Introduction

Primary liver cancer (PLC) is the most common malignancy of the digestive system and the fourth leading cause of cancer-related mortality worldwide.1 PLC encompasses three main subtypes: hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (CHC). Among these, HCC accounts for 80–90% of cases, while ICC represents 10%-15%.2 CHC is the rarest subtype, accounting for 1–4.7% of PLC.3 Typically, PLC is diagnosed at advanced stages, resulting in limited treatment options and a poor 5-year survival rate.4 Early detection and timely treatment are critical for improving survival outcomes.5 Current clinical guidelines recommend biannual surveillance for HCC in high-risk patients using abdominal ultrasonography (US) and serum alpha-fetoprotein (AFP) testing.6,7 The efficiency of the US in detecting early-stage HCC is limited by the operator’s expertise and patient-specific characteristics. The combination of US and AFP has been reported to have a sensitivity of only 63% for early-stage HCC detection.8 Furthermore, AFP alone demonstrates limited sensitivity, ranging from 40%-60% and approximately 40% of HCC patients, particularly those in early stages, have normal AFP levels.9,10 These findings highlight the need for more reliable serum biomarkers for early liver cancer detection.

Des-gamma-carboxy prothrombin (DCP) and lens culinaris agglutinin-reactive AFP (AFP-L3), have been utilized for HCC screening.11–14 The combination of DCP and AFP-L3 with AFP has shown promise in improving sensitivity for early HCC detection.15–18 Lim et al demonstrated that combining AFP, DCP, and AFP-L3 improved the sensitivity and specificity to 87.0% and 60.1%, respectively, in diagnosing HCC among cirrhotic patients.19 Other liver function biomarkers, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), have also shown potential in aiding HCC diagnosis and prognosis.20,21 Novel diagnostic algorithms, such as GALAD (gender, age, AFP, AFP-L3, and DCP), have been developed to further enhance HCC detection.22,23 The GALAD score significantly improves diagnostic performance, with reported sensitivity and specificity of approximately 85.6% and 93.3%, respectively, outperforming individual biomarkers.24,25 Similarly, the ASAP (age, sex, AFP, and DCP) score, derived from Chinese cohort of HBV-infected patients, demonstrated sensitivity and specificity of approximately 76.1% and 90.4%, respectively, for early HCC detection.26 Nevertheless, the performance of GALAD and ASAP diminishes in AFP-negative HCC case, emphasizing the need for optimized biomarker panels.27

ICC, the second most prevalent subtype of PLC, has a dismal 5-year overall survival (OS) rate of approximately 9%.28 Although ICC diagnosis primarily relies on imaging and tissue biopsy, serum biomarkers such as carbohydrate antigen 50 (CA50), carbohydrate antigen 242 (CA242), carbohydrate antigen 199 (CA199), and carcinoembryonic antigen (CEA) are also clinically significant.29 Elevated CA50 serum levels have been observed in ICC patients, distinguishing them from non-ICC controls. Furthermore, higher CA50 levels are associated with poorer clinical outcomes and shorter survival in ICC patients.30 Similarly, serum CA242 levels are significantly lower in ICC patients after treatment, indicating its potential as a biomarker for treatment monitoring.31 ICC patients often exhibit elevated serum CA199 levels, which demonstrate 72% sensitivity and 84% specificity for ICC diagnosis.32 Additionally, elevated serum levels of CA199 and CEA are frequently observed in patients with locally advanced or metastatic ICC.33–35 Considering the pathological characteristics of CHC patients are similar to those of HCC and ICC, the serum levels of AFP, DCP, CEA, and CA199 are elevated in CHC patients.28 However, these biomarkers have not been considered effective enough for clinical application as indicators for ICC and CHC diagnosis.

To improve the early diagnosis of PLC, it is necessary to combine the current tumor biomarkers to enhance the diagnosis accuracy for PLC. This study retrospectively evaluated the diagnostic performance of a biomarker panel comprising AFP, AFP-L3, DCP, and CA199 in combination for diagnosing PLC.

Materials and MethodsEthics Statement

The study was approved by the Ethics Committee of Huadong Hospital (project number 20160089) and conducted in accordance with the Declaration of Helsinki. All participants were over 18 years old and voluntarily provided written informed consent.

Patient Subjects

This retrospective study included 94 patients diagnosed with primary liver cancer (PLC), 128 patients diagnosed with benign liver disease (BLD), and 79 normal controls (NC). The PLC group comprised 39 patients with hepatocellular carcinoma (HCC), 14 patients with intrahepatic cholangiocarcinoma (ICC), 4 patients with combined hepatocellular-cholangiocarcinoma (CHC), and 37 diagnosed with HCC based on imaging. All participants were recruited from Huadong Hospital between June 2020 and June 2024. PLC diagnoses were confirmed through histopathological examination or, when unavailable, radiological evidence following the Liver Imaging Reporting and Data System (LI-RADS). Data were collected from newly diagnosed, untreated PLC patients. Exclusion criteria were as follows: (1) PLC recurrence; (2) non-PLC liver metastases; (3) presence of other tumors; and (4) treatment with warfarin or vitamin K, which could affect DCP serum levels. Tumor staging was determined according to the eighth edition of the Union for International Cancer Control (UICC) TNM classification and Barcelona Clinical Liver Cancer (BCLC) staging system.36 The BLD group included patients diagnosed with chronic liver disease (CLD), liver fibrosis (LF) or cirrhosis (LC).

Biomarkers Determination

Tumor biomarkers tests: AFP, CEA, and CA199 levels were measured using the Roche Cobas E601 electrochemical immunoluminescence analyzer and its corresponding reagent kit (Roche Diagnostics, Geneva, Switzerland). AFP-L3 levels were measured by the μTASWako i30 auto-analyzer and its reagent kit (Wako Pure Chemical Corporation, Osaka, Japan). DCP levels were measured using the ARCHITECT immunoassay system and its supporting reagent kit (Abbott Diagnostics, Chicago, IL, USA). CA50 and CA242 levels were analyzed using the Smart 6500 analyzer and its supporting reagent kit (Tellgen Corporation, Shanghai, China).

Liver function tests: ALT, AST, ALP, and gamma-glutamyl transferase (GGT) were measured using the Roche Cobas 8000 analyzer and its supporting reagent kit (Roche Diagnostics, Geneva, Switzerland). Total bilirubin (TB), direct bilirubin (DB), and albumin (ALB) were also measured using the Roche Cobas 8000 analyzer and its supporting reagent kit (Roche Diagnostics, Geneva, Switzerland). Prothrombin time (PT) was assessed with the ACL Top 700 automatic coagulation analyzer (Instrumentation Laboratory Company, Barcelona, Spanish), while Platelet (PLT) count was measured using the Mindray BC-6800 hematological analyzer (Mindray Corporation, Shenzhen, China). Hepatitis B surface antigen (HBsAg) and anti-HCV antibodies were evaluated with the Architect I2000 (Abbott Diagnostics, Chicago, IL, USA).

Calculation of the GALAD and ASAP Score

The GALAD score was calculated based on gender, age, AFP, AFP-L3, and DCP levels using the following formula: GALAD score = −10.08 + 1.67 × gender (1 for males, 0 for females) + 0.09 × age + 0.04 × AFP-L3% + 2.34 × log10AFP + 1.33 × log10DCP.18 The ASAP score was calculated using the following equation: ASAP score = −7.58 + 0.05 × age - 0.58 × sex (1 for females, 0 for males) + 0.42 × Ln (AFP) + 1.11 × Ln (DCP).26

Statistical Analysis

Statistical analyses were performed using R (https://www.r-project.org), Prism (GraphPad, La Jolla, CA), and SPSS 28.0.1.0 (SPSS Inc., an IBM Company, and Chicago, IL, USA). Categorical variables were presented as frequencies and percentages, while continuous variables were reported as means (standard deviation) or medians (interquartile range). Continuous variables were compared using analysis of variance (ANOVA), Student’s t-test or the Mann–Whitney U-test, while categorical variables were compared using chi-square tests. Receiver operating characteristic (ROC) curves were used to determine the area under the curve (AUC) for individual biomarkers (AFP, AFP-L3, DCP, CA199), the GALAD score, and the ASAP score, and combinations of these four serum biomarkers in predicting PLC. Youden’s index was used to determine optimal cutoff values for sensitivity and specificity comparisons. Positive predictive value (PPV) and negative predictive value (NPV) were calculated based on the following clinical thresholds: AFP ≥ 20 ng/mL, AFP-L3 > 10%, DCP > 40 mAU/mL, and CA199 > 34 U/mL. Univariate and multivariate logistic regression analyses were conducted to identify the independent prognostic factors. Based on these factors, a predictive nomogram model was developed. A two-tailed value of p<0.05 was considered statistically significant.

ResultsCharacteristics of the Enrolled Population

A total of 301 subjects were included in this retrospective study. The general clinical information of subjects in the NC, BLD, and PLC groups is summarized in Table 1. The mean age of participants in the BLD and PLC groups was higher than that in the NC group (62.39, 63.94, and 57.44 years, respectively). The PLC group also exhibited a higher prevalence of males (77.66%). Serum concentrations of tumor biomarkers, including AFP, DCP, AFP-L3, CEA, CA199, and CA50, were significantly elevated in PLC patients compared to those in the BLD group or normal controls (p < 0.001). Similarly, the PLC group demonstrated significantly higher levels of liver function biomarkers, such as ALT, AST, TB, DB, ALP, GGT, and PT (p < 0.001), while ALB and PLT levels were significantly lower compared to other groups (p< 0.001).

Table 1 Baseline Characteristics of the Enrolled Population

Demographic and Clinicopathologic Characteristics Between AFP-Negative and AFP-Positive PLC Patients

To explore the differences between AFP-positive and AFP-negative PLC patients, we analyzed their clinicopathological data (Table 2). There were no significant differences in age (p = 0.982), sex distribution (p = 0.368), or HBsAg status (p = 0.894), with HBsAg positivity observed in 75% in the AFP-negative group and 76.19% in the AFP-positive patients. Tumor biomarker analysis revealed that AFP-L3 levels were significantly higher in the AFP-positive group (27.46%) than in the AFP-negative group (0.5%) (p = 0.001). Similarly, DCP levels were significantly higher in AFP-positive patients (315.2 mAU/mL) compared to AFP-negative patients (33.91 mAU/mL) (p = 0.002). No significant differences were observed in other biomarkers, such as CEA (p = 0.369) and CA242 (p = 0.422). Among liver function biomarkers, AST levels were significantly elevated in AFP-positive PLC patients (p = 0.009). However, histological grade showed no significant differences, with most cases in both groups classified as moderately differentiated tumors (76.92% in AFP-negative and 66.67% in AFP-positive, p = 0.358). Other clinicopathological factors, including microvascular invasion (MVI) status (p = 0.104), tumor count (p = 0.519), and BCLC stage (p = 0.191), also showed no significant differences. In summary, AFP-positive PLC patients exhibited significantly higher levels of AFP-L3, DCP, and AST compared to AFP-negative patients, while other demographic, tumor biomarkers and clinicopathological characteristics were similar.

Table 2 Demographic and Clinicopathologic Characteristics of AFP-Negative and AFP-Positive PLC Patients

Evaluation of PLC-Associated Serum Biomarkers in PLC, BLD, and NC Groups

To determine the differences in serum levels of PLC-associated tumor serum biomarkers, data for seven biomarkers were compared across groups (Figure 1). Serum levels of AFP and DCP were significantly higher in the HCC group compared with those in the BLD groups (p < 0.001 and p < 0.001, respectively) (Figure 1A-B). However, AFP levels were not elevated in the ICC and CHC groups compared with the BLD group (Figure 1A), while DCP serum levels were elevated in the CHC group compared with those in the BLD group (p < 0.05) (Figure 1B).

Figure 1 Serum levels of AFP (A), DCP (B), AFP-L3 (C), CA199 (D), CEA (E), CA50 (F), CA242 (G), GALAD score (H), ASAP (I) in NC, BLD, HCC, ICC, and CHC groups. ***p < 0.001, **p < 0.01, *p < 0.05, ns p > 0.05, no significance.

Furthermore, the GALAD and ASAP scores were also significantly higher in the HCC and CHC groups than that in the BLD group (p < 0.001 and p < 0.05, respectively) (Figure 1H-I). AFP-L3 levels were higher in the HCC, ICC, and CHC groups compared to the BLD group (p < 0.001, p < 0.05, and p < 0.01, respectively) (Figure 1C). Interestingly, the serum levels of CA199 in HCC, ICC, and CHC groups were higher than those in the BLD group (p < 0.05, p < 0.001, and p < 0.001, respectively) (Figure 1D). However, CEA levels were only elevated in the ICC group (p < 0.001) (Figure 1E). CA242 levels were elevated in ICC and CHC groups, but not in the HCC group (p < 0.01, p < 0.001, and p > 0.05, respectively) (Figure 1G). No significant differences in CA50 levels were observed between the HCC, ICC, CHC, and BLD groups (Figure 1F). These findings indicate that AFP, AFP-L3, DCP, and CA199, may serve as individual diagnostic biomarkers for HCC.

Evaluation of the Diagnostic Values of AFP, AFP-L3, DCP, CA199, and Their Combination for PLC Patients

ROC curve analysis was used to evaluate the diagnostic performance of the four biomarker panels including AFP, AFP-L3, DCP, and CA199, and their combination, as well as the GALAD score and the ASAP scores for differentiating PLC from BLD (Figure 2A and Table 3). The area under the ROC curve (AUC) for AFP, AFP-L3, DCP, and CA199 were 0.5928, 0.7229, 0.764, and 0.6523, respectively. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are presented in Table 3. CA199 exhibited the highest sensitivity of 0.8723 while AFP showed the highest specificity of 0.9375. Combining AFP, AFP-L3, DCP, and CA199 improved diagnostic performance, increasing the AUC to 0.8492, surpassing both the GALAD score (AUC = 0.7751) and the ASAP score (AUC = 0.7927). The ASAP score exhibited a high PPV (0.82) for the PLC detection compared to BLD. We subsequently validated the diagnostic performance of these biomarkers in the PLC group compared with the NC group (Figure 2B), and the AUC for AFP, AFP-L3, DCP, and CA199 were 0.7267, 0.7938, 0.801, and 0.7912, respectively (Table 4). The combination of AFP, AFP-L3, DCP, and CA199 increased the AUC to 0.9480, which surpassed that of the GALAD score (AUC = 0.9022) and the ASAP score (AUC = 0.9016). The combination of AFP, AFP-L3, DCP, and CA199 exhibited a sensitivity of 0.8404 and a specificity of 0.9873. These findings suggested that combining AFP, AFP-L3, DCP, and CA199 could enhance their diagnostic ability to differentiate patients with PLC from those with BLD or NC.

Table 3 The ROC Curve Analyses of AFP, AFP-L3, DCP, CA199, and Their Combination P4, GALAD, and ASAP Between PLC and BLD Patients

Table 4 The ROC Curve Analyses of AFP, AFP-L3, DCP, CA199, and Their Combination P4, GALAD, and ASAP Between PLC Patients and NC Group

Figure 2 The diagnostic performance of AFP, AFP-L3, DCP, CA199, their combination P4, GALAD, and ASAP for PLC patients. (A) ROC curves of AFP, AFP-L3, DCP, CA199, their combination P4, GALAD, and ASAP in distinguishing the PLC patients from the BLD patients. (B) ROC curves of AFP, AFP-L3, DCP, CA199, their combination P4, GALAD, and ASAP in distinguishing the PLC patients from the NC group.

Nomogram for Predicting PLC Based on Logistic Regression

Univariate and multivariate logistic regression analyses identified sex, AFP-L3, DCP, and CA199, as independent predictors of PLC when compared to BLD (Table 5). In the multivariate logistic regression analysis, male (OR: 3.99, 95% CI 1.92–8.71; p = 0.001), increased AFP-L3 (OR: 1.071, 95% CI 1.061–1.32; p = 0.001), elevated DCP (OR: 1.001, 95% CI 1–1.002; p = 0.02), increased CA199 (OR: 1.001, 95% CI 1–1.005; p = 0.04) were independent predictors of PLC. Based on these factors, a nomogram was established for PLC diagnosis (Figure 3A). The nomogram demonstrated strong discriminatory power, with a C-index of 0.878 (Figure 3B).

Table 5 Univariate and Multivariate Logistic Regression Analysis Between PLC Patients and BLD Patients

Figure 3 Selected candidate indicators and developed a visualized diagnosis model of PLC. (A) The diagnostic nomogram for differentiating PLC from BLD patients. In the sex line, 0 represents female, and 1 represents male. (B) The calibration curves for the nomogram. The x-axis represents the nomogram-predicted probability and y-axis represents the actual probability of primary liver cancer.

Discussion

Primary liver cancer (PLC), one of the most common malignant tumors, poses a significant threat to public health. Currently, AFP and US are the most commonly recommended tests for PLC surveillance in cirrhotic patients.37,38 However, due to the often asymptomatic nature of early-stage PLC, many PLC patients are diagnosed at advanced stages. Early diagnosis is crucial for effective treatment, improved prognosis, and enhanced long-term survival.39 This underscores the urgent need for efficient, non-invasive prognostic tools to improve PLC detection.

Various biomarkers, such as AFP, AFP-L3, and DCP, have been extensively studied for PLC detection.25,40 Although AFP is widely used for PLC surveillance, about 30–50% of PLC patients are AFP-negative.19 In our cohort, 55.4% of PLC patients in our cohort were AFP-negative. Moreover, AFP showed a relatively low PPV of 0.51, indicating a higher rate of false positives, likely due to AFP’s non-specific elevation in BLD patients such as chronic hepatitis or cirrhosis. Several studies have shown that DCP and AFP-L3 improve diagnostic accuracy, particularly for AFP-negative HCC, and are potential predictors of early HCC recurrence.40–43 Consistent with other studies, we found that DCP had the highest NPV (0.74) and AFP-L3 exhibited the highest PPV (0.79) for distinguishing PLC from BLD patients, suggesting fewer false positives and false negatives compared to AFP. However, false negatives in DCP may occur in early-stage PLC cancers, and false positives in AFP-L3 might still occur in BLD patients. Given these limitations, combining complementary biomarkers may enhance diagnostic accuracy. A previous study demonstrated that CA199 in the liver cancer group was significantly higher than that in the benign lesion group, and CA199 in the liver cancer patients, with a combination of AFP, and imaging (eg US or CT) was achieving an AUC of 0.962.44 In our study, CA199 was significantly elevated in the HCC, ICC, and CHC groups. Intriguingly, the combination of AFP, AFP-L3, DCP, and CA199 achieved superior diagnostic performance, with AUCs of 0.8492 and 0.9480 for discriminating PLC patients from BLD and NC groups, respectively.

As previously mentioned, The GALAD and ASAP scores are well-established tools for estimating HCC risk and have demonstrated improved sensitivity for HCC detection.26,45,46 Consistent with these reports, we found that GALAD and the ASAP scores outperform individual biomarkers (AFP, AFP-L3, or DCP) in detecting PLC. Moreover, our results demonstrated that the combination of AFP, AFP-L3, DCP, and CA199 surpasses the ASAP and GALAD scores in diagnostic performance. This improved performance may be attributed to the inclusion of CA199, which is elevated not only in HCC but also in ICC and CHC. Differences in the study populations may also contribute to these findings, as GALAD and ASAP studies typically focus on solely on HCC patients, whereas our study included HCC, ICC, and CHC patients, in which CA199 is not only elevated in HCC but also significantly elevated in ICC and CHC patients. We also developed a nomogram model to predict PLC risk based on independent predictors, including sex, AFP-L3, DCP, and CA199. This model highlights the potential of these factors as predictive tools for liver cancer. Notably, the combined biomarker tests are convenient and noninvasive, which may improve surveillance compliance in high-risk populations.

Despite its strengths, our study has some limitations. It was conducted at a single center with a relatively small sample size, necessitating further validation in external and prospective studies. Additionally, long-term follow-up is required to confirm the diagnostic accuracy of these biomarkers across diverse populations. Future research should also evaluate the cost-effectiveness of combining AFP, AFP-L3, DCP, and CA199 compared to current standards, such as AFP and US.

Conclusion

In conclusion, our study demonstrates that the combination of AFP, AFP-L3, DCP, and CA199 provides superior diagnostic accuracy compared to the GALAD and ASAP scores for distinguishing PLC patients. These findings suggest that this biomarker panel represents a promising, non-invasive tool for early PLC detection.

Abbreviations

AFP, alpha-fetoprotein; AFP-L3, lens culinaris agglutinin-reactive AFP; ALT, alanine aminotransferase; ALB, albumin; ALP, alkaline phosphatase; AST, aspartate transaminase; ASAP, age, sex, AFP, DCP; CA199, carbohydrate antigen 199; CA242, carbohydrate antigen 242; CA50, carbohydrate antigen 50; CEA, carcinoembryonic antigen; CHC, hepatocellular-cholangiocarcinoma; DB, direct bilirubin; DCP, des-gamma-carboxy prothrombin; GALAD, gender, age, AFP, AFP-L3, and DCP; GGT, gamma-glutamyl transferase; HCC, hepatocellular carcinoma; ICC, intrahepatic cholangiocarcinoma; MVI, microvascular invasion; NPV, negative predictive value; PLC, primary liver cancer; PLT, platelet; PT, prothrombin time; PPV, positive predictive value; TB, total bilirubin; US, ultrasonography.

Funding

This research was supported by the National Natural Science Foundation of China (82172933, 82272987); National Key R&D Program of China (2023YFC3606600); Science and Technology Commission of Shanghai Municipality Rising-Star Program (23QA1403000), Project of Shanghai Science and Technology Commission (23015830300); Excellent Project of Shanghai Municipal Health Commission (20244Z0018); Shanghai “Rising Stars of Medical Talent” Youth Development Program of Outstanding Youth Medical Talents (2024-70).

Disclosure

The authors report no conflicts of interest in this work.

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