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Int. J. Mol. Sci. 2013, 14(12), 23559-23580; doi:10.3390/ijms141223559

Article
Evaluation of Individual and Combined Applications of Serum Biomarkers for Diagnosis of Hepatocellular Carcinoma: A Meta-Analysis
Bin Hu 1,, Xiaohui Tian 1,, Jie Sun 1 and Xiangjun Meng 1,2,*
1
Department of Gastroenterology, Shanghai First People’s Hospital, School of Medicine, Shanghai Jiaotong University, 100 Haining Road, Shanghai 200080, China; E-Mails: hubinsjtu@163.com (B.H.); tianxiaohui01@163.com (X.T.); Sunjie0426@126.com (J.S.)
2
Department of Gastroenterology, Shanghai Sixth People’s Hospital, School of Medicine, Shanghai Jiaotong University, 600 Yishan Road, Shanghai 200233, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed; E-Mail: meng_xiangjun@yahoo.com; Tel.: +86-138-1617-0604; Fax: +86-21-6324-1331.
Received: 10 September 2013; in revised form: 31 October 2013 / Accepted: 7 November 2013 /
Published: 2 December 2013

Abstract

: The clinical value of Serum alpha-fetoprotein (AFP) to detect early hepatocellular carcinoma (HCC) has been questioned due to its low sensitivity and specificity found in recent years. Other than AFP, several new serum biomarkers including the circulating AFP isoform AFP-L3, des-gamma-carboxy prothrombin (DCP) and Golgi protein-73 (GP73) have been identified as useful HCC markers. In this investigation, we review the current knowledge about these HCC-related biomarkers, and sum up the results of our meta-analysis on studies that have addressed the utility of these biomarkers in early detection and prognostic prediction of HCC. A systematic search in PubMed, Web of Science, and the Cochrane Library was performed for articles published in English from 1999 to 2012, focusing on serum biomarkers for HCC detection. Data on sensitivity and specificity of tests were extracted from 40 articles that met the inclusion criteria, and the summary receiver operating characteristic curve (sROC) was obtained. A meta-analysis was carried out in which the area under the curve (AUC) for each biomarker or biomarker combinations (AFP, DCP, GP73, AFP-L3, AFP + DCP, AFP + AFP-L3, and AFP + GP73) was used to compare the diagnostic accuracy of different biomarker tests. The AUC of AFP, DCP, GP73, AFP-L3, AFP + DCP, AFP + AFP-L3, and AFP + GP73 are 0.835, 0.797, 0.914, 0.710, 0.874, 0.748, and 0.932 respectively. A combination of AFP + GP73 is superior to AFP in detecting HCC and differentiating HCC patients from non-HCC patients, and may prove to be a useful marker in the diagnosis and screening of HCC. In addition, the AUC of GP73, AFP + DCP and AFP + GP73 are better than that of AFP. The clinical value of GP73, AFP + DCP, or AFP + GP73 as serological markers for HCC diagnosis needs to be addressed further in future studies.
Keywords:
hepatocellular carcinoma; biomarkers; detection; diagnosis

1. Introduction

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. The incidence and mortality rates of HCC are almost equal because most HCC patients are diagnosed at an advanced stage. Therefore, the prognosis of HCC patients is generally poor, with a five-year survival rate lower than 5%. Alpha-fetoprotein (AFP) is the most commonly used serological biomarker in clinical practice. AFP, along with hepatic ultrasonography, is employed in the detection of HCC in high-risk patients with cirrhosis [1]. However, the clinical diagnostic accuracy of AFP is unsatisfactory due to low sensitivity and specificity. Therefore, there is an urgent need for developing better HCC-specific biomarkers [2,3]. Recent studies have identified other potential biomarkers for early detection of HCC, including the circulating AFP isoform AFP-L3, des-gamma-carboxy prothrombin (DCP), and Golgi protein-73 (GP73), although these biomarkers have been used in the clinic [46], the practical value of these markers has yet to be fully evaluated. So, it is meaningful to assess the value of these markers individually or for combined application in the clinic.

2. Results and Discussion

2.1. Results

A total of 40 studies [7] were included in the meta-analysis, 35 for AFP (biomarker 1), 15 for DCP (biomarker 2), nine for GP73 (biomarker 3), 15 for AFP-L3 (biomarker 4), eight for AFP+DCP (biomarker 5), three for AFP+AFP-L3 (biomarker 6), and two for AFP+GP73 (biomarker 7). The literature search strategy is depicted below (Table 1). Literature screening was performed at four levels (Figure 1). We extracted data from the selected papers on authors, country, year of publication, journal, number of patients, test methods and results, sensitivity, specificity, and cut-off points for the biomarkers (Table 2).

In this meta-analysis, AFP was considered to be the reference biomarker. The area under the curve (AUC) and S-values were the major indicators. The AUC for 1, 2, 3, 4, 5, 6 and 7 biomarkers or the associated compounds were 0.835, 0.797, 0.914, 0.710, 0.874, 0.748, and 0.932 respectively. It was found that the AUC of biomarkers 3, 5 and 7 were superior to that of the reference biomarker 1, while the AUC of biomarkers 2, 4 and 6 were inferior to that of the reference biomarker 1 (Table 3). The S-value represents the positive rate of the biomarkers for detecting HCC. Using biomarker 1 as a reference marker, the S-values of biomarkers 2, 3, 4, 5 and 6 were not significantly different from that of AFP (p > 0.05). The S-value of biomarker 7 was significantly different as compared with AFP (p < 0.05) (Table 4). The plots for calculating the pooled AUC, and the S- and D-values for each biomarker are shown in (Figure 2).

2.2. Discussion

HCC remains one of the most common malignant tumors. Early diagnosis and early surgical extraction are imperative for improving the survival of HCC patients [1,4,5]. AFP, a specific glycoprotein produced primarily by the fetal liver has been the most practical and widely used serum biomarker for HCC diagnosis. However, its sensitivity and specificity varied significantly ranging from 40%–65% and 76%–96%, respectively [45,48,49]. The AFP value is often elevated at a milder level in patients with chronic hepatitis C infection in the absence of HCC [27]. An AFP value >400 ng/mL is considered ideal for diagnosing HCC [27]. Even though normal AFP levels can be seen in approximately one-third of patients with HCC [49], a large number of HCC patients have AFP values <400 ng/mL, making them very difficult to undergo detection and prognosis of HCC [49]. Other conventional clinical tumor associated biomarkers such as CEA, CA199, CA724 etc. are not practical in the clinic because of their poor sensitivity and specificity. The present situation requires an urgent need to explore new markers to overcome these drawbacks in liver cancer diagnosis.

An ideal serum biomarker should be both sensitive and specific for HCC detection at an early stage, and be easy to test, reproduce, as well as be non-invasive [50]. With the latest developments in molecular techniques, several new HCC-specific biomarkers including AFP-L3, DCP and GP73 have been discovered [51,52]. These new markers have been investigated for their diagnostic accuracy and potential for HCC detection [53,54]. However, the clinical usefulness of these biomarkers needs to be carefully evaluated and validated. Thus, we aimed to evaluate the utility of the biomarkers individually, as well as their combined application in the early detection of HCC and for their usefulness in therapeutic decision-making.

AFP-L3, one of the AFP isoforms, has a high binding affinity to lectin Lens culinaris agglutinin. It has been reported that AFP-L3 is a more valuable index than total AFP for early diagnosis of HCC [24,51]. The proportion of AFP-L3 over the total AFP concentration has been used as a marker for early diagnosis and assessment of the therapeutic effect as well as prognosis of HCC [51]. AFP-L3 was found to be associated with liver dysfunction, poor differentiation, and other biologically malignant characteristics [48]. If total AFP concentration is below 10 ng/mL, the absolute value of AFP-L3 would be hard to be detected. However, AFP-L3 instead of AFP can be detected in the serum of some patients with tumors smaller than two centimeters in size. Generally, AFP-L3 has been detected in approximately one-third of HCC patients with cutoff values of 10%–15% (percentage of AFP-L3 over AFP) [2,55]. Therefore, percentage of AFP-L3 is often used when AFP concentration is above 10 ng/mL, especially within levels between 10–200 ng/mL [8]. In the clinic, it is a diagnostic dilemma for patients with total AFP values of 10–200 ng/mL [8]. For these cases, AFP-L3 may be a better complement index for diagnosing HCC when combined with AFP. However, because its sensitivity and specificity range from 36%–96%, and 89%–94%, respectively [34,48,49,53,56,57], drawing a conclusion requires caution. For example, Nouso K et al. found that the sensitivity of AFP-L3 in patients of HCC with low AFP (under 20 ng/mL) was 51.5%, 13.3%, and 8.7% at cutoff levels of 5%, 10%, and 15%, respectively [58]. Leerapun, A. et al. found that in patients with total AFP values of 10–200 ng/mL, the AFP-L3 was greater than 10% and had a sensitivity of 71% and a specificity of 63% for diagnosing HCC [8]. With an AFP-L3 greater than 35%, the specificity for HCC diagnosis reached 100% while reducing sensitivity to 33% [8]. Nonetheless, the combination of AFP-L3% and AFP significantly improved the specificity (100%) of diagnosis of HCC [8]. Thus, they proposed that the AFP-L3% should be taken as a potential marker for AFP in the diagnosis of HCC, although no prognostic significance for AFP-L3% was observed after adjustment for total AFP [8]. Our meta-analysis result showed that AFP-L3 alone or combined with AFP was not superior to AFP (AUC: 0.710, 0.748 vs. 0.835) (Table 3). However, significant variations for interpreting the present studies arose due to different assay methods, cutoff values, and study populations.

As a new serum biomarker in patients with HCC, DCP, or prothrombin, induced by vitamin K absence-II (PIVKA-II) was first described by Liebman et al. [59]. DCP is an abnormal protein that is increased in the serum of patients with HCC. With a cutoff value of 40 mAU/mL, the sensitivity and specificity of DCP for diagnosing HCC ranges from 28%–89%, and 87%–96% respectively [19,25,33,6065]. DCP at a 125 mAU/mL had a high sensitivity (89%) and specificity (95%) for differential diagnosis of HCC from cirrhosis and chronic hepatitis [25]. The areas under the ROC curve for DCP (125 mAU/mL) and AFP (11 ng/mL) were 0.928 vs. 0.810, respectively [25]. Another case-controlled study comparing the AFP value of diagnosing HCC with DCP showed that a cutoff value of 60 mAU/mL for DCP had a higher sensitivity than AFP (with a cutoff value 20 ng/mL) (75.5% vs. 68.4%) [60]. Therefore, DCP is considered as a valuable complement prognostic predictor for HCC [66,67]. Nevertheless, just as our meta-analysis showed, DCP was not statistically better than AFP (AUC: 0.797 vs. 0.835), albeit combined measurement of DCP and AFP was superior to AFP alone (AUC: 0.874 vs. 0.835) (Table 3). As a biomarker of HCC, more appropriate and accurate evaluations of DCP are expected in future studies.

GP73, also called Golgi phosphoprotein 2 (GOLPH2), is a resident Golgi transmembrane glycoprotein with 400 amino acids and the 73kDa molecular weight was found up-regulated in expression in virus-infected hepatocyte [52]. Several recent studies indicate that GP73 is one of the most promising serum markers for HCC. Although there are studies reporting that the sensitivity of GP73 was higher than that of AFP in the diagnosis of early HCC (62% vs. 25%), the potential clinical value of GP73 as a better serum biomarker than AFP remains controversial [7,17,31,54,68]. A large cohort study of HCC by Mao et al. showed that GP73 was a valuable tumor biomarker for HCC and was superior to AFP with respect to sensitivity and specificity after comparing the adjusted factors such as the likelihood ratio and predictive value, which are independent of the age and gender of the subjects [23]. They also found that combined measurement of GP73 and AFP could further increase the sensitivity for the detection of HCC. Using 8.5 relative units as the cut-off value, the sensitivity and specificity of serum GP73 for HCC were 74.6% (95% confidence interval (CI), 71.5%–77.6%) and 97.4% (95% CI, 96.8%–98.3%) [23]. Combined measurement of GP73 and AFP increased the sensitivity for HCC to 89.2% (95% CI, 86.7%–91.5%), with a specificity of 85.2% (95% CI, 83.4%–86.4%) [23], which indicated that serum GP73 was dramatically elevated in patients with HCC and that the sensitivity and specificity of GP73 for HCC might be superior to that of AFP [23]. Another meta-analysis by Zhou et al. found that, as an independent marker for diagnosis of HCC, GP73 is comparable to AFP [69]. In our study, the result showed that GP73 and AFP combined were dramatically superior to AFP (AUC: 0.914, 0.932 vs. 0.835), which was similar to the results above (Table 3). However, there were several drawbacks in our study. For instance, the data extracted from the paper was not enough to conclude for certain that GP73 and AFP were more effective when applied together. Moreover, the assay methods and the cutoff values varied in these studies.

The results of this meta-analysis showed that the AUC of biomarkers 3, 5 and 7 were superior to that of the reference biomarker AFP, while the AUC of biomarkers 2, 4, and 6 were inferior to that of the reference biomarker AFP. Based on our tendency to conclude that GP73, together with DCP + AFP, with the combined measurement of GP73 + AFP could increase detection of HCC, this may be superior to AFP for diagnosis and screening in the early stages of HCC. Using biomarker 1 (AFP) as the reference marker, there is no significant difference in the S-value between biomarkers 2, 3, 4, 5 and 6 (p > 0.05), while the S-value of biomarker 7 (AFP + GP73) was significantly different (p < 0.05), suggesting that biomarker 7 may be more valuable and useful in clinical practice, although the compound value of AFP + GP73 still calls for further research and evaluation.

The clinical value of a biomarker depends not only on the high sensitivity and specificity but also on the universality and availability for practice. Technologies of testing assays for these biomarkers vary, including: ELISA, LiBASys, μTAS, IAUEC, ECLIA, EIA, LAEC, and immunoblot. Some may not been used worldwide and their costs differ significantly. However, with the optimization and improvement of technology, such problems are expected to be resolved. The costs of these technologies could not be extracted from the original citation papers, which suggests that in future studies, financial factors should be taken into consideration. According to the inclusion criteria, although the obtained reports of each biomarker may bring potential bias, they do not affect statistical analysis with Empower Stats software package (Empower Stats, X & Y Solutions, Boston, MA, USA). Therefore, more caution needs to be taken with the results.

3. Experimental Section

3.1. Identification of Studies

To assess biomarkers for HCC, a comprehensive literature search of original research articles published between January 1999 and July 2012 (cut-off date 1 July) was conducted using the PubMed database ( http://www.ncbi.nlm.nih.gov/pubmed), Web of Science ( http://www.isiknowledge.com), and Cochrane Library ( http://www.thecochranelibrary.com).

3.2. Literature Screening

Literature screening was performed at four levels. At level 1, we excluded reviews, letters, case reports, editorials, and comments. At level 2, we excluded the articles in which biomarkers were not evaluated for their utility in detecting HCC and content duplicated articles. At level 3, we ensured the articles included serum biomarkers: AFP, DCP, GP73, or AFP-L3 for diagnosing HCC in their studies. The data pertaining to other biomarkers were excluded from further analysis. Articles were further screened to ensure that the data in those studies pertained to patients with HCC and appropriate control populations. At level 4, only reports with sensitivity and specificity data for the biomarkers were selected. A total of 40 reports met the inclusion criteria for meta-analysis.

3.3. Data Extraction

Data extracted from the selected papers included authors, country, year of publication, journal, number of patients, test methods and results, sensitivity, specificity, and cut-off points for the biomarkers.

3.4. Statistical Analysis

Data from disparate reports were summarized by the method described by Littenberg and Moses using a logistic transformation and linear regression we generated a summary receiver operating characteristic (sROC) curve [70]. The ROC curve has been demonstrated to be useful for summarizing the diagnostic accuracy of multiple reports, comparing technologies, detecting outliers, and identifying the optimum operating point of the test.

The sensitivity and specificity of the biomarkers from the included studies were logistically transformed, and then a linear regression line was fitted through the data points. This line was back-transformed to obtain the sROC curve [7], which is a compact description of the accuracy of the diagnostic test in many populations [48,70]. We did not extrapolate the curve beyond the range of the logistically transformed data.

The AUC, as one of the serum biomarkers, was obtained from the included studies. For each biomarker, the pooled AUC was calculated with the inverse standard errors as weights. The pooled AUC, together with similarly pooled standard errors, was used to compare the accuracy of these diagnostic tests [7].

Two parameters were used to describe the meta-analysis data S, a sum of the two transforms, is related to the frequency when the test is positive, which depends on the test threshold D, the difference between the two transforms, is a measure of the successful degree of the test to discriminate between the populations of healthy subjects and HCC [70].

AFP was taken as the reference marker for performing comparisons with other biomarkers. Student’s t-test was used to compare the pooled AUC of AFP and the pooled AUC of each new biomarker. Statistical analysis was performed using the Empower Stats software package (Empower Stats, X & Y Solutions, Boston, MA, USA). Differences were considered statistically significant at a p < 0.05.

4. Conclusions

The results of our meta-analysis suggested that AFP+GP73 is superior to AFP alone in diagnosing HCC and differentiating HCC patients from non-HCC patients, therefore, it might be a useful compound marker in the diagnosis and screening of potential HCC patients. In addition, the values of GP73, AFP + DCP and AFP + GP73 are also better than that of AFP alone. The clinical value of GP73, AFP + DCP, or AFP + GP73 as serological markers for early diagnosis of HCC needs to be evaluated further in future studies using stricter criteria.

Acknowledgments

Statistical analysis was performed using the Empower Stats software package (Empower Stats, X & Y Solutions, Boston, MA, USA), which was supported and directed by Changzhong Chen in the Department of Biostatistics of Harvard University.

We are very grateful to the authors of the primary studies included in our meta-analysis; due to their contributions, this work could be carried out successfully.

Conflicts of Interest

The authors declare no conflict of interest.

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Ijms 14 23559f1 1024
Figure 1. Literature screening was performed at four levels. Level 1, reviews, letters, case reports, editorials, and comments were excluded from the papers identified using the above search strategy. Level 2, articles in which biomarkers were not evaluated for their utility in detecting hepatocellular carcinoma (HCC) were excluded. The full texts of reports that met the above criteria were obtained with duplicate articles excluded. Level 3, the content of the articles was analyzed to ensure that the serum biomarkers in the study included Alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin (DCP), Golgi protein-73 (GP73), or circulating AFP isoform AFP-L3, and that these biomarkers were used just for diagnosing HCC. The data pertaining to other biomarkers were excluded from further analysis. Articles were further screened to ensure that the studies included data pertaining to patients with HCC and appropriate control populations. At Level 4, only reports with sensitivity and specificity data for the biomarkers were selected. A total of 40 reports met the inclusion criteria and were selected for meta-analysis.

Click here to enlarge figure

Figure 1. Literature screening was performed at four levels. Level 1, reviews, letters, case reports, editorials, and comments were excluded from the papers identified using the above search strategy. Level 2, articles in which biomarkers were not evaluated for their utility in detecting hepatocellular carcinoma (HCC) were excluded. The full texts of reports that met the above criteria were obtained with duplicate articles excluded. Level 3, the content of the articles was analyzed to ensure that the serum biomarkers in the study included Alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin (DCP), Golgi protein-73 (GP73), or circulating AFP isoform AFP-L3, and that these biomarkers were used just for diagnosing HCC. The data pertaining to other biomarkers were excluded from further analysis. Articles were further screened to ensure that the studies included data pertaining to patients with HCC and appropriate control populations. At Level 4, only reports with sensitivity and specificity data for the biomarkers were selected. A total of 40 reports met the inclusion criteria and were selected for meta-analysis.
Ijms 14 23559f1 1024
Ijms 14 23559f2 1024
Figure 2. Plots for calculating the area under the curve (AUC) and D (log odds ratio) against S (implicit threshold) for biomarker 1 to biomarker 7. The S-value represents the positive rate of the biomarker for detecting HCC. S = logit (TPR) + logit (FPR), where TPR is the true positive rate (sensitivity) and FPR is the false positive rate (1-specificity) for AFP. D represents the ability of distinguishing HCC and the control. D = logit (TPR) − logit (FPR) (a, c, e, g, i, k, m). The AUC of biomarkers 1 to biomarker 7 are 0.835, 0.797, 0.914, 0.710, 0.874, 0.748, 0.932 respectively (b, d, f, h, j, l, n).

Click here to enlarge figure

Figure 2. Plots for calculating the area under the curve (AUC) and D (log odds ratio) against S (implicit threshold) for biomarker 1 to biomarker 7. The S-value represents the positive rate of the biomarker for detecting HCC. S = logit (TPR) + logit (FPR), where TPR is the true positive rate (sensitivity) and FPR is the false positive rate (1-specificity) for AFP. D represents the ability of distinguishing HCC and the control. D = logit (TPR) − logit (FPR) (a, c, e, g, i, k, m). The AUC of biomarkers 1 to biomarker 7 are 0.835, 0.797, 0.914, 0.710, 0.874, 0.748, 0.932 respectively (b, d, f, h, j, l, n).
Ijms 14 23559f2 1024
Table Table 1. Literature search strategy.

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Table 1. Literature search strategy.
DatabaseSearch criteriaFilters/Limits
PubMedSearch ((hepatocellular carcinoma [MeSH Terms]) and biological markers [MeSH Terms]) and (“1999” [Date—Publication]: “2012” [Date—Publication])Humans; English
Web of Science(hepatoma * or liver cell neoplasm * or hepatocellular neoplasm * or liver cell cancer * or hepatocellular cancer * or liver cell tumo * or hepatocellular tumo * or liver cell carcinom * or hepatocellular carcinom *) and (Biomarker * or Biological Marker * or Biologic Marker * or Biochemical Marker * or Serum Marker * or Clinical Marker *)Publication date: 1999–present and language: English
Cochrane Library#1 MeSH descriptor: [Carcinoma, Hepatocellular] explode all trees
#2 MeSH descriptor: [Biological Markers] explode all trees
Dates: from 1999–2012

*A comprehensive literature search of original research articles published between January 1999 and July 2012 (cut-off date 1 July) assessing biomarkers for hepatocellular carcinoma (HCC) was conducted using the PubMed database, Web of Science, and Cochrane Library.

Table Table 2. Data from the selected papers.

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Table 2. Data from the selected papers.
StudyCountry/DistrictPublication YearNumberBiomarkerTest MethodsSensitivitySpecificityCutoffJournal
Leerapun [8]USA200752AFP-L3LiBASys0.7100.63010%Clin. Gastroenterol. Hepatol.
0.3301.00035%

Cedrone [9]Italy200074AFPELISA0.2000.990200 ug/LHepato Gastroenterol.
0.5500.88020 ug/L

Wang [10]Taiwan, China200561AFPELISA0.5900.77020 ng/mLWorld J. Gastroenterol.
DCPEIA0.7700.86040 mAU/mL
AFP + DCPELISA + EIA0.8360.68220 ng/mL/40 mAU/mL

Durazo [11]USA2008144AFPIAUEC0.6900.87025 ng/mLJ. Gastroenterol. Hepatol.
DCPELISA0.8700.85084 mAU/mL
AFP-L3LAEC0.5600.90010%

Gianluigi [12]Italy2007499AFPELISA0.4100.94018.8 ng/mL/20.5 IU/mLClin. Chim. Acta

Giannelli [13]Italy2005120AFPELISA0.4500.87012.6 ng/mL/13.7 IU/mLInt. J. Cancer

Toyoda [14]Japan2011270AFP-L3μTAS0.3940.7707%Cancer Sci.

Okuda [15]Japan199960DCPEIA0.6170.82130 mAU/mLCancer
0.6000.86335 mAU/mL
0.6000.92340 mAU/mL
0.5830.94945 mAU/mL
0.5670.96650 mAU/mL
0.5670.96655 mAU/mL
0.5330.97460 mAU/mL

Hsia [16]Taiwan, China200726AFPELISA0.6200.88020 ng/mLEur. J. Surg. Oncol.

Hu [17]China201031AFPELISA0.4800.97036 ug/LMed. Oncol.
AFP + GP73ELISA/western blotting0.7700.8407.4 RU

Hussein [18]Egypt200849AFPELISA0.9000.9307.7 ng/mLIndian J. Cancer

Ikoma [19]Japan200263AFPELISA0.5100.83020 ng/mLHepato Gastroenterol.
DCPELISA0.3900.96016 mAU/mL
AFP + DCPELISA/ELISA0.8300.84020/16

Ertle [20]Germany2011170AFPELISA0.3100.960200 ng/mLJ. Hepatol.
DCPLiBASys0.6000.9407.5 ng/mL
AFP-L3LiBASys0.4100.99010%

Yamamoto [21]Japan2009714AFPimmunometric assay0.6490.82911 ng/mLAnn. Surg. Oncol.
0.6080.86113 ng/mL
0.5130.90820 ng/mL
0.3040.986100 ng/mL
0.2470.990200 ng/mL
DCPtwo-step enzyme immunoassay0.7340.94720 mAU/mL
0.6280.99430 mAU/mL
0.5590.99840 mAU/mL
0.4191.000100 mAU/mL
0.3911.000125 mAU/mL

Volk [22]USA200784AFPIAUEC0.8600.930150 mAU/mLCancer Biomark.
0.6900.91023 ng/mL
AFP-L3IAUEC0.5700.8803%
AFP + DCPIAUEC/ELISA0.8800.89023 ng/mL/150 mAU/mL

Mao [23]China2010789AFP + GP73ELISA/Immunoblot0.8920.85235 ng/mL/8.5 RUGut
GP73Immunoblot0.7500.9708.5 RU
AFPELISA/Immunoblot0.5800.85035 ug/L

Yamamoto [24]Japan201096AFPELISA0.4000.83015 ng/mLJ. Gastroenterol.
0.3900.87020 ng/mL
0.2800.960124 ng/mL
0.2200.960200 ng/mL
DCPImmunoblot0.7700.58020 mAU/mL
0.5900.81030 mAU/mL
0.5500.91040 mAU/mL
0.5200.96060 mAU/mL
AFP-L3IAUEC0.2400.9205%
0.2200.96010%
0.1700.97015%
0.1500.97020%
AFP + DCPImmunoblot0.6900.79020 ng/mL/40 mAU/mL
0.6800.83020 ng/mL/60 mAU/mL
0.5900.900400 ng/mL/40 mAU/mL
0.5700.950400 ng/mL/60 mAU/mL
AFP + AFP-L3Immunoblot/IAUEC0.4000.87020 ng/mL/irrespective
0.2600.96020–400 ng/mL/10%
0.2600.97020–400 ng/mL/15%
0.1800.990400 ng/mL/irrespective

Marrero [25]USA200355AFPIAUEC0.7700.79011 ng/mLHepatology
0.6800.86020 ng/mL
0.4700.980100 ng/mL
0.3401.000400 ng/mL
DCPELISA0.8900.950125 mAU/mL
0.8700.970150 mAU/mL
AFP + DCPIAUEC/ELISA0.8800.950logAFP + 4.6 * logDCP

Colombo [26]Italy200155AFPELISA0.3900.76020 ng/mlClin. Liver Dis.
0.1300.970100 ng/mL

Nguyen [27]USA2002163AFPELISA0.7840.61110 ng/mLHepatology
0.6300.79920 ng/mL
0.5060.89350 ng/mL
0.4140.973100 ng/mL
0.3211.000200 ng/mL

Marrero [28]USA2009419AFPLiBASys0.5900.90020 ng/mLGastroenterology
DCPELISA0.7400.700150 mAU/mL
AFP-L3LiBASys0.4200.97010%
AFP + DCPELISA + LiBASys0.8600.63020 ng/mL/150 mAU/mL

Shafie [29]Egypt201231AFPELISA0.8100.85032.64 ng/mLLife Sci. J.
0.7700.60028.51 ng/mL
AFP + GP73ELISA0.8700.95032.64 ng/mL/7.62 ng/mL
0.9000.90028.51 ng/mL/7.62 ng/mL

Morota [30]Japan2011UNAFPELISA0.6300.92015.3 ug/LClin. Chem. Lab. Med.
AFP + GP73ELISA0.8900.62094.7 ug/L

Marrero [31]USA2005144AFPELISA0.3000.96099 ug/LJ. Hepatol.
AFP + GP73ELISA/Immunoblot0.6900.75010.0 RU

Tong [32]USA200131AFPELISA0.4100.95024 ng/mLJ. Gastroenterol. Hepatol.
0.4100.94021 ng/mL
0.4500.94019 ng/mL
0.5500.93016 ng/mL
0.5900.91013 ng/mL
0.8600.89011 ng/mL
0.8600.8508 ng/mL

Nomura [33]Japan199936AFPELISA0.5900.76020 ng/mLAm. J. Gastroenterol.
DCPImmunoblot0.2800.96040 mAU/mL
AFP-L3ECLIA0.2200.94010%

Oka [34]Japan2001388AFPELISA0.5500.49020 ng/mLJ. Gastroenterol. Hepatol.
AFP-L3LAEC0.2800.93010%
0.2100.99015%

Ozkan [35]Turkey201175AFPELISA0.8200.9504.36 ug/LDigestion
0.6900.95013 ng/mL
0.7600.9508.46 ng/mL
0.6000.98020 ng/mL
0.4601.000100 ng/mL
0.3901.000200 ng/mL
AFP + GP73ELISA0.0700.95020 ng/mL/2.36 ug/L
0.8200.09020 ng/mL/0.078 ng/mL
0.0000.95020 ng/mL/24.43 ng/mL

Peng [36]Taiwan, China1999205AFPELISA0.4501.000200 ug/LHepato Gastroenterol.
0.6500.87020 ug/L

Porta [37]Italy200830AFPELISA0.6300.88012.8 ng/mLAnn. Oncol.

Romeo [38]Italy201086AFPμ TAS0.4650.86820 ng/mLJ. Clin. Oncol.
DCPμ TAS0.6630.8420.5 ng/mL
0.5580.9210.75 ng/mL
0.2091.0002.5 ng/mL
0.1511.0007.5 ng/mL
AFP-L3μ TAS0.7790.5265 ng/ml
0.5580.7897 ng/mL
0.3140.89510 ng/mL
AFP + DCPμ TAS0.6980.78920 ng/mL + 0.75 ng/mL
AFP + AFP-L3μ TAS0.6510.71120 ng/mL + 10 ng/mL
0.7910.68420 ng/mL + 1010 ng/mL

Sassa [39]Japan199961AFPELISA0.0801.000200 ng/mLEur. J. Gastroenterol. Hepatol.
DCPimmunoassay0.4400.95040 mAU/mL
AFP-L3LAEC0.2300.99010%
AFP + DCPELISA/immunoassay0.4800.990200ng/mL/40 mAU/mL
AFP + AFP-L3ELISA/LAEC0.2500.990200 ng/mL/10%

Shi [40]China201173AFPELISA0.7500.750400 ug/LTechnol. Cancer Res. Treat
AFP + GP73ELISA0.9800.950400 ug/L/100.0 ug/L

Shimauch [41]Japan199921AFP-L3Immunoblot0.3300.93010%Oncol. Rep.
DCPImmunoblot0.4300.97040mAU/mL

Sterling [42]USA200974AFPLiBASys0.9900.200200 ng/mLClin. Gastroenterol. Hepatol.
0.6080.71120 ng/mL
DCPLiBASys0.7600.58040 mAU/mL
LiBASys0.3900.9007.5 ng/mL
AFP+DCPLiBASys0.7030.63420 ng/mL/7.5 ng/mL
AFP + AFP-L3LiBASys0.6890.66420 ng/mL/10
AFP-L3LiBASys0.3650.91610%

Sun [43]China200979AFP-L3ACSC method0.8480.92510J. Gastroenterol. Hepatol.

Tian [44]China2010153AFPELISA0.9500.47013.6 ug/LInt. J. Cancer
AFP + GP73ELISA0.7500.520113.8 ug/L

Trevisani [45]Italy2001170AFPELISA0.6000.90620 ug/LJ. Hepatol.
0.6240.89416 ug/L
0.3120.988100 ug/L
0.2240.994200 ug/L
0.1710.994400 ug/L

Wang [46]USA2009164AFPELISA0.9500.210NKCancer Epidemiol. Biomark. Prev.
0.5000.980NK
0.7500.640NK
0.9000.360NK
0.9500.210NK
1.0000.040NK

GP73Immunoblot0.5000.970NK
0.7500.860NK
0.9000.540NK
0.9500.350NK
1.0000.250NK

AFP + GP73ELISA/Immunoblot0.5000.990NK
0.7500.870NK
0.9000.680NK
0.9500.550NK
1.0000.130NK

LI [47]China200950AFPELISA0.2500.97011.23 ng/mLHepatology
AFP + GP73Immunoblot0.6200.88014.37 RU

AFP: alpha-fetoprotein; DCP: des-gamma-carboxy prothrombin; GP73: Golgi protein-73; AFP-L3: Alpha-fetoprotein L3 isoform, ELISA: enzyme-linked immunosorbent assay; ACSC: Lens culinaris agglutinin (LCA)-coupled spin column; EIA: conventional enzyme immunoassay; μTAS: micro-total analysis system; IAUEC: immunometric assays utilizing enhanced chemiluminescence; ECLIA: immunoassay using the electrochemiluminescence detection system; LiBASys: LiBASys automated immunologic analyzer; LAEC: lecithin-affinity electrophoresis coupled with antibody-affinity blotting; NK: not known.*: Multiply.

Table Table 3. The area under the curve (AUC) for each marker in the meta-analysis.

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Table 3. The area under the curve (AUC) for each marker in the meta-analysis.
BiomarkerNumber of StudiesAUC
1 (AFP)350.835
2 (DCP)150.797
3 (GP73)90.914
4 (AFP-L3)150.710
5 (AFP+DCP)80.874
6 (AFP+AFP-L3)30.748
7 (AFP+GP73)30.932

AFP: alpha-fetoprotein; DCP: des-gamma-carboxy (abnormal) prothrombin; GP73: golgi protein-73; AFP-L3: Alpha-fetoprotein L3 isoform.

Table Table 4. The S-values of the biomarkers in the meta-analysis.

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Table 4. The S-values of the biomarkers in the meta-analysis.
EstimateStd. Errort ValuePr (>|t|)
(Intercept)2.392570.1519715.744<2 × 10−16
S.Biomarker.10.135940.045322.9990.00314
S.Biomarker.20.292190.102222.8590.00483
S.Biomarker.30.153260.082451.8590.06492
S.Biomarker.4−0.196430.12226−1.6070.11012
S.Biomarker.5−0.286620.20797−1.3780.17009
S.Biomarker.60.219130.171221.2800.20250
S.Biomarker.7−0.165970.132001.2570.21050
Biomarker.20.080540.361710.2230.82409
Biomarker.30.131950.314040.4200.67494
Biomarker.4−0.788030.47604−1.6550.09983
Biomarker.50.431570.409731.0530.29381
Biomarker.60.776440.706801.0990.27364
Biomarker.71.216880.486572.5010.01341

The statistical significance level was set to p < 0.05. The S-value, which represents the positive rate of the biomarker for detecting HCC, was calculated as follows: S = logit (TPR) + logit (FPR), where TPR is the true positive rate (sensitivity) and FPR is the false positive rate (1-specificity).

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