Distinct microbiome composition and reduced interactions in patients with pancreatic cancer

doi:10.21203/rs.3.rs-4992405/v1

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

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.

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.

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/m²) (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. 1A, 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. 2A).

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: R² = 0.07, p = 0.014; feces: R² = 0.13, p = 0.001; blood: R² = 0.21, p = 0.001) (Fig. 2B).

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. Studies 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

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.

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.

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No competing interests reported.

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

Distinct microbiome composition and reduced interactions in patients with pancreatic cancer

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Methods

Patients

Sample collection and preparation

16S rRNA amplicon sequencing

Data analysis and visualization

Results

Baseline characteristics

Overview of microbiome composition

Alpha and beta diversities

Differentially abundant taxa between patients with pancreatic cancer and healthy controls

Microbial interactions in blood samples of patients with pancreatic cancer

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1