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Next generation digital PCR measurement of hepatitis B virus copy number in formalin-fixed paraffin-embedded hepatocellular carcinoma tissue.

Hepatocellular carcinoma (HCC) [6] is one of most common neoplasms, with >700 000 cases diagnosed in 2008 (1). In the US, approximately 6000 new HCC cases are diagnosed each year that either are intrinsically resistant to chemotherapy or initially respond to it but later develop resistance (2). The mortality of HCC is extremely high, with only a 5%-9% overall 5-year survival rate from the time of clinical diagnosis (3). The incidence of HCC is related to the hepatitis B viral load and the duration of infection, indicating that hepatitis B virus (HBV) has a cumulative effect on tumorigenesis (3, 4). More than 80% of HCC patients in eastern Asia and sub-Saharan Africa also have HBV infection (3, 5, 6). HBV is a small, enveloped DNA virus that consists of 3.2-kb partially double-stranded relaxed circular DNA (rcDNA). The rcDNA can be converted into a stable covalently closed circular DNA (cccDNA) in the nucleus, after which it serves as the original template for viral replication and plays an important role in HBV persistence in the nucleus of infected hepatocytes (7), which may explain HBV reactivation and why HBV cannot be completely eliminated by antiviral agents (7, 8). Inactive HBV infection also has a substantial risk of HCC due to HBV integration and DNA damage (9).

Currently, the diagnosis of HBV infection is routinely performed with serological tests and quantitative real-time PCR (qPCR) (10, 11). However, false-negative results are common with these methods because of HBV surface antigen (HBsAg) variation and low HBV copy number. In particular, the precision limitations of qPCR have prevented distinguishing between small differences in copy number among samples (12), especially in cases of low copy number. It is difficult to accurately justify the results of qPCR when the quantification cycle (Cq) value of an unknown sample is near the cutoff. This phenomenon is frequent in the evaluation of HBV from formalin-fixed paraffin-embedded (FFPE) tissues. In clinical practice, patients with decompensated cirrhosis and detectable HBV DNA require urgent antiviral treatment (8). Thus, there is a need to develop more analytically sensitive and accurate methods to determine HBV infection.

Droplet digital PCR (ddPCR) is a new method of digital PCR that enables the absolute quantification of nucleic acid without the use of calibration curves (13). This method relies on limiting dilutions of the PCR volume and Poisson statistics (9, 14). After the dilutions, the PCR mix is distributed into fractions, and each reaction is independently interrogated for the copy number of the target nucleic acids at a single-molecule sensitivity (15). The absolute number of target nucleic acid in the original sample can be calculated with Poisson statistics from the ratio of positive to total partitions (14, 16-19). Evidence has shown that ddPCR is more analytically sensitive than qPCR (18, 20 ). Because of its single-molecule sensitivity, ddPCR is particularly applicable for detecting the copy number of a viral load (21, 22). Importantly, ddPCR also has the potential to improve HBV measurements because of its ability to quantify nucleic acid targets with high precision and accuracy. Taken together, these advantages show that ddPCR has better analytical sensitivity, precision, accuracy, and day-to-day reproducibility compared with qPCR, indicating that the results from ddPCR are more reliable and applicable than those from qPCR (17). In this study, we used a ddPCR system to measure the HBV copy number in FFPE hepatocellular carcinoma samples, and also investigated the association of HBV copy number with tumor stage and clinical features.

Materials and Methods


A total of 132 HCC patients were enrolled in this study between June 2005 and January 2012 at the Zhongnan Hospital of Wuhan University. Informed consent was obtained from each participant, and the ethics committee of Zhongnan Hospital, Wuhan University, China, approved the study. DNA templates were extracted with DEXPAT Easy kit (Takara) from FFPE tumor tissues of HCC patients, as confirmed by pathology. The DNA was then purified with the AxyPrep DNA Gel Extraction Kit (Axygen) according to the manufacturer's instructions. A total of 27 DNA samples from healthy individuals who had nonpathologic liver function and no history of HBV infection [HBsAg, HBV surface antibody (HBsAb), HBeAg, HBeAb, and HBcAb were negative by serological tests, and HBV DNA was undetectable by qPCR] served as negative controls.


We quantified HBV copy number with the QX100[TM] Droplet Digital(tm) PCR system (Bio-Rad). The 20-[micro]L ddPCR mixture consisted of 10 [micro]L 2 X ddPCR supermix for probes (Bio-Rad); 900 nmol/L HBV sense (5'CTCTCTTTACGCGGTCTC-3') and HBV antisense (5'-GTCGTTGACATTGCTGAG-3') primers, which produced a 161-bp amplicon; 250 nmol/L HBV probe (5'-CCGTCTGTGCCTTCTCATCTGC-3'); and 4 [micro]L DNA sample. The mixture was placed into the DG8 cartridge, 70 [micro]L of droplet generation oil was added, and droplets were formed in the droplet generator (Bio-Rad). After processing, the droplets were transferred to a 96-well PCR plate (Eppendorf). We carried out PCR amplification on a C1000 thermal cycler (Bio-Rad) with a thermal profile beginning at 95[degrees]C for 10 min, followed by 45 cycles of 94[degrees]C for 30 s and 57[degrees]C for 60 s, 1 cycle of 98[degrees]C for 10 min, and ending at 4[degrees]C. After amplification, the plate was loaded on the droplet reader (BioRad). We included no-template controls for detecting PCR contamination. We used pBlue-HBV plasmid containing 1.3-fold HBV genome (from Dr. Yin Zhu's laboratory, State Key Laboratory of Virology, College of Life Sciences, Wuhan University, China) as a reference standard. ddPCR data were analyzed with QuantaSoft analysis software (Bio-Rad). The quantification measurements of the target molecule were presented as the copy numbers per microliter of DNA sample.


We quantified HBV cccDNA in FFPE tumor tissues of HCC patients as described previously (23).


We assessed serological tests of HBsAg, HBsAb, HBeAg, HBeAb, HBcAb, and anti-HCV using Architect chemiluminescent enzyme immunoassays (Abbott Architect i system). Clinical preoperative biochemical parameters and tumor biomarkers were measured with an automated chemistry analyzer (Abbott-Aeroset, Abbott Diagnostics) with commercial kits, including serum alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, indirect bilirubin, total protein, albumin, globulin, y-glutamyltransferase, alkaline phosphatase, 5-nucleotidase, total biliary acid, cholinesterase, prealbumin, glucose, blood urea nitrogen, creatinine, uric acid, retinol-binding protein, cystatin C, carcinoembryonic antigen, and a-fetoprotein (AFP).


All data were analyzed with SPSS version 19.0. We used generalized linear models to analyze the association of HBV DNA and cccDNA copy numbers with tumor stages and clinical features after adjusting for sex, age, smoking, and alcohol consumption. We used the [kappa] statistic to investigate the concordance between the HBV results from ddPCR and HBsAg serological testing. The mean (SE) was used for normally distributed data, and the median with interquartile range was used for skewed data. All statistical tests were 2-sided, and the level of statistical significance was set at P < 0.05.



Among the 132 patients enrolled in this study, the mean age was 49.4 (12.3) years, and the male:female ratio was 108:24. The serological tests for HBV and HCV indicated that the majority of HCC patients (81.1%, 107/132) were HBsAg positive. The etiologies in HBsAg-negative HCC were nonalcoholic fatty liver disease (28.0%, 7/25), alcohol intake (12.0%, 3/25), diabetes mellitus (8.0%, 2/25), HCV infection (4.0%, 1/25), and schistosome infection (4.0%, 1/25). The remaining patients (44.0%, 11/25) had a cryptogenic etiology. To exclude the interference of HCV, we removed 1 patient who was seropositive for anti-HCV. Of the 131 HCC patients included in the analysis, 87 had stage I disease, 13 had stage II disease, 18 had stage III disease, and 13 had stage IV disease on the basis of the tumor-nodes-metastasis (TNM) staging system. According to the Barcelona Clinic liver cancer (BCLC) staging system, 1 patient had stage 0 disease, 36 had stage A disease, 76 had stage B disease, and 18 had stage C disease. Patient demographic and clinicopathological information is presented in Table 1.


We initially analyzed the DNA samples without purification, but the results were not acceptable. There were many impurities in the DNA samples, which led to large variations in the data. The data variation was small and the reproducibility was high when we used purified DNA samples (Fig. 1). With QuantaSoft analysis, we found that all of the DNA samples (n = 131) from HCC patients had hepatitis B viral load, regardless of a HBsAg-positive (n = 107) or -negative (n = 24) serological test result, and that the HBV copy numbers ranged from 1.1 (SD 0.7) to 175.5 (SD 8.2) copies/[micro]l. To investigate if the false-positive result could be completely excluded in the detection of FFPE samples by ddPCR, hepatitis B viral load was detected in the negative controls (n = 27), and the mean copy number of HBV DNA was 0.2 (SD 0.1) copies/^! at 99% confidence level. We validated that the HBV DNA copy numbers in FFPE tissues of HCC patients were much higher than those in the negative controls. To verify the accuracy of our ddPCR data, the copy number of pBlue-HBV plasmid as a reference standard was analyzed by ddPCR and showed a high degree of linearity and correlation ([R.sup.2] = 0.9991) (see Supplemental Fig. 1, which accompanies the online version of this article at vol61/issue1). We also further investigated the concordance between the HBV results from the ddPCR and HBsAg serological tests with the [kappa] statistic. The k statistic tests the agreement between 2 methods; [kappa] < 0.80 indicates poor agreement. Our data showed that there were significant differences between the methods (P < 0.001) and that ddPCR had a higher positive incidence. The incidence of false-negative serological test results was 18.3%, which was not satisfactory for clinical diagnosis. HBV infection was present even for patients who were seronegative for HBsAg.


Next, we used generalized linear models to analyze the associations of hepatitis B viral load with tumor stage and clinical features after adjusting for sex, age, smoking, and alcohol consumption. Notably, we found that the hepatitis B viral load was correlated with the TNM stage (n = 131, P = 7.745; 95% CI 2.011-13.478; P = 0.008) (Fig. 2A). Moreover, the copy numbers of HBV were associated with BCLC stage (n = 131, [beta] = 9.601; 95% CI 0.229-18.973; P = 0.045) (Fig. 2B). In addition to tumor stage, the viral load of HBV was positively correlated with serum cholinesterase (n = 130, p = 0.004; 95% CI 0.001-0.006; P = 0.006). No significant association was found between the HBV copy number and the other clinical features examined in our study.


Because the quantification of HBV DNA included single- and double-stranded DNA, it was important to explore the relationship between HBV cccDNA in liver tissues and disease progression. We successfully detected HBV cccDNA copy numbers per hepatocyte in 39 of 131 samples (see online Supplemental Table 1). We applied generalized linear models to analyze the association of cccDNA copy numbers with tumor stage and clinical features after adjusting for sex, age, smoking, and alcohol consumption. As a result, cccDNA copy numbers were correlated with the Child-Pugh score (n = 39, [beta] = 7.619; 95% CI 1.719-13.518; P = 0.011) (Fig. 3a) and serum AFP (n = 35, P = 7.818 X [10.sup.-5]; 95% CI 4.038 X [10.sup.-5] -1.160 X [10.sup.-4]; P = 5.049 X [ 10.sup.-5]) (Fig. 3B). However, no significant association was found among the HBV cccDNA copy numbers, tumor classifications, and other clinical features examined in our study.


The incidence of and mortality associated with hepatocellular carcinoma are increasing worldwide, and accurate, noninvasive biomarkers for the early diagnosis of HCC are urgently needed to reduce HCC-related morbidity and mortality (24). Recently, we developed an online trapping/ capillary hydrophilic-interaction liquid chromatography/ insource fragmentation/tandem mass spectrometry system for HCC detection by quantifying 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5-hmC) in genomic DNA from HCC tumor samples. However, the limits of detection for 5-mC and 5-hmC were 0.06 and 0.19 fmol, and the imprecision and recovery of the method were poor, with relative SDs and relative errors of 14.9% and 15.8%, respectively (25). Thus, we developed a novel assay that is more analytically sensitive for identifying patients with a low hepatitis B viral load and can be applied for the early detection and prognosis of HCC.

Our results demonstrated that ddPCR can be used to determine the HBV copy number in clinical FFPE samples and suggested that the copy numbers of HBV DNA in liver tissue correlate with TNM and BCLC stages of HCC. Our data showed a direct positive correlation between the hepatitis B viral load and cccDNA in liver tissue and the development of HCC, which indicated that cccDNA secures HBV persistence and reactivation in hepatocytes and that HBV replication is associated with tumor development. Because HBV integrates into the host genome and the copy numbers of HBV increase as the tumor develops, monitoring HBV DNA and cccDNA has the advantage of indicating the development of malignancy. HBV infection has been associated with >80% of HCC patients (26); the ideal end point for treatment of chronic HBV infection is cccDNA elimination (7) and HBsAg loss (8). Our results further confirmed the oncogenicity of HBV, which supports the viewpoints that the expressions of HBV proteins themselves have oncogenic potency and that the integration of HBV DNA into the host genome promotes carcinogenic mechanisms in the host genome (26-29).

With ddPCR, we discovered that all of the HCC patients in our study had a hepatitis B viral load, even in cases that were seronegative for HBsAg. This result demonstrated that the serological tests for HBV are not analytically sensitive enough to diagnose HBV infection because variant HBV antigens cannot be recognized by the specific antibody. We also found that all of the DNA samples had detectable HBV copy numbers, which indicated that each patient in our study was infected with HBV. Our results demonstrated the oncogenicity of HBV.

Next, we found that as the copy number of HBV increased, the serum cholinesterase activity increased as well. Cholinesterase is a protein that is primarily synthesized by the liver, and the serum cholinesterase activity indicates the liver function reserve (30). The serum cholinesterase activity is related to the synthetic activity of the hepatocytes and some hepatic diseases, such as chronic hepatitis and cirrhosis, resulting in changes in the enzyme activity (31). Cholinesterase activity is closely related to the damage of hepatocytes and the HBV DNA copy number, resulting in more damage to hepatocytes. This is not confirmatory of our results, possibly because of the abnormal proliferation of malignant cells, which leads to a high amount of synthesized cholinesterase. This abnormal phenomenon should be studied further.

In this study, we developed a ddPCR assay for the measurement of HBV copy number in FFPE hepatocellular carcinoma tissue. With this method, we successfully detected the hepatitis B viral load in FFPE DNA samples from 131 clinical cases of HCC. Our results indicated that hepatitis B virus infection is a fundamental factor in hepatocellular carcinogenesis because it influences tumor occurrence and development. We also demonstrated that ddPCR can improve the analytical sensitivity and specificity of measurements in nucleic acids at the single-molecule level and is suitable for HBV detection.

Thus, next generation digital PCR, as a high-sensitivity measure of detecting low-copy-number HBV viral load, not only helps HBsAg-negative patients with liver disease by obtaining an earlier diagnosis, but also provides an important theoretical basis for HCC chemotherapy and/or immunosuppression. Moreover, HBV DNA reduction to undetectable concentrations by ddPCR should ideally be achieved to avoid drug resistance and prevent recurrent hepatitis after liver transplantation. Overall, the detection of HBV copy number is essential for clinical diagnosis, decision to treat and subsequent monitoring, assessment endpoints of therapy, and evaluating antiviral efficacy for chronic HBV infection patients (8). The results of the current study suggest that HBV DNA monitoring by ddPCR will be critical to discover the failure of HBV treatment and liver transplantation as early as possible, and our ddPCR technique for analysis of HBV DNA can be used as part of routine diagnostic procedures to establish early detection of hepatocellular carcinoma.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contribution to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: S.-M. Liu, National Basic Research Program of China (973 Program) (2012CB720600, 2012CB720605) andthe National Natural Science Foundation of China (81271919, 81472023). Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.

Acknowledgments: We thank Professor Dongping Xu (Institute of Infectious Diseases and Liver Failure Research Center of Beijing 302 Hospital of PLA, China) for technique support of cccDNA detection.


(1.) Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: Globocan 2008. Int J Cancer 2010; 127:2893-917.

(2.) Wang J, Chan JY, Fong CC, Tzang CH, Fung KP, Yang M. Transcriptional analysis of doxorubicin-induced cytotoxicity and resistance in human hepatocellular carcinoma cell lines. Liver Int 2009; 29:1338-47.

(3.) Forner A, Llovet JM, Bruix J. Hepatocellular carcinoma. Lancet 2012; 379:1245-55.

(4.) Chen CJ, Yang HI, Su J, Jen CL, You SL, Lu SN, et al. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level JAMA 2006; 295: 65-73.

(5.) El-Serag HB. Hepatocellular carcinoma. N Engl J Med 2011; 365:1118-27.

(6.) Sherman M. Hepatocellular carcinoma: epidemiology, surveillance, and diagnosis. Semin Liver Dis 2010; 30: 3-16.

(7.) Lucifora J, Xia Y, Reisinger F, Zhang K, Stadler D, Cheng X, et al. Specific and nonhepatotoxic degradation of nuclear hepatitis B virus cccDNA. Science 2014; 343: 1221-8.

(8.) European Association for the Study of the Liver. EASL clinical practice guidelines: management of chronic hepatitis B virus infection. J Hepatol 2012; 57:167- 85.

(9.) Chen JD, Yang HI, Iloeje UH, You SL, Lu SN, Wang LY, et al. Carriers of inactive hepatitis B virus are still at risk for hepatocellular carcinoma and liver-related death. Gastroenterology 2010; 138:1747-U38.

(10.) Hino O, Kitagawa T, Sugano H. Relationship between serum and histochemical markers for hepatitis-B virus and rate of viral integration in hepatocellular carcinomas in Japan. Int J Cancer 1985; 35:5-10.

(11.) Ide T, Kumashiro R, Koga Y, Tanaka E, Hino T, Hisamochi A, et al. A real-time quantitative polymerase chain reaction method for hepatitis B virus in patients with chronic hepatitis B treated with lamivudine. Am J Gastroenterol 2003; 98:2048-51.

(12.) Belgrader P, Tanner SC, Regan JF, Koehler R, Hindson BJ, Brown AS. Droplet digital PCR measurement of Her2 copy number alteration in formalin-fixed paraffin-embedded breast carcinoma tissue. Clin Chem 2013; 59:991-4.

(13.) Hindson BJ, Ness KD, Masquelier DA, Belgrader P, Heredia NJ, Makarewicz AJ, et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem 2011; 83:8604-10.

(14.) Morisset D, Stebih D, Milavec M, Gruden K, Zel J. Quantitative analysis of food and feed samples with droplet digital PCR. PLoS One 2013; 8.

(15.) Miotke L, Lau BT, Rumma RT, Ji HP. High sensitivity detection and quantitation of DNA copy number and single nucleotide variants with single color droplet digital PCR. Anal Chem 2014; 86:2618-24.

(16.) Beck J, Bierau S, Balzer S, Andag R, Kanzow P, Schmitz J, et al. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin Chem 2013; 59:1732-41.

(17.) Hindson CM, Chevillet JR, Briggs HA, Gallichotte EN, Ruf IK, Hindson BJ, et al. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat Methods 2013; 10:1003-5.

(18.) Heredia NJ, Belgrader P, Wang SL, Koehler R, Regan J, Cosman AM, et al. Droplet digital(tm) PCR quantitation of Her2 expression in FFPE breast cancer samples. Methods 2013; 59:S20-S3.

(19.) Hayden RT, Gu Z, Ingersoll J, Abdul-Ali D, Shi L, Pounds S, Caliendo AM. Comparison of droplet digital PCR to real-time PCR for quantitative detection of cytomegalovirus. J Clin Microbiol 2013; 51:540-6.

(20.) Whale AS, Huggett JF, Cowen S, Speirs V, Shaw J, Ellison S, et al. Comparison of microfluidic digital PCR and conventional quantitative PCR for measuring copy number variation. Nucleic Acids Res 2012; 40.

(21.) Kiselinova M, Pasternak AO, De Spiegelaere W, Vogelaers D, Berkhout B, Vandekerckhove L. Comparison of droplet digital PCR and seminested real-time PCR for quantification of cell-associated HIV-1 RNA. PLoS One 2014; 9:e85999.

(22.) White RA 3rd, Quake SR, Curr K. Digital PCR provides absolute quantitation of viral load for an occult RNA virus. J Virol Methods 2012; 179:45-50.

(23.) Li W, Zhao J, Zou Z, Liu Y, Li B, Sun Y, et al. Analysis of hepatitis B virus intrahepatic covalently closed circular DNA and serum viral markers in treatment-naive patients with acute and chronic HBV infection. PLoS One 2014; 9:e89046.

(24.) Qi J, Wang J, Katayama H, Sen S, Liu SM. Circulating microRNAs (cmiRNAs) as novel potential biomarkers for hepatocellular carcinoma. Neoplasma 2013; 60:135-42.

(25.) Chen ML, Shen F, Huang W, Qi JH, Wang YS, Feng YQ, et al. Quantification of 5-methylcytosine and 5-hydroxymethylcytosine in genomic DNA from hepatocellular carcinoma tissues by capillary hydrophilic-interaction liquid chromatography/quadrupole TOF mass spectrometry. Clin Chem 2013; 59:824-32.

(26.) Li XJ, Zhang JB, Yang ZW, Kang JT, Jiang SZ, Zhang T, et al. The function of targeted host genes determines the oncogenicity of HBV integration in hepatocellular carcinoma. J Hepatol 2014; 60:975-84.

(27.) Sung WK, Zheng HC, Li SY, Chen RH, Liu X, Li YR, et al. Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nat Genet 2012; 44:765-U188.

(28.) Jiang SZ, Yang ZW, Li WJ, Li XJ, Wang YF, Zhang JB, et al. Re-evaluation of the carcinogenic significance of hepatitis B virus integration in hepatocarcinogenesis. PLoS One 2012; 7.

(29.) Neuveut C, Wei Y, Buendia MA. Mechanisms of HBV-related hepatocarcinogenesis. J Hepatol 2010; 52: 594-604.

(30.) Donadon M, Cimino M, Procopio F, Morenghi E, Montorsi M, Torzilli G. Potential role of cholinesterases to predict short-term outcome after hepatic resection for hepatocellular carcinoma. Updates Surg 2013; 65: 11-8.

(31.) Ogunkeye OO, Roluga AI. Serum cholinesterase activity helps to distinguish between liver disease and non-liver disease aberration in liver function tests. Pathophysiology 2006; 13:91-3.

Jing-Tao Huang, [1] [dagger] Ying-Juan Liu, [2] ([dagger] ) Jin Wang, [3] [ dagger] Zhi-Gao Xu, [4] Ying Yang, [1] Fan Shen, [1] Xing-hui Liu, [5] Xin Zhou, [1] and Song-Mei Liu [1,2] *

[1] Center for Gene Diagnosis, [2] Medical Research Center, and [ 4] Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China; [3] Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX; [5] Department of Clinical Laboratory, Gongli Hospital, Second Military Medicine University, Shanghai, China.

([dagger]) Jing-Tao Huang, Ying-Juan Liu, and Jin Wang contributed equally to the work, and both should be considered as first authors.

* Address correspondence to this author at: Fax 27-67812610; e-mail

Received July 10, 2014; accepted September 19, 2014.

Previously published online at DOI: 10.1373/clinchem.2014.230227

[6] Nonstandard abbreviations: HCC, hepatocellular carcinoma; HBV, hepatitis B virus; rcDNA, relaxed circular DNA; cccDNA, covalently closed circular DNA; qPCR, quantitative real-time PCR; HBsAg, HBV surface antigen; FFPE, formalin-fixed paraffin- embedded; ddPCR, droplet digital PCR; HBsAb, HBV surface antibody; AFP, [alpha]- fetoprotein; TNM, tumor-nodes-metastasis; BCLC, Barcelona Clinic liver cancer; 5-mC, 5- methylcytosine; 5-hmC, 5-hydroxymethylcytosine.

Table 1. Characteristics of HCC patients.

Baseline variable                  HCC patients,
                                       n (%)
  Male                               107 (81.7)
  Female                              24 (18.3)
  Yes                                 46 (35.1)
  No                                  85 (64.9)
  Yes                                 35 (26.7)
  No                                  96 (73.3)
TNM staging
  I                                   87 (66.4)
  II                                  13 (9.9)
  III                                 18 (13.8)
  IV                                  13 (9.9)
BCLC staging
  0                                    1 (0.8)
  A                                   36 (27.5)
  B                                   76 (58.0)
  C                                   18 (13.7)
Child-Pugh classification
  A                                  116 (88.5)
  B                                   15 (11.5)
Tumor size (cm)
  <5.0                                37 (28.2)
  [greater than or equal to] 5.0      94 (71.8)
  Positive                           107 (81.7)
  Negative                            24 (18.3)
  Positive                             9 (6.9)
  Negative                           122 (93.1)
  Positive                            85 (64.9)
  Negative                            46 (35.1)
  Positive                            37 (28.2)
  Negative                            94 (71.8)
  Positive                            80 (61.1)
  Negative                            51 (38.9)
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Title Annotation:Cancer Diagnostics
Author:Huang, Jing-Tao; Liu, Ying-Juan; Wang, Jin; Xu, Zhi-Gao; Yang, Ying; Shen, Fan; Liu, Xing-hui; Zhou,
Publication:Clinical Chemistry
Article Type:Report
Date:Jan 1, 2015
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