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Xu W, Yu M, Wu Y, Jie Y, Li X, Zeng X, Yang F, Chong Y. Plasma-Derived Exosomal SncRNA as a Promising Diagnostic Biomarker for Early Detection of HBV-Related Acute-on-Chronic Liver Failure. Front Cell Infect Microbiol 2022; 12:923300. [PMID: 35873157 PMCID: PMC9301338 DOI: 10.3389/fcimb.2022.923300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives The small noncoding RNAs (sncRNAs) including microRNAs and the noncanonical sncRNAs [i.e., tRNA-derived small RNAs (tsRNAs) and rRNA-derived small RNAs (rsRNAs)] are a vital class of gene regulators in response to a variety of diseases. We focus on an sncRNA signature enriched in hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF) to develop a plasma exosome-based noninvasive biomarker for human ACLF. Methods In this work, sncRNAs related to HBV-ACLF were identified by small RNA sequencing (RNA-seq) in plasma exosomes collected from 3 normal subjects, 4 chronic hepatitis B (CHB) patients with flare, and 6 HBV-ACLF patients in the discovery cohort. Thereafter, the differentially expressed sncRNAs were further verified in a validation cohort (n = 313) using the newly developed molecular signature incorporating different mi/ts/rsRNAs (named as MTR-RNAs) through qRT-PCR assays. Subsequently, using the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model analysis, we developed an MTR-RNA classifier for early detection of ACLF. Results The identified sncRNAs (hsa-miR-23b-3p, hsa-miR-223-3p, hsa-miR-339-5p, tsRNA-20, tsRNA-46, and rsRNA-249) were specifically differentially expressed in plasma exosomes of HBV-ACLF. The MTR-RNA signature (AUC = 0.787) containing the above sncRNAs distinguished HBV-ACLF cases among normal subjects with 71.67% specificity and 74.29% sensitivity, CHB patients with flare (AUC = 0.694, 85.71% sensitivity/59.5% specificity), and patients with CHB/cirrhosis (AUC = 0.785, 57.14% sensitivity/94.59% specificity). Notably, it revealed 100% specificity/94.80% sensitivity in detecting patients or normal people. Conclusions Our as-constructed plasma-derived exosomal sncRNA signature can serve as a reliable biomarker for ACLF detection and also be adopted to be the pre−triage biomarker for selecting cases that can gain benefits from adjuvant treatment.
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Ning Q, Chen T, Wang G, Xu D, Yu Y, Mao Q, Li T, Li L, Li J, Lu X, Li J, Li Z, Zhang W, Xiao Y, Meng Q, Mi Y, Shang J, Yu Y, Zhao Y, Zhao C, Zhao H, Huang J, Peng J, Tang H, Tang X, Hu J, Hu B, Guo W, Zheng B, Chen B, Zhang Y, Wei J, Sheng J, Chen Z, Wang M, Xie Q, Wang Y, Wang FS, Hou J, Duan Z, Wei L, Jia J. Expert Consensus on Diagnosis and Treatment of End-Stage Liver Disease Complicated with Infections. INFECTIOUS DISEASES & IMMUNITY 2022; 2:168-178. [DOI: 10.1097/id9.0000000000000055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Indexed: 10/13/2023]
Abstract
Abstract
End-stage liver disease (ESLD) is a life-threatening clinical syndrome that markedly increases mortality in patients with infections. In patients with ESLD, infections can induce or aggravate the occurrence of liver decompensation. Consequently, infections are among the most common complications of disease progression. There is a lack of working procedure for early diagnosis and appropriate management for patients with ESLD complicated by infections as well as local and international guidelines or consensus. This consensus assembled up-to-date knowledge and experience across Chinese colleagues, providing data on principles as well as working procedures for the diagnosis and treatment of patients with ESLD complicated by infections.
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Affiliation(s)
- Qin Ning
- Department of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Tao Chen
- Department of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guiqiang Wang
- Department of Infectious Disease, Center for Liver Disease, Peking University First Hospital, Beijing 100034, China
| | - Dong Xu
- Department of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yanyan Yu
- Department of Infectious Disease, Center for Liver Disease, Peking University First Hospital, Beijing 100034, China
| | - Qing Mao
- Department of Infectious Diseases, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Taisheng Li
- Department of Infectious Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jun Li
- Department of Infectious Disease, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
| | - Xiaoju Lu
- Center of Infectious Diseases, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jiabin Li
- Department of Infectious Diseases, First Affiliated Hospital of Anhui Medical University, Hefei 230031, China
| | - Zhiwei Li
- Department of Infectious Diseases, Shengjing Hospital, Affiliated Hospital of China Medical University, Shenyang 110801, China
| | - Wenhong Zhang
- Department of Infectious Diseases, Institute of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yonghong Xiao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Qinghua Meng
- Department of Severe Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Yuqiang Mi
- Nankai University Second People's Hospital, Tianjin 300071, China
| | - Jia Shang
- Department of Infectious Disease, People's Hospital of Henan Province, Zhengzhou 450003, China
| | - Yunsong Yu
- Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China
| | - Yingren Zhao
- Department of Infectious Diseases, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Caiyan Zhao
- Department of Infectious Diseases, Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 050051, China
| | - Hong Zhao
- Department of Infectious Disease, Center for Liver Disease, Peking University First Hospital, Beijing 100034, China
| | - Jianrong Huang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Jie Peng
- State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Hong Tang
- Center of Infectious Diseases, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xiaoping Tang
- Research Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou 510060, China
| | - Jinhua Hu
- Liver Failure Treatment and Research Center, The Fifth Medical Center, China PLA General Hospital, Beijing 100039, China
| | - Bijie Hu
- Department of Infectious Diseases, Zhongshan Hospital of Fudan University, Shanghai 200032, China
| | - Wei Guo
- Department of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Bo Zheng
- Institute of Clinical Pharmacology, Peking University First Hospital, Beijing 100034, China
| | - Baiyi Chen
- Department of Infectious Diseases, The First Hospital of China Medical University, Shenyang 110002, China
| | - Yuexin Zhang
- Center of Infectious Diseases, First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Jia Wei
- Department of Infectious Disease, The Second People's Hospital, Kunming 650201, China
| | - Jifang Sheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Zhi Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
| | - Minggui Wang
- Department of Infectious Diseases, Institute of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Qing Xie
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Yuming Wang
- Department of Infectious Diseases, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Fu-Sheng Wang
- Liver Failure Treatment and Research Center, The Fifth Medical Center, China PLA General Hospital, Beijing 100039, China
| | - Jinlin Hou
- State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Zhongping Duan
- Artificial Liver Center, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Lai Wei
- Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Peking University Hepatology Institute, Peking University People's Hospital, Beijing 100044, China
| | - Jidong Jia
- Liver Research Center, Beijing Friendship Hospital, Capital Medial University; Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis & National Clinical Research Center for Digestive Diseases, Beijing 100050, China
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Yu M, Li X, Lu Y, Jie Y, Li X, Shi X, Zhong S, Wu Y, Xu W, Liu Z, Chong Y. Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients With Severe Acute Exacerbation. Front Med (Lausanne) 2021; 8:748915. [PMID: 34790679 PMCID: PMC8591055 DOI: 10.3389/fmed.2021.748915] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/21/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Patients with chronic hepatitis B (CHB) with severe acute exacerbation (SAE) are at a progression stage of acute-on-chronic liver failure (ACLF) but uniform models for predicting ACLF occurrence are lacking. We aimed to present a risk prediction model to early identify the patients at a high risk of ACLF and predict the survival of the patient. Methods: We selected the best variable combination using a novel recursive feature elimination algorithm to develop and validate a classification regression model and also an online application on a cloud server from the training cohort with a total of 342 patients with CHB with SAE and two external cohorts with a sample size of 96 and 65 patients, respectively. Findings: An excellent prediction model called the PATA model including four predictors, prothrombin time (PT), age, total bilirubin (Tbil), and alanine aminotransferase (ALT) could achieve an area under the receiver operating characteristic curve (AUC) of 0.959 (95% CI 0.941-0.977) in the development set, and AUC of 0.932 (95% CI 0.876-0.987) and 0.905 (95% CI 0.826-0.984) in the two external validation cohorts, respectively. The calibration curve for risk prediction probability of ACLF showed optimal agreement between prediction by PATA model and actual observation. After predictive stratification into different risk groups, the C-index of predictive 90-days mortality was 0.720 (0.675-0.765) for the PATA model, 0.549 (0.506-0.592) for the end-stage liver disease score model, and 0.648 (0.581-0.715) for Child-Turcotte-Pugh scoring system. Interpretation: The highlypredictive risk model and easy-to-use online application can accurately predict the risk of ACLF with a poor prognosis. They may facilitate risk communication and guidetherapeutic options.
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Affiliation(s)
- Mingxue Yu
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyong Li
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yaxin Lu
- The Department of Clinical Data Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yusheng Jie
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinhua Li
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xietong Shi
- The Department of Infectious Disease, Jieyang People's Hospital (Jieyang Affiliated Hospital of Sun Yat-sen University), Jieyang, China
| | - Shaolong Zhong
- The Department of Infectious Disease, Jieyang People's Hospital (Jieyang Affiliated Hospital of Sun Yat-sen University), Jieyang, China
| | - Yuankai Wu
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wenli Xu
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zifeng Liu
- The Department of Clinical Data Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yutian Chong
- TheDepartment of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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