Plasma MicroRNA Panel to Diagnose Hepatitis B Virus–Related Hepatocellular Carcinoma

2011 ◽  
Vol 29 (36) ◽  
pp. 4781-4788 ◽  
Author(s):  
Jian Zhou ◽  
Lei Yu ◽  
Xue Gao ◽  
Jie Hu ◽  
Jiping Wang ◽  
...  

Purpose More than 60% of patients with hepatocellular carcinoma (HCC) do not receive curative therapy as a result of late clinical presentation and diagnosis. We aimed to identify plasma microRNAs for diagnosing hepatitis B virus (HBV) –related HCC. Patients and Methods Plasma microRNA expression was investigated with three independent cohorts including 934 participants (healthy, chronic hepatitis B, cirrhosis, and HBV-related HCC), recruited between August 2008 and June 2010. First, we used microarray to screen 723 microRNAs in 137 plasma samples for diagnosing HCC. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using a training cohort (n = 407) and then validated using an independent cohort (n = 390). Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results We identified a microRNA panel (miR-122, miR-192, miR-21, miR-223, miR-26a, miR-27a and miR-801) that provided a high diagnostic accuracy of HCC (AUC = 0.864 and 0.888 for training and validation data set, respectively). The satisfactory diagnostic performance of the microRNA panel persisted regardless of disease status (AUCs for Barcelona Clinic Liver Cancer stages 0, A, B, and C were 0.888, 0.888, 0.901, and 0.881, respectively). The microRNA panel can also differentiate HCC from healthy (AUC = 0.941), chronic hepatitis B (AUC = 0.842), and cirrhosis (AUC = 0.884), respectively. Conclusion We found a plasma microRNA panel that has considerable clinical value in diagnosing early-stage HCC. Thus, patients who would have otherwise missed the curative treatment window can benefit from optimal therapy.

2019 ◽  
Vol 39 (6) ◽  
Author(s):  
Han Shi ◽  
Hongyan He ◽  
Suvash Chandra Ojha ◽  
Changfeng Sun ◽  
Juan Fu ◽  
...  

Abstract Background: It has been reported that polymorphisms of signal transducer and activator of transcription (STAT) 3 and STAT4 might be associated with susceptibility to hepatitis B virus (HBV) infection and risk of chronic hepatocellular carcinoma (HCC). Owing to limitation of sample size and inconclusive results, we conducted a meta-analysis to clarify the association. Methods: We identified relevant studies by a systematic search of Medline/PubMed, Embase, Web of Science and the Cochrane Library up to 20 February 2019. The strength of the association measured by odds ratios (OR) with 95% confidence intervals (CIs) was studied. All the statistical analyses were conducted based on Review Manager 5.3 software. Results: A total of 5242 cases and 2717 controls from five studies were included for the STAT3 polymorphism, 5902 cases and 7867 controls from nine studies for the STAT4 polymorphism. Our results suggested that STAT3 rs1053004 polymorphism was a significant risk factor of chronic HBV infection (C vs. T: OR = 1.17, 95% CI: 1.07–1.29, PA=0.0007; CC + CT vs. TT: OR = 1.38, 95% CI: 1.09–1.76, PA=0.008). Validation with all the genetic models revealed that rs7574865 polymorphism of STAT4 gene was closely associated with chronic HBV infection (PA<0.01) and chronic hepatitis B (CHB)-related HCC (PA<0.05). Meanwhile, the authenticity of the above meta-analysis results was confirmed by trial sequential analysis (TSA). Conclusions: The meta-analysis showed that STAT3 rs1053004 polymorphism may be the risk for developing chronic HBV infection but not associated with HCC. The present study also indicates that STAT4 rs7574865 polymorphism increased the risk of chronic HBV infection and HCC.


1992 ◽  
Vol 136 (9) ◽  
pp. 1115-1121 ◽  
Author(s):  
Chung-Cheng Hsieh ◽  
Anastasia Tzonou ◽  
Xenophon Zavitsanos ◽  
Evagelia Kaklamani ◽  
Shou-Jen Lan ◽  
...  

2007 ◽  
Vol 42 (9) ◽  
pp. 761-768 ◽  
Author(s):  
Zhi Yong Gao ◽  
Tong Li ◽  
Jia Wang ◽  
Ji Mei Du ◽  
Ya Juan Li ◽  
...  

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