Identification reproducible microbiota biomarkers for the diagnosis of cirrhosis and hepatocellular carcinoma

被引:0
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作者
Huarong Zhang
Junling Wu
Yijuan Liu
Yongbin Zeng
Zhiyu Jiang
Haidan Yan
Jie Lin
Weixin Zhou
Qishui Ou
Lu Ao
机构
[1] Fujian Medical University,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, the School of Basic Medical Sciences
[2] Fujian Medical University,Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering
[3] The First Affiliated Hospital of Fujian Medical University,Department of Gastroenterology
[4] The First Affiliated Hospital of Fujian Medical University,Department of Laboratory Medicine, Gene Diagnosis Research Center
[5] The First Affiliated Hospital of Fujian Medical University,Fujian Key Laboratory of Laboratory Medicine
来源
AMB Express | / 13卷
关键词
Diagnostic biomarkers; Gut microbiota; Hepatocellular carcinoma; Liver cirrhosis; Reproducible genera;
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摘要
Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence in China, which is mainly related to chronic hepatitis B (CHB) and liver cirrhosis (LC) caused by hepatitis B virus (HBV) infection. This study aimed to identify reproducible gut microbial biomarkers across Chinese population for LC and HCC diagnosis. In this study, a group of 21 CHB, 25 LC, 21 HCC and 15 healthy control (HC) were examined, and used as the training data. Four published faecal datasets from different regions of China were collected, totally including 121 CHB, 33 LC, 70 HCC and 96 HC. Beta diversity showed that the distribution of community structure in CHB, LC, HCC was significantly different from HC. Correspondingly, 14 and 10 reproducible differential genera across datasets were identified in LC and HCC, respectively, defined as LC-associated and HCC-associated genera. Two random forest (RF) models based on these reproducible genera distinguished LC or HCC from HC with an area under the curve (AUC) of 0.824 and 0.902 in the training dataset, respectively, and achieved cross-region validations. Moreover, AUCs were greatly improved when clinical factors were added. A reconstructed random forest model on eight genera with significant changes between HCC and non-HCC can accurately distinguished HCC from LC. Conclusively, two RF models based on 14 reproducible LC-associated and 10 reproducible HCC-associated genera were constructed for LC and HCC diagnosis, which is of great significance to assist clinical early diagnosis.
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