Method for quantifying fucosylated alpha-fetoprotein

Abstract

The present invention relates to a method for providing information necessary for the diagnosis of liver cancer by measuring the percentage of fucosylated AFP (AFP-Fuc%). In the present invention, AFP present at a low concentration in serum is quantified with excellent sensitivity by performing direct mass spectrometry on total fucosylated AFP without glycan removal or the use of antibodies or lectins that recognize fucosylation sites, and hepatocellular carcinoma is detected with significantly improved accuracy compared to conventional GALAD scores using Lens culinaris agglutinin-reactive fraction (AFP-L3) as a marker. In particular, the present invention can be effectively used to establish treatment strategies early and improve patient survival rates by distinguishing early hepatocellular carcinoma from common non-tumor liver diseases with high reliability.

Description

Quantification method of fucosylated alpha-fetoprotein

[0001] The present invention relates to a method for accurately quantifying fucosylated alpha-fetoprotein (AFP) in a biological sample through mass spectrometry and a method for diagnosing liver cancer using the same.

[0002]

[0003] Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortality worldwide. While the causes of liver cancer are diverse, including hepatitis C virus (HCV), hepatitis B virus (HBV), and non-alcoholic steatohepatitis (NASH), HBV infection has been reported as a major cause of HCC. Early diagnosis is crucial to reduce mortality from HCC, and currently, alpha-fetoprotein (AFP), lectinenz culinaris aglutinin reactive fraction (AFP-L3), and des-gamma-carboxy prothrombin (DCP), also known as PIVKA-II, have been proven as HCC-specific biomarkers. AFP has been used as a serological marker for HCC for decades, and AFP-L3, which has higher diagnostic sensitivity, can detect the onset of HCC earlier than AFP. Previous studies have proposed the use of combination biomarkers, which have demonstrated better diagnostic performance than single biomarkers for the early diagnosis of hepatocellular carcinoma. Since the development of the GALAD scoring system based on sex, age, AFP-L3, AFP, and DCP, many studies have verified the diagnostic performance of GALAD for hepatocellular carcinoma.

[0004] The aforementioned biomarkers are analyzed using immunoassays such as electrochemiluminescence immunoassay (ECLIA) or liquid-phase binding assay (LiBA). However, these immunoassays have some disadvantages, such as cross-reactivity or limited sensitivity, and in particular, lectins recognize not only specific glycans of the target glycoprotein but also glycans on the antibody used to detect the target glycoprotein.

[0005] Meanwhile, mass spectrometry (MS) is widely used for the direct identification and quantification of protein biomarkers and has been applied in clinical fields for decades. Although MS accurately analyzes post-translational modifications (PTMs), such as protein subtypes or glycosylations, it has disadvantages, including limited automation, which makes the process time-consuming, the need for manual intervention, and the requirement for specialized user training on sample preparation. To overcome these limitations, one-step sample preparation or automated systems are continuously being developed; however, there is a need to develop simple, powerful, and accurate automated sample preparation methods to simultaneously quantify multiple marker combinations and save manpower and time, particularly in clinical settings.

[0006]

[0007] Throughout this specification, numerous papers and patent documents are referenced and cited. The disclosures of the cited papers and patent documents are incorporated by reference into this specification in their entirety to more clearly explain the state of the art to which the present invention pertains and the content of the present invention.

[0008]

[0009] The inventors have made diligent research efforts to develop an efficient diagnostic system capable of more accurately predicting the risk of developing hepatocellular carcinoma (HCC) based on alpha-fetoprotein (AFP) in the blood, which is a key diagnostic marker for liver cancer. As a result, they discovered that by measuring the ratio of fucosylated AFP (AFP-Fuc%) through mass spectrometry of glycosylated AFP, it is possible to accurately and directly quantify intact glycoproteins without removing sugar chains or using antibodies or lectins specific to the fucosylation site, thereby enabling a highly reliable quantitative assessment of the risk of developing liver cancer, specifically hepatocellular carcinoma (HCC).

[0010] Therefore, the objective of the present invention is to provide a method for providing information necessary for the diagnosis of liver cancer through the measurement of the ratio of fucosylated AFP (AFP-Fuc%).

[0011] Other objects and advantages of the present invention will become more apparent from the following detailed description of the invention, claims, and drawings.

[0012]

[0013] According to one aspect of the present invention, the present invention provides a method for providing information necessary for the diagnosis of liver cancer, comprising the step of measuring the proportion of fucosylated AFP (AFP-Fuc%) among the total alpha-fetoprotein (AFP) in a biological sample separated from a subject.

[0014] The inventors have made diligent research efforts to develop an efficient diagnostic system capable of predicting the risk of developing liver cancer, specifically hepatocellular carcinoma (HCC), with high reliability based on the expression level of AFP in the blood, which is a key diagnostic marker for liver cancer. As a result, they discovered that liver cancer, specifically hepatocellular carcinoma, can be diagnosed with significantly improved accuracy compared to conventional technology by measuring the ratio of fucosylated AFP (AFP-Fuc%) through direct mass spectrometry of total glycosylated AFP without antibodies or lectins that recognize the removal of sugar chains or fucosylation sites, and by detecting the increase in mass value caused by the binding of fucose to the sugar chain.

[0015] In this specification, the term “subject” refers to an individual that provides a sample for measuring the proportion of fucosylated AFP and is ultimately the subject of analysis regarding the development of liver cancer. The subject includes, without limitation, humans, mice, rats, guinea pigs, dogs, cats, horses, cattle, pigs, monkeys, chimpanzees, baboons, or rhesus monkeys, and specifically, humans. Since the method of the present invention provides information for predicting not only the current development of liver cancer but also the metabolic and genetic risk of future liver cancer development, the subject of the present invention may be a liver cancer patient or a healthy individual that has not yet developed liver cancer.

[0016] In this specification, the term “biological sample” refers to any sample containing the aforementioned fucosylated AFP obtained from mammals, including humans, and includes, but is not limited to, tissues, organs, cells, or cell culture media. More specifically, the biological sample may be cancer tissue, cancer cells, culture media thereof, or blood, and more specifically, whole blood, plasma, or serum.

[0017] In this specification, the term “whole blood” generally refers to blood composed of unclotted plasma and cellular components. Plasma makes up about 50 to 60% of the volume of whole blood, and cellular components (e.g., red blood cells, white blood cells, or platelets) may make up about 40 to 50%.

[0018] In this specification, the term “plasma” refers to the liquid component of blood and functions as a transport medium for supplying nutrients to the cells and organs of the body.

[0019] In this specification, the term “serum” refers to a pale yellow liquid collected from blood. Specifically, it refers to the pale yellow body fluid component remaining after removing the red coagulant, which forms as the fluidity of the blood decreases when the blood is left unattended after collection.

[0020] In this specification, the term “ratio of fucosylated AFP (AFP-Fuc%)” refers to the mass percentage of fucosylated AFP (AFP-Fuc) among the total AFP proteins in a biological sample, specifically blood, and can be measured by directly quantifying fucosylated peptides and non-fucosylated peptides while maintaining core fucosylation. Specifically, as described below, it can be measured by directly quantifying fucosylated AFP and non-fucosylated AFP through mass spectrometry, and, for example, can be calculated by dividing the peak area of ​​fucosylated AFP (VNFTEIQK_5410) by the sum of the peak areas of non-fucosylated AFP (VNFTEIQK_5400) and fucosylated AFP (VNFTEIQK_5410).

[0021] In this specification, the term “diagnosis” includes the determination of an individual’s susceptibility to a specific disease, the determination of whether an individual currently possesses a specific disease, and the determination of the prognosis of an individual afflicted with a specific disease.

[0022] According to a specific embodiment of the present invention, the AFP-Fuc% is measured by quantifying the fucosylated AFP through mass spectrometry as described above.

[0023] In this specification, the term “mass spectrometry” refers to a procedure for determining the presence and quantity of a target compound based on its quantitative mass value. Mass spectrometry is performed by filtering, detecting, and measuring ions using the mass-to-charge ratio (m / z value). Generally, mass spectrometry includes (1) a step of ionizing and charging a compound and (2) a step of measuring the molecular weight of the charged compound and calculating the m / z value. The calculated m / z value is used as a reference to identify and quantify a target compound in a complex mixture. Mass spectrometry in this specification can be performed through any type of mass spectrometry method utilizing the above-described principles.

[0024] According to a specific embodiment of the present invention, the mass spectrometry is performed by a mass spectrometry method selected from the group consisting of MALDI-TOF (Matrix-Assisted Laser Desorption / Ionization Time of Flight) mass spectrometry, SELDI-TOF (Surface Enhanced Laser Desorption / Ionization Time of Flight) mass spectrometry, ESI-TOF (Electrospray ionization time-of-flight) mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), and LC-MS / MS (liquid chromatography-mass spectrometry / mass spectrometry).

[0025] According to a more specific embodiment of the present invention, the detection condition of the mass spectrometry is selected from the group consisting of Multiple Reaction Monitoring (MRM), Parallel Reaction Monitoring (PRM), and Single Reaction Monitoring (SRM).

[0026]

[0027] More specifically, the detection condition of the mass spectrometry above is Multiple Reaction Monitoring (MRM), and more specifically, Liquid Chromatography Multiple Reaction Monitoring (LC-MRM).

[0028] In this specification, the term “multi-reaction monitoring” refers to an analytical technique capable of selectively separating, detecting, and quantifying specific analytes to monitor changes in their concentration over time. It involves using a first mass filter (Q1) to selectively transfer parent ions among the ion fragments generated from an ionization source to a collision tube, after which the parent ions reaching the collision tube collide with an internal collision gas to decompose into daughter ions and are sent to a second mass filter (Q2), where only characteristic ions are transferred to a detection unit, thereby enabling the detection of information regarding the target component. The MRM method exhibits excellent selectivity and sensitivity and is suitable for simultaneously measuring multiple peptides. According to the present invention, LC-MRM demonstrates superior sensitivity in the quantification of fucosylated AFP compared to the liquid-phase binding analysis method (LiBA), thereby exhibiting higher diagnostic performance at low AFP levels.

[0029] According to a specific embodiment of the present invention, the method of the present invention further comprises the step of concentrating AFP in a sample by contacting a biological sample separated from a target with an antibody that specifically recognizes AFP or an antigen-binding fragment thereof.

[0030] In this specification, the term “antibody” means a peptide that recognizes a specific epitope of AFP and binds specifically to it, and includes not only the complete antibody form but also antigen-binding fragments (antibody fragments) of a full-length antibody molecule.

[0031] In this specification, the term “antigen-binding fragment of an antibody” refers to a fragment having a significant antigen-antibody binding function within a full-length antibody molecule, and includes Fab, F(ab'), F(ab')2, Fv, and nanobodies (nanobody or sybody), etc.

[0032] In this specification, the term “specifically binding” has the same meaning as “specifically recognizing,” and refers to the specific interaction between an antigen and an antibody (or a fragment thereof) through an immunological reaction.

[0033] The process of concentrating AFP using an anti-AFP antibody or its antigen-binding fragment can be performed, for example, by adding a bead-antibody complex (beads-Ab) to a sample to be analyzed and stirring. The beads may be magnetic beads with a surface that is tosylated or epoxidized, and the magnetic beads may be bound to streptavidin, protein G, or protein A.

[0034] According to a specific embodiment of the present invention, the method of the present invention further comprises the step of desialylating glycosylated AFP in a biological sample separated from a subject. According to the present invention, desialylation of glycoproteins has significantly improved analytical sensitivity, particularly in mass spectrometry, VNFTEIQK_5410, VNFTEIQK_5412, and VNFTEIQK_5411 have been integrated into VNFTEIQK_5410, and VNFTEIQK_5402 and VNFTEIQK_5401 have been integrated into VNFTEIQK_5400. Desialylation can be performed by adding a desialylase, for example, comprising α2-3 neuraminidase or α2-3,6,8 neuraminidase.

[0035] According to a specific embodiment of the present invention, the method of the present invention additionally includes the step of measuring the gender of the subject, the age of the subject, and the log value of the total AFP concentration (ng / mL) in the biological sample separated from the subject.

[0036] According to a more specific embodiment of the present invention, the method of the present invention quantitatively evaluates the likelihood of liver cancer development based on the sum of each weighted measurement value, by assigning a higher weight in the order of the ratio of fucosylated AFP to the total AFP in a biological sample separated from a subject (AFP-Fuc portion); the log value of the concentration of total AFP (ng / mL) in a biological sample separated from a subject; the gender of the subject; and the age of the subject.

[0037] More specifically, the method of the present invention is carried out by obtaining a score (GAFA score) that quantitatively evaluates the likelihood of developing liver cancer using the following [Equation 1]:

[0038] [Equation 1]

[0039] GAFA score =ax Gender +bx Age +cx AFP-Fuc portion +dx 1og10AFP -e

[0040] In the above Equation 1,

[0041] The above Gender is the gender of the subject, defined as 1 for male and 0 for female;

[0042] The above Age is the age of the subject;

[0043] The above AFP-Fuc portion is the ratio of fucosylated AFP to the total AFP in the biological sample separated from the subject;

[0044] The above 1og10AFP is the logarithm of the total AFP concentration (ng / mL) in the biological sample separated from the subject;

[0045] The above a is a rational number from 0.4 to 0.7;

[0046] The above b is a rational number from 0.08 to 0.12;

[0047] The above c is a rational number from 11 to 14.5;

[0048] The above d is a rational number from 0.5 to 0.9;

[0049] The above e is a rational number from 5.8 to 8.8.

[0050] More specifically,

[0051] The above a is a rational number from 0.45 to 0.7;

[0052] The above b is a rational number from 0.09 to 0.1;

[0053] The above c is a rational number from 11.1 to 14.1;

[0054] The above d is a rational number from 0.6 to 0.8;

[0055] The above e is a rational number from 6.5 to 7.8.

[0056] More specifically,

[0057] The above a is a rational number from 0.484 to 0.692;

[0058] The above b is a rational number between 0.096 and 0.098;

[0059] The above c is a rational number between 11.168 and 14.03;

[0060] The above d is a rational number between 0.665 and 0.795;

[0061] The above e is a rational number between 6.948 and 7.65.

[0062] More specifically,

[0063] The above a is a rational number from 0.55 to 0.6;

[0064] The above b is a rational number between 0.0965 and 0.0975;

[0065] The above c is a rational number from 11.5 to 13;

[0066] The above d is a rational number from 0.7 to 0.75;

[0067] The above e is a rational number from 7.1 to 7.4.

[0068] Most specifically, a is 0.588, b is 0.097, c is 12.599, d is 0.73, and e is 7.299.

[0069] According to a specific embodiment of the present invention, if the GAFA score calculated by the above formula is -0.6 or higher, more specifically -0.56 or higher, the subject is determined to have an increased risk of liver cancer, and more specifically, is determined to have developed liver cancer.

[0070]

[0071] According to a specific embodiment of the present invention, the method of the present invention additionally includes, in addition to the step of measuring the proportion of fucosylated AFP (AFP-Fuc%) of total AFP in a biological sample, the step of measuring the sex of the subject, the age of the subject, the log value of the concentration of total AFP (ng / mL) in the biological sample separated from the subject, and the concentration of DCP (des-gamma-carboxy prothrombin) in the biological sample separated from the subject.

[0072] According to a more specific embodiment of the present invention, the method of the present invention quantitatively evaluates the likelihood of liver cancer development based on the sum of each weighted measurement value, by assigning a higher weight in the order of the proportion of fucosylated AFP (AFP-Fuc portion) of the total AFP in a biological sample separated from a subject; the concentration of DCP in a biological sample separated from a subject; the log value of the concentration of the total AFP (ng / mL) in a biological sample separated from a subject; the gender of the subject; and the age of the subject.

[0073]

[0074] More specifically, the method of the present invention additionally comprises the step of obtaining a score (GAFAD score) that quantitatively evaluates the likelihood of developing liver cancer using [Equation 2] below:

[0075] [Equation 2]

[0076] GAFAD score = -fx Gender +gx Age +hx AFP-Fuc portion +ix 1og10AFP +jx 1og10DCP -k

[0077] In the above Equation 2,

[0078] The above Gender, Age, FP-Fuc%, and 1og10AFP are the same as defined in Formula 1 of Claim 6, and

[0079] The above 1og10DCP is the log value of the concentration (ng / mL) of DCP in a biological sample separated from the subject;

[0080] The above f is a rational number from -0.3 to 0.6;

[0081] The above g is a rational number from 0.09 to 0.13;

[0082] The above h is a rational number from 6 to 13.5;

[0083] The above i is a rational number from 0.2 to 0.85;

[0084] The above j is a rational number from 6 to 7;

[0085] The above k is a rational number between 15 and 17.5.

[0086] More specifically,

[0087] The above f is a rational number between -0.25 and 0.55;

[0088] The above g is a rational number from 0.1 to 0.12;

[0089] The above h is a rational number from 6.5 to 13;

[0090] The above i is a rational number from 0.25 to 0.8;

[0091] The above j is a rational number from 6.4 to 6.7;

[0092] The above k is a rational number from 15.5 to 17.

[0093] More specifically,

[0094] The above f is a rational number from -0.217 to 0.521;

[0095] The above g is a rational number from 0.102 to 0.11;

[0096] The above h is a rational number from 6.831 to 12.873;

[0097] The above i is a rational number between 0.266 and 0.716;

[0098] The above j is a rational number between 6.478 and 6.624;

[0099] The above k is a rational number between 15.751 and 16.925.

[0100] More specifically,

[0101] The above f is a rational number from 0.1 to 0.17;

[0102] The above g is a rational number from 0.104 to 0.108;

[0103] The above h is a rational number from 8.5 to 11;

[0104] The above i is a rational number between 0.45 and 0.55;

[0105] The above j is a rational number between 6.5 and 6.6;

[0106] The above k is a rational number between 16 and 16.6.

[0107] Most specifically, f is 0.152, g is 0.106, h is 9.852, i is 0.491, j is 6.551, and k is 16.338.

[0108]

[0109] According to a specific embodiment of the present invention, if the GAFAD score calculated by the above formula is -0.4 or higher, more specifically -0.39 or higher, the subject is determined to have an increased risk of liver cancer, and more specifically, is determined to have developed liver cancer.

[0110]

[0111] According to a specific embodiment of the present invention, the liver cancer is hepatocellular carcinoma (HCC), and more specifically, stage 1 hepatocellular carcinoma (HCC 1).

[0112] Hepatocellular carcinoma accounts for approximately 80% of all liver cancer patients and is caused by hepatitis B, hepatitis C, cirrhosis, alcoholic liver disease, diabetic liver disease, and cirrhosis. Since these causative liver diseases have a high prevalence, early diagnosis of progression from these liver diseases to hepatocellular carcinoma is essential for improving patient survival rates. The present invention can be usefully employed to distinguish early hepatocellular carcinoma from non-tumor liver diseases such as infection and cirrhosis with high reliability.

[0113]

[0114] The features and advantages of the present invention are summarized as follows:

[0115] (a) The present invention provides a method for providing information necessary for the diagnosis of liver cancer through the measurement of the ratio of fucosylated AFP (AFP-Fuc%).

[0116] (b) The present invention quantifies AFP present at low concentrations in serum with excellent sensitivity by performing direct mass spectrometry on total glycosylated AFP without using antibodies or lectins that recognize the removal or fucosylation sites of the sugar chains, and detects hepatocellular carcinoma with significantly improved accuracy compared to the conventional GALAD score that uses the lectin lens culinaris aglutinin reaction fraction (AFP-L3) as a marker.

[0117] (c) The present invention can be usefully utilized to establish treatment strategies early and improve patient survival rates in hepatocellular carcinoma, which is difficult to diagnose early, by distinguishing between general non-tumor liver disease and early hepatocellular carcinoma with high reliability.

[0118]

[0119] Figure 1 is a schematic diagram showing the clinical analysis procedure in the development, validation, and diagnosis using the analysis method of the present invention.

[0120] Figure 2 shows the results of a sensitivity comparison for the quantification of fucosylated AFP using the LC-MRM (Liquid Chromatography-Multiple Reaction Monitoring) assay and the LiBA (Liquid-Phase Binding Assay) assay. Figure 2a shows the measurements for a low-concentration calibration standard and a middle-fucosylated sample (10 points, 5 days, fucosylation: 48.2%, AFP range: 0.1-1.3 ng / mL). Figure 2b shows the measurements for clinical serum (N=525, fucosylation range: 0.3%-98.9%, AFP range: 0.5-14189 ng / mL) and clinical serum with less than 10% fucosylation (dotted box, N=409). Figure 2c shows measurements for clinical serum groups classified according to clinical characteristics and clinical serum groups with less than 10% fucosylation (dotted box) [1: healthy, 2: chronic hepatitis, 3: cirrhosis, 4: HCC stage 1, 5: HCC stage 2, 6: HCC stage 3].

[0121] Figure 3 shows the results of evaluating diagnostic performance by ROC curve analysis between LiBA (AFP-L3) and LC-MRM (AFP-Fuc%) at the lower 30% of AFP concentrations (Figure 3a) or various AFP reference values ​​(Figures 3b-3d). (Figure 3a) AFP < 2.3 ng / mL, n=165, p < 0.001. (Figure 3b) AFP < 7 ng / mL, n=298, p < 0.001. (Figure 3c) AFP < 10 ng / mL, n=331, p < 0.005. (Figure 3d) AFP < 20 ng / mL, n=382, p < 0.05.

[0122] Figure 4 shows the clinical group distributions of the multivariate models GAFAD, GAFA, and GALAD. GAFAD is the result of combining sex and age information with the fucosylated AFP ratio, AFP, and DCP, whereas GALAD is the result of combining data on sex, age, AFP-L3, AFP, and DCP. Each dot represents an individual sample, and the black horizontal line represents the median. The cut-off values ​​for GAFAD (-0.39), GAFA (-0.56), and GALAD (-0.03 and -0.63), along with their corresponding sensitivities and specificities, are shown.

[0123] Figure 5 shows the results of comparing the diagnostic performance between individual biomarkers and biomarker combinations through ROC curve analysis. The results are shown for the entire group including non-liver cancer (healthy individuals, chronic hepatitis patients, and cirrhosis patients) and liver cancer (stages 1-3) patients (Figure 5a), the very early liver cancer (HCC stage 1) and liver disease (chronic hepatitis, cirrhosis) groups (Figure 5b), and the liver cancer and liver disease patient groups with negative AFP values ​​(Figure 5c). Each variable applied to derive the GAFAD and GALAD scores is as described above.

[0124] Figure 6 is a schematic diagram summarizing the automated clinical analysis process, showing Process 1 (A), which shows the immunoprecipitation and desiarylation procedures; Process 2 (B), which shows the reduction, alkylation, protein purification and trypsin degradation steps; and Process 3 (C), which shows the Evotip manufacturing process.

[0125] Figure 7 shows the results of measuring the mass spectra (Figures 7a-7c) and retention times (Figure 7d) for the generated ions. Figure 7a is the generated ion spectrum of the GYQELLEK peptide representing the AFP protein. Figure 7b is the generated ion spectrum of the VNFTEIQK_5400 peptide representing the non-fucosylated AFP protein. Figure 7c is the generated ion spectrum of the VNFTEIQK_5410 peptide representing the fucosylated AFP protein. In Figures 7a-7c, red represents the quantified ion, and blue represents the qualified ion. Figure 7d shows the retention times of the quantified and qualified ions of GYQELLEK (left), VNFTEIQK_5400 (center), and VNFTEIQK_5410 (right).

[0126] Figure 8 shows the digestion rate with a fixed enzyme amount (5 μg) (Figs. 8a, 8b) and the results of enzyme amount optimization with a fixed digestion time (16 hours) (Figs. 8c, 8d). Figs. 8a and 8c, non-glycosylated AFP (GYQELLEK). Figs. 8b and 8d, glycosylated AFP (VNFTEIQK).

[0127] Figure 9 shows the results of desiarylation time optimization with a fixed enzyme amount (500 units) applied to the target peptide (Figures 9a, 9b) and enzyme amount optimization with a fixed digestion time (1 hour) applied (Figures 9c, 9d). Figures 9a and 9c, non-glycosylated AFP (GYQELLEK). Figures 9b and 9d, glycosylated AFP (VNFTEIQK).

[0128] Figure 10 shows the results of the linearity evaluation between the assigned values ​​and the measured values ​​over a period of two months of clinical analysis measuring instruments, displayed on a log scale, for non-glycopeptide (GYQELLEK) (Figure 10a), non-fucosylated peptide (VNFTEIQK_5400) (Figure 10b), and fucosylated peptide (VNFTEIQK_5410) (Figure 10c), respectively.

[0129] Figure 11 is a figure showing the results of a log-scale linearity evaluation of the analytical measurement range between the assigned value and the measured value, showing the results for the non-glycopeptide (GYQELLEK) (Fig. 11a), the non-fucosylated peptide (VNFTEIQK_5400), and the fucosylated peptide (VNFTEIQK_5410) (Fig. 11c), respectively.

[0130] Figure 12 shows the results of evaluating the matrix effect. The matrix effect was evaluated by calculating the recovery rate after adding standard proteins to six different clinical serums, and the average recovery rate of the six matrices ranged from 100.5% to 109.9%.

[0131] Figure 13 shows the results of evaluating freeze-thaw stability for non-glycopeptide (GYQELLEK) (Figure 13a), fucosylation ratio (calculated by dividing the peak area of ​​VNFTEIQK_5410 by the sum of the peak areas of VNFTEIQK_5400 and VNFTEIQK_5410) (Figure 13b), non-fucosylated peptide (VNFTEIQK_5400) (Figure 13c), and fucosylated peptide (VNFTEIQK_5410) (Figure 13d).

[0132] Figure 14 shows the results of comparing and evaluating each method through Deming-fit regression analysis (Figures 14a-14b) and bias estimation by Bland-Altman plots (Figures 14c-14d) for two target peptides [Comparison of AFP between LC-MRM and ECLIA (Figures 14a, 14c, N=126); Comparison of AFP-Fuc% between LC-MRM and LiBA (Figures 14b, 14d, N=117)].

[0133] Figure 15 is a graph showing the error range of the coefficients in each formula for deriving the GAFA score (Figure 15a) and the GAFAD score (Figure 1b).

[0134]

[0135] The present invention will be described in more detail below through examples. These examples are intended solely to explain the invention more specifically, and it will be obvious to those skilled in the art that the scope of the invention is not limited by these examples according to the gist of the invention.

[0136]

[0137] Examples

[0138] Experimental method

[0139] Experimental materials

[0140] Labeled or (C 1315NArg / Lys) unlabeled recombinant AFP protein was purchased from Sino Biological Inc. (Beijing, China) and Origene Technologies Inc. (Rockville, MD, USA), respectively, and mouse monoclonal anti-AFP antibody was purchased from Abfrontier (Seoul, Korea), respectively. The anti-AFP antibody was covalently bound to Dynabeads protein G (Thermo Fisher Scientific, Waltham, MA, USA) for immunoprecipitation. IAA (Iodoacetamide), Tris-HCl (pH 8.0), Tris(2-carboxyethyl)phosphine hydrochloride solution (TCEP, pH 7.0), triamnonium bicarbonate buffer (TEAB, pH 8.5), sodium borate (pH 9.5), ammonium sulfate, human serum, and sodium dodecyl sulfate (SDS) were purchased from Sigma-Aldrich (St. Louis, MO, USA). α2-3,6,8 neuraminidase was purchased from NEB (Ipswich, MA, USA), and sequencing-grade modified trypsin was obtained from Promega (Madison, WI, USA). Sera-Mag SpeedBeads carboxylate-modified [E3] magnetic particles were purchased from Cytiva (Marlborough, MA, USA). PBS (Phosphate buffered saline, pH 7.4) and stable isotope-labeled internal standard peptide (IS, GYQELLEK 13 C 15N) was purchased from Thermo Fisher Scientific, and glycine buffer (pH 2.5) was purchased from bioWORLD (Dublin, OH, USA). Methyl alcohol and acetonitrile (ACN) were purchased from TEDIA (Fairfield, OH, USA), and ethanol was purchased from Merck (Darmstadt, Germany). Formic acid (FA) was purchased from FUJIFILM Wako Pure Chemical Corp. (Osaka, Japan), and isopropyl alcohol (IPA) was purchased from JT Baker / Avantor (Center Valley, PA, USA).

[0141]

[0142] Sample Collection

[0143] Samples were collected from 50 healthy individuals, 120 patients with chronic hepatitis, 120 patients with liver cirrhosis, 80 patients with stage 1 hepatocellular carcinoma, 80 patients with stage 2 hepatocellular carcinoma, and 75 patients with stage 3 hepatocellular carcinoma at Ajou University Hospital, and tumor stages were classified according to the modified International Union Against Cancer (mUICC) staging system. All experiments performed in this invention were conducted in accordance with the ethical guidelines of the 1975 Helsinki Declaration, and the research protocol was approved by the Institutional Review Board of Ajou University Hospital (AJOUIRB-KSP-2016-365). All samples were divided into three parts and stored at -80°C until use.

[0144]

[0145] Immunological analysis

[0146] Serum AFP levels were measured by electrochemiluminescence immunoassay (ECLIA) according to the manufacturer's instructions (Cobas e801, Roche Diagnostics), and serum DCP levels were measured by chemiluminescence microparticle immunoassay (CMIA) according to the manufacturer's instructions (MultiSR, Abbott Diagnostics). Serum AFP-L3 ratios were measured by liquid-phase binding assay (LiBA) using capillary domain electrophoresis according to the manufacturer's instructions (μTAS i30, Wako Pure Chemical Industries).

[0147]

[0148] Automated sample preparation

[0149] All serum samples were automatically prepared on a Bravo 96-channel liquid processing platform (Agilent Technologies). Sample preparation proceeded in the order of AFP protein enrichment using AFP monoclonal antibodies, desialylation using α2-3,6,8 neuraminidase, protein cleanup using Sera-Mag E3 beads for trypsin digestion, and LC-MRM after Evotip preparation.

[0150] From the remaining human serum samples (n=130) from other immunoassay tests conducted by the Seegene Medical Foundation, 126 serum samples were selected and diluted with depleted AFP serum to create a broad spectrum of AFP concentrations. These serums were used in two types of calibrators: 1) an analytical measurement range calibrator covering 0.07–8600 ng / mL AFP with 48.2% fucosylation; and 2) a clinical analysis calibrator covering 1–533 ng / mL AFP with 8.7% fucosylation. These calibrators were also diluted with depleted AFP serum. All diluted serums were verified using ECLIA and LiBA.

[0151]

[0152] Quality Control and Depleted AFP Serum (CLSI #1)

[0153] Quality control (QC) was prepared using low-fucosylated AFP protein and high-fucosylated AFP protein. Low-fucosylated AFP protein (<5%, 100 μg / mL) was obtained through in-house isolation and purification from residual serum, while high-fucosylated AFP protein (>95%, 100 μg / mL) was purchased from Fitzgerald Industries International (Acton, MA, USA). QC1 and QC4 were prepared by using low-fucosylated protein (in-house production) to produce low and high concentrations of AFP, respectively, and diluted with depleted AFP serum. QC2 and QC5 were prepared using high-fucosylated protein (Fitzgerald) for low and high concentrations of AFP, respectively, and diluted with depleted AFP serum. QC3 was prepared by mixing QC4 and QC5. All QCs were verified using ECLIA and LiBA.

[0154] Pooled human serum (Sigma-Aldrich) was used to prepare depleted AFP serum. After filtering the serum using a 0.45 μm filter, monoclonal anti-antibodies conjugated with magnetic beads were added to the filtered serum (at a 1:40 ratio), and the mixture was incubated overnight at 4°C using a rotary incubator, after which the supernatant was separated using a magnet. After repeating this immunoprecipitation four times, the depleted AFP serum was stored at -70°C until use.

[0155]

[0156] AFP protein enrichment and desiallylation

[0157] According to the manufacturer's instructions, 2.5 μL of Dynabeads (100 μg / μL) were incubated overnight at 37°C with 3.85 μL of monolonic anti-AFP antibody (2.61 μg / μL). After removing the supernatant from the Bravo deck, the Dynabeads-Ab complex was added to the diluted serum sample (100 μL of serum and 300 μL of 1x PBS) and incubated at room temperature for 2 hours with stirring. The supernatant was discarded four times and washed three times with 1x PBS buffer. Subsequently, after removing the supernatant, 50 μL of neuraminidase solution for desiallylation (neuraminidase stock 50 units) was added and incubated at 37°C for 1 hour without shaking, followed by washing with 0.1x PBS. For elution, 0.1 M glycine and 50% acetonitrile at pH 2.5 were used as the elution buffer, and elution was performed at 300 rpm for 10 minutes at room temperature. Finally, 10% SDS buffer of 1 M Tris-HCl (pH 8.0) was added for neutralization and denaturation.

[0158]

[0159] Bead-based trypsin decomposition

[0160] For reduction, TCEP (final concentration 5 mM) was added and incubated at 55°C for 15 minutes. Subsequently, IAA was added for alkylation and incubated in the dark at room temperature for 30 minutes (final concentration 40 mM). 20 μL of Sera-Mag E3 beads were bound to the protein in 50% ethanol buffer while stirring at room temperature for 20 minutes. The supernatant was discarded, and after washing with methanol, trypsin solution (1 μg trypsin, 0.5 pmol IS in 50 mM TEAB) was added and incubated overnight at 37°C at 1000 rpm.

[0161]

[0162] EVOTIP preparation

[0163] The tryptic peptide sample was loaded onto the magnetic deck up to the sample loading step after adding 100 μL of 50 mM TEAB. The Evotips (EV2011, Evosep, Odense, Denmark) were rinsed with 20 μL of solvent B (80% ACN, 0.1% FA) and immersed in IPA solution until all Evotips turned pale white. After equilibrating with 20 μL of solvent A (0.1% FA) and loading the sample, the Evotips were washed twice with 20 μL of solvent A using a centrifuge at 800 g for 60 seconds at every step. Finally, 100 μL of solvent A was added to the Evotips, and they were centrifuged at 800 g for 10 seconds to maintain a wet state. The prepared Evotips were transferred to an autosampler for LC-MRM analysis.

[0164]

[0165] LC-MS / MS analysis

[0166] Quantification of target peptides was performed by combining a Sciex Triple Quad 6500+ mass spectrometer (Sciex, Framingham, MA, USA) equipped with an OptiFlow Turbo V ion source with an Evosep One LC system (Evosep Biosystems, Odense, Denmark). The LC-MS / MS system was controlled by the Evosep plugin (version 2.3.58.0) of Chronos (version 5.1.8.0) and Analyst (version 1.7.2, Sciex) software for method setup and data acquisition.

[0167] The Evosep One method was designed to process 60 samples per day with a 21-minute gradient, a 24-minute cycle time, and a flow rate of 1.0 μL / min (60 SPD method). Chromatographic separation was performed using an analytical column (8 cm x 150 μm, 1.5 μm, EV1109, Evosep) equipped with a column heater set to 40°C. Mobile phases A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile) were used for the Evosep gradient.

[0168] The mass spectrometer operated in cation, multiple reaction monitoring (MRM) mode. The source / gas parameters were as follows: Curtain gas, 20; collision gas, 8; ion spray voltage, 4500; temperature, 200; ion source gas 1, 10; ion source gas 2, 30. The residence time of the mass transfer was 40 ms, and the cycle time was 0.7651 s. The Q1 quadrupole and Q3 quadrupole were set to unit resolution (0.7 Da).

[0169]

[0170] LC-MRM analysis

[0171] All samples were eluted online using the Evosep One system (Evosep Biosystems) and analyzed using the Triple Quad 6500+ mass spectrometer (Sciex). Data were processed using the quantitative tools of Analyst 1.7.2 (Sciex).

[0172]

[0173] Data analysis

[0174] MRM raw data was processed using the quantification tools of Analyst 1.7.2. All statistical analyses (ROC curves, ANOVA, Bland-Altman plots, Deming-fit regression, correlation analysis) and visualizations were performed using MedCalc software version 19.7 (Ostend, Belgium).

[0175]

[0176] Validation

[0177] The assay was validated for two months according to CLSI guidelines by evaluating precision, accuracy, assay retrieval, calibration curve, immunoprecipitation retrieval, comparison with immunoassays, assay specificity, assay sensitivity, carryover, matrix effects, dilution integrity, and stability. Statistical analysis was performed using MedCalc 19.7 software.

[0178]

[0179] Scoring Optimization and Diagnostic Evaluation

[0180] Data analysis and the development of a new scoring system were performed using the logistic regression model for machine learning from the open-source R software version 4.3.2 (The R Foundation). Machine learning was performed using the Carat R package (version 6.0-94), and the data were randomly split into 75% training data and 25% test data for unbiased model evaluation. Additionally, 10-fold cross-validation was used to enhance the robustness and generalizability of the model. The GALAD score was calculated according to the following formula: Z = 1.67 x Gender + 0.09 x Age + 0.04 x AFP-L3(%) + 2.34 x log10 AFP(ng / mL) + 1.33 x log10 DCP(ng / mL) -10.08. Gender was defined as 1 for male and 0 for female. Receiver operating characteristic (ROC) curves were constructed as 95% confidence intervals (CI) to evaluate sensitivity, specificity, and area under the curve (AUC).

[0181]

[0182] Experimental results

[0183] Clinical Analysis Overview

[0184] The overall plan for the analytical method development and evaluation process is illustrated in Fig. 1. The inventors established a sample pretreatment step to directly detect glycopeptides while maintaining the N-glycan structure as a specific standard. In addition, both the LC-MRM conditions and the sample pretreatment step were optimized, and all sample pretreatment steps were automated and further refined (see Figs. 6-9 and Table 1). The automated method was validated for two months according to CLSI guidelines to evaluate analytical performance, and the developed GAFAD scoring algorithm was evaluated for diagnostic performance in comparison to the conventional GALAD algorithm in distinguishing between non-hepatocellular carcinoma and hepatocellular carcinoma.

[0185] Target Target Poptide sandwich isotope type precursor m / z (charge) product ion type fragment m / z (charge) DPEPCECXPAFPGYQELLEK376-383unlabeled490.3 (+2)y6*759.4 (+1)30102215y5631.4 (+1)30102225y4502.3 (+1)30102825GYQELLE[K]376-383labeled494.3 (+2)y6*767.4 (+1)60102415y5639.4 (+1)60102430y4510.3 (+1)60103020AFP-Fuc%**VNFTEIQK_5400250-257unlabeled867.7 (+3)PEP_3300*1037.5 (+2)40102620 PEP_43001118.5 (+2)40102220PEP_01001181.6 (+1)40104620VNFTEIQK_5410250-257unlabeled916.4 (+3)PEP_3310*1110.5 (+2)30102620 PEP_43101191.5 (+2)30102420PEP_01101327.7 (+1)30103820PEP_01001181.6 (+1)30105220

[0186]

[0187] Clinical analysis verification

[0188] The calibration curves for the quantification of AFP and glycosylated AFP remained linear across the clinical analysis range for 2 months (1–533 ng / mL for AFP, 1–487 ng / mL for non-fucosylated AFP, and 0.1–46 ng / mL for fucosylated AFP; Table 2 and Fig. 10). Coefficient of determination (R²) 2 The linear fit (R) exceeded 0.99, the slope was close to 1, and the y-intercept was close to 0 (Table 3 and Fig. 10). Linear fit (R 2 The analytical measurement range, including the lower limit of quantification (LLOQ) and upper limit of quantification (ULOQ) at >0.99, was 0.07–8600 ng / mL for AFP and 0.03–2150 ng / mL for both non-fucosylated AFP and fucosylated AFP (Fig. 11). This range is fivefold. The analytical sensitivity and specificity for LLOQ met all criteria (Table 4-5). For the LLOQ of glycosylated AFP, the lowest concentration meeting the criterion of CV<20% after tripling for 5 days was measured at 0.27 ng / mL for non-fucosylated AFP and 0.25 ng / mL for fucosylated AFP, respectively. However, the fucosylation ratio of glycosylated AFP can be clinically used up to 0.03 ng / mL, which is higher than the LOD (SNR > 3).

[0189] Analysis of inaccuracy and accuracy in five different quality control (QC) samples over a two-month period showed that repeatability, inter-day precision, and in-laboratory precision were <17.1% (Table 6). Accuracy focused on inaccuracy was found to be acceptable when tested using bias estimation methods (Table 7). The analytical recovery rates for AFP, non-fucosylated AFP, and fucosylated AFP in three QC samples were 92.8–114.4%, 88.9–93.9%, and 84.8–104.0%, respectively (Table 8). Analytical quantification was acceptable when samples were diluted 20,000-fold (Table 9). No carryover was observed in blanks exceeding the upper limit of the measurement interval sample for the clinical analysis range (Table 10). The matrix effect for six matrix samples was less than 10% in accuracy (Fig. 12). The average recovery rate of the immunoprecipitate was 105% for the AFP standard protein (Table 11). AFP concentrations were preserved with short-term storage stability (up to 7 days) at 4°C or room temperature, long-term storage stability (up to 4 weeks) at -20°C, and freeze-thaw cycle stability (up to 3 cycles) (Fig. 13). Comparisons for each method demonstrated analytical equivalence between the LC-MRM assay and immunoassays including ECLIA and LiBA (Table 12 and Fig. 14).

[0190] Target Measurement Blank Calibrator 1 Calibrator 2 Calibrator 3 Calibrator 4 Calibrator 5 Calibrator 6 Calibrator 7 Calibrator 8 Calibrator 9 Calibrator 10AFP(GYQELLEK) Estimated Concentration (ng / mL) 0.0 1.0 2.0 4.0 8.0 16.7 34.0 68.3 139 27 35 33 Mean 0.0 1.1 2.1 4.2 8.4 16.7 33.3 66.9 140 27 05 25 SD 0.0 0.2 0.3 0.6 1.3 2.7 4.9 8.8 20.1 34.7 58.9 CV(%) -15.3 14.6 14.3 14.9 16.0 14.8 13.2 14.4 12.8 11.2 Bias -2.3 5.2 5.5 5.7 -0.1 -2.1 -2.0 0.5 -0.9 -1.4 Non-fucosylated AFP(VNFTEIQK_5400) Estimated Concentration (ng / mL) 0 1 0 1 9 3 8 7 6 1 5 2 3 0 4 6 0 8 1 2 2 2 4 3 4 8 7 Fucylated AFP(VNFTEIQK_5410) Estimated Concentration (ng / mL) 0 0 1 0 2 0 4 0 7 1 4 2 9 5 8 1 1 6 2 3 2 4 6 4 AFP-Fuc% Estimated Value (%) 0 8 7 8 7 8 7 8 7 8 7 8 7 8 7 8 7 8 7 7 8 7 7 LiBA (AFP-Fuc%)N / AN / AN / AN / A6.66.87.77.77.98.28.3MeanN / A13.111.19.58.89.18.98.99.08.99.3SD-4.22.31.71.0 1.10.80.90.90.80.8CV(%)-32.120.918.211.012.18.99.710.08.48.7Bias-50.728.18.81.64.11.82.73.02.47.2

[0191]

[0192] Target Slope Intercept R 2 MeanSDMeanSDMeanSDAFP (GYQELLEK)0.98660.10820.39052.84760.99890.0016 Non-fucosylated AFP (VNFTEIQK_5400)1.00230.11551.09333.23370.99720.0059 Fucosylated AFP (VNFTEIQK_5410)1.07830.2596-0.32030.41560.99780.0039

[0193]

[0194] blank sampleLLOQS / NPrecisionRecovery of assay (%)Peak ratio2736513.3--Concentration (ng / mL)0.0050.071-8.0108.4

[0195]

[0196] Peak Area of ​​Target Peptide Blank Sample Peak Area of ​​LLOQ Interference (%) 1AFP (GYQELLEK)_unlabeled 1013114638.82N / A129030.03N / A133350.0Mean 1013125672.91AFP (GYQELLEK)_labeled 42168130500.02N / A76403840.03N / A74799110.0Mean 42173111150.01Non-fucosylated AFP(VNFTEIQK_5400)N / A20830.02N / A25130.03N / A10230.0Mean 018730.01Fucosylated AFP(VNFTEIQK_5410)N / A8640.02N / A10200.03N / A6400.0Mean08420.0

[0197] Target QC Level NMean Repetitiveness Between-Day In-laboratory SD R % CVSD B % CVSD WL %CVAFP (ng / mL)Q1Low7011.91.08.11.613.31.915.6Q2Low709.80.66.61.010.21.212.1Q3Mid70184.114.17.717. 29.322.212.1Q4High70346.125.77.444.813.051.714.9Q5High67371.530.58.229.17.842.111.3AFP-Fuc (%)Q1Low694.30.716.70.23.70.717.1Q2High6994.52.02.10.80.82.12.3Q3Mid7053.52 .03.80.51.02.13.9Q4Low703.80.37.30.26.30.49.6Q5High7095.20.40.40.00.00.40.4

[0198]

[0199] SDR, Standard Deviation of Repeatability; SDB, Between-Day Standard Deviation; SDWL, Within-Laboratory Standard Deviation

[0200] Target QC Level NMean Validation Interval Bias Estimation Lower Limit Upper Limit Unit (ng / mL or %) Difference (%) AFP (ng / mL) Q1 Low 70 11.9 7.1 15.5 0.6 5.1 Accept Q2 Low 70 9.8 5.9 11.2 1.2 14.4 Accept Q3 Mid 70 18 4.1 14 22 36 -4.9 -2.6 Accept Q4 High 70 34 6.1 25 64 90 -26.9 -7.2 Accept Q5 High 67 37 1.5 30 44 74 -17.5 -4.5 Accept tAFP-Fuc(%)Q1Low694.32.44.60.721.3AcceptQ2High6994.588.495.41.31.4AcceptQ3Mid7053.5 50.256.60.10.1AcceptQ4Low703.82.84.20.38.6AcceptQ5High7095.290.791.93.94.3Acceptable

[0201] Target Measurement QC_LowQC_MidQC_HighAFP(GYQELLEK) Estimated Concentration (ng / mL) 8.5 51 8 9.0 37 3.0 Measured Concentration (ng / mL) 9.7 8 1 8 4.1 34 6.1 Spiking Recovery (%) 114.49 7.49 2.8 Non-fucosylated AFP (VNFTEIQK_5400) Estimated Concentration (ng / mL) 0.46 78 7.95 35 8.8 Measured Concentration (ng / mL) 0.43 97 8.94 319.0 Spiking Recovery (%) 93.98 9.88 8.9 Fucosylated AFP (VNFTEIQK_5410) Estimated Concentration (ng / mL) 8.08 101.11 4.18 Measured Concentration (ng / mL) 8.40 103.41 2.03 Spiking Recovery (%) 104.01 02.38 4.8 AFP-Fuc% Estimated Ratio (%) 91.95 3.43.5 Measured Ratio (%) 93.25 3.53.8 Spiking Recovery (%) 101.41 00.11 08.6

[0202]

[0203] Target Dilution Scale Measurement Concentration (ng / mL) SDCV (%) Dilution-Corrected Concentration (ng / mL) Recovery (%) AFP (GYQELLEK) 085925174859.73.48739102100891.51.78901104200361.23.3719584500150.00.376758925003.10.00.9776090200000.40.04.8724884 Non-Fucosylated AFP(VNFTEIQK_5400)04451591510.81.24576103100452.86.24455100200200.42.13940895009.40.22.2468210525001.60.425.2408992200000.20.02.94464100FukoSilhwa AFP(VNFTEIQK_5410)0414158469.11.14231102100403.69.0402597200170.53. 13456835008.80.11.5440210625001.50.426.3367889200000.30.04.35043122 Fuc(%)00.48250.4440.0051.195921000.4360.0071.516912000.4290.0020.50 6895000.4450.0020.4029225000.4330.0030.69590200000.4860.0030.694101

[0204]

[0205] Peptide Type Peak Area of ​​Blank Sample Peak Area of ​​LLOQ Performance Rate (%) 1 AFP (GYQELLEK)_unlabeled 15 6 21 14 6 31 3.6 21 37 5 12 90 31 0.7 3 20 8 41 33 35 15.6 Mean 16 7 41 25 6 71 3.3 1 AFP (GYQELLEK)_labeled N / A6 81 30 50 0.0 2 N / A7 6 40 38 40.0 3 N / A7 47 99 11 0.0 Mean N / A7 31 11 15 0.0 1 Non-fucosylated AFP(VNFTEIQK_5400) N / A2 83 0.0 2 N / A2 5 13 0.0 3 N / A1 23 0.0 Mean N / A1 87 30.0 1 Fucosylated AFP(VNFTEIQK_5410)N / A8640.02N / A10200.03N / A6400.0MeanN / A8420.0

[0206]

[0207] Target Concentration (ng / mL) Measurement QC1 AFP (GYQELLEK) 94.0 Peak Area 11,992,248 Peak Area 12,347,644 IP (Immunoprecipitation) Recovery (%) 97% AFP (GYQELLEK) 397.1 Peak Area 56,644,730 Peak Area 49,752,934 IP Recovery (%) 114% IP Recovery (%) Average Value 105%

[0208] Target Comparison Equipment Sample Size Regression Equation R (95% CI) Mean Bias (95% CI) Slope (95% CI) Intercept (95% CI) AFP LC-MRM vs ECLIA 126 1.131 (1.020 to 1.243) -13.687 (-27.918 ~0.545) 0.933 (0.906 ~ 0.953) 0.387 (-3.999 ~ 4.772) AFP-Fuc% LC-MRM vs LiBA 117 1.024 (1.005 to 1.042) 1.701 (0.650 ~ 2.752) 0.992 (0.989 ~ 0.995) 7.059 (4.679 ~ 9.439)

[0209]

[0210] Healthy (N=50) Chronic Hepatitis (N=120) Cirrhosis (N=120) HCC Stage 1 (N=80) HCC Stage 2 (N=80) HCC Stage 3 (N=75) Age <60 50(100%) 113(94.2%) 103(85.8%) 56(70%) 55(68.8%) 52(69.3%) ≥60 7(5.8%) 17(14.2%) 24(30%) 25(31.3%) 23(30.7%) Gender Male 9(18%) 71(59.2%) 91(75.8%) 61(76.3%) 61(76.3%) 60(80%) Female 41(82%) 49(40.8%) 29(24.2%) 19(23.8%) 19(23.8%) 15(20%) Etiology Anti-HBV-positive 120(100%) 120(100%) 80(100%) 80(100%) 75(100%) Tumor Size ≤ 280 (100%) 8 (10%) 4 (5.3%) 2 - 106 3 (78.8%) 54 (72%) ≥ 109 (11.3%) 17 (22.7%) Number of Tumors 180 (100%) 65 (81.3%) 34 (45.3%) 2 - 314 (17.5%) 23 (30.7%) > 31 (1.3%) 18 (24%) Vascular Infiltration No 80 (100%) 80 (100%) 42 (56%) Yes 33 (44%)

[0211] Diagnostic performance indicator AFPAFP-L3 (or AFP-Fuc%)LiBA(Cutoff > 10 ng / mL)LC-MRM(Cutoff > 14.12 ng / mL)LiBA(Cutoff > 10%)LC-MRM(Cutoff > 9.2%)True Positive 139 12899 109(▲10)False Negative 96 107 136 126(▼10)True Positive 237 245 279 279(-)False Positive 534 51 111(-)Accuracy 71.6% 71.0% 72.0% 73.9%(▲1.9%)Sensitivity 59.1% 54.5% 42.1% 46.4%(▲4.3%)Specificity 81.7% 84.5% 96.2% 96.2%(-)Precision 72.4% 74.0% 90.0% 90.8%(▲0.8%)

[0212] Sensitivity comparison with LiBA

[0213] The LC-MRM assay demonstrated superior sensitivity in quantifying fucosylated AFP compared to LiBA. Evaluating two sample groups—1) low-concentration calibrators and 2) clinical serum (N=525)—the low-concentration calibrators showed an AFP range of 0.1–1.3 ng / mL and a moderate AFP-Fuc% (10 points, 15 replicates per point, 48.2% fucosylated, Fig. 2a). The clinical serum (N=525) ranged from 0.5–14,189 ng / mL for AFP and 0.3–98.9% for AFP-Fuc% (Fig. 2b). In the first sample group (low-concentration calibrator), the sample group with low AFP and intermediate AFP-Fuc% detected all diluted samples, whereas LiBA failed to detect fucosylated AFP in samples with AFP levels below 0.7 ng / mL (Fig. 2a). In the second sample group (525 clinical serums), LiBA failed to detect fucosylated AFP in 309 out of 525 patient samples; in some samples, it failed to detect AFP despite having high AFP levels or high degrees of fucosylation, whereas the LC-MRM assay detected fucosylated AFP in all 525 patient samples. The graph filtered for less than 10% fucosylation better illustrates samples that cannot be detected by LiBA but are detectable only by the LC-MRM assay (N=409 for fucosylation less than 10%, dotted box in Fig. 2b). In particular, when clinical serum was classified according to clinical characteristics (Table 13), a high proportion of values ​​were undetectable by LiBA (Fig. 2c). The median value filtered by the reference value (fucosylation 10%) was 0 in all groups, and some of the undetectable values ​​accounted for 98% in the healthy group, 81% in chronic hepatitis, and 64% in cirrhosis (dotted box in Fig. 2c). In hepatocellular carcinoma, 51% of stage 1, 35% of stage 2, and 25% of stage 3 were also undetectable.

[0214]

[0215] Comparison of diagnostic performance with LiBA

[0216] Although the accuracy of both AFP-Fuc% (or AFP-L3) and AFP was very similar, the precision of AFP was significantly lower than that of AFP-Fuc% (or AFP-L3), with an AFP-Fuc% cutoff of 9.2% and an AFP-L3 cutoff of 10% (Table 14). In particular, the accuracy of LC-MRM was the highest at 90.8%, and the difference in diagnostic power between AFP-Fuc% and AFP-L3 became more pronounced as the AFP concentration fell below the threshold. When filtered by the bottom 30% of AFP concentrations or various AFP threshold values, the diagnostic performance of AFP-Fuc% by LC-MRM in the diagnosis of hepatocellular carcinoma was superior to that of AFP-L3 by LiBA (Figs. 3a-3d). The AUC values ​​were 0.511 for LiBA and 0.710 for LC-MRM (Fig. 3a, AFP<2.3 ng / mL, n=165, p<0.001), 0.593 for LiBA and 0.705 for LC-MRM (Fig. 3b, AFP<7 ng / mL, n=298, p<0.001), 0.590 for LiBA and 0.690 for LC-MRM (Fig. 3c, AFP<10 ng / mL, n=331, p<0.005), and 0.636 for LiBA and 0.697 for LC-MRM (Fig. 3d, AFP<20 ng / mL, n=382, p<0.05).

[0217]

[0218] Development of new scores and evaluation of diagnostic performance

[0219] The inventors developed a new scoring system based on the derived factors using a machine learning approach. A total of 525 samples, including 290 non-HCC samples and 235 HCC samples, were divided into a training set for score development and a test set for validation. For early diagnosis, the disease groups were divided into three subgroups for analysis (Table 15). The GAFAD and GAFA functions were developed using logistic regression with machine learning methods and calculated using the following formulas:

[0220] [Equation1] GAFAD= -0.152 x Gender + 0.106 x Age + 9.852 x AFP-Fuc portion + 0.491 x log10 AFP(ng / mL) + 6.551 x log10 DCP (ng / mL) - 16.338

[0221] [Equation2] GAFA= 0.588 x Gender + 0.097 x Age + 12.599 x AFP-Fuc portion + 0.73 x log10 AFP (ng / mL) - 7.299

[0222] Training Set (N=395) Test Set (N=130) Healthy Individuals (N=36) HBV (N=92) LC (N=90) HCC Phase 1 (N=61) HCC Phase 2 (N=59) HCC Phase 3 (N=57) Healthy Individuals (N=14) HBV (N=28) LC (N=30) HCC Phase 1 (N=19) HCC Phase 2 (N=21) HCC Phase 3 (N=18) Age, Mean (SD) <60 32.7 (8.0) 43.9 (9.1) 48.2 (6.7) 52.8 (5.5) 50.4 (6.5) 50.7 (7.3) 31.9 (9.4) 38.4 (9.3) 48.3 (8.16) 50.7 (5.5) 50.8 (8.3) 50.3 (6.6)≥60 -63.6 (2.7)64.5 (4.0)63.7 (4.5)67.6 (6.3)67.4 (6.3)-63.5 (4.9)-65.6 (4.6)65.8 (3.1)62.0 (1.4) Gender Male 9 (25%) 55 (60%) 69 (77%) 46 (75%) 46 (78%) 45 (79%) -16 (57%) 22 (73%) 15 (79%) 15 (71%) 15 (83%) Female 27 (75%) 37 (40%) 21 (23%) 15 (25%) 13 (22%) 12 (21%) 14 (100%) 12 (43%) 8 (27%) 4 (21%) 6 (29%) 3 (17%) Etiology - HBV-Positive - 92 (100%) 90 (100%) 61 (100%) 59 (100%) 57 (100%) -28 (100%) 30 (100%) 19 (100%) 21 (100%) 18 (100%) Tumor Size ≤2 cm - 61 (100%) 8 (14%)3 (5%)---19 (100%)-1 (6%)2-10 cm----45 (76%)41 (72%)----18 (86%)13 (72%)≥10 cm----6 (10%)13 (23%)----3 (14%)4 (22%)Number of Tumors1---61 (100%)45 (76%)28 (49%)---19 (100%)20 (95%)6 (33%)2-3----13 (22%)17 (30%)----1 (5%)6 (33%)>3----1 (2%)12 (21%)-----6 (33%)Vascular Infiltration No-----27 (47%)-----6 (33%)Yes-----30 (53%)-----12 (67%)Marker, Median[IQR]AFP (ng / mL)3.30 [2.17~4.71]4.03 [2.55~8.20]6.32 [3.59~13.11]13.53 [4.30~106.19]21.20 [4.98~348.08]47.83 [4.40~320.70]3.07 [2.37~6.09]3.48 [2.70~7.66]6.24 [3.53~18.24]8.53 [4.34~155.78]14.51 [4.64~1937.47]35.24 [6.20~494.09]DCP (mAU / mL)20.7 [18.0~24.3]20.9 [18.85~25.30]20.25 [16.90~26.00]29.70 [19.58~51.28]70.20 [28.38~381.40]362.30 [71.25~6030.7]23.00 [18.AFP-Fuc (%)3.04 [2.32~3.84]3.38 [2.48~4.91]4.56 [3.16~5.65]4.93 [3.00~14.42]7.67 [3.95~28.73]15.77 [5.52~50.933.06 [2.54~4.52]2.77 [2.19~4.30]3.92 [3.06~5.93]3.95 [2.90~22.94]14.74 [5.41~36.98]12.39 [5.33~32.12]GALAD-5.30 [-5.95~-3.64]-2.06 [-3.54~-0.31]-0.66 [-1.61~0.52]1.20 [-0.97~3.98]2.65 [0.51~6.55]4.78 [1.53~8.81]-4.92 [-6.44~-4.14]-2.50 [-3.52~-1.03]-0.60 [-1.81~0.68]1.47 [-0.94~4.88]2.04 [0.19~7.53]4.51 [1.48~8.58]GAFAD-4.06 [-4.43~-2.54]-2.12 [-3.03~-1.52]-1.46 [-2.55~-0.62]1.32 [-0.77~3.68]4.10 [0.98~9.55]9.21 [4.72~17.92]-3.72 [-4.32~-2.59]-2.53 [-3.64~-1.44]-1.05 [-2.20~-0.81]0.15 [-0.50~5.27]3.41 [0.75~16.94]12.82 [1.72~16.76]GAFA-3.28 [-4.15~-2.39]-1.58 [-2.40~-0.66]-0.65 [-1.40~0.11]0.44 [-0.55~1.66]1.03 [-0.06~3.99]1.85 [0.05~7.28]-3.65 [-4.25~-2.29]-2.33 [-2.77~-1.52]-0.87 [-1.37~-0.13]0.52 [-0.58~2.67]1.80 [0.33~5.30]1.00 [-0.13~4.82](1) Whole group Non-HCCHCC (2) Subgroup liver disease Very early HCC (3) Subgroup liver disease AFP-negative HCC (AFP <20 ng / mL) AFP-negative HCC (AFP <20 ng / mL).

[0223] Each coefficient value of Equation 1(GAFAD) 1. Statistical Value X Intercept Age Gender MRM_log.AFPMRM_AFP_Fuclog.PIVKAMin.-60.140175080.101737948-3.047563075-3.4049717242.989503490.6771355181st Qu.-25.958680180.122136527-1.036104548-1.035967316.8309845436.106340777Median-18.382881910.1350811820.3924184 36-0.04280492715.765957596.624116218Mean-24.444020040.1533041160.217007857-0.34907112125.224952810.25654823rd Qu.-16.924609490.1816207481.6142809840.7163035540.3005093411.17793974Ma x.-8.3477198940.2476085933.1230514841.28412546370.8237666730.755190742. Difference from final coefficients Min.43.8020.0042.8963.8966.8625.8741st Qu.9.6210.0160.8841.5273.0210.445Median2.0450.0290.5440.5345.9140.073Mean8.1060.0470.3690.84015.3733.7063rd Qu.0.5870.0761.7660.22530.4494.627Max.7.9900.1423.2750.79360.97224.204Maximum difference 43.8020.1423.2753.89660.97224.204 Minimum value 0.58 70.00 40.36 90.22 53.02 10.07 33. Coefficient -kg-fihj Final coefficient -16.33 80.10 6 -0.15 20.49 19.85 26.55 1 Maximum range -60.14 ~ 27.46 4 -0.036 ~ 0.24 8 -3.42 7 ~ 3.12 3 -3.40 5 ~ 4.38 7 -51.12 ~ 70.82 4 -17.65 3 ~ 30.75 5 Intermediate range -25.95 5 ~ -6.71 70.03 ~ 0.18 2 -3.048 ~ 2.74 4 -3.405 ~ 4.387 -20.597 ~ 40.30 10.677 ~ 12.425 min_range -16.925 ~ -15.75 10.102 ~ 0.11 -0.521 ~ 0.21 7 0.266 ~ 0.71 6 6.831 ~ 12.8 7 3 6.478 ~ 6.624.

[0224] Each coefficient value of Equation 2 (GAFA) 1. Statistical Value X Intercept Age Gender MRM_log.AFPMRM_AFP_FucMin.-11.082482610.069666424-1.083538512-0.5282793662.8134224131st Qu.-8.7111070350.097686214-0.3500750540.27266709110.14000407Median-8.1613952640.1084881470. 758781140.47541843614.03024432Mean-8.0771884070.11171960.6921464240.43122366415.747421763rd Qu.-6.9479136210.1376648361.687380770.79497967820.95944827Max.-5.4364235440.1443287682.3326598861.02667070930.438986692. Difference from final coefficient Min.3.7830.0271.6721.2589.7861st Qu.1.4120.0010.9380.4572.459Median0.8620.0110.1710.2551.431Mean0.7780.0150.1040.2993.1483rd Qu.0.3510.0411.0990.0658.360Max.1.8630.0471.7450.29717.840Max.Difference 3.7830.0471.7451.25817.840Min.Difference 0.3510.0010.1040.0651.4313. Coefficient e (-inclusive value) badc Final Coefficient -7.29 90.09 70.58 80.73 12.599 Max Range -11.082 ~ -3.51 60.05 ~ 0.144 -1.157 ~ 2.333 -0.528 ~ 1.988 -5.241 ~ 30.439 Middle Range -9.162 ~ -5.43 60.056 ~ 0.138 -1.084 ~ 2.26 0.273 ~ 1.18 72.813 ~ 22.385 Min Range -7.65 ~ -6.94 80.096 ~ 0.09 80.484 ~ 0.69 20.665 ~ 0.79 511.168 ~ 14.03

[0225] To clearly distinguish between the high-risk hepatocellular carcinoma group (chronic hepatitis or cirrhosis) and the hepatocellular carcinoma group, the optimal cutoff value, AUC, diagnostic sensitivity, specificity, accuracy, and precision were calculated. In particular, the new score GAFA is a method for determining liver cancer risk using a formula combining sex, age, the fraction of fucosylated AFP, and AFP concentration, while GAFAD uses a formula that additionally combines DCP with this. A comparison of GAFAD and GALAD showed that GAFAD was superior in the diagnosis of hepatocellular carcinoma. When diagnostic performance was evaluated with specificity fixed at 90%, significant differences were observed in the sensitivity of each marker and score (Table 18). Compared to GALAD, GAFAD achieved a 16% improvement in sensitivity, a 7% absolute improvement in accuracy, and a 3% improvement in precision, supporting the enhanced diagnostic utility of the GAFAD model compared to the existing multi-marker approach, GALAD.

[0226] In both the training and test sets, the AUC values ​​for single markers (AFP, AFP-Fuc%, DCP) ranged from 0.716 to 0.846, while the multi-marker based scores (GAFA, GALAD, GAFAD) were higher at 0.866 to 0.947. Notably, GAFAD recorded an AUC of 0.938 in the entire patient population (HCC vs. non-HCC), which was significantly superior to GALAD's 0.887 (p<0.001) (Table 19 and Fig. 5a). When comparing patients in the very early stage (HCC stage 1, ≤2cm) with the liver disease patient group (chronic hepatitis, cirrhosis), GAFAD had an AUC of 0.879, which was significantly higher than GALAD (0.785) (p<0.0001, Fig. 5b), and GAFAD also demonstrated the best diagnostic performance in the AFP-negative subgroup (occurring in many HCC patients). The AUC of GAFAD was 0.883, which was significantly higher than that of GALAD (0.755) (p<0.0001, Fig. 5c). This supports the utility of diagnosis using GAFAD in the patient group with low AFP dependency.

[0227] In addition to the analysis of accuracy and sensitivity / specificity, multi-marker models were compared using AUC-PR, F1 score, IDI, and NRI. GAFAD recorded an AUC-PR of 0.933 and an F1 score of 0.849 compared to GALAD, demonstrating improved balance and reclassification ability across all metrics. Furthermore, in the Hosmer-Lemeshow test, GALAD showed discrepancy (p<0.001), whereas GAFAD demonstrated excellent goodness of fit (p=0.321) (Table 20).

[0228] The study cohort of the present invention was a more diagnostically challenging group because the difference in biomarker values ​​between HCC and non-HCC was narrower and there was more overlap compared to the existing GALAD study. Nevertheless, GAFAD improved the AUC from 0.887 to 0.938 (ΔAUC=0.051) compared to GALAD, which demonstrates excellent diagnostic reliability, particularly in early and AFP-negative HCC patients.

[0229] Diagnostic Performance Indicators GALADGAFAGAFAD Cutoff > 0.89 Cutoff > 0.34 Cutoff > -0.34 True Positives 155 146 193 (▲38) False Negatives 808 942 (▼38) True Positives 261 261 261 (-) False Positives 292 929 (-) Accuracy 79.24% 77.52% 86.48% (▲7.24%) Sensitivity 65.96% 62.13% 82.13% (▲16.17%) Specificity 90.00% 90.00% 90.00% (-) Precision 84.24% 83.43% 86.94% (▲2.7%)

[0230] (1) HCC vs. non-HCC (2) Very early HCC (single node, ≤2cm) vs. liver disease (3) AFP-negative HCC vs. liver disease Cutoff AUROC (95% CI) Sensitivity (%) Specificity (%) Cutoff AUROC (95% CI) Sensitivity (%) Specificity (%) Cutoff AUROC (95% CI) Sensitivity (%) Specificity (%) Training Set AFP (Established Cutoff) 200.716 (0.669-0.760) 46.89 88.53 200.659 (0.596-0.718) 36.07 86.26 200.549 (0.486-0.609) -- AFP-Fuc % (Youden Index) 8.30.736(0.690-0.779) 48.599 4.95 8.30.616(0.552-0.677) 32.799 4.51 8.30.634(0.574-0.691) 27.669 4.51 DCP (Established Cutoff) 400.846(0.807-0.880) 61.029 8.174 00.716(0.655-0.772) 29.519 7.80 400.826(0.776-0.869) 53.199 7.80 GALAD (Established Cutoff)-0.63 0.879(0.843-0.910)85.3166.51-0.63 0.779(0.722-0.830)73.7760.44-0.63 0.741(0.685-0.792)72.3460.44GAFAD (Youden Index)-0.03 0.879(0.843-0.910)79.6676.611.27 0.779(0.722-0.830)49.1892.86-0.49 0.741(0.685-0.792)71.2863.74GAFAD (Youden Index) 0.1 10.937 (0.908 -0.959) 78.5 395.4 10.1 10.880 (0.832 -0.918) 63.9 394.5 10.1 10.882 (0.838 -0.918) 64.8 994.5 1 GAFA (Youden Index) -0.03 0.866 (0.829 -0.898) 71.1 983.03 -0.7 90.804 (0.748 -0.852) 85.2 559.34 -1.2 00.742 (0.686 -0.792) 91.4 947.80 Test Set AFP (Established Cutoff)200.747(0.663-0.819)46.5587.50200.698(0.583-0.797)42.1184.48200.513(0.404-0.620)--AFP-Fuc % (Youden Index)8.30.781(0.700-0.848)51.7294.448.30.625(0.508-0.733)31.5893.108.30.645(0.537-0.744)22.5893.10DCP (Established Cutoff)400.845(0.772-0.903)48.2898.61400.802(0.696-0.884)15.7998.28400.755(0.653-0.840)29.0398.28GALAD (Established Cutoff)-0.630.910(0.847-0.953)91.3875.00-0.630.803(0.696-0.885)73.6868.97-0.630.801(0.703-0.878)83.8768.97GALAD (Youden Index)-0.03 0.910(0.847-0.953)79.31 83.33 1.25 0.803(0.696-0.885)52.63 94.83-0.47 0.801(0.703-0.878)83.87 72.41 GAFAD (Youden Index)-0.69 0.947(0.893-0.978)91.38 88.89-0.69 0.888(0.796-0.948)84.21 86.21-0.69 0.894(0.811-0.950)83.87 86.21 GAFAD (Youden Index)-0.560.905(0.841-0.949)86.2179.17-0.750.830(0.727-0.906)78.9570.69-0.560.821(0.725-0.894)80.6574.14.

[0231] Statistical Matrix Scoring Model GALADGAFADGAFAAUC-PR(95% Bootstrap CI) 0.881 (0.853-0.907) 0.933 (0.896-0.952) 0.869 (0.840-0.897) F1 max 0.778 0.849 0.766 IDI (95% CI) 1 (Reference) 0.506 (0.759-1.039) 0.315 (0.280-0.351) P value < 0.001 < 0.001 NRI (95% CI) 1 (Reference) 1.407 (1.288-1.526) 0.899 (0.759-1.039) P value < 0.001 < 0.001 Calibration Curve Chi-squared (Δ -2 LL)260.5399.4262.9 Cox & Snell R²0.3910.5330.394 Nagelkerke R²0.5240.7130.527 Odds Ratio (OR, 95% CI)105.1(51.6-214.0)1062.3(355.6-3173.4)314.2(124.4-793.5)P-value<0.001<0.001<0.001 Hosmer-Lemeshow (P-value)<0.0010.3210.008

[0232]

[0233] Foregoing, specific parts of the present invention have been described in detail. It is evident to those skilled in the art that such specific descriptions are merely preferred embodiments and do not limit the scope of the invention. Accordingly, the actual scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for providing information necessary for the diagnosis of liver cancer, comprising the step of measuring the proportion of fucosylated AFP (AFP-Fuc%) among the total alpha-fetoprotein (AFP) in a biological sample separated from a subject.

2. A method according to claim 1, characterized in that the AFP-Fuc% is measured by quantifying fucosylated AFP through mass spectrometry.

3. The method according to claim 2, wherein the mass spectrometry is performed by a mass spectrometry method selected from the group consisting of MALDI-TOF (Matrix-Assisted Laser Desorption / Ionization Time of Flight) mass spectrometry, SELDI-TOF (Surface Enhanced Laser Desorption / Ionization Time of Flight) mass spectrometry, ESI-TOF (Electrospray ionization time-of-flight) mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), and LC-MS / MS (liquid chromatography-mass spectrometry / mass spectrometry).

4. A method according to claim 3, characterized in that the detection condition of the mass spectrometry is selected from the group consisting of Multiple Reaction Monitoring (MRM), Parallel Reaction Monitoring (PRM), and Single Reaction Monitoring (SRM).

5. A method according to claim 4, characterized in that the detection condition of the mass spectrometry is Multiple Reaction Monitoring (MRM).

6. The method of claim 1, wherein the method further comprises the step of concentrating AFP in a sample by contacting a biological sample separated from a subject with an antibody that specifically recognizes AFP or an antigen-binding fragment thereof.

7. The method of claim 1, characterized in that the method additionally comprises the step of desializing glycosylated AFP in a biological sample separated from a target.

8. The method of claim 1, wherein the method additionally comprises the step of measuring the sex of the subject, the age of the subject, and the log value of the total AFP concentration (ng / mL) in the biological sample separated from the subject.

9. The method of claim 8, characterized in that it quantitatively evaluates the likelihood of liver cancer development based on the sum of each weighted measurement value, by assigning higher weights in the order of the ratio of fucosylated AFP to the total AFP in a biological sample separated from a subject (AFP-Fuc portion); the log value of the concentration of total AFP (ng / mL) in a biological sample separated from a subject; the sex of the subject; and the age of the subject.

10. The method of claim 9, characterized in that the method is performed by obtaining a score (GAFA score) that quantitatively evaluates the probability of developing liver cancer using the following [Equation 1]: [Equation 1] GAFA score =ax Gender +bx Age +cx AFP-Fuc portion +dx 1og10AFP -e In the above Equation 1, The above Gender is the gender of the subject, defined as 1 for male and 0 for female; The above Age is the age of the subject; The above AFP-Fuc portion is the ratio of fucosylated AFP to the total AFP in the biological sample separated from the subject; The above 1og10AFP is the logarithm of the total AFP concentration (ng / mL) in the biological sample separated from the subject; The above a is a rational number from 0.4 to 0.7; The above b is a rational number from 0.08 to 0.12; The above c is a rational number from 11 to 14.5; The above d is a rational number from 0.5 to 0.9; The above e is a rational number from 5.8 to 8.

8.

11. The method of claim 1, wherein the method further comprises the step of measuring the sex of the subject, the age of the subject, the log value of the total AFP concentration (ng / mL) in the biological sample separated from the subject, and the concentration of DCP (des-gamma-carboxy prothrombin) in the biological sample separated from the subject.

12. The method of claim 11, characterized in that it quantitatively evaluates the likelihood of liver cancer development based on the sum of each weighted measurement value, by assigning higher weights in the order of the ratio of fucosylated AFP to the total AFP in a biological sample separated from a subject (AFP-Fuc portion%); the concentration of DCP in a biological sample separated from a subject; the log value of the total AFP concentration (ng / mL) in a biological sample separated from a subject; the sex of the subject; and the age of the subject.

13. A method according to claim 12, characterized in that the method is performed by obtaining a score (GAFAD score) that quantitatively evaluates the probability of developing liver cancer using [Equation 2] below: [Equation 2] GAFAD score = -fx Gender +gx Age +hx AFP-Fuc portion +ix 1og10AFP +jx 1og10DCP -k In the above Equation 2, The above Gender, Age, AFP-Fuc portion, and 1og10AFP are the same as defined in Formula 1 of Claim 6, and The above 1og10DCP is the log value of the concentration (ng / mL) of DCP in a biological sample separated from the subject; The above f is a rational number from -0.3 to 0.6; The above g is a rational number from 0.09 to 0.13; The above h is a rational number from 6 to 13.5; The above i is a rational number from 0.2 to 0.85; The above j is a rational number from 6 to 7; The above k is a rational number between 15 and 17.

5.

14. A method according to claim 1, characterized in that the biological sample is whole blood, plasma, or serum.

15. A method according to claim 1, characterized in that the liver cancer is hepatocellular carcinoma (HCC).

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