Tuo Deng1,2, Jialiang Li1,2, Bangjie He1,2, Bo Chen1,2, Fangting Liu3, Ziyan Chen1,2, Jiuyi Zheng1,2, Zhehao Shi1,2, Tan Zhang1,2, Liming Deng1,2, Haitao Yu1,2, Jinhuan Yang1,2, Lijun Wu1,2, Yunfeng Shan1,2, Zhengping Yu1,2, Xiaolei Chen4, Yi Wang5, Gang Chen6,7. 1. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Fuxue Road, Wenzhou, 325035, Zhejiang, China. 2. Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China. 3. Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, The South of Shangcai Village, Ouhai District, Wenzhou, 325005, Zhejiang Province, China. 4. Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, The South of Shangcai Village, Ouhai District, Wenzhou, 325005, Zhejiang Province, China. chenxiaolei0577@126.com. 5. Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Chashan High Education Zone, Wenzhou, 325035, Zhejiang, China. wang.yi@wmu.edu.cn. 6. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Fuxue Road, Wenzhou, 325035, Zhejiang, China. chen.gang@wmu.edu.cn. 7. Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China. chen.gang@wmu.edu.cn.
Abstract
BACKGROUND: Primary liver cancer has high mortality and morbidity worldwide. However, the characteristic of gut microbiota profile and its correlation with inflammation status in liver cancer patients remains largely unknown, and a gut microbiome-based diagnostic model for liver cancer is still absent. METHODS: Here, we provided a comprehensive analysis based on fecal 16S rRNA sequencing and clinical data in a cohort consisting of 40 healthy volunteers, 143 hepatocellular carcinoma (HCC) patients, and 46 cholangiocarcinoma (CCA) patients. RESULTS: Our results indicated a distinct shift of gut microbiota composition between two primary liver cancer types and compared with healthy volunteers. Based on the diversity constitute of gut microbiome taxonomy and random forest algorithm, eight genera with mean abundance above 0.1% were selected to construct the classification model with half of the randomly selected cohort. Based on this signature, high diagnostic accuracy in the validation cohort to classify liver cancer types (AUC = 0.989, 0.967, 0.920 for Control, HCC, CCA separately) was achieved. Further analysis showed increased Gram-negative bacteria and elevated inflammatory response markers in CCA group versus HCC group. The correlation analysis between inflammatory response markers and composition of gut microbiome revealed decreased potentially beneficial genus and increased opportunistic pathogens positively correlated with adverse prognostic inflammatory response markers. CONCLUSION: Generally, our study established the gut microbiome-based signature for liver cancer prediction and screening and revealed that gut microbiome characteristic in primary liver cancer was correlated with adverse inflammatory response markers in liver cancer.
BACKGROUND: Primary liver cancer has high mortality and morbidity worldwide. However, the characteristic of gut microbiota profile and its correlation with inflammation status in liver cancer patients remains largely unknown, and a gut microbiome-based diagnostic model for liver cancer is still absent. METHODS: Here, we provided a comprehensive analysis based on fecal 16S rRNA sequencing and clinical data in a cohort consisting of 40 healthy volunteers, 143 hepatocellular carcinoma (HCC) patients, and 46 cholangiocarcinoma (CCA) patients. RESULTS: Our results indicated a distinct shift of gut microbiota composition between two primary liver cancer types and compared with healthy volunteers. Based on the diversity constitute of gut microbiome taxonomy and random forest algorithm, eight genera with mean abundance above 0.1% were selected to construct the classification model with half of the randomly selected cohort. Based on this signature, high diagnostic accuracy in the validation cohort to classify liver cancer types (AUC = 0.989, 0.967, 0.920 for Control, HCC, CCA separately) was achieved. Further analysis showed increased Gram-negative bacteria and elevated inflammatory response markers in CCA group versus HCC group. The correlation analysis between inflammatory response markers and composition of gut microbiome revealed decreased potentially beneficial genus and increased opportunistic pathogens positively correlated with adverse prognostic inflammatory response markers. CONCLUSION: Generally, our study established the gut microbiome-based signature for liver cancer prediction and screening and revealed that gut microbiome characteristic in primary liver cancer was correlated with adverse inflammatory response markers in liver cancer.
Authors: Jun Yu; Qiang Feng; Sunny Hei Wong; Dongya Zhang; Qiao Yi Liang; Youwen Qin; Longqing Tang; Hui Zhao; Jan Stenvang; Yanli Li; Xiaokai Wang; Xiaoqiang Xu; Ning Chen; William Ka Kei Wu; Jumana Al-Aama; Hans Jørgen Nielsen; Pia Kiilerich; Benjamin Anderschou Holbech Jensen; Tung On Yau; Zhou Lan; Huijue Jia; Junhua Li; Liang Xiao; Thomas Yuen Tung Lam; Siew Chien Ng; Alfred Sze-Lok Cheng; Vincent Wai-Sun Wong; Francis Ka Leung Chan; Xun Xu; Huanming Yang; Lise Madsen; Christian Datz; Herbert Tilg; Jian Wang; Nils Brünner; Karsten Kristiansen; Manimozhiyan Arumugam; Joseph Jao-Yiu Sung; Jun Wang Journal: Gut Date: 2015-09-25 Impact factor: 23.059