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Annals of Medicine logoLink to Annals of Medicine
. 2022 Dec 8;55(1):42–61. doi: 10.1080/07853890.2022.2153163

A meta-analysis and of clinical values of 11 blood biomarkers, such as AFP, DCP, and GP73 for diagnosis of hepatocellular carcinoma

Bing-yao Pang a,*, Yan Leng b,*, Xiaoli Wang c, Yi-qiang Wang c, Li-hong Jiang c,✉
PMCID: PMC9744221  PMID: 36476015

Abstract

Background

Hepatocellular carcinoma lacks ideal diagnostic biomarkers. There is a lack of scientific evaluation of relevant promising biomarkers as well. Therefore this study reanalyzes the related studies of 11 blood biomarkers of HCC, and compares the diagnostic value of these biomarkers for HCC systematically.

Methods

The relevant literatures on the diagnostic value in HCC of 11 blood indexes in recent 5 years were searched in PubMed, Embase, and Cochrane libraries. Data were extracted and analyzed.

Results

Finally, 83 literature studies were brought into meta-analysis. The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The AUC and sum of sensitivity and specificity of the combination of AFP and other biomarkers were all significantly higher than that of AFP, including AFP + AFP-L3 + DCP, AFP + DCP, AFP/DCP, AFP + GPC3. Among other biomarkers, the AUC and sum of sensitivity and specificity of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, and OPN were significantly higher than that of AFP. In this study, GP73 had the highest sum of sensitivity and specificity (1.78) and AUC (0.95).

Conclusions

The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The combination of AFP and other biomarkers improved the diagnostic efficiency. The diagnostic value of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, and OPN was higher than that of AFP. GP73 had the best diagnostic value for HCC with the highest sum of sensitivity and specificity (1.78) and AUC (0.95).

KEY MESSAGES

  • The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The combination of AFP and other biomarkers improved the diagnostic efficiency of HCC.

  • The diagnostic value of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, and OPN was higher than that of AFP.

  • GP73 had the best diagnostic value for HCC.

Keywords: Biomarkers, HCC, the diagnostic value, meta-analysis

1. Introduction

Hepatocellular carcinoma(HCC) has become an important public health problem worldwide because of its high mortality [1]. In Asia, HCC secondary to hepatitis B virus is more common. HCC secondary to alcoholic liver disease is also increasing in western developed countries. Due to the symptoms of HCC are not obvious in the early stage, many patients are in the advanced stage when diagnosed, missing the best opportunity for treatment. The early diagnosis of HCC can not only improve the survival time and quality of life of patients but also save the cost of treatment. Therefore, the early diagnosis is the breakthrough focus of HCC treatment.

Alpha fetoprotein (AFP) is the most commonly used biomarker for the diagnosis of HCC, but the diagnostic value of AFP is gradually being doubted because of its low sensitivity, especially for early HCC [2]. Many studies were devoted to finding biomarkers with better diagnostic value in HCC [3].

Recently, many new biomarkers attracted public attention, such as AFP-L3, DCP, DKKI, GP73, and so on. Alpha-fetoprotein-L3(AFP-L3), as a heterogeneous body of AFP, mainly comes from HCC cells. Des-γ-carboxyprothrombin (DCP), also known as protein induced by vitamin K absence or antagonist II (PIVKAII), can appear in the serum of patients with vitamin K absence or HCC [4]. Dickkopf-1 (DKK1) is a secretory glycoprotein that inhibits Wnt signalling pathway by binding to Wnt receptor LRP5/6 [5]. Wnt signalling pathway is an important mechanism for the occurrence and development of HCC and other tumours. Golgi protein 73 (GP73), a type II transmembrane glycoprotein resident in golgi apparatus, is expressed in a small amount in normal liver while it can be specifically expressed, especially around connective tissue and cirrhotic nodules, when liver diseases, such as HCC occur [6]. Glypican-3 (GPC3) is a heparan sulphate glycoprotein on the surface of cell membrane, which is a specific antigen related to HCC [7]. Osteopontin (OPN) is a kind of protein, widely distributed in a variety of tissues and cells. It can participate in tissue repair, self-metabolism, and other functions. The expression level of OPN is closely related to the clinicopathological features of HCC, such as envelope infiltration, vascular invasion, lymph node metastasis, and clinical stage [8,9]. A-L-fucosidase (AFU) is an acidic hydrolase, which is mainly involved in the catabolism of macromolecular substances containing fucosyl, such as glycolipids, glycoproteins, mucopolysaccharides. Junna reported the over expression of AFU in the serum of patients with primary HCC, suggesting that AFU might be a potential marker for the early diagnosis of HCC [10]. Carbohydrate antigen199 (CA199), glycolipid on cell membrane is a kind of glycoprotein antigen that can recognize tumour specific macromolecules [11,12]. Heat shock protein 90alpha (Hsp90alpha) is a multifunctional molecular chaperone, which is widely involved in physiological activities, such as cell signal transduction, hormone response, and transcriptional regulation, maintaining the normal physiological function of cells [13]. Hsp90alpha keeps silent in normal cells while active in tumour cells. Midkine (MDK) is a secretory cytokine, which can participate in the occurrence and development of malignant tumours by promoting division, promoting angiogenesis, and antiapoptosis [14,15]. In recent years, there were more and more studies on the diagnostic value of the above biomarkers for HCC, so which biomarker had a better diagnostic value?

In this study, the meta-analysis was used to reanalyze the related studies of 11 biomarkers, so as to analyze and compare the diagnostic value of biomarkers for HCC more systematically and scientifically.

2. Materials and methods

2.1. Literature search

This meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines. The relevant literatures on the diagnostic value of 11 blood indexes, such as AFP, AFP-L3, and DCP in HCC in recent 5 years were comprehensively and systematically searched in PubMed, Embase, and Cochrane libraries. The keywords used for retrieval included three parts: (1) (AFP OR alpha-fetoprotein) OR (AFP-L3 OR alpha-fetoprotein-L3 OR lens culinaris-agglutinin-reactive fraction of AFP) OR (PIVKA-II OR DCP OR des-γ-carboxyprothrombin) OR (dickkopf-1 OR DKK1) OR (GP73 OR golgi protein 73) OR (glypican-3 OR GPC3) OR (osteopontin OR OPN) OR (α-L-fucosidase OR AFU) OR (carbohydrate antigen199 OR CA199) OR (heat shock protein 90alpha OR Hsp90alpha) OR (midkine OR MDK) (2) HCC OR hepatocellular carcinoma OR liver cancer OR hepatocellular OR hepatoma (3) sensitivity OR specificity. The above three parts were connected by ‘and’. The retrieved literature was published from January 2017 to January 2022.

2.2. Inclusion and exclusion criteria

The inclusion criteria of literature were as follows:

  1. The diagnostic value of at least one of the 11 biomarkers was described to ensure that we could extract and calculate key indicators directly or indirectly, including sensitivity, specificity, true positive (TP), false positive (FP), false negative (FN) and true negative (TN);

  2. The diagnosis of HCC was based on recognized guidelines, such as histopathology or other appropriate diagnostic criteria;

  3. The specimen type was serum;

  4. The control group was consisted of patients with chronic liver disease, such as liver cirrhosis or hepatitis, or patients with benign liver disease like liver cyst or healthy people.

The exclusion criteria were defined as follows:

  1. The article provided incomplete diagnostic information;

  2. Meta analysis, systematic review, case review, case report, letter;

  3. The control group involved other malignant tumours, especially gastrointestinal tumours;

  4. The article from non-human research;

  5. The article with <20 research samples;

2.3. Data extraction

Two authors screened each record retrieved and extracted data independently. Any differences were resolved through discussion until a consensus was reached or a third author was consulted. Following information was extracted from qualified Literature: author, year of publication, patient country, comprehensive sensitivity and specificity of biomarkers, TP, FP, FN, and TN, type, and number of cases. Finally, the pooled sensitivity and specificity, diagnostic odds ratio, area under the curve, positive likelihood ratio, and negative likelihood ratio were evaluated.

2.4. Statistical analysis

The stata software (midas) was used for meta-analysis. Meta-disc software was used to calculate Spearman’s correlation coefficient values to evaluate the threshold effect. p < 0.05 was considered as a significant manifestation of threshold effect. At the same time, the Cochran Q test and I-squared test were performed to estimate the existence and severity of heterogeneity. p < 0.1, and I2 > 50% were considered significant manifestations of heterogeneity. The Deeks’ funnel plot was used for publication bias analysis. p < 0.05 was considered to have potential publication bias.

3. Results

The flow chart of the inclusion and exclusion of studies in the review is shown in Figure 1. As shown in the figure, a total of 1751 documents were retrieved from three databases, of which 620 duplicates were eliminated and 1131 were left. Among the leaving documents, there were 784 unrelated to the subject, 192 reviews or meta-analysis, seven non-human related studies, 20 non serum samples, 23 with incomplete date, six with other malignancies in the control group, 10 without recognized diagnostic guidelines for HCC, and six studies with <20 samples. Finally, 83 studies were included in the meta-analysis.

Figure 1.

Figure 1.

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Flow chart of research.

3.1. AFP and AFP combined with other biomarkers

Among the 83 literature studies, 43 were related to the diagnostic value of AFP, six were related to AFP + AFP-L3, six were related to AFP + AFP-L3 + DCP, 10 were related to AFP + DCP, six were related to AFP/DCP, and eight were related to AFP + GPC3. In the above multi-biomarker combination types, ‘A + B’ represents series diagnosis, which can be diagnosed as positive if they are all positive, while ‘A/B’ is parallel diagnosis, and one positive item can be diagnosed as positive. The study ID, region, and other research characteristics were summarized in Table 1.

Table 1.

Main characteristics of the included literature related to AFP and AFP combined with other biomarkers in this study.

Markers Study id [Ref] Region Case/control Sample size (case/control) TP FP FN TN Se (%) Sp (%)
AFP Mashaly, A. H. 2018 [14] Egypt HCC/CLD 44/31 23 1 21 30 52.27 96.77
  Qian, Y. 2020 [16] China HCC/CLD 118/75 68 27 50 48 57.63 64.00
  Lee, J. 2021 [17] Korea HCC/CLD 184/134 83 11 101 123 45.00 91.80
  Sultanik, P. 2017 [18] France HCC/CLD 46/116 29 21 17 95 63.00 82.00
  Kim, M. N. 2019 [19] Korea HCC/CLD 64/328 42 22 22 306 65.60 93.30
  Park, S. J. 2017 [20] Korea HCC/CLD 79/77 49 6 30 71 62.03 92.21
  Xu, F. 2021 [21] China HCC/CLD 308/60 212 10 96 50 68.80 83.30
  Loglio, A. 2020 [22] Italy HCC/CLD 64/148 36 9 28 139 56.00 94.00
  Ye, F. 2021 [23] China HCC/CLD 133/101 80 9 53 92 60.15 91.09
  Wang, F. 2021 [24] China HCC/CLD 186/235 118 58 68 177 63.40 75.30
  Omran, M. M. 2021 [25] Egypt HCC/CLD 155/60 42 0 113 60 27.00 100.00
  Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 25 3 30 52 45.50 94.50
  Lambrecht, J. 2021 [27] Germany HCC/CLD 122/145 97 31 25 114 79.17 78.87
  Zhou, P. C. 2020 [28] China HCC/CLD 20/20 6 0 14 20 30.00 100.00
  Zhang, X. 2020 [29] China HCC/CLD 288/286 210 53 78 233 72.90 81.50
  Hendy, O. M. 2020 [30] Egypt HCC/CLD 40/30 25 2 15 28 62.50 93.33
  Mao, L. 2017 [31] China HCC/CLD 82/29 37 10 45 19 45.12 65.52
  Jasirwan, C. O. 2020 [32] Indonesia HCC/CLD 132/198 109 57 23 141 82.60 71.20
  Lee, H. A. 2021 [33] Korea HCC/CLD 160/462 126 181 34 281 78.80 61.00
  Yang, M. Y. 2021 [34] China HCC/CLD 314/300 194 114 120 186 61.80 62.00
  Schotten, C. 2021 [35] Germany HCC/CLD 196/377 119 15 77 362 60.70 96.00
  Liu, D. 2021 [36] China HCC/CLD 123/143 65 9 58 134 52.80 93.70
  Omran, M. M. 2020 [37] Egypt HCC/CLD 104/92 30 0 74 92 29.00 100.00
  Li, B. 2020 [38] China HCC/CLD 104/95 60 13 44 82 57.70 86.30
  Yang, T. 2019 [39] China HCC/CLD 289/211 193 44 96 167 66.80 79.20
  Wang, Q. 2019 [40] China HCC/CLD 176/359 114 82 62 277 64.80 77.20
  Shimagaki, T. 2019 [41] Japan HCC/CLD 185/108 93 16 92 92 50.30 85.20
  Hu, J. 2018 [42] China HCC/CLD 369/176 213 26 156 150 57.70 85.20
  Guo, W. 2018 [43] China HCC/CLD 200/101 114 20 86 81 57.00 80.00
  Chuaypen, N. 2018 [44] Thailand HCC/CLD 150/150 97 4 53 146 64.70 97.30
  Tian, M. M. 2017 [45] China HCC/CLD 120/146 66 56 54 90 55.00 61.60
  Shaker, M. K. 2017 [46] Egypt HCC/CLD 50/25 35 1 15 24 70.00 96.00
  Ismail, M. M. 2017 [47] Saudi Arabia HCC/CLD 66/99 45 12 21 87 68.20 87.90
  Hu, N. 2017 [48] China HCC/CLD 80/80 47 20 33 60 58.75 75.00
  Fu, Y. 2017 [49] China HCC/CLD 531/171 408 29 123 142 76.82 83.01
  Liu, H. H. 2020 [50] China HCC/CLD 105/54 38 5 67 49 36.19 90.74
  Tang, X. Q. 2017 [51] China HCC/CLD 176/190 130 32 46 158 73.90 82.90
  Ali, O. M. 2020 [52] Egypt HCC/CLD 30/30 22 4 8 26 72.20 86.20
  Sai, W. L. 2021 [53] China HCC/CLD 126/96 93 21 33 75 73.81 78.13
  Alzamzamy, A. 2021 [54] Egypt HCC/CLD 40/60 26 10 14 50 65.00 83.30
  Ricco G. 2018 [55] Italy HCC/CLD 258/130 135 32 123 98 52.50 75.20
  Caviglia GP. 2021 [56] Italy HCC/CLD 72/119 55 37 17 82 76.40 68.90
  Caviglia GP. 2020 [57] Italy HCC/CLD 149/200 107 68 42 132 72.00 66.00
AFP + AFP-L3 Song, T. 2020 [58] China HCC/CLD 100/67 54 7 46 60 53.50 89.60
  Pan, Y. 2019 [59] China HCC/CLD, HC 125/280 72 23 53 257 57.60 91.80
  Hu, R. Z. 2019 [60] China HCC/CLD 56/91 36 8 20 83 64.28 90.90
  Park, S. J. 2017 [20] Korea HCC/CLD 79/77 35 2 44 75 44.30 97.40
  Choi, J. 2019 [61] Korea HCC/CLD 42/168 33 22 9 146 79.00 87.00
  Chen, H. 2018 [62] China HCC/CLD 202/441 83 44 119 397 40.90 90.00
AFP + AFP-L3 + DCP Song, T. 2020 [58] China HCC/CLD 100/67 67 11 33 56 66.70 83.10
  Pan, Y. 2019 [59] China HCC/CLD, HC 125/280 65 18 60 262 52.00 93.60
  Hu, R. Z. 2019 [60] China HCC/CLD 56/91 31 2 25 89 55.35 98.48
  Park, S. J. 2017 [20] Korea HCC/CLD 79/77 30 0 49 77 37.97 100.00
  Choi, J. 2019 [61] Korea HCC/CLD 42/168 35 42 7 126 83.00 75.00
  Chen, H. 2018 [62] China HCC/CLD, HC 202/644 149 64 53 580 73.70 90.00
AFP + DCP Park, S. J. 2017 [20] Korea HCC/CLD 79/77 40 0 39 77 51.00 100.00
  Xu, F. 2021 [21] China HCC/CLD, BLD 308/120 293 20 15 100 95.10 83.30
  Yang, T. 2019 [39] China HCC/CLD 289/211 254 40 35 171 87.80 81.00
  Wang, Q. 2019 [40] China HCC/CLD 176/359 129 22 47 337 73.30 93.90
  Tang, X. Q. 2017 [51] China HCC/CLD 176/190 154 25 22 165 87.50 87.00
  Pan, Y. 2019 [59] China HCC/CLD, HC 125/280 78 21 47 259 62.00 92.50
  Wu, M. 2020 [63] China HCC/CLD, HC 198/176 160 52 38 124 80.80 70.60
  Maeda, T. 2019 [64] Japan HCC/CLD, HC 304/152 224 13 80 139 73.70 91.70
  Wu, J. 2018 [65] China HCC/CLD, HC 143/131 126 44 17 87 88.10 66.41
  Ricco G. 2018 [55] Italy HCC/CLD 258/130 225 62 33 68 87.40 52.20
AFP/DCP Park, S. J. 2017 [20] Korea HCC/CLD 79/77 65 29 14 48 82.28 62.34
  Lee, Q. 2021 [66] China HCC/CLD 158/62 114 2 44 60 72.20 96.80
  Loglio, A. 2020 [22] Italy HCC/CLD 64/148 51 21 13 127 80.00 86.00
  Sultanik, P. 2017 [18] France HCC/CLD 46/116 40 28 6 88 87.00 76.00
  Wu, J. 2018 [65] China HCC/CLD, HC 143/131 76 9 67 122 53.02 93.13
  Pan, Y. 2019 [59] China HCC/CLD, HC 125/280 114 53 11 227 91.20 81.10
AFP + GPC3 Wu, M. 2020 [63] China HCC/CLD, HC 198/176 172 50 26 126 86.90 71.70
  Zhu, X. 2019 [67] China HCC/CLD 47/89 40 16 7 73 85.01 81.62
  Taketomi, A. 2020 [68] Egypt HCC/CLD, HC 25/125 23 0 2 125 92.00 100.00
  Tang, X. Y. 2020 [69] China HCC/HC, BLD 166/144 146 32 20 112 87.82 77.86
  Shimizu, Y. 2020 [70] Japan HCC/CLD 105/95 72 10 33 85 68.30 89.40
  Liu, S. 2020 [71] China HCC/HC 210/127 185 22 25 105 88.10 82.68
  Sun, B. 2017 [7] China HCC/CLD, HC 76/100 65 4 11 96 85.50 96.00
  Ali, O. M. 2020 [52] Egypt HCC/CLD 30/30 27 4 3 26 91.00 86.00
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CLD: chronic liver disease; BLD: benign liver disease; HC: healthy control; CH: cirrhosis; TP: true positive; FP: false positive; FN: false negative; TN: true negative; Se: sensitivity; Sp: specificity; Ref: reference.

We combined the data from individual diagnostic tests for AFP and data from the study on combined analysis of AFP and other biomarkers. The sensitivity and specificity after combination are shown in Figure 2, and the SROC curve is shown in Figure 3. Forty-three literatures related to AFP in recent 5 years were included in the study. The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. To better compare the diagnostic value, we compared the sum of sensitivity and specificity.

Figure 2.

Figure 2.

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The sensitivity and specificity of AFP and its combined with other biomarkers (A, AFP; B, AFP + AFP-L3; C, AFP + AFP-L3 + DCP; D, AFP + DCP; E, AFP/DCP; F, AFP + GPC3).

Figure 3.

Figure 3.

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The SROC curve of AFP and its combined with other biomarkers (A, AFP; B, AFP + AFP-L3; C, AFP + AFP-L3 + DCP; D, AFP + DCP; E, AFP/DCP; F, AFP + GP73).

Among studies on combined analysis of AFP and other biomarkers, the sum of sensitivity and specificity of AFP + AFP-L3 + DCP was significantly higher than that of AFP, as were AFP + DCP, AFP/DCP, and AFP + GPC3. The AUC of AFP + AFP-L3, AFP + AFP-L3 + DCP, AFP + DCP, AFP/DCP, and AFP + GPC3 were higher than that of AFP. The combination of AFP and other biomarkers improved the diagnostic efficiency. AFP + GPC3 had the highest sum of sensitivity and specificity (1.75) and AUC (0.91). The pooled sensitivity, specificity, diagnostic odds ratio, area under the curve, positive likelihood ratio, and negative likelihood ratio are shown in Table 2.

Table 2.

The pooled sensitivity, specificity, diagnostic odds ratio, area under the curve, positive likelihood ratio, and negative likelihood ratio of AFP and AFP combined with other biomarkers.

Markers Sensitivity (95% CI) Specificity (95% CI) PLR (95% CI) NLR (95% CI) DOR (95% CI) AUC
AFP 0.61 [0.57, 0.65] 0.87 [0.83, 0.90] 4.50 [3.60, 5.80] 0.45 [0.41, 0.49] 10.00 [8.00, 13.00] 0.78
AFP + AFP-L3 0.56 [0.45, 0.66] 0.90 [0.88, 0.92] 5.80 [4.70, 7.31] 0.49 [0.39, 0.61] 12.00 [8.00, 18.00] 0.90
AFP + AFP-L3 + DCP 0.62 [0.49, 0.74] 0.93 [0.84, 0.97] 9.00 [4.50, 17.90] 0.40 [0.30, 0.54] 22.00 [13.00, 38.00] 0.85
AFP + DCP 0.81 [0.73, 0.88] 0.86 [0.75, 0.93] 6.00 [3.40, 10.60] 0.21 [0.15, 0.30] 28.00 [17.00, 47.00] 0.90
AFP/DCP 0.80 [0.68, 0.88] 0.85 [0.74, 0.92] 5.30 [3.20, 8.90] 0.24 [0.16, 0.36] 22.00 [13.00, 38.00] 0.89
AFP + GPC3 0.86 [0.80, 0.90] 0.89 [0.79, 0.95] 8.10 [3.80, 17.20] 0.16 [0.12, 0.22] 50.00 [20.00, 127.00] 0.91
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PLR: positive likelihood ratio; NLR: negative likelihood ratio; DOR: diagnostic odds ratio; CI: confidence interval; AUC: area under the curve.

We used meta-disc software to calculate Spearman’s correlation coefficient values to evaluate the threshold effect. Spearman’s relevance coefficient value was 0.407 (p = 0.154) in AFP, 0.371 (p = 0.468) in AFP + AFP-L3, 0.886 (p = 0.089) in AFP + AFP-L3 + DCP, 0.717 (p = 0.224) in AFP + DCP, 0.714 (p = 0.111) in AFP/DCP, −0.190 (p = 0.651) in AFP + GPC3. There was no threshold effect in the analysis of each group.

Cochran Q test and I-squared test were performed to estimate the existence and severity of heterogeneity. p < 0.1, and I2 > 50% were considered significant manifestations of heterogeneity. It was found that AFP + AFP-L3 + DCP, AFP, AFP + AFP-L3, AFP + DCP, AFP/DCP, and AFP + GPC3 was heterogeneous.

Meta-regression analysis was performed to analyze the sources of heterogeneity. Potential sources of heterogeneity included research time, publication time, race, average age of HCC group, male proportion of HCC group, proportion of early HCC, male proportion of control group, and average age of control group. The source of heterogeneity of AFP also included cut-off values. The inclusion studies of AFP + AFP-L3, and AFP + AFP-L3 + DCP were all from the same race. The results were shown in Figure 4.

Figure 4.

Figure 4.

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The univariable meta-regression and subgroup analysis of AFP and its combined with other biomarkers (A, AFP; B, AFP + AFP-L3; C, AFP + AFP-L3 + DCP; D, AFP + DCP; E, AFP/DCP; F, AFP + GPC3) (year1, research time; year2, publication time; age1, average age of HCC group; male1, male proportion of HCC group; n, proportion of early HCC; male2, male proportion of control group; age2, average age of control group).

As shown in Figure 4, the meta-regression results of AFP + AFP-L3 showed that the heterogeneity of AFP + AFP-L3 sensitivity may be derived from the male proportion of HCC group, the heterogeneity of AFP + AFP-L3 + DCP specificity may be derived from the male proportion of HCC group and control group, the heterogeneity of AFP/DCP sensitivity may be derived from the early HCC proportion of HCC group, and the heterogeneity of specificity may be derived from the male proportion of HCC group. The source of heterogeneity in other groups was not found.

The sensitivity analysis results were shown in Figure 5. It could be seen from the figure that the meta-analysis results of all markers were relatively stable.

Figure 5.

Figure 5.

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The sensitivity analysis of AFP and its combined with other biomarkers (A, AFP; B, AFP + AFP-L3; C, AFP + AFP-L3 + DCP; D, AFP + DCP; E, AFP/DCP; F, AFP + GPC3).

We used stata software to analyze the potential publication bias of each group. There was no potential publication bias in each group.

3.2. Other biomarkers

Among the 83 literature studies, four were related to AFU, seven were related to AFP-L3, five were related to CA199, 19 were related to DCP, six were related to DKK1, nine were related to GP73, eight were related to GPC3, four were related to Hsp90alpha, five were related to midkine, four were related to OPN, The study ID, region and other research characteristics were summarized in Table 3.

Table 3.

Main characteristics of the included literature related to other biomarkers in this study.

Markers Study id [Ref] Region Case/control Sample size (case/control) TP FP FN TN Se (%) Sp (%)
DCP Lee, H. A. 2021 [33] Korea HCC/CLD 160/462 100 20 60 442 62.50 96.00
  Park, S. J. 2017 [20] Korea HCC/CLD 79/77 56 23 23 54 70.89 70.13
  Xu, F. 2021 [21] China HCC/CLD, BLD 308/120 274 10 34 110 89.00 91.70
  Loglio, A. 2020 [22] Italy HCC/CLD 64/148 41 13 23 135 64.00 91.00
  Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 30 6 25 49 54.60 88.60
  Yang, T. 2019 [39] China HCC/CLD 289/211 229 17 60 194 79.00 91.90
  Wang, Q. 2019 [40] China HCC/CLD 176/359 143 23 33 336 81.30 93.60
  Shimagaki, T. 2019 [41] Japan HCC/CLD 185/108 122 14 63 94 66.00 87.00
  Lee, J. 2021 [17] Korea HCC/CLD 184/134 68 6 116 128 37.00 95.50
  Ji, J. 2021 [72] China HCC/CLD 183/601 154 58 29 543 84.08 90.43
  Tang, X. Q. 2017 [51] China HCC/CLD 176/190 145 8 31 182 82.40 95.90
  Sultanik, P. 2017 [18] France HCC/CLD 46/116 34 9 12 107 74.00 92.00
  Wu, J. 2018 [65] China HCC/CLD, HC 143/131 110 18 33 113 76.92 86.26
  Pan, Y. 2019 [59] China HCC/CLD, HC 125/280 101 26 24 254 80.80 90.70
  Ao, J. 2021 [73] Japan HCC/CLD, HC 275/118 170 4 105 114 61.80 96.90
  Wu, M. 2020 [63] China HCC/CLD, HC 198/176 59 5 139 171 29.80 97.20
  Caviglia GP. 2020 [57] Italy HCC/HC 149/200 101 32 48 168 68.00 84.00
  Caviglia GP. 2021 [56] Italy HCC/CLD 72/119 54 17 18 102 75.00 85.70
  Ricco G. 2018 [55] Italy HCC/CLD 258/130 203 49 55 81 78.70 62.50
GPC3 Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 33 2 22 53 60.00 96.40
  Jing, J. S. 2017 [74] China HCC/CLD 39/31 35 1 4 30 89.70 96.77
  Wang, Y. Y. 2017 [75] China HCC/CLD 60/50 39 3 21 47 65.00 94.00
  Wu, M. 2020 [63] China HCC/CLD, HC 198/176 116 28 82 148 58.60 84.40
  Liu, S. 2020 [71] China HCC/HC 210/134 167 20 43 114 79.52 85.07
  Sun, B. 2017 [7] China HCC/CLD, HC 76/100 57 13 19 87 75.00 87.00
  Caviglia GP. 2020 [57] Italy HCC/HC 149/200 109 98 40 102 73.00 51.00
  Caviglia GP. 2021 [56] Italy HCC/CLD 72/119 45 21 27 98 62.50 82.40
GP73 Wei, H. 2020 [76] China HCC/CLD 60/60 48 35 12 25 80.00 41.70
  Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 35 11 20 44 63.60 80.00
  Jing, J. S. 2017 [74] China HCC/CLD 39/31 36 5 3 26 92.31 83.87
  Ismail, M. M. 2017 [47] Saudi Arabia HCC/CLD 66/99 66 0 0 99 100.00 100.00
  Farag, R. M. 2019 [77] Saudi Arabia HCC/CLD, HC 145/155 138 8 7 147 95.00 95.00
  Wu, M. 2020 [63] China HCC/CLD, HC 198/176 47 1 151 175 23.70 99.40
  Yanming, L. 2019 [78] China HCC/CLD, HC 93/628 71 125 22 503 76.34 80.09
  Elzefzafy, W.M. 2021 [79] Egypt HCC/CLD, HC 30/60 29 27 1 33 95.00 55.00
DKK1 Piratvisuth, T. 2022 [80] Thailand HCC/CLD 308/773 67 77 241 696 21.60 90.00
  Mao, L. 2017 [31] China HCC/CLD 82/57 73 32 9 25 89.02 43.86
  ElShayeb, A. F. 2021 [81] Egypt HCC/CLD 86/89 77 18 9 71 89.00 80.00
  Eldeeb, M. K. 2020 [82] Egypt HCC/CLD 30/30 12 3 18 27 40.00 90.00
  Awad, A. E. 2019 [83] Egypt HCC/CLD, HC 55/35 48 6 7 29 87.30 82.90
  Zhu, M. 2020 [8] China HCC/CLD 101/221 65 80 36 141 64.36 63.80
AFP-L3 Lee, H. A. 2021 [33] Korea HCC/CLD 160/462 114 90 46 372 71.30 81.00
  Song, T. 2020 [58] China HCC/CLD 100/67 28 3 72 64 28.30 95.50
  Park, S. J. 2017 [20] Korea HCC/CLD 79/77 40 13 39 64 50.63 83.12
  Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 16 2 39 53 29.10 96.40
  Pan, Y. 2019 [59] China HCC/CLD, HC 125/280 88 24 37 256 70.40 91.40
  Liu, R. 2021 [84] China HCC/CLD, HC 89/119 66 12 23 107 74.20 89.90
  Yanming, L. 2019 [78] China HCC/CLD, HC 93/628 63 116 30 512 67.74 81.52
AFU Liu, D. 2021 [36] China HCC/CLD 123/143 88 47 35 96 71.50 67.10
  Xing, H. 2019 [85] China HCC/CLD 187/214 124 98 63 116 66.31 54.21
  Junna, Z. 2017 [10] China HCC/HC 36/36 28 13 8 23 78.00 64.00
  ElShayeb, A. F. 2021 [81] Egypt HCC/CLD 86/89 64 45 22 44 74.00 50.00
CA199 Li, H. 2020 [11] China HCC/CLD 86/85 71 31 15 54 82.94 63.95
  Han, Y. 2021 [86] China HCC/CH, BLD 118/62 58 11 60 51 48.90 81.50
  Piratvisuth, T. 2022 [80] Thailand HCC/CLD 308/773 99 77 209 696 32.00 90.00
  Huang, X. 2018 [87] China HCC/CH 62/40 42 13 20 27 68.43 68.36
  Zhao, L. 2017 [88] China HCC/CH, BLD 149/178 24 16 125 162 16.10 90.80
Hsp90alpha Han, Y. 2021 [86] China HCC/CH, BLD 118/62 113 15 5 47 95.70 75.00
  Fu, Y. 2017 [49] China HCC/CLD, HC 531/743 496 72 35 671 93.32 90.27
  Wei, W. 2020 [13] China HCC/HC 659/230 442 22 217 208 67.07 90.43
  Tang, Y. 2020 [89] China HCC/CH, BLD 409/179 297 12 112 167 72.60 93.30
Midkine Mashaly, A. H. 2018 [14] Egypt HCC/CLD 44/31 36 5 8 26 81.82 83.87
  Omran, M. M. 2020 [37] Egypt HCC/CLD 104/92 79 19 25 73 76.00 79.00
  Hodeib, H. 2017 [90] Egypt HCC/CLD, HC 35/105 34 4 1 101 98.40 96.20
  ElShayeb, A. F. 2021 [81] Egypt HCC/CLD 86/89 86 9 0 80 100.00 90.00
  Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 47 20 8 35 85.50 63.60
OPN Zhu, M. 2020 [8] China HCC/CLD 101/221 80 45 21 176 79.21 79.64
  Al-Zoubi, S. 2017 [91] Syria HCC/CLD, HC 26/42 18 7 8 35 69.00 84.00
  Malov, S. I. 2021 [26] Russia HCC/CLD 55/55 44 13 11 42 80.00 76.60
  Hodeib, H. 2017 [90] Egypt HCC/CLD, HC 35/105 34 5 1 100 97.10 95.30
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CLD: chronic liver disease; BLD: benign liver disease; HC: healthy control; CH: cirrhosis; TP: true positive; FP: false positive; FN: false negative; TN: true negative; Se: sensitivity; Sp: specificity; Ref: reference.

We combined the data from individual diagnostic tests for 10 biomarkers. The sensitivity and specificity after combination are shown in Figure 6, and the SROC curve is shown in Figure 7. Compared to AFP, the sum of sensitivity and specificity of DCP was significantly higher, as were GPC3, GP73, Hsp90alpha, midkine, and OPN. In addition, Midkine and GP73 had the highest sum of sensitivity and specificity (1.78).

Figure 6.

Figure 6.

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The sensitivity and specificity of other biomarkers (A, DCP; B, GPC3; C, GP73; D, DK-K1; E, AFP-L3; F, AFU; G, CA199; H, Hsp90alpha; I, Midkine, J, OPN).

Figure 7.

Figure 7.

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The SROC curve of other biomarkers (A, DCP; B, GPC3; C, GP73; D, DK-K1; E, AFP-L3; F, AFU; G, CA199; H, Hsp90alpha; I, Midkine, J, OPN).

Taking AFP as the reference, the results showed that the AUC of DCP, GPC3, GP73, DKK1, AFP-L3, Hsp90alpha, Midkine, and OPN were significantly higher than that of AFP. The AUC of GP73 (0.95) was the highest. The pooled sensitivity, specificity, diagnostic odds ratio, area under the curve, positive likelihood ratio, and negative likelihood ratio are shown in Table 4.

Table 4.

The pooled sensitivity, specificity, diagnostic odds ratio, area under the curve, positive likelihood ratio, and negative likelihood ratio of other biomarkers.

Markers Sensitivity (95% CI) Specificity (95% CI) PLR (95% CI) NLR (95% CI) DOR (95% CI) AUC
DCP 0.71 [0.64, 0.77] 0.91 [0.87, 0.93] 7.8 [5.70, 10.50] 0.32 [0.26, 0.40] 24.00 [16.00, 36.00] 0.90
GPC3 0.71 [0.63, 0.77] 0.87 [0.77, 0.93] 5.50 [2.90, 10.40] 0.33 [0.26, 0.43] 17.00 [7.00, 37.00] 0.82
GP73 0.88 [0.65, 0.96] 0.90 [0.68, 0.98] 8.90 [2.30, 33.90] 0.14 [0.04, 0.45] 66.00 [9.00, 502.00] 0.95
DKK1 0.70 [0.44, 0.87] 0.77 [0.62, 0.87] 3.10 [2.00, 4.80] 0.39 [0.20, 0.77] 8.00 [3.00, 18.00] 0.81
AFP-L3 0.56 [0.42, 0.70] 0.89 [0.83, 0.94] 5.40 [3.70, 7.70] 0.49 [0.36, 0.66] 11.00 [7.00, 18.00] 0.85
AFU 0.71 [0.66, 0.75] 0.59 [0.51, 0.66] 1.70 [1.40, 2.10] 0.50 [0.40, 0.63] 3.00 [2.00, 5.00] 0.71
CA199 0.50 [0.27, 0.72] 0.83 [0.71, 0.90] 2.90 [2.40, 3.50] 0.61 [0.42, 0.88] 5.00 [3.00, 7.00] 0.78
Hsp90alpha 0.87 [0.69, 0.95] 0.89 [0.84, 0.93] 8.20 [5.60, 11.90] 0.15 [0.06, 0.37] 55.00 [24.00, 126.00] 0.94
Midkine 0.93 [0.76, 0.98] 0.85 [0.73, 0.93] 6.40 [3.10, 13.30] 0.08 [0.02, 0.33] 78.00 [11.00, 582.00] 0.94
OPN 0.85 [0.70, 0.93] 0.85 [0.74, 0.92] 5.60 [2.80, 11.40] 0.18 [0.08, 0.41] 31.00 [7.00, 137.00] 0.91
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PLR: positive likelihood ratio; NLR: negative likelihood ratio; DOR: diagnostic odds ratio; CI: confidence interval; AUC: area under the curve.

Spearman’s relevance coefficient value was 0.000 (p = 1.000) in AFU, 0.571 (p = 0.180) in AFP-L3, 1.220 (p = 0.230) in CA199, 0.207 (p = 0.395) in DCP, 0.714 (p = 0.111) in DKK1, −0.143 (p = 0.736) in GP73, −0.286 (p = 0.493) in GPC3, 0.800 (p = 0.200) in Hsp90alpha, −0.600 (p = 0.285) in Midkine, −0.200 (p = 0.800) in OPN. There was no threshold effect in the analysis of each group.

Cochran Q test and I-squared test were performed to estimate the existence and severity of heterogeneity. It was found that AFU, AFP-L3, CA199, DCP, DKK1, GP73, GPC3, Hsp90alpha, Midkine, OPN was heterogeneous.

Meta-regression analysis was performed to analyze the sources of heterogeneity of meta-analysis involving more than five articles. Potential sources of heterogeneity included research time, publication time, race, cut-off values, average age of HCC group, male proportion of HCC group, proportion of early HCC, male proportion of control group, and average age of control group.

As shown in Figure 8, the meta-regression results showed that the heterogeneity of DCP sensitivity may be derived from the male proportion of HCC group and the early HCC proportion of HCC group, the heterogeneity of GPC3 sensitivity may be derived from the male proportion of HCC group, the heterogeneity of GP73 sensitivity may be derived from the male proportion of control group, the heterogeneity of DKK1 sensitivity and specificity may be derived from the male proportion of control group. The source of heterogeneity in other groups was not found.

Figure 8.

Figure 8.

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The univariable meta-regression and subgroup analysis of other biomarkers (A, DCP; B, GPC3; C, GP73; D, DKK1; E, AFP-L3) (year1, research time; year2, publication time; age1, average age of HCC group; male1, male proportion of HCC group; n, proportion of early HCC; male2, male proportion of control group; age2, average age of control group).

The sensitivity analysis results are shown in Figure 9. It could be seen from the figure that the meta-analysis results of all markers were relatively stable.

Figure 9.

Figure 9.

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The sensitivity analysis of other biomarkers (A, DCP; B, GPC3; C, GP73; D, DK-K1; E, AFP-L3; F, AFU; G, CA199; H, Hsp90alpha; I, Midkine, J, OPN).

We used Deeks’ funnel plot to analyze the potential publication bias of each group. Among the analysis of each group, DCP (p = 0.05) and GPC3 (p = 0.04) have potential publication bias. For the publication bias test results of DCP and GPC3, we further tested the publication bias by using Egger’ funnel plot, Begger’ funnel plot, trim and fill methods. The results are shown in Figure 10.

Figure 10.

Figure 10.

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Publication bias analysis of DCP and GPC3 (A, Deeks’ funnel plot; B, Beggs’ funnel plot; C, Egger’ funnel plot; D, filled funnel plot) (Left, DCP; Right, GPC3).

As shown in Figure 10, the Egger test result of DCP is p = 0.50, and the Egger test result of GPC3 is p = 0.286. We analyzed the analysis results of DCP and GPC3 by the trim and fill method. Seven studies were added to DCP. The combined RR value and 95% effect interval before repair were 5.003 (3.766, 6.240), and the combined RR value and 95% effect interval after repair were 14.885 (3.523, 62.893). Four studies were added to GPC3. The combined RR value and 95% effect interval before repair were 3.606 (2.864, 4.348), and the combined RR value and 95% effect interval after repair were 11.676 (5.443, 25.047). The combined RR value before and after repair showed no significant impact on the study.

4. Discussion

The early diagnosis is the key to the treatment of HCC. AFP, as the most commonly used clinical biomarker of HCC, has been questioned for its diagnostic value. However, there is no ideal recognized biomarker of HCC at present [92]. There are more and more studies on new biomarkers with better diagnostic value for HCC, lack of scientific evaluation yet. In view of this, we tried to use meta-analysis to compare 11 biomarkers of HCC which were played more attention by public. According to our investigation, there are few such studies to conduct such a wide range of literature research, screen and evaluate so many biomarkers at the same time.

The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively, which were consistent with the results of previous studies. It was found that the combination of AFP and other biomarkers improved the diagnostic efficiency. The sum of sensitivity and specificity of AFP + GP73 was the highest (1.75), and AUC was the highest (0.91) as well.

The sum of sensitivity and specificity of DCP was significantly higher than that of AFP, as were GPC3, GP73, Hsp90alpha, midkine, and OPN. Midkine and GP73 had the highest sum of sensitivity and specificity (1.78), higher than AFP + GP73 in the combination diagnosis of AFP and other biomarkers of HCC. The AUC of DCP, GPC3, GP73, DKK1, AFP-L3, Hsp90alpha, midkine, and OPN were significantly higher than that of AFP and GP73’s (0.95) was the highest. It indicated the above biomarkers had higher diagnostic value for HCC, and expected to become ideal biomarkers for HCC diagnosis, worthy of further research.

GP73 was a type II transmembrane glycoprotein that existed in Golgi apparatus. It was generally believed that GP73 was mainly expressed in bile duct epithelial cells in normal liver, with no or only a small amount of expression in hepatocytes and low serum GP73 level; However, hepatocytes in patients with liver cancer produced a large amount of GP73 and further release it into serum, resulting in a significant increase in serum GP73 level [90].

As a potential biomarker of tumour diagnosis and prognosis, GP73 had become a research hotspot. Previous studies had shown that in the diagnosis of early HCC, the sensitivity of GP73 was 62%, which was much higher than that of AFP (sensitivity was 25%), indicating that GP73 was better than AFP in the diagnosis of early HCC [89]. In a study on the population of China, it was found that the serum GP73 level of liver cancer patients infected with hepatitis B virus was the highest in all detection groups [93]. According to this study, it was shown that in the study, GP73 had the highest AUC (0.95), and sum of sensitivity and specificity (1.78). The data of this study shows that GP73 has the best diagnostic value. but the number of relevant research cases was small, which still needed a lot of clinical data for further support.

The diagnostic value of individual biomarker may not be significantly different from that of multi-biomarker combined assays. Perhaps we can find enough good biomarkers from an individual biomarker instead of focusing on multi-biomarker combined assays.

A large number of case–control studies investigated the diagnostic accuracy of serologic biomarkers; However, only few studies investigated the predictiveness of these biomarkers. Serologic biomarkers that monitor the risk stratification of HCC development in patients are crucial to personalize surveillance strategies and thus to improve early HCC detection by optimizing resource allocation.

Loglio et al. reported that in Caucasian patients with HBV compensatory cirrhosis who received long-term NUC treatment, AFP higher than 7 ng/mL showed excellent specificity (99.6%), indicating the occurrence of HCC within a year [94]. El-Derany MO reported HCC development in NASH was associated with higher serum AFP, IL-13 levels [95]. Choi et al. found that the level of DCP in HCC patients began to increase half a year before diagnosis, and the level of AFP-L3 began to increase one year before diagnosis, but there was no significant change in the control group [61]. Li et al. found that the AFP, AFP-L3, ALT, and AFP-L3/AFP increased significantly in patients with HCC 3 years before the diagnosis of HCC [96]. Shakado suggested that the elevation of AFP and DCP levels at 24 weeks after the completion of IFN and ribavirin therapy were strongly associated with the incidence of HCC irrespective of virological response among Japanese hepatitis C virus-related liver cirrhosis patients [97]. Gatselis et al. reported that the combination of GP73 and the cartilage oligomeric matrix protein (COMP) seems efficient to detect the development of HCC in patients with chronic liver diseases [98].

5. Conclusions

The pooled sensitivity and specificity of AFP were 0.61 and 0.87, respectively. The AUC of AFP were 0.78. The combination of AFP and other biomarkers improved the diagnostic efficiency. The diagnostic value of biomarkers including DCP, GPC3, GP73, Hsp90alpha, midkine, OPN was higher than that of AFP. GP73 had the best diagnostic value for HCC with the highest sum of sensitivity and specificity (1.78) and AUC (0.95).

Funding Statement

This study was funded by The National Key R&D Program of China (2017YFC1700305).

Author contributions

All authors provided substantial intellectual contribution to the study to qualify for authorship. Bingyao Pang, Yan Leng, and Lihong Jiang conceived the study design. Bingyao Pang, Xiaoli Wang, and Yiqiang Wang collected and processed the data. Bingyao Pang and Yan Leng prepared the manuscript. Bingyao Pang, Yan Leng, and Lihong Jiang edited the manuscript and provided valuable comments. Bingyao Pang, Yan Leng, and Lihong Jiang approved the final version to be published. All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

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