Abstract
Although liquid biopsy has garnered increasing attention in recent years for diagnosing hepatocellular carcinoma (HCC), serum biomarkers continue to hold significant value for HCC diagnosis due to their simple operation, cost-effectiveness, and high efficiency. This study aimed to screen for the optimal diagnostic combinations of alpha fetoprotein (AFP), a protein induced by vitamin K deficiency or antagonist II (PIVKA-II), golgi glycoprotein 73 (GP73), and routine clinical indicators for diagnosing hepatitis B-associated HCC (HBV-HCC). A retrospective analysis was conducted on 358 HBV-HCC patients treated at Taizhou People’s Hospital from August 2015 to October 2021; 124 patients with chronic hepatitis B (CHB) and 241 patients with hepatitis B cirrhosis composed the control group. With liver pathology as the gold standard, the concordance between the screened indicators and liver pathology for HCC diagnosis was analyzed by Cohen’s kappa coefficient. In the CHB group, AFP, PIVKA-II, and GP73 were statistical significance, and the triple biomarker combination achieved the highest AUC (0.908) for HCC diagnosis, surpassing the efficacy of both individual indicators and two biomarker combinations. In both the Child‒Pugh A and Child‒Pugh B&C cirrhosis groups, AFP and PIVKA-II were significantly different between patients with and without HCC, and the AUC values of AFP combined with PIVKA-II for HCC diagnosis were 0.969 and 0.956, respectively. Using liver pathology as the gold standard, the Kappa values of the above combinations in the three groups were 0.866, 0.780, and 0.800, respectively. The triple combination of AFP, PIVKA-II, and GP73 in the CHB group and the combination of AFP and PIVKA-II in both the Child‒Pugh A and Child‒Pugh B&C cirrhosis groups had excellent diagnostic accuracy for HCC, consistent with liver pathology, and were superior to the diagnostic ability of individual biomarkers.
Keywords: Chronic hepatitis B, Hepatocellular carcinoma, AFP, PIVKA-II, GP73
Subject terms: Hepatocellular carcinoma, Hepatitis B
Introduction
Primary liver cancer is currently the fourth most common malignant tumor and the second leading cause of cancer death in China, seriously threatening people’s lives and health1,2. Primary liver cancer comprises mainly hepatocellular carcinoma (HCC) (75‒85% of cases), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-CCA), with hepatitis B virus (HBV) and hepatitis C virus (HCV) chronic infection responsible for 21–55% of HCC worldwide3. HBV chronic infection accounts for 86% of HCC patients in China4.
The combination of liver ultrasound and blood alpha-fetoprotein (AFP) testing is the fundamental approach for screening and monitoring HCC but is characterized by both suboptimal sensitivity and specificity. A meta-analysis showed that the sensitivity of liver ultrasound combined with AFP for early HCC detection is only 635. The protein induced by vitamin K deficiency or antagonist II (PIVKA-II) is an abnormal product formed by incomplete carboxylation of the prothrombin precursor synthesized by the liver6. It was reported that PIVKA-II exhibited suboptimal sensitivity in diagnosing HCC, with a sensitivity and specificity of 59.92% and 89.35% for all-cause HCC, respectively, and 62.31% and 92.32% for HBV-HCC, respectively7. However, PIVKA-II plays an important role in monitoring early HCC and AFP-negative HCC8. Golgi protein 73 (GP73) is a type II Golgi membrane protein that is mainly expressed by bile duct epithelial cells in the normal liver9. One study reported that HBV suppresses the host immune response by activating GP73, subsequently promoting the development of HCC10. A meta-analysis showed that GP73 has significant diagnostic value for HCC but performs poorly in distinguishing HCC from cirrhosis11. GALAD is a scoring system for evaluating liver cancer in nonalcoholic steatohepatitis patients12. Liquid biopsy has emerged as a pivotal research focus in the clinical diagnosis and treatment of tumors, including circulating tumor DNA (ctDNA), cell-free RNA (cfRNA), circulating tumor cells (CTCs), proteins, and extracellular vesicles (EVs)13–16. However, the current complexity of liquid biopsy and its high detection costs hinder its routine clinical implementation, which renders it inaccessible to most primary hospitals. The screening of routine clinical indicators and the exploration of different diagnostic strategies for liver cancer are other directions.
The purpose of this study was to identify and screen for the most effective diagnostic combinations of AFP, PIVKA-II, GP73, and routine clinical indicators in patients with HBV-HCC.
Materials and methods
Patients
A total of 358 patients with HBV-HCC, 124 patients with chronic hepatitis B (CHB), and 241 patients with hepatitis B cirrhosis were recruited from August 2015 to October 2021 at Taizhou People’s Hospital. Among them, 382 patients had liver pathological data. All patients signed the informed consent for liver puncture. The inclusion criteria were as follows: (1) Older than 18 years old; (2) Complete clinical data; (3) The diagnosis of CHB and hepatitis B cirrhosis adhered strictly to the Chinese “Guidelines for the Prevention and Treatment of Chronic Hepatitis B (version 2022)”17, and the diagnosis of HCC was in accordance with the “Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition)”18. The exclusion criteria were as follows: (1) Overlapping with other viral infections or liver diseases caused by other factors, such as HCV infection, human immunodeficiency virus infection, drug-induced liver injury, nonalcoholic fatty liver disease, alcoholic fatty liver disease, and autoimmune hepatitis; (2) The presence of malignancies in other primary locations, including secondary liver cancer; (3) Pregnant and lactating women. None of the patients consumed vitamin K or vitamin K antagonists. The patient screening process is shown in Fig 1.
Fig. 1.
The patient screening process.
At baseline, 403 out of 723 enrolled patients received antiviral treatment. Among those who received antiviral treatment, 48 received interferon treatment, while the remaining patients were treated with nucleoside analogs or a combination of antiviral therapies. Patients who had not received antiviral treatment at baseline were given antiviral treatment after completing relevant examinations and meeting the criteria for antiviral therapy.
This study was approved by the ethics committee of Taizhou People’s Hospital (ky2021-081-01). This study was a retrospective study, the data was the routine examinations of patients, and the patient’s privacy was not involved, so informed consent was exempted.
Data collection
Clinical data included sex, age, laboratory tests (routine blood tests, biochemical detection, tumor marker detection, coagulation function, pathogen detection, HBV marker detection, and HBV DNA quantification), imaging examinations (dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI)), and liver histopathology.
Examination of the specimen: A blood cell analyzer was used for complete blood count, and an Abbott i2000SR automated chemiluminescent immunoassay analyzer was used to detect AFP and PIVKA-II, and GP73 was measured using the up-converting phosphor technology immunoassay analyzer UPT-3 A produced by Beijing Hotgen Biotech Co., Ltd. along with the corresponding quantitative detection kit. The reference concentrations of AFP, PIVKA-II, and GP73 were 0-8.78 ng/ml, 0–32 mAU/ml, and 0-150 ng/ml, respectively. Liver function and kidney function were tested by an Olympus 5421 automatic biochemical analyzer. HBV markers were detected by a Roche Elecsys electrochemiluminescence automatic immune analyzer. HBV DNA was quantified by a Roche Cobas TaqMan 48 virus quantification system, and the lower limit of quantification was 1.3 lg IU/ml (20 IU/ml).
Pathological detection
The pathological data were obtained from multiple sources. First, they involve puncture biopsies of liver nodules which cannot rule out the presence of HCC and do not exhibit the characteristic imaging features of HCC, with the aim of obtaining a definitive pathological diagnosis. Second, pathological data of resected liver cancer specimens were collected. Third, liver biopsy evaluations were conducted to assess inflammation and fibrosis in HBV-infected patients, as well as in those with liver disease who were serum HBsAg-negative and HBcAb-positive. Liver biopsy was performed under the guidance of ultrasound, utilizing a TSK 16G liver biopsy needle to procure liver nodule(s). Informed consent and signatures were obtained from the patients before the procedure. The seven-point baseline sampling method was used for the resected specimens. The pathological specimens were evaluated by an intermediate and a senior pathologist, initially examined by the intermediate pathologist and subsequently reviewed by the senior pathologist. In the event of any queries, the researcher and pathologist jointly deliberated and reached a consensus based on the criteria.
Grouping
The training cohort for screening of indicators
A total of 341 patients with clinical diagnoses and lacking pathological data were selected as the training cohort. These patients were divided into the CHB group (56 patients with HCC and 74 without), the Child‒Pugh A hepatitis B cirrhosis group (43 patients with HCC and 39 without), and the Child‒Pugh B&C hepatitis B cirrhosis group (88 patients with HCC and 41 without). The baseline data was shown in Table 1.
Table 1.
Comparison of baseline data between HCC patients and non-HCC patients in different groups.
| Variables | CHB | Child‒Pugh A cirrhosis | Child‒Pugh B&C cirrhosis | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HCC (n = 56) | Without HCC (n = 74) | Z/χ2 | P | HCC (n = 43) | Without HCC (n = 39) | Z/χ2 | P | HCC (n = 88) | Without HCC (n = 41) | Z/χ2 | P value | |
| Sex (M/F) | 44/12 | 58/16 | 0.001 | 0.979 | 34/9 | 31/8 | 0.05 | 0.944 | 68/20 | 31/10 | 0.43 | 0.835 |
| Age (year) | 65(54, 75) | 46(38, 51) | – 1.936 | 0.056 | 58(50, 65) | 53(47, 60) | – 1.232 | 0.218 | 60(52, 69) | 57(49, 65) | – 1.318 | 0.187 |
| AFP (ng/mL) | 10.94(3.31,205.81) | 2.93(2.03,6.66) | – 4.589 | < 0.001 | 33.07(14.58,582.26) | 3.15(1.62,7.84) | – 6.777 | < 0.001 | 36.43(19.45,253.87) | 6.22(3.78,12.01) | – 7.345 | < 0.001 |
| PIVKA-II (mAU/mL) | 93.14(21.74,964.14) | 20.15(17.13,25.99) | – 4.364 | < 0.001 | 473.88(109.6,1650.7) | 22.53(14.59,26.19) | – 6.628 | < 0.001 | 337.85(71.87,3378.65) | 22.18(16.1,33.45) | – 7.488 | < 0.001 |
| GP73 (ng/mL) | 60.98(35.34,144.92) | 49.15(33.69,81.16) | – 3.783 | < 0.001 | 207.04(138.5,396.74) | 51.79(39.41,77.31) | – 6.355 | < 0.001 | 239.56(132.60,537.34) | 80.6(37.70,124.56) | – 5.858 | < 0.001 |
| CEA (ng/mL) | 3.09(2.33,3.87) | 2.13(1.43,3.59) | – 1.760 | 0.077 | 2.81(1.68,4.35) | 2.97(2.13,4.62) | – 0.921 | 0.357 | 3.26(2.14,5.84) | 2.89(2.21,3.86) | – 1.186 | 0.236 |
| CA199 (ng/mL) | 9.31(4.21,17.23) | 6.03(3.24,12.11) | – 1.783 | 0.060 | 8.9(5.3,14.6) | 8.72(3.32,14.90) | – 0.913 | 0.361 | 12.77(6.08,34.56) | 4.80(2.64,12.66) | – 1.903 | 0.060 |
| FER (ng/mL) | 162.23(78.13,394.3) | 196.25(130.4,362.8) | – 0.950 | 0.342 | 247.9(105.78,445.35) | 183.3(76.51,208.33) | – 1.408 | 0.159 | 269(150.25,406.95) | 105.1(45.91,285.83) | – 1.500 | 0.130 |
| RBC (× 1012/L) | 4.22(3.90,4.62) | 4.81(4.36,5.14) | – 1.391 | 0.170 | 4.53(4.22,5.00) | 4.67(4.23,5.03) | – 0.571 | 0.568 | 3.93(3.47,4.45) | 4.03(3.11,4.47) | – 0.341 | 0.733 |
| HGB (g/L) | 133.5(119.25,145.5) | 149.5(137,159.25) | – 1.774 | 0.072 | 139.5(130.5,152.75) | 144(135.25,156.75) | – 0.856 | 0.392 | 124(108.25,139) | 128(91.5,145.5) | – 0.071 | 0.944 |
| WBC (× 109/L) | 5.23(4.27,7.45) | 5.1(4.31,6.31) | – 0.764 | 0.445 | 5.29(4.11,6.65) | 4.04(3.09,5.38) | – 3.248 | 0.001 | 4.28(3.11,5.89) | 4.13(2.38,5.12) | – 1.571 | 0.116 |
| NEUT (× 109/L) | 2.70(1.01,4.33) | 2.45(1.77,3.65) | – 2.665 | 0.008 | 3.26(2.33,4.4) | 2.39(1.85,5.03) | – 3.358 | 0.001 | 3.20(2.15,4.35) | 2.33(1.95,3.60) | – 0.392 | 0.695 |
| LYMPH (×109/L) | 1.76(1.39,1.97) | 1.61(1.38,1.96) | – 1.107 | 0.268 | 1.44(0.97,1.75) | 1.28(0.74,1.79) | – 3.1 | 0.002 | 1.01(0.72,1.48) | 0.97(0.57,1.69) | – 1.613 | 0.096 |
| MONO (× 10^9/L) | 0.38(0.27,0.55) | 0.36(0.28,0.42) | – 1.319 | 0.187 | 0.40(0.3,0.55) | 0.28(0.2,0.35) | – 3.606 | < 0.001 | 0.36(0.25,0.53) | 0.23(0.19,0.39) | – 1.898 | 0.059 |
| EO (× 109/L) | 0.09(0.05,0.14) | 0.08(0.04,0.13) | – 0.278 | 0.781 | 0.11(0.06,0.14) | 0.08(0.03,0.12) | – 1.464 | 0.143 | 0.07(0.04,0.13) | 0.05(0.03,0.12) | – 0.044 | 0.965 |
| BASO (× 109/L) | 0.02(0.02,0.04) | 0.02(0.02,0.04) | – 0.423 | 0.672 | 0.03(0.01,0.04) | 0.02(0.01,0.04) | – 0.621 | 0.535 | 0.02(0.01,0.04) | 0.02(0.01,0.04) | – 0.309 | 0.758 |
| PLT (× 10^9/L) | 157.5(119,210) | 172(132.75,203) | – 0.856 | 0.392 | 123(87,167.5) | 119.5(73.5,151) | – 1.153 | 0.249 | 76.5(52,134.75) | 79(57.5,126) | – 0.939 | 0.348 |
| Tbil (umol/L) | 15.35(12.33,21.13) | 16.05(12.6,22.48) | – 0.576 | 0.565 | 16.2(11.45,22) | 18.05(13.48,22.15) | – 1.274 | 0.203 | 22.2(15.7,39) | 19.9(14.6,31.1) | – 1.753 | 0.082 |
| Dbil (umol/L) | 3.6(2.75,5.95) | 3.25(2.38,4.63) | – 1.540 | 0.124 | 3.7(2.9,5.05) | 3.4(2.7,4.8) | – 1.002 | 0.316 | 6.9(4.2,12.38) | 4.2(3,6.15) | – 0.68 | 0.496 |
| Ibil (umol/L) | 12.4(9.28,15.2) | 12.9(10,17.98) | – 1.317 | 0.188 | 12.85(7.68,15.4) | 14.1(10.4,17.6) | – 1.881 | 0.060 | 17(11.13,27.4) | 15.6(10.8,24.05) | – 0.867 | 0.386 |
| TP (g/L) | 69.4(65.1,74.1) | 72.55(67.3,75.7) | – 1.762 | 0.078 | 73.3(65.3,77.5) | 72.7(69.5,76.1) | – 0.41 | 0.682 | 67.05(61.4,72.3) | 68.3(63,72.85) | – 0.477 | 0.520 |
| Alb (g/L) | 39.9(36.6,43.35) | 43.9(40.83,45.73) | – 0.578 | 0.560 | 40.75(38,44.45) | 42.65(39.53,45.03) | – 1.207 | 0.228 | 33.15(30.1,37.63) | 38.5(31.05,42.95) | – 1.763 | 0.078 |
| Glo (g/L) | 29.9(26.25,33.15) | 28(25.68,31.33) | – 1.878 | 0.060 | 31.1(27.2,34.9) | 29.6(27.4,33.1) | – 0.661 | 0.509 | 31.95(28,36.78) | 29.6(27.1,33.35) | – 1.715 | 0.086 |
| ALT (U/L) | 20(16.25,31.75) | 30.5(18,55.25) | – 0.397 | 0.691 | 30(18.25,61.75) | 32.5(19.25,47) | – 0.176 | 0.860 | 35(22.5,65.25) | 23(15,39.5) | – 1.865 | 0.070 |
| AST (U/L) | 26.5(22.25,47.5) | 26(21,39.75) | – 1.183 | 0.249 | 35(25,49.75) | 38(24,50) | – 0.031 | 0.975 | 53(35,93.75) | 29(22,54.5) | – 1.878 | 0.061 |
| ALP (U/L) | 110.5(75,126.5) | 83(67.5,97.75) | – 0.049 | 0.960 | 104(75.75,134.5) | 114(90.25,134) | – 0.442 | 0.659 | 147.5(97.25,191.75) | 98(78.5,137) | – 4.406 | 0.001 |
| GGT (U/L) | 50.5(25.25,90) | 25.5(17,46.75) | – 1.776 | 0.071 | 43(27,88) | 40(27,75.25) | – 0.651 | 0.515 | 82(37.25,199.25) | 29(19,66) | – 2.763 | 0.006 |
| TBA (umol/L) | 6.3(2.83,10.65) | 2.95(1.6,5.48) | – 1.010 | 0.302 | 4.8(2.75,10.85) | 4.8(2.4,10.4) | – 0.197 | 0.844 | 26(9.4,49.8) | 10.7(4.55,27.45) | – 2.236 | 0.051 |
| CG (mg/L) | 2(1.1,2.9) | 1.2(1,1.9) | – 1.603 | 0.108 | 1.8(1.1,3.5) | 1.7(0.9,3.7) | – 0.379 | 0.704 | 8.7(3.2,23.3) | 2.95(1.1,7.38) | – 1.728 | 0.084 |
| P-ALB (mg/L) | 191.5(132.25,231.75) | 248.5(220.75,302.75) | – 1.218 | 0.241 | 214(183.5,241) | 234(190,258) | – 1.295 | 0.195 | 87(58,147) | 157(78.75,218.5) | – 1.12 | 0.263 |
| ADA (U/L) | 14(11.25,21) | 10.5(8,14.25) | – 1.724 | 0.085 | 15(11.5,18.5) | 16(12,22.5) | – 0.973 | 0.331 | 25(19.5,32) | 19(12.5,20.5) | – 1.499 | 0.130 |
| AFU (U/L) | 25(21,35) | 25(21,32) | – 0.576 | 0.565 | 30(23,36) | 33(26.75,38.25) | – 1.213 | 0.225 | 36(27,44) | 24.5(20,30.25) | – 2.502 | 0.012 |
| CHE (U/L) | 5706.5(4577,7240) | 7879.5(6715.5,8580) | – 1.872 | 0.064 | 6258.5(5330,7243.75) | 6784(5512,8162) | – 1.589 | 0.112 | 3634(2548,4545) | 5374.5(2609,6313.75) | – 1.766 | 0.077 |
| LDH (U/L) | 226(187,340.5) | 190(173,210.25) | – 1.760 | 0.077 | 197(172,240) | 202(173.5,233) | – 0.246 | 0.806 | 238(199.75,306.5) | 191(167.25,241.75) | – 1.452 | 0.147 |
| HBDH (U/L) | 146(124.75,172.25) | 132(125.5,154) | – 1.418 | 0.156 | 150.5(129,169.25) | 139(125.5,157) | – 0.967 | 0.333 | 161.5(135.5,203.5) | 137(122.5,191.75) | – 0.281 | 0.779 |
| CK (U/L) | 78(61.5,102.5) | 92(72.5,121) | – 1.414 | 0.157 | 77.5(54.75,115.25) | 94(73,163) | – 1.563 | 0.118 | 67(48.75,149.5) | 80(52.5,101) | – 0.695 | 0.487 |
| CK-MB (U/L) | 13(10,19) | 12(10,15) | – 0.677 | 0.499 | 13(11,19) | 14(11,22) | – 0.372 | 0.710 | 25(16.75,38.5) | 20(10,42.5) | – 1.015 | 0.310 |
| ACE (U/L) | 33(22.25,46.75) | 34(27.25,48) | – 0.742 | 0.458 | 41(28,52.75) | 40(26,64) | – 0.603 | 0.546 | 54(35,70) | 44.5(32.75,66.5) | – 0.741 | 0.459 |
| MYO (µg/L) | 46.25(33.93,56.525) | 40.3(32.93,51.8) | – 0.704 | 0.481 | 41.9(31.08,53.95) | 48.4(39.6,55.1) | – 0.853 | 0.394 | 47.75(36.48,68.18) | 44.9(35.58,58.9) | – 0.406 | 0.685 |
| HCY (µmol/L) | 13.6(10.78,18.38) | 10.8(9.08,13.83) | – 0.258 | 0.810 | 12.55(10.15,15.45) | 11.2(10.3,13.4) | – 1.195 | 0.232 | 10.65(8.93,13.28) | 10.9(9.75,13.83) | – 0.459 | 0.646 |
| BUN (mmol/L) | 5.39(4.49,6.64) | 4.86(4.12,5.57) | – 1.760 | 0.077 | 5.33(3.87,6.25) | 5.85(4.85,6.48) | – 1.831 | 0.067 | 5.21(4.03,6.42) | 5.6(4.18,6.48) | – 0.153 | 0.878 |
| CREA (µmol/L) | 68.65(58.48,76) | 67.3(58.15,76.4) | – 0.049 | 0.961 | 63.6(56.65,71) | 64.5(58.45,76.33) | – 0.845 | 0.398 | 62.5(55.4,73.7) | 62.9(53.83,79.85) | – 0.283 | 0.777 |
| UA (µmol/L) | 323(256,374.25) | 342(285.5,404.5) | – 1.644 | 0.100 | 292.75(253,334.25) | 354.5(283,409.25) | – 1.881 | 0.060 | 62.5(55.4,73.7) | 300.5(238.5,371.5) | – 1.273 | 0.203 |
| β2MG (mg/L) | 2.07(1.76,2.52) | 1.73(1.5,1.99) | – 1.196 | 0.244 | 1.9(1.67,2.32) | 1.94(1.63,2.44) | – 0.073 | 0.941 | 2.39(1.88,3.24) | 2.10(1.74,2.64) | – 1.770 | 0.076 |
| Cys-C (mg/L) | 0.91(0.76,1.01) | 0.74(0.63,0.85) | – 1.036 | 0.300 | 0.9(0.78,1.095) | 0.82(0.73,1.04) | – 1.172 | 0.241 | 0.95(0.84,1.205) | 0.84(0.73,1.04) | – 1.977 | 0.056 |
| PIIINP (ng/mL) | 6.47(4.39,8.56) | 5.648(3.81,8.72) | – 0.399 | 0.690 | 8.87(5.24,19.54) | 7.26(4.80,11.41) | – 1.497 | 0.134 | 12.63(8.65,26.46) | 6.41(5.13,1.04) | – 1.741 | 0.083 |
| IV-Col (ng/mL) | 44.21(25.53,83.98) | 31.92(21.08,47.17) | – 1.724 | 0.085 | 42.38(23.92,76.04) | 53.81(34.75,76.13) | – 1.253 | 0.210 | 127.40(76.44,226.62) | 67.12(34.81,113.78) | – 1.778 | 0.075 |
| LN (ng/mL) | 83.23(57.36,128.58) | 72.81(49.84,93.82) | – 1.672 | 0.094 | 81.95(19.39,131.63) | 99.58(77.75,153.20) | – 1.89 | 0.059 | 160.22(75.98,243.25) | 105.60(75.40,172.83) | – 1.859 | 0.073 |
| HA (ng/mL) | 93.87(68.27,138.9) | 58.80(42.56,101.23) | – 1.680 | 0.092 | 103.61(78.03,233.12) | 92.36(63.83,177.74) | – 0.754 | 0.451 | 288.39(197.32,756.38) | 153.94(80.80,283.91) | – 2.233 | 0.052 |
| PT (s) | 12.65(11.7,13.6) | 11.6(11.1,12.3) | – 4.629 | 0.001 | 12.2(11.7,13.6) | 11.6(11.7,13.4) | – 0.501 | 0.616 | 14.75(13.58,16.73) | 13.05(12.05,14.4) | – 1.876 | 0.062 |
| INR | 1.04(0.97,1.15) | 0.97(0.93,1.04) | – 1.830 | 0.069 | 1.03(0.98,1.1525) | 1.06(0.98,1.17) | – 0.978 | 0.328 | 1.26(1.15,1.42) | 1.11(1.02,1.26) | – 2.210 | 0.054 |
| APTT (s) | 28.8(26.28,30.4) | 28.2(26.75,29.48) | – 0.625 | 0.532 | 28.4(26.53,31.45) | 28.1(26.43,30.2) | – 0.726 | 0.468 | 32.35(29.15,37.38) | 27.6(25.6,30.85) | – 1.621 | 0.094 |
| FBG (g/L) | 2.99(2.26,3.91) | 2.1(1.93,2.45) | – 1.731 | 0.082 | 2.35(1.95,3.26) | 2.19(1.97,2.65) | – 1.391 | 0.164 | 2.15(1.58,2.98) | 2.01(1.70,2.5) | – 0.375 | 0.707 |
| D-Dimer (µg/L) | 190(77,467) | 180(140,290) | – 0.227 | 0.821 | 282(160,410) | 305.5(158,480) | – 0.691 | 0.489 | 680(170,1760) | 400(180,1355) | – 1.707 | 0.088 |
| IgG (g/L) | 13.1(11,14.7) | 12.15(9.76,13.95) | – 1.075 | 0.283 | 13.2(10.01,16.65) | 13.05(11.33,15.13) | – 0.479 | 0.632 | 16.1(11.4,22.6) | 13.3(12.5,15.5) | – 1.763 | 0.078 |
| IgA (g/L) | 2.34(2.10,3.12) | 2.14(1.67,2.60) | – 1.575 | 0.115 | 2.15(1.81,3.85) | 2.41(1.84,3.14) | – 0.505 | 0.613 | 3.64(2.61,5.33) | 2.66(1.80,3.95) | – 1.278 | 0.201 |
| IgM (g/L) | 0.73(0.64,0.83) | 1.01(0.71,1.28) | – 1.774 | 0.072 | 1.64(1.23,1.82) | 1.55(1.28,1.79) | – 0.402 | 0.688 | 0.98(0.7,1.47) | 0.84(0.59,1.04) | – 1.455 | 0.146 |
| C3 (g/L) | 1.04(0.92,1.24) | 0.1(0.88,1.1) | – 1.698 | 0.089 | 0.96(0.82,1.12) | 0.97(0.79,1.02) | – 0.842 | 0.400 | 0.75(0.64,0.99) | 0.87(0.78,0.97) | – 1.56 | 0.119 |
| C4 (g/L) | 0.24(0.18,0.29) | 0.19(0.15,0.23) | – 1.868 | 0.068 | 0.20(0.16,0.3) | 0.2(0.13,0.21) | – 1.298 | 0.194 | 0.13(0.09,0.19) | 0.16(0.13,0.22) | – 1.318 | 0.187 |
| HBsAg (+/–) | 56/0 | 74/0 | – | 43/0 | 39/0 | – | – | 88/0 | 41/0 | – | – | |
| HBsAb (+/–) | 1/55 | 3/71 | 0.550 | 0.458 | 3/40 | 3/36 | 0.015 | 0.901 | 5/83 | 4/37 | 0.715 | 0.398 |
| HBeAg (+/–) | 13/43 | 24/50 | 0.934 | 0.334 | 8/35 | 7/32 | 0.006 | 0.939 | 25/63 | 8/33 | 0.234 | 0.628 |
| HBeAb (+/–) | 34/22 | 52/22 | 1.893 | 0.169 | 35/8 | 27/12 | 1.641 | 0.200 | 46/42 | 25/16 | 0.475 | 0.491 |
| HBcAb (+/–) | 56/0 | 74/0 | – | – | 43/0 | 39/0 | – | – | 88/0 | 41/0 | – | – |
| HBV-DNA (+/–) | 31/25 | 37/37 | 0.788 | 0.375 | 17/26 | 13/26 | 0.360 | 0.848 | 40/48 | 13/28 | 0.001 | 0.979 |
Significant values are bold.
CHB: chronic hepatitis B; HCC: hepatocellular carcinoma; AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II; GP73: Golgi protein 73; CEA: carcinoembryonic antigen; CA199: carbohydrate antigen199; FER: ferritin; RBC: red blood cell; HGB: hemoglobin; WBC: white blood cell; NEUT: neutrophil; LYMPH: lymphocyte; MONO: monocyte; EO: eosinophil; BASO: basophil; PLT: platelet; Tbil: total bilirubin; Dbil: direct bilirubin; Ibil: indirect bilirubin; TP: total protein; Alb: albumin; Glo: gloglobulin; ALT: alanine transaminase; AST: aspartate transaminase; ALP: alkaline phosphatase; GGT: γ-glutamyl translerase; TBA: total bile acid; CG: cholyglycine; P-ALB: prealbumin; ADA: adenosine deaminase; AFU: α-L-fucosidase; CHE: cholinesterase; LDH: lactate dehydrogenase; LBDH: hydroxybutyrate-dehydrogenase; CK: creatine kinase; CK-MB: creatine kinase-MB; ACE: angiotensin converting enzyme; MYO: myoglobin; HCY: homocysteine; BUN: blood urea nitrogen; CREA: homocysteine.
The validation cohort for validation of the screened indicators
The validation cohort consisted of 382 patients with liver pathology data, including 171 HCC patients. The median age was 56 (48, 64). The number of males and female was 307 and 75, respectively. Among the 171 HCC patients, 124 underwent surgical resection. These patients were categorized into the CHB group (55 patients with HCC and 50 without), the Child‒Pugh A hepatitis B cirrhosis group (56 patients with HCC and 91 without), and the Child‒Pugh B&C hepatitis B cirrhosis group (60 patients with HCC and 70 without).
The staging of HCC
This study adopted the China liver cancer (CNLC) staging system. The CNLC staging system is divided into stages Ia, Ib, IIa, IIb, IIIa, IIIb, and IV. The recommended treatment for patients with CNLC stages Ia, Ib, and IIa HCC and enough liver function reserve is surgical resection18. Among the 358 HCC patients in the study, there were 191 patients with CNLC stages Ia or Ib, 51 patients with CNLC stage IIa, 38 with CNLC stage IIb, 41 with CNLC stage IIIa, 18 with CNLC stage IIIb, and 19 with CNLC stage IV. Approximately 70% of patients belong to the early to middle stage of HCC.
Statistical analysis
SPSS 26.0 statistical software was used for data analysis and processing, and P < 0.05 indicated that the results were significantly different. The chi-square test was used to analyze the level of concordance between patients with and without HCC in each group, and the Kolmogorov‒Smirnov test was used to assess the normality of the measurement data. The measurement data in this study exhibited a non‒normal distribution; thus, the median and quartiles were used to describe the non‒normally distributed data. The constituent ratio was used to describe the count variable, and statistical charts were drawn. The Mann‒Whitney U test was used to compare the measurement data of patients with and without HCC within each group. The Pearson chi-square test was used to analyze the differences in classification data across groups. After identifying indicators with statistically significant results from the univariate analysis, a collinearity analysis was carried out and combined with references to exclude the mutual influence of individual variables. These refined variables were subsequently included in a multivariate analysis. Binary logistic regression was used to analyze the relationships between the levels of pertinent indicators and clinical variables in patients with HCC. The receiver operating characteristic (ROC) curve was generated for diagnostic combinations of multiple indicators, and the area under the curve (AUC) was calculated. Using liver pathology as the gold standard, the consistency between the screened indicators and liver pathology for HCC diagnosis was analyzed with Cohen’s kappa.
Results
Data screening of the training cohort
Comparison of baseline data between HCC patients and non-HCC patients
In the CHB group, AFP, PIVKA-II, GP73, NEUT, and PT of HCC patients were significantly greater than those of patients without HCC (P < 0.05). In the Child‒Pugh A cirrhosis group, the AFP, PIVKA-II, GP73, white blood cell (WBC), neutrophil (NEUT), lymphocyte (LYMPH), and monocyte (MONO) counts of HCC patients were significantly greater than those of patients without HCC (P < 0.05). In the Child‒Pugh B&C cirrhosis group, the levels of AFP, PIVKA-II, GP73, alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), and α-l-fucosidase (AFU) in patients with HCC were notably elevated compared to those in patients without HCC (P < 0.05). The results are presented in Table 1. Before entering the multivariate analysis, collinearity analysis was conducted for the indicators of each group separately. Collinearity analysis revealed that the variance inflation factor (VIF) of WBC and NEUT exceeded 10 in the Child-Pugh A cirrhosis group. Considering that WBC counts included the NEUT, LYMPH, and MONO counts, WBC counts were excluded from the analysis, and subsequently, AFP, PIVKA-II, GP73, NEUT, LYMPH, and MONO counts were included in the multivariate analysis.
Multivariate analysis
Multivariate analysis revealed that AFP, PIVKA-II, and GP73 exhibited statistical significance in the CHB group, as shown in Table 2. AFP and PIVKA-II were significantly different between HCC patients and those without HCC in both the Child-Pugh A cirrhosis group and the Child-Pugh B&C cirrhosis group, as shown in Tables 3 and 4.
Table 2.
Multivariate analysis of the CHB group.
| B | Standard error | Wald | Degree of freedom | Exp(B) | 95% CI | P value | |
|---|---|---|---|---|---|---|---|
| AFP | 0.012 | 0.005 | 6.102 | 1 | 1.012 | 1.002–1.021 | 0.014 |
| PIVKA-II | 0.036 | 0.018 | 4.137 | 1 | 1.037 | 1.001–1.074 | 0.042 |
| GP73 | 0.014 | 0.005 | 6.695 | 1 | 1.014 | 1.003–1.024 | 0.010 |
| NEUT | 0.058 | 0.041 | 1.927 | 1 | 1.059 | 0.977–1.149 | 0.165 |
| PT | 0.648 | 0.727 | 0.794 | 1 | 1.911 | 0.460–7.945 | 0.373 |
| Constants | – 4.327 | 1.085 | 15.905 | 1 | 0.013 | 0.000 |
AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II; GP73: Golgi protein 73; NEUT: neutrophils; PT: prothrombin time.
Table 3.
Multivariate analysis of the child‒pugh class A cirrhosis group.
| B | Standard error | Wald | Degree of freedom | Exp(B) | 95% CI | P value | |
|---|---|---|---|---|---|---|---|
| AFP | 0.023 | 0.012 | 3.937 | 1 | 1.023 | 1.000-1.047 | 0.047 |
| PIVKA-II | 0.063 | 0.026 | 5.931 | 1 | 1.065 | 1.012–1.121 | 0.015 |
| GP73 | 0.080 | 0.041 | 3.831 | 1 | 1.083 | 1.000-1.174 | 0.065 |
| NEUT | 0.091 | 0.048 | 4.151 | 1 | 1.090 | 0.995–1.187 | 0.071 |
| MONO | – 2.619 | 1.888 | 1.924 | 1 | 0.073 | 1.002–1.949 | 0.165 |
| LYMPH | 0.010 | 0.010 | 0.945 | 1 | 1.010 | 0.990–1.031 | 0.331 |
| Constants | – 6.551 | 1.960 | 11.167 | 1 | 0.001 | 0.001 |
AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II; GP73: Golgi protein 73; NEUT: neutrophils; MONO: monocyte; LYMPH: lymphocyte.
Table 4.
Multivariate analysis of the child‒pugh class B&C cirrhosis group.
| B | Standard error | Wald | Degree of freedom | Exp(B) | 95% CI | P value | |
|---|---|---|---|---|---|---|---|
| AFP | 0.121 | 0.057 | 4.446 | 1 | 1.129 | 1.009–1.263 | 0.035 |
| PIVKA-II | 0.142 | 0.071 | 3.943 | 1 | 1.152 | 1.002–1.325 | 0.047 |
| GP73 | 0.011 | 0.007 | 2.399 | 1 | 1.011 | 0.997–1.026 | 0.121 |
| ALP | – 0.019 | 0.018 | 1.060 | 1 | 0.982 | 0.948–1.017 | 0.303 |
| GGT | 0.004 | 0.006 | 0.426 | 1 | 1.004 | 0.992–1.017 | 0.514 |
| AFU | 0.010 | 0.010 | 0.945 | 1 | 1.010 | 0.990–1.031 | 0.331 |
| Constants | – 8.079 | 3.242 | 6.210 | 1 | 0.000 | 0.013 |
AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II; GP73: Golgi protein 73; ALP: alkaline phosphatase; GGT: γ-glutamyl translerase; AFU: α-L-fucosidase.
Evaluation of the screened indicators in the diagnosis of HCC
In the CHB group, the AUCs of AFP, PIVKA-II, and GP73, both individually and in combination, exceeded 0.75 for HCC diagnosis, with the AUC of the triple combination of AFP, PIVKA-II, and GP73 attaining the largest value of 0.908 (95% CI: 0.811-1.000). The sensitivity and specificity (SE 87.0%, SP 97.3%, PPV 95.2%, NPV 92.3%) of the triple combination of AFP, PIVKA-II, and GP73 for HCC diagnosis were better than those of other combinations and individual indicators, as shown in Fig. 2; Table 5. The AUC for the combined use of AFP and PIVKA-II for HCC diagnosis in the Child‒Pugh class A cirrhosis group was 0.969 (95% CI: 0.931–1.000), with excellent sensitivity and specificity (94.1% for SE, 94.4% for SP, 94.9% for PPV, and 94.4% for NPV), surpassing the performance of individual indicators, as shown in Fig. 3; Table 6. Similarly, for the Child-Pugh B&C cirrhosis group, the AUC for the combined use of AFP and PIVKA-II for HCC diagnosis was 0.956 (95% CI: 0.922–0.989), and the sensitivity and specificity of AFP combined with PIVKA-II (SE 89.1%, SP 95.1%, PPV 96.4%, NPV 79.6%) were the highest, as presented in Fig. 4; Table 7.
Fig. 2.
AUC-ROC of each indicator for HCC diagnosis in the CHB group.
Table 5.
AUC-ROC of each indicator for HCC diagnosis in the CHB group.
| Variable | AUC | Standard error | P value | 95% CI | Cutoff value | SE% | SP% | PPV% | NPV% |
|---|---|---|---|---|---|---|---|---|---|
| AFP | 0.886 | 0.042 | < 0.0001 | 0.803–0.969 | 13.24 | 58.9 | 87.8 | 78.6 | 73.9 |
| PIVKA-II | 0.781 | 0.070 | < 0.0001 | 0.644–0.918 | 31.56 | 72.1 | 88.1 | 81.6 | 81.3 |
| GP73 | 0.767 | 0.069 | 0.001 | 0.632–0.902 | 138.76 | 62.1 | 92.3 | 81.8 | 81.4 |
| AFP + PIVKA-II | 0.874 | 0.053 | < 0.0001 | 0.769–0.979 | – | 65.1 | 94.9 | 90.3 | 78.9 |
| AFP + GP73 | 0.890 | 0.049 | < 0.0001 | 0.793–0.986 | – | 75.9 | 88.5 | 78.6 | 86.8 |
| AFP + PIVKA-II + GP73 | 0.908 | 0.050 | < 0.0001 | 0.811-1.000 | – | 87.0 | 97.3 | 95.2 | 92.3 |
AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II; GP73: Golgi protein 73.
Fig. 3.
AUC-ROC of each indicator for HCC diagnosis in the Child‒Pugh class A cirrhosis group.
Table 6.
AUC-ROC of each indicator for HCC diagnosis in the child‒pugh class A cirrhosis group.
| Variable | AUC | Standard error | P value | 95% CI | Cutoff value | SE% | SP% | PPV% | NPV% |
|---|---|---|---|---|---|---|---|---|---|
| AFP | 0.896 | 0.034 | < 0.0001 | 0.829–0.963 | 15.34 | 75.0 | 81.8 | 81.8 | 75.0 |
| PIVKA-II | 0.945 | 0.029 | < 0.0001 | 0.888–1.000 | 32.67 | 89.7 | 94.4 | 94.6 | 89.5 |
| AFP + PIVKA-II | 0.969 | 0.019 | < 0.0001 | 0.931–1.000 | – | 94.1 | 94.4 | 94.9 | 94.4 |
AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II.
Fig. 4.
AUC-ROC of each indicator for HCC diagnosis in the Child‒Pugh B&C cirrhosis group.
Table 7.
AUC-ROC of each indicator for HCC diagnosis in the child‒pugh B&C cirrhosis group.
| Variable | AUC | Standard error | P value | 95% CI | Cutoff value | SE% | SP% | PPV% | NPV% |
|---|---|---|---|---|---|---|---|---|---|
| AFP | 0.910 | 0.029 | < 0.0001 | 0.854–0.966 | 15.99 | 78.4 | 90.2 | 94.5 | 66.1 |
| PIVKA-II | 0.934 | 0.023 | < 0.0001 | 0.889–0.980 | 48.04 | 84.4 | 87.8 | 91.9 | 83.7 |
| AFP + PIVKA-II | 0.956 | 0.017 | < 0.0001 | 0.922–0.989 | – | 89.1 | 95.1 | 96.4 | 79.6 |
AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II.
Consistency verification analysis
Using liver pathology as the gold standard, the aforementioned combinations were verified. The consistency of the combination of AFP, PIVKA-II, and GP73 in diagnosing HCC among the CHB group was excellent, attaining a kappa value of 0.866. The kappa value of the combination of AFP and PIVKA-II for HCC diagnosis was 0.780 in the Child-Pugh A cirrhosis group and 0.800 in the Child-Pugh B&C cirrhosis group. The results are shown in Table 8.
Table 8.
Consistency verification analysis of the diagnostic combinations in different groups.
| Group | Combination | Liver pathology | Kappa value | P value | ||
|---|---|---|---|---|---|---|
| HCC | Non-HCC | |||||
| CHB | AFP + PIVKA-II + GP73 | HCC | 52 | 4 | 0.866 | 0.049 |
| Non-HCC | 3 | 46 | ||||
| Child‒Pugh A cirrhosis | AFP + PIVKA-II | HCC | 46 | 5 | 0.780 | < 0.001 |
| Non-HCC | 10 | 86 | ||||
| Child‒Pugh B&C cirrhosis | AFP + PIVKA-II | HCC | 55 | 8 | 0.800 | < 0.001 |
| Non-HCC | 5 | 62 | ||||
CHB: chronic hepatitis B; HCC: hepatocellular carcinoma; AFP: alpha-fetoprotein; PIVKA-II: protein induced by vitamin K deficiency or antagonist II; GP73: Golgi protein 73.
Discussion
The occurrence of primary liver cancer in China is mostly based on hepatitis B cirrhosis19, and approximately 70% of HCC patients are in an advanced stage of the disease at the initial diagnosis20. Although liquid biopsy has attracted attention in diagnosing HCC in recent years13–16, the detection of serum biomarkers remains a preferred method for rapid clinical decision-making in primary hospitals due to its advantages of simple operation, low cost, high efficiency and high throughput. This study compared the clinical routine indicators of HBV-HCC patients with different disease stages, identified those with significant diagnostic value for HBV-HCC, and subsequently evaluated their individual and combined effectiveness in diagnosing HBV-HCC.
In this study, univariate analysis revealed that patients with HCC exhibited significantly greater levels of NEUT in peripheral blood than patients without HCC, both within the CHB group (P = 0.008) and the Child‒Pugh A cirrhosis group (P = 0.001). This finding is consistent with the findings of Li, who reported that the level of NEUT in the peripheral blood of HCC patients was greater than that in the peripheral blood of CHB patients and healthy controls21. NEUT has powerful phagocytic capabilities and serves as the initial effector cell that swiftly arrives at the site of infection, inflammation, or tissue injury. Research indicates that among NEUT, PLT, LYMPH, neutrophil–lymphocyte ratio (NLR), systemic immune–inflammation index (SII), and other indicators, only NEUT is independently associated with tumor–node–metastasis (TNM) stage and physical status (PST) of patients with HCC; furthermore, NEUT is significantly and independently associated with poor survival22. The heterogeneity of the tumor immune microenvironment, which is composed of various immune cells and stromal cells, is a major contributor to tumor metastasis, recurrence, and drug resistance23. A systematic analysis revealed that higher levels of NEUT infiltration in tumor tissue predicted lower overall survival (OS) and disease-free survival (DFS)24. High infiltration of NEUT around the tumor is positively correlated with angiogenic progression of the tumor invasive margin in HCC patients25. Neutrophil extracellular traps (NETs) are considered to be important factors in cancer progression. Zhan et al. reported that the levels of NETs increased in the serum and liver tissue specimens of HCC patients, especially HBV-HCC patients26. Univariate analysis of this study also revealed that the levels of peripheral LYMPH and MONO in HCC patients were greater than those in non-HCC patients in the Child-Pugh A cirrhosis group (P values were 0.002 and < 0.001, respectively). Patients with liver cirrhosis exhibit a reduced level of peripheral LYMPH27. A similar phenomenon was also found in this study. As the disease progressed, the peripheral LYMPH levels in the three groups gradually decreased, as shown in Table 1. The present study also revealed that the peripheral LYMPH level was greater in patients with HCC than in patients without HCC in the Child‒Pugh class A cirrhosis group (P = 0.002). Li reported that the levels of peripheral LYMPH and MONO in HBV-HCC patients were lower than those in CHB patients21. After 3 years of follow-up, CHB patients with lower levels of peripheral LYMPH and MONO had a greater incidence of HCC, but the study did not further compare the differences between cirrhotic and noncirrhotic patients, nor did it analyze the differences between HCC patients and those without HCC within the cirrhosis subgroup21. Patients with decompensated cirrhosis, especially those with acute-onset chronic liver failure, exhibit leukocytosis, neutrophilia, and lymphopenia in peripheral blood28. These phenomena resemble the alterations observed in the levels of peripheral blood immune cells among patients with HCC21. This may also be the reason why this study did not observe differences in the levels of peripheral blood immune cells between HCC patients and non-HCC patients in the Child‒Pugh B&C cirrhosis group.
In this study, multivariate analysis revealed that the screened indicators differed among the three groups in the training cohort. AFP, PIVKA-II, and GP73 were significantly different in the CHB group, and the AUC of the triplet combination for HCC diagnosis was 0.908. AFP and PIVKA-II were significantly different in both Child-Pugh A and Child-Pugh B&C cirrhosis groups, and the AUCs of AFP + PIVKA-II for HCC diagnosis were 0.969 and 0.956, respectively. The efficacy of combination patterns for the diagnosis of HCC was greater than that of individual indicators. AFP, PIVKA-II, and GP73 are serological markers for the diagnosis of HCC. Several studies have reported the diagnostic value of different combinations of these serum indicators for HCC. A meta-analysis including five serum markers (AFP, AFP-L3, PIVKA-II, DKK-1 and GP73) showed that AFP + GP73 had the highest AUC (0.93) for HCC diagnosis in the pairwise combination, and AFP + AFP-L3 + PIVKA-II had the highest diagnostic value in the triple combinations (AUC = 0.9)29. Another study showed that the detection rate of elevated PIVKA-II was greater than that of AFP 6–18 months before HCC diagnosis, and the sensitivity, accuracy, and negative predictive value of the combination of PIVKA-II and AFP for HCC diagnosis could increase to 67%, 90% and 85%, respectively, preserving 100% specificity30. A study on a North American cirrhotic population showed that the sensitivity of the GALAD, which includes five indicators (sex, age, AFP, AFP-L3, and PIVKA-II), for diagnosing HCC was significantly improved31. In the validation cohort of this study, utilizing liver pathology as the gold standard, the kappa values for the combined use of AFP, PIVKA-II, and GP73 for HCC diagnosis exceeded 0.75 in each group, indicating good consistency with the diagnostic efficacy of liver pathology.
In this study, GP73 was not significantly different in either the Child-Pugh A or Child-Pugh B&C cirrhosis groups. Several studies have reported the diagnostic value of GP73 for HCC (not limited to HBV-related HCC), specifically AFP-negative HCC11,32–34. Research indicated that high GP73 expression was signiffcantly correlated with higher tumour grade and later tumour stage35. However, serum GP73 is also elevated in patients with liver cirrhosis and has a certain diagnostic value for liver cirrhosis36–39. A meta analysis indicated that GP73 has a high diagnostic value for HCC and a moderate value for differentiating HCC from liver cirrhosis11. Therefore, the accuracy of GP73 in diagnosing HCC in patients with cirrhosis may be affected. This may explain why, in the validation analysis, the multivariate analysis of GP73 did not show statistical significance between HCC and non-HCC patients in the cirrhosis group; additionally, the kappa value was slightly lower in the cirrhosis group (combination of AFP and PIVKA-II) compared to the CHB group (combination of AFP, PIVKA-II, and GP73).
The limitations of this study are as follows: (1) Because this was a retrospective study, the enrolled patients did not undergo AFP-L3 testing during the initial evaluation period, which is also a diagnostic marker of HCC40; (2) This study did not collect clinical data from patients prior to their HCC diagnosis, consequently resulting in a lack of research on the early diagnosis of HCC.
Conclusion
The diagnostic efficacy of combining AFP, PIVKA-II, and GP73 for diagnosing HBV-HCC without cirrhosis, as well as combining AFP and PIVKA-II for diagnosing HBV-HCC with cirrhosis, is consistent with that of liver pathology, surpassing the accuracy of individual biomarkers. Therefore, the combination of multiple serum biomarkers exhibits greater clinical significance in guiding HCC clinical decision-making than a single biomarker.
Author contributions
H.R. and N.Y. contributed to data collection, statistical analysis, initial drafting, and literature review. W.W. participated in scientific discussions. L.B. and X.L. conceived the writing strategy, provided guidance, revised the manuscript, and finalized the draft.
Funding
This manuscript was supported by Taizhou People’s Hospital Scientifc Research Fund Project (QDJJ202108).
Data availability
The data supporting the results of the manuscript can be obtained from the corresponding author.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Hu Rui and Ni Yueqin.
Contributor Information
Li Bangtao, Email: taotemp2011@163.com.
Xiao Li, Email: xiaoli24tz@163.com.
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Data Availability Statement
The data supporting the results of the manuscript can be obtained from the corresponding author.