4.7 Article

Circulating Microbiota-Based Metagenomic Signature for Detection of Hepatocellular Carcinoma

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SCIENTIFIC REPORTS
卷 9, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-019-44012-w

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资金

  1. Ministry of Health and Welfare, Republic of Korea
  2. Korea Health Technology R&D Project through the Korea Health Industry Development Institute - Ministry of Health Welfare [HI16C2037]
  3. Bio-Synergy Research Project of the Ministry of Science, ICT and Future Planning through the National Research Foundation (NRF) [2013M3A9C4078158]
  4. Basic Science Research Program through the NRF - Ministry of Education [2017R1D1A1B03034639]
  5. SNUH Research Fund [04-2017-0640]
  6. Liver Research Foundation of Korea
  7. National Research Foundation of Korea [2017R1D1A1B03034639] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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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 165 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.

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