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Sci Rep. 2019;9(1):7536.

Circulating Microbiota-Based Metagenomic Signature for Detection of Hepatocellular Carcinoma.

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제1저자 조은주/임상섭 · 교신저자 윤정환/박태성

소속 (8)
  1. Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
  2. Department of Statistics, Seoul National University, Seoul, Korea.
  3. Department of Health and Safety Convergence Science, Korea University, Seoul, Korea.
  4. Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea.
  5. Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul National University College of Medicine, Seoul, Korea.
  6. Department of Internal Medicine, Inje University Haeundae Paik Hospital, Busan, Korea.
  7. Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea. yoonjh@snu.ac.kr.
  8. Department of Statistics, Seoul National University, Seoul, Korea. tspark@stats.snu.ac.kr.

Abstract

Circulating microbial dysbiosis is associated with chronic liver disease including nonalcoholic steatohepatitis and alcoholic liver disease. In this study, we evaluated whether disease-specific alterations of circulating microbiome are present in patients with cirrhosis and hepatocellular carcinoma (HCC), and their potential as diagnostic biomarkers for HCC. We performed cross-sectional metagenomic analyses of serum samples from 79 patients with HCC, 83 with cirrhosis, and 201 matching healthy controls, and validated the results in the same number of subjects. Serum bacterial DNA was analyzed using high-throughput pyrosequencing after amplification of the V3-V4 hypervariable regions of 16S rDNA. Blood microbial diversity was significantly reduced in HCC, compared with cirrhosis and control. There were significant differences in the relative abundances of several bacterial taxa that correlate with the presence of HCC, thus defining a specific blood microbiome-derived metagenomic signature of HCC. We identified 5 microbial gene markers-based model which distinguished HCC from controls with an area under the receiver-operating curve (AUC) of 0.879 and a balanced accuracy of 81.6%. In the validation, this model accurately distinguished HCC with an AUC of 0.875 and an accuracy of 79.8%. In conclusion, circulating microbiome-based signatures may be potential biomarkers for the detection HCC.

Figures

Figure 1
Figure 1 Reduced blood microbial diversities in the hepatocellular carcinoma (n = 79) and cirrhosis groups (n = 83) compared with control group (n = 201). α-diversity (Shannon index) of the three groups (a) at the phylum and (b) at the genus levels. p values from Kruskal–Wallis tests are shown.
Figure 2
Figure 2 Differences of microbiome between the three groups. The PCoA plots based on (a) unweighted UniFrac distance and (b) weighted UniFrac distance. The control samples are colored as green, cirrhosis as gray, and HCC samples as red.
Figure 3
Figure 3 Relative abundances of abundant OTUs among the three groups. Boxplots of the relative abundances of abundant OTUs (a) at phylum and (b) at the genus levels in the three groups. Abundant OTUs are selected by relative abundance means of OTUs > 1% in total samples and listed by decreasing order of the mean values.
Figure 4
Figure 4 Receiver operating characteristic curve of 5-genera signature for the test set.

그림 출처: PubMed Central 공개 논문 (PMC6525191)

Conflict of interest statement

The authors declare no competing interests.

그 밖의 정보
출판 형태
Journal Article, Research Support, Non-U.S. Gov't
저널
Scientific reports · eISSN 2045-2322
전자 출판
2019-05-17
MeSH
Biomarkers, Tumor / genetics · Carcinoma, Hepatocellular / blood / diagnosis / microbiology · Cross-Sectional Studies · DNA, Bacterial / blood · DNA, Ribosomal / blood / genetics · Dysbiosis / blood / microbiology · Female · Humans · Liver Cirrhosis / blood / microbiology · Liver Neoplasms / blood / diagnosis / microbiology · Male · Metagenome / genetics · Microbiota / genetics · Middle Aged · RNA, Ribosomal, 16S / genetics · Republic of Korea
Substances
Biomarkers, Tumor · DNA, Bacterial · DNA, Ribosomal · RNA, Ribosomal, 16S
참고문헌
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