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

Authors :
Jae Ri Kim
Kyulhee Han
Youngmin Han
Nayeon Kang
Tae-Seop Shin
Hyeon Ju Park
Hongbeom Kim
Wooil Kwon
Seungyeoun Lee
Yoon-Keun Kim
Taesung Park
Jin-Young Jang
Source :
Biology, Vol 10, Iss 3, p 219 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
20797737
Volume :
10
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.4f2fb9279d8f41048874271fb9f511ce
Document Type :
article
Full Text :
https://doi.org/10.3390/biology10030219