Primary liver cancer screening method based on quadruple biomarker detection

Abstract

The invention belongs to the technical field of primary liver cancer diagnosis, and provides a primary liver cancer screening method based on quadruple biomarker detection, and the method comprises the following steps: collecting gender data, AFP data, AFP-L3 data, DCP data and CA199 data, and carrying out column diagram model matching; the construction process of the column diagram model comprises the steps of research patient determination, tumor biomarker data collection, liver function data collection, GALAD score and ASAP score calculation, statistical analysis, individual diagnosis biomarker screening, diagnosis value evaluation and column diagram construction. The invention provides a convenient and non-invasive detection means through the column diagram model constructed by the AFP data, the AFP-L3 data, the DCP data and the CA199 data, and improves the diagnostic performance of the primary liver cancer and the monitoring compliance of high-risk groups.

Description

Technical Field

[0001] The present invention relates to the technical field of primary liver cancer diagnosis, and particularly to a primary liver cancer screening method based on the detection of four combined biomarkers. Background Art

[0002] Primary liver cancer is abbreviated as PLC, hepatocellular carcinoma as HCC, intrahepatic cholangiocarcinoma as ICC, hepatocyte to cholangiocarcinoma as CHC, abdominal ultrasound as US, serum alpha-fetoprotein as AFP, abnormal prothrombin as DCP, alanine aminotransferase as ALT, aspartate aminotransferase as AST, alkaline phosphatase as ALP, 5-year overall survival as OS, carbohydrate antigen 50 as CA50, carbohydrate antigen 242 as CA242, carbohydrate antigen 199 as CA199, carcinoembryonic antigen as CEA, total bilirubin as TB, direct bilirubin as DB, gamma-glutamyl transferase as GGT, albumin as ALB, prothrombin as PT, platelets as PLT, hepatitis B virus surface antigen as HBsAg, and hepatitis C antibody as HCV-Ab. PLC is the most common malignant tumor of the digestive system and the fourth leading cause of cancer-related deaths globally. PLC includes three main subtypes: HCC, ICC, and CHC. Among them, HCC accounts for 80 to 90% of cases, while ICC accounts for 10% to 15%. CHC is the rarest subtype, accounting for 1 to 4.7% of PLC. Usually, PLC is diagnosed at an advanced stage, resulting in limited treatment options and low 5-year survival rates. Early detection and timely treatment are crucial for improving survival outcomes. Current clinical guidelines recommend biannual HCC surveillance testing using US and AFP in high-risk patients. The efficiency of B-ultrasound in detecting early HCC is limited by the operator's expertise and patient-specific characteristics. It has been reported that the combination of US and AFP has a sensitivity of only 63% for early HCC detection. In addition, AFP alone shows limited sensitivity, ranging from 40% to 60%, and approximately 40% of HCC patients, especially early-stage patients, have normal AFP levels. These findings highlight the need for more reliable serum biomarkers for the detection of early liver cancer. DCP and AFP-L3 have been used for HCC screening. The combination of DCP and AFP-L3 with AFP has shown promise in improving the sensitivity of early HCC detection. Studies have demonstrated that the combined use of AFP, DCP, and AFP-L3 increases the sensitivity and specificity to 87.0% and 60.1% respectively in diagnosing HCC in cirrhotic patients. Other liver function biomarkers, such as ALT, AST, and ALP, also show potential in assisting HCC diagnosis and prognosis. New diagnostic algorithms, such as GALAD based on gender, age, AFP, AFP-L3, and DCP, have been developed to further enhance HCC detection. The GALAD score significantly improves diagnostic performance, with reported sensitivities and specificities of approximately 85.6% and 93.3% respectively, which are superior to individual biomarkers. Similarly, the ASAP score based on age, gender, AFP, and DCP shows sensitivities and specificities of approximately 76.1% and 90.4% respectively for early HCC detection.However, the performance of GALAD and ASAP was weakened in AFP-negative HCC patients, emphasizing the need for optimized biomarker panels. ICC is the second most common subtype in PLC, with a 5-year OS of approximately 9%. Although ICC diagnosis mainly relies on imaging examinations and tissue biopsy, serum biomarkers such as CA50, CA242, CA199, and CEA also have clinical significance. Elevated CA50 serum levels are observed in ICC patients, distinguishing them from non-ICC controls. In addition, higher CA50 levels are associated with worse clinical outcomes and shorter survival in ICC patients. Similarly, serum CA242 levels are significantly reduced after treatment in ICC patients, indicating its potential as a biomarker for treatment monitoring. ICC patients often show elevated serum CA199 levels, which have a sensitivity of 72% and a specificity of 84% for the diagnosis of ICC. In addition, elevated serum CA199 and CEA levels are frequently observed in patients with locally advanced or metastatic ICC. Considering that the pathological characteristics of CHC patients are similar to those of HCC and ICC, elevated serum AFP, DCP, CEA, and CA199 levels can also be seen in CHC patients.

[0003] However, these biomarkers are considered insufficient indicators for the diagnosis of ICC and CHC. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a primary liver cancer screening method based on quadruple biomarker detection to improve the diagnostic performance of primary liver cancer.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A primary liver cancer screening method based on quadruple biomarker detection, comprising:

[0007] Collect target detection user's gender data, AFP data, AFP-L3 data, DCP data and CA199 data;

[0008] The gender data, the AFP data, the AFP-L3 data, the DCP data and the CA199 data are matched using a pre-constructed nomogram model to obtain a prediction result of primary liver cancer; the construction process of the nomogram model includes:

[0009] Identify a number of study patients;

[0010] Collecting tumor biomarker data of the patients in the study;

[0011] Collect liver function data of the patients in the study;

[0012] Calculate the GALAD score and ASAP score based on the tumor biomarker data;

[0013] Use R software, Prism software, and SPSS 28.0.1.0 software to perform statistical analysis on the tumor biomarker data, the liver function data, the GALAD score, and the ASAP score, and obtain the characteristics of the enrolled population and the basic and clinicopathological characteristics of AFP-negative PLC patients and AFP-positive PLC patients;

[0014] Perform between-group analysis on each biomarker in the tumor biomarker data according to the characteristics of the enrolled population, the basic characteristics, and the clinicopathological characteristics to obtain individual diagnostic biomarkers; the individual diagnostic biomarkers include: the AFP data, the AFP-L3 data, the DCP data, and the CA199 data;

[0015] Evaluate the individual diagnostic biomarkers using ROC curve, positive predictive value, and negative predictive value to obtain the evaluation results. If the evaluation results meet the preset requirements, determine the independent predictors of the individual diagnostic biomarkers according to univariate and multivariate logistic regression, and construct the nomogram model using the independent predictors.

[0016] Preferably, the study patients include: patients with hepatocellular carcinoma, patients with intrahepatic cholangiocarcinoma, patients with mixed cell type hepatocellular carcinoma, patients with benign liver diseases, and normal control patients.

[0017] Preferably, the tumor biomarker data includes: the AFP data, CEA data, the CA199 data, the AFP-L3 data, the DCP data, CA50 data, and CA242 data.

[0018] Preferably, the liver function data includes: ALT data, AST data, ALP data, GGT data, TB data, DB data, ALB data, PT data, PLT data, HBsAg data, and anti-HCV antibody data.

[0019] Preferably, the expression formula of the GALAD score is:

[0020] GALAD score = -10.08 + 1.67 × gender (1 for male, 0 for female) + 0.09 × age + 0.04 × AFP-L3% + 2.34 × log10 AFP + 1.33 × log10 DCP;

[0021] Among them, AFP-L3% represents the AFP-L3 data; AFP represents the AFP data; DCP represents the DCP data.

[0022] Preferably, the ASAP score expression is as follows:

[0023] ASAP score = -7.58 + 0.05 × age - 0.58 × gender (1 for female, 0 for male) + 0.42 × Ln(AFP) + 1.11 × Ln(DCP);

[0024] Wherein, AFP represents the AFP data; DCP represents the DCP data.

[0025] Preferably, the characteristics of the enrolled population include: age, the gender data, HBsAg status, and HCV-Ab status; the basic characteristics include: the tumor biomarker data and the liver function data; the clinicopathological characteristics include: tumor type, pathological grade, TNM staging data, and BCLC staging data.

[0026] Preferably, the screening criteria for the individual diagnostic biomarker include: p value less than 0.05.

[0027] The present invention discloses the following technical effects:

[0028] The present invention provides a primary liver cancer screening method based on the detection of four combined biomarkers. By using the nomogram model constructed with AFP data, AFP-L3 data, DCP data, and CA199 data, the defect of poor detection performance of conventional biomarkers is solved, and the combined detection of multiple biomarkers is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is a schematic diagram of the primary liver cancer screening process based on the detection of four combined biomarkers provided by the embodiments of the present invention;

[0031] Figure 2 It is a comparison diagram of 7 biomarkers and 2 algorithms provided by the embodiments of the present invention, Figure 2 A is the comparison diagram of serum AFP levels, Figure 2 B is the comparison diagram of serum DCP levels, Figure 2 C is the comparison diagram of serum AFP-L3 levels, Figure 2 D is the comparison diagram of serum CA199 levels, Figure 2 E is the comparison diagram of serum CEA levels, Figure 2F is a comparison chart of serum CA50 levels, Figure 2 G is a comparison chart of serum CA242 levels, Figure 2 H is a comparison chart of GALAD levels, Figure 2 I is a comparison chart of ASAP levels;

[0032] Figure 3 It is a schematic diagram for evaluating the diagnostic value of 4 biomarkers and their combinations provided by the embodiments of the present invention, Figure 3 A is the ROC curve for distinguishing PLC patients from BLD group patients, Figure 3 B is the ROC curve for distinguishing PLC patients from NC group patients;

[0033] Figure 4 It is a schematic diagram of the PLC nomogram model provided by the embodiments of the present invention, Figure 4 A is the diagnostic nomogram for distinguishing PLC and BLD patients, Figure 4 B is the calibration curve of the nomogram. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] The purpose of the present invention is to provide a primary liver cancer screening method based on the detection of four biomarkers, so as to improve the diagnostic performance of primary liver cancer.

[0036] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0037] Figure 1 It is a schematic diagram of the primary liver cancer screening process based on the detection of four biomarkers provided by the embodiments of the present invention. As Figure 1 shown, the present invention provides a primary liver cancer screening method based on the detection of four biomarkers, including:

[0038] Step 100: Collect the gender data, AFP data, AFP-L3 data, DCP data, and CA199 data of the target detection user;

[0039] Step 200: Use the pre-constructed nomogram model to match the gender data, AFP data, AFP-L3 data, DCP data, and CA199 data to obtain the primary liver cancer prediction result; the construction process of the nomogram model includes:

[0040] Step 201: Determine a number of research patients;

[0041] Step 202: Collect tumor biomarker data of the research patients;

[0042] Step 203: Collect liver function data of the research patients;

[0043] Step 204: Calculate GALAD score and ASAP score according to the tumor biomarker data;

[0044] Step 205: Use R software, Prism software, and SPSS 28.0.1.0 software to perform statistical analysis on the tumor biomarker data, liver function data, GALAD score, and ASAP score, and obtain the characteristics of the enrolled population and the basic and clinicopathological characteristics of AFP-negative PLC patients and AFP-positive PLC patients;

[0045] Step 206: Perform between-group analysis on each biomarker in the tumor biomarker data according to the characteristics, basic characteristics, and clinicopathological characteristics of the enrolled population to obtain individual diagnostic biomarkers; The individual diagnostic biomarkers include: AFP data, AFP-L3 data, DCP data, and CA199 data;

[0046] Step 207: Evaluate the individual diagnostic biomarkers using ROC curve, positive predictive value, and negative predictive value to obtain the evaluation results. If the evaluation results meet the preset requirements, determine the independent predictors of the individual diagnostic biomarkers according to univariate and multivariate logistic regression, and construct a nomogram model using the independent predictors.

[0047] Specifically, the research patients include: hepatocellular carcinoma patients, intrahepatic cholangiocarcinoma patients, mixed cell type hepatocellular carcinoma patients, benign liver disease patients, and normal control patients.

[0048] Preferably, the tumor biomarker data includes: AFP data, CEA data, CA199 data, AFP-L3 data, DCP data, CA50 data, and CA242 data.

[0049] Optionally, the liver function data includes: ALT data, AST data, ALP data, GGT data, TB data, DB data, ALB data, PT data, PLT data, HBsAg data, and anti-HCV antibody data.

[0050] Specifically, the expression formula of the GALAD score is:

[0051] GALAD score = -10.08 + 1.67 × sex (male 1, female 0) + 0.09 × age + 0.04 × AFP-L3% + 2.34 × log10AFP + 1.33 × log10DCP;

[0052] Among them, AFP-L3% represents AFP-L3 data; AFP represents AFP data; DCP represents DCP data.

[0053] Specifically, the ASAP score expression is:

[0054] ASAP score = -7.58 + 0.05 × age - 0.58 × sex (1 for female and 0 for male) + 0.42 × Ln (AFP) + 1.11 × Ln (DCP);

[0055] Among them, AFP represents AFP data; DCP represents DCP data.

[0056] Furthermore, the characteristics of the enrolled population include: age, gender data, HBsAg status and HCV-Ab status; the basic characteristics include: the tumor biomarker data and the liver function data; the clinical pathological characteristics include: tumor type, pathological grade, TNM staging data and BCLC staging data.

[0057] Preferably, screening criteria for individual diagnostic biomarkers include: a p-value less than 0.05.

[0058] Specifically, the characteristics include the detection data of the following four biomarkers: AFP (alpha-fetoprotein), AFP-L3 (alpha-fetoprotein isoform), DCP (de-γ-carboxy prothrombin) and CA199 (carbohydrate antigen 199). The expression levels of these biomarkers are used for screening and prediction of primary liver cancer. The characteristics of the enrolled population include:

[0059] Age: Patients of different age groups may have different biomarker expression levels, and age is an important factor affecting the risk of liver cancer.

[0060] Gender: Gender (male or female) is associated with the incidence of liver cancer.

[0061] HBsAg (hepatitis B surface antigen) and HCV-Ab (hepatitis C antibody) status: Hepatitis B and hepatitis C virus infection are major risk factors for liver cancer.

[0062] The basic features include:

[0063] Tumor marker levels: The expression levels of AFP, AFP-L3, DCP, CEA, CA199, CA242, and CA50, which are used to distinguish patients with liver cancer from those without liver cancer.

[0064] Clinical serological marker characteristics: including TB (total bilirubin), DB (direct bilirubin), ALT (alanine aminotransferase), AST (aspartate aminotransferase), ALP (alkaline phosphatase), GGT (γ-glutamyl transferase), ALB (albumin), PLT (platelet count), and PT (prothrombin time). These indicators reflect the liver function status.

[0065] The described clinicopathological features include:

[0066] Tumor type: such as hepatocellular carcinoma, intrahepatic cholangiocarcinoma, mixed cell type hepatocellular carcinoma, etc. Different types of tumors may exhibit different biomarker expression patterns.

[0067] Pathological grade: The degree of tumor differentiation (such as well-differentiated, moderately-differentiated, poorly-differentiated) affects the expression of biomarkers.

[0068] TNM staging and BCLC staging: The staging of tumors is significantly correlated with the expression levels of AFP, AFP-L3, and DCP. The biomarker levels in advanced patients are usually higher.

[0069] Optionally, ethical statement: This study was approved by the ethics committee of a certain hospital and conducted in accordance with the Helsinki Declaration. All participants were over 18 years old and voluntarily provided written informed consent.

[0070] Specifically, 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 control (NC) patients. The PLC group included 39 patients with hepatocellular carcinoma (HCC), 14 patients with intrahepatic cholangiocarcinoma (ICC), 4 patients with hepatocellular-cholangiocarcinoma (CHC), and 37 patients with HCC diagnosed by imaging. All participants were recruited from a certain hospital between June 2020 and June 2024. PLC diagnosis was confirmed by histopathological examination, or in cases where it was not available, by radiological evidence from the Liver Imaging Reporting and Data System (LI-RADS). Data were collected from newly diagnosed, untreated PLC patients. The exclusion criteria were as follows: 1) PLC recurrence; 2) non-PLC liver metastasis; 3) presence of other tumors; 4) warfarin or vitamin K treatment, which may affect the DCP serum level. Tumor staging was determined according to the eighth edition of the International Union Against Cancer (UICC) TNM classification and the Barcelona Clinic Liver Cancer (BCLC) staging system; the BLD group included patients diagnosed with chronic liver disease (CLD), liver fibrosis (LF), or liver cirrhosis (LC).

[0071] Preferably, biomarker assay: Tumor biomarker detection: Measure AFP, CEA, and CA199 levels using the Roche Cobas E601 electrochemiluminescence immunoassay analyzer and its corresponding kit. Measure AFP-L3 levels using the Wakoi30 automatic analyzer and its kit. Measure DCP levels using the Abbott ARCHITECT immunoassay system and its supporting kit. Analyze CA50 and CA242 levels using the Thermo Fisher Smart6500 analyzer and its supporting kit.

[0072] Furthermore, liver function tests: Measure ALT, AST, ALP, and gamma-glutamyl transferase (GGT) using the Roche Cobas 8000 analyzer and its supporting kit. Also measure total bilirubin (TB), direct bilirubin (DB), and albumin (ALB) using the Roche Cobas 8000 analyzer and its supporting kit. Evaluate prothrombin time (PT) using the Werfen ACL Top 700 automated coagulation analyzer, while measure platelet (PLT) count using the Mindray BC-6800 hematology analyzer. Evaluate hepatitis B surface antigen (HBsAg) and anti-HCV antibody using the Abbott Architect I2000.

[0073] Calculation of GALAD and ASAP scores. The GALAD score is calculated using the following formula based on gender, age, AFP, AFP-L3, and DCP levels: GALAD score = -10.08 + 1.67 × gender (1 for male, 0 for female) + 0.09 × age + 0.04 × AFP-L3% + 2.34 × log10AFP + 1.33 × log10DCP; The ASAP score is calculated using the following formula: ASAP score = -7.58 + 0.05 × age - 0.58 × gender (1 for female, 0 for male) + 0.42 × Ln(AFP) + 1.11 × Ln(DCP).

[0074] Specifically, statistical analysis was performed using R, Prism, and SPSS 28.0.1.0. Categorical variables were expressed as frequencies and percentages, while continuous variables were reported as mean (standard deviation) or median (interquartile range). Analysis of variance (ANOVA), t-tests, or Mann-Whitney U tests were used to compare continuous variables, and chi-square tests were used to compare categorical variables. Receiver operating characteristic (ROC) curves were used to determine the area under the curve (AUC), GALAD score, and ASAP score of individual biomarkers (AFP, AFP-L3, DCP, CA199), as well as the combination of these four serum biomarkers for predicting PLC. The Youden index was used to determine the optimal cut-off value for comparing sensitivity and specificity. Positive predictive value (PPV) and negative predictive value (NPV) were calculated according to the following clinical cut-offs: AFP ≥ 20 ng / mL, AFP-L3 > 10%, DCP > 40 mAU / mL, CA199 > 34 U / mL. Univariate and multivariate logistic regression analyses were performed to identify independent prognostic factors. Based on these factors, a predictive nomogram model was developed in this example. p < 0.05 was considered statistically significant.

[0075] Furthermore, the characteristics of the enrolled population were analyzed. A total of 301 subjects were included in this retrospective study. Table 1 summarizes the general clinical information of the subjects in the NC, BLD, and PLC groups. The mean ages of the participants in the BLD and PLC groups were higher than those in the NC group (62.39, 63.94, and 57.44 years, respectively). The prevalence of males in the PLC group was also higher (77.66%). The serum tumor biomarker concentrations (including AFP, DCP, AFP-L3, CEA, CA199, and CA50) in PLC patients were significantly higher than those in the BLD group or normal controls (p < 0.001). Similarly, the PLC group showed higher levels of liver function biomarkers such as ALT, AST, TB, DB, ALP, GGT, and PT (p < 0.001), while the levels of ALB and PLT were significantly lower compared to the other groups (p < 0.001).

[0076] Table 1

[0077]

[0078]

[0079] Specifically, the demographic and clinicopathological characteristics of AFP-negative and AFP-positive PLC patients. To explore the differences between AFP-positive and AFP-negative PLC patients, the clinicopathological data of the above patients were analyzed in this example (Table 2). There were no significant differences in age (p = 0.982), gender distribution (p = 0.368), or HBsAg status (p = 0.894). HBsAg positivity was observed in 75% of the AFP-negative group and 76.19% of the AFP-positive patients. Tumor biomarker analysis showed that the AFP-L3 level in the AFP-positive group (27.46%) was significantly higher than that in the AFP-negative group (0.5%) (p = 0.001). Similarly, the DCP level in AFP-positive patients (315.2 mAU / mL) was significantly higher than that in 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 the liver function biomarkers, the AST level in AFP-positive PLC patients was significantly elevated (p = 0.009). However, histological grading showed no significant difference, and most cases in both groups were classified as moderately differentiated tumors (76.92% in the AFP-negative group and 66.67% in the AFP-positive group, p = 0.358). Other clinicopathological factors, including the 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, compared with AFP-negative patients, the AFP-L3, DCP, and AST levels in AFP-positive PLC patients were significantly elevated, while other demographic, tumor biomarker, and clinicopathological characteristics were similar.

[0080] Table 2

[0081]

[0082]

[0083]

[0084] The Child-Pugh classification is a grading standard used to quantitatively evaluate the liver reserve function of cirrhotic patients (including bilirubin, albumin, ascites, hepatic encephalopathy, and PT), thereby classifying liver function into three grades: A, B, and C.

[0085] Specifically, the evaluation of PLC-related serum biomarkers in the PLC, BLD, and NC groups. To determine the differences in the serum levels of PLC-related tumor serum biomarkers, the data of 7 biomarkers in each group were compared (refer to Figure 2)。Compared with the BLD group, the serum levels of AFP and DCP in the HCC group were significantly elevated (p < 0.001 and p < 0.001, respectively) (see Figure 2 A and Figure 2 B). However, compared with the BLD group, the AFP levels in the ICC and CHC groups were not elevated (see Figure 2 A), while compared with the BLD group, the serum level of DCP in the CHC group was elevated (p < 0.05) (see Figure 2 B). In addition, the GALAD and ASAP scores in the HCC and CHC groups were also significantly higher than those in the BLD group (p < 0.001 and p < 0.05, respectively) (see Figure 2 H and Figure 2 I). Compared with the BLD group, the AFP-L3 levels in the HCC, ICC, and CHC groups were higher (p < 0.001, p < 0.05, and p < 0.01, respectively) (see Figure 2 C). The serum CA199 levels in the HCC, ICC, and CHC groups were higher than those in the BLD group (p < 0.05, p < 0.001, and p < 0.001) (see Figure 2 D). However, the CEA level was elevated only in the ICC group (p < 0.001) (see Figure 2 E). The CA242 levels in the ICC and CHC groups were elevated, but not in the HCC group (p < 0.01 and p < 0.001, respectively) (see Figure 2 G). No significant difference in the CA50 level was observed among the HCC, ICC, CHC, and BLD groups (see Figure 2 F). These findings suggest that AFP, AFP-L3, DCP, and CA199 can be used as individual diagnostic biomarkers for HCC.

[0086] Furthermore, the diagnostic value of AFP, AFP-L3, DCP, CA199, and their combined detection in PLC patients was evaluated. ROC curve analysis was used to evaluate the diagnostic performance of the four biomarker groups of AFP, AFP-L3, DCP, and CA199 and their combinations, as well as the GALAD score and ASAP score for differentiating PLC and BLD (see Figure 3A and Table 3). The areas under the ROC curves (AUC) of 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 referred to Table 3. The sensitivity of CA199 was the highest, at 0.8723, while the specificity of AFP was the highest, at 0.9375. The combination P4 of AFP, AFP-L3, DCP, and CA199 improved the diagnostic performance, increasing the AUC to 0.8492, exceeding the GALAD score (AUC = 0.7751) and the ASAP score (AUC = 0.7927). Compared with BLD, the ASAP score showed a higher PPV (0.82) in the detection of PLC. Compared with the NC group, the diagnostic performance of these biomarkers in the PLC group was subsequently verified in this example (refer to Figure 3 B), and the AUCs of AFP, AFP-L3, DCP, and CA199 were 0.7267, 0.7938, 0.801, and 0.7912, respectively (refer to Table 4). The combination P4 of AFP, AFP-L3, DCP, and CA199 increased the AUC to 0.9480, exceeding the GALAD score (AUC = 0.9022) and the ASAP score (AUC = 0.9016). The combination P4 of AFP, AFP-L3, DCP, and CA199 showed a sensitivity of 0.8404 and a specificity of 0.9873. These findings indicate that the combination of AFP, AFP-L3, DCP, and CA199 can improve their diagnostic ability to distinguish PLC patients from BLD or NC patients.

[0087] Table 3

[0088]

[0089]

[0090] Table 4

[0091]

[0092] Specifically, a nomogram for predicting PLC based on logistic regression. Compared with BLD, univariate and multivariate logistic regression analyses identified gender, AFP-L3, DCP, and CA199 as independent predictors of PLC (refer to Table 5). In the multivariate logistic regression analysis, male (OR: 3.99, 95% CI 1.92 - 8.71; p = 0.001), elevated 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), and increased CA199 (OR: 1.001, 95% CI 1 - 1.005; p = 0.04) were independent predictors of PLC. Based on these factors, a nomogram for PLC diagnosis was established (refer to Figure 4 A, in the gender row, 0 represents female and 1 represents male). The nomogram showed strong discriminatory ability, with a C-index of 0.878 (refer to Figure 4 B).

[0093] Table 5

[0094]

[0095]

[0096] The beneficial effects of the present invention are as follows:

[0097] The nomogram model constructed by the present invention using AFP data, AFP-L3 data, DCP data, and CA199 data provides a convenient and non-invasive detection method, improving the diagnostic performance of primary liver cancer and the monitoring compliance of high-risk populations.

[0098] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0099] Specific examples are used in this article to elaborate on the principles and implementation methods of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, there will be changes in the specific implementation methods and application scopes according to the idea of the present invention. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for primary liver cancer screening based on quadruple biomarker detection, characterized in that: include: Collect target detection user's gender data, AFP data, AFP-L3 data, DCP data and CA199 data; Using a pre-constructed nomogram model to match the gender data, the AFP data, the AFP-L3 data, the DCP data, and the CA199 data, to obtain a prediction result of primary liver cancer; The construction process of the nomogram model includes: Identify a number of study patients; Collecting tumor biomarker data of the patients in the study; Collect liver function data of the patients in the study; Calculating the GALAD score and the ASAP score according to the tumor biomarker data; The tumor biomarker data, the liver function data, the GALAD score, and the ASAP score were statistically analyzed using R software, Prism software, and SPSS28.0.1.0 software to obtain the characteristics of the enrolled population and the basic characteristics and clinical pathological characteristics of AFP-negative PLC patients and AFP-positive PLC patients; Performing intergroup analysis on each marker in the tumor biomarker data according to the characteristics of the enrolled population, the basic characteristics and the clinical pathological characteristics to obtain individual diagnostic biomarkers; the individual diagnostic biomarkers include: the AFP data, the AFP-L3 data, the DCP data and the CA199 data; The individual diagnostic biomarkers are evaluated by ROC curve, positive predictive value, and negative predictive value to obtain evaluation results. If the evaluation results meet the preset requirements, the independent predictive factors of the individual diagnostic biomarkers are determined according to univariate and multivariate logistic regression, and the nomogram model is constructed using the independent predictive factors.

2. A method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The research patients include: patients with hepatocellular carcinoma, patients with intrahepatic cholangiocarcinoma, patients with mixed cell type liver cancer, patients with benign liver disease and normal control patients.

3. A method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The tumor biomarker data includes: the AFP data, the CEA data, the CA199 data, the AFP-L3 data, the DCP data, the CA50 data and the CA242 data.

4. A method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The liver function data include: ALT data, AST data, ALP data, GGT data, TB data, DB data, ALB data, PT data, PLT data, HBsAg data and anti-HCV antibody data.

5. The method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The expression of the GALAD score is: GALAD score = -10.08 + 1.67 × sex (male 1, female 0) + 0.09 × age + 0.04 × AFP-L3% + 2.34 × log10AFP + 1.33 × log10DCP; Among them, AFP-L3% represents the AFP-L3 data; AFP represents the AFP data; DCP represents the DCP data.

6. The method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The ASAP score expression is: ASAP score = -7.58 + 0.05 × age - 0.58 × sex (1 for female and 0 for male) + 0.42 × Ln (AFP) + 1.11 × Ln (DCP); Among them, AFP represents the AFP data; DCP represents the DCP data.

7. The method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The characteristics of the enrolled population include: age, gender data, HBsAg status and HCV-Ab status; the basic characteristics include: the tumor biomarker data and the liver function data; the clinical pathological characteristics include: tumor type, pathological grade, TNM staging data and BCLC staging data.

8. The method for primary liver cancer screening based on quadruple biomarker detection according to claim 1, characterized in that: The screening criteria for the individual diagnostic biomarkers include: a p value less than 0.05.

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