Microbiome Markers of Pancreatic Cancer Based on Bacteria-Derived Extracellular Vesicles Acquired from Blood Samples: A Retrospective Propensity Score Matching Analysis

Study information

Needs review

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

33805810

DOI Digital object identifier for electronic documents.

10.3390/biology10030219

URI Uniform resource identifier for web resources.

Authors

Kim J.R., Han K., Han Y., Kang N., Shin T.S., Park H.J., Kim H., Kwon W., Lee S., Kim Y.K., Park T., Jang J.Y.

Journal

Biology

Year

2021

Keywords:

early diagnosis, microbial extracellular vesicles, microbiome markers, pancreatic cancer, propensity score matching

Novel biomarkers for early diagnosis of pancreatic cancer (PC) are necessary to improve prognosis. We aimed to discover candidate biomarkers by identifying compositional differences of microbiome between patients with PC (n = 38) and healthy controls (n = 52), using microbial extracellular vesicles (EVs) acquired from blood samples. Composition analysis was performed using 16S rRNA gene analysis and bacteria-derived EVs. Statistically significant differences in microbial compositions were used to construct PC prediction models after propensity score matching analysis to reduce other possible biases. Between-group differences in microbial compositions were identified at the phylum and genus levels. At the phylum level, three species (Verrucomicrobia, Deferribacteres, and Bacteroidetes) were more abundant and one species (Actinobacteria) was less abundant in PC patients. At the genus level, four species (Stenotrophomonas, Sphingomonas, Propionibacterium, and Corynebacterium) were less abundant and six species (Ruminococcaceae UCG-014, Lachnospiraceae NK4A136 group, Akkermansia, Turicibacter, Ruminiclostridium, and Lachnospiraceae UCG-001) were more abundant in PC patients. Using the best combination of these microbiome markers, we constructed a PC prediction model that yielded a high area under the receiver operating characteristic curve (0.966 and 1.000, at the phylum and genus level, respectively). These microbiome markers, which altered microbial compositions, are therefore candidate biomarkers for early diagnosis of PC.

Experiment 1

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Curated date: 2025/10/20

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Subjects

Location of subjects: China

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Blood serum Serum,Blood serum,blood serum

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Pancreatic carcinoma cancer of pancreas,cancer of the pancreas,carcinoma of exocrine pancreas,carcinoma of pancreas,carcinoma of the pancreas,exocrine cancer,exocrine pancreas carcinoma,exocrine pancreatic carcinoma,pancreas cancer,pancreas carcinoma,pancreatic cancer,pancreatic cancer (not islets),pancreatic carcinoma,pancreatic carcinoma, familial,Pancreatic carcinoma

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Healthy controls

Group 1 name Corresponds to the case (exposed) group for case-control studies: Pancreatic cancer patients

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Patients that have pancreatic cancer

Group 0 sample size Number of subjects in the control (unexposed) group: 52

Group 1 sample size Number of subjects in the case (exposed) group: 38

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: raw counts

Statistical test: ANCOM; DESeq2; edgeR; metagenomeSeq

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: No

Confounders controlled for Confounding factors that have been accounted for by stratification or model adjustment: age, sex

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: increased

Signature 1

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Source: Figure 3

Description: Log2 counts of abundant operational taxonomy units (OTUs) in the Pancreatic Cancer group and the control group.

Abundance in Group 1: increased abundance in Pancreatic cancer patients

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Bacteroides
Deferribacterota
Verrucomicrobiota

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Signature 2

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Source: Figure 3

Description: Log2 counts of abundant operational taxonomy units (OTUs) in PC and control group.

Abundance in Group 1: decreased abundance in Pancreatic cancer patients

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Actinomycetota

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Experiment 2

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Differences from previous experiment shown

Subjects

Lab analysis

Statistical Analysis

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: increased

Signature 1

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Source: Table 2

Description: Microbial composition at the phylum level using nine statistical methods

Abundance in Group 1: decreased abundance in Pancreatic cancer patients

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Pseudomonadota
Spirochaetota
Cyanobacteriota
Planctomycetota
Armatimonadota
Chloroflexota
Acidobacteriota

Revision editor(s): Temmie