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Difference in fecal and oral microbiota between pancreatic cancer and benign/low-grade malignant tumor patients

BMC Microbiology volume 24, Article number: 527 (2024) Cite this article

Abstract

Background

Significant gaps exist in understanding the gastrointestinal microbiota in patients with pancreatic cancer (PCA) versus benign or low-grade malignant pancreatic tumors (NPCA). This study aimed to analyze these microbiota characteristics and explore their potential use in distinguishing malignant pancreatic lesions.

Methods

Between September 2020 and May 2024, fecal and oral samples were collected from 121 patients undergoing surgical resection or diagnostic biopsy of pancreatic lesions, including 75 patients with PCA and 46 patients with NPCA, and 16s rRNA sequencing was performed. Random forest models based using fecal and oral microbiota data were developed to diagnose PCA and NPCA, with performance assessed using the leave-one-out cross validation method.

Results

The Shannon index and PCoA analysis revealed significant differences in oral microbiota composition between PCA and NPCA (p < 0.001 and p = 0.001, respectively). Fecal microbiome richness differed significantly (p = 0.02), though composition similarity was noted (p = 0.238). LEfSe identified 16 and 23 genera with significant differences in fecal and oral microbiomes, respectively. Random forest classifiers based on fecal and oral microbiota achieved areas under the curves (AUCs) of 89.4% and 96.3%, respectively, for distinguishing PCA and NPCA. In the mucinous tumor cohort, oral and fecal microbiome classifiers outperformed CA19-9, yielding AUCs of 83.0% and 85.2%, respectively.

Conclusion

Fecal and oral microbiota compositions were significantly different between PCA and NPCA patients. Random forest classifiers utilizing fecal and oral microbiota data effectively distinguish between benign or low-grade malignant and malignant pancreatic lesions.

Peer Review reports

Introduction

Pancreaticoduodenectomy and distal pancreatectomy are the primary surgical procedures for resecting pancreatic tumors. With advancements in physical examinations and abdominal imaging technologies, the detection rate of pancreatic tumors has increased [1, 2]. However, accurately differentiating between benign and malignant pancreatic lesions preoperatively remains challenging. Computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS) often guide surgical decisions upon idenfitication of pancreatic masses. According to the International Study Group of Pancreatic Surgery, surgery may proceed without histological evidence confirmation based on recent consensus [3].

Nevertheless, Gerritsen et al. analyzed 1629 cases of suspected malignant pancreatic masses undergoing pancreaticoduodenectomy, revealing benign pathology in 107 patients (6.6%) postoperatively [4]. Similarly, Wojcicki et al. reported benign pathology in 7.25% of patients (34/469) who underwent pancreaticoduodenectomy for suspected malignancy [5]. In the randomized controlled trial (DIPLOMA) involving 261 patients with suspected pancreatic body and tail cancer, 9.0% (23 cases) were found to have non-malignant lesions upon postoperative pathology [6]. Despite the increasing safety of pancreatic surgery due to advancements in surgical techniques, with postoperative mortality below 3.5% in high-volume centers [7,8,9], it may still result in severe postoperative complications. It can also lead to long-term effects such as diabetes and exocrine insufficiency, impacting quality of life and increasing healthcare costs [10,11,12]. Therefore, it is evident that efforts should be made to avoid unnecessary surgical treatments, as surgical treatment is rarely required for (a) symptomatic benign diseases. Early and precise detection of pancreatic cancer (PCA) remains pivotal timely intervention, significantly enhancing prognosis and quality of life.

Tumor markers, CT, MRI, EUS, PET/CT, and fine-needle aspiration (FNA) constitute essential clinical tools for discerning the nature of pancreatic tumors [13,14,15]. Carbohydrate antigen19-9 (CA 19 − 9) serves as the most commonly utilized tumor marker in PCA diagnosis, but its sensitivity ranges approximately from 0.72 to 0.86, with a specificity of around 0.68–0.80 [13, 14]. Approximately 10% of patients are Lewis antigen-negative [15], further limiting its diagnostic efficacy in PCA. CT and MRI are primary imaging modalities for distinguishing pancreatic tumor characteristics, although contrast-enhanced scanning is required, posing challenges in differentiating between partially cystic and solid masses [4, 16]. Percutaneous fine-needle biopsy and EUS-guided biopsy offer higher sensitivity but are significantly operator-dependent and invasive [17, 18].

In the past decade, microbiome research has rapidly progressed, revealing the critical roles of fecal and oral microbiota in maintaining internal homeostasis. Studies employing 16 S rRNA and metagenomics have highlighted dysbiosis of the fecal and oral microbiota as closely linked to PCA development and progression, indicating their potential as screening tools for this disease [19, 20]. In recent years, several studies have explored the differences in the fecal and oral microbiomes between pancreatic ductal adenocarcinoma (PDAC), chronic pancreatitis (CP), and intraductal papillary mucinous neoplasms (IPMN), but the results have been inconsistent [20,21,22]. Unlike the studies above, this study takes a clinical perspective, collecting oral and fecal samples from patients diagnosed with pancreatic lesions, and classifying them as benign or low-grade malignant and malignant pancreatic tumors based on postoperative pathological results. Diagnostic models will be constructed based on fecal and oral microbiota characteristics, and the diagnostic efficacy of the fecal and oral microbiota in distinguishing benign from malignant pancreatic lesions will be compared, providing a reference for early clinical identification of malignant tumors.

Method

Study design and study participants

This prospective study was conducted at Peking Union Medical College Hospital (PUMCH) in accordance with the principles of the Declaration of Helsinki (64th Fortaleza Brazil, October 2013) and adhered to the guidelines of the Strengthening The Organization and Reporting of Microbiome Studies (STORMS) [23]. Ethical approval was obtained from the Institutional Review Board of PUMCH (ZS-2727), and all participants provided written informed consent prior to sample collection. The clinical trial number was NCT06659237.

Fecal and oral samples collected from 121 patients with pancreatic tumors who underwent surgical resection or fine-needle biopsy at PUMCH between November 2020 and May 2024 were collected. This study strictly adhered to predefined inclusion and exclusion criteria. Inclusion criteria encompassed patients with a pancreatic tumor detected via imaging with no prior treatment before sample collection. Exclusion criteria included: [1] Current or previous diagnoses of (a) other malignancies, (b) infectious diseases, (c) oral or gastrointestinal disorders (d) psychiatric or neurodegenerative disorders; [2] Specific medical procedures or interventions within defined periods, including (a) antibiotic, hormone therapy, or immunosuppressant within the past three months, (b) gastrointestinal reconstructive surgery within the past three months, (c) frequent use of cathartics, antidiarrheals, or therapeutic doses of probiotics within the past month, (d) oral or gastrointestinal examinations within the past three days.

Patients were categorized into two groups based on their final pathology reports. Those diagnosed with cancer or high-grade mucinous tumors were classified into the malignant group (PCA), whereas patients with diagnoses such as low-grade intraductal papillary mucinous neoplasm (IPMN), mucinous cystadenoma, serous cystadenoma, chronic pancreatitis, neuroendocrine tumors, or solid pseudopapillary tumors were classified into the benign/low-grade malignant group (NPCA).

Sample collection

Two trained medical professionals collected oral and fecal samples. Prior to the collection, patients rinsed their mouths for 15 min. Oral samples were obtained from various sites using swabs, including the tooth surface, tongue coating, and buccal mucosa. Fecal samples were stored in sterile tubes immediately after defecation. All samples were placed in a cryopreservation box immediately after collection and stored at -80 °C within 1 h for further analysis.

16s rRNA extraction and sequencing

DNA was extracted using the E.Z.N.A. Stool DNA Kit (D4015; Omega, Inc., USA). The V3-V4 region of the 16 S rRNA gene was amplified using PCR with the universal primers 341 F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) [24]. PCR products were purified using AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA) and quantified using a Qubit (Invitrogen, USA). DNA sequencing was performed on the NovaSeq PE250 platform.

Sequencing processing and bioinformatic analysis

Paired-end raw sequences were merged using FLASH (v1.2.8) [25], and clean sequences were obtained after quality checking with fqtrim (v0.9.4). Chimeric sequences were filtered using Vsearch software (v2.3.4). After dereplication using DADA2, the ASV (Amplicon Sequence Variants) feature table and feature sequence were obtained. Alpha and beta diversity were calculated using QIIME2 after normalization to the minimum ASV count across all samples and visualized using the R package. Sequence alignment was performed with BLAST, and representative sequences were annotated using the SILVA database (Release 138, https://www.arbsilva.de/documentation/release138/). Differential fecal and oral microbiota taxa were identified using Linear Discriminant Analysis Effect Size (LEfSe) [26]. Functional enrichment analysis of the microbiota was conducted with PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) [27].

Diagnostic classifier establishment

A random forest algorithm was applied to select discriminatory markers based on oral and fecal microbial data and to construct diagnostic classifiers. To evaluate the performance and robustness of our diagnostic classifier, we utilized leave-one-out cross validation (LOOCV), a standard technique that minimizes bias by using all but one sample for training and the remaining single sample for testing. This process was repeated for each sample in the dataset, ensuring that each observation served as a test instance once. The area under the receiver operating characteristic curve (AUC) was used to assess the classifier performance.

Statistical analysis

Quantitative data are expressed as mean ± standard deviation (SD) or median (interquartile range (IQR)). The independent samples t-test or Mann-Whitney U test was used for comparisons, as appropriate. Categorical data were presented as frequencies and percentages, and comparisons were performed using Pearson’s chi-squared test, continuity correction, or Fisher’s exact test. All statistical analyses were conducted using R software (v.4.4.0). Random Forest classifier was performed using the RandomForest R package (v.4.7.1.1). Other R packages applied in the study included: VennDiagram (v.1.7.3), ggplot2 (v.3.5.1), circlize (v.0.4.16), ggalluvial (v.0.12.5), car, picrust2.2.0b, vegan (v.2.5.4), pROC (v.1.18.5), and caret (v.6.0.94). Statistical significance was defined as a two-sided p-value < 0.05.

Results

Characteristics and microbiome composition of patients with PCA and NPCA

After rigorous inclusion and exclusion criteria was met, fecal and oral samples were collected from 75 patients with PCA and 46 patients with NPCA without treatment. In the samples from 121 patients, 7,137,752 sequences from fecal samples were mapped to 13,316 ASVs and 8,114,367 sequences from oral samples were mapped to 9,887 ASVs. The rarefaction curves for both groups of samples based on the Shannon index reached a plateau, indicating that our sequencing depth was sufficient (Additional file 1: Supplementary Fig. 1). The baseline characteristics were well-balanced between the PCA and NPCA groups. Detailed tumor types in each group are shown in Table 1.

Table 1 Characterisitics of PCA and NPCA patients
Full size table

16 S rRNA analysis identified 34 phyla and 872 genera in the fecal samples and 31 phyla and 753 genera in the oral samples (Additional file 1: Supplementary Fig. 2). Firmicutes, Actinobacteria, and Proteobacteria were the predominant phyla in both PCA and NPCA fecal microbiome samples. The predominant genera in the PCA group were Bifidobacterium, Escherichia-Shigella, Bacteroides, Faecalibacterium, and Streptococcus, whereas the NPCA group was dominated by Bifidobacterium, Bacteroides, Escherichia-Shigel, Klebsiella, and Faecalibacterium. In the oral samples, at the phylum level, PCA was dominated by Firmicutes, Proteobacteria, and Bacteroidetes, whereas NPCA was dominated by Firmicutes, Proteobacteria, and Actinobacteria. At the genus level, the PCA group was dominated by Streptococcus, Veillonella, Neisseria, Haemophilus, and Leptotrichia, whereas the NPCA group was dominated by Streptococcus, Haemophilus, Gemella, Veillonella, and Neisseria. (Additional file 1: Supplementary Fig. 3).

Significant differences in the microbiome of PCA and NPCA patients

Shannon index showed a significant decrease in the fecal microbiome (p = 0.02), and a significant increase (p < 0.001) in the oral microbiome in patients with PCA compared to those with NPCA (Fig. 1). Similar trends were observed with the Simpson index in both the fecal and oral microbiomes (p = 0.07, p < 0.001, respectively, Additional file 1: Supplementary Fig. 4). Unweighted and weighted unifrac principal coordinates analysis (PCoA) revealed significant difference in the beta diversity of the oral microbiome between patients with PCA and NPCA (R2 = 0.1026, p = 0.001, R2 = 0.1149, p = 0.001, Additional file 1: Supplementary Fig. 5 (B), Fig. 2(B)). However, there were no significant differences in the beta diversity of the fecal microbiome (R2 = -0.0125, p = 0.63; R2 = 0.0141, p = 0.238, Additional file 1: Supplementary Fig. 5(A), Fig. 2(A)). In the cyst cohort, consisting of 9 cases of PCA and 28 cases of NPCA, we also conducted diversity analyses. There were no significant differences in α- and β-diversity of fecal microbiota between the two groups. In the oral microbiota, however, the Shannon index was significantly higher in the PCA group compared to the NPCA group (p = 0.02). Additionally, weighted PCoA analysis showed a slight distinction between the two groups (R² = 0.1658, p = 0.081).

Fig. 1
figure 1

The Shannon index of the fecal microbiome (A) and oral microbiome (B) at the genus level in PCA and NPCA patients

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Fig. 2
figure 2

The weighted PCoA analysis of the fecal microbiome (A) and oral microbiome (B) between PCA and NPCA patients

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A comparison of genus abundance between PCA and NPCA patients using LEfSe identified 16 and 23 genera with significant changes in the fecal and oral microbiomes, respectively (Fig. 3). Four genera including Dialister, Ruminococcus_gnavus_group, Prevotella_9, and Lachnoclostridium were significantly enriched in the fecal microbiome of PCA patients compared to NPCA patients. In the oral microbiome, 16 genera including Veillonella, Prevotella_7, Leptotrichia, Fusobacterium, Prevotella and Porphyromonas were significantly enriched in PCA patients. No differential genera were shared between the fecal and oral microbiomes.

Fig. 3
figure 3

The LEfSe analysis of the differential genera in the fecal microbiome (A) and oral microbiome (B) between PCA and NPCA patients

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Microbiota function prediction

Alternations in microbial communities are associated with changes in microbiota function. Despite similarities in the dominant genera between the two groups, these changes are crucial for understanding their contribution to PCA occurrence. PICRUSt2 functional prediction analysis showed significant enrichment in the sensory system at KEGG level 2 in the fecal microbiome of the PCA group. The oral microbiome in the PCA group was particularly enriched in energy metabolism, cellular processes and signaling, glycan biosynthesis and metabolism, and cancer pathways et al. (Additional file 1: Supplementary Fig. 6).

Establishment of a diagnostic classifier for differentiating PCA and NPCA based on the microbiome profiles

To translate our findings to clinical applications, a random forest algorithm was used to construct a diagnostic classifier. Top ten microbial features based on the value of mean decrease accuracy were selected as biomarkers to construct the fecal and oral microbiome random forest classifiers. The top 10 contributing microbiomes in fecal and oral samples are shown in Fig. 4. The ROC curves revealed that the fecal microbiome classifier had an AUC of 0.894 (95% CI: 0.835–0.954) and the oral microbiome classifier had an AUC of 0.963 (95% CI: 0.934–0.991). The diagnostic efficacy of CA19-9 was also analyzed, with an AUC value of 0.831 (95% CI: 0.756–0.905), which is lower than those of the fecal and oral microbiome classifiers (Fig. 5A). When fecal and oral microbiome were combined with CA19-9, the performance of both classifiers slightly improved. The AUC value of the classifier combining CA19-9 with the fecal microbiome was 0.909 (95% CI: 0.851–0.968), and that of the classifier combining CA19-9 with the oral microbiome was 0.963 (95% CI: 0.935–0.991) (Fig. 5B).

Fig. 4
figure 4

Characteristic variables of the fecal microbiome (A) and oral microbiota (B) in the random forest model, ranked by Mean Decreasing Accuracy

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Fig. 5
figure 5

ROC curve of the microbiome classifiers. 10 fecal genera and 10 oral genera were used to construct the fecal microbiome classifier and oral microbiome classifier. (A) The fecal and oral microbiome classifiers effectively discriminate PCA from NPCA, with AUC values of 0.894 (95% CI: 0.835–0.954) and 0.963 (95% CI: 0.934–0.991), respectively. (B) The fecal and oral microbiome classifiers combined with CA19-9 slightly improved the classifiers’ performance, with AUC values of 0.909 (95% CI: 0.851–0.968) and 0.963 (95% CI: 0.935–0.991), respectively. (C) The fecal microbiome and oral microbiome classifiers can effectively discriminate malignant tumors in the pancreatic mucinous tumor cohort, with AUC values of 0.830 and 0.852, respectively. However, the discriminative performance of CA19-9 is lower, with an AUC value of 0.693

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Performance of the classifiers in detecting malignant tumors in mucinous lesions

We investigated the applicability of the established classifiers for assessing the nature of mucinous tumors, which have the potential to evolve into malignant tumors. In a cohort of mucinous tumors (n = 19), both the fecal and oral microbiome classifiers accurately identified malignant tumors, with AUCs of 0.830 and 0.852, respectively. In contrast, CA19-9 performed poorly with an AUC of 0.693 (Fig. 5C).

Discussion

Managing pancreatic lesions presents a significant challenge in clinical practice, as accurately distinguishing malignant tumors based solely on clinical and radiological features can be difficult [28, 29]. Our study revealed significant differences in fecal and oral microbiomes between benign or low-grade malignant and malignant pancreatic tumors. Analyzing these microbiomes, which are accessible through noninvasive means, has proven to be a promising method for identifying malignancies. The diagnostic classifiers based on these microbiomes achieved AUC values of up to 0.894 and 0.963 for oral and fecal samples, respectively. With these high AUCs, our microbiome-based random forest classifiers show promise for identifying malignancies, potentially enabling earlier surgical intervention and improving patient outcomes.

Previous studies have reported significant differences in the diversity and composition of the fecal and oral microbiota between patients with PCA and healthy individuals. Classifiers constructed from fecal and oral microbiota can serve as noninvasive screening tools for PCA [20, 30, 31]. Nagata et al. found higher alpha diversity in the oral microbiota of PCA patients than in healthy individuals, while fecal microbiota showed lower diversity [20]. Similarly, our study observed increased alpha diversity in the oral microbiota and decreased diversity in the fecal microbiota in the PCA group compared to the NPCA group. These findings suggest that increased bacterial diversity in the oral microbiota and decreased bacterial diversity in the fecal microbiota may be correlated with the onset of PCA.

Among the 16 differential fecal microbiota identified by LEfSe, Dialister, Ruminococcus gnavus group, Prevotella_9, and Lachnoclostridium were significantly enriched in the PCA group. Prevotella_9 was the top contributor to the classifier. Both Dialister and Lachnoclostridium, anaerobic bacteria belonging to the phylum Firmicutes, have been reported to be more abundant in gastrointestinal tumors such as hepatocellular carcinoma (HCC) and colorectal cancer. Dialisters have a predictive value for HCC and colorectal cancer recurrence [32,33,34], whereas Lachnoclostridium is linked to colorectal cancer diagnosis and intratumoral tertiary lymphoid structures in HCC [35, 36]. The Ruminococcus gnavus group includes Ruminococcus gnavus and genetically similar strains. Ruminococcus gnavus, a member of the Firmicutes phylum and Lachnospiraceae family, can degrade various complex carbohydrates and is typically present in low abundance in the gut of healthy individuals. It is considered a pathogen associated with inflammatory bowel disease [37] and is capable of producing a glucorhamnan polysaccharide that induces inflammatory responses via the Toll-like receptor 4 [38]. Prevotella_9 is significantly enriched in the gut microbiota of cancer patients [39] and has also been associated with worse overall survival and a poor response to immune checkpoint inhibitors in liver cancer [40]. These bacteria may influence cancer development and progression by affecting local and systemic inflammatory responses.

Among the 23 oral genera identified by LEfSe, Prevotella_7, Prevotella and Megaspharea were among the top 10 contributors to the classifier and were significantly enriched in the PCA group. Prevotella, belonging to the phylum Bacteroidetes, was significantly enriched in the PCA group, with both Prevotella and Prevotella_7 significantly contributing to the oral microbiome classifier. Prevotella is closely associated with periodontitis through the arachidonic acid metabolism pathway [34, 35]. Prevotella spp. is significantly enriched in the oral microbiota of patients with colorectal cancer and oral squamous cell carcinoma [41, 42]. In PCA, some studies reported significant enrichment of Prevotella spp., including Prevotella intermedia and Prevotella pallens [20, 43]. Megasphaera, a genus from the phylum Firmicutes, was found in our study to be associated with pancreatic cancer in oral microbiota。Previous studies have reported that Megasphaera in the gut microbiota is significantly related to gastric and liver cancers [44, 45]. Moreover, compared to healthy individuals, Megasphaera micronuciformis is significantly elevated in the salivary microbiota of patients with colorectal polyps and pancreatic cancer [20, 46]. However, the specific mechanisms through which oral Prevotella and Megasphaera influence cancer development remain unclear.

Many studies have explored the fecal and oral microbiota of patients with PCA and healthy individuals, however, few have focused on microbiome differences among pancreatic lesion types. Kartal et al. found significant differences in fecal microbiota α- and β-diversity between pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP), with a diagnostic model based on fecal microbiota showing good performance in distinguishing PDAC, yielding an AUC of 0.75. However, the diagnostic performance of the oral microbiota was less effective [22]. In Japan, Nagata et al. collected fecal samples from patients with IPMN and PCA for metagenomic sequencing and revealed significant differences in species composition (p < 0.001). A classifier constructed using random forest achieved an AUC value of 0.70 (0.62–0.78) in distinguishing PCA from IPMN, and the AUC increased when combined with CA19-9 [20]. In contrast, Sidiropoulos et al. found no significant differences in gut microbiome biodiversity between PDAC and IPMN, with their model failing to effectively differentiate between the two [21]. Our study included pancreatic lesions such as SPT, SCN, MCN, NET, IPMN, and PCA. Differentiating some tumors radiologically and based on CA19-9 levels can be challenging. We found that both fecal and oral microbiomes could effectively discriminate against malignant pancreatic tumors with AUC values of 0.894 and 0.963, respectively. Furthermore, both the fecal and oral microbiome classifiers demonstrated superior discriminative abilities compared to CA19-9, with the oral microbiome classifier outperforming the fecal microbiome classifier. Similarly, in studies comparing patients with PCA to healthy individuals, the diagnostic performance of the oral microbiota was superior to that of the gut microbiota [20, 47]. This suggests that oral microbiota may be more suitable for the differential diagnosis and screening of PCA. Additionally, when we applied fecal and oral microbiome classifiers to mucinous tumors, they accurately differentiated malignant mucinous tumors and performed significantly better than CA19-9. Therefore, oral and fecal microbiomes are promising biomarkers for identifying the malignancy of mucinous tumors. However, further exploration and validation with larger sample sizes are required.

This study had several limitations. First, the study population was limited to patients admitted to our institution. To improve the performance and reliability of the classifiers, more patients from both our institution and other institutions are required for internal and external validation. Second, we used genus-level variables to construct classifiers. In the future, leveraging the results of metagenomic sequencing data could provide biomarkers at the species level and offer information on bacterial functionality. Thirdly, pancreatic exocrine insufficiency can affect the composition of the gut microbiota [48]. Due to sample limitations, we were unable to obtain data on pancreatic exocrine function, which may have impacted our results. Future studies should consider incorporating pancreatic exocrine function data to reduce potential confounding factors.

Conclusion

In this study, we identified distinct differences in the fecal and oral microbiota profiles between PCA and benign or low-grade malignant tumors. Non-invasive classifiers based on these microbiota features show promise in distinguishing patients with PCA from those with benign or low-grade malignant tumors. These findings warrant further validation in larger, diverse cohorts to confirm their diagnostic utility and potential for clinical application.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA008306) that are publicly accessible at https://bigd.big.ac.cn/gsa-human/browse/HRA008306.

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Acknowledgements

We thank all the subjects who volunteered to participate in this study. We also extend our gratitude to the Clinical Biobank (ISO 20387) at Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, for storing the oral and fecal samples.

Funding

This work was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS, 2021-I2M-1-002) and the project of National Natural Science Foundation of China (Grant No. 82073238).

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Authors and Affiliations

  1. Department of General Surgery, Peking Union Medical College Hospital (PUMCH), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China

    Pengyu Li, Hanyu Zhang, Xingyu Gao, Lixin Chen, Haomin Chen, Shuai Yuan, Weijie Chen & Menghua Dai

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  1. Pengyu Li
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  2. Hanyu Zhang
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  3. Xingyu Gao
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  4. Lixin Chen
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  5. Haomin Chen
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  7. Weijie Chen
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  8. Menghua Dai
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Contributions

Conceptualization, Investigation: Pengyu Li and Menghua Dai. Sample collection: Pengyu Li, Hanyu Zhang, Xingyu Gao and Shuai Yuan. Data Analysis: Pengyu Li, Lixin Chen, Haomin Chen and Weijie Chen. Writing - original draft preparation: Pengyu Li. Writing - review and editing: Menghua Dai.

Corresponding author

Correspondence to Menghua Dai.

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Ethical approval was obtained from the Institutional Review Board of Peking Union Medical College Hospital (ZS-2727), and all participants provided written informed consent.

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The authors declare no competing interests.

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Li, P., Zhang, H., Gao, X. et al. Difference in fecal and oral microbiota between pancreatic cancer and benign/low-grade malignant tumor patients. BMC Microbiol 24, 527 (2024). https://doi.org/10.1186/s12866-024-03687-6

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Keywords

BMC Microbiology

ISSN: 1471-2180

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