Metabolic and functional pathways of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08)

Metabolic and functional pathways of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08) | 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 Article Metabolic and functional pathways of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08) RYO MATOBA, Hiroshi Iijima, Yasuhiro Sakamoto, Ryohei Kawabata, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7899663/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract We analyzed the differences in bacterial composition between 475 Japanese patients with advanced gastric cancer (median age 70 years, median BMI 20.0) and 106 healthy individuals using a comprehensive metagenome shotgun analysis. Among the patients with advanced gastric cancer, 71% were male, 37.% patients had relapsed, and 55.5% previously underwent gastrectomy. Bifidobacterium , Anaerostipes , and Parabacteroides were predominant in healthy individuals, whereas Streptococcus , Lactobacillus , and Odoribacter were predominant in patients with advanced gastric cancer. Additionally, Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that butanoate and pyruvate metabolism was enriched in healthy individuals, whereas factors such as ABC transporters and ribosomes were enriched in patients with advanced gastric cancer. Clustering analysis broadly classified patients with advanced gastric cancer and healthy individuals into two clusters; however, clustering using pathway data enabled a clearer classification of patients with advanced gastric cancer and healthy individuals than clustering using flora analysis. Moreover, healthy individuals showed higher bacterial flora diversity than patients with advanced gastric cancer. Thus, we successfully identified the molecular characteristics of microbial genera and functional pathways in the gut of patients with advanced gastric cancer. Our results suggest that fecal metagenomic shotgun sequencing analysis can be used to detect gastric cancer. Biological sciences/Cancer Health sciences/Gastroenterology Biological sciences/Microbiology Health sciences/Oncology gastric cancer fecal microbiome metagenomic shotgun functional pathway molecular diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Microbiome research and development has been gaining momentum worldwide in recent decades and is expected to a promising tool for understanding "personalized medicine," as represented by the Precision Medicine Initiative. Additionally, microbiome analysis will become important as a tool to monitor the state of health before illness occurs 1 , 2 . Particularly, gut microbiota analysis is known to be closely related to autoimmunity. Proteins, metabolites, and other substances produced by gut bacteria affect the immune cells in the gut, which in turn strongly influence autoimmunity 3 – 7 . Therefore, it may be possible to predict individual health conditions by examining gut microbiota. Moreover, specific intestinal microbiota and their metabolites are correlated with the efficacy of immunotherapy 8 – 11 . Furthermore, recent advances in NGS technology have been remarkable, resulting in large amounts of sequencing data that can be obtained simultaneously. Microbiome sequencing can be performed in two ways: 16S ribosomal RNA analysis for the identification of bacterial species, and metagenome shotgun analysis to obtain a large amount of random sequencing data. Although the metagenome shotgun method requires a large amount of sequence data, it provides information on bacterial species, diversity, non-bacterial (viruses, eukaryotic microorganisms, etc.) information, and functional data such as metabolic enzymes from the sequence information 12 13 . This study aimed to identify patient-specific microbiome markers in patients with gastric cancer by performing a comprehensive metagenome shotgun analysis of the intestinal microbiota in patients with gastric cancer compared to fecal microbiome data from healthy individuals. Results Cancer Patient and Health characteristics Overall, 501 patients were enrolled in the DELIVER trial (JACCRO GC-08) from 67 institutes between March 2018 and August 2019. Of them, samples from 487 patients were available for clinical data, and biomarker analysis, and 475 fecal samples were available for genome shotgun sequencing. Metagenomic data were obtained for each sample, with a minimum of 5 Gbp, and an average mapping rate of 98.2%. The same metagenomic data for 106 healthy individuals were analyzed from clinical data and the Fastq files, using the database from a previously published paper 14 . Although background comparisons of all patients with gastric cancer and healthy controls showed no bias in BMI, it showed differences in age and sex (Table 1 ). Analysis of the metagenomic shotgun data revealed 36 phyla and 1122 genera (Supplemental table 1 and 2). Functional pathways were classified based on sequencing data, and 350 pathways were identified. Using the 36 different phyla, we compared data from gastric cancer patients and healthy controls but did not find any significant differences (Fig. 1 ). Profiling of differences between Cancer patients and healthy individuals We performed a comparative analysis between patients with gastric cancer and healthy individuals using genus data, for whom more than 80% of the data were available. The results, although unclear, showed a tendency for healthy individuals and patients with GC to be divided into several clusters (Fig. 2 a, Supplemental table 3 and Supplemental Fig. 1). Subsequently, we compared the genera between patients with gastric cancer and healthy controls and found Anaerostipes , Bifidobacterium , and Parabacteroides were significantly higher in healthy individuals. Whereas, Odoribacter , Lactobacillus , and Streptococcus were significantly more abundant in patients with cancer. (Fig. 2 b) Specific bacteria and functional pathway of Cancer Patients Next, a similar analysis was performed focusing on functional pathways, with 234 pathways for which the expressed data were available for more than 80% of the samples (Supplemental table 4 ). A cluster analysis was performed using the expression values of these pathways. The results showed that the expression patterns of the pathways clearly distinguished healthy individuals from patients with gastric cancer (Fig. 3 a, Supplemental table 5 and Supplemental Fig. 2), compared to the analysis using the genus. This implies that functional pathways in healthy individuals and patients with gastric cancer are different. Furthermore, the results showed that genes related to the butanoate and pyruvate metabolism pathways were downregulated in patients with gastric cancer, while genes related to the ABC transporter pathway and ribosome synthesis were upregulated in patients with gastric cancer compared to healthy controls (Fig. 3 b). Also, clinical information on gastric cancer patients, such as ECOG performance score, diagnosis status, gastrectomy status and Helicobacter pylori infection status, was analyzed for its association with the gut microbiota. Although Eubacterium and Streptococcus were found to be associated with ECOG status, Lactobacillus and Veillonella with diagnosis status, and Veillonella , Alistipes , and Ribosome of pathway with gastrectomy, there were no definite difference in the amount of expression was observed (Supplemental Fig. 3). Interestingly, Streptococcus , Lactobacillus , Alistipes , Bifidobacterium and Parabacteroides were more abundant in the presence of H. pylori infection patents, but there was no significant difference, statistically. Analysis of age-sex matched pair samples Although differences were identified in fecal samples from patients with gastric cancer and healthy controls, our analyses showed that the age distributions of patients with gastric cancer and healthy controls were different. Therefore, we examined whether the markers obtained would remain significant when the 73 patients with gastric cancer and 24 healthy subjects were matched by age and sex (Table 2). The results showed that all six genus and four pathway markers were statistically significant and showed different expression profiles in the fecal analysis of patients with gastric cancer and healthy individuals (Fig. 4 a, b). Furthermore, these age- and sex-matched samples were analyzed for the Complexity of Genus. Shannon analysis revealed that healthy individuals had higher bacterial flora diversity than patients with advanced gastric cancer (Fig. 4 c). Discussion This study aimed to identify patient-specific microbiome markers in patients with gastric cancer and found that genes related to the butanoate and pyruvate metabolic pathways were downregulated in patients with gastric cancer. Butyrate is a typical short-chain fatty acid implicated in cancer immunotherapy efficacy in prostate cancer 15 , 16 . Whereas, pyruvic acid is a key component of the metabolic pathway network, and has been implicated in cancer growth 17 , 18 . The activities of these metabolic pathways are decreased in patients with gastric cancer. In contrast the ABC transporter pathway was found to be elevated in patients with gastric cancer. This pathway is supposedly related to single nucleotide polymorphisms and anti-cancer chemotherapy effects, and is activated in patients gastric cancer. Additionally, its expression level has been reported to be related to malignancy in pancreatic cancer 19 . Furthermore, activation of ribosome synthesis indicates ribosomal protein gene copy number changes and abnormal expression, suggesting that the nucleosome is in a stress-activated state. Our analysis revealed that Anaerostipes , Bifidobacterium , and Parabacteroides were significantly higher in healthy individuals, while Odoribacter , Lactobacillus , and Streptococcus were significantly more abundant in patients with cancer. Anaerostipes and Bifidobacterium are more common in healthy individuals than in patients with colorectal cancer 20 . Odoribacter , which belongs to the order Bacteroidales , is a common short-chain fatty acid-producing member of the human intestinal microbiota 21 . Decreased abundance of Odoribacter has been linked to different microbiota-associated diseases, such as non-alcoholic fatty liver disease, cystic fibrosis, and inflammatory bowel disease 22 . Parabacteroides are secondary bile acid-producing bacteria that may be beneficial for longevity. Notably, Lactobacillus are also found more frequently in patients with gastric cancer, as previous studies have reported an increase in intestinal Lactobacillus in the presence of H. pylori infection 23 . Thus, the decrease in gastric acid secretion associated with gastric mucosal atrophy may have been affected. Streptococcus and Lactobacillus are also increase in the intestinal microbiota of patients with pancreatic cancer, and there may be a functional relationship between them 24 . We analyzed whether there was any correlation between gut microbiome and clinicopathologic, and found that several genus and pathway were related significantly. Lactobacillus was abundant in ‘advanced’ than ‘relapse’ and Veillonella was abundant in ‘relapse’ than ‘advanced’ in cancer patients. Veillonella was also abundant in previous gastrectomy, those data suggested to be related to salivary microbiome 25 . Currently, 16S rRNA analysis, which is a method for classifying and identifying bacteria at the species level, is the mainstream method for analyzing the microbiome. However, metagenomic analysis, which analyzes all genomic DNA-encoding genes, is expected to become crucial in the following years. Although metagenomic analysis is time-consuming and costly, it can provide information on both phylogenetic and functional composition. Thus, it is possible to analyze the functional genetic information of the microbiome using metagenomic analysis. Recent studies have reported an association between the efficacy of cancer immunotherapy and intestinal microbiota, suggesting that microbiota analysis could play an important role in the selection of specific treatment modalities. Although data acquisition has just begun, we hope that microbiome testing will be incorporated into health checkup menus in the near future. Conclusion We identified the molecular characteristics of the microbial genera and functional pathways in the gut of patients with advanced gastric cancer. Bifidobacterium, Anaerostipes , and Parabacteroides were predominant in the healthy individuals, whereas Streptococcus, Lactobacillus , and Odoribacter were predominant in patients with advanced gastric cancer. Furthermore, pathway analysis showed that butanoate and pyruvate metabolism were enriched in the healthy individuals, whereas factors such as ABC transporters and ribosomes were enriched in the patients with advanced gastric cancer. Cluster analysis broadly classified patients with advanced gastric cancer and healthy individuals, and clustering using pathway data enabled a clearer classification of patients with advanced gastric cancer and healthy individuals than clustering using flora analysis. This analysis represents characteristics of a specific group of gastric cancer patients and therefore provides limited results. In order to generalize these findings, it is believed that analysis data from a larger number of gastric cancer patients would be necessary. Materials and methods Sample collection This study enrolled patients with advanced or recurrent unresectable gastric or gastroesophageal junction cancer that was histologically confirmed to be adenocarcinoma. Fecal samples were collected by patients before treatment with nivolumab (in DELIVER trial), at home immediately after evacuation, using scoop collection tubes containing 3 mL GuSCN solution from the TechnoSuruga Laboratory 26 . DNA extraction and metagenome shotgun sequencing Genomic information in fecal samples was measured by genome shotgun sequencing. We extracted DNA from the feces of patients with advanced gastric cancer enrolled in an observational/translational study, the DELIVER trial (JACCRO GC-08: UMIN000030850), which evaluated the clinical outcomes of nivolumab and developed host-related biomarkers for nivolumab in 500 patients with advanced gastric cancer. Briefly, metagenomic DNA was extracted using zirconia beads added to the specimens. The bacteria were crushed (5 m/s, 2 min), purified and eluted using magnetic beads 26 . DNA quality was assessed by agarose gel electrophoresis, and the concentration was confirmed using a NanoPhotometer spectrophotometer (IMPLEN, CA, USA) and a Qubit 2.0 Flurometer (Life Technologies, CA, USA). Sequence libraries were prepared with the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, MA, USA) using 1 µg of genomic DNA. The AMPure XP system was used for library purification and the quality of the libraries was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Sequencing was performed on a NovaSeq6000 (Illumina, Inc., San Diego, CA) with 150 bp paired ends. Data analysis Subsequently, genome annotation, assignment of bacterial strains, assignment of functions, and metabolic pathways were performed using custom pipelines implemented in Bash and Python scripts. This pipeline includes steps for quality control, trimming, assembly, and annotation of metagenomic reads. The quality control step involved assessing the quality of raw reads before trimming using FastQC (version 0.11.8, www.bioinformatics.babraham.ac.uk/projects/fastqc ). The pipeline trimmed the adapter sequences and low-quality bases from the reads using TrimGalore (version 0.6.3; www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ). Contaminant removal included the removal human reads using the BMTagger tool (version 3.101, ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/bmtagger/ ) to ensure the analysis focused on microbial content. Thereafter, the cleaned reads were assembled de novo using SPAdes (version 3.11.1) 27 , and contigs shorter than 500 bp were discarded. Gene prediction and annotation were performed during the gene annotation step using the Prokka software (version 1.14.5) 28 . The annotation process generated files containing predicted genes in the GFF format, which were subsequently converted to the GTF format for further analysis. A gene catalog was the created using DIAMOND (version 0.9.25) 29 by aligning the predicted genes against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. This produced a table of gene counts for each sample, which was used to generate the functional profiles. The reads were mapped to the gene catalog using Bowtie2 (version 2.2.1) 30 , and the results were processed using SAM tools (version 1.3.1) 31 to obtain sorted BAM files. Gene expression levels were quantified using HTSeq count (version 0.11.2) 32 , followed by conversion to TPM (Transcripts Per Million) using a custom Python script. Gene abundance tables were constructed for each sample. Functional annotation and pathway analysis were performed by mapping the predicted genes to KEGG orthology (KO) terms using DIAMOND. Cluster analyses were performed using the MeV tool (webmev.tm4.org). Declarations Data availability The datasets generated during this study are available in the National Center for Biotechnology Information (NCBI) BioProject Repository https://www.ncbi.nlm.nih.gov/bioproject under BioProject : PRJDB20360 (PSUB025696). Acknowledgments We thank the patients, their families, and the investigators who participated in the DELIVER trial (JACCRO GC-08). Competing interests RM and HI are employee of DNA Chip Research Inc. The other authors declare no competing financial interests that may be affected by the research reported in the enclosed paper. Funding This work was supported by Ono Pharmaceutical Co.Ltd., Bristol-Myers Squibb and Japan Clinical Cancer Research Organization (JACCRO). Author contributions Conception and design: RM, HI, WI, MF, and YS. Data collection and assembly: YS, RK, AI, YA, YK, MA, JM, MT, AM, TS, MT, HY, JH, HO, JK, TY, HK, TN, KM, WI, and YS. Data analysis and interpretation: RM, HI and YS. Manuscript writing: All authors Final approval manuscript: All authors Accountable for all aspects of the work: All authors Ethics approval and consent The authors declare that they obtained appropriate Japan Clinical Cancer Research Organization Institutional Review Board approval and followed the principles outlined in the Declaration of Helsinki for all human experimental investigations. Informed consent was obtained from all the participants at each institute. Additional information Correspondence and requests for materials should be addressed to RM, YS. References Lynch, S. V. & Pedersen, O. The Human Intestinal Microbiome in Health and Disease. N Engl. J. Med. 375 , 2369–2379 (2016). Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352 , 565–569 (2016). Amoroso, C. et al. The Role of Gut Microbiota Biomodulators on Mucosal Immunity and Intestinal Inflammation. Cells 9 (2020). Lee, Y. K., Menezes, J. S., Umesaki, Y. & Mazmanian, S. K. Proinflammatory T-cell responses to gut microbiota promote experimental autoimmune encephalomyelitis. Proc. Natl. Acad. Sci. U S A . 108 (Suppl 1), 4615–4622 (2011). Toor, D. et al. Dysbiosis Disrupts Gut Immune Homeostasis and Promotes Gastric Diseases. Int J. Mol. Sci 20 (2019). Macia, L. et al. Microbial influences on epithelial integrity and immune function as a basis for inflammatory diseases. Immunol. Rev. 245 , 164–176 (2012). Luu, M. et al. Regulation of the effector function of CD8(+) T cells by gut microbiota-derived metabolite butyrate. Sci. Rep. 8 , 14430 (2018). Jia, D. et al. Microbial metabolite enhances immunotherapy efficacy by modulating T cell stemness in pan-cancer. Cell 187 , 1651–1665e1621 (2024). Elinav, E., Garrett, W. S., Trinchieri, G. & Wargo, J. The cancer microbiome. Nat. Rev. Cancer . 19 , 371–376 (2019). Park, J. S. et al. Targeting PD-L2-RGMb overcomes microbiome-related immunotherapy resistance. Nature 617 , 377–385 (2023). Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359 , 97–103 (2018). Wensel, C. R., Pluznick, J. L., Salzberg, S. L. & Sears, C. L. Next-generation sequencing: insights to advance clinical investigations of the microbiome. J Clin. Invest 132 (2022). Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469 , 967–977 (2016). Nishijima, S. et al. The gut microbiome of healthy Japanese and its microbial and functional uniqueness. DNA Res. 23 , 125–133 (2016). Murphy, S. et al. Ketogenic Diet Alters the Epigenetic and Immune Landscape of Prostate Cancer to Overcome Resistance to Immune Checkpoint Blockade Therapy. Cancer Res. 84 , 1597–1612 (2024). Lachance, G. et al. The gut microbiome-prostate cancer crosstalk is modulated by dietary polyunsaturated long-chain fatty acids. Nat. Commun. 15 , 3431 (2024). Bader, D. A. et al. Mitochondrial pyruvate import is a metabolic vulnerability in androgen receptor-driven prostate cancer. Nat. Metab. 1 , 70–85 (2019). Vincent, E. E. et al. Mitochondrial Phosphoenolpyruvate Carboxykinase Regulates Metabolic Adaptation and Enables Glucose-Independent Tumor Growth. Mol. Cell. 60 , 195–207 (2015). Cervenkova, L. et al. Protein expression and localization of ABC transporters in pancreatic adenocarcinoma: Prognostic role of ABCC8. Pancreatology . 23, 978–987 (2023). Chen, W., Liu, F., Ling, Z., Tong, X. & Xiang, C. Human intestinal lumen and mucosa-associated microbiota in patients with colorectal cancer. PLoS One . 7 , e39743 (2012). Hiippala, K. et al. Novel Odoribacter splanchnicus Strain and Its Outer Membrane Vesicles Exert Immunoregulatory Effects in vitro. Front. Microbiol. 11 , 575455 (2020). Zhou, J. et al. Programmable probiotics modulate inflammation and gut microbiota for inflammatory bowel disease treatment after effective oral delivery. Nat. Commun. 13 , 3432 (2022). Bühling, A., Radun, D., Müller, W. A. & Malfertheiner, P. Influence of anti-Helicobacter triple-therapy with metronidazole, omeprazole and clarithromycin on intestinal microflora. Aliment. Pharmacol. Ther. 15 , 1445–1452 (2001). Hashimoto, S. et al. Changes in intestinal bacteria and imbalances of metabolites induced in the intestines of pancreatic ductal adenocarcinoma patients in a Japanese population: a preliminary result. Scand. J. Gastroenterol. 58 , 193–198 (2023). Huang, K. et al. Salivary Microbiota for Gastric Cancer Prediction: An Exploratory Study. Frontiers Cell. Infect. Microbiology 11 (2021). Hosomi, K. et al. Method for preparing DNA from feces in guanidine thiocyanate solution affects 16S rRNA-based profiling of human microbiota diversity. Sci. Rep. 7 , 4339 (2017). Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19 , 455–477 (2012). Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30 , 2068–2069 (2014). Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods . 12 , 59–60 (2015). Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods . 9 , 357–359 (2012). Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 , 2078–2079 (2009). Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31 , 166–169 (2015). Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.xlsx Supplemental table 1. Metagenomic shotgun data revealed 36 Phylum species SupplementalTable2.xlsx Supplemental table 2. Metagenomic shotgun data revealed 1122 Genus species SupplementalTable3.xlsx Supplemental table 3. Cluster order matrix of Sample and Genus species in Figure 2(a) SupplementalTable4.xlsx Supplemental table 4. Expressed data from 234 pathways were detected in more than 80% of the samples SupplementalTable5.xlsx Supplemental table 5. Cluster order matrix of Sample and KEGG Pathway in Figure 3(a) SupplementalFigure1.jpg Supplemental Figure 1. High resolution image of Figure 2(a). SupplementalFigure2.jpg Supplemental Figure 2. High resolution image of Figure 3(a) SupplementalFigure3.jpg Supplemental Figure 3. The several genus species and KEGG pathway were correlated with the clinicopathologies. Tables.docx Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers invited by journal 17 Nov, 2025 Editor invited by journal 27 Oct, 2025 Editor assigned by journal 24 Oct, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 24 Oct, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7899663","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550433947,"identity":"d1f9f5c6-f8ab-4dbf-b3f3-98e35e86db12","order_by":0,"name":"RYO MATOBA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYJACZgYGG8YGMMMAImJASANQSxrpWg5DtRADdNvPH3xcUHNedjv7AcbPBQV3GPjbGxiKC/BoMTuTzGw849ht4509CczSMwyeMUicOcBgPAOflgPJbNI8bLcTNxzI/8bMY3CYwUAigcGYB5+W84+BWv6dS9xw/gEbkVpuAG3hbTuQuOFGAtFaHhsbz+xLNt5w4wGzNI/BMx6JMwcb8PvlfOLDxwXf7GQ3nE9g/Mzz544cf3vzMWN8IYYODgCdxNhmTIIOhgMggvkxKVpGwSgYBaNg2AMAVOtKJzxm0OgAAAAASUVORK5CYII=","orcid":"","institution":"DNA Chip Research (Japan)","correspondingAuthor":true,"prefix":"","firstName":"RYO","middleName":"","lastName":"MATOBA","suffix":""},{"id":550433948,"identity":"89e4a83f-f825-4187-968c-e52d99306aa0","order_by":1,"name":"Hiroshi Iijima","email":"","orcid":"","institution":"DNA Chip Research (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Iijima","suffix":""},{"id":550433949,"identity":"87a81074-dd96-4076-bea3-cb3604ad99ec","order_by":2,"name":"Yasuhiro Sakamoto","email":"","orcid":"","institution":"Osaki Citizen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Sakamoto","suffix":""},{"id":550433950,"identity":"eee00044-124f-4f12-acad-9ebce5139460","order_by":3,"name":"Ryohei Kawabata","email":"","orcid":"","institution":"Sakai City Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ryohei","middleName":"","lastName":"Kawabata","suffix":""},{"id":550433951,"identity":"d485c114-a896-4d34-b793-73a7323fed07","order_by":4,"name":"Atsushi Ishiguro","email":"","orcid":"","institution":"Teine Keijinkai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Ishiguro","suffix":""},{"id":550433952,"identity":"4932c28e-c93c-47b8-ad6b-33ce09f0b2c5","order_by":5,"name":"Yusuke Akamaru","email":"","orcid":"","institution":"Osaka Rosai Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Akamaru","suffix":""},{"id":550433953,"identity":"aa6e8085-16ef-4a91-b779-c2a7aee66695","order_by":6,"name":"Yosuke Kito","email":"","orcid":"","institution":"Ishikawa Prefectural Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yosuke","middleName":"","lastName":"Kito","suffix":""},{"id":550433954,"identity":"78de40d0-b5b3-45bb-9f06-3c3341186827","order_by":7,"name":"Masaki Aizawa","email":"","orcid":"","institution":"Niigata Cancer Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Masaki","middleName":"","lastName":"Aizawa","suffix":""},{"id":550433955,"identity":"dd84735f-b462-48fa-a0dd-2fbc17284de7","order_by":8,"name":"Jin Matsuyama","email":"","orcid":"","institution":"Higashiosaka City medical center","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Matsuyama","suffix":""},{"id":550433956,"identity":"cf63a550-b339-46f1-9ef9-61791061f96b","order_by":9,"name":"Masazumi Takahashi","email":"","orcid":"","institution":"Yokohama Municipal Citizen's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Masazumi","middleName":"","lastName":"Takahashi","suffix":""},{"id":550433957,"identity":"b55c816d-b5b6-430f-8363-56e19748b332","order_by":10,"name":"Akitaka Makiyama","email":"","orcid":"","institution":"Gifu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Akitaka","middleName":"","lastName":"Makiyama","suffix":""},{"id":550433958,"identity":"d4c9c961-e690-4e18-aa28-56a5547a9faf","order_by":11,"name":"Takahisa Suzuki","email":"","orcid":"","institution":"Kure Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Takahisa","middleName":"","lastName":"Suzuki","suffix":""},{"id":550433959,"identity":"1b955557-d25e-438b-ab50-1360cf01c890","order_by":12,"name":"Masahiro Tsuda","email":"","orcid":"","institution":"Hyogo Prefectural Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Tsuda","suffix":""},{"id":550433960,"identity":"d5ad272f-18a7-4e57-84c3-d60c27c9e1bb","order_by":13,"name":"Hisateru Yasui","email":"","orcid":"","institution":"Kobe City Medical Center General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hisateru","middleName":"","lastName":"Yasui","suffix":""},{"id":550433961,"identity":"2fe3c229-90d2-4345-9c86-2ea4840151ad","order_by":14,"name":"Jun Hihara","email":"","orcid":"","institution":"Hiroshima City Asa Citizens Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Hihara","suffix":""},{"id":550433962,"identity":"02f4bdad-8068-4cd4-946d-5b78bbb64ce1","order_by":15,"name":"Hiroyuki Okuda","email":"","orcid":"","institution":"Keiyukai Sapporo Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Okuda","suffix":""},{"id":550433963,"identity":"3e5b681f-0f26-4f60-b185-99e9942746fb","order_by":16,"name":"Junji Kawada","email":"","orcid":"","institution":"Yao Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junji","middleName":"","lastName":"Kawada","suffix":""},{"id":550433964,"identity":"ac08688a-cf60-46a8-958b-a213b0a3145a","order_by":17,"name":"Takashi Yoshioka","email":"","orcid":"","institution":"Yamagata University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Yoshioka","suffix":""},{"id":550433965,"identity":"bdc9989c-19f5-431c-9775-3c8e926af1f2","order_by":18,"name":"Hisato Kawakami","email":"","orcid":"","institution":"Tohoku University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hisato","middleName":"","lastName":"Kawakami","suffix":""},{"id":550433966,"identity":"177555c3-7ba8-4529-9c8f-b5ffc6d15ed0","order_by":19,"name":"Takako Eguchi Nakajima","email":"","orcid":"","institution":"Kyoto University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Takako","middleName":"Eguchi","lastName":"Nakajima","suffix":""},{"id":550433967,"identity":"6380bcc5-1a7d-440d-aad8-6131644b840e","order_by":20,"name":"Kei Muro","email":"","orcid":"","institution":"Aichi Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Kei","middleName":"","lastName":"Muro","suffix":""},{"id":550433968,"identity":"b3b12410-56fb-48b5-9af2-8cc5ca19867b","order_by":21,"name":"Wataru Ichikawa","email":"","orcid":"","institution":"Showa University Fujigaoka Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wataru","middleName":"","lastName":"Ichikawa","suffix":""},{"id":550433969,"identity":"3a280773-c24f-4e9c-afb8-a73b30996578","order_by":22,"name":"Masashi Fujii","email":"","orcid":"","institution":"Japan Clinical Cancer Research Organization","correspondingAuthor":false,"prefix":"","firstName":"Masashi","middleName":"","lastName":"Fujii","suffix":""},{"id":550433970,"identity":"429617b8-1121-486c-a6ad-0c030170d8f1","order_by":23,"name":"Yu Sunakawa","email":"","orcid":"","institution":"St. Marianna University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Sunakawa","suffix":""}],"badges":[],"createdAt":"2025-10-19 15:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7899663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7899663/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-47830-x","type":"published","date":"2026-04-10T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96968089,"identity":"44d34e54-609f-4fa9-bcc6-958a630f9af9","added_by":"auto","created_at":"2025-11-28 06:58:40","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150622,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/2724d551f6f30af185879c46.jpg"},{"id":96968096,"identity":"467b6116-7ec0-4a83-b94d-aa99a4a9b22d","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1513209,"visible":true,"origin":"","legend":"","description":"","filename":"JACCROGC08RMatoba2025v3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/501edb8883b1ba55956d9f62.docx"},{"id":96968091,"identity":"edd3880e-4fa2-4a4d-88ac-df17a3bab6e5","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":280733,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/70b19ace8275be05fe91ce7a.jpg"},{"id":96968106,"identity":"1a9d3bd6-7f17-45ed-a000-b46627d2f74e","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":259229,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/798c0cc9d7c3be52b947fe15.jpg"},{"id":97136693,"identity":"d902aee3-9825-4cd0-9ace-0c63f47d0b40","added_by":"auto","created_at":"2025-12-01 09:56:53","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126554,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/69b64eb84a3d51f26303b250.jpg"},{"id":96968101,"identity":"3295b6cd-e27b-4d49-9a73-71a30916bd25","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"json","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20869,"visible":true,"origin":"","legend":"","description":"","filename":"27f227582c354b409e53ce19e493f5d2.json","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/baebc67c18dd86fa55b5ffa2.json"},{"id":96968115,"identity":"fc654df0-eb3b-4b07-bc21-2c23b85b7745","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6801489,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/f36cff975475ff5575bb21f5.jpg"},{"id":96968110,"identity":"df5f2234-5dbb-44c0-81cd-33a6443d552a","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9868735,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/6d988e23cccc3c72b837052e.jpg"},{"id":97137538,"identity":"1f6a3cef-7dcf-47ef-a91c-4116ea1b15d0","added_by":"auto","created_at":"2025-12-01 09:57:53","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137302,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/dbd23adcb8b4c66ada6d14f8.jpg"},{"id":97137473,"identity":"5631f1fd-f2de-439c-a562-62eaace9a336","added_by":"auto","created_at":"2025-12-01 09:57:48","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206322,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/3c175b17c08773fb7b1034ac.xlsx"},{"id":96968127,"identity":"82d51893-e66b-4196-8086-b702082be34d","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3682273,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/5324d9555d0388ee36cfd4f1.xlsx"},{"id":97136705,"identity":"ac892750-05a3-4300-8083-1937346a96b2","added_by":"auto","created_at":"2025-12-01 09:56:55","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":522855,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/d9dbc116784cbf8af5bcfafa.xlsx"},{"id":97137793,"identity":"ef043dcd-b69e-4a65-a3f7-7421b1964401","added_by":"auto","created_at":"2025-12-01 09:58:10","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1469757,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/75b6eeb50b4afa079f83d4d5.xlsx"},{"id":96968093,"identity":"b9810e4f-e481-4dcf-ba6a-f65716ef5a47","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1360622,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/3d1ba943f03f2b51e60780f9.xlsx"},{"id":97137442,"identity":"fb68a02a-f0bb-4679-aea3-5043f14d8746","added_by":"auto","created_at":"2025-12-01 09:57:47","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91478,"visible":true,"origin":"","legend":"","description":"","filename":"27f227582c354b409e53ce19e493f5d21enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/3d1371e3add40888cc1603c2.xml"},{"id":96968131,"identity":"f3a05ebd-a720-46ea-a782-63570677627f","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"jpg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150622,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/4f80664d7ac78624d88b9da3.jpg"},{"id":97136819,"identity":"19903e14-6221-4a44-8ca9-19f949ff53bb","added_by":"auto","created_at":"2025-12-01 09:57:02","extension":"jpg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":280733,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/743ba8aa8b25b8f9f84e121e.jpg"},{"id":96968125,"identity":"16a5dc45-909c-4b93-b350-3e945186de5e","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"jpg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":259229,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/4c53eee26d72f2ed10725474.jpg"},{"id":97137502,"identity":"c77c6c4c-11fb-41f1-8528-f00d533bd699","added_by":"auto","created_at":"2025-12-01 09:57:50","extension":"jpg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126554,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/455c4cc46aa5fa5e37a54e3c.jpg"},{"id":96968118,"identity":"746c19fb-e948-4542-97c1-c189ff7dd9e4","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"emf","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84512,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/0cbfaa8300cc05070faecd48.emf"},{"id":96968119,"identity":"2a29b570-9067-46c0-bd93-165fd64a9268","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190348,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/3b4a5a0efa518b03d2725e81.jpeg"},{"id":96968133,"identity":"92b559dc-3062-483a-91c4-188b8c38d156","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"jpeg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":578534,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/0f5a91caeb1ded83ab6de047.jpeg"},{"id":96968129,"identity":"538ca393-29a3-4811-8bfd-ee22561e7d79","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":559176,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/477755ebdc29b3d3e7ee6118.png"},{"id":96968137,"identity":"73d99119-a478-4902-875d-a8c8a4e4b638","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":580597,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/d4a69cf4cf054750154b2b31.png"},{"id":97136429,"identity":"63bd1d4e-5517-42c4-8b31-132d53218edd","added_by":"auto","created_at":"2025-12-01 09:56:33","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":192022,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/dfc72701be8163c6942b4189.png"},{"id":96968099,"identity":"df3e8634-6505-4235-ab1a-025e168b0b01","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68894,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/5dc66c4b3cfe6ebf23394fcf.png"},{"id":96968103,"identity":"a8a923e2-27e7-4507-b83b-ec10ebc04844","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157281,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/2d94c67fc0584ca15b93c6ce.png"},{"id":96968120,"identity":"65057111-1db2-43cd-a823-8f638ae3c9ae","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127271,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/6548c8094d095bece071753e.png"},{"id":97138141,"identity":"061d15a4-6867-4f63-9b43-37b95adcd593","added_by":"auto","created_at":"2025-12-01 09:58:32","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40985,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/8e9e30ea24e10b2828513321.png"},{"id":96968130,"identity":"7d26fccf-327e-4964-b82a-c483335e5a8a","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205995,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/96acb5c81556e176f2e035d6.png"},{"id":96968113,"identity":"fc1f43cd-a914-4a74-b9a0-d5ad6a58b4ce","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26444,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/1edcd52bbdccb1ba313539f5.png"},{"id":97138256,"identity":"5d4bb6d1-1a21-4507-b64c-275e529194ea","added_by":"auto","created_at":"2025-12-01 09:58:41","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88322,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/7c81497883c7a2ca7a7c894f.png"},{"id":96968111,"identity":"3a96e560-2359-41fa-a581-be348cf0db05","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96873,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/c633cae16852f2215a434746.png"},{"id":96968134,"identity":"3ed5da90-9701-45e7-84ec-c96d9ff61416","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74768,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/50dda3dec5e02c6c6f334c4d.png"},{"id":96968135,"identity":"dbce90db-6fcb-44c5-8e7e-9c83d0962db0","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28430,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/2d23d528001abf974d4cb7ae.png"},{"id":96968116,"identity":"2a925a2d-8300-484d-970a-bfe160a01ae5","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"xml","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90129,"visible":true,"origin":"","legend":"","description":"","filename":"27f227582c354b409e53ce19e493f5d21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/0aa4e23439db33c1030ec0d4.xml"},{"id":96968138,"identity":"9d805c94-ef6c-4db5-a533-fe726799fa10","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"html","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107863,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/489c7abfb0b93cc40cce270e.html"},{"id":96968090,"identity":"9afdb8f0-a663-4d7a-a357-c647462aec18","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150622,"visible":true,"origin":"","legend":"\u003cp\u003ePhylum distributions of relative abundance in patients with gastric cancer and healthy individuals. No differences were observed between patients with cancer and healthy individuals in the phylum distribution of the gut microbiome.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/bbe5177e66bb61370cdfd957.jpg"},{"id":96968098,"identity":"abf2b05d-14a7-4b16-82ec-f43075ce295f","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":280733,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent expression of Genus between healthy individuals and patients with advanced gastric cancer\u003c/p\u003e\n\u003cp\u003e(a) Clustering analysis using Genus data broadly classified the patients with advanced gastric cancer and healthy individuals. C: Cancer patient cluster, H: Health control cluster, C/H: Cancer patient and Health control cluster. Detail were listed in Supplemental table 3 and Supplemental Figure 1.\u003c/p\u003e\n\u003cp\u003e(b) \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were predominant in the healthy individuals, whereas \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eOdoribacter\u003c/em\u003e were predominant in patients with advanced gastric cancer.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/d2c41ffe9cf45fe804f83f46.jpg"},{"id":96968108,"identity":"d32aa579-e1b8-466d-92e8-8266e7bf865a","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":259229,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent activities of KEGG Pathway between healthy individuals and patients with advanced gastric cancer\u003c/p\u003e\n\u003cp\u003e(a) Clustering analysis using KEGG pathway data enabled clearer classification of the patients with advanced gastric cancer and healthy individuals than the clustering using Genus analysis. Detail were listed in Supplemental table 5 and Supplemental Figure 2.\u003c/p\u003e\n\u003cp\u003e(b) Butanoate and Pyruvate metabolism were enriched in the healthy individuals, whereas factors such as ABC transporters and Ribosomes were enriched in patients with advanced gastric cancer.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/aca2f4c4a727835e199802ef.jpg"},{"id":97136630,"identity":"5ee2751f-853e-4f69-a6b2-0c3621eed147","added_by":"auto","created_at":"2025-12-01 09:56:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126554,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent expression of Genus and activities of KEGG Pathway between Age-Matched healthy individuals and patients with advanced gastric cancer\u003c/p\u003e\n\u003cp\u003e(a) (b) The results in Figure 3 are reproduced in the analysis focused on the Age-Matched sample.\u003c/p\u003e\n\u003cp\u003e(c) Complexity of Genus: Healthy individuals had higher bacterial flora diversity compared to patients with advanced gastric cancer based on Shannon index.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/bd66f1c4a3f0ce5517f8e4bc.jpg"},{"id":106809332,"identity":"d8cd9fc0-b9b7-41b1-853c-b2a7a60caf9b","added_by":"auto","created_at":"2026-04-13 16:09:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1563637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/4919ea38-b310-4156-a38e-6e771f303685.pdf"},{"id":96968097,"identity":"e3211c15-ca17-4d8b-9df8-35472f6b1c31","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":206322,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental table 1. Metagenomic shotgun data revealed 36 Phylum species\u003c/p\u003e","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/35b1ce6937af33ff65858ed3.xlsx"},{"id":97136682,"identity":"aece3b4f-1506-48e6-8eab-40e153b73b10","added_by":"auto","created_at":"2025-12-01 09:56:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3682273,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental table 2. Metagenomic shotgun data revealed 1122 Genus species\u003c/p\u003e","description":"","filename":"SupplementalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/cce7d458a1466ce1725a03ca.xlsx"},{"id":96968092,"identity":"f973ac51-08fd-472f-8f03-a2f49d268030","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":522855,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental table 3. Cluster order matrix of Sample and Genus species in Figure 2(a)\u003c/p\u003e","description":"","filename":"SupplementalTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/2f13dd4199044470647d2dd8.xlsx"},{"id":96968114,"identity":"50e15e34-f723-4212-ba7b-10304d562b72","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1469757,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental table 4. Expressed data from 234 pathways were detected in more than 80% of the samples\u003c/p\u003e","description":"","filename":"SupplementalTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/15e5b204dd6a93b37af4a5fa.xlsx"},{"id":97136687,"identity":"ad12c8d6-61e4-4c85-a6e5-4385269ce383","added_by":"auto","created_at":"2025-12-01 09:56:53","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1360622,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental table 5. Cluster order matrix of Sample and KEGG Pathway in Figure 3(a)\u003c/p\u003e","description":"","filename":"SupplementalTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/42393547459a9af23cfb0b90.xlsx"},{"id":97138081,"identity":"dfcd3498-26ac-4afc-bb9d-d0b002a559db","added_by":"auto","created_at":"2025-12-01 09:58:28","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6801489,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Figure 1. High resolution image of Figure 2(a).\u003c/p\u003e","description":"","filename":"SupplementalFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/f164c55cee6de85481927ecc.jpg"},{"id":96968109,"identity":"c559fed7-076e-4164-9694-80d85b9ce1bb","added_by":"auto","created_at":"2025-11-28 06:58:41","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9868735,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Figure 2. High resolution image of Figure 3(a)\u003c/p\u003e","description":"","filename":"SupplementalFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/1624a45a0eba85699de57c1e.jpg"},{"id":96968123,"identity":"c2dbd990-f079-4d53-a174-fe474edd9f0d","added_by":"auto","created_at":"2025-11-28 06:58:42","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":137302,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Figure 3. The several genus species and KEGG pathway were correlated with the clinicopathologies.\u003c/p\u003e","description":"","filename":"SupplementalFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/5f03a3f236e741e8046035fc.jpg"},{"id":97136691,"identity":"0d23b653-09e3-44c9-b3a9-59c06c2a1625","added_by":"auto","created_at":"2025-12-01 09:56:53","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":23313,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7899663/v1/7a32c398213d2d91927b2620.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic and functional pathways of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicrobiome research and development has been gaining momentum worldwide in recent decades and is expected to a promising tool for understanding \"personalized medicine,\" as represented by the Precision Medicine Initiative. Additionally, microbiome analysis will become important as a tool to monitor the state of health before illness occurs \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eParticularly, gut microbiota analysis is known to be closely related to autoimmunity. Proteins, metabolites, and other substances produced by gut bacteria affect the immune cells in the gut, which in turn strongly influence autoimmunity \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, it may be possible to predict individual health conditions by examining gut microbiota. Moreover, specific intestinal microbiota and their metabolites are correlated with the efficacy of immunotherapy \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eFurthermore, recent advances in NGS technology have been remarkable, resulting in large amounts of sequencing data that can be obtained simultaneously. Microbiome sequencing can be performed in two ways: 16S ribosomal RNA analysis for the identification of bacterial species, and metagenome shotgun analysis to obtain a large amount of random sequencing data. Although the metagenome shotgun method requires a large amount of sequence data, it provides information on bacterial species, diversity, non-bacterial (viruses, eukaryotic microorganisms, etc.) information, and functional data such as metabolic enzymes from the sequence information \u003csup\u003e12 13\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study aimed to identify patient-specific microbiome markers in patients with gastric cancer by performing a comprehensive metagenome shotgun analysis of the intestinal microbiota in patients with gastric cancer compared to fecal microbiome data from healthy individuals.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCancer Patient and Health characteristics\u003c/h2\u003e\u003cp\u003eOverall, 501 patients were enrolled in the DELIVER trial (JACCRO GC-08) from 67 institutes between March 2018 and August 2019. Of them, samples from 487 patients were available for clinical data, and biomarker analysis, and 475 fecal samples were available for genome shotgun sequencing. Metagenomic data were obtained for each sample, with a minimum of 5 Gbp, and an average mapping rate of 98.2%. The same metagenomic data for 106 healthy individuals were analyzed from clinical data and the Fastq files, using the database from a previously published paper \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Although background comparisons of all patients with gastric cancer and healthy controls showed no bias in BMI, it showed differences in age and sex (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnalysis of the metagenomic shotgun data revealed 36 phyla and 1122 genera (Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2). Functional pathways were classified based on sequencing data, and 350 pathways were identified. Using the 36 different phyla, we compared data from gastric cancer patients and healthy controls but did not find any significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProfiling of differences between Cancer patients and healthy individuals\u003c/h3\u003e\n\u003cp\u003eWe performed a comparative analysis between patients with gastric cancer and healthy individuals using genus data, for whom more than 80% of the data were available. The results, although unclear, showed a tendency for healthy individuals and patients with GC to be divided into several clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplemental table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplemental Fig.\u0026nbsp;1). Subsequently, we compared the genera between patients with gastric cancer and healthy controls and found \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were significantly higher in healthy individuals. Whereas, \u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e were significantly more abundant in patients with cancer. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSpecific bacteria and functional pathway of Cancer Patients\u003c/h3\u003e\n\u003cp\u003eNext, a similar analysis was performed focusing on functional pathways, with 234 pathways for which the expressed data were available for more than 80% of the samples (Supplemental table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A cluster analysis was performed using the expression values of these pathways. The results showed that the expression patterns of the pathways clearly distinguished healthy individuals from patients with gastric cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Supplemental table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplemental Fig.\u0026nbsp;2), compared to the analysis using the genus. This implies that functional pathways in healthy individuals and patients with gastric cancer are different. Furthermore, the results showed that genes related to the butanoate and pyruvate metabolism pathways were downregulated in patients with gastric cancer, while genes related to the ABC transporter pathway and ribosome synthesis were upregulated in patients with gastric cancer compared to healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlso, clinical information on gastric cancer patients, such as ECOG performance score, diagnosis status, gastrectomy status and \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection status, was analyzed for its association with the gut microbiota. Although \u003cem\u003eEubacterium\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e were found to be associated with ECOG status, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e with diagnosis status, and \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, and Ribosome of pathway with gastrectomy, there were no definite difference in the amount of expression was observed (Supplemental Fig.\u0026nbsp;3). Interestingly, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e were more abundant in the presence of \u003cem\u003eH. pylori\u003c/em\u003e infection patents, but there was no significant difference, statistically.\u003c/p\u003e\n\u003ch3\u003eAnalysis of age-sex matched pair samples\u003c/h3\u003e\n\u003cp\u003eAlthough differences were identified in fecal samples from patients with gastric cancer and healthy controls, our analyses showed that the age distributions of patients with gastric cancer and healthy controls were different. Therefore, we examined whether the markers obtained would remain significant when the 73 patients with gastric cancer and 24 healthy subjects were matched by age and sex (Table\u0026nbsp;2). The results showed that all six genus and four pathway markers were statistically significant and showed different expression profiles in the fecal analysis of patients with gastric cancer and healthy individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). Furthermore, these age- and sex-matched samples were analyzed for the Complexity of Genus. Shannon analysis revealed that healthy individuals had higher bacterial flora diversity than patients with advanced gastric cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify patient-specific microbiome markers in patients with gastric cancer and found that genes related to the butanoate and pyruvate metabolic pathways were downregulated in patients with gastric cancer. Butyrate is a typical short-chain fatty acid implicated in cancer immunotherapy efficacy in prostate cancer \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Whereas, pyruvic acid is a key component of the metabolic pathway network, and has been implicated in cancer growth \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The activities of these metabolic pathways are decreased in patients with gastric cancer. In contrast the ABC transporter pathway was found to be elevated in patients with gastric cancer. This pathway is supposedly related to single nucleotide polymorphisms and anti-cancer chemotherapy effects, and is activated in patients gastric cancer. Additionally, its expression level has been reported to be related to malignancy in pancreatic cancer \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Furthermore, activation of ribosome synthesis indicates ribosomal protein gene copy number changes and abnormal expression, suggesting that the nucleosome is in a stress-activated state.\u003c/p\u003e\u003cp\u003eOur analysis revealed that \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were significantly higher in healthy individuals, while \u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e were significantly more abundant in patients with cancer. \u003cem\u003eAnaerostipes\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e are more common in healthy individuals than in patients with colorectal cancer \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003cem\u003eOdoribacter\u003c/em\u003e, which belongs to the order \u003cem\u003eBacteroidales\u003c/em\u003e, is a common short-chain fatty acid-producing member of the human intestinal microbiota \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Decreased abundance of \u003cem\u003eOdoribacter\u003c/em\u003e has been linked to different microbiota-associated diseases, such as non-alcoholic fatty liver disease, cystic fibrosis, and inflammatory bowel disease \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eParabacteroides\u003c/em\u003e are secondary bile acid-producing bacteria that may be beneficial for longevity. Notably, \u003cem\u003eLactobacillus\u003c/em\u003e are also found more frequently in patients with gastric cancer, as previous studies have reported an increase in intestinal \u003cem\u003eLactobacillus\u003c/em\u003e in the presence of \u003cem\u003eH. pylori\u003c/em\u003e infection \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Thus, the decrease in gastric acid secretion associated with gastric mucosal atrophy may have been affected.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e are also increase in the intestinal microbiota of patients with pancreatic cancer, and there may be a functional relationship between them \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe analyzed whether there was any correlation between gut microbiome and clinicopathologic, and found that several genus and pathway were related significantly. \u003cem\u003eLactobacillus\u003c/em\u003e was abundant in \u0026lsquo;advanced\u0026rsquo; than \u0026lsquo;relapse\u0026rsquo; and \u003cem\u003eVeillonella\u003c/em\u003e was abundant in \u0026lsquo;relapse\u0026rsquo; than \u0026lsquo;advanced\u0026rsquo; in cancer patients. \u003cem\u003eVeillonella\u003c/em\u003e was also abundant in previous gastrectomy, those data suggested to be related to salivary microbiome \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, 16S rRNA analysis, which is a method for classifying and identifying bacteria at the species level, is the mainstream method for analyzing the microbiome. However, metagenomic analysis, which analyzes all genomic DNA-encoding genes, is expected to become crucial in the following years. Although metagenomic analysis is time-consuming and costly, it can provide information on both phylogenetic and functional composition. Thus, it is possible to analyze the functional genetic information of the microbiome using metagenomic analysis.\u003c/p\u003e\u003cp\u003eRecent studies have reported an association between the efficacy of cancer immunotherapy and intestinal microbiota, suggesting that microbiota analysis could play an important role in the selection of specific treatment modalities. Although data acquisition has just begun, we hope that microbiome testing will be incorporated into health checkup menus in the near future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified the molecular characteristics of the microbial genera and functional pathways in the gut of patients with advanced gastric cancer. \u003cem\u003eBifidobacterium, Anaerostipes\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were predominant in the healthy individuals, whereas \u003cem\u003eStreptococcus, Lactobacillus\u003c/em\u003e, and \u003cem\u003eOdoribacter\u003c/em\u003e were predominant in patients with advanced gastric cancer. Furthermore, pathway analysis showed that butanoate and pyruvate metabolism were enriched in the healthy individuals, whereas factors such as ABC transporters and ribosomes were enriched in the patients with advanced gastric cancer. Cluster analysis broadly classified patients with advanced gastric cancer and healthy individuals, and clustering using pathway data enabled a clearer classification of patients with advanced gastric cancer and healthy individuals than clustering using flora analysis. This analysis represents characteristics of a specific group of gastric cancer patients and therefore provides limited results. In order to generalize these findings, it is believed that analysis data from a larger number of gastric cancer patients would be necessary.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eSample collection\u003c/h2\u003e\n \u003cp\u003eThis study enrolled patients with advanced or recurrent unresectable gastric or gastroesophageal junction cancer that was histologically confirmed to be adenocarcinoma. Fecal samples were collected by patients before treatment with nivolumab (in DELIVER trial), at home immediately after evacuation, using scoop collection tubes containing 3 mL GuSCN solution from the TechnoSuruga Laboratory \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDNA extraction and metagenome shotgun sequencing\u003c/h2\u003e\n \u003cp\u003eGenomic information in fecal samples was measured by genome shotgun sequencing.\u003c/p\u003e\n \u003cp\u003eWe extracted DNA from the feces of patients with advanced gastric cancer enrolled in an observational/translational study, the DELIVER trial (JACCRO GC-08: UMIN000030850), which evaluated the clinical outcomes of nivolumab and developed host-related biomarkers for nivolumab in 500 patients with advanced gastric cancer.\u003c/p\u003e\n \u003cp\u003eBriefly, metagenomic DNA was extracted using zirconia beads added to the specimens. The bacteria were crushed (5 m/s, 2 min), purified and eluted using magnetic beads \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. DNA quality was assessed by agarose gel electrophoresis, and the concentration was confirmed using a NanoPhotometer spectrophotometer (IMPLEN, CA, USA) and a Qubit 2.0 Flurometer (Life Technologies, CA, USA). Sequence libraries were prepared with the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, MA, USA) using 1 \u0026micro;g of genomic DNA. The AMPure XP system was used for library purification and the quality of the libraries was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Sequencing was performed on a NovaSeq6000 (Illumina, Inc., San Diego, CA) with 150 bp paired ends.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eSubsequently, genome annotation, assignment of bacterial strains, assignment of functions, and metabolic pathways were performed using custom pipelines implemented in Bash and Python scripts. This pipeline includes steps for quality control, trimming, assembly, and annotation of metagenomic reads.\u003c/p\u003e\n \u003cp\u003eThe quality control step involved assessing the quality of raw reads before trimming using FastQC (version 0.11.8, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bioinformatics.babraham.ac.uk/projects/fastqc\u003c/span\u003e\u003c/span\u003e). The pipeline trimmed the adapter sequences and low-quality bases from the reads using TrimGalore (version 0.6.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bioinformatics.babraham.ac.uk/projects/trim_galore/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eContaminant removal included the removal human reads using the BMTagger tool (version 3.101, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/bmtagger/\u003c/span\u003e\u003c/span\u003e) to ensure the analysis focused on microbial content. Thereafter, the cleaned reads were assembled de novo using SPAdes (version 3.11.1) \u003csup\u003e27\u003c/sup\u003e, and contigs shorter than 500 bp were discarded. Gene prediction and annotation were performed during the gene annotation step using the Prokka software (version 1.14.5) \u003csup\u003e28\u003c/sup\u003e. The annotation process generated files containing predicted genes in the GFF format, which were subsequently converted to the GTF format for further analysis. A gene catalog was the created using DIAMOND (version 0.9.25) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e by aligning the predicted genes against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. This produced a table of gene counts for each sample, which was used to generate the functional profiles. The reads were mapped to the gene catalog using Bowtie2 (version 2.2.1) \u003csup\u003e30\u003c/sup\u003e, and the results were processed using SAM tools (version 1.3.1) \u003csup\u003e31\u003c/sup\u003e to obtain sorted BAM files. Gene expression levels were quantified using HTSeq count (version 0.11.2) \u003csup\u003e32\u003c/sup\u003e, followed by conversion to TPM (Transcripts Per Million) using a custom Python script. Gene abundance tables were constructed for each sample. Functional annotation and pathway analysis were performed by mapping the predicted genes to KEGG orthology (KO) terms using DIAMOND. Cluster analyses were performed using the MeV tool (webmev.tm4.org).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during this study are available in the\u0026nbsp;National Center for Biotechnology Information (NCBI)\u0026nbsp;BioProject Repository https://www.ncbi.nlm.nih.gov/bioproject under BioProject : PRJDB20360 (PSUB025696).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients, their families, and the investigators who participated in the DELIVER trial (JACCRO GC-08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM and HI are employee of DNA Chip Research Inc. The other authors declare no competing financial interests that may be affected by the research reported in the enclosed paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Ono Pharmaceutical Co.Ltd., Bristol-Myers Squibb and Japan Clinical Cancer Research Organization (JACCRO).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: RM, HI, WI, MF, and YS. Data collection and assembly: YS, RK, AI, YA, YK, MA, JM, MT, AM, TS, MT, HY, JH, HO, JK, TY, HK, TN, KM, WI, and YS. Data analysis and interpretation: RM, HI and YS. Manuscript writing: All authors Final approval manuscript: All authors Accountable for all aspects of the work: All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they obtained appropriate Japan Clinical Cancer Research Organization Institutional Review Board approval and followed the principles outlined in the Declaration of Helsinki for all human experimental investigations. Informed consent was obtained from all the participants at each institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to RM, YS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLynch, S. V. \u0026amp; Pedersen, O. The Human Intestinal Microbiome in Health and Disease. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e375\u003c/b\u003e, 2369\u0026ndash;2379 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e352\u003c/b\u003e, 565\u0026ndash;569 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmoroso, C. et al. The Role of Gut Microbiota Biomodulators on Mucosal Immunity and Intestinal Inflammation. \u003cem\u003eCells\u003c/em\u003e 9 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, Y. K., Menezes, J. S., Umesaki, Y. \u0026amp; Mazmanian, S. K. Proinflammatory T-cell responses to gut microbiota promote experimental autoimmune encephalomyelitis. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e108\u003c/b\u003e (Suppl 1), 4615\u0026ndash;4622 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToor, D. et al. Dysbiosis Disrupts Gut Immune Homeostasis and Promotes Gastric Diseases. \u003cem\u003eInt J. Mol. Sci\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacia, L. et al. Microbial influences on epithelial integrity and immune function as a basis for inflammatory diseases. \u003cem\u003eImmunol. Rev.\u003c/em\u003e \u003cb\u003e245\u003c/b\u003e, 164\u0026ndash;176 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuu, M. et al. Regulation of the effector function of CD8(+) T cells by gut microbiota-derived metabolite butyrate. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 14430 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia, D. et al. Microbial metabolite enhances immunotherapy efficacy by modulating T cell stemness in pan-cancer. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e187\u003c/b\u003e, 1651\u0026ndash;1665e1621 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElinav, E., Garrett, W. S., Trinchieri, G. \u0026amp; Wargo, J. The cancer microbiome. \u003cem\u003eNat. Rev. Cancer\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e, 371\u0026ndash;376 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark, J. S. et al. Targeting PD-L2-RGMb overcomes microbiome-related immunotherapy resistance. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e617\u003c/b\u003e, 377\u0026ndash;385 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e359\u003c/b\u003e, 97\u0026ndash;103 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWensel, C. R., Pluznick, J. L., Salzberg, S. L. \u0026amp; Sears, C. L. Next-generation sequencing: insights to advance clinical investigations of the microbiome. \u003cem\u003eJ Clin. Invest\u003c/em\u003e \u003cb\u003e132\u003c/b\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanjan, R., Rani, A., Metwally, A., McGee, H. S. \u0026amp; Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. \u003cem\u003eBiochem. Biophys. Res. Commun.\u003c/em\u003e \u003cb\u003e469\u003c/b\u003e, 967\u0026ndash;977 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNishijima, S. et al. The gut microbiome of healthy Japanese and its microbial and functional uniqueness. \u003cem\u003eDNA Res.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 125\u0026ndash;133 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurphy, S. et al. Ketogenic Diet Alters the Epigenetic and Immune Landscape of Prostate Cancer to Overcome Resistance to Immune Checkpoint Blockade Therapy. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, 1597\u0026ndash;1612 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLachance, G. et al. The gut microbiome-prostate cancer crosstalk is modulated by dietary polyunsaturated long-chain fatty acids. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 3431 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBader, D. A. et al. Mitochondrial pyruvate import is a metabolic vulnerability in androgen receptor-driven prostate cancer. \u003cem\u003eNat. Metab.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 70\u0026ndash;85 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVincent, E. E. et al. Mitochondrial Phosphoenolpyruvate Carboxykinase Regulates Metabolic Adaptation and Enables Glucose-Independent Tumor Growth. \u003cem\u003eMol. Cell.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 195\u0026ndash;207 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCervenkova, L. et al. Protein expression and localization of ABC transporters in pancreatic adenocarcinoma: Prognostic role of ABCC8. \u003cem\u003ePancreatology\u003c/em\u003e. 23, 978\u0026ndash;987 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, W., Liu, F., Ling, Z., Tong, X. \u0026amp; Xiang, C. Human intestinal lumen and mucosa-associated microbiota in patients with colorectal cancer. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, e39743 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiippala, K. et al. Novel Odoribacter splanchnicus Strain and Its Outer Membrane Vesicles Exert Immunoregulatory Effects in vitro. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 575455 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, J. et al. Programmable probiotics modulate inflammation and gut microbiota for inflammatory bowel disease treatment after effective oral delivery. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 3432 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026uuml;hling, A., Radun, D., M\u0026uuml;ller, W. A. \u0026amp; Malfertheiner, P. Influence of anti-Helicobacter triple-therapy with metronidazole, omeprazole and clarithromycin on intestinal microflora. \u003cem\u003eAliment. Pharmacol. Ther.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1445\u0026ndash;1452 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHashimoto, S. et al. Changes in intestinal bacteria and imbalances of metabolites induced in the intestines of pancreatic ductal adenocarcinoma patients in a Japanese population: a preliminary result. \u003cem\u003eScand. J. Gastroenterol.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 193\u0026ndash;198 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, K. et al. Salivary Microbiota for Gastric Cancer Prediction: An Exploratory Study. \u003cem\u003eFrontiers Cell. Infect. Microbiology\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosomi, K. et al. Method for preparing DNA from feces in guanidine thiocyanate solution affects 16S rRNA-based profiling of human microbiota diversity. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 4339 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. \u003cem\u003eJ. Comput. Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 455\u0026ndash;477 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeemann, T. Prokka: rapid prokaryotic genome annotation. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 2068\u0026ndash;2069 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuchfink, B., Xie, C. \u0026amp; Huson, D. H. Fast and sensitive protein alignment using DIAMOND. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 59\u0026ndash;60 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangmead, B. \u0026amp; Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 357\u0026ndash;359 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, H. et al. The Sequence Alignment/Map format and SAMtools. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 2078\u0026ndash;2079 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnders, S., Pyl, P. T. \u0026amp; Huber, W. HTSeq\u0026ndash;a Python framework to work with high-throughput sequencing data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 166\u0026ndash;169 (2015).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gastric cancer, fecal microbiome, metagenomic shotgun, functional pathway, molecular diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7899663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7899663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe analyzed the differences in bacterial composition between 475 Japanese patients with advanced gastric cancer (median age 70 years, median BMI 20.0) and 106 healthy individuals using a comprehensive metagenome shotgun analysis. Among the patients with advanced gastric cancer, 71% were male, 37.% patients had relapsed, and 55.5% previously underwent gastrectomy. \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were predominant in healthy individuals, whereas \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eOdoribacter\u003c/em\u003e were predominant in patients with advanced gastric cancer. Additionally, Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that butanoate and pyruvate metabolism was enriched in healthy individuals, whereas factors such as ABC transporters and ribosomes were enriched in patients with advanced gastric cancer. Clustering analysis broadly classified patients with advanced gastric cancer and healthy individuals into two clusters; however, clustering using pathway data enabled a clearer classification of patients with advanced gastric cancer and healthy individuals than clustering using flora analysis. Moreover, healthy individuals showed higher bacterial flora diversity than patients with advanced gastric cancer. Thus, we successfully identified the molecular characteristics of microbial genera and functional pathways in the gut of patients with advanced gastric cancer. Our results suggest that fecal metagenomic shotgun sequencing analysis can be used to detect gastric cancer.\u003c/p\u003e","manuscriptTitle":"Metabolic and functional pathways of gut microbiota in patients with gastric cancer: The DELIVER trial (JACCRO GC-08)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 06:58:36","doi":"10.21203/rs.3.rs-7899663/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-18T06:33:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T17:02:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T07:23:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T13:11:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308029852072132761121227809033122613418","date":"2025-12-08T22:57:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121780127758408139392779720951042989370","date":"2025-12-08T07:14:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34764389895592655782145122923049015909","date":"2025-12-08T04:27:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17735652910607351612567927755772349766","date":"2025-12-08T03:39:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T14:16:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195849606531592292012199470750117173751","date":"2025-11-20T03:31:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-18T02:53:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-27T09:30:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T11:01:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T08:57:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-24T08:53:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"622a309e-2d69-4329-aee1-a137b54e1eb0","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58565854,"name":"Biological sciences/Cancer"},{"id":58565855,"name":"Health sciences/Gastroenterology"},{"id":58565856,"name":"Biological sciences/Microbiology"},{"id":58565857,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-13T16:05:36+00:00","versionOfRecord":{"articleIdentity":"rs-7899663","link":"https://doi.org/10.1038/s41598-026-47830-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-10 15:57:49","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2025-11-28 06:58:36","video":"","vorDoi":"10.1038/s41598-026-47830-x","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47830-x","workflowStages":[]},"version":"v1","identity":"rs-7899663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7899663","identity":"rs-7899663","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}