion of indole-3-aldehyde [ 8 ]. In pancreatic cancer, the tumor-specific microbiome has been shown to suppress tumor growth by activating M1 macrophages and CD8 + T cells following microbial ablation [ 5 ]. Helicobacter pylori is a well-established carcinogen [ 10 – 12 ]. Furthermore, H. pylori has been reported to modulate the tumor immune microenvironment by inducing regulatory T cells (Tregs), thereby contributing to the immune suppressive environment [ 13 , 14 ]. These findings suggest that diverse bacterial species influence cancer development and tumor immunity. Secreted extracellular vesicles (EVs) released by both prokaryotic and eukaryotic cells can carry bacterial DNA (b-DNA) when secreted by bacteria [ 15 , 16 ]. Although bacteria were once believed to be absent from the bloodstream under normal conditions, recent evidence has shown that circulating EVs can contain bacterial components, including b-DNA [ 17 – 19 ]. In urothelial carcinoma, a higher proportion of Firmicutes DNA in serum EVs correlates with reduced tumor-infiltrating T cells, decreased T cell activation, and poorer prognosis in patients treated with anti-programmed cell death protein 1 (PD-1) therapy [ 20 ]. Similarly, tumor-specific b-DNA signatures have been detected in serum EVs from patients with renal cell carcinoma and shown to distinguish patients from healthy donors (HDs) [ 21 ]. These findings suggest that GC may also have a distinct b-DNA profile in serum EVs, which could serve as a potential diagnostic biomarker for GC. In this study, we evaluated the diagnostic utility of b-DNA profile in serum EVs for GC. Specifically, we aimed to develop a minimally invasive method for early-stage GC detection and identify potential bacterial biomarkers present in the serum EVs of patients with GC. Materials and Methods Patients’ recruitment and data This study included patients with histologically proven pStage I–III GC who underwent curative resection between 2015 and 2023 at Osaka University Hospital, Japan. Clinicopathological factors were obtained from medical records. Tumors were staged according to the Japanese classification of gastric carcinoma [ 22 ]. Histological type classification was based on the Lauren classification [ 23 ]. Relapse-free survival (RFS) was defined as the time from the operation to either disease progression or death from any cause. Overall survival (OS) was defined as the time from the operation to death from any cause. Written informed consent was obtained from each patient in accordance with the Declaration of Helsinki. Comparison of bacteria information in serum EVs A flow diagram of the study is shown in Fig. 1 a. Differences in b-DNA information in serum EVs were investigated between HDs and patients with GC in a discovery cohort (2015–2016), and the differences were confirmed in a validation cohort (2017–2023). HDs were defined as those without a current malignant disease or a medical history of cancer. Collection of EVs and isolation of bacteria-derived DNA Serum EVs collection and isolation were performed as described previously (Fig. 1 b) [ 20 , 21 ]. Whole blood samples were collected in Venoject II tubes (TERUMO, Tokyo, Japan) immediately before surgery. Within 3 h after sample collection, all samples were centrifuged at 1200 × g for 15 min, and supernatants were stored at − 80℃. No medications containing antibiotics or probiotics were routinely administered before surgery. For EV isolation, serum samples were centrifuged at 2000 × g for 30 min and filtered with a 0.2-µm syringe filter (KURABO, Osaka, Japan). Serum EVs were isolated using the Exosome Isolation Kit (serum) (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. EVs isolated from serum samples were confirmed using transmission electron microscopy (TEM), nanoparticle analysis, and western blotting for OmpA, a bacteria-derived EV marker (Fig. 1 c–e). b-DNA from serum EVs was purified using the QIAamp® Circulating Nucleic Acid Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s protocol. TEM analysis TEM was performed according to the method previously reported [ 24 ]. EV samples were placed on a Formvar carbon-coated nickel grid for 1 h and fixed with 2% paraformaldehyde before observation with an HT7800 (HITACHI, Tokyo, Japan). Nanoparticle measurement The size and concentration of the EVs were measured using qNano Gold (Izon Science, Christchurch, New Zealand). Data were analyzed using the Izon Control Suite Software (V3.February 3, 2001). Western blotting analysis EV samples were lysed with Laemmli sodium dodecyl sulfate sample buffer without 2-mercaptoethanol and separated on a 12% gel by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, followed by transfer onto a polyvinylidene difluoride membrane using a Bio-Rad semidry transfer system (1 h, 12 V). The membranes were probed with anti- Escherichia coli OmpA Pab (1:1000) primary antibodies at 4°C overnight. Membranes were incubated with horseradish peroxidase-conjugated secondary antibodies against mouse immunoglobulin (1:5000) for 1 h at room temperature. Chemiluminescence was detected using an Amersham Imager 680 (GE Healthcare). Because OmpA varies in length among bacterial species, serum EV proteins were detected at different locations than in E. coli [ 25 ]. 16S metagenomic sequencing The PCR-amplified V1–V2 regions of the bacterial 16S ribosomal RNA gene were sequenced on a MiSeq platform (Illumina, San Diego, CA, USA). QIIME version 2.202002 was used to process the raw sequencing data. The Simpson and Shannon indices indicated population diversity in the samples. Linear discriminant analysis (LDA) Effect Size (LEfSe) analysis was calculated using the Microbiome Analyst web platform, and only LDA scores ≥ 3.5 were listed as previously reported (Fig. 1 b) [ 21 ]. Multicolor flow cytometry Tumor samples were collected from surgically dissected specimens. Fresh tumor tissues were minced and digested to a single-cell suspension using a Tumor Dissociation Kit for humans (Miltenyi Biotec, Germany) and a gentle MACS Dissociator (Miltenyi Biotec) according to the manufacturer’s instructions. The cell suspension was applied to a 70-µm nylon cell strainer (BD Biosciences, U.S.A.), and red blood cells were lysed using BD Pharm Lyse for 2 min. Dead cells and debris were removed through centrifugation in an isodensity Percoll solution (Pharmacia Biotech). Surface marker staining was performed after FcR blocking for 15 min using Human TruStain FcX Fc receptor blocking solution (BioLegend, U.S.A.). Cells were incubated with antibodies against surface antigens and fixable viability dye (eBioscience, U.S.A.) at 4°C for 30 min. After incubation, cells were washed, fixed, and permeabilized with a fix/perm solution (BD Biosciences) at 4°C for 15 min. Cells were then stained with antibodies against intracellular molecules at 4°C for 30 min. Cells were analyzed on a BD LSRFortessa using the FACSDiva software (BD Biosciences). Details of the antibody clones used for staining the cell molecules are shown in Table S1 . The representative staining patterns and gating strategies are shown in Fig. S1 . Statistical analysis Statistical analyses and visual quantification were performed using the JMP software (JMP 16.1, SAS Institute). Associations were assessed by case-case comparisons using the chi-square test for categorical variables and the Mann–Whitney U test for continuous variables. The diagnostic abilities were evaluated using the Receiver Operating Characteristic analysis. The formula for the BAF index was created using logistic regression analysis, as previously reported [ 21 ]. The Kaplan–Meier method was used to calculate survival rates, and log-rank tests were used to compare the two groups. Statistical significance was set at a two-sided P-value of < 0.05. Results Patient characteristics The clinical characteristics of the patients with GC and HDs are presented in Table 1 . The median age of patients with GC was 75 years, and 66.3% were males. Compared with HDs, patients with GC were older and had a higher percentage of males. The pathological stage of GC was I in 39 patients (43.8%), II in 24 patients (27.0%), and III in 26 patients (29.2%). H. pylori infection was detected in 44 patients (55.6%). Table 1 Patient characteristics GC HD P value n = 89 n = 25 Age Median [IQR] 75 [68–81] 63 [50–72] < 0.001 Sex Male / Female 59 / 30 11 / 14 0.039 Location U / M / L 26 / 28 / 35 - Lauren Classification Intestinal / Diffuse 55 / 34 - pT status 1 / 2 / 3 / 4 39 / 18 / 17 / 4 - pN status 0 / 1 / 2 / 3 40 / 18 / 11 / 21 - pStage 1 / 2 / 3 39 / 24 / 26 - H.Pylori Negative / Positive 35 / 44 - Differences in bacterial information from serum EVs EVs isolated from serum samples were confirmed using TEM, nanoparticle analysis, and western blotting for OmpA, a bacteria-derived EV marker (Fig. 1 c–e). There were no significant differences in the total amount of bacterial DNA between patients with GC and HDs (Fig. 1 f). Patients with GC showed significantly lower α-diversity, as assessed by the Simpson and Shannon indices, compared with HDs (Fig. 1 g). In the principal coordinate analysis based on the Jaccard index, b-DNA profiles in serum EVs from patients with GC were significantly different from those in HDs (P = 0.002) (Fig. 1 h). LEfSe analysis of b-DNA information in serum EVs of patients with GC and HDs The b-DNA profiles of serum EVs were compared between patients with GC (2015–2016; n = 14) and HDs (n = 15) as the discovery cohort using LEfSe analysis. Analysis at the bacterial phylum level (level 2) revealed that patients with GC exhibited higher LDA scores for Firmicutes and lower LDA scores for two operational taxonomic units (OTUs), Bacteroidetes and Actinobacteria (Fig. 2 a). The proportion of Bacteroidetes was significantly higher and that of Actinobacteria tended to be higher in HDs than in patients with GC, whereas the proportion of Firmicutes tended to be higher in patients with GC than in HDs (Fig. S2 a). The results of the discovery cohort were confirmed in a validation cohort, where b-DNA information in serum EVs was compared between patients with GC (2017–2023; n = 75) and HDs (n = 10) using LEfSe analysis. Patients with GC had higher LDA scores for Firmicutes and lower LDA scores for six OTUs, including Bacteroidetes and Actinobacteria (Fig. 2 b), indicating that the same OTUs were identified in both cohorts. These results were further supported by the relative abundance of each bacterial phylum. The proportion of Bacteroidetes tended to be higher and that of Actinobacteria was significantly higher in HDs than in patients with GC, whereas that of Firmicutes tended to be higher in patients with GC than in HDs (Fig. S2 b). BAF index using b-DNA information showed high sensitivity for patients with GC Next, we investigated whether the bacterial profiles of serum EVs could be used to distinguish patients with GC from HDs. A novel scoring system, the BAF index, was developed using the proportion of b-DNAs from Bacteroidetes, Actinobacteria, and Firmicutes and was calculated as follows: BAF index = 1.55732699032807 + (0.000073939795799846 × Firmicutes) - (0.0000087290440622304 × Bacteroidetes) - (0.00011995497036724 × Actinobacteria). In the discovery cohort, the area under the curve (AUC) for diagnosing GC was 0.641 for Bacteroidetes, 0.630 for Actinobacteria, 0.624 for Firmicutes, and 0.714 for the BAF index (Fig. 2 c, Fig. S3a). When the cutoff value for the BAF index was set to 1.604, based on Receiver Operating Characteristic analysis to optimize both sensitivity and specificity, the sensitivity was 78.6%, the specificity was 73.3%, and the accuracy was 75.9%. In the validation cohort, the AUC for diagnosing GC were 0.760 for Actinobacteria, 0.621 for Firmicutes, and 0.760 for the BAF index (Fig. 2 d, Fig. S3b). Applying the same cutoff value (1.604) from the discovery cohort, the sensitivity was 42.7%, the specificity was 100.0%, and the accuracy was 56.0%. The BAF index distinguished patients with pStage I GC from HDs, with an AUC value of 0.621, sensitivity of 33.3%, specificity of 80.0%, and accuracy of 51.6%. (Fig. S3c). Furthermore, a logistic regression analysis showed that the BAF index was an independent factor for GC diagnosis in all patients (odds ratio [OR] 4.40, 95% confidence interval [CI]: 1.39–13.92, P = 0.012) as well as older age (Table 2 ). Table 2 Univariate and multivariate analyses for GC diagnosis in the whole cohort Univariate Multivariate OR (95% CI) P-Value OR (95% CI) P-Value Age < 65 ≥ 65 Reference 4.94 (1.91–12.8) 0.001 Reference 5.45 (1.88–15.8) 0.002 Sex Female Male Reference 2.50 (1.01–6.18) 0.047 Reference 1.92 (0.70–5.30) 0.21 BAF index < 1.604 ≥ 1.604 Reference 3.57 (1.23–10.4) 0.019 Reference 4.40 (1.39–13.9) 0.012 Abbreviations: CI = confidence interval; OR = odds ratio. We also compared the positivity of conventional tumor markers, CEA and CA19-9, with that of the BAF index in patients with GC by applying the same cutoff value (1.604) from the discovery cohort. The positivity was 33.7% for CEA, 15.7% for CA19-9, and 47.2% for the BAF index in patients with GC (Table 3 ). Among the patients with pStage I GC, the positivity was 28.2% with CEA, 10.3% with CA19-9, and 33.3% with the BAF index. Given its higher sensitivity and relatively higher diagnostic accuracy, the BAF index shows promise as a screening tool for GC. Table 3 Positivity of markers in patients with gastric cancer and healthy donors GC_all stages (n = 89) GC_Stage I (n = 39) HDs (n = 25) CEA + / - 30 / 59 (34% / 66%) 11 / 28 (28% / 72%) NE CA19-9 + / - 14 / 75 (16% / 84%) 4 / 35 (10% / 90%) NE BAF index* + / - 42 / 47 (47% / 53%) 13 / 26 (33% / 67%) 5 / 20 (20% / 80%) * The cutoff value for the BAF index was 1.604. Abbreviations: NE, not evaluated; The association of b-DNA profile in serum EVs with GC progression and prognosis The proportions of b-DNAs in serum EVs from HDs and patients with GC, stratified by pStage, are shown in Fig. 2 e. As the GC stage advanced, the relative abundance of Bacteroidetes and Actinobacteria gradually decreased, whereas that of Firmicutes increased. Accordingly, the BAF index increased in the advanced stages, showing significant differences between HDs and pStage II (p = 0.01) and pStage III (p = 0.004) and between pStage I and pStage III (p = 0.045) (Fig. 2 f). When patients with GC were divided into two groups based on the median value of the BAF index value, RFS was significantly better, and OS tended to be better in patients with a low BAF index than in those with a high BAF index (5-years RFS rate: 91.3% vs. 66.5%; p = 0.042, 5-years OS rate: 84.2% vs. 64.9%; p = 0.19) (Fig. 2 g, Table S2 ). The association of b-DNA profile in serum EVs with intratumoral immune cell status Among the 87 patients with GC, fresh tumor tissue samples were available for 67 patients, from which tumor-infiltrating lymphocytes (TILs) were isolated and analyzed using flow cytometry (Fig. S1 ). The frequencies of PD-1 and CD103 expression on CD8 + T cells, CD4 + FoxP3 − T cells, and CD4 + FoxP3 + regulatory T cells (Tregs) were assessed, and the relationship between b-DNA profiles and TIL characteristics was evaluated. PD-1 expression was significantly higher in CD4 + FoxP3 − T cells and lower in Tregs of patients with high Bacteroidetes abundance, whereas the opposite trend was observed in patients with high Firmicutes abundance (Fig. S4a). Furthermore, patients with a high BAF index also showed significantly increased frequency of PD-1 expression in CD4 + FoxP3 − T cells and decreased frequency in Tregs (Fig. 3 ). Because PD-1 expression in T cells generally indicates an exhausted status, these findings suggest that a high BAF index may be associated with an immunosuppressive tumor microenvironment, potentially contributing to unfavorable clinical outcomes. Discussion This study demonstrated the clinical significance of bacterial profiles in serum EVs from patients with GC. The DNA composition in serum EVs from patients with GC differed markedly from that of HDs, showing consistently higher levels of Firmicutes and lower levels of Bacteroidetes and Actinobacteria in both the discovery and validation cohorts. Based on these three bacterial signatures, we developed a BAF index that enables the highly sensitive detection of GC. A high BAF index is significantly associated with advanced tumor stage and poor prognosis, potentially reflecting an immunosuppressive tumor microenvironment. EVs are universal intercellular signaling vehicles found in both eukaryotes and bacterial systems [ 16 ]. Recent studies have identified bacterial components in the human bloodstream [ 17 , 18 ], and EVs containing these components have been reported to modulate systemic immune responses [ 26 , 27 ]. Furthermore, we previously examined the relationship between b-DNA information in EVs and tumor immune status, focusing on its association with immunotherapy efficacy [ 20 , 21 ]. In urothelial carcinoma, lower levels of Firmicutes in serum EVs are correlated with increased infiltration and activation of tumor-infiltrating T cells, resulting in an improved response to anti-PD-1 therapy [ 20 ]. Similarly, in renal cell carcinoma, lower levels of Bacteroidia in serum EVs were associated with a lower abundance of tumor-infiltrating Tregs, contributing to the enhanced therapeutic efficacy of anti-PD-1 therapy [ 21 ]. These findings suggest that specific b-DNA signatures in serum EVs may reflect the tumor immune status and predict the response to immunotherapy. Experimentally, EVs derived from Cutibacterium acnes have been shown to promote tumor growth in renal cell carcinoma both in vitro and in vivo, and C. acnes DNA has been detected in serum EVs of patients with renal cell carcinoma [ 28 ]. These results imply that EVs released by specific bacteria within tumor tissues may contribute to tumor progression. In this study, specific b-DNA signatures in serum EVs from patients with GC were associated with an immunosuppressive tumor microenvironment. However, the correlation with immunotherapy efficacy could not be evaluated because of the lack of data from patients treated with immunotherapy. Further investigation is warranted to determine whether b-DNA profiles in serum EVs from patients with GC are predictive of the response to immunotherapy. In our previous study, b-DNA profiles from various samples, including serum EVs and stools, were analyzed using 16S RNA sequencing. These findings suggest that the bacterial content of serum EVs may reflect that of the gut microbiota [ 20 ]. Consistent with this, our results showed an increased proportion of Firmicutes and decreased proportions of Bacteroidetes and Actinobacteria in the b-DNA content of serum EVs. Previous studies on the gastric microbiota of patients with GC have reported enrichment of Firmicutes and a decrease in Bacteroidetes and Actinobacteria [ 29 – 31 ]. These findings support the possibility that EVs originate from the gastrointestinal tract and subsequently enter the bloodstream. One of the main challenges in GC screening is the low sensitivity of conventional tumor markers. Reported sensitivity ranges from 16–68% for CEA and 14–68% for CA19-9 [ 2 – 4 ]. In our cohort, the BAF index demonstrated a higher sensitivity than CEA and CA19-9. For patients with pStage I GC, the sensitivity of the BAF index was better than that of the CEA and CA19-9. These results indicate that the BAF index offers superior diagnostic performance compared with conventional tumor markers, particularly in early-stage GC. Therefore, the BAF index may serve as a promising screening tool for the early detection of GC. This study had some limitations. First, it was a retrospective study with a small cohort size. Large-scale multicenter studies are required to validate the findings of the present study. Second, detailed background information on HDs was not examined in this study. Comprehensive profiling of patients with HDs, including information on comorbidities, should be considered for a more accurate assessment of the screening performance of the BAF index. In conclusion, the bacterial profile of serum EVs may enable minimally invasive and highly sensitive diagnosis of GC and prognostic prediction in patients with GC. Further large-scale validation studies are warranted to confirm these findings and explore their clinical utility. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, Osaka University has a joint research laboratory with Shionogi & Co., Ltd. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kaoru Fujikawa, Takuro Saito, Atsunari Kawashima, Kentaro Jingushi. The first draft of the manuscript was written by Kaoru Fujikawa and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Osaka (Date.2014.01.06. / No. 13266-39.) Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Not applicable. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660 He CZ, Zhang KH, Li Q, Liu XH, Hong Y, Lv NH (2013) Combined use of AFP, CEA, CA125 and CAl9-9 improves the sensitivity for the diagnosis of gastric cancer. BMC Gastroenterol 13:87. https://doi.org/10.1186/1471-230x-13-87 Liang Y, Wang W, Fang C, Raj SS, Hu WM, Li QW, Zhou ZW (2016) Clinical significance and diagnostic value of serum CEA, CA19-9 and CA72-4 in patients with gastric cancer. Oncotarget 7:49565–49573. https://doi.org/10.18632/oncotarget.10391 Shimada H, Noie T, Ohashi M, Oba K, Takahashi Y (2014) Clinical significance of serum tumor markers for gastric cancer: a systematic review of literature by the Task Force of the Japanese Gastric Cancer Association. Gastric Cancer 17:26–33. https://doi.org/10.1007/s10120-013-0259-5 Pushalkar S, Hundeyin M, Daley D, Zambirinis CP, Kurz E, Mishra A, Mohan N, Aykut B, Usyk M, Torres LE, Werba G, Zhang K, Guo Y, Li Q, Akkad N, Lall S, Wadowski B, Gutierrez J, Kochen Rossi JA, Herzog JW, Diskin B, Torres-Hernandez A, Leinwand J, Wang W, Taunk PS, Savadkar S, Janal M, Saxena A, Li X, Cohen D, Sartor RB, Saxena D, Miller G (2018) The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov 8:403–416. https://doi.org/10.1158/2159-8290.Cd-17-1134 Nejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, Rotter-Maskowitz A, Weiser R, Mallel G, Gigi E, Meltser A, Douglas GM, Kamer I, Gopalakrishnan V, Dadosh T, Levin-Zaidman S, Avnet S, Atlan T, Cooper ZA, Arora R, Cogdill AP, Khan MAW, Ologun G, Bussi Y, Weinberger A, Lotan-Pompan M, Golani O, Perry G, Rokah M, Bahar-Shany K, Rozeman EA, Blank CU, Ronai A, Shaoul R, Amit A, Dorfman T, Kremer R, Cohen ZR, Harnof S, Siegal T, Yehuda-Shnaidman E, Gal-Yam EN, Shapira H, Baldini N, Langille MGI, Ben-Nun A, Kaufman B, Nissan A, Golan T, Dadiani M, Levanon K, Bar J, Yust-Katz S, Barshack I, Peeper DS, Raz DJ, Segal E, Wargo JA, Sandbank J, Shental N, Straussman R (2020) The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science 368:973–980. https://doi.org/10.1126/science.aay9189 Fu A, Yao B, Dong T, Chen Y, Yao J, Liu Y, Li H, Bai H, Liu X, Zhang Y, Wang C, Guo Y, Li N, Cai S (2022) Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell 185:1356–1372e1326. https://doi.org/10.1016/j.cell.2022.02.027 Bender MJ, McPherson AC, Phelps CM, Pandey SP, Laughlin CR, Shapira JH, Medina Sanchez L, Rana M, Richie TG, Mims TS, Gocher-Demske AM, Cervantes-Barragan L, Mullett SJ, Gelhaus SL, Bruno TC, Cannon N, McCulloch JA, Vignali DAA, Hinterleitner R, Joglekar AV, Pierre JF, Lee STM, Davar D, Zarour HM, Meisel M (2023) Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment. Cell 186:1846–1862e1826. https://doi.org/10.1016/j.cell.2023.03.011 Chung IY, Kim J, Koh A (2024) The Microbiome Matters: Its Impact on Cancer Development and Therapeutic Responses. J Microbiol 62:137–152. https://doi.org/10.1007/s12275-024-00110-7 Marshall BJ, Warren JR (1984) Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet 1:1311–1315. https://doi.org/10.1016/s0140-6736(84)91816-6 Fukase K, Kato M, Kikuchi S, Inoue K, Uemura N, Okamoto S, Terao S, Amagai K, Hayashi S, Asaka M (2008) Effect of eradication of Helicobacter pylori on incidence of metachronous gastric carcinoma after endoscopic resection of early gastric cancer: an open-label, randomised controlled trial. Lancet 372:392–397. https://doi.org/10.1016/s0140-6736(08)61159-9 Wang F, Meng W, Wang B, Qiao L (2014) Helicobacter pylori-induced gastric inflammation and gastric cancer. Cancer Lett 345:196–202. https://doi.org/10.1016/j.canlet.2013.08.016 Nagase H, Takeoka T, Urakawa S, Morimoto-Okazawa A, Kawashima A, Iwahori K, Takiguchi S, Nishikawa H, Sato E, Sakaguchi S, Mori M, Doki Y, Wada H (2017) ICOS(+) Foxp3(+) TILs in gastric cancer are prognostic markers and effector regulatory T cells associated with Helicobacter pylori. Int J Cancer 140:686–695. https://doi.org/10.1002/ijc.30475 Urakawa S, Yamasaki M, Makino T, Kurokawa Y, Yamamoto K, Goto K, Haruna M, Hirata M, Morimoto-Okazawa A, Kawashima A, Iwahori K, Mizushima T, Sato E, Mori M, Doki Y, Wada H (2021) The impact of ICOS(+) regulatory T cells and Helicobacter pylori infection on the prognosis of patients with gastric and colorectal cancer: potential prognostic benefit of pre-operative eradication therapy. Cancer Immunol Immunother 70:443–452. https://doi.org/10.1007/s00262-020-02696-4 Kang CS, Ban M, Choi EJ, Moon HG, Jeon JS, Kim DK, Park SK, Jeon SG, Roh TY, Myung SJ, Gho YS, Kim JG, Kim YK (2013) Extracellular vesicles derived from gut microbiota, especially Akkermansia muciniphila, protect the progression of dextran sulfate sodium-induced colitis. PLoS ONE 8:e76520. https://doi.org/10.1371/journal.pone.0076520 Kalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Sci 367. https://doi.org/10.1126/science.aau6977 Dorward DW, Schwan TG, Garon CF (1991) Immune capture and detection of Borrelia burgdorferi antigens in urine, blood, or tissues from infected ticks, mice, dogs, and humans. J Clin Microbiol 29:1162–1170. https://doi.org/10.1128/jcm.29.6.1162-1170.1991 Jones EJ, Booth C, Fonseca S, Parker A, Cross K, Miquel-Clopés A, Hautefort I, Mayer U, Wileman T, Stentz R, Carding SR (2020) The Uptake, Trafficking, and Biodistribution of Bacteroides thetaiotaomicron Generated Outer Membrane Vesicles. Front Microbiol 11:57. https://doi.org/10.3389/fmicb.2020.00057 Tomiyama E, Matsuzaki K, Fujita K, Shiromizu T, Narumi R, Jingushi K, Koh Y, Matsushita M, Nakano K, Hayashi Y, Wang C, Ishizuya Y, Kato T, Hatano K, Kawashima A, Ujike T, Uemura M, Takao T, Adachi J, Tomonaga T, Nonomura N (2021) Proteomic analysis of urinary and tissue-exudative extracellular vesicles to discover novel bladder cancer biomarkers. Cancer Sci 112:2033–2045. https://doi.org/10.1111/cas.14881 Jingushi K, Kawashima A, Saito T, Kanazawa T, Motooka D, Kimura T, Mita M, Yamamoto A, Uemura T, Yamamichi G, Okada K, Tomiyama E, Koh Y, Matsushita M, Kato T, Hatano K, Uemura M, Tsujikawa K, Wada H, Nonomura N (2022) Circulating extracellular vesicles carrying Firmicutes reflective of the local immune status may predict clinical response to pembrolizumab in urothelial carcinoma patients. Cancer Immunol Immunother 71:2999–3011. https://doi.org/10.1007/s00262-022-03213-5 Uemura T, Kawashima A, Jingushi K, Motooka D, Saito T, Nesrine S, Oka T, Okuda Y, Yamamoto A, Yamamichi G, Tomiyama E, Ishizuya Y, Yamamoto Y, Kato T, Hatano K, Tsujikawa K, Wada H, Nonomura N (2023) Bacteria-derived DNA in serum extracellular vesicles are biomarkers for renal cell carcinoma. Heliyon 9:e19800. https://doi.org/10.1016/j.heliyon.2023.e19800 (2011) Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer 14:101–112. https://doi.org/10.1007/s10120-011-0041-5 Lauren P, THE TWO HISTOLOGICAL MAIN TYPES OF GASTRIC CARCINOMA: DIFFUSE AND SO-CALLED INTESTINAL-TYPE CARCINOMA. AN ATTEMPT AT A HISTO-CLINICAL CLASSIFICATION (1965) Acta Pathol Microbiol Scand 64:31–49. https://doi.org/10.1111/apm.1965.64.1.31 Lässer C, Eldh M, Lötvall J (2012) Isolation and characterization of RNA-containing exosomes. J Vis Exp:e3037. https://doi.org/10.3791/3037 Futse JE, Buami G, Kayang BB, Koku R, Palmer GH, Graça T, Noh SM (2019) Sequence and immunologic conservation of Anaplasma marginale OmpA within strains from Ghana as compared to the predominant OmpA variant. PLoS ONE 14:e0217661. https://doi.org/10.1371/journal.pone.0217661 Kuehn MJ, Kesty NC (2005) Bacterial outer membrane vesicles and the host-pathogen interaction. Genes Dev 19:2645–2655. https://doi.org/10.1101/gad.1299905 Ellis TN, Kuehn MJ (2010) Virulence and immunomodulatory roles of bacterial outer membrane vesicles. Microbiol Mol Biol Rev 74:81–94. https://doi.org/10.1128/mmbr.00031-09 Jingushi K, Kawashima A, Tanikawa S, Saito T, Yamamoto A, Uemura T, Sassi N, Ishizuya Y, Yamamoto Y, Kato T, Hatano K, Hase H, Nonomura N, Tsujikawa K (2024) Cutibacterium acnes-derived extracellular vesicles promote tumor growth in renal cell carcinoma. Cancer Sci 115:2578–2587. https://doi.org/10.1111/cas.16202 Yu G, Torres J, Hu N, Medrano-Guzman R, Herrera-Goepfert R, Humphrys MS, Wang L, Wang C, Ding T, Ravel J, Taylor PR, Abnet CC, Goldstein AM (2017) Molecular Characterization of the Human Stomach Microbiota in Gastric Cancer Patients. Front Cell Infect Microbiol 7:302. https://doi.org/10.3389/fcimb.2017.00302 Gunathilake M, Lee J, Choi IJ, Kim YI, Yoon J, Sul WJ, Kim JF, Kim J (2020) Alterations in Gastric Microbial Communities Are Associated with Risk of Gastric Cancer in a Korean Population: A Case-Control Study. Cancers (Basel) 12. https://doi.org/10.3390/cancers12092619 Png CW, Lee WJJ, Chua SJ, Zhu F, Yeoh KG, Zhang Y (2022) Mucosal microbiome associates with progression to gastric cancer. Theranostics 12:48–58. https://doi.org/10.7150/thno.65302 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.pdf Supplementary Fig. S1 Representative flow cytometry images. Supplementary Fig. S2 Comparison of the abundance of bacteria-derived DNA (b-DNA) in serum extracellular vesicles (EVs) between patients with GC gastric cancer (GC) and healthy donors (HDs). (a) Comparison of the abundance of Bacteroidetes, Actinobacteria, and Firmicutes between patients with GC and HDs in the discovery cohort. (b) Comparison of the abundance of Bacteroidetes, Actinobacteria, and Firmicutes between patients with GC and HDs in the cohort and validation cohorts. Comparison between the two groups was performed using the Mann–Whitney U test. Supplementary Fig. S3 The ROC curve for the diagnostic accuracy in patients with gastric cancer (GC). (a) The ROC curve showing the diagnostic performance of Bacteroidetes, Actinobacteria, and Firmicutes in the discovery cohort. (b) The ROC curve showing the diagnostic performance of Bacteroidetes, Actinobacteria, and Firmicutes in the validation cohort. (c) The ROC curve showing the diagnostic performance of the BAF index in patients with GC with pStage I disease. Supplementary Fig. S4 Correlation between bacteria-derived DNA (b-DNA) and tumor-infiltrating lymphocytes (TILs). (a) The frequency of PD-1 expression in CD4 + FoxP3 - T cells in high and low patients with Bacteroidetes, Actinobacteria, and Firmicutes. (b) The frequency of PD-1 expression in CD4 + FoxP3 + T cells in high and low patients with Bacteroidetes, Actinobacteria, and Firmicutes. Comparison between the two groups was performed using the Mann–Whitney U test. SupplementaryTable.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 04 Aug, 2025 First submitted to journal 03 Aug, 2025 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. 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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-7281626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496725121,"identity":"b5a4f78f-d2f8-4138-ae8f-ba4061d0aaa2","order_by":0,"name":"Kaoru Fujikawa","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Kaoru","middleName":"","lastName":"Fujikawa","suffix":""},{"id":496725123,"identity":"3727987e-6117-4f59-a8d9-d0ebd9fcbbbc","order_by":1,"name":"Takuro 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Osaka","correspondingAuthor":false,"prefix":"","firstName":"Hisashi","middleName":"","lastName":"Wada","suffix":""},{"id":496725145,"identity":"5b87b807-9a2e-41eb-81ca-f9eaf84a09a4","order_by":15,"name":"Hidetoshi Eguchi","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Hidetoshi","middleName":"","lastName":"Eguchi","suffix":""},{"id":496725146,"identity":"887addaa-024f-4c9a-8f7f-74ea9c827fd1","order_by":16,"name":"Yuichiro Doki","email":"","orcid":"","institution":"The University of Osaka","correspondingAuthor":false,"prefix":"","firstName":"Yuichiro","middleName":"","lastName":"Doki","suffix":""}],"badges":[],"createdAt":"2025-08-03 06:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7281626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7281626/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-025-04175-0","type":"published","date":"2025-10-24T16:17:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88644694,"identity":"2c7e330e-f381-4cca-9f1b-43651c4d1b64","added_by":"auto","created_at":"2025-08-08 16:20:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":349134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtracellular vesicle (EV) collection from serum and isolation of bacteria-derived DNA (b-DNA).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Study flow diagram. (b) Methods of processing and analyzing serum samples. (c–d) Representative results of transmission electron microscopic (c) and nanoparticle (d) analysis of EVs isolated from serum samples. A black bar indicates 1 µm. (e) Western blot analysis of serum EVs from a patient with gastric cancer (GC) and a healthy donor (HD) using an anti-OmpA antibody. (f) DNA amount in EVs in 100 μL of serum from 89 patients with GC and 15 HDs. Comparison between the two groups was performed using the Mann–Whitney U test. (G) Alpha diversity analysis (left: Simpson, right: Shannon) for serum EVs from 89 patients with GC and 15 HDs. Comparison between the two groups was performed using the Mann–Whitney U test. (h) Beta diversity plots for serum EVs from 89 patients with GC and 15 HDs.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7281626/v1/d7813c2b587f6821ca1fd91d.png"},{"id":88644697,"identity":"d64a8793-dfd8-4b82-9619-2163de83d92c","added_by":"auto","created_at":"2025-08-08 16:20:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the abundance of bacteria-derived DNA (b-DNA) in serum extracellular vesicles (EVs) between patients with gastric cancer (GC) and healthy donors (HDs).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a–b) Linear discriminant analysis Effect Size (LEfSe). Distinctive bacterial information detected in serum EVs from patients with GC and HDs in the discovery (a) and validation (b) cohorts. The biological classification classes are denoted by initials (p: phylum). (c–d) The ROC curve shows the diagnostic performance of the BAF index in patients with GC in the discovery (c) and validation cohorts (d). (e) Distribution of b-DNA in patients with HDs and GC stratified by pStage. (f) BAF index in HDs and patients with GC stratified by pStage. (g) Relapse-free survival (RFS) and overall survival (OS) after surgery in patients with GC and high and low BAF indices. Statistical analyses were performed using log-rank tests.\u003c/p\u003e","description":"","filename":"floatimage22.png","url":"https://assets-eu.researchsquare.com/files/rs-7281626/v1/af52bc7ba857ddd5f6979cd1.png"},{"id":88645736,"identity":"5d210bd7-c090-4580-b43d-3cff8e377182","added_by":"auto","created_at":"2025-08-08 16:28:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between bacteria-derived DNA (b-DNA) and tumor-infiltrating lymphocytes (TILs).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantification of immune cell populations in GC TILs. The frequency of CD8\u003csup\u003e +\u003c/sup\u003e\u0026nbsp;in CD3\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;in CD3\u003csup\u003e+\u003c/sup\u003e T cells, CD45RA\u003csup\u003e-\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T cells, PD-1\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD103\u003csup\u003e+\u003c/sup\u003e in CD8\u003csup\u003e+\u003c/sup\u003e T cells, FoxP3\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;in CD4\u003csup\u003e+\u003c/sup\u003e T cells, PD-1\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;in FoxP3\u003csup\u003e-\u003c/sup\u003eCD4\u003csup\u003e+ \u003c/sup\u003eT cells, PD-1\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;in FoxP3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+ \u003c/sup\u003eT cells, CD103\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;in FoxP3\u003csup\u003e-\u003c/sup\u003eCD4\u003csup\u003e+ \u003c/sup\u003eT cells, and CD103\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;in FoxP3\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+ \u003c/sup\u003eT cells were analyzed between high and low BAF index. Comparison between the two groups was performed using the Mann–Whitney U test.\u003c/p\u003e","description":"","filename":"floatimage34.png","url":"https://assets-eu.researchsquare.com/files/rs-7281626/v1/b09550d34b091a2fdc3ecc7a.png"},{"id":94490383,"identity":"528532a6-87d6-41eb-820d-3d3aa7ea1e4a","added_by":"auto","created_at":"2025-10-27 17:09:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1512565,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7281626/v1/e2f0ae55-7de3-4d26-82e7-ec116c013c81.pdf"},{"id":88645734,"identity":"380fd09f-0119-4e22-a21f-63858a72229e","added_by":"auto","created_at":"2025-08-08 16:28:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":404448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S1 Representative flow cytometry images.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S2 Comparison of the abundance of bacteria-derived DNA (b-DNA) in serum extracellular vesicles (EVs) between patients with GC gastric cancer (GC) and healthy donors (HDs).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Comparison of the abundance of Bacteroidetes, Actinobacteria, and Firmicutes between patients with GC and HDs in the discovery cohort. (b) Comparison of the abundance of Bacteroidetes, Actinobacteria, and Firmicutes between patients with GC and HDs in the cohort and validation cohorts. Comparison between the two groups was performed using the Mann–Whitney U test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S3 The ROC curve for the diagnostic accuracy in patients with gastric cancer (GC).\u003c/strong\u003e (a) The ROC curve showing the diagnostic performance of Bacteroidetes, Actinobacteria, and Firmicutes in the discovery cohort. (b) The ROC curve showing the diagnostic performance of Bacteroidetes, Actinobacteria, and Firmicutes in the validation cohort. (c) The ROC curve showing the diagnostic performance of the BAF index in patients with GC with pStage I disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S4 Correlation between bacteria-derived DNA (b-DNA) and tumor-infiltrating lymphocytes (TILs). \u003c/strong\u003e(a) The frequency of PD-1 expression in CD4\u003csup\u003e+\u003c/sup\u003eFoxP3\u003csup\u003e-\u003c/sup\u003e T cells in high and low patients with Bacteroidetes, Actinobacteria, and Firmicutes. (b) The frequency of PD-1 expression in CD4\u003csup\u003e+\u003c/sup\u003eFoxP3\u003csup\u003e+\u003c/sup\u003e T cells in high and low patients with Bacteroidetes, Actinobacteria, and Firmicutes. Comparison between the two groups was performed using the Mann–Whitney U test.\u003c/p\u003e","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7281626/v1/a1c2227db2bd55a38969e76c.pdf"},{"id":88644703,"identity":"a015935e-ca81-4bb0-bd13-f957e3690e18","added_by":"auto","created_at":"2025-08-08 16:20:47","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":147486,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7281626/v1/3c42ca2de5abd58790e01e2c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bacteria-derived DNA in serum extracellular vesicles as a biomarker for gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) accounts for approximately 10% of all malignancies worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Since early-stage GC often lacks clinical symptoms, there is an urgent need to establish a biomarker that enables the easy and accurate diagnosis of GC. Although carcinoembryonic antigen (CEA) and cancer antigen 19\u0026ndash;9 (CA19-9) are commonly used tumor markers, their positivity rates in early-stage GC are below 10%, making them insufficient for screening purposes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, the National Comprehensive Cancer Network (NCCN) guidelines do not recommend their use for diagnostic screening [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, the development of a simple, sensitive, and noninvasive screening test for the early detection of GC is imperative.\u003c/p\u003e\u003cp\u003eRecent studies have demonstrated the presence of bacteria within tumors and their influence on tumorigenesis and antitumor immune responses [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, \u003cem\u003eLactobacillus reuteri\u003c/em\u003e within melanoma tumors promotes interferon-γ production by CD8\u003csup\u003e+\u003c/sup\u003e T cells via the secretion of indole-3-aldehyde [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In pancreatic cancer, the tumor-specific microbiome has been shown to suppress tumor growth by activating M1 macrophages and CD8\u003csup\u003e+\u003c/sup\u003e T cells following microbial ablation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u003cem\u003eHelicobacter pylori\u003c/em\u003e is a well-established carcinogen [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, \u003cem\u003eH. pylori\u003c/em\u003e has been reported to modulate the tumor immune microenvironment by inducing regulatory T cells (Tregs), thereby contributing to the immune suppressive environment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings suggest that diverse bacterial species influence cancer development and tumor immunity.\u003c/p\u003e\u003cp\u003eSecreted extracellular vesicles (EVs) released by both prokaryotic and eukaryotic cells can carry bacterial DNA (b-DNA) when secreted by bacteria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although bacteria were once believed to be absent from the bloodstream under normal conditions, recent evidence has shown that circulating EVs can contain bacterial components, including b-DNA [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In urothelial carcinoma, a higher proportion of Firmicutes DNA in serum EVs correlates with reduced tumor-infiltrating T cells, decreased T cell activation, and poorer prognosis in patients treated with anti-programmed cell death protein 1 (PD-1) therapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, tumor-specific b-DNA signatures have been detected in serum EVs from patients with renal cell carcinoma and shown to distinguish patients from healthy donors (HDs) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings suggest that GC may also have a distinct b-DNA profile in serum EVs, which could serve as a potential diagnostic biomarker for GC.\u003c/p\u003e\u003cp\u003eIn this study, we evaluated the diagnostic utility of b-DNA profile in serum EVs for GC. Specifically, we aimed to develop a minimally invasive method for early-stage GC detection and identify potential bacterial biomarkers present in the serum EVs of patients with GC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003ePatients\u0026rsquo; recruitment and data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study included patients with histologically proven pStage I\u0026ndash;III GC who underwent curative resection between 2015 and 2023 at Osaka University Hospital, Japan. Clinicopathological factors were obtained from medical records. Tumors were staged according to the Japanese classification of gastric carcinoma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Histological type classification was based on the Lauren classification [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Relapse-free survival (RFS) was defined as the time from the operation to either disease progression or death from any cause. Overall survival (OS) was defined as the time from the operation to death from any cause. Written informed consent was obtained from each patient in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of bacteria information in serum EVs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA flow diagram of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. Differences in b-DNA information in serum EVs were investigated between HDs and patients with GC in a discovery cohort (2015\u0026ndash;2016), and the differences were confirmed in a validation cohort (2017\u0026ndash;2023). HDs were defined as those without a current malignant disease or a medical history of cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollection of EVs and isolation of bacteria-derived DNA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSerum EVs collection and isolation were performed as described previously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Whole blood samples were collected in Venoject II tubes (TERUMO, Tokyo, Japan) immediately before surgery. Within 3 h after sample collection, all samples were centrifuged at 1200 \u0026times;\u003cem\u003eg\u003c/em\u003e for 15 min, and supernatants were stored at \u0026minus;\u0026thinsp;80℃. No medications containing antibiotics or probiotics were routinely administered before surgery. For EV isolation, serum samples were centrifuged at 2000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 30 min and filtered with a 0.2-\u0026micro;m syringe filter (KURABO, Osaka, Japan). Serum EVs were isolated using the Exosome Isolation Kit (serum) (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer\u0026rsquo;s protocol. EVs isolated from serum samples were confirmed using transmission electron microscopy (TEM), nanoparticle analysis, and western blotting for OmpA, a bacteria-derived EV marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec\u0026ndash;e). b-DNA from serum EVs was purified using the QIAamp\u0026reg; Circulating Nucleic Acid Kit (QIAGEN, Hilden, Germany) according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTEM analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTEM was performed according to the method previously reported [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. EV samples were placed on a Formvar carbon-coated nickel grid for 1 h and fixed with 2% paraformaldehyde before observation with an HT7800 (HITACHI, Tokyo, Japan).\u003c/p\u003e\u003cp\u003e\u003cb\u003eNanoparticle measurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe size and concentration of the EVs were measured using qNano Gold (Izon Science, Christchurch, New Zealand). Data were analyzed using the Izon Control Suite Software (V3.February 3, 2001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern blotting analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEV samples were lysed with Laemmli sodium dodecyl sulfate sample buffer without 2-mercaptoethanol and separated on a 12% gel by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, followed by transfer onto a polyvinylidene difluoride membrane using a Bio-Rad semidry transfer system (1 h, 12 V). The membranes were probed with anti-\u003cem\u003eEscherichia coli\u003c/em\u003e OmpA Pab (1:1000) primary antibodies at 4\u0026deg;C overnight. Membranes were incubated with horseradish peroxidase-conjugated secondary antibodies against mouse immunoglobulin (1:5000) for 1 h at room temperature. Chemiluminescence was detected using an Amersham Imager 680 (GE Healthcare). Because OmpA varies in length among bacterial species, serum EV proteins were detected at different locations than in \u003cem\u003eE. coli\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003e16S metagenomic sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PCR-amplified V1\u0026ndash;V2 regions of the bacterial 16S ribosomal RNA gene were sequenced on a MiSeq platform (Illumina, San Diego, CA, USA). QIIME version 2.202002 was used to process the raw sequencing data. The Simpson and Shannon indices indicated population diversity in the samples. Linear discriminant analysis (LDA) Effect Size (LEfSe) analysis was calculated using the Microbiome Analyst web platform, and only LDA scores\u0026thinsp;\u0026ge;\u0026thinsp;3.5 were listed as previously reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulticolor flow cytometry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTumor samples were collected from surgically dissected specimens. Fresh tumor tissues were minced and digested to a single-cell suspension using a Tumor Dissociation Kit for humans (Miltenyi Biotec, Germany) and a gentle MACS Dissociator (Miltenyi Biotec) according to the manufacturer\u0026rsquo;s instructions. The cell suspension was applied to a 70-\u0026micro;m nylon cell strainer (BD Biosciences, U.S.A.), and red blood cells were lysed using BD Pharm Lyse for 2 min. Dead cells and debris were removed through centrifugation in an isodensity Percoll solution (Pharmacia Biotech). Surface marker staining was performed after FcR blocking for 15 min using Human TruStain FcX Fc receptor blocking solution (BioLegend, U.S.A.). Cells were incubated with antibodies against surface antigens and fixable viability dye (eBioscience, U.S.A.) at 4\u0026deg;C for 30 min. After incubation, cells were washed, fixed, and permeabilized with a fix/perm solution (BD Biosciences) at 4\u0026deg;C for 15 min. Cells were then stained with antibodies against intracellular molecules at 4\u0026deg;C for 30 min. Cells were analyzed on a BD LSRFortessa using the FACSDiva software (BD Biosciences). Details of the antibody clones used for staining the cell molecules are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The representative staining patterns and gating strategies are shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses and visual quantification were performed using the JMP software (JMP 16.1, SAS Institute). Associations were assessed by case-case comparisons using the chi-square test for categorical variables and the Mann\u0026ndash;Whitney U test for continuous variables. The diagnostic abilities were evaluated using the Receiver Operating Characteristic analysis. The formula for the BAF index was created using logistic regression analysis, as previously reported [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Kaplan\u0026ndash;Meier method was used to calculate survival rates, and log-rank tests were used to compare the two groups. Statistical significance was set at a two-sided P-value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical characteristics of the patients with GC and HDs are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of patients with GC was 75 years, and 66.3% were males. Compared with HDs, patients with GC were older and had a higher percentage of males. The pathological stage of GC was I in 39 patients (43.8%), II in 24 patients (27.0%), and III in 26 patients (29.2%).\u0026nbsp;\u003cem\u003eH. pylori\u003c/em\u003e infection was detected in 44 patients (55.6%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;89\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 [68\u0026ndash;81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 [50\u0026ndash;72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale / Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 / 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 / 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU / M / L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 / 28 / 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLauren Classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntestinal / Diffuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 / 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epT status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 / 2 / 3 / 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 / 18 / 17 / 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epN status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 / 1 / 2 / 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 / 18 / 11 / 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 / 2 / 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 / 24 / 26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eH.Pylori\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative / Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 / 44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in bacterial information from serum EVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEVs isolated from serum samples were confirmed using TEM, nanoparticle analysis, and western blotting for OmpA, a bacteria-derived EV marker (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec\u0026ndash;e). There were no significant differences in the total amount of bacterial DNA between patients with GC and HDs (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef). Patients with GC showed significantly lower \u0026alpha;-diversity, as assessed by the Simpson and Shannon indices, compared with HDs (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eg). In the principal coordinate analysis based on the Jaccard index, b-DNA profiles in serum EVs from patients with GC were significantly different from those in HDs (P\u0026thinsp;=\u0026thinsp;0.002) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eh).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLEfSe analysis of b-DNA information in serum EVs of patients with GC and HDs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe b-DNA profiles of serum EVs were compared between patients with GC (2015\u0026ndash;2016; n\u0026thinsp;=\u0026thinsp;14) and HDs (n\u0026thinsp;=\u0026thinsp;15) as the discovery cohort using LEfSe analysis. Analysis at the bacterial phylum level (level 2) revealed that patients with GC exhibited higher LDA scores for Firmicutes and lower LDA scores for two operational taxonomic units (OTUs), Bacteroidetes and Actinobacteria (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The proportion of Bacteroidetes\u003c/p\u003e\n\u003cp\u003ewas significantly higher and that of Actinobacteria tended to be higher in HDs than in patients with GC, whereas the proportion of Firmicutes tended to be higher in patients with GC than in HDs (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003eThe results of the discovery cohort were confirmed in a validation cohort, where b-DNA information in serum EVs was compared between patients with GC (2017\u0026ndash;2023; n\u0026thinsp;=\u0026thinsp;75) and HDs (n\u0026thinsp;=\u0026thinsp;10) using LEfSe analysis. Patients with GC had higher LDA scores for Firmicutes and lower LDA scores for six OTUs, including Bacteroidetes and Actinobacteria (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb), indicating that the same OTUs were identified in both cohorts. These results were further supported by the relative abundance of each bacterial phylum. The proportion of Bacteroidetes tended to be higher and that of Actinobacteria was significantly higher in HDs than in patients with GC, whereas that of Firmicutes tended to be higher in patients with GC than in HDs (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBAF index using b-DNA information showed high sensitivity for patients with GC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we investigated whether the bacterial profiles of serum EVs could be used to distinguish patients with GC from HDs. A novel scoring system, the BAF index, was developed using the proportion of b-DNAs from Bacteroidetes, Actinobacteria, and Firmicutes and was calculated as follows: BAF index\u0026thinsp;=\u0026thinsp;1.55732699032807 + (0.000073939795799846 \u0026times; Firmicutes) - (0.0000087290440622304 \u0026times; Bacteroidetes) - (0.00011995497036724 \u0026times; Actinobacteria).\u003c/p\u003e\n\u003cp\u003eIn the discovery cohort, the area under the curve (AUC) for diagnosing GC was 0.641 for Bacteroidetes, 0.630 for Actinobacteria, 0.624 for Firmicutes, and 0.714 for the BAF index (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec, Fig. S3a). When the cutoff value for the BAF index was set to 1.604, based on Receiver Operating Characteristic analysis to optimize both sensitivity and specificity, the sensitivity was 78.6%, the specificity was 73.3%, and the accuracy was 75.9%.\u003c/p\u003e\n\u003cp\u003eIn the validation cohort, the AUC for diagnosing GC were 0.760 for Actinobacteria, 0.621 for Firmicutes, and 0.760 for the BAF index (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed, Fig. S3b). Applying the same cutoff value (1.604) from the discovery cohort, the sensitivity was 42.7%, the specificity was 100.0%, and the accuracy was 56.0%. The BAF index distinguished patients with pStage I GC from HDs, with an AUC value of 0.621, sensitivity of 33.3%, specificity of 80.0%, and accuracy of 51.6%. (Fig. S3c). Furthermore, a logistic regression analysis showed that the BAF index was an independent factor for GC diagnosis in all patients (odds ratio [OR] 4.40, 95% confidence interval [CI]: 1.39\u0026ndash;13.92, P\u0026thinsp;=\u0026thinsp;0.012) as well as older age (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate analyses for GC diagnosis in the whole cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e4.94 (1.91\u0026ndash;12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e5.45 (1.88\u0026ndash;15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e2.50 (1.01\u0026ndash;6.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e1.92 (0.70\u0026ndash;5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAF index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.604\u003c/p\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e3.57 (1.23\u0026ndash;10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003e4.40 (1.39\u0026ndash;13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;confidence interval; OR\u0026thinsp;=\u0026thinsp;odds ratio.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWe also compared the positivity of conventional tumor markers, CEA and CA19-9, with that of the BAF index in patients with GC by applying the same cutoff value (1.604) from the discovery cohort. The positivity was 33.7% for CEA, 15.7% for CA19-9, and 47.2% for the BAF index in patients with GC (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the patients with pStage I GC, the positivity was 28.2% with CEA, 10.3% with CA19-9, and 33.3% with the BAF index. Given its higher sensitivity and relatively higher diagnostic accuracy, the BAF index shows promise as a screening tool for GC.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePositivity of markers in patients with gastric cancer and healthy donors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGC_all stages\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGC_Stage I\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHDs\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ / -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 / 59\u003c/p\u003e\n \u003cp\u003e(34% / 66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 / 28\u003c/p\u003e\n \u003cp\u003e(28% / 72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA19-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ / -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 / 75\u003c/p\u003e\n \u003cp\u003e(16% / 84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 / 35\u003c/p\u003e\n \u003cp\u003e(10% / 90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAF index*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+ / -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 / 47\u003c/p\u003e\n \u003cp\u003e(47% / 53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 / 26\u003c/p\u003e\n \u003cp\u003e(33% / 67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 / 20\u003c/p\u003e\n \u003cp\u003e(20% / 80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e* The cutoff value for the BAF index was 1.604.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: NE, not evaluated;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThe association of b-DNA profile in serum EVs with GC progression and prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportions of b-DNAs in serum EVs from HDs and patients with GC, stratified by pStage, are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee. As the GC stage advanced, the relative abundance of Bacteroidetes and Actinobacteria gradually decreased, whereas that of Firmicutes increased. Accordingly, the BAF index increased in the advanced stages, showing significant differences between HDs and pStage II (p\u0026thinsp;=\u0026thinsp;0.01) and pStage III (p\u0026thinsp;=\u0026thinsp;0.004) and between pStage I and pStage III (p\u0026thinsp;=\u0026thinsp;0.045) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef). When patients with GC were divided into two groups based on the median value of the BAF index value, RFS was significantly better, and OS tended to be better in patients with a low BAF index than in those with a high BAF index (5-years RFS rate: 91.3% vs. 66.5%; p\u0026thinsp;=\u0026thinsp;0.042, 5-years OS rate: 84.2% vs. 64.9%; p\u0026thinsp;=\u0026thinsp;0.19) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eg, Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association of b-DNA profile in serum EVs with intratumoral immune cell status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 87 patients with GC, fresh tumor tissue samples were available for 67 patients, from which tumor-infiltrating lymphocytes (TILs) were isolated and analyzed using flow cytometry (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The frequencies of PD-1 and CD103 expression on CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003eFoxP3\u003csup\u003e\u0026minus;\u003c/sup\u003e T cells, and CD4\u003csup\u003e+\u003c/sup\u003eFoxP3\u003csup\u003e+\u003c/sup\u003e regulatory T cells (Tregs) were assessed, and the relationship between b-DNA profiles and TIL characteristics was evaluated. PD-1 expression was significantly higher in CD4\u003csup\u003e+\u003c/sup\u003eFoxP3\u003csup\u003e\u0026minus;\u003c/sup\u003e T cells and lower in Tregs of patients with high \u003cem\u003eBacteroidetes\u003c/em\u003e abundance, whereas the opposite trend was observed in patients with high \u003cem\u003eFirmicutes\u003c/em\u003e abundance (Fig. S4a). Furthermore, patients with a high BAF index also showed significantly increased frequency of PD-1 expression in CD4\u003csup\u003e+\u003c/sup\u003eFoxP3\u003csup\u003e\u0026minus;\u003c/sup\u003e T cells and decreased frequency in Tregs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Because PD-1 expression in T cells generally indicates an exhausted status, these findings suggest that a high BAF index may be associated with an immunosuppressive tumor microenvironment, potentially contributing to unfavorable clinical outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated the clinical significance of bacterial profiles in serum EVs from patients with GC. The DNA composition in serum EVs from patients with GC differed markedly from that of HDs, showing consistently higher levels of \u003cem\u003eFirmicutes\u003c/em\u003e and lower levels of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e in both the discovery and validation cohorts. Based on these three bacterial signatures, we developed a BAF index that enables the highly sensitive detection of GC. A high BAF index is significantly associated with advanced tumor stage and poor prognosis, potentially reflecting an immunosuppressive tumor microenvironment.\u003c/p\u003e\u003cp\u003eEVs are universal intercellular signaling vehicles found in both eukaryotes and bacterial systems [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent studies have identified bacterial components in the human bloodstream [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and EVs containing these components have been reported to modulate systemic immune responses [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, we previously examined the relationship between b-DNA information in EVs and tumor immune status, focusing on its association with immunotherapy efficacy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In urothelial carcinoma, lower levels of \u003cem\u003eFirmicutes\u003c/em\u003e in serum EVs are correlated with increased infiltration and activation of tumor-infiltrating T cells, resulting in an improved response to anti-PD-1 therapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, in renal cell carcinoma, lower levels of \u003cem\u003eBacteroidia\u003c/em\u003e in serum EVs were associated with a lower abundance of tumor-infiltrating Tregs, contributing to the enhanced therapeutic efficacy of anti-PD-1 therapy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings suggest that specific b-DNA signatures in serum EVs may reflect the tumor immune status and predict the response to immunotherapy. Experimentally, EVs derived from \u003cem\u003eCutibacterium acnes\u003c/em\u003e have been shown to promote tumor growth in renal cell carcinoma both in vitro and in vivo, and \u003cem\u003eC. acnes\u003c/em\u003e DNA has been detected in serum EVs of patients with renal cell carcinoma [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These results imply that EVs released by specific bacteria within tumor tissues may contribute to tumor progression. In this study, specific b-DNA signatures in serum EVs from patients with GC were associated with an immunosuppressive tumor microenvironment. However, the correlation with immunotherapy efficacy could not be evaluated because of the lack of data from patients treated with immunotherapy. Further investigation is warranted to determine whether b-DNA profiles in serum EVs from patients with GC are predictive of the response to immunotherapy.\u003c/p\u003e\u003cp\u003eIn our previous study, b-DNA profiles from various samples, including serum EVs and stools, were analyzed using 16S RNA sequencing. These findings suggest that the bacterial content of serum EVs may reflect that of the gut microbiota [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consistent with this, our results showed an increased proportion of \u003cem\u003eFirmicutes\u003c/em\u003e and decreased proportions of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e in the b-DNA content of serum EVs. Previous studies on the gastric microbiota of patients with GC have reported enrichment of \u003cem\u003eFirmicutes\u003c/em\u003e and a decrease in \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings support the possibility that EVs originate from the gastrointestinal tract and subsequently enter the bloodstream.\u003c/p\u003e\u003cp\u003eOne of the main challenges in GC screening is the low sensitivity of conventional tumor markers. Reported sensitivity ranges from 16\u0026ndash;68% for CEA and 14\u0026ndash;68% for CA19-9 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In our cohort, the BAF index demonstrated a higher sensitivity than CEA and CA19-9. For patients with pStage I GC, the sensitivity of the BAF index was better than that of the CEA and CA19-9. These results indicate that the BAF index offers superior diagnostic performance compared with conventional tumor markers, particularly in early-stage GC. Therefore, the BAF index may serve as a promising screening tool for the early detection of GC.\u003c/p\u003e\u003cp\u003eThis study had some limitations. First, it was a retrospective study with a small cohort size. Large-scale multicenter studies are required to validate the findings of the present study. Second, detailed background information on HDs was not examined in this study. Comprehensive profiling of patients with HDs, including information on comorbidities, should be considered for a more accurate assessment of the screening performance of the BAF index.\u003c/p\u003e\u003cp\u003eIn conclusion, the bacterial profile of serum EVs may enable minimally invasive and highly sensitive diagnosis of GC and prognostic prediction in patients with GC. Further large-scale validation studies are warranted to confirm these findings and explore their clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Department of Clinical Research in Tumor Immunology, Graduate School of Medicine, Osaka University has a joint research laboratory with Shionogi \u0026amp; Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kaoru Fujikawa, Takuro Saito, Atsunari Kawashima, Kentaro Jingushi. The first draft of the manuscript was written by Kaoru Fujikawa and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Osaka (Date.2014.01.06. / No. 13266-39.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209\u0026ndash;249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe CZ, Zhang KH, Li Q, Liu XH, Hong Y, Lv NH (2013) Combined use of AFP, CEA, CA125 and CAl9-9 improves the sensitivity for the diagnosis of gastric cancer. BMC Gastroenterol 13:87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-230x-13-87\u003c/span\u003e\u003cspan address=\"10.1186/1471-230x-13-87\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang Y, Wang W, Fang C, Raj SS, Hu WM, Li QW, Zhou ZW (2016) Clinical significance and diagnostic value of serum CEA, CA19-9 and CA72-4 in patients with gastric cancer. Oncotarget 7:49565\u0026ndash;49573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.10391\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.10391\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShimada H, Noie T, Ohashi M, Oba K, Takahashi Y (2014) Clinical significance of serum tumor markers for gastric cancer: a systematic review of literature by the Task Force of the Japanese Gastric Cancer Association. Gastric Cancer 17:26\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10120-013-0259-5\u003c/span\u003e\u003cspan address=\"10.1007/s10120-013-0259-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePushalkar S, Hundeyin M, Daley D, Zambirinis CP, Kurz E, Mishra A, Mohan N, Aykut B, Usyk M, Torres LE, Werba G, Zhang K, Guo Y, Li Q, Akkad N, Lall S, Wadowski B, Gutierrez J, Kochen Rossi JA, Herzog JW, Diskin B, Torres-Hernandez A, Leinwand J, Wang W, Taunk PS, Savadkar S, Janal M, Saxena A, Li X, Cohen D, Sartor RB, Saxena D, Miller G (2018) The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov 8:403\u0026ndash;416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/2159-8290.Cd-17-1134\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.Cd-17-1134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, Rotter-Maskowitz A, Weiser R, Mallel G, Gigi E, Meltser A, Douglas GM, Kamer I, Gopalakrishnan V, Dadosh T, Levin-Zaidman S, Avnet S, Atlan T, Cooper ZA, Arora R, Cogdill AP, Khan MAW, Ologun G, Bussi Y, Weinberger A, Lotan-Pompan M, Golani O, Perry G, Rokah M, Bahar-Shany K, Rozeman EA, Blank CU, Ronai A, Shaoul R, Amit A, Dorfman T, Kremer R, Cohen ZR, Harnof S, Siegal T, Yehuda-Shnaidman E, Gal-Yam EN, Shapira H, Baldini N, Langille MGI, Ben-Nun A, Kaufman B, Nissan A, Golan T, Dadiani M, Levanon K, Bar J, Yust-Katz S, Barshack I, Peeper DS, Raz DJ, Segal E, Wargo JA, Sandbank J, Shental N, Straussman R (2020) The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science 368:973\u0026ndash;980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aay9189\u003c/span\u003e\u003cspan address=\"10.1126/science.aay9189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu A, Yao B, Dong T, Chen Y, Yao J, Liu Y, Li H, Bai H, Liu X, Zhang Y, Wang C, Guo Y, Li N, Cai S (2022) Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell 185:1356\u0026ndash;1372e1326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2022.02.027\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2022.02.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBender MJ, McPherson AC, Phelps CM, Pandey SP, Laughlin CR, Shapira JH, Medina Sanchez L, Rana M, Richie TG, Mims TS, Gocher-Demske AM, Cervantes-Barragan L, Mullett SJ, Gelhaus SL, Bruno TC, Cannon N, McCulloch JA, Vignali DAA, Hinterleitner R, Joglekar AV, Pierre JF, Lee STM, Davar D, Zarour HM, Meisel M (2023) Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment. Cell 186:1846\u0026ndash;1862e1826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2023.03.011\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2023.03.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung IY, Kim J, Koh A (2024) The Microbiome Matters: Its Impact on Cancer Development and Therapeutic Responses. J Microbiol 62:137\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12275-024-00110-7\u003c/span\u003e\u003cspan address=\"10.1007/s12275-024-00110-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarshall BJ, Warren JR (1984) Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet 1:1311\u0026ndash;1315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(84)91816-6\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(84)91816-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFukase K, Kato M, Kikuchi S, Inoue K, Uemura N, Okamoto S, Terao S, Amagai K, Hayashi S, Asaka M (2008) Effect of eradication of Helicobacter pylori on incidence of metachronous gastric carcinoma after endoscopic resection of early gastric cancer: an open-label, randomised controlled trial. Lancet 372:392\u0026ndash;397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(08)61159-9\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(08)61159-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang F, Meng W, Wang B, Qiao L (2014) Helicobacter pylori-induced gastric inflammation and gastric cancer. Cancer Lett 345:196\u0026ndash;202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.canlet.2013.08.016\u003c/span\u003e\u003cspan address=\"10.1016/j.canlet.2013.08.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagase H, Takeoka T, Urakawa S, Morimoto-Okazawa A, Kawashima A, Iwahori K, Takiguchi S, Nishikawa H, Sato E, Sakaguchi S, Mori M, Doki Y, Wada H (2017) ICOS(+) Foxp3(+) TILs in gastric cancer are prognostic markers and effector regulatory T cells associated with Helicobacter pylori. Int J Cancer 140:686\u0026ndash;695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ijc.30475\u003c/span\u003e\u003cspan address=\"10.1002/ijc.30475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUrakawa S, Yamasaki M, Makino T, Kurokawa Y, Yamamoto K, Goto K, Haruna M, Hirata M, Morimoto-Okazawa A, Kawashima A, Iwahori K, Mizushima T, Sato E, Mori M, Doki Y, Wada H (2021) The impact of ICOS(+) regulatory T cells and Helicobacter pylori infection on the prognosis of patients with gastric and colorectal cancer: potential prognostic benefit of pre-operative eradication therapy. Cancer Immunol Immunother 70:443\u0026ndash;452. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00262-020-02696-4\u003c/span\u003e\u003cspan address=\"10.1007/s00262-020-02696-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang CS, Ban M, Choi EJ, Moon HG, Jeon JS, Kim DK, Park SK, Jeon SG, Roh TY, Myung SJ, Gho YS, Kim JG, Kim YK (2013) Extracellular vesicles derived from gut microbiota, especially Akkermansia muciniphila, protect the progression of dextran sulfate sodium-induced colitis. PLoS ONE 8:e76520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0076520\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0076520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Sci 367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aau6977\u003c/span\u003e\u003cspan address=\"10.1126/science.aau6977\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDorward DW, Schwan TG, Garon CF (1991) Immune capture and detection of Borrelia burgdorferi antigens in urine, blood, or tissues from infected ticks, mice, dogs, and humans. J Clin Microbiol 29:1162\u0026ndash;1170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/jcm.29.6.1162-1170.1991\u003c/span\u003e\u003cspan address=\"10.1128/jcm.29.6.1162-1170.1991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones EJ, Booth C, Fonseca S, Parker A, Cross K, Miquel-Clop\u0026eacute;s A, Hautefort I, Mayer U, Wileman T, Stentz R, Carding SR (2020) The Uptake, Trafficking, and Biodistribution of Bacteroides thetaiotaomicron Generated Outer Membrane Vesicles. Front Microbiol 11:57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2020.00057\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.00057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomiyama E, Matsuzaki K, Fujita K, Shiromizu T, Narumi R, Jingushi K, Koh Y, Matsushita M, Nakano K, Hayashi Y, Wang C, Ishizuya Y, Kato T, Hatano K, Kawashima A, Ujike T, Uemura M, Takao T, Adachi J, Tomonaga T, Nonomura N (2021) Proteomic analysis of urinary and tissue-exudative extracellular vesicles to discover novel bladder cancer biomarkers. Cancer Sci 112:2033\u0026ndash;2045. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cas.14881\u003c/span\u003e\u003cspan address=\"10.1111/cas.14881\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJingushi K, Kawashima A, Saito T, Kanazawa T, Motooka D, Kimura T, Mita M, Yamamoto A, Uemura T, Yamamichi G, Okada K, Tomiyama E, Koh Y, Matsushita M, Kato T, Hatano K, Uemura M, Tsujikawa K, Wada H, Nonomura N (2022) Circulating extracellular vesicles carrying Firmicutes reflective of the local immune status may predict clinical response to pembrolizumab in urothelial carcinoma patients. Cancer Immunol Immunother 71:2999\u0026ndash;3011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00262-022-03213-5\u003c/span\u003e\u003cspan address=\"10.1007/s00262-022-03213-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUemura T, Kawashima A, Jingushi K, Motooka D, Saito T, Nesrine S, Oka T, Okuda Y, Yamamoto A, Yamamichi G, Tomiyama E, Ishizuya Y, Yamamoto Y, Kato T, Hatano K, Tsujikawa K, Wada H, Nonomura N (2023) Bacteria-derived DNA in serum extracellular vesicles are biomarkers for renal cell carcinoma. Heliyon 9:e19800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e19800\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e19800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e(2011) Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer 14:101\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10120-011-0041-5\u003c/span\u003e\u003cspan address=\"10.1007/s10120-011-0041-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLauren P, THE TWO HISTOLOGICAL MAIN TYPES OF GASTRIC CARCINOMA: DIFFUSE AND SO-CALLED INTESTINAL-TYPE CARCINOMA. AN ATTEMPT AT A HISTO-CLINICAL CLASSIFICATION (1965) Acta Pathol Microbiol Scand 64:31\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/apm.1965.64.1.31\u003c/span\u003e\u003cspan address=\"10.1111/apm.1965.64.1.31\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026auml;sser C, Eldh M, L\u0026ouml;tvall J (2012) Isolation and characterization of RNA-containing exosomes. J Vis Exp:e3037. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3791/3037\u003c/span\u003e\u003cspan address=\"10.3791/3037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFutse JE, Buami G, Kayang BB, Koku R, Palmer GH, Gra\u0026ccedil;a T, Noh SM (2019) Sequence and immunologic conservation of Anaplasma marginale OmpA within strains from Ghana as compared to the predominant OmpA variant. PLoS ONE 14:e0217661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0217661\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0217661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuehn MJ, Kesty NC (2005) Bacterial outer membrane vesicles and the host-pathogen interaction. Genes Dev 19:2645\u0026ndash;2655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gad.1299905\u003c/span\u003e\u003cspan address=\"10.1101/gad.1299905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEllis TN, Kuehn MJ (2010) Virulence and immunomodulatory roles of bacterial outer membrane vesicles. Microbiol Mol Biol Rev 74:81\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mmbr.00031-09\u003c/span\u003e\u003cspan address=\"10.1128/mmbr.00031-09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJingushi K, Kawashima A, Tanikawa S, Saito T, Yamamoto A, Uemura T, Sassi N, Ishizuya Y, Yamamoto Y, Kato T, Hatano K, Hase H, Nonomura N, Tsujikawa K (2024) Cutibacterium acnes-derived extracellular vesicles promote tumor growth in renal cell carcinoma. Cancer Sci 115:2578\u0026ndash;2587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cas.16202\u003c/span\u003e\u003cspan address=\"10.1111/cas.16202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu G, Torres J, Hu N, Medrano-Guzman R, Herrera-Goepfert R, Humphrys MS, Wang L, Wang C, Ding T, Ravel J, Taylor PR, Abnet CC, Goldstein AM (2017) Molecular Characterization of the Human Stomach Microbiota in Gastric Cancer Patients. Front Cell Infect Microbiol 7:302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2017.00302\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2017.00302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGunathilake M, Lee J, Choi IJ, Kim YI, Yoon J, Sul WJ, Kim JF, Kim J (2020) Alterations in Gastric Microbial Communities Are Associated with Risk of Gastric Cancer in a Korean Population: A Case-Control Study. Cancers (Basel) 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers12092619\u003c/span\u003e\u003cspan address=\"10.3390/cancers12092619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePng CW, Lee WJJ, Chua SJ, Zhu F, Yeoh KG, Zhang Y (2022) Mucosal microbiome associates with progression to gastric cancer. Theranostics 12:48\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.65302\u003c/span\u003e\u003cspan address=\"10.7150/thno.65302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"16S rRNA gene, bacterial DNA, extracellular vesicles, gastric cancer, tumor-infiltrating lymphocyte, T cell exhaustion","lastPublishedDoi":"10.21203/rs.3.rs-7281626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7281626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBacterial flora is present in various parts of the human body, and recent studies have detected bacterial 16S rRNA genes in the bloodstream. Distinct blood microbiomes have been identified in various diseases, including cancer, and are thought to play a role in disease pathogenesis. In this study, we conducted a 16S rRNA metagenomic analysis of serum extracellular vesicles from 89 patients with gastric cancer (GC) and 15 healthy donors and identified lower levels of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e and higher levels of \u003cem\u003eFirmicutes\u003c/em\u003e in patients with GC than in healthy donors. By integrating this characteristic bacterial DNA profile, we developed a BAF index, defined as the ratio of \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e to \u003cem\u003eFirmicutes\u003c/em\u003e, which exhibited high sensitivity for detecting GC in both the discovery and validation cohorts, suggesting its potential utility as a screening tool. A high BAF index was significantly associated with an advanced tumor stage and poor prognosis. Moreover, a high BAF index was linked to an immunosuppressive tumor microenvironment, which may contribute to the unfavorable outcomes observed in these patients. These findings indicate that circulating bacterial signatures may serve as promising biomarkers for GC.\u003c/p\u003e","manuscriptTitle":"Bacteria-derived DNA in serum extracellular vesicles as a biomarker for gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 16:20:42","doi":"10.21203/rs.3.rs-7281626/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T06:49:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T02:23:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T03:10:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179822269210323493865736594253148724878","date":"2025-08-14T02:30:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151097003305829286312756900934435028529","date":"2025-08-11T08:55:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59082659509162599608794485865620245859","date":"2025-08-09T08:42:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270825091192019872155318907630852143768","date":"2025-08-06T16:28:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T07:11:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T13:56:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T13:56:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2025-08-03T06:33:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ce5e585b-83bd-43d4-ba2d-f518e204f77e","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:24:10+00:00","versionOfRecord":{"articleIdentity":"rs-7281626","link":"https://doi.org/10.1007/s00262-025-04175-0","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2025-10-24 16:17:12","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-08-08 16:20:42","video":"","vorDoi":"10.1007/s00262-025-04175-0","vorDoiUrl":"https://doi.org/10.1007/s00262-025-04175-0","workflowStages":[]},"version":"v1","identity":"rs-7281626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7281626","identity":"rs-7281626","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}