• OPEN ACCESS

Diagnostic Efficacy and Possible Underlying Mechanisms of Noninvasive Clinical Markers in Hepatocellular Carcinoma

  • Chao-Xu Fu1,
  • Jun Li2,
  • Zheng-Da Chen3,
  • Yan-Ping Cao4,
  • Hong-Ling Zhang5,
  • Hong-Ting Sui6,
  • Bao-Cheng Shan7,
  • Lei Xu1,
  • Yang Zhou1,
  • Min Zhou1,
  • En-Yue Yang8,*  and
  • Hong-Xin Piao8,* 
 Author information
Journal of Clinical and Translational Hepatology 2023;11(4):889-898

DOI: 10.14218/JCTH.2022.00285

Abstract

Background and Aims

In this study, we aimed to evaluate the diagnostic values of alpha-fetoprotein (AFP), soluble AXL (sAXL), des-γ-carboxy prothrombin (DCP), the aspartate aminotransferase-to-platelet ratio index (APRI), and the gamma-glutamyl transpeptidase-to-platelet ratio (GPR) in hepatocellular carcinoma (HCC) and the possible underlying mechanisms of the correlations between them.

Methods

We collected serum samples from 190, 128, and 75 patients with HCC, cirrhosis, and chronic viral hepatitis, and from 82 healthy subjects. Serum levels of AFP, sAXL, and DCP were determined, and APRI and GPR values were calculated. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic value of single and combined biomarkers.

Results

We detected significant differences between the HCC group and other groups regarding serum AFP, sAXL, DCP, and APRI levels. GPR significantly differed between the HCC group and other groups, except for the liver cirrhosis group. AFP, sAXL, DCP, APRI, and GPR had positive correlations with each other, and AFP showed a higher area under the curve (AUC) and Youden index values, while APRI and DCP showed the highest sensitivity and specificity. Also, when AFP was combined with sAXL, DCP, APRI, and GRP, the highest AUC (0.911) and a higher net reclassification improvement value were obtained compared with those obtained for the individual biomarkers.

Conclusions

AFP, sAXL, DCP, APRI, and GPR are independent risk factors for HCC, and the diagnostic performance of AFP combined with sAXL, DCP, APRI, and GPR for HCC diagnosis was superior to that of the individual biomarkers.

Keywords

Hepatocellular carcinoma, HCC-specific biomarkers, Alpha-fetoprotein, Chronic viral hepatitis Cirrhosis

Introduction

Hepatocellular carcinoma (HCC) is a highly malignant cancer, and unfortunately, its early symptoms are difficult to detect. Further, it progresses rapidly and has a low 5-year overall survival rate.1 Therefore, to improve its prognosis, early diagnostic methods with high sensitivity and good specificity are important. Abdominal ultrasonography, liver biopsy, magnetic resonance imaging, using noninvasive markers, and computed tomography are the main methods for diagnosing liver cancer.2 To ensure patient safety and reduce invasive examinations and the impact of computed tomography on the body, recent clinical studies have focused on noninvasive markers for HCC diagnosis.3

Early diagnosis of HCC based on the levels of serum alpha-fetoprotein (AFP) is widely accepted by the international medical community.4 Studies have also shown that about 50% of patients with HCC, especially those with early and small HCCs, are AFP negative. Novel biomarkers and related scoring indicators have been proposed to complement AFP and improve the accuracy of HCC diagnosis.5,6 However, the clinical value of those markers is debatable and determining their application values is challenging. Further in-depth studies are needed improve HCC diagnosis.

The TAM receptor family of receptor tyrosine kinases comprises TYRO3, AXL, and MER, and specifically, AXL is mainly involved in regulating platelet aggregation and maintaining vascular integrity.7 The upstream regulator of AXL, RAB10, is associated with an advanced stage and a large tumor size in patients with HCC,8 and following AXL cleavage by a disintegrin and metalloproteinases 10 and 17 via a protein kinase C-dependent pathway, soluble AXL (sAXL) can be detected in serum.9

Des-γ-carboxy prothrombin (DCP) is a prothrombin precursor produced in patients with HCC. It does not interact with other coagulation factors,10 and many studies have shown that elevated DCP levels are associated with tumors in patients with HCC.11,12 However, the specific mechanisms underlying this association remain unclear. The aminotransferase-to-platelet ratio index (APRI) is also widely used to noninvasively assess liver fibrosis.13 A retrospective study revealed that both fibrosis-4 and APRI predicted the risk of liver cancer.14 Also, the gamma-glutamyl transpeptidase-to-platelet ratio (GPR), which was first proposed by Lemoine et al.15 in 2016, was used by Park et al.16 to predict HCC in Korean patients with chronic hepatitis B.

With the increasing use of noninvasive markers to assess HCC, studies aimed at clarifying the limitations of these markers are required. Therefore, the aim of this study was to analyze the predictive power of noninvasive markers of HCC and the possible mechanisms of the association between them in HCC.

Methods

Patients and serum samples

Serum samples were collected from 190 patients with HCC, 128 patients with liver cirrhosis (LC), 75 patients with chronic viral hepatitis (VH), and 82 healthy controls (HC) between August 2014 and January 2019 at the Phase I Drug Clinical Trial Unit of the Affiliated Hospital of Yanbian University, Yanji, China. Patients with chronic VH had either hepatitis B virus or hepatitis C virus infection. Baseline patient characteristics were collected and recorded. Venous blood samples were also collected from the participants in the morning after fasting for at least 8 h and then sent to the laboratory, where they were frozen and stored until index testing.

HCC and VH were diagnosed according to the guidelines of the American Association for the Study of Liver Diseases and tumor staging was performed based on the Barcelona Clinical Liver Cancer (BCLC) system. Also, cirrhosis was assessed by the investigators based on histological and clinical evidence related to decompensation. The healthy subjects had no family history of liver cancer, no history of liver-related diseases, no abnormal hepatobiliary manifestations, and no abnormal laboratory test results.

Clinical information

Using the electronic medical record system of the Affiliated Hospital of Yanbian University, China, we collected information related to age, sex, ethnicity, and clinical indicators. Data based on imaging examinations of the patients with HCC and cirrhosis were also obtained.

Determination of serum AFP, sAXL, and DCP levels

Serum AFP levels were determined using a chemiluminescent immunoassay analyzer (UniCel DxI 800, Beckman Coulter, Brea, CA, USA) at the Department of Laboratory Medicine, Affiliated Hospital of Yanbian University, Yanji, China. Enzyme-linked immunosorbent assay (ELISA) kits (human AXL DuoSet ELISA, R&D Systems, Minneapolis, MN, USA; human abnormal prothrombin ELISA, Hotgen, Beijing, China) were used to detect sAXL and DCP levels.

Calculation of APRI and GPR indices

APRI was calculated as APRI = 100 × [AST (IU/L)/AST upper limit of normal (ULN)]/[platelet count (PLT) (109/L)]. GPR was calculated as GPR = 100 × GGT (IU/L)/PLT (109/L)].

Statistical analysis

SPSS software version 26.0 (IBM Corp., Armonk, NY, USA) was used for the statistical analysis, and p<0.05 was considered statistically significant. For multiple comparisons, the Kruskal-Wallis test, which is a nonparametric test, was used, but for bilateral between-group variability analysis, the Mann–Whitney test, which is also a nonparametric test, was performed. Bivariate correlation analysis was used to explore the correlation between the various biomarkers, and binary logistic regression analysis was used to assess the variance of various biomarker combinations. To evaluate the diagnostic value of each biomarker, receiver operating characteristic (ROC) curves were used. The net reclassification improvement (NRI) value was determined using an “extreme smart analysis platform” for analysis and processing.

Results

Clinical characteristics of patients with HCC, LC, VH, and HC

In this study, we enrolled 475 participants. The baseline clinical characteristics are in Table 1 which shows there were no significant differences between the different groups in age and HBV and HCV infections (p>0.05). The numbers of men in the HCC, LC, VH, and HC groups were 120 (63.2%), 65 (50.8%), 37 (49.3%), and 29 (35.4%), respectively.

Table 1

Clinical characteristics of the study participants

CharacteristicHCCLCVHHCp-value
Study participants1901287582
Age60.37±9.5858.76±10.6057.46±12.2653.46±11.56>0.05
BCLC stage (0/A/B/C/D)4/44/76/45/21
Male120 (63.2%)65 (50.8%)37 (49.3%)29 (35.4%)<0.05
HBV/HCV81/10971/5740/35>0.05
PLT (*109/L)112.00 (71.25–170.00)80.00 (56.00–125.50)197.00 (156.00–254.00)247.50 (210.00–271.00)<0.05
AST (U/L)49.00 (30.00–73.00)54.00 (35.50–80.50)25.00 (19.00–36.50)18.50 (16.00–22.00)<0.05
ALT (U/L)36.00 (23.00–55.00)46.00 (23.50–75.50)22.00 (17.00–64.50)14.00 (11.00–21.00)<0.05
GGT (U/L)64.50 (38.00–149.00)47.00 (26.50–102.5)25.00 (16.00–86.00)16.00 (13.00–22.00)<0.05
ALB (g/L)39.00 (34.00–43.00)40.00 (32.00–43.00)45.00 (44.00–48.00)48.00 (47.00–49.00)<0.05
TBIL (µmol/L)18.35 (13.30–26.38)24.20 (16.60–37.45)14.60 (12.65–18.95)12.40 (10.50–15.60)<0.05
AFP (ng/mL)60 (6.46–761.75)5.60 (3.99–8.29)2.93 (1.99–4.23)2.42 (1.86–3.26)<0.05
sAXL (ng/mL)33.55 (28.79–37.06)29.98 (26.72–33.68)20.82 (14.39–28.31)11.39 (9.87–15.83)<0.05
DCP (ng/mL)40.12 (5.76–70.11)9.04 (5.06–10.66)7.84 (7.05–11.44)4.62 (1.88–9.05)<0.05
GPR1.12 (0.55–2.29)0.90 (0.44–1.69)0.21 (0.14–0.49)0.11 (0.09–0.17)<0.05
APRI1.21 (0.52–2.08)1.45 (0.71–3.08)0.30 (0.20–0.63)0.20 (0.14–0.26)<0.05

As shown in Figure 1 and Table 1, the median AFP concentration corresponding to the HCC group was 60 ng/mL, which was significantly higher than those in the LC (5.60 ng/mL), VH (2.93 ng/mL), and HC (2.42 ng/mL) groups (p<0.05). Further, the median concentration of sAXL for the HCC group (33.55 ng/mL) was significantly higher than those in the LC (29.98 ng/mL), VH (20.82 ng/mL), and HC (11.39 ng/mL) groups (p<0.05). The HCC group also had a significantly higher median DCP concentration (40.12 ng/mL) than the LC (9.04 ng/mL), VH (7.84 ng/mL), and HC (4.62 ng/mL) groups (p<0.05). However, no significant differences were seen between the LC and VH groups (p>0.05). Our results also indicated a significantly higher median APRI concentration in the HCC group (1.21) than in the LC (1.45), VH (0.30), and HC (0.20) groups (p<0.05). The HCC group also had a higher median GPR concentration (1.12) than the VH (0.21) and HC (0.11) groups (p<0.05). However, there was no significant difference between the HCC and LC groups (p>0.05). As shown in Table 2, the median concentration of AFP, sAXL, DCP, APRI, and GPR increased with the BCLC stage of HCC, but the differences were not statistically significant (p>0.05).

Distribution of the values of serum biomarkers AFP, sAXL, DCP, APRI, and GPR in the HCC, LC, VH, and HC groups.
Fig. 1  Distribution of the values of serum biomarkers AFP, sAXL, DCP, APRI, and GPR in the HCC, LC, VH, and HC groups.

AFP, alpha-fetoprotein; APRI, aminotransferase-to-platelet ratio index; AUC, area under the receiver operating characteristic curve; CI, confidence interval; DCP, des-γ-carboxy prothrombin; GPR, gamma-glutamyl transpeptidase-to-platelet ratio; HC, healthy control; HCC, hepatocellular carcinoma; LC, liver cirrhosis; sAXL, soluble AXL; VH, viral hepatitis.

Table 2

AFP, sAXL, DCP, APRI, and GPR concentration in Barcelona Clinic Liver Cancer staging

FactorBCLC(0+A)BCLC(B)BCLC(C)Zp-value
AFP21.27 (4.30–253.46)35.82 (7.18–300.58)106.12 (10.10–1545.94)4.0610.131
sAXL32.39 (20.72–34.55)32.61 (28.10–37.47)34.08 (30.02–37.16)2.8090.246
DCP31.29 (3.09–54.51)38.74 (5.99–69.62)46.11 (5.40–274.10)2.2200.340
APRI0.81 (0.45–2.06)1.19 (0.51–2.38)1.29 (0.56–2.05)2.1710.338
GPR0.92 (0.38–1.49)0.90 (0.47–2.07)1.37 (0.61–2.72)4.3050.116

Key indicators related to HCC

We divided the participants into two groups. One included patients with liver cancer, and the other group included non-HCC patients with cirrhosis or VH and the HCs. To determine the key risk factors involved in the development of HCC, we first performed correlation analysis, which showed that AFP, sAXL, DCP, APRI, and GPR were positively correlated (Fig. 2). The correlations between the biomarkers in all groups were above 0.44, with the strongest correlation seen between APRI and GPR (coefficient=0.81), and the weakest between DCP and GPR (coefficient=0.44). Univariate analysis showed that the odds ratios for age, sex, AFP, sAXL, DCP, APRI, and GPR were all above 1. To reduce the interference of confounding factors, we conducted multivariate analysis to determine the significant risk factors. The results thus obtained were consistent with those obtained following univariate analysis (p<0.05; Table 3).

Correlation analysis of test indicators.
Fig. 2  Correlation analysis of test indicators.

AFP, alpha-fetoprotein; APRI, aminotransferase-to-platelet ratio index; DCP, des-γ-carboxy prothrombin; GPR, gamma-glutamyl transpeptidase-to-platelet ratio; sAXL, soluble AXL; red represents positive correlation; blue represents negative correlation.

Table 3

Univariate and multivariate analyses of risk factors associated with HCC

ParameterUnivariate analysis
Multivariate analysis
OR (95% CI)p-valueOR (95% CI)p-value
Age1.031 (1.013–1.050)<0.051.049 (1.008–1.092)<0.05
Sex (male/female)2.015 (1.384–2.934)<0.053.428 (1.471–7.989)<0.05
AFP15.532 (9.642–25.020)<0.054.480 (1.701–11.802)<0.05
sAXL6.356 (4.199–9.619)<0.052.857(1.157–7.055)<0.05
DCP37.488 (13.306–105.622)<0.0516.172 (3.425–76.359)<0.05
GPR1.765 (1.367–2.280)<0.053.373(1.129–10.076)<0.05
APRI6.905 (4.099–11.630)<0.052.830 (1.019–7.859)<0.05

Diagnostic accuracy of APRI, GPR, and serum biomarkers for detecting HCC

The area under the ROC curve (AUC), sensitivities, specificities, and optimal threshold values of AFP, sAXL, DCP, APRI, and GPR for the diagnosis of HCC are shown in Table 4. the AUC and Youden’s index values corresponding to AFP were higher than those of the other markers. APRI had the highest sensitivity, but DCP showed the highest specificity. We also evaluated the diagnostic value of the combined biomarkers and found that the AUC corresponding to the combined biomarkers was greater than 0.8. Of all the two-marker combinations used for HCC diagnosis, the combination of sAXL with DCP had the highest AUC (0.882), with a higher sensitivity and Youden’s index (0.8211 and 0.6648, respectively) compared with those of all the other two-marker combinations. Also, the specificity of AFP combined with DCP was the highest (0.9453) considering all the two-marker combinations. Considering that we evaluated five markers in this study, the significance of analyzing only two-marker combinations could be limited, so we also analyzed three-, four-, and five-marker combinations. The results revealed that among all the three-marker combinations, that of AFP, sAXL, and DCP showed the highest AUC and Youden’s index (0.904 and 0.7085, respectively). Further, the combination of sAXL, DCP, and GPR showed the highest sensitivity (0.8418), while the combination of AFP, DCP, and GPR showed the highest specificity (0.9407). We also saw that among all the four-marker combinations, AFP, sAXL, DCP, and APRI showed the highest AUC, Youden’s index, and specificity (0.910, 0.7162, and 0.9576, respectively), but the combination of sAXL, DCP, APRI, and GPR had the highest sensitivity (0.8523). Interestingly, the combination of all five markers, AFP, sAXL, DCP, APRI, and GPR, had the highest AUC (0.911), and thus, the greatest diagnostic value for HCC.

Table 4

Combined diagnostic performance of APRI, GPR and serum markers in the detection of HCC

MarkerAUC (95%CI)Sensitivity (%)Specificity (%)Youden’s index (%)Cutoff value
AFP0.825 (0.786–0.859)73.4584.5057.9410.31
sAXL0.752 (0.711–0.791)74.2172.6646.8730.02
DCP0.738 (0.686–0.786)53.6899.2252.9019.60
APRI0.683 (0.636–0.727)88.0750.4238.480.38
GPR0.730 (0.685–0.772)79.1060.0039.100.46
AFP+sAXL0.832 (0.793–0.866)77.4078.4955.89
AFP+DCP0.830 (0.783–0.870)71.7594.5366.28
AFP+APRI0.819 (0.778–0.855)71.2686.0357.29
AFP+GPR0.835 (0.795–0.870)73.7181.2254.94
sAXL+DCP0.882 (0.842–0.915)82.1184.3766.48
DCP+APRI0.822 (0.773–0.864)69.3285.5954.91
DCP+GPR0.823 (0.775–0.865)64.4194.0758.47
AFP+sAXL+DCP0.904 (0.865–0.935)80.2390.6270.85
AFP+sAXL+APRI0.838 (0.798–0.873)72.4186.6759.08
AFP+sAXL+GPR0.832 (0.792–0.867)65.7189.7855.49
AFP+DCP+APRI0.851 (0.805–0.890)72.9993.2266.21
AFP+DCP+GPR0.854 (0.808–0.892)73.7194.0767.78
sAXL+DCP+APRI0.890 (0.849–0.923)83.5285.5969.12
sAXL+DCP+GPR0.890 (0.849–0.924)84.1883.9068.08
DCP+APRI+GPR0.837 (0.790–0.878)65.3491.5356.87
AFP+sAXL+DCP+APRI0.910 (0.871–0.940)75.8695.7671.62
AFP+sAXL+DCP+GPR0.909 (0.870–0.939)77.7193.2270.93
AFP+sAXL+APRI+GPR0.838 (0.799–0.873)70.1187.1157.23
AFP+DCP+APRI+GPR0.853 (0.807–0.891)73.5694.9268.48
sAXL+DCP+APRI+GPR0.891 (0.849–0.924)85.2382.2067.43
AFP+sAXL+DCP+APRI+GPR0.911 (0.872–0.941)83.9188.1472.04

We further examined whether multimarker combinations had better diagnostic values than individual markers and whether there were significant differences between the diagnostic values of the different multimarker combinations. First, we compared 26 groups of multimarker combinations with five groups of individual markers. The results are shown in Figure 3 and Table 5. Next, we selected combinations with AUCs >0.9 for comparison with the single group. We saw that four groups of multimarker combinations differed significantly from the five groups of individual markers (p<0.05). However, we observed no significant differences among the four groups of multimarker combinations (p>0.05). Our NRI calculations also indicated that the above multimarker combinations had significantly higher prediction accuracies than the individual markers, and that multimarker combinations were superior to individual markers for diagnosing HCC. Also, the predictive accuracies of AFP, sAXL, DCP, APRI, and GPR in the HCC group were higher than their values for the other three groups, and interestingly, AFP, sAXL, and DCP were all identified as part of the four multimarker combinations with high prediction accuracy.

Pairwise comparison of multiple marker combinations and individual markers to assess their diagnostic performance in HCC using ROC curves.
Fig. 3  Pairwise comparison of multiple marker combinations and individual markers to assess their diagnostic performance in HCC using ROC curves.

AFP, alpha-fetoprotein; APRI, aminotransferase-to-platelet ratio index; DCP, des-γ-carboxy prothrombin; GPR, gamma-glutamyl transpeptidase-to-platelet ratio; sAXL, soluble AXL.

Table 5

Assessment of diagnostic values in HCC

GroupStandard Errorz statisticp-(AUC)NRI (%)
AFP+sAXL+DCP+APRI+GPR vs.AFP0.01972.5860.009737
sAXL0.01693.7960.000127.6
DCP0.02367.029<0.0520.7
APRI0.02652.2390.025171.1
GPR0.02382.1760.029667
AFP+sAXL+DCP+APRI vs.AFP0.01962.5420.011042.9
sAXL0.01633.8680.000162
DCP0.02366.973<0.0519.5
APRI0.02652.2030.027671.2
GPR0.02452.0710.038469.8
AFP+sAXL+DCP+GPR vs.AFP0.01942.5300.011442.3
sAXL0.01504.142<0.0551
DCP0.02337.041<0.0519.8
APRI0.02432.3650.018071.8
GPR0.02322.1510.031570.4
AFP+sAXL+DCP vs.AFP0.01932.5310.011436.6
sAXL0.01534.064<0.0534.5
DCP0.02307.110<0.0520.1
APRI0.02432.3600.018670.2
GPR0.02302.1580.030967
AFP+sAXL+DCP+APRI+GPR vs. AFP+sAXL+DCP+APRI0.00220.4590.64600.3
AFP+sAXL+DCP+APRI+GPR vs. AFP+sAXL+DCP+GPR0.00430.4350.66361.7
AFP+sAXL+DCP+APRI+GPR vs. AFP+sAXL+DCP0.00410.5030.61532.6
AFP+sAXL+DCP+APRI vs. AFP+sAXL+DCP+GPR0.00330.2480.80443.4
AFP+sAXL+DCP+APRI vs. AFP+sAXL+DCP0.00340.3010.76355.2
AFP+sAXL+DCP+GPR vs. AFP+sAXL+DCP0.00080.2290.81855.2

Discussion

Even though about 50% of patients with HCC are AFP negative, AFP is a first-line clinical biomarker for monitoring and diagnosing HCC.17 Several biomarkers for HCC diagnosis, such as sAXL, DCP, Golgi protein-73, and lectin-binding AFP-3 have been identified; however, they are still under clinical evaluation.18,19 Further, even though the pathogenesis of liver fibrosis-HCC remains unclear, related fibrosis scores, such as APFI, GPR, and fibrosis-4 scores have been widely used to predict HCC development.20,21 Therefore, we measured and calculated the levels of sAXL, DCP, ARPI, and GPR to test and verify their ability regarding HCC diagnosis. The results showed that using a single marker assay, the diagnostic accuracy of AFP was higher than that of the other detection markers. So AFP remains a reliable and first-line marker for diagnosing HCC in the Chinese population. Many studies have shown that reasonable multimarker combinations have better sensitivity and specificity than single markers.22,23 In this study, univariate and multivariate analysis showed that age, sex, AFP, sAXL, DCP, APRI, and GPR were independent risk factors for HCC progression. We also saw significantly higher serum sAXL, DCP, ARRI, and GPR levels in the HCC group than the LC, VH, and HC groups. Those markers may be useful for predicting HCC progression. Also, regular screening for markers or indicators can aid early HCC detection and improve survival rates. Appropriate biomarkers are important for the early screening, diagnosis, treatment, evaluation, and prognosis of liver cancer.24 The biomarkers that are used are mainly blood-, histochemical-, and drug resistance-related biomarkers. Recently, circulating biomarkers have been extensively studied. Although there are still limitations to be overcome for their widespread clinical application to become possible, basic research regarding those biomarkers is relatively mature. For example, it has been confirmed that CD4 + CD25 + Foxp3 + regulatory T cells have a key role in the immune microenvironment of HCC and have some prognostic significance.25 In this study, the diagnostic significance of widely used blood biomarkers, AFP, sAXL, and DCP, and two noninvasive indicators, APRI and GPR, were investigated. We used an AFP cutoff value of 10.31 ng/mL for the diagnosis of HCC, with a sensitivity of 73.45% and a specificity of 84.50%. Compared with AFP, sAXL, APRI, and GPR had higher sensitivities at their respective critical values, while DCP showed a higher specificity. We speculated that combining multiple biomarkers might further improve diagnostic performance, and we were not disappointed. Based on our results, it was easy to confirm that the combined use of AFP, DCP, and sAXL significantly improved diagnostic performance. Even though no significant differences in diagnostic value were seen following the inclusion of APRI and GPR, their inclusion still played a certain role in our NRI. The results reported here, which indicated an increase in the overall AUC for HCC detection following the inclusion of APRI and GPR, is supported by published evidence. Specifically, a meta-analysis of whether combined testing of serum markers is effective for improving the clinical value of biomarkers in HCC decision making showed a higher diagnostic potential for combined testing than a single randomized double combination and that combined testing of AFP, AFP-L3, DCP is of clinical importance in HCC decision making.26 The results of a large-scale multicenter analysis also suggested that combining AFP and sAXL shows high potential as an accurate surveillance marker in patients at high risk for HCC.27 Similarly, other studies have established a new simple score for the diagnosis of HCC, i.e. the Hepatocellular Carcinoma Multidisciplinary Clinic-Cairo University (HMC-CU) score.28 Whether the score applies in the Chinese population needs to be further verified. Also, DCP and sAXL assay results cannot be directly compared extensively owing to the use of different techniques and antibodies; however, the strengths of the study include skill and quality control measures to ensure that study procedures are performed accurately. Also, the results may help clinicians diagnose HCC with greater ease, especially at the early stages.

Further investigation of biomarker-related signaling pathways may help to further clarify the mechanism of HCC occurrence and development,29 to the end of providing a reliable theoretical basis for clinical intervention and treatment. In our initial analysis, we saw positive correlations between AFP, sAXL, DCP, APRI, and GPR, with all the correlation coefficients greater than 0.44. In this study, we further compared the combined diagnostic efficacy of multiple biomarkers for HCC with those of a single biomarker. Thus, we saw that the multimarker groups that showed significantly higher diagnostic values compared with the single biomarkers all included the three markers, AFP, sAXL, and DCP.

Previous studies have shown that AFP promotes HCC proliferation and progression and that there exists an interaction between AFP and retinoic acid receptors.30 It has also been reported that retinoic acid receptors and retinoid X receptors significantly inhibit the secretion and expression of hepatocyte growth factor (HGF).31 Notably, HGF is a cellular mesenchymal-epithelial transition factor (c-Met) ligand that can interact with and lead to c-Met phosphorylation, resulting in many effects, such as cell proliferation and invasion. Numerous studies have suggested that c-Met is a target receptor for DCP and that DCP-positive HCC is often accompanied by high levels of phosphorylated c-Met and a high activation rate of its downstream signaling pathways after DCP binding to c-Met.32 Also, c-Met has been shown to interact functionally with AXL both in vitro and in vivo, possibly via signaling. Further, drug resistance can occur in patients with tumor recurrence through kinase interactions,33 and reportedly endogenous and exogenous AXL overexpression induces tumorigenesis, with GAS6/AXL signaling reported to contribute to tumor cell survival. The serum levels of both GAS6 and sAXL are positively correlated with increased tumor staging, and the level of GAS6 is even higher than that of sAXL. It has also been reported that the binding of GAS6 to AXL receptors activates cancer progression, and that AXL expression is upregulated in HCC. Elevated sAXL levels have also been seen in cirrhosis and early to advanced stages of HCC.34 Considering previous findings and the study results, there may be a novel mechanism involving the AFP-HGF/c-MET-AXL/GAS6 axis by which the progression of HCC is promoted; however, further in vitro and ex vivo experiments are needed to verify this speculation.

In simple terms, c-Met is the target receptor of DCP, while sAXL is the soluble part of AXL, and our results in this study revealed that DCP, sAXL, and their combined detection showed good performance regarding HCC diagnosis (c-Met and AXL are tyrosine kinases). So we believe that targeted drugs related to tyrosine kinase inhibitors are worthy of attention for the diagnosis and treatment of HCC. Further, the efficacy and safety of drugs targeting c-MET and AXL, such as cabozantinib, in patients with advanced HCC have also been reported recently. As expected, an earlier study showed that cabozantinib improves the overall and progression-free survival of treated patients with HCC.35 Further, in a Phase Ib study, cabozantinib treatment transformed locally advanced HCC into a resectable tumor by enhancing antitumor immunity.36 Therefore, tyrosine kinase receptor inhibitors, such as c-Met/AXL targets may improve the status of patients with HCC; however, a large number of clinical trials are still needed for validation.

This study had limitations. First, we did not obtain complete in vivo and in vitro experimental results to verify the potential mechanisms of the interactions between biomarkers. Second, the patient population was small given our strict exclusion criteria and the univariate and multivariate analyses performed.

Conclusions

The results indicated that AFP, sAXL, DCP, APRI, and GPR were independent risk factors for HCC. The diagnostic performance of AFP with sAXL, DCP, APRI, and GPR was superior to that of the individual biomarkers in relation to HCC diagnosis. Further, although no significant differences in diagnostic performance were seen on comparing the diagnostic values based on AFP, sAXL, and DCP with or without APRI and GPR, including APRI and GPR resulted in an increased NRI. Also, the correlation between the expression of the biomarkers suggested there might be pathways, such as the AFP-HGF/c-MET-AXL/GAS6 axis, that promote HCC progression; however, further validation studies are needed.

Abbreviations

AFP: 

alpha-fetoprotein

APRI: 

aminotransferase-to-platelet ratio index

AUC: 

area under the curve

c-Met: 

cellular mesenchymal-epithelial transition factor

DCP: 

des-γ-carboxy prothrombin

GPR: 

gamma-glutamyl transpeptidase-to-platelet ratio

HC: 

healthy controls

HCC: 

hepatocellular carcinoma

HGF: 

hepatocyte growth factor

LC: 

liver cirrhosis

ROC: 

Receiver operating characteristic

sAXL: 

soluble AXL

VH: 

viral hepatitis

Declarations

Ethical statement

This study was approved by the Ethics Committee of the Affiliated Hospital of Yanbian University, Yanji, China. The study was part of a project named, “Case-control study on factors influencing disease progression of hepatitis C and hepatitis B: National Twelfth 5-Year Plan Science and Technology Major Project” (approval number 20130064). All the participants signed a written informed consent form, and the study conducted in compliance with the principles of the Declaration of Helsinki and the Ethical Review of Biomedical Research Involving Human Beings.

Data sharing statement

No additional data are available.

Funding

This study was funded by National Natural Science Foundation of China (grant number 82260506) and the National Science and Technology Major Project (Prevention and Control of Major Infectious Diseases such as AIDS and Viral Hepatitis) of the Twelfth 5-Year Plan (2012ZX10002003).

Conflict of interest

The authors have no conflicts of interest related to this publication.

Authors’ contributions

Data organization and drafting of the manuscript (CXF), data collection (JL), data analysis and interpretation (ZDC), sample collection and processing (YPC, HLZ, BCS, LX, YZ, MZ), study concept and design, administrative, technical, or material support (HXP, EYY).

References

  1. Sun Y, Wu L, Zhong Y, Zhou K, Hou Y, Wang Z, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell 2021;184(2):404-421.e16 View Article PubMed/NCBI
  2. Di Tommaso L, Spadaccini M, Donadon M, Personeni N, Elamin A, Aghemo A, et al. Role of liver biopsy in hepatocellular carcinoma. World J Gastroenterol 2019;25(40):6041-6052 View Article PubMed/NCBI
  3. Chen X, Gole J, Gore A, He Q, Lu M, Min J, et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat Commun 2020;11(1):3475 View Article PubMed/NCBI
  4. Luo P, Yin P, Hua R, Tan Y, Li Z, Qiu G, et al. A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology 2018;67(2):662-675 View Article PubMed/NCBI
  5. Wang T, Zhang KH. New Blood Biomarkers for the Diagnosis of AFP-Negative Hepatocellular Carcinoma. Front Oncol 2020;10:1316 View Article PubMed/NCBI
  6. Zhang YS, Chu JH, Cui SX, Song ZY, Qu XJ. Des-gamma-carboxy prothrombin (DCP) as a potential autologous growth factor for the development of hepatocellular carcinoma. Cell Physiol Biochem 2014;34(3):903-915 View Article PubMed/NCBI
  7. Hedrich V, Breitenecker K, Djerlek L, Ortmayr G, Mikulits W. Intrinsic and Extrinsic Control of Hepatocellular Carcinoma by TAM Receptors. Cancers (Basel) 2021;13(21):5448 View Article PubMed/NCBI
  8. Wang W, Jia WD, Hu B, Pan YY. RAB10 overexpression promotes tumor growth and indicates poor prognosis of hepatocellular carcinoma. Oncotarget 2017;8(16):26434-26447 View Article PubMed/NCBI
  9. Flem-Karlsen K, Nyakas M, Farstad IN, McFadden E, Wernhoff P, Jacobsen KD, et al. Soluble AXL as a marker of disease progression and survival in melanoma. PLoS One 2020;15(1):e0227187 View Article PubMed/NCBI
  10. Ono M, Ohta H, Ohhira M, Sekiya C, Namiki M. Measurement of immunoreactive prothrombin precursor and vitamin-K-dependent gamma-carboxylation in human hepatocellular carcinoma tissues: decreased carboxylation of prothrombin precursor as a cause of des-gamma-carboxyprothrombin synthesis. Tumour Biol 1990;11(6):319-326 View Article PubMed/NCBI
  11. Nguyen HB, Le XT, Nguyen HH, Vo TT, Le MK, Nguyen NT, et al. Diagnostic Value of hTERT mRNA and in Combination With AFP, AFP-L3%, Des-gamma-carboxyprothrombin for Screening of Hepatocellular Carcinoma in Liver Cirrhosis Patients HBV or HCV-Related. Cancer Inform 2022;21:11769351221100730 View Article PubMed/NCBI
  12. Song PP, Xia JF, Inagaki Y, Hasegawa K, Sakamoto Y, Kokudo N, et al. Controversies regarding and perspectives on clinical utility of biomarkers in hepatocellular carcinoma. World J Gastroenterol 2016;22(1):262-274 View Article PubMed/NCBI
  13. Itakura J, Kurosaki M, Setoyama H, Simakami T, Oza N, Korenaga M, et al. Applicability of APRI and FIB-4 as a transition indicator of liver fibrosis in patients with chronic viral hepatitis. J Gastroenterol 2021;56(5):470-478 View Article PubMed/NCBI
  14. Zhang X, Guan L, Tian H, Zeng Z, Chen J, Huang D, et al. Risk Factors and Prevention of Viral Hepatitis-Related Hepatocellular Carcinoma. Front Oncol 2021;11:686962 View Article PubMed/NCBI
  15. Lemoine M, Shimakawa Y, Nayagam S, Khalil M, Suso P, Lloyd J, et al. The gamma-glutamyl transpeptidase to platelet ratio (GPR) predicts significant liver fibrosis and cirrhosis in patients with chronic HBV infection in West Africa. Gut 2016;65(8):1369-1376 View Article PubMed/NCBI
  16. Kim MN, Lee JH, Chon YE, Ha Y, Hwang SG. Fibrosis-4, aspartate transaminase-to-platelet ratio index, and gamma-glutamyl transpeptidase-to-platelet ratio for risk assessment of hepatocellular carcinoma in chronic hepatitis B patients: comparison with liver biopsy. Eur J Gastroenterol Hepatol 2020;32(3):433-439 View Article PubMed/NCBI
  17. Galle PR, Foerster F, Kudo M, Chan SL, Llovet JM, Qin S, et al. Biology and significance of alpha-fetoprotein in hepatocellular carcinoma. Liver Int 2019;39(12):2214-2229 View Article PubMed/NCBI
  18. Virzi A, Gonzalez-Motos V, Tripon S, Baumert TF, Lupberger J. Profibrotic Signaling and HCC Risk during Chronic Viral Hepatitis: Biomarker Development. J Clin Med 2021;10(5):977 View Article PubMed/NCBI
  19. Song X, Wu A, Ding Z, Liang S, Zhang C. Soluble Axl Is a Novel Diagnostic Biomarker of Hepatocellular Carcinoma in Chinese Patients with Chronic Hepatitis B Virus Infection. Cancer Res Treat 2020;52(3):789-797 View Article PubMed/NCBI
  20. Zhu YF, Tan YF, Xu X, Zheng JL, Zhang BH, Tang HR, et al. Gamma-glutamyl transpeptidase-to-platelet ratio and the fibrosis-4 index in predicting hepatitis B virus-related hepatocellular carcinoma development in elderly chronic hepatitis B patients in China: A single-center retrospective study. Medicine (Baltimore) 2019;98(50):e18319 View Article PubMed/NCBI
  21. Zhang C, Wu J, Xu J, Xu J, Xian J, Xue S, et al. Association between Aspartate Aminotransferase-to-Platelet Ratio Index and Hepatocellular Carcinoma Risk in Patients with Chronic Hepatitis: A Meta-Analysis of Cohort Study. Dis Markers 2019;2019:2046825 View Article PubMed/NCBI
  22. Fang YS, Wu Q, Zhao HC, Zhou Y, Ye L, Liu SS, et al. Do combined assays of serum AFP, AFP-L3, DCP, GP73, and DKK-1 efficiently improve the clinical values of biomarkers in decision-making for hepatocellular carcinoma? A meta-analysis. Expert Rev Gastroenterol Hepatol 2021;15(9):1065-1076 View Article PubMed/NCBI
  23. Hou SC, Xiao MB, Ni RZ, Ni WK, Jiang F, Li XY, et al. Serum GP73 is complementary to AFP and GGT-II for the diagnosis of hepatocellular carcinoma. Oncol Lett 2013;6(4):1152-1158 View Article PubMed/NCBI
  24. Luo P, Wu S, Yu Y, Ming X, Li S, Zuo X, et al. Current Status and Perspective Biomarkers in AFP Negative HCC: Towards Screening for and Diagnosing Hepatocellular Carcinoma at an Earlier Stage. Pathol Oncol Res 2020;26(2):599-603 View Article PubMed/NCBI
  25. Granito A, Muratori L, Lalanne C, Quarneti C, Ferri S, Guidi M, et al. Hepatocellular carcinoma in viral and autoimmune liver diseases: Role of CD4+ CD25+ Foxp3+ regulatory T cells in the immune microenvironment. World J Gastroenterol 2021;27(22):2994-3009 View Article PubMed/NCBI
  26. Fang YS, Wu Q, Zhao HC, Zhou Y, Ye L, Liu SS, et al. Do combined assays of serum AFP, AFP-L3, DCP, GP73, and DKK-1 efficiently improve the clinical values of biomarkers in decision-making for hepatocellular carcinoma? A meta-analysis. Expert Rev Gastroenterol Hepatol 2021;15(9):1065-1076 View Article PubMed/NCBI
  27. Dengler M, Staufer K, Huber H, Stauber R, Bantel H, Weiss KH, et al. Soluble Axl is an accurate biomarker of cirrhosis and hepatocellular carcinoma development: results from a large-scale multicenter analysis. Oncotarget 2017;8(28):46234-46248 View Article PubMed/NCBI
  28. Abdelaziz AO, Nabil MM, Omran DA, Abdelmaksoud AH, Asem N, Shousha HI, et al. Hepatocellular Carcinoma Multidisciplinary Clinic-Cairo University (HMC-CU) score: A new simple score for diagnosis of HCC. Arab J Gastroenterol 2020;21(2):102-105 View Article PubMed/NCBI
  29. Gao YX, Yang TW, Yin JM, Yang PX, Kou BX, Chai MY, et al. Progress and prospects of biomarkers in primary liver cancer (Review). Int J Oncol 2020;57(1):54-66 View Article PubMed/NCBI
  30. Zhang C, Zhang J, Wang J, Yan Y, Zhang C. Alpha-fetoprotein accelerates the progression of hepatocellular carcinoma by promoting Bcl-2 gene expression through an RA-RAR signalling pathway. J Cell Mol Med 2020;24(23):13804-13812 View Article PubMed/NCBI
  31. Chattopadhyay N, Butters RR, Brown EM. Agonists of the retinoic acid- and retinoid X-receptors inhibit hepatocyte growth factor secretion and expression in U87 human astrocytoma cells. Brain Res Mol Brain Res 2001;87(1):100-108 View Article PubMed/NCBI
  32. Cui SX, Yu XF, Qu XJ. Roles and Signaling Pathways of Des-γ-Carboxyprothrombin in the Progression of Hepatocellular Carcinoma. Cancer Invest 2016;34(9):459-464 View Article PubMed/NCBI
  33. Yeh CY, Shin SM, Yeh HH, Wu TJ, Shin JW, Chang TY, et al. Transcriptional activation of the Axl and PDGFR-α by c-Met through a ras- and Src-independent mechanism in human bladder cancer. BMC Cancer 2011;11:139 View Article PubMed/NCBI
  34. Holstein E, Binder M, Mikulits W. Dynamics of Axl Receptor Shedding in Hepatocellular Carcinoma and Its Implication for Theranostics. Int J Mol Sci 2018;19(12):4111 View Article PubMed/NCBI
  35. Kelley RK, Miksad R, Cicin I, Chen YH, Klümpen HJ, Kim S, et al. Efficacy and safety of cabozantinib for patients with advanced hepatocellular carcinoma based on albumin-bilirubin grade. Br J Cancer 2022;126(4):569-575 View Article PubMed/NCBI
  36. Ho WJ, Zhu Q, Durham J, Popovic A, Xavier S, Leatherman J, et al. Neoadjuvant Cabozantinib and Nivolumab Converts Locally Advanced HCC into Resectable Disease with Enhanced Antitumor Immunity. Nat Cancer 2021;2(9):891-903 View Article PubMed/NCBI
  • Journal of Clinical and Translational Hepatology
  • pISSN 2225-0719
  • eISSN 2310-8819
  • Copyright © 2022 JCTH. All Rights Reserved.
  • Published by Xia & He Publishing Inc.
  • Address: 14090 Southwest Freeway, Suite 300, Sugar Land, Texas 77478, USA
  • Email: service@xiahepublishing.com