Distinct microbiome composition and reduced interactions in patients with pancreatic cancer

Distinct microbiome composition and reduced interactions in patients with pancreatic cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct microbiome composition and reduced interactions in patients with pancreatic cancer Bomi Kim, Sujin Oh, Soomin Yang, Jinwoo Ahn, Kwangrok Jung, Jong-Chan Lee, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4992405/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The results of microbiome composition in patients with malignancy have been inconsistent across studies and are affected by various factors. This study aimed to identify microbiome composition of saliva, feces, and blood in patients with pancreatic cancer. Results Overall, 31 patients with pancreatic cancer and 24 healthy controls were sex- and age-matched. Microbiome analysis of saliva, fecal, and blood samples was conducted using 16S rRNA amplicon sequencing. Baseline characteristics were comparable between patients and controls. Saliva showed insignificant difference in alpha diversity ( p = 0.42), whereas feces and blood exhibited a significant difference in Shannon’s index (feces: 6.19 vs. 6.52, p = 0.013; blood: 8.00 vs. 7.49, p < 0.001) between patients and controls. Beta diversity analysis revealed significant differences between saliva, fecal, and blood samples ( p = 0.014, 0.001, and 0.001, respectively). Distinct microbiome compositions were identified in patients, with higher abundance of Lactobacillus , Enterobacter , and Prevotella in saliva, fecal, and blood samples, respectively. Based on microbial network analysis, patients with pancreatic cancer showed lower clustering coefficient (71% vs. 99%) and higher average path length (1.67 vs. 0.68) than healthy controls, suggesting a more compact network and stronger microbial interactions in healthy controls. Conclusions This study identified a distinctive microbiome in patients with pancreatic cancer, indicating the presence of Lactobacillus , Enterobacter , and Prevotella . A less condensed and robust microbial interaction network was observed in blood samples of patients with pancreatic cancer. These findings provide a basis for research on the connection between the microbiome and pancreatic cancer. Pancreatic cancer microbiome saliva feces blood Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The incidence of pancreatic cancer is increasing, and it is expected to become the second leading cause of cancer-related deaths worldwide by 2030 [ 1 , 2 ]. The average 5-year survival rate in pancreatic cancer has reached approximately 12%, and the 5-year survival rate in patients with localized disease is only 44% [ 1 ]. The tumor microenvironment (TME) plays an important role in tumor growth, metastasis, and disease characteristics. The TME comprises various cell types that interact with each other [ 3 ]. The microbiome, a constituent of the TME, modulates the inflammatory response, which can drive carcinogenesis [ 4 , 5 ]. Microbiome analysis has been reported to be a potential diagnostic tool for malignancies [ 6 ]. Studies on the microbiome of saliva and feces from patients with pancreatic cancer have reported variations in diversity and taxonomy [ 7 – 12 ]. This inconsistency is attributable to differences between individuals, sample sites, and lifestyle variables, such as diet, medication, and familial factors [ 13 , 14 ]. Although human blood is considered sterile, modern sequencing techniques have detected bacterial genetic material even in the blood of healthy individuals [ 15 ]. Distinct blood microbial profiles have been reported in several malignancies [ 6 , 16 – 19 ]. This underscores the importance of blood microbiome analysis in cancer. There are limited studies on microbiome composition in patients with pancreatic cancer. This study aimed to characterize microbiome composition of saliva, fecal, and blood samples in patients with pancreatic cancer. Methods Patients We collected saliva, fecal, and blood samples from patients with pancreatic cancer in a single tertiary teaching hospital between December 2019 and May 2022. Samples from healthy controls were acquired from the Periodontal Human Specimen Storage Registry at Seoul National University Bundang Hospital, with approval for secondary research. We reviewed a database of patients with pancreatic cancer. A survey was conducted among patients with pancreatic cancer and healthy controls to obtain information on the underlying medical conditions and oral care practices. When assessing smoking history, individuals who had quit for ≥ 6 months were categorized as the nonsmoking group and those who had quit within the past 6 months were classified as the smoking group. Experienced periodontists evaluated periodontal health parameters, including periodontal probing depth and missing teeth count. Written informed consent was obtained from all participants prior to inclusion in the study. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Seoul National University Bundang Hospital (no. B-2110-714-303). Sample collection and preparation Patients with pancreatic cancer and healthy controls were instructed to abstain from oral hygiene practices for a minimum of 2 h before saliva collection. Fecal samples were self-collected by the participants using a sterile spatula, placed in a sterile container designed for feces, and immediately stored in a freezer until transportation on ice to the laboratory. Venous blood samples were aseptically collected by trained personnel. Upon arrival at the laboratory, all samples, excluding fecal samples, were stored at − 80°C until DNA extraction. DNA was extracted from 1 mL of thawed sample using QIAamp DNA Microbiome Kit (QIAGEN, Venlo, the Netherlands), following the manufacturer’s protocol. 16S rRNA amplicon sequencing DNA quality was assessed using Qubit dsDNA HS Assay Kits (Thermo Fisher Scientific Inc., Waltham, MA, USA). Polymerase chain reaction (PCR) targeting V3 and V4 hypervariable regions of 16S rRNA genes was conducted using KAPA HiFi HotStart ReadyMix PCR Kit (Roche, Basel, Switzerland) following the manufacturer’s instructions. The primer sequences used for PCR amplification were as follows: 519F: 5′-CCTACGGGNGGCWGCAG-3′ and 806R: 5′-GACTACHVGGGTATCTAATCC-3′. Libraries were constructed utilizing Nextera XT DNA Library Preparation Kit (Illumina Inc., San Diego, CA, USA), and the amplified samples were pooled to achieve a final loading concentration of 8 pM. Subsequently, paired-end (2 × 300 bp) sequencing was performed using the MiSeq platform (Illumina). Data analysis and visualization The reads were processed using a Divisive Amplicon Denoising Algorithm (DADA2)-based pipeline within the Quantitative Insights Into Microbial Ecology (QIIME2) 22.2 platform. This process involved generation of an amplicon sequence variant (ASV) table through quality-based filtering and trimming, read deduplication, ASV inference, paired-end merging, and chimera removal. ASVs were taxonomically classified against the 99% SILVA rRNA taxonomy. To rectify artifactual biases, feature tables were normalized via rarefaction. For alpha diversity analysis, including observed features, Shannon’s entropy, Pielou’s evenness, and Faith’s phylogenetic diversity were calculated. To evaluate dissimilarities between microbial compositions of each sample, beta diversity indices, such as the Bray–Curtis index, and unweighted UniFrac distance were calculated. Principal coordinate analysis (PCoA) was used to visualize overall trends in sample dissimilarities. Moreover, permutation multivariate analysis of variance was performed to quantify the strength of associations between microbial composition and sample variables. To identify differentially abundant taxa between sample groups, we performed analysis of compositions of microbiomes with bias correction (ANCOM-BC), which can estimate unknown sampling fractions and correct for bias resulting from differences through a log-linear regression model. Then, we used Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2), which can predict microbial functions based on 16S marker gene sequences. To examine variations in microbial metabolism, predicted orthologs were collapsed into the Kyoto Encyclopedia of Genes and Genomes pathways, followed by differential abundance (DA) analysis using ANOVA-Like Differential Expression tool version 2 (ALDEx2). Correction for multiple testing was performed using the Benjamini–Hochberg method; thus, Q -values of < 0.05 were considered to indicate statistical significance for both DA methods. To examine interactions between microbiomes, co-occurrence network analysis was performed using sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) via graphical lasso algorithms. Signed distance was computed to transform associations into dissimilarities. Topological properties of networks, including clustering coefficient, average path length, average dissimilarity, modularity, edge density, and positive edge ratio, were examined using the igraph package in R. Only nodes with > 3 degrees are shown in the figures. Statistical analyses and data visualization were performed using R software (ver. 4.1.2; R Development Core Team, Vienna, Austria). QIIME artifacts were imported into the R environment using the qiime2R package and then transformed into phyloseq objects using the phyloseq package. Centered log-ratio transformation of raw feature counts was performed before conducting statistical analyses of microbial abundance. The Wilcoxon rank-sum test was conducted to compare nonparametric distributions of alpha diversity between sample groups, and p- values of < 0.05 were considered to indicate statistical significance. Results Baseline characteristics Study participants included 31 patients with pancreatic cancer and 24 healthy controls. No significant difference was noted in the baseline characteristics between the two groups (Table 1 ). The median ages of patients with pancreatic cancer and healthy controls were 69 and 65 years, respectively ( p = 0.81). In total, males constituted 58.1% (n = 18) and 66.7% (n = 16) of patients with pancreatic cancer and healthy controls, respectively ( p = 0.61). The prevalence rates of diabetes mellitus ( p = 0.31), smoking history ( p = 0.93), and drinking history ( p = 0.20) were not significantly different between the two groups. Although body mass index was significantly different between the two groups, none of the groups met the criteria for obesity (median body mass index: 21.7 and 24.7 kg/m² for patients with pancreatic cancer and healthy controls, respectively). Pancreatic cancer was more frequent in the head than in the body or tail (n = 17, 54.8% vs. n = 14, 45.2%). In total, 14 (45.2%) tumors were resectable, 7 (22.6%) were borderline resectable, 5 (16.1%) were locally advanced, and 5 (16.1%) were metastatic. The median serum level of carbohydrate antigen 19 − 9 (CA 19 − 9) was 41.0 U/mL. Periodontal information revealed no difference in daily toothbrushing frequency (≤ 2 times a day: 51.9% vs. 40.0%, p = 0.52), median severity of severe periodontitis (4.0 vs. 4.0, p = 0.91), and number of missing teeth (1.5 vs. 2.0, p = 0.42) between the two groups. Table 1 Baseline characteristics and periodontal information between the patient with pancreatic cancer and healthy control Pancreatic cancer (n = 31, %) Healthy control (n = 24, %) p-value Median age (year) (range) 69 (36–83) 65 (36–80) 0.81 Male 18 (60.0) 16 (66.7) 0.61 Diabetes (n = 31/23) 8 (25.8) 4 (17.4) 0.31 Smoking (n = 25/23) 2 (8.0) 2 (8.7) 0.93 Alcohol (n = 25/12) 9 (36.0) 7 (58.3) 0.20 BMI (SD) (kg/m 2 ) (n = 31/17) 21.7 (2.2) 24.7(2.7) < 0.001 Tumor location and resectability Head/Body/Tail 17 (54.8)/9 (29.0)/5 (16.1) Resectable/Borderline resectable/ Locally advanced/metastatic 14 (45.2)/7 (22.6)/ 5 (16.1)/5 (16.1) Median CA 19 − 9 (SD) (U/ml) 41.0 (6,131.4) Periodontal information Tooth brushing/day ≤ 2 (n = 27/10) 14 (51.9) 4 (40.0) 0.52 Median severe periodontitis (SD) 4.0 (10.8) 4.0 (10.7) 0.91 Severe periodontitis 22 (73.3) 17 (70.8) 1.00 Median missing (SD) 1.5 (7.4) 2.0 (4.1) 0.42 Missing 20 (66.7) 16 (66.7) 1.00 Median missing & severe periodontitis (SD) 7.5 (13.0) 5.5 (12.6) 0.33 BMI, body mass index; SD, Standard Deviation; CA 19 − 9, Carbohydrate antigen 19 − 9 Overview of microbiome composition Microbiome composition was analyzed at phylum and genus levels (Fig. 1 A, B). At the phylum level, Firmicutes was dominant in the saliva, fecal, and blood samples of patients with pancreatic cancer and healthy controls. Saliva samples exhibited a high abundance of Actinobacteria, whereas fecal samples showed a prevalence of Verrucomicrobia. At the genus level, distinct variations in microbial composition were observed between saliva, fecal, and blood samples of the two groups. PCoA results of unweighted UniFrac distance confirmed differences in microbial composition of saliva, fecal, and blood samples between patients with pancreatic cancer and healthy controls (Supplementary Fig. 1). Alpha and beta diversities No significant difference was observed in the alpha diversity indices between saliva samples of patients with pancreatic cancer and those of healthy controls (median Shannon index: 6.45 vs. 6.22; p = 0.42). However, Shannon indices of fecal samples were higher in healthy controls than in patients with pancreatic cancer (median Shannon index: 6.19 vs. 6.52; p = 0.013). The blood microbiome showed higher richness and evenness in patients with pancreatic cancer than in healthy controls (median Shannon index: 8.00 vs. 7.49; p < 0.001; Fig. 2 A). Beta diversity analysis using the Bray–Curtis distance revealed significant differences in microbial composition of saliva, fecal, and blood samples between patients with pancreatic cancer and healthy controls (saliva: R 2 = 0.07, p = 0.014; feces: R 2 = 0.13, p = 0.001; blood: R 2 = 0.21, p = 0.001) (Fig. 2 B). Differentially abundant taxa between patients with pancreatic cancer and healthy controls DA analysis revealed distinct microbial profiles between the two groups. Saliva samples of patients with pancreatic cancer had a significantly higher abundance of Cyanobacteria, Bulleidia , Lactobacillus , and Saccharimonadaceae than those of healthy controls. Furthermore, Enterobacter and Sellimonas were more abundant in the fecal samples of patients with pancreatic cancer than in those of healthy controls, whereas Alistipes , Ruminococcus , and Slackia were more abundant in the fecal samples of healthy controls than in those of patients with pancreatic cancer. Acetobacter , Butyricicoccus , Ochrobactrum , Prevotella , Ralstonia , Ruminococcus , Sellimonas , Weeksellaceae , and Lachnospiraceae were enriched in the blood samples of patients with pancreatic cancer, whereas Actinobacteria, Verrucomicrobia, Akkermansia , Enterococcus , Erysipelatoclostridium , Gemella , Neisseria , Parvimonas , Rothia , and Streptococcus were enriched in the blood samples of healthy controls (Fig. 3 ). Microbial interactions in blood samples of patients with pancreatic cancer The microbial interactions in blood samples differed between patients with pancreatic cancer and healthy controls (Table 2 , Fig. 4 ). The co-occurrence network of the blood microbiome had a higher clustering coefficient for healthy controls (99%) than for patients with pancreatic cancer (71%). The average path length, a metric indicating the compactness and strength of microbial interactions, was calculated by determining the average number of steps along the shortest paths for all possible pairs of network nodes. The average path length was lower in healthy controls (0.68) than in patients with pancreatic cancer (1.67). This suggests a more compact network and stronger microbial interactions in healthy controls than in patients with pancreatic cancer (Table 2 ). Table 2 Comparison of network topological properties between patients with pancreatic cancer and healthy controls Pancreatic cancer Healthy control Clustering coefficient 0.711 0.992 Modularity 0.487 0.150 Positive edge percentage 74.340 99.378 Edge density 0.017 0.053 Natural connectivity 0.010 0.049 Average dissimilarity 0.994 0.983 Average path length 1.670 0.684 Table 3. S tudies on the association between microbiome and pancreatic cancer at oral (A), stool (B), and blood (C) (A) Author (publication year, country) Sample size alpha-diversity Result: increased Result: decreased Wei AL et al (2020, China) 25 PC 41/ HC 69 ↓ Bacilli, Streptococcus , Firmicutes, Actinomyces, Rothia, Leptotrichia, Lactobacillus , Escherichia_coli, Enterobacteriales Selenomonas, Porphyromnas, Prevotella, Capnocytophaga, Alloprevotella, Tannerella, Neisseria Fan X et al (2018, US) 21 PC 361/HC 371 Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans Fusobacteria, Leptotrichia Lu H et al (2019, China) 22 PC 30/ HC 25 ↑ Leptotrichia, Fusobacterium, Rothia, Actinomyces, Corynebacterium, Atopobium, Peptostreptococcus, Catonella, Oribacterium, Filifactor, Campylobacter, Moraxella, Tannerella Haemophilus, Porphyromonas, Paraprevotella Olson SH et al (2017,Canada) 23 PC 40/ IPMN 39/ HC 58 ↓ Firmicutes, Bacilli, Lactobacillales, Streptococcaceae, Streptococcus, Streptococcus thermophilus Proteobacteria, Gammaproteobacteria, Pasteurellales, Pasteurellaceae, Haemophilus, Haemophilus parainfluenzae, Betaproteobacteria, Neisseriales, Neisseriaceae, Neisseria, Neisseria flavescens Chen T et al (2023,China) 20 PC 40/CP 15/HC 39 Firmicutes, Verrucomicrobia, Veillonella,Peptostreptococcus,Akkermansia,Parvimonas,Solobacterium,Olsenella, Escherichia, Shigella Vogtmann E et al (2020, Iran) 24 PC 273/HC 285 ↑ Enterobacteriaceae, Lachnospiraceae G7, Bacteroidaceae, Staphylococcaceae Haemophilus Torres PJ et al (2015, US) 8 PC 8/other diseases 78/ HC 22 Leptotrichia , Porphyromonas Neisseria, Aggregatibacter Our study (2024, Korea) PC 31/HC 24 Cyanobacteria, Bulleidia, Lactobacillus , Saccharimonadaceae, Chloroplast (B) Author (publication year, country) Sample size Alpha-diversity Result: increased Result: decreased Matsukawa H et al (2021, Japan) 28 PC 24/HC 18 Klebsiella pneumoniae, Clostridium bolteae, C. symbiosum, Streptococcus mutans, Alistipes shahii, Bacteroides species, Parabacteroides species, Lactobacillus Ren Z et al (2017, China) 9 PC 24/HC 18 Prevotella, Veillonella, Klebsiella, Selenomonas, Hallella, Enterobacter , Cronobacter Gemmiger, Bifidobacterium, Coprococcus, Clostridium IV, Blautia, Flavonifractor, Anaerostipes, Butyricicoccus, Dorea Yang J et al (2023, China) 29 PC 44 /HC 50 ↑ Streptococcus Chen T et al (2023, China) 20 PC 40/CP 15/ HC 39 Prevotella, Coprobacter. Proteobacteria, Peptostreptococcus, Actinomyces, Bifidobacterium,Campylobacter, Coprobacillus, Escherichia-Shigella Hashimoto S et al (2023, Japan) 26 PC 5/HC 68 Actinomyces, Streptococcus, Veillonella, Lactobacillus Anaerostipes Kartal E et al (2022, EU) 27 PC 57/CP 29/HC 50 Veillonella atypica, Fusobacterium nucleatum/hwasookii, Alloscardovia omnicolens Romboutsia timonensis, Faecalibacterium prausnitzii, Bacteroides coprocola, Bifidobacterium bifidum Half E et al (2019, Israel) 12 PC 30/pre-cancerous lesions 6 /HC 13 /NAFLD 16 Bacteroidetes, Veillonellaceae, Akkermansia, Odoribacter. Firmicutes, Clostridiacea, Lachnospiraceae, Ruminococcaceae Our study (2024, Korea) PC 31/HC 24 ↓ Enterobacter , Sellimonas Alistipes, Ruminococcus , Slackia (C) Author (publication year, country) Sample size Alpha-diversity Result: increased Result: decreased Our study (2024, Korea) PC 31/HC 24 ↑ Acetobacter, Butyricicoccus, Ochrobactrum, Prevotella, Ralstonia, Ruminococcus, Sellimonas, Weeksellaceae Lachnospiraceae Actinobacteria, Verrucomicrobia, Akkermansia, Enterococcus, Erysipelatoclostridium, Gemella, Neisseria, Parvimonas, Rothia, Streptococcus HC, healthy controls; PC, patients with pancreatic cancer; IPMN, intraductal papillary mucinous neoplasm; CP, Chronic pancreatitis ; NAFLD, non-alcoholic fatty liver disease Discussion Studies have analyzed the differences in the microbiome of patients with pancreatic cancer and healthy controls. However, the results have been inconsistent, and studies focusing on blood samples are limited. The current study compared the microbiome profiles of patients with pancreatic cancer and healthy controls based on 16S rRNA sequencing of saliva, fecal, and blood samples. This study identified features that differentiated the microbial composition of patients with pancreatic cancer from that of healthy controls. Previous studies on saliva samples have shown inconsistent findings regarding alpha diversity in patients with pancreatic cancer [ 22 – 25 ]. However, alpha diversity showed no significant difference between patients with pancreatic cancer and healthy controls in the current study, consistent with the findings of a few studies [ 8 , 20 ]. Fecal samples of patients with pancreatic cancer exhibited decreased alpha diversity and a significant difference in beta diversity, consistent with the findings of other studies [ 19 , 30 , 31 ]. Network analysis of the blood microbiome revealed a higher clustering coefficient and lower average path length in healthy controls than in patients with pancreatic cancer, indicating greater complexity and strength of microbial interactions. Therefore, in patients with pancreatic cancer, the complexity and compactness of microbial interactions are reduced. This result is consistent with that of other studies reporting similar patterns in microbial interaction network in other cancer types [ 32 ]. This study found distinctive microbiomes, such as Lactobacillus, Enterobacter , and Prevotella in saliva, fecal, and blood samples of patients with pancreatic cancer, respectively. Lactobacillus was consistently elevated in the saliva of patients with pancreatic cancer, which is consistent with the findings of other studies [ 25 ]. In contrast, an increased abundance of Lactobacillus was reported in the fecal samples of patients with pancreatic cancer [ 27 ]. In a mouse model of pancreatic cancer, Lactobacillus influenced macrophage activity, potentially contributing to rapid disease progression and mortality [ 33 ]. In a comparison of saliva samples between patients with precancerous lesions and squamous cell carcinoma, Lactobacillus was more abundant in patients with cancer [ 34 ]. The abundance of Enterobacter in the fecal samples of patients with pancreatic cancer was consistent with that reported in other studies [ 9 , 35 ]. Enterobacter was more abundant in tumor [ 31 , 35 ] and bile samples of patients with pancreatic cancer [ 35 ]. In a mouse study, Enterobacter induced chronic pancreatitis, elevating the risk of pancreatic cancer development [ 36 ]. Consistently, Enterobacteriaceae was abundant in pancreatic cancer [ 37 ]. Prevotella was more abundant in the saliva samples of healthy controls than in those of patients with pancreatic cancer [ 25 ]. In contrast, Prevotella had a higher prevalence in the tumors [ 35 ] and feces [ 9 ] of patients with pancreatic cancer. These findings underscore the complex interplay between Lactobacillus , Enterobacter , and Prevotella , and cancer, warranting further investigation. This study had several limitations. First, the study population was small, although it was comparable to other studies. Second, as this is a single center study, studies from several institutions are needed for generalizing the findings. Third, because this study conducted a cross-sectional microbiome analysis, additional experimental models must establish causality between microbial taxa and pancreatic cancer. In conclusion, this study identified significant microbial taxa such as Lactobacillus , Enterobacter , and Prevotella in patients with pancreatic cancer. Network analysis revealed reduced complexity, strength, and compactness of microbial interaction patterns in the blood samples of patients with pancreatic cancer. Our findings can serve as a guide for future research on the complex connection between the microbiome and pancreatic cancer. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the Seoul National University Bundang Hospital (Approval number B-2110-714-303). Consent for publication Informed consent was obtained from all participants included in the study. Availability of data and materials All data generated or analyzed during this study are included in this article and the online supplementary material. Further inquiries can be directed to the corresponding author, and additional data will be available upon reasonable request. Conflict of interest The authors declare no conflicts of interest. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript Author contributions The present study was designed by Cheol Min Shin, Hyo-Jung Lee, Hye Seung Lee, Jaihwan Kim, and Kyoung Un Park The manuscript was written by Bomi Kim and Sujin Oh Statistical analyses were performed by Bomi Kim and Sujin Oh The samples and clinical information were recorded and summarized by Soomin Yang, Jinwoo Ahn, Kwangrok Jung, Jong-Chan Lee, and Jin-Hyeok Hwang Acknowledgments We would like to thank all patients who generously participated in this study. References Siegel RL, Miller KD, Wagle NS, Jemal A, Cancer statistics. 2023. CA Cancer J Clin. 2023;73(1):17–48; 10.3322/caac.21763 Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913–21. 10.1158/0008-5472.CAN-14-0155 . Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. 10.1016/j.cell.2011.02.013 . Zambirinis CP, Pushalkar S, Saxena D, Miller G. Pancreatic cancer, inflammation, and microbiome. Cancer J. 2014;20(3):195–202. 10.1097/PPO.0000000000000045 . Yu Q, Jobin C, Thomas RM. Implications of the microbiome in the development and treatment of pancreatic cancer: Thinking outside of the box by looking inside the gut. Neoplasia. 2021;23(2):246–56. 10.1016/j.neo.2020.12.008 . Poore GD, Kopylova E, Zhu Q, Carpenter C, Fraraccio S, Wandro S, et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature. 2020;579(7800):567–74. 10.1038/s41586-020-2095-1 . Zhou W, Zhang D, Li Z, Jiang H, Li J, Ren R, et al. The fecal microbiota of patients with pancreatic ductal adenocarcinoma and autoimmune pancreatitis characterized by metagenomic sequencing. J Transl Med. 2021;19(1):215. 10.1186/s12967-021-02882-7 . Torres PJ, Fletcher EM, Gibbons SM, Bouvet M, Doran KS, Kelley ST. Characterization of the salivary microbiome in patients with pancreatic cancer. PeerJ. 2015;3:e1373. 10.7717/peerj.1373 . Ren Z, Jiang J, Xie H, Li A, Lu H, Xu S, et al. Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China. Oncotarget. 2017;8(56):95176–91. 10.18632/oncotarget.18820 . Petrick JL, Wilkinson JE, Michaud DS, Cai Q, Gerlovin H, Signorello LB, et al. The oral microbiome in relation to pancreatic cancer risk in African Americans. Br J Cancer. 2022;126(2):287–96. 10.1038/s41416-021-01578-5 . Nagata N, Nishijima S, Kojima Y, Hisada Y, Imbe K, Miyoshi-Akiyama T, et al. Metagenomic Identification of Microbial Signatures Predicting Pancreatic Cancer From a Multinational Study. Gastroenterology. 2022;163(1):222–38. 10.1053/j.gastro.2022.03.054 . Half E, Keren N, Reshef L, Dorfman T, Lachter I, Kluger Y, et al. Fecal microbiome signatures of pancreatic cancer patients. Sci Rep. 2019;9(1):16801. 10.1038/s41598-019-53041-4 . Song SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458. 10.7554/eLife.00458 . Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat Med. 2018;24(4):392–400. 10.1038/nm.4517 . Potgieter M, Bester J, Kell DB, Pretorius E. The dormant blood microbiome in chronic, inflammatory diseases. FEMS Microbiol Rev. 2015;39(4):567–91. 10.1093/femsre/fuv013 . Woerner J, Huang Y, Hutter S, Gurnari C, Sanchez JMH, Wang J, et al. Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes. Nat Commun. 2022;13(1):1038. 10.1038/s41467-022-28678-x . Cho EJ, Leem S, Kim SA, Yang J, Lee YB, Kim SS, et al. Circulating Microbiota-Based Metagenomic Signature for Detection of Hepatocellular Carcinoma. Sci Rep. 2019;9(1):7536. 10.1038/s41598-019-44012-w . Cheng HS, Tan SP, Wong DMK, Koo WLY, Wong SH, Tan NS. The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges. Int J Mol Sci. 2023;24(6). 10.3390/ijms24065633 . An J, Kwon H, Lim W, Moon BI. Staphylococcus aureus-Derived Extracellular Vesicles Enhance the Efficacy of Endocrine Therapy in Breast Cancer Cells. J Clin Med. 2022;11(7). 10.3390/jcm11072030 . Chen T, Li X, Li G, Liu Y, Huang X, Ma W, et al. Alterations of commensal microbiota are associated with pancreatic cancer. Int J Biol Markers. 2023;38(2):89–98. 10.1177/03936155231166721 . Fan X, Alekseyenko AV, Wu J, Peters BA, Jacobs EJ, Gapstur SM, et al. Human oral microbiome and prospective risk for pancreatic cancer: a population-based nested case-control study. Gut. 2018;67(1):120–7. 10.1136/gutjnl-2016-312580 . Lu H, Ren Z, Li A, Li J, Xu S, Zhang H, et al. Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls. J Oral Microbiol. 2019;11(1):1563409. 10.1080/20002297.2018.1563409 . Olson SH, Satagopan J, Xu Y, Ling L, Leong S, Orlow I, et al. The oral microbiota in patients with pancreatic cancer, patients with IPMNs, and controls: a pilot study. Cancer Causes Control. 2017;28(9):959–69. 10.1007/s10552-017-0933-8 . Vogtmann E, Han Y, Caporaso JG, Bokulich N, Mohamadkhani A, Moayyedkazemi A, et al. Oral microbial community composition is associated with pancreatic cancer: A case-control study in Iran. Cancer Med. 2020;9(2):797–806. 10.1002/cam4.2660 . Wei AL, Li M, Li GQ, Wang X, Hu WM, Li ZL, et al. Oral microbiome and pancreatic cancer. World J Gastroenterol. 2020;26(48):7679–92. 10.3748/wjg.v26.i48.7679 . Hashimoto S, Tochio T, Funasaka K, Funahashi K, Hartanto T, Togashi Y, 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. 2023;58(2):193–8. 10.1080/00365521.2022.2114812 . Kartal E, Schmidt TSB, Molina-Montes E, Rodriguez-Perales S, Wirbel J, Maistrenko OM, et al. A faecal microbiota signature with high specificity for pancreatic cancer. Gut. 2022;71(7):1359–72. 10.1136/gutjnl-2021-324755 . Matsukawa H, Iida N, Kitamura K, Terashima T, Seishima J, Makino I, et al. Dysbiotic gut microbiota in pancreatic cancer patients form correlation networks with the oral microbiota and prognostic factors. Am J Cancer Res. 2021;11(6):3163–75. Yang J, Ma Y, Tan Q, Zhou B, Yu D, Jin M, et al. Gut Streptococcus is a microbial marker for the occurrence and liver metastasis of pancreatic cancer. Front Microbiol. 2023;14:1184869. 10.3389/fmicb.2023.1184869 . Li JJ, Zhu M, Kashyap PC, Chia N, Tran NH, McWilliams RR, et al. The role of microbiome in pancreatic cancer. Cancer Metastasis Rev. 2021;40(3):777–89. 10.1007/s10555-021-09982-2 . Kohi S, Macgregor-Das A, Dbouk M, Yoshida T, Chuidian M, Abe T, et al. Alterations in the Duodenal Fluid Microbiome of Patients With Pancreatic Cancer. Clin Gastroenterol Hepatol. 2022;20(2):e196–227. 10.1016/j.cgh.2020.11.006 . Liu X, Shao L, Liu X, Ji F, Mei Y, Cheng Y, et al. Alterations of gastric mucosal microbiota across different stomach microhabitats in a cohort of 276 patients with gastric cancer. EBioMedicine. 2019;40:336–48. 10.1016/j.ebiom.2018.12.034 . Hezaveh K, Shinde RS, Klotgen A, Halaby MJ, Lamorte S, Ciudad MT et al. Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity. Immunity. 2022;55(2):324 – 40 e8; 10.1016/j.immuni.2022.01.006 Li Z, Dou L, Zhang Y, He S, Zhao D, Hao C, et al. Characterization of the Oral and Esophageal Microbiota in Esophageal Precancerous Lesions and Squamous Cell Carcinoma. Front Cell Infect Microbiol. 2021;11:714162. 10.3389/fcimb.2021.714162 . Nejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science. 2020;368(6494):973–80. 10.1126/science.aay9189 . Maekawa T, Fukaya R, Takamatsu S, Itoyama S, Fukuoka T, Yamada M, et al. Possible involvement of Enterococcus infection in the pathogenesis of chronic pancreatitis and cancer. Biochem Biophys Res Commun. 2018;506(4):962–9. 10.1016/j.bbrc.2018.10.169 . Geller LT, Barzily-Rokni M, Danino T, Jonas OH, Shental N, Nejman D, et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science. 2017;357(6356):1156–60. 10.1126/science.aah5043 . Additional Declarations No competing interests reported. Supplementary Files supplefigure1.tif Supplementary Fig. 1 The microbial composition relationships between the sample types were assessed between healthy controls and patients with pancreatic cancer. Principal coordinate analysis (PCoA) was employed to visualize the relationships between samples using unweighted UniFrac distance matrices. HC, healthy control; PC, patient with pancreatic cancer Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4992405","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351990507,"identity":"c79dffb6-a6e0-4db7-ab22-5d6171970e7f","order_by":0,"name":"Bomi Kim","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bomi","middleName":"","lastName":"Kim","suffix":""},{"id":351990517,"identity":"e3d63192-69a4-4bea-9afc-33edd16d19dc","order_by":1,"name":"Sujin Oh","email":"","orcid":"","institution":"Department of Laboratory Medicine, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sujin","middleName":"","lastName":"Oh","suffix":""},{"id":351990518,"identity":"adec0238-539a-47fa-85a0-d185c779f7b7","order_by":2,"name":"Soomin Yang","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Soomin","middleName":"","lastName":"Yang","suffix":""},{"id":351990520,"identity":"a83309fd-164e-44b9-bd40-8ef494c4efa7","order_by":3,"name":"Jinwoo Ahn","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinwoo","middleName":"","lastName":"Ahn","suffix":""},{"id":351990521,"identity":"151c5255-d82d-4f2e-9dfc-3e00ba164257","order_by":4,"name":"Kwangrok Jung","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kwangrok","middleName":"","lastName":"Jung","suffix":""},{"id":351990522,"identity":"e2a4581b-dc81-4aa6-b1dd-596d4d8d92b0","order_by":5,"name":"Jong-Chan Lee","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jong-Chan","middleName":"","lastName":"Lee","suffix":""},{"id":351990523,"identity":"7ac6301c-8472-490d-bb62-473411cf4db1","order_by":6,"name":"Jin-Hyeok Hwang","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jin-Hyeok","middleName":"","lastName":"Hwang","suffix":""},{"id":351990524,"identity":"c11638df-5b5c-4689-9168-7548ab6011e8","order_by":7,"name":"Cheol Min Shin","email":"","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Cheol","middleName":"Min","lastName":"Shin","suffix":""},{"id":351990527,"identity":"b017e73d-9344-4519-81df-6b9ec20fa210","order_by":8,"name":"Hyo-Jung Lee","email":"","orcid":"","institution":"Department of Periodontology, Section of Dentistry, Seoul National University Bundang Hospital, Seoul National University School of Dentistry","correspondingAuthor":false,"prefix":"","firstName":"Hyo-Jung","middleName":"","lastName":"Lee","suffix":""},{"id":351990534,"identity":"69234cb5-4a93-4dc5-aea3-12ebbe52512f","order_by":9,"name":"Hye Seung Lee","email":"","orcid":"","institution":"Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hye","middleName":"Seung","lastName":"Lee","suffix":""},{"id":351990535,"identity":"1aea463f-7c0e-4364-84ed-022ddde41e93","order_by":10,"name":"Jaihwan Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3RMQrCMBSA4SeCLk9dWxx6hRahdVGvkhDo1AMIDnaKh9BDZNStUDBLdVa6KEJdHBx10hQcnNKOgvmnEPKRFwJgMv1iCRCgsVq0488O1iaY1CcAjZJYpCbp5sn5fl6nTmBfiwvC2AHcnbTE3hNm0Sz1NssoGCAwL+4sXC1xMyAW5XlD5FGrj9Ak0GvpB1OEPRSZiKMsFJnXImF5CxUH8BVJCXS4ntiKDCl/MZFFvr1ypcdxqyfdDNnxycORkLKwbtOZ08NQT9Q3kO85ASpeUtZOqs+YTCbTf/cGC25EdqXUWlQAAAAASUVORK5CYII=","orcid":"","institution":"Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jaihwan","middleName":"","lastName":"Kim","suffix":""},{"id":351990537,"identity":"2ad4794b-d4f9-4bc0-943b-cf4f9e793b4d","order_by":11,"name":"Kyoung Un Park","email":"","orcid":"","institution":"Department of Laboratory Medicine, Seoul National University Bundang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kyoung","middleName":"Un","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-08-28 15:59:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4992405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4992405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66930374,"identity":"424526a5-5749-4ab8-b362-0351a107d92a","added_by":"auto","created_at":"2024-10-18 06:53:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68165,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance in each specimen. Bar plots represent the relative abundance of predominant microbiota constituents at the phylum (A) and genus (B) levels for each sample. HC, healthy control; PC, patient with pancreatic cancer\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4992405/v1/b171efe218254f9bafcb97d6.png"},{"id":66930379,"identity":"6cbb53fb-9d60-4cb0-88eb-d4d1887a783f","added_by":"auto","created_at":"2024-10-18 06:54:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41886,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of microbial diversity between patients with pancreatic cancer and matched healthy controls in each specimen. Alpha diversity, measured via Shannon’s entropy, was higher in fecal samples and lower in blood samples of healthy controls. (A). Beta diversity analysis using the Bray–Curtis distance indicated significant differences among sample types, allowing differentiation between patients with pancreatic cancer and healthy controls (B). HC, healthy control; PC, patient with pancreatic cancer. Statistical significance (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4992405/v1/8f81ed5304665a5b2d86931b.png"},{"id":66930378,"identity":"6497e627-3ce5-4917-98b7-43ed3edc8386","added_by":"auto","created_at":"2024-10-18 06:54:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46173,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot showing the effect size of the difference between the abundance of each taxon in patients with pancreatic cancer and healthy controls. Effect sizes were estimated via differential abundance analysis using analysis of compositions of microbiomes with bias correction (ANCOM-BC) and expressed as log-fold change divided by the estimate of standard error. HC, healthy control; PC, patient with pancreatic cancer; P, phylum level; G, genus level; B, blood sample; F, fecal sample; LFC, log-fold change\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4992405/v1/01f4ed3c2f27205674ba334a.png"},{"id":66930380,"identity":"bf37ce6d-2c95-40ab-b4b5-7cd3bfa2d2e2","added_by":"auto","created_at":"2024-10-18 06:54:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54811,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analysis of blood samples from patients with pancreatic cancer (A) and healthy controls (B). Lines between dots indicate the significant correlation of species (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The size of the node is proportional to the relative abundance of species. The nodes are colored according to the phylum to which the species belongs\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4992405/v1/6118015a3fddb5b005d77e75.png"},{"id":68873570,"identity":"6bc4d248-1977-41fa-ab6a-b02802556739","added_by":"auto","created_at":"2024-11-13 04:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":874275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4992405/v1/cd142f40-9a52-4a2c-8c53-cf0239f30a91.pdf"},{"id":66930371,"identity":"3188e172-f9af-4dfa-b72b-627e61dbfe14","added_by":"auto","created_at":"2024-10-18 06:53:55","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2456050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 1\u003c/strong\u003e The microbial composition relationships between the sample types were assessed between healthy controls and patients with pancreatic cancer. Principal coordinate analysis (PCoA) was employed to visualize the relationships between samples using unweighted UniFrac distance matrices. HC, healthy control; PC, patient with pancreatic cancer\u003c/p\u003e","description":"","filename":"supplefigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4992405/v1/4429a150c74dfabf44318141.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct microbiome composition and reduced interactions in patients with pancreatic cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eThe incidence of pancreatic cancer is increasing, and it is expected to become the second leading cause of cancer-related deaths worldwide by 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The average 5-year survival rate in pancreatic cancer has reached approximately 12%, and the 5-year survival rate in patients with localized disease is only 44% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) plays an important role in tumor growth, metastasis, and disease characteristics. The TME comprises various cell types that interact with each other [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The microbiome, a constituent of the TME, modulates the inflammatory response, which can drive carcinogenesis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Microbiome analysis has been reported to be a potential diagnostic tool for malignancies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies on the microbiome of saliva and feces from patients with pancreatic cancer have reported variations in diversity and taxonomy [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This inconsistency is attributable to differences between individuals, sample sites, and lifestyle variables, such as diet, medication, and familial factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough human blood is considered sterile, modern sequencing techniques have detected bacterial genetic material even in the blood of healthy individuals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Distinct blood microbial profiles have been reported in several malignancies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This underscores the importance of blood microbiome analysis in cancer.\u003c/p\u003e \u003cp\u003eThere are limited studies on microbiome composition in patients with pancreatic cancer. This study aimed to characterize microbiome composition of saliva, fecal, and blood samples in patients with pancreatic cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe collected saliva, fecal, and blood samples from patients with pancreatic cancer in a single tertiary teaching hospital between December 2019 and May 2022. Samples from healthy controls were acquired from the Periodontal Human Specimen Storage Registry at Seoul National University Bundang Hospital, with approval for secondary research. We reviewed a database of patients with pancreatic cancer. A survey was conducted among patients with pancreatic cancer and healthy controls to obtain information on the underlying medical conditions and oral care practices. When assessing smoking history, individuals who had quit for \u0026ge;\u0026thinsp;6 months were categorized as the nonsmoking group and those who had quit within the past 6 months were classified as the smoking group. Experienced periodontists evaluated periodontal health parameters, including periodontal probing depth and missing teeth count.\u003c/p\u003e \u003cp\u003e Written informed consent was obtained from all participants prior to inclusion in the study. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Seoul National University Bundang Hospital (no. B-2110-714-303).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and preparation\u003c/h2\u003e \u003cp\u003e Patients with pancreatic cancer and healthy controls were instructed to abstain from oral hygiene practices for a minimum of 2 h before saliva collection. Fecal samples were self-collected by the participants using a sterile spatula, placed in a sterile container designed for feces, and immediately stored in a freezer until transportation on ice to the laboratory. Venous blood samples were aseptically collected by trained personnel. Upon arrival at the laboratory, all samples, excluding fecal samples, were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until DNA extraction. DNA was extracted from 1 mL of thawed sample using QIAamp DNA Microbiome Kit (QIAGEN, Venlo, the Netherlands), following the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA amplicon sequencing\u003c/h2\u003e \u003cp\u003eDNA quality was assessed using Qubit dsDNA HS Assay Kits (Thermo Fisher Scientific Inc., Waltham, MA, USA). Polymerase chain reaction (PCR) targeting V3 and V4 hypervariable regions of 16S rRNA genes was conducted using KAPA HiFi HotStart ReadyMix PCR Kit (Roche, Basel, Switzerland) following the manufacturer\u0026rsquo;s instructions. The primer sequences used for PCR amplification were as follows: 519F: 5\u0026prime;-CCTACGGGNGGCWGCAG-3\u0026prime; and 806R: 5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;. Libraries were constructed utilizing Nextera XT DNA Library Preparation Kit (Illumina Inc., San Diego, CA, USA), and the amplified samples were pooled to achieve a final loading concentration of 8 pM. Subsequently, paired-end (2 \u0026times; 300 bp) sequencing was performed using the MiSeq platform (Illumina).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and visualization\u003c/h2\u003e \u003cp\u003eThe reads were processed using a Divisive Amplicon Denoising Algorithm (DADA2)-based pipeline within the Quantitative Insights Into Microbial Ecology (QIIME2) 22.2 platform. This process involved generation of an amplicon sequence variant (ASV) table through quality-based filtering and trimming, read deduplication, ASV inference, paired-end merging, and chimera removal. ASVs were taxonomically classified against the 99% SILVA rRNA taxonomy. To rectify artifactual biases, feature tables were normalized via rarefaction.\u003c/p\u003e \u003cp\u003eFor alpha diversity analysis, including observed features, Shannon\u0026rsquo;s entropy, Pielou\u0026rsquo;s evenness, and Faith\u0026rsquo;s phylogenetic diversity were calculated. To evaluate dissimilarities between microbial compositions of each sample, beta diversity indices, such as the Bray\u0026ndash;Curtis index, and unweighted UniFrac distance were calculated. Principal coordinate analysis (PCoA) was used to visualize overall trends in sample dissimilarities. Moreover, permutation multivariate analysis of variance was performed to quantify the strength of associations between microbial composition and sample variables. To identify differentially abundant taxa between sample groups, we performed analysis of compositions of microbiomes with bias correction (ANCOM-BC), which can estimate unknown sampling fractions and correct for bias resulting from differences through a log-linear regression model. Then, we used Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2), which can predict microbial functions based on 16S marker gene sequences. To examine variations in microbial metabolism, predicted orthologs were collapsed into the Kyoto Encyclopedia of Genes and Genomes pathways, followed by differential abundance (DA) analysis using ANOVA-Like Differential Expression tool version 2 (ALDEx2). Correction for multiple testing was performed using the Benjamini\u0026ndash;Hochberg method; thus, \u003cem\u003eQ\u003c/em\u003e-values of \u0026lt;\u0026thinsp;0.05 were considered to indicate statistical significance for both DA methods.\u003c/p\u003e \u003cp\u003eTo examine interactions between microbiomes, co-occurrence network analysis was performed using sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) via graphical lasso algorithms. Signed distance was computed to transform associations into dissimilarities. Topological properties of networks, including clustering coefficient, average path length, average dissimilarity, modularity, edge density, and positive edge ratio, were examined using the \u003cem\u003eigraph\u003c/em\u003e package in R. Only nodes with \u0026gt;\u0026thinsp;3 degrees are shown in the figures.\u003c/p\u003e \u003cp\u003eStatistical analyses and data visualization were performed using R software (ver. 4.1.2; R Development Core Team, Vienna, Austria). QIIME artifacts were imported into the R environment using the \u003cem\u003eqiime2R\u003c/em\u003e package and then transformed into phyloseq objects using the \u003cem\u003ephyloseq\u003c/em\u003e package. Centered log-ratio transformation of raw feature counts was performed before conducting statistical analyses of microbial abundance. The Wilcoxon rank-sum test was conducted to compare nonparametric distributions of alpha diversity between sample groups, and \u003cem\u003ep-\u003c/em\u003evalues of \u0026lt;\u0026thinsp;0.05 were considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eStudy participants included 31 patients with pancreatic cancer and 24 healthy controls. No significant difference was noted in the baseline characteristics between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median ages of patients with pancreatic cancer and healthy controls were 69 and 65 years, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81). In total, males constituted 58.1% (n\u0026thinsp;=\u0026thinsp;18) and 66.7% (n\u0026thinsp;=\u0026thinsp;16) of patients with pancreatic cancer and healthy controls, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61). The prevalence rates of diabetes mellitus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31), smoking history (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93), and drinking history (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20) were not significantly different between the two groups. Although body mass index was significantly different between the two groups, none of the groups met the criteria for obesity (median body mass index: 21.7 and 24.7 kg/m\u0026sup2; for patients with pancreatic cancer and healthy controls, respectively). Pancreatic cancer was more frequent in the head than in the body or tail (n\u0026thinsp;=\u0026thinsp;17, 54.8% vs. n\u0026thinsp;=\u0026thinsp;14, 45.2%). In total, 14 (45.2%) tumors were resectable, 7 (22.6%) were borderline resectable, 5 (16.1%) were locally advanced, and 5 (16.1%) were metastatic. The median serum level of carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9) was 41.0 U/mL. Periodontal information revealed no difference in daily toothbrushing frequency (\u0026le;\u0026thinsp;2 times a day: 51.9% vs. 40.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52), median severity of severe periodontitis (4.0 vs. 4.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91), and number of missing teeth (1.5 vs. 2.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42) between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and periodontal information between the patient with pancreatic cancer and healthy control\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic cancer \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;31, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy control \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;24, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (year) (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (36\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (36\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (n\u0026thinsp;=\u0026thinsp;31/23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (n\u0026thinsp;=\u0026thinsp;25/23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (n\u0026thinsp;=\u0026thinsp;25/12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (SD) (kg/m\u003csup\u003e2\u003c/sup\u003e) (n\u0026thinsp;=\u0026thinsp;31/17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.7 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location and resectability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead/Body/Tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (54.8)/9 (29.0)/5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResectable/Borderline resectable/\u003c/p\u003e \u003cp\u003eLocally advanced/metastatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.2)/7 (22.6)/\u003c/p\u003e \u003cp\u003e5 (16.1)/5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (SD) (U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.0 (6,131.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriodontal information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTooth brushing/day\u0026thinsp;\u0026le;\u0026thinsp;2 (n\u0026thinsp;=\u0026thinsp;27/10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian severe periodontitis (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere periodontitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian missing (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian missing \u0026amp; severe periodontitis (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; SD, Standard Deviation; CA 19\u0026thinsp;\u0026minus;\u0026thinsp;9, Carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOverview of microbiome composition\u003c/h2\u003e \u003cp\u003eMicrobiome composition was analyzed at phylum and genus levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). At the phylum level, Firmicutes was dominant in the saliva, fecal, and blood samples of patients with pancreatic cancer and healthy controls. Saliva samples exhibited a high abundance of Actinobacteria, whereas fecal samples showed a prevalence of Verrucomicrobia. At the genus level, distinct variations in microbial composition were observed between saliva, fecal, and blood samples of the two groups. PCoA results of unweighted UniFrac distance confirmed differences in microbial composition of saliva, fecal, and blood samples between patients with pancreatic cancer and healthy controls (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAlpha and beta diversities\u003c/h2\u003e \u003cp\u003eNo significant difference was observed in the alpha diversity indices between saliva samples of patients with pancreatic cancer and those of healthy controls (median Shannon index: 6.45 vs. 6.22; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42). However, Shannon indices of fecal samples were higher in healthy controls than in patients with pancreatic cancer (median Shannon index: 6.19 vs. 6.52; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). The blood microbiome showed higher richness and evenness in patients with pancreatic cancer than in healthy controls (median Shannon index: 8.00 vs. 7.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeta diversity analysis using the Bray\u0026ndash;Curtis distance revealed significant differences in microbial composition of saliva, fecal, and blood samples between patients with pancreatic cancer and healthy controls (saliva: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; feces: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; blood: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially abundant taxa between patients with pancreatic cancer and healthy controls\u003c/h2\u003e \u003cp\u003eDA analysis revealed distinct microbial profiles between the two groups. Saliva samples of patients with pancreatic cancer had a significantly higher abundance of Cyanobacteria, \u003cem\u003eBulleidia\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eSaccharimonadaceae\u003c/em\u003e than those of healthy controls. Furthermore, \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eSellimonas\u003c/em\u003e were more abundant in the fecal samples of patients with pancreatic cancer than in those of healthy controls, whereas \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eSlackia\u003c/em\u003e were more abundant in the fecal samples of healthy controls than in those of patients with pancreatic cancer. \u003cem\u003eAcetobacter\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eOchrobactrum\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eSellimonas\u003c/em\u003e, \u003cem\u003eWeeksellaceae\u003c/em\u003e, and \u003cem\u003eLachnospiraceae\u003c/em\u003e were enriched in the blood samples of patients with pancreatic cancer, whereas Actinobacteria, Verrucomicrobia, \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eErysipelatoclostridium\u003c/em\u003e, \u003cem\u003eGemella\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eParvimonas\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e were enriched in the blood samples of healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial interactions in blood samples of patients with pancreatic cancer\u003c/h2\u003e \u003cp\u003eThe microbial interactions in blood samples differed between patients with pancreatic cancer and healthy controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The co-occurrence network of the blood microbiome had a higher clustering coefficient for healthy controls (99%) than for patients with pancreatic cancer (71%). The average path length, a metric indicating the compactness and strength of microbial interactions, was calculated by determining the average number of steps along the shortest paths for all possible pairs of network nodes. The average path length was lower in healthy controls (0.68) than in patients with pancreatic cancer (1.67). This suggests a more compact network and stronger microbial interactions in healthy controls than in patients with pancreatic cancer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of network topological properties between patients with pancreatic cancer and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy control\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustering coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive edge percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdge density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage dissimilarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage path length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3. S\u003c/strong\u003e\u003cstrong\u003etudies on the association between microbiome and pancreatic cancer at\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eoral (A), stool (B), and blood (C)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(A)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003eAuthor (publication year, country)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1429%;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003ealpha-diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eResult: increased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4286%;\"\u003e\n \u003cp\u003eResult: decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eWei AL et al \u0026nbsp;(2020, China)\u003csup\u003e25\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 41/ HC 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eBacilli, \u003cem\u003eStreptococcus\u003c/em\u003e, Firmicutes, \u003cem\u003eActinomyces, Rothia, Leptotrichia, \u003cstrong\u003e\u003cu\u003eLactobacillus\u003c/u\u003e\u003c/strong\u003e, Escherichia_coli, Enterobacteriales\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003eSelenomonas, \u003cem\u003ePorphyromnas, Prevotella, Capnocytophaga, Alloprevotella, Tannerella, Neisseria\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eFan X et al (2018, US)\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 361/HC 371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003e\u003cem\u003ePorphyromonas gingivalis, Aggregatibacter actinomycetemcomitans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003eFusobacteria, Leptotrichia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eLu H et al (2019, China)\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 30/ HC 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eLeptotrichia, \u003cem\u003eFusobacterium, Rothia, Actinomyces, Corynebacterium, Atopobium, Peptostreptococcus, Catonella, Oribacterium, Filifactor, Campylobacter, Moraxella, Tannerella\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003e\u003cem\u003eHaemophilus, Porphyromonas, Paraprevotella\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eOlson SH\u0026nbsp;et al (2017,Canada)\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 40/ IPMN 39/ HC 58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eFirmicutes, Bacilli, Lactobacillales, Streptococcaceae, Streptococcus, \u003cem\u003eStreptococcus thermophilus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003eProteobacteria, Gammaproteobacteria, Pasteurellales, Pasteurellaceae, \u003cem\u003eHaemophilus, Haemophilus parainfluenzae, Betaproteobacteria, Neisseriales, Neisseriaceae, Neisseria, Neisseria flavescens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eChen T et al (2023,China)\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 40/CP 15/HC 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eFirmicutes, Verrucomicrobia, \u0026nbsp;\u003cem\u003eVeillonella,Peptostreptococcus,Akkermansia,Parvimonas,Solobacterium,Olsenella, Escherichia, Shigella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eVogtmann E et al (2020, Iran)\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 273/HC 285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eEnterobacteriaceae, Lachnospiraceae G7, Bacteroidaceae, Staphylococcaceae\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003e\u003cem\u003eHaemophilus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eTorres PJ et al (2015, US)\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 8/other diseases 78/ HC 22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003e\u003cem\u003eLeptotrichia\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003e\u003cem\u003eNeisseria, Aggregatibacter\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003eOur study (2024, Korea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1429%;\"\u003e\n \u003cp\u003ePC 31/HC 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eCyanobacteria, \u003cem\u003eBulleidia, \u003cstrong\u003e\u003cu\u003eLactobacillus\u003c/u\u003e\u003c/strong\u003e, Saccharimonadaceae, Chloroplast\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(B)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.166%;\"\u003e\n \u003cp\u003eAuthor (publication year, country)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.86981%;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.00858%;\"\u003e\n \u003cp\u003eAlpha-diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.4807%;\"\u003e\n \u003cp\u003eResult: increased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.475%;\"\u003e\n \u003cp\u003eResult: decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eMatsukawa H et al (2021, Japan)\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 24/HC 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae, Clostridium bolteae, C. symbiosum, Streptococcus mutans, Alistipes shahii, Bacteroides species, Parabacteroides species, Lactobacillus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eRen Z et al (2017, China)\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 24/HC 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003ePrevotella, Veillonella, Klebsiella, Selenomonas, Hallella, \u003cstrong\u003e\u003cu\u003eEnterobacter\u003c/u\u003e,\u003c/strong\u003e Cronobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u003cem\u003eGemmiger, Bifidobacterium, Coprococcus, Clostridium IV, Blautia, Flavonifractor, Anaerostipes, Butyricicoccus, Dorea\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eYang J et al (2023, China)\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 44 /HC 50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eChen T et al (2023, China)\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 40/CP\u0026nbsp;15/ HC 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u003cem\u003ePrevotella, Coprobacter. Proteobacteria, Peptostreptococcus, Actinomyces, Bifidobacterium,Campylobacter, Coprobacillus, Escherichia-Shigella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eHashimoto S et al (2023, Japan)\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 5/HC 68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u003cem\u003eActinomyces, Streptococcus, Veillonella, Lactobacillus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u003cem\u003eAnaerostipes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eKartal \u0026nbsp;E et al (2022, EU)\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 57/CP 29/HC 50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u003cem\u003eVeillonella atypica, Fusobacterium nucleatum/hwasookii, \u0026nbsp; \u0026nbsp; Alloscardovia omnicolens\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u003cem\u003eRomboutsia timonensis, Faecalibacterium prausnitzii, Bacteroides coprocola, Bifidobacterium bifidum\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eHalf E et al (2019, Israel)\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 30/pre-cancerous lesions 6 /HC 13 /NAFLD 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003eBacteroidetes, Veillonellaceae, \u003cem\u003eAkkermansia, Odoribacter.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003eFirmicutes, Clostridiacea, Lachnospiraceae, Ruminococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.166%;\"\u003e\n \u003cp\u003eOur study (2024, Korea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.86981%;\"\u003e\n \u003cp\u003ePC 31/HC 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.00858%;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4807%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eEnterobacter\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, Sellimonas\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.475%;\"\u003e\n \u003cp\u003e\u003cem\u003eAlistipes, Ruminococcus , Slackia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(C)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.5714%;\"\u003e\n \u003cp\u003eAuthor (publication year, country)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.5714%;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003eAlpha-diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.4286%;\"\u003e\n \u003cp\u003eResult: increased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4286%;\"\u003e\n \u003cp\u003eResult: decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5714%;\"\u003e\n \u003cp\u003eOur study (2024, Korea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5714%;\"\u003e\n \u003cp\u003ePC 31/HC 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4286%;\"\u003e\n \u003cp\u003e\u003cem\u003eAcetobacter, Butyricicoccus, Ochrobactrum, Prevotella, Ralstonia, Ruminococcus, Sellimonas, Weeksellaceae \u0026nbsp; \u0026nbsp; Lachnospiraceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.4286%;\"\u003e\n \u003cp\u003eActinobacteria, Verrucomicrobia, Akkermansia, Enterococcus, Erysipelatoclostridium, Gemella, Neisseria, Parvimonas, Rothia, Streptococcus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHC, healthy controls; PC, patients with pancreatic cancer; IPMN, intraductal papillary mucinous neoplasm; CP, Chronic pancreatitis ; NAFLD, non-alcoholic fatty liver disease\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies have analyzed the differences in the microbiome of patients with pancreatic cancer and healthy controls. However, the results have been inconsistent, and studies focusing on blood samples are limited. The current study compared the microbiome profiles of patients with pancreatic cancer and healthy controls based on 16S rRNA sequencing of saliva, fecal, and blood samples. This study identified features that differentiated the microbial composition of patients with pancreatic cancer from that of healthy controls.\u003c/p\u003e \u003cp\u003ePrevious studies on saliva samples have shown inconsistent findings regarding alpha diversity in patients with pancreatic cancer [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, alpha diversity showed no significant difference between patients with pancreatic cancer and healthy controls in the current study, consistent with the findings of a few studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Fecal samples of patients with pancreatic cancer exhibited decreased alpha diversity and a significant difference in beta diversity, consistent with the findings of other studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork analysis of the blood microbiome revealed a higher clustering coefficient and lower average path length in healthy controls than in patients with pancreatic cancer, indicating greater complexity and strength of microbial interactions. Therefore, in patients with pancreatic cancer, the complexity and compactness of microbial interactions are reduced. This result is consistent with that of other studies reporting similar patterns in microbial interaction network in other cancer types [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found distinctive microbiomes, such as \u003cem\u003eLactobacillus, Enterobacter\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e in saliva, fecal, and blood samples of patients with pancreatic cancer, respectively. \u003cem\u003eLactobacillus\u003c/em\u003e was consistently elevated in the saliva of patients with pancreatic cancer, which is consistent with the findings of other studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, an increased abundance of \u003cem\u003eLactobacillus\u003c/em\u003e was reported in the fecal samples of patients with pancreatic cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In a mouse model of pancreatic cancer, \u003cem\u003eLactobacillus\u003c/em\u003e influenced macrophage activity, potentially contributing to rapid disease progression and mortality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In a comparison of saliva samples between patients with precancerous lesions and squamous cell carcinoma, \u003cem\u003eLactobacillus\u003c/em\u003e was more abundant in patients with cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The abundance of \u003cem\u003eEnterobacter\u003c/em\u003e in the fecal samples of patients with pancreatic cancer was consistent with that reported in other studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. \u003cem\u003eEnterobacter\u003c/em\u003e was more abundant in tumor [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and bile samples of patients with pancreatic cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In a mouse study, \u003cem\u003eEnterobacter\u003c/em\u003e induced chronic pancreatitis, elevating the risk of pancreatic cancer development [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consistently, Enterobacteriaceae was abundant in pancreatic cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cem\u003ePrevotella\u003c/em\u003e was more abundant in the saliva samples of healthy controls than in those of patients with pancreatic cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, \u003cem\u003ePrevotella\u003c/em\u003e had a higher prevalence in the tumors [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and feces [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] of patients with pancreatic cancer. These findings underscore the complex interplay between \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e, and cancer, warranting further investigation.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the study population was small, although it was comparable to other studies. Second, as this is a single center study, studies from several institutions are needed for generalizing the findings. Third, because this study conducted a cross-sectional microbiome analysis, additional experimental models must establish causality between microbial taxa and pancreatic cancer.\u003c/p\u003e \u003cp\u003eIn conclusion, this study identified significant microbial taxa such as \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e in patients with pancreatic cancer. Network analysis revealed reduced complexity, strength, and compactness of microbial interaction patterns in the blood samples of patients with pancreatic cancer. Our findings can serve as a guide for future research on the complex connection between the microbiome and pancreatic cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the Seoul National University Bundang Hospital (Approval number B-2110-714-303).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article and the online supplementary material. Further inquiries can be directed to the corresponding author, and additional data will be available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was designed by\u0026nbsp;Cheol Min Shin, Hyo-Jung Lee, Hye Seung Lee, Jaihwan Kim,\u0026nbsp;and\u0026nbsp;Kyoung Un Park\u003c/p\u003e\n\u003cp\u003eThe manuscript was written by\u0026nbsp;Bomi Kim and Sujin Oh\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed by Bomi Kim and\u0026nbsp;Sujin Oh\u003c/p\u003e\n\u003cp\u003eThe samples and clinical information were recorded and summarized by Soomin Yang, Jinwoo Ahn, Kwangrok Jung, Jong-Chan Lee, and Jin-Hyeok Hwang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all patients who generously participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A, Cancer statistics. 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21763\u003c/span\u003e\u003cspan address=\"10.3322/caac.21763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74(11):2913\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/0008-5472.CAN-14-0155\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-14-0155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2011.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2011.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZambirinis CP, Pushalkar S, Saxena D, Miller G. Pancreatic cancer, inflammation, and microbiome. Cancer J. 2014;20(3):195\u0026ndash;202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/PPO.0000000000000045\u003c/span\u003e\u003cspan address=\"10.1097/PPO.0000000000000045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Q, Jobin C, Thomas RM. Implications of the microbiome in the development and treatment of pancreatic cancer: Thinking outside of the box by looking inside the gut. Neoplasia. 2021;23(2):246\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neo.2020.12.008\u003c/span\u003e\u003cspan address=\"10.1016/j.neo.2020.12.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoore GD, Kopylova E, Zhu Q, Carpenter C, Fraraccio S, Wandro S, et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature. 2020;579(7800):567\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-020-2095-1\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2095-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou W, Zhang D, Li Z, Jiang H, Li J, Ren R, et al. The fecal microbiota of patients with pancreatic ductal adenocarcinoma and autoimmune pancreatitis characterized by metagenomic sequencing. J Transl Med. 2021;19(1):215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-021-02882-7\u003c/span\u003e\u003cspan address=\"10.1186/s12967-021-02882-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres PJ, Fletcher EM, Gibbons SM, Bouvet M, Doran KS, Kelley ST. Characterization of the salivary microbiome in patients with pancreatic cancer. PeerJ. 2015;3:e1373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.1373\u003c/span\u003e\u003cspan address=\"10.7717/peerj.1373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Z, Jiang J, Xie H, Li A, Lu H, Xu S, et al. Gut microbial profile analysis by MiSeq sequencing of pancreatic carcinoma patients in China. Oncotarget. 2017;8(56):95176\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.18820\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.18820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrick JL, Wilkinson JE, Michaud DS, Cai Q, Gerlovin H, Signorello LB, et al. The oral microbiome in relation to pancreatic cancer risk in African Americans. Br J Cancer. 2022;126(2):287\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41416-021-01578-5\u003c/span\u003e\u003cspan address=\"10.1038/s41416-021-01578-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagata N, Nishijima S, Kojima Y, Hisada Y, Imbe K, Miyoshi-Akiyama T, et al. Metagenomic Identification of Microbial Signatures Predicting Pancreatic Cancer From a Multinational Study. Gastroenterology. 2022;163(1):222\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2022.03.054\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2022.03.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalf E, Keren N, Reshef L, Dorfman T, Lachter I, Kluger Y, et al. Fecal microbiome signatures of pancreatic cancer patients. Sci Rep. 2019;9(1):16801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-019-53041-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-53041-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, et al. Cohabiting family members share microbiota with one another and with their dogs. Elife. 2013;2:e00458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7554/eLife.00458\u003c/span\u003e\u003cspan address=\"10.7554/eLife.00458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat Med. 2018;24(4):392\u0026ndash;400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.4517\u003c/span\u003e\u003cspan address=\"10.1038/nm.4517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotgieter M, Bester J, Kell DB, Pretorius E. The dormant blood microbiome in chronic, inflammatory diseases. FEMS Microbiol Rev. 2015;39(4):567\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/femsre/fuv013\u003c/span\u003e\u003cspan address=\"10.1093/femsre/fuv013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoerner J, Huang Y, Hutter S, Gurnari C, Sanchez JMH, Wang J, et al. Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes. Nat Commun. 2022;13(1):1038. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-022-28678-x\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-28678-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho EJ, Leem S, Kim SA, Yang J, Lee YB, Kim SS, et al. Circulating Microbiota-Based Metagenomic Signature for Detection of Hepatocellular Carcinoma. Sci Rep. 2019;9(1):7536. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-019-44012-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-44012-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng HS, Tan SP, Wong DMK, Koo WLY, Wong SH, Tan NS. The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges. Int J Mol Sci. 2023;24(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24065633\u003c/span\u003e\u003cspan address=\"10.3390/ijms24065633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn J, Kwon H, Lim W, Moon BI. Staphylococcus aureus-Derived Extracellular Vesicles Enhance the Efficacy of Endocrine Therapy in Breast Cancer Cells. J Clin Med. 2022;11(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm11072030\u003c/span\u003e\u003cspan address=\"10.3390/jcm11072030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Li X, Li G, Liu Y, Huang X, Ma W, et al. Alterations of commensal microbiota are associated with pancreatic cancer. Int J Biol Markers. 2023;38(2):89\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/03936155231166721\u003c/span\u003e\u003cspan address=\"10.1177/03936155231166721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan X, Alekseyenko AV, Wu J, Peters BA, Jacobs EJ, Gapstur SM, et al. Human oral microbiome and prospective risk for pancreatic cancer: a population-based nested case-control study. Gut. 2018;67(1):120\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2016-312580\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2016-312580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu H, Ren Z, Li A, Li J, Xu S, Zhang H, et al. Tongue coating microbiome data distinguish patients with pancreatic head cancer from healthy controls. J Oral Microbiol. 2019;11(1):1563409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/20002297.2018.1563409\u003c/span\u003e\u003cspan address=\"10.1080/20002297.2018.1563409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlson SH, Satagopan J, Xu Y, Ling L, Leong S, Orlow I, et al. The oral microbiota in patients with pancreatic cancer, patients with IPMNs, and controls: a pilot study. Cancer Causes Control. 2017;28(9):959\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10552-017-0933-8\u003c/span\u003e\u003cspan address=\"10.1007/s10552-017-0933-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogtmann E, Han Y, Caporaso JG, Bokulich N, Mohamadkhani A, Moayyedkazemi A, et al. Oral microbial community composition is associated with pancreatic cancer: A case-control study in Iran. Cancer Med. 2020;9(2):797\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.2660\u003c/span\u003e\u003cspan address=\"10.1002/cam4.2660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei AL, Li M, Li GQ, Wang X, Hu WM, Li ZL, et al. Oral microbiome and pancreatic cancer. World J Gastroenterol. 2020;26(48):7679\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v26.i48.7679\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v26.i48.7679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashimoto S, Tochio T, Funasaka K, Funahashi K, Hartanto T, Togashi Y, 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. 2023;58(2):193\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00365521.2022.2114812\u003c/span\u003e\u003cspan address=\"10.1080/00365521.2022.2114812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKartal E, Schmidt TSB, Molina-Montes E, Rodriguez-Perales S, Wirbel J, Maistrenko OM, et al. A faecal microbiota signature with high specificity for pancreatic cancer. Gut. 2022;71(7):1359\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2021-324755\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2021-324755\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsukawa H, Iida N, Kitamura K, Terashima T, Seishima J, Makino I, et al. Dysbiotic gut microbiota in pancreatic cancer patients form correlation networks with the oral microbiota and prognostic factors. Am J Cancer Res. 2021;11(6):3163\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Ma Y, Tan Q, Zhou B, Yu D, Jin M, et al. Gut Streptococcus is a microbial marker for the occurrence and liver metastasis of pancreatic cancer. Front Microbiol. 2023;14:1184869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2023.1184869\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2023.1184869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JJ, Zhu M, Kashyap PC, Chia N, Tran NH, McWilliams RR, et al. The role of microbiome in pancreatic cancer. Cancer Metastasis Rev. 2021;40(3):777\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10555-021-09982-2\u003c/span\u003e\u003cspan address=\"10.1007/s10555-021-09982-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohi S, Macgregor-Das A, Dbouk M, Yoshida T, Chuidian M, Abe T, et al. Alterations in the Duodenal Fluid Microbiome of Patients With Pancreatic Cancer. Clin Gastroenterol Hepatol. 2022;20(2):e196\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cgh.2020.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2020.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Shao L, Liu X, Ji F, Mei Y, Cheng Y, et al. Alterations of gastric mucosal microbiota across different stomach microhabitats in a cohort of 276 patients with gastric cancer. EBioMedicine. 2019;40:336\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2018.12.034\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2018.12.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHezaveh K, Shinde RS, Klotgen A, Halaby MJ, Lamorte S, Ciudad MT et al. Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity. Immunity. 2022;55(2):324\u0026thinsp;\u0026ndash;\u0026thinsp;40 e8; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2022.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2022.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Dou L, Zhang Y, He S, Zhao D, Hao C, et al. Characterization of the Oral and Esophageal Microbiota in Esophageal Precancerous Lesions and Squamous Cell Carcinoma. Front Cell Infect Microbiol. 2021;11:714162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2021.714162\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2021.714162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNejman D, Livyatan I, Fuks G, Gavert N, Zwang Y, Geller LT, et al. The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science. 2020;368(6494):973\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aay9189\u003c/span\u003e\u003cspan address=\"10.1126/science.aay9189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaekawa T, Fukaya R, Takamatsu S, Itoyama S, Fukuoka T, Yamada M, et al. Possible involvement of Enterococcus infection in the pathogenesis of chronic pancreatitis and cancer. Biochem Biophys Res Commun. 2018;506(4):962\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbrc.2018.10.169\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrc.2018.10.169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeller LT, Barzily-Rokni M, Danino T, Jonas OH, Shental N, Nejman D, et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science. 2017;357(6356):1156\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aah5043\u003c/span\u003e\u003cspan address=\"10.1126/science.aah5043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pancreatic cancer, microbiome, saliva, feces, blood","lastPublishedDoi":"10.21203/rs.3.rs-4992405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4992405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe results of microbiome composition in patients with malignancy have been inconsistent across studies and are affected by various factors. This study aimed to identify microbiome composition of saliva, feces, and blood in patients with pancreatic cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 31 patients with pancreatic cancer and 24 healthy controls were sex- and age-matched. Microbiome analysis of saliva, fecal, and blood samples was conducted using 16S rRNA amplicon sequencing. Baseline characteristics were comparable between patients and controls. Saliva showed insignificant difference in alpha diversity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42), whereas feces and blood exhibited a significant difference in Shannon\u0026rsquo;s index (feces: 6.19 vs. 6.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013; blood: 8.00 vs. 7.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between patients and controls. Beta diversity analysis revealed significant differences between saliva, fecal, and blood samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, 0.001, and 0.001, respectively). Distinct microbiome compositions were identified in patients, with higher abundance of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e in saliva, fecal, and blood samples, respectively. Based on microbial network analysis, patients with pancreatic cancer showed lower clustering coefficient (71% vs. 99%) and higher average path length (1.67 vs. 0.68) than healthy controls, suggesting a more compact network and stronger microbial interactions in healthy controls.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identified a distinctive microbiome in patients with pancreatic cancer, indicating the presence of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e. A less condensed and robust microbial interaction network was observed in blood samples of patients with pancreatic cancer. These findings provide a basis for research on the connection between the microbiome and pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"Distinct microbiome composition and reduced interactions in patients with pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 06:53:39","doi":"10.21203/rs.3.rs-4992405/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50ac48d2-a177-4473-a77b-fb23c959992a","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-13T03:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 06:53:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4992405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4992405","identity":"rs-4992405","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}