KR20180131269A - A method and kit for assessing risk of hepatocellular carcinoma using metabolite profiling - Google Patents

Abstract

The present invention relates to a diagnosis method for evaluating the risk level associated with liver cancer based in a specific biomarker. Provided is a method for evaluating the prognosis or risk level of liver cancer comprises the steps of: collecting a biological sample from a subject; (b) measuring the concentration of at least one metabolite selected from the group consisting of tyrosine, aspartate aminotransferase (AST), 5-hydroxyhexanoic acid, tauroursodeoxycholic acid, lysophosphatidylcholines 16 : 1, lysophosphatidylcholine 20 : 3, oleamide, and androsterone sulfate; and (c) comparing the measured level of the metabolite to a measured value of a normal control sample.

Description

METHOD AND KIT FOR ASSESSING RISK OF HEPATOCELLULAR CARCINOMA USING METABOLITE BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and kit for evaluating the prognosis or risk of liver cancer using metabolic profiling,

The present invention relates to a diagnostic method and a kit for evaluating the risk of liver cancer by measuring the concentration of a specific biomarker.

Cancer is a disease in which the cell cycle is not regulated and continues cell division. It grows rapidly as it infiltrates the surrounding tissues and spreads or transits to each part of the body, threatening life. Cancer of the liver is called liver cancer, and liver cancer is one of the highest incidence cancer in the world. In Korea, liver cancer mortality rate is very high at 23 per 100,000 population, and about 10% of the total mortality rate of Koreans is related to hepatitis, liver cirrhosis and liver cancer.

Hepatocellular carcinomas can be classified as hepatocellular carcinoma (HCC) in which metastatic hepatocellular carcinoma originating from hepatocellular carcinoma and metastatic hepatocellular carcinoma in which other cancers are transferred to the liver. However, since primary hepatocellular carcinoma accounts for 90% Means primary liver cancer (HCC).

Causes of liver cancer include hepatitis B and hepatitis C virus, alcoholic epilepsy, metabolic chronic epilepsy, and toxic substances. Hepatitis B and C viruses can cause chronic liver disease or liver cancer such as chronic hepatitis or cirrhosis, and many people are infected mainly in Asia and Africa. Of the hepatocellular carcinoma patients in Korea, 65-80% are known to be carriers of hepatitis B antigens.

However, in recent years, obesity, diabetes mellitus, etc. have increased, and liver cancer patients due to metabolic chronic epilepsy are increasing. The metabolic syndrome progresses progressively, causing metabolic chronic epilepsy, and liver cancer may develop in the long term. Metabolic syndrome is associated with various metabolic diseases, such as hypertension, diabetes and other metabolic disorders such as diabetes mellitus with abdominal obesity and concurrently refers to the state of simultaneous occurrence.

Because liver cancer has a low survival rate and poor prognosis, prevention and early detection are important.

Research on new biomarkers as a method for early diagnosis of liver cancer is underway. Biomarkers are generally indicators of changes in the body through the use of proteins, DNA, RNA, and metabolites. By using biomarkers, we can objectively determine the normal or pathological state of a subject, Can be measured. Biomarkers are called internal phenotypes of certain diseases and can be used to find the cause of a specific disease or to measure the risk of an onset.

Metabolomics, which collectively analyze metabolites and metabolic circuits in cells, are widely used to detect biomarkers of disease. A specific amount of a metabolite present in a large amount or a small amount at the onset of cancer can be used as a cancer specific marker and a biological specimen can be taken from a patient suspected of having developed cancer to observe the change pattern of a cancer- . DNA, mRNA, and proteins have been studied as specific markers for the diagnosis of liver cancer. However, comprehensive understanding of metabolic changes among liver cancer patients and healthy persons is still insufficient.

The present inventors have identified metabolites associated with hepatocellular carcinoma and identified a correlation with hepatocellular carcinoma, thereby providing a method of diagnosing the risk or prognosis of hepatocellular carcinoma.

Numerous papers and patent documents are referenced and cited throughout this specification. The disclosures of the cited papers and patent documents are incorporated herein by reference in their entirety to better understand the state of the art to which the present invention pertains and the content of the present invention.

Disclosure of Invention Technical Problem [8] Accordingly, the present invention has been made in view of the above problems, and it is an object of the present invention to provide a method for diagnosing the risk or prognosis of liver cancer by measuring the level of a specific metabolite.

According to an aspect of the present invention, there is provided a method of detecting a biological sample, comprising: (a) obtaining a biological sample from a subject; (b), wherein the biological sample contains tyrosine, aspartate aminotransferase (AST), lysoPC (16: 1), lysoPC (20: 3), oleamide, 5-hydroxyhexanoic acid, Measuring at least one metabolite concentration selected from the group consisting of androsterone sulfate, and tauroursodeoxycholic acid (TUDCA); And (c) comparing the level of the measured metabolite to the measured value in a normal control sample. A method for assessing the prognosis or risk of a liver cancer is provided.

In one embodiment, in step (b), the compound selected from the group consisting of alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γ-GTP), leucine, phenylalanine and arachidonic acid Lt; RTI ID = 0.0 > metabolite < / RTI >

In one embodiment, the concentration of one or more metabolites selected from the group consisting of L-palmitoylcarnitine, LPA (16: 0) and LPA (18: 1) can do.

In one embodiment, the likelihood of developing liver cancer can be determined after 7 years from the reference time point.

According to another aspect of the present invention there is provided a method for the production of a pharmaceutical composition comprising tyrosine, aspartate aminotransferase, lysoPC (16: 1), lysoPC (20: 3), oleamide, 5-hydroxyhexanoic acid, , Androsterone sulfate, and TUDCA (tauroursodeoxycholic acid). The kit for diagnosing the risk of liver cancer is provided.

In one embodiment, at least one metabolite selected from the group consisting of alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γ-GTP), leucine, phenylalanine, and arachidonic acid And may further include a metering device for the metering device.

In one embodiment, it may further comprise a quantitative device for one or more metabolites selected from the group consisting of L-palmitoylcarnitine, LPA (16: 0) and LPA (18: 1) .

According to one aspect of the present invention, by measuring the concentration of a specific metabolite in serum, the possibility of developing liver cancer can be effectively predicted.

In particular, the method provides preemptive countermeasures with information on the likelihood of developing liver cancer at an average of 7 years from baseline.

It should be understood that the effects of the present invention are not limited to the effects described above, but include all effects that can be deduced from the details of the present invention or the composition of the invention described in the claims.

Figure 1 compares plasma metabolites between the control group (n = 134) and the HCC group (hepatocellular cancer group, n = 75). The score plot of the positive ion mode (a) and negative ion mode (b) obtained from the OPLS-DA model and the covariance [p] of the positive ion mode (c) and the negative ion mode (d) obtained from the OPLS- correlation [p (corr)].
Figure 2 is a metabolic path analysis result indicating a path arranged according to a score based on enrichment analysis (y axis) and topology analysis (x axis). The color and size of each circle represent the P - value and the pathway impact value, respectively.
Figure 3 is a matrix showing the correlation between aspartate aminotransferase (AST) and the seven major metabolites in the control and hepatocellular cancer groups. Correlations were derived using Pearson's correlation coefficient. The red color indicates a positive correlation, while the blue color indicates a negative correlation.
Figure 4 is a matrix showing the correlation between major metabolites and biochemical characteristics in the control group and the HCC group (Hepatocellular cancer group). Correlations were derived using Pearson's correlation coefficient. The red color indicates a positive correlation, while the blue color indicates a negative correlation.
Figure 5 depicts the ROC curve for tyrosine, aspartate aminotransferase, 5-hydroxyhexanoic acid and TUDCA (tauroursodeoxycholic acid) on liver cancer incidence.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

When an element is referred to as " comprising ", it means that it can include other elements, not excluding other elements unless specifically stated otherwise.

Unless otherwise defined, can be performed by molecular biology, microbiology, protein purification, protein engineering, and DNA sequencing and routine techniques commonly used in the art of recombinant DNA within the skill of those skilled in the art. These techniques are known to those skilled in the art and are described in many standardized textbooks and references.

Unless otherwise defined herein, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

Various scientific dictionaries, including the terms contained herein, are well known and available in the art. Although any methods and materials similar or equivalent to those described herein are found to be used in the practice or testing of the present application, some methods and materials have been described. It is not intended that the invention be limited to the particular methodology, protocols, and reagents, as they may be used in various ways in accordance with the context in which those skilled in the art use them.

Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings.

According to an aspect of the present invention, there is provided a method of detecting a biological sample, comprising: (a) obtaining a biological sample from a subject; (b), wherein the biological sample contains tyrosine, aspartate aminotransferase (AST), lysoPC (16: 1), lysoPC (20: 3), oleamide, 5-hydroxyhexanoic acid, Measuring at least one metabolite concentration selected from the group consisting of androsterone sulfate, and tauroursodeoxycholic acid (TUDCA); And (c) comparing the level of the measured metabolite to the measured value in a normal control sample. A method for assessing the prognosis or risk of a liver cancer is provided.

The subject may be a human subject to be diagnosed, and the biological sample is a sample separated from the subject to be evaluated for the risk of a liver cancer-related disease. The biological sample may be a tissue, a cell, a blood, a plasma, a peritoneal fluid, , Urine, feces, and the like. Preferably, the biological sample may be blood, and specifically may be plasma separated from blood.

The normal control group may be a human being who does not develop cancer or has no cancer-stained record for an average of 7 years.

In addition, each of the metabolites can be individually analyzed to diagnose the prognosis or risk of liver cancer, but the accuracy of the diagnosis can be improved by analyzing the level of the relevant metabolites as a whole.

The metabolites can be determined by studying the pathological pathways of liver cancer through metabolomics. The term " metabolomics " is one of the fields of biology that collectively analyzes and studies metabolites and metabolic circuits in cells. The term " metabolite " refers to the metabolome of a small molecule occurring in a cellular process, which can be classified mainly in blood, and the metabolism is based on analyzing the metabolite extracted from blood, The application is mainly to study metabolic diseases.

The metabolites may be enzymes produced in the liver, branched amino acids, aromatic amino acids, glycerol phospholipids, metabolites of fatty acids, and bile acids.

The term "tyrosine" is a kind of aromatic amino acid and plays an important role in hormone biosynthesis such as adrenaline (adrenocortical hormone) and thyroxine (thyroid hormone). The tyrosine is biosynthesized from phenylalanine in the liver in vivo. Amino acid imbalance was observed in patients with liver cancer and liver cirrhosis, especially tyrosine, an aromatic amino acid. Therefore, an increase in tyrosine can be an indicator of the risk of developing liver cancer.

The "AST (aspartate aminotransferase)" is an aspartic acid amino acid transporter enzyme, which is a hepatic enzyme produced in the liver in order to conduct the urea cycle in the liver. It has been reported that hepatic enzymes increase in the liver of patients with liver disease. These liver enzymes include ALT (alanine aminotransferase) and γ-GTP (γ-glutamyl transpeptidase) as well as AST. In particular, according to the stepwise regression analysis, since AST is one of the independent variables affecting the development of liver cancer, the increase of AST can be an important index to predict the risk of liver cancer.

The above "lysoPC (16: 1) and lysoPC (20: 3)" are a kind of lysophosphatidylcholine (lysoPC). Recent studies have reported that lysoPC is reduced in patients with liver cancer and that the conversion of phosphatidylcholine (PC) rapidly increases in patients with a diagnosis of tumors, resulting in down regulation of lysoPC. Thus, a decrease in lysoPC (16: 1) and lysoPC (20: 3) may be an indicator of the risk of developing liver cancer.

The term " oleamide " is one of the unsaturated fatty acid amides, which helps induce sleep in the brain and plays an important role in the central nervous system. Since the onset of liver cancer affects not only amino acid metabolism but also lipid metabolism, the value of the oleamide, which is an unsaturated fatty acid, may change. In addition, oleamide can be a specific marker of liver cancer because it is not associated with cirrhosis.

The term " 5-hydroxyhexanoic acid " is a fatty acid metabolite, and " androseron sulfate " is an androgen metabolite. Although there has been no study on the incidence of hepatocellular carcinoma of 5-hydroxyhexane and androsterone sulfate, the values of 5-hydroxyhexane and androsterone sulfate may be changed because of the influence of various metabolism upon the development of liver cancer.

The above-mentioned " TUDCA (tauroursodeoxycholic acid) " is a hydrophilic bile acid which is produced at a very low level in vivo. The TUDCA reduces cell death due to unfolded protein response (UPR) signaling leading to endoplasmic reticulum stress as well as liver disease such as fatty liver disease, alcoholic liver disease, viral hepatitis and liver cancer. When in vivo hepatocarcinogenesis occurs, protective action may increase the level of TUDCA in vivo, and an increase in TUDCA may be a specific marker of liver cancer.

Therefore, when the concentration of any one or more metabolites selected from the group consisting of tyrosine, AST, 5-hydroxyhexane, and TUDCA is higher than the value measured in the normal control sample in the step (c) And when the concentration of any one or more metabolites selected from the group consisting of lysoPC (16: 1), lysoPC (20: 3), oleamide, and androsterone sulfate is low, It can be concluded that subjects are at high risk of developing liver cancer.

Meanwhile, in step (b), one or more metabolites selected from the group consisting of alanine aminotransferase (ALT), γ-glutamyl transpeptidase (γ-GTP), leucine, phenylalanine and arachidonic acid The concentration of at least one metabolite selected from the group consisting of L-palmitoylcarnitine, LPA (16: 0) and LPA (18: 1) can be measured in step (b) Can be further measured.

The level of the metabolites can be further analyzed and compared with the normal control group to further improve the reliability of the diagnosis.

The ALT (alanine aminotransferase) and γ-GTP (γ-glutamyl transpeptidase) are enzymes produced in the liver, such as AST. As described above, liver-produced enzymes have been reported to increase in patients with liver disease, and the increase in ALT and γ-GTP may be a specific marker of liver cancer.

In addition, 20-year follow-up studies have reported that the incidence of hepatocarcinoma among people with elevated AST and γ-GTP is higher than those with normal levels. Thus, levels of liver enzymes such as AST, ALT, and γ-GTP may be elevated before liver cancer develops.

The leucine is one of the branched amino acids, and the phenylalanine is an aromatic amino acid and is an essential amino acid in vivo. Amino acid imbalance is observed in patients with liver cancer and liver cirrhosis, and the levels of leucine and phenylalanine can be specific markers of liver cancer.

The arachidonic acid is one of the unsaturated fatty acids and is an essential fatty acid such as linoleic acid and linolenic acid. It has been reported that the levels of various free fatty acids in liver cancer patients and normal people are different. Especially, the arachidonic acid plays an important role in the angiogenesis process, and since the angiogenesis process is a feature frequently observed in patients with liver cancer, the increase of arachidonic acid may be a specific marker of liver cancer.

The L-palmitoylcarnitine is an ester derivative of carnitine which is involved in fatty acid metabolism. Differences in carnitine levels between liver cancer patients and normal individuals have been reported, and L-palmitoyl carnitine levels may be a specific marker of liver cancer.

The LPA (16: 0) and the LPA (18: 1) are lysophospholipids in the form of a hydroxyl group in which the acyl group of the phosphatidic acid is advantageous. The incidence of liver cancer affects the phospholipid metabolism, so the levels of LPAs may change. Thus, the values of LPA (16: 0) and LPA (18: 1) may be specific markers of liver cancer.

Therefore, in step (c), it is preferable that at least one selected from the group consisting of alanine aminotransferase (ALT), γ-glutamyl transpeptidase (γ-GTP), leucine, phenylalanine and arachidonic acid L-palmitoylcarnitine, LPA (16: 0), and L-carnitine carnitine were used as the control, and when the concentration of the metabolite was higher than that of the normal control sample, LPA (18: 1) is lower than the value measured in the normal control sample, it can be judged that the subject has a high risk of developing liver cancer.

On the other hand, the above method can determine the possibility of developing liver cancer after 7 years from the reference time point. The " reference time point " means metabolomic profiling, that is, a specific time point at which the concentration measurement of the metabolite is performed.

Another aspect of the present invention is to provide a pharmaceutical composition comprising tyrosine, aspartate aminotransferase (AST), lysoPC (16: 1), lysoPC (20: 3), oleamide, 5-hydroxyhexanoic acid, , Androsterone sulfate, and TUDCA (tauroursodeoxycholic acid). The present invention also provides a kit for diagnosing the risk of liver cancer.

The quantification device is for measuring the concentration of each metabolite, and may be exemplified by chromatography, mass spectrometry, and the like.

The chromatography can be carried out in a liquid chromatography (LC), a liquid-solid chromatography (LSC), a paper chromatography (PC), a thin layer chromatography (TLC), a gas-solid chromatography (GSC) ), Foam chromatography (FC), emulsion chromatography (EC), gas-liquid chromatography (GLC), ion chromatography (IC), gel filtration chromatography (GFC) or gel permeation chromatography , Preferably liquid chromatography, more preferably ultra-performance liquid chromatography (UPLC).

The mass spectrometer may be a Fourier transform mass spectrometer (FTMS). Specifically, an LTQ-Orbitrap-XL mass spectrometer may be used.

The above-mentioned chromatography can separate each metabolite using different mobility for each molecule, and the mass spectrometer can identify the metabolite through structure information as well as molecular weight information of the object to be analyzed.

The diagnostic kit can be used for quantitative determination of one or more metabolites selected from the group consisting of alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γ-GTP), leucine, phenylalanine, and arachidonic acid Device. ≪ / RTI >

The diagnostic kit may further comprise a quantification device for one or more metabolites selected from the group consisting of L-palmitoylcarnitine, LPA (16: 0) and LPA (18: 1).

Hereinafter, the present invention will be described in more detail by way of examples.

Experimental Method

Subject

The research related to the present invention was named "Korean Cancer Prevention Study-II (KCPS-II)" by referring to data collected from the Korean Metabolic Syndrome Research Initiative since 2005.

The subjects of this study were regular health checkers from 2004 to 2013 at the health promotion center in Seoul and Gyeonggi Province, South Korea. Of the 156,701 volunteers who agreed to provide blood samples, 75 liver cancer patients (HCC group) and 134 healthy volunteers (normal control group) were selected at a 1: 2 ratio.

Survey and Human Measurement

All participants were interviewed according to the structured questionnaire:

BMI, systolic and diastolic blood pressure after resting for at least 15 minutes, waist circumference between lower ribs and iliac crest, and smoking history (smoking inexperienced, smokers, current smoker) and drinking history, weight and weight according to light attire.

Blood collection and biochemical analysis

For clinical chemistry analysis, serum separated from peripheral venous blood was obtained from each participant after 12 hours of fasting and stored at -70 ° C until analysis.

(LDL-cholesterol), HDL-cholesterol, aspartate aminotransferase (ALT), alanine aminotransaminase (ALT) and hs-CRP (serum high-density lipoprotein cholesterol) sensitivity C-reactive protein) was measured using an automatic analyzer.

Each measurement was performed according to the internal and external quality control procedures required by the Korean Association of Laboratory Quality Control.

The agreement of each biomarker among individual hospitals was high (correlation coefficient ranged from 0.96 to 0.99).

Liver cancer HCC ) Diagnosis

Data on the incidence of hepatocellular carcinoma were obtained from National Cancer Registry records and hospitals where patients were treated. Data not reported in the National Cancer Registry refer to hospital hospital records.

Overall Non-targeting ) Serum Metabolism  Profiling

UPLC - LTQ - Orbitrap XL  MS analysis

The sample preparation procedure was performed according to the conventional literature (Nutr Metab 2016; 13: 3).

The chromatographic separation procedure was carried out on an Acquity UPLC-BEH-C18 column (2.1 x 50 mm, 1.7 [mu] m; Waters Milford, MA, USA) equipped with a Thermo UPLC system (Ultimate 3000 BioRS, Dionex-Thermo Fischer Scienific, Bremen, Germany) , ESI cation mode and ESI anion mode.

Each sample (5 [mu] L) was injected into the column and maintained at 50 [deg.] C.

The cation mode was carried out using Solution A, a solution of LC-MS grade water (Fisher Scientific, Fair Lawn, NJ, USA) containing 0.1% formic acid and LC-MS grade methanol (Fisher Scientific, Fair Lawn, NJ , USA) was used.

The ratio of methanol was increased from 0% to 100% for 15 minutes, maintained at 100% for 4 minutes, and decreased from 100% to 0% for 2 minutes.

The anion mode was averaged over a sample injected with LC-MS grade water (Fisher Scientific, Fair Lawn, NJ, USA) containing 6.5 mM ammonium bicarbonate and was diluted to 5 with 5 mM ammonium bicarbonate % water (Fisher Scientific). The ratio of the elution solution was increased from 0% to 100% for 15 minutes, maintained at 100% for 5 minutes, and decreased from 100% to 0% for 2 minutes.

The total flow rates of the cation and anion modes were fixed at 0.4 mL / min and 0.35 mL / min, respectively.

MS was performed with a LTQ-Orbitrap-XL mass spectrometer (Thermo Fisher Scientific, Waltham, Mass., USA) operating in full scan mode for Fourier transformed MS. The mass resolution is 60,000 and the spray voltage is 5 kV. The flow rates of the nitrogen sheath gas and the auxiliary gas are 50 and 5 (arbitrary units), respectively. The capillary voltage, tube-lens voltage, and capillary temperature were kept constant at 35 V, 120 V, and 360 ° C, respectively.

MS data were collected in the m / z 50-1,000 range. For quality control, an integrated control sample was prepared by mixing with each sample, and a sample was injected every fifth time. The MS / MS spectra of the metabolites were obtained via a collision energy ramp at 20-55%.

Data processing and Metabolism  Sympathy

All relevant data including retention time, m / z ratio, and ionic strength were collected using SIEVE 2.2 data analysis software (Thermo Fisher Scientific, Waltham, Mass., USA). The analytical parameters are as follows: m / z range 50-1,000 m / z; 5 ppm in width; And a retention time width of 2.5 minutes.

Metabolites were searched using the following databases: Human Metabolome (www.hmdb.ca); Lipid MAPS (www.lipidmaps.org); KEGG (www.genome.jp/kegg); MassBank (www.massbank.jp); And ChemSpider (www.chemspider.com).

Potential metabolites were identified via MS / MS. The potential metabolites were identified by comparing the spectra obtained from MS / MS with reference values in the above database.

Statistical analysis

Statistical analysis was performed using SPSS v. 23.0 (IBM, Chicago, IL, USA) was used. Distorted variables are logarithmically transformed. For descriptive purposes, the mean value is expressed as an untransformed value. Results are expressed as mean ± standard error (SE). Both sides P - the test-values (two-tailed P -value) < 0.05 was considered statistically significant. Independent t-tests were used to compare the parameters between the two groups.

Pearson's correlation coefficient was used to evaluate the relationship between the variables. Was calculated values, q is less than 0.05 - - R q package used in the calibration the error detection rate (FDR, False discovery rate-corrected ) the 'fdrtool' was considered to be a value having a significance.

Stepwise regression analysis was performed to identify the major independent predictors of liver cancer incidence.

On the basis of the stepwise regression analysis, an optimal cut-off value for each biochemical characteristic was determined using receiver-operating characteristic curve analysis.

In addition, a heat map was generated to visualize and evaluate the correlation between metabolites and biochemical characteristics.

All spectra data were exported to SIMCA-P ⁺ 14.0 (Umetrics, Umea, Sweden) for multivariate analysis. Prior to multivariate statistical analysis, Pareto scaling was applied to all data.

The models were compared using orthogonal partial least squares discriminant analysis (OPLS-DA). The validity of the model was evaluated using the parameters of R ² Y and Q ² Y and the cross-validation analysis of variance (CV-ANOVA).

Metabolic pathway analysis

Metaboanalyst 3.0 (http://metaboanalyst.ca) was used for metabolic pathway analysis. This program is based on the information of several databases (KEGG (http://www.genome.jp/kegg/), Human Metabolome Database (http://www.hmdb.ca), etc.) Lt; / RTI &gt; The MetaboAnalyst analyzed metabolic pathways by integrating pathway enrichment analysis and pathway topology analysis.

Experiment result

Experimental Example  1: Clinical features at baseline

Over an average of 7 years, 75 of the 156,701 volunteers aged 20 to 84 years developed liver cancer. HCC group and normal control group were divided into 75 patients who were hospitalized with liver cancer and 134 patients whose age and sex matched without cancer.

Triglyceride, total cholesterol, HDL-cholesterol, LDL-cholesterol, and hs (p <0.05), as well as the BMI, systolic and diastolic blood pressures of the normal control (n = 134) and HCC The blood concentrations of -CRP are shown in Table 1.

The levels of AST ( P <0.001), ALT ( P <0.001) and γ-GTP ( P <0.001) were higher in the HCC group.

division Control group (n = 134) HCC  Group (n = 75) P ^a P ^b Age (years) 52.7 ± 0.81 52.8 ± 1.18 0.968 0.983 Male / Female n, (%) 121 (90.3) / 13 (9.7) 67 (89.3) / 8 (10.7) 0.824 0.793 Current smoker n, (%) 42 (33.3) 31 (45.6) 0.093 - Drinker n, (%) 103 (82.4) 48 (71.6) 0.083 - Drinking frequency (g / day) ^∮ 18.2 ± 3.24 22.3 ± 7.24 0.235 0.359 Weight (kg) 69.2 ± 0.89 69.7 ± 1.17 0.744 0.892 Height (cm) 168.0 + - 0.61 168.5 ± 0.88 0.604 0.601 Waist circumference (cm) 84.9 ± 0.88 86.8 ± 1.07 0.154 0.305 BMI (kg / m ² ) 24.5 ± 0.25 24.6 ± 0.38 0.868 0.966 Systolic blood pressure (mmHg) 122.8 ± 1.3 121.5 ± 1.59 0.552 0.730 Diastolic blood pressure (mmHg) 77.4 ± 1.00 76.5 ± 1.18 0.590 0.803 Glucose (mg / dL) ^∮ 96.8 ± 1.78 98.7 ± 2.70 0.546 0.623 Triglyceride (mg / dL) ^∮ 135.1 ± 7.20 120.4 + 9.24 0.133 0.064 Total cholesterol (mg / dL) ^∮ 188.6 ± 2.76 179.6 ± 3.41 0.049 0.068 HDL-cholesterol (mg / dL) ^∮ 50.6 ± 0.93 51.0 + - 1.36 0.871 0.803 LDL-cholesterol (mg / dL) ^∮ 114.2 + - 2.44 106.8 ± 3.52 0.063 0.101 AST (IU / L) ^∮ 24.1 ± 0.63 42.9 ± 4.08 <0.001 <0.001 ALT (IU / L) ^∮ 27.5 ± 1.56 49.7 ± 8.95 <0.001 <0.001 γ-GTP (U / L) ^∮ 42.9 ± 4.98 90.7 ± 15.4 <0.001 <0.001 White blood cell count (x10 ³ / μL) ^∮ 6.08 ± 0.15 5.66 ± 0.17 0.093 0.031 hs-CRP (mg / L) ∮ 0.21 + 0.03 0.22 ± 0.06 0.509 0.325

Each value is expressed as mean ± standard error; ^∮ is the value checked by log conversion. P ^a - value derived from independent t-test. P ^b -values derived from independent t-tests after correction for drinking and smoking.

Experimental Example  2 : UPLC - LTQ - orbitrap  Serum using MS Metabolism  Profiling

Total ion chromatographic data (cation and anion modes) for the serum metabolites obtained at baseline were analyzed using an OPLS-DA score plot. In particular, the OPLS-DA was significantly lower than that of the control group (n = 134) and the HCC group (baseline and 7-year hepatocellular carcinoma, n = 75) Metabolites were compared (Fig. 1A).

The quality of the OPLS-DA was tested using the R ² and Q ² values to assess the suitability of the model and the predictive power of each model.

Referring to FIG. 1, the model shows goodness of fit of 88.2% (R ² Y = 0.882) and predictive power of 65.8% (Q ² Y = 0.658). These results suggest that the degree of connectivity of the OPLS-DA model is high and that it has acceptable predictive power.

Also, OPLS-DA was statistically analyzed in the negative ion mode with the fitness R ² Y = 0.798 and the predictive power Q ² Y = 0.604 (Fig. 1b).

These results suggest that the two groups can be distinguished based on the difference in the presence of metabolites.

In order to extract the potential variables contributing to the identified differences, p (1) covariance from the OPLS-DA model using the Pareto scaling in the cation mode (Fig. 1c) and the negative ion mode An S-plot of p (corr) (1) was generated. When the metabolite has a high or low p (corr) value, it has a higher relevance in distinguishing the two groups.

Experimental Example  3: Serum Metabolite  Sympathy

Variables (metabolites), which play an important role in the classification of each group, were selected according to the Variable Important in the Projection (VIP) parameter. This means that when the VIP value exceeds 1.5, the difference between the sample groups is highly related.

In a comparison of baseline metabolic levels between control (baseline and 7 years without cancer, n = 134) and HCC group (baseline, no liver cancer, but 7 years with liver cancer; n = 75) The VIP value of the body exceeded 1.5. Of these, 55 metabolites have been identified in the past and the rest are new.

Tables 2 and 3 show serum metabolites in the control and HCC groups.

Compared with the control group, in the HCC group, isovaleraldehyde, 4-hydroxynonenal, azelaic acid, 3-hydroxysebacic acid, 3-hydroxydodecanedioic acid, 3-hydroxytetradecanedioic acid, oleamide (q = 2.17x10 ^-5 ), phenyl (3-hydroxydodecanedioic acid) alanyl-phenylalanine (phenylalanylphenylalanine), dihydro-epi Andros Theron sulfate (dehydroepiandrosterone sulfate), Andros Theron sulfate (androsterone sulfate), L- palmitoyl carnitine (L-palmitoylcarnitine, q = 1.26x10 -5), LPA (16 two kinds of (14: 0, 15: 0, 16: 1 ( q = 1.81x10 ^-10 ), 16: 0, 17: 0, 18: 4, 18: 3, 18 : 28 metabolites, including 5): 2, 18: 1 (q = 2.09x10 -5), 20: 5, 20: 4, 20: 3 (q = 1.97x10 -8), 22: 6, 22 Was significantly lower.

Compared to the control, the HCC group leucine ^{(leucine, q = 1.84x10 -4)} , 5- hydroxy-hexanes (5-hydroxyhexanoic acid, q = 7.47x10 -3), phenylalanine (phenylalanine, q = 9.22x10 ^-5) , tyrosine ^{(tyrosine, q = 3.24x10 -9)} , arachidonic acid ^{(arachidonic acid, q = 2.51x10 -2} ), 8, 11, 14- trien-eicosyl four acid (8, 11, 14-eicosatrienoic acid), glycoside 9, including ^{-; (4 TUDCA, q =} 3.35x10 tauroursodeoxycholic acid) Ur sode DCA acid (glycoursodeoxycholic acid), glycolic acid ^{(glycolic acid, q = 1.18x10 -2} ), and tau by Ur sode DCA acid The levels of metabolites were significantly higher.

In addition, the level of linoleic acid was significantly higher in the HCC group than in the control group.

Identification of upregulated serum metabolites in the control and HCC groups m / z
[M + H] †
[M-H] ‡ The Metabolism VIP t -Test Cohen's d P q 132.102 C ₆ H ₁₃ NO ₂ L-Leucine † 3.682 <0.001 1.84E-04 0.591 133.086 C ₆ H ₁₂ O ₃ 5-Hydroxyhexanoic acid † 1.631 0.008 7.47E-03 0.461 166.086 C ₉ H ₁₁ NO ₂ L-Phenylalanine † 3.353 <0.001 9.22E-05 0.615 182.081 C ₉ H ₁₁ NO ₃ L-Tyrosine † 3.844 <0.001 3.24E-09 1.192 205.097 C ₁₁ H ₁₂ N ₂ O ₂ L-Tryptophan † 2.608 0.776 0.287 0.041 253.217 C ₁₆ H ₃₀ O ₂ Palmitoleic acid ‡ 2.871 0.180 0.098 0.220 279.232 C ₁₈ H ₃₂ O ₂ Linoleic acid ‡ 4.466 0.098 0.062 0.267 295.226 C ₁₈ H ₃₀ O ₃ 9-HOTE † 1.961 0.145 0.084 0.255 295.227 C ₁₈ H ₃₂ O ₃ Alpha-dimerphecolic acid ‡ 2.116 0.116 0.071 0.302 301.216 C ₂₀ H ₃₀ O ₂ Eicosapentaenoic acid ‡ 1.968 0.302 0.142 0.157 303.232 C ₂₀ H ₃₂ O ₂ Arachidonic acid ‡ 4.243 0.032 2.51E-02 0.373 305.248 C ₂₀ H ₃₄ O ₂ 8,11,14-Eicosatrienoic acid ‡ 2.401 0.005 5.23E-03 0.502 319.227 C ₂₀ H ₃₂ O ₃ 15 (S) -HETE ‡ 2.321 0.235 0.119 0.227 325.216 C ₂₂ H ₂₈ O ₂ Etonogestrel † 1.713 0.850 0.306 0.027 327.232 C ₂₂ H ₃₂ O ₂ Docosahexaenoic acid ‡ 4.127 0.117 0.071 0.260 329.247 C ₂₂ H ₃₄ O ₂ Docosapentaenoic acid ‡ 1.537 0.189 0.101 0.211 343.227 C ₂₂ H ₃₂ O ₃ 8-HDoHE ‡ 2.557 0.271 0.132 0.209 448.306 C ₂₆ H ₄₃ NO ₅ Glycoursodeoxycholic acid ‡ 4.341 0.001 1.24E-03 0.941 464.300 C ₂₆ H ₄₃ NO ₆ Glycocholic acid ‡ 2.171 0.013 1.18E-02 0.759 498.288 C ₂₆ H ₄₅ NO ₆ S Tauroursodeoxycholic acid ‡ 2.320 <0.001 3.55E-04 1.126

† is the metabolite obtained in the ESI-cation mode ( M / Z [M + H]) and ‡ is the metabolite obtained in the ESI-anion mode ( M / Z [MH]). VIP is a variable importance measure. P -values were derived from independent t-tests between the control group (n = 134) and the HCC group (n = 75). The q - value is the P - value that corrects the error detection rate (FDR). Cohen's d is the magnitude of the impact on the comparison of the differences between the two means divided by the pooled standard deviation. Define the magnitude of the influence as "small" if d = 0.20, "medium" if d = 0.50, and "large" if d = 0.80. The trend of change was determined by comparing the relative peak intensities of the metabolites in the HCC group compared to the control group.

Identification of down-regulated serum metabolites in the control and HCC groups m / z
[M + H] †
[M-H] ‡ The Metabolism VIP t -Test Cohen's d P q 87.080 C ₅ H ₁₀ O Isovaleraldehyde † 1.547 <0.001 1.22E-04 -0.540 157.122 C ₉ H ₁₆ O ₂ 4-Hydroxynonenal † 1.585 <0.001 1.32E-04 -0.536 189.112 C ₉ H ₁₆ O ₄ Azelaic acid † 1.590 <0.001 7.39E-05 -0.554 219.122 C ₁₀ H ₁₈ O ₅ 3-Hydroxysebacic acid † 2.118 <0.001 6.91E-05 -0.551 227.201 C ₁₄ H ₂₈ O ₂ Myristic acid ‡ 1.571 0.093 0.060 -0.244 229.143 C ₁₂ H ₂₀ O ₄ Traumatic acid † 3.256 <0.001 6.85E-05 -0.544 247.154 C ₁₂ H ₂₂ O ₅ 3-Hydroxydodecanedioic acid † 3.515 <0.001 6.94E-05 -0.546 255.232 C ₁₆ H ₃₂ O ₂ Palmitic acid ‡ 5.146 0.735 0.276 -0.049 275.185 C ₁₄ H ₂₆ O ₅ 3-Hydroxytetradecanedioic acid † 3.790 <0.001 1.12E-04 -0.525 277.216 C ₁₈ H ₃₀ O ₂ Gamma-linolenic acid ‡ 1.725 0.270 0.131 -0.160 281.248 C ₁₈ H ₃₄ O ₂ Oleic acid ‡ 5.867 0.938 0.327 -0.011 282.279 C ₁₈ H ₃₅ NO Oleamide † 2.817 <0.001 2.17E-05 -0.580 283.263 C ₁₈ H ₃₆ O ₂ Stearic acid ‡ 3.144 0.356 0.161 -0.133 301.216 C ₂₀ H ₂₈ O ₂ All-trans-retinoic acid † 2.211 0.904 0.319 -0.017 313.154 C ₁₈ H ₂₀ N ₂ O ₃ Phenylalanylphenylalanine † 3.472 <0.001 5.30E-10 -0.893 367.157 C ₁₉ H ₂₈ O ₅ S Dehydroepiandrosterone sulfate ‡ 2.382 <0.001 4.26E-04 -0.548 369.173 C ₁₉ H ₃₀ O ₅ S Androsterone sulfate ‡ 2.784 <0.001 1.08E-06 -0.780 400.342 C ₂₃ H ₄₅ NO ₄ L-Palmitoylcarnitine † 1.518 <0.001 1.26E-05 -0.624 409.235 C ₁₉ H ₃₉ O ₇ P LPA (16: 0) ‡ 4.089 <0.001 4.64E-07 -0.810 435.250 C ₂₁ H ₄₁ O ₇ P LPA (18: 1) ‡ 2.338 <0.001 4.18E-06 -0.731 468.307 C ₂₂ H ₄₆ NO ₇ P LysoPC (14: 0) † 4.560 <0.001 3.22E-10 -1.051 480.344 C ₂₄ H ₅₀ NO ₆ P LysoPC (P-16: 0) † 2.151 0.820 0.299 -0.033 482.323 C ₂₃ H ₄₈ NO ₇ P ysoPC (15: 0) † 2.860 <0.001 3.77E-08 -0.865 494.323 C ₂₄ H ₄₈ NO ₇ P LysoPC (16: 1) 5.700 <0.001 1.81E-10 -1.066 496.339 C ₂₄ H ₅₀ NO ₇ P LysoPC (16: 0) 9.992 <0.001 4.62E-06 -0.728 510.355 C ₂₅ H ₅₂ NO ₇ P LysoPC (17: 0) † 3.721 <0.001 2.26E-06 -0.760 516.308 C ₂₆ H ₄₆ NO ₇ P LysoPC (18: 4) † 2.067 <0.001 3.88E-08 -0.821 518.323 C ₂₆ H ₄₈ NO ₇ P LysoPC (18: 3) † 4.068 <0.001 8.63E-09 -0.942 520.338 C ₂₆ H ₅₀ NO ₇ P LysoPC (18: 2) † 7.483 <0.001 1.41E-08 -1.070 522.354 C ₂₆ H ₅₂ NO ₇ P LysoPC (18: 1) † 6.090 <0.001 2.09E-05 -0.766 542.323 C ₂₈ H ₄₈ NO ₇ P LysoPC (20: 5) † 5.908 <0.001 3.32E-11 -1.117 544.338 C ₂₈ H ₅₀ NO ₇ P LysoPC (20: 4) † 5.182 <0.001 5.67E-09 -0.958 546.354 C ₂₈ H ₅₂ NO ₇ P LysoPC (20: 3) † 4.878 <0.001 1.97E-08 -0.812 568.338 C ₃₀ H ₅₀ NO ₇ P LysoPC (22: 6) † 4.158 <0.001 6.43E-08 -0.875 570.354 C ₃₀ H ₅₂ NO ₇ P LysoPC (22: 5) † 1.931 0.001 1.11E-03 -0.494

† is the metabolite obtained in the ESI-cation mode ( M / Z [M + H]) and ‡ is the metabolite obtained in the ESI-anion mode ( M / Z [MH]). VIP is a variable importance measure. P -values were derived from independent t-tests between the control group (n = 134) and the HCC group (n = 75). The q - value is the P - value that corrects the error detection rate (FDR). Cohen's d is the magnitude of the impact on the comparison of the differences between the two means divided by the pooled standard deviation. Define the magnitude of the influence as "small" if d = 0.20, "medium" if d = 0.50, and "large" if d = 0.80. The trend of change was determined by comparing the relative peak intensities of the metabolites in the HCC group compared to the control group.

Experimental Example  4: Metabolic pathway associated with the development of liver cancer in the subject

Metabolic pathway analysis was performed using MetaboAnalyst 3.0, a web-based analysis module, to identify the most relevant pathways in the selected metabolites (FIG. 2).

All subjects with or without liver cancer metastatic at baseline; Phenylalanine metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; Valine, leucine and isoleucine decomposition; Valine, leucine and isoleucine biosynthesis; Tryptophan metabolism; Glycerol phospholipid metabolism; Linoleic acid metabolism; Arachidonic acid metabolism; Early bile acid biosynthesis; And fatty acid metabolism (Table 4).

Metabolic pathway analysis Metabolic pathway Hits P FDR Impact Tyrosine metabolism L-Tyrosine 2.64E-19 2.64E-19 0.04724 Phenylalanine metabolism L-Tyrosine 7.92E-19 4.16E-18 0.11906 L-Phenylalanine Phenylalanine, tyrosine and
tryptophan biosynthesis L-Tyrosine 1.83E-16 6.39E-16 0.008 L-Phenylalanine L-Tryptophan Valine, leucine and isoleucine
degradation L-Leucine 7.29E-12 1.70E-11 0.02232 Valine, leucine and isoleucine
biyosynthesis L-Leucine 7.29E-12 1.70E-11 0.01325 Tryptophan metabolism L-Tryptophan 1.44E-08 2.76E-08 0.10853 Glycerophospholipid metabolism LysoPC (18: 1) 3.11E-06 5.45E-06 0.10429 Linoleic acid metabolism Linoleic acid 2.57E-05 4.15E-05 0.65625 Gamma-linoleic acid Arachidonic acid metabolism Arachidonic acid 6.30E-05 9.45E-05 0.2255 15 (S) -HETE Primary bile acid biosynthesis Glycocholic acid 0.001403 0.001841 0.00846 Fatty acid metabolism Palmitic acid 0.002902 0.003585 0.02959 L-Palmitoylcarnitine

Hits are actually matched to metabolites uploaded to MetaboAnalyst. The FDR is a P -value that corrects the False Discovery Rate. Impact is the pathway impact value calculated from the path topology analysis. Influence value 0 is not displayed.

Metabolic pathway analysis revealed that tyrosine belongs to the metabolism of tyrosine, and the coefficient of influence is 0.047. The phenylalanine metabolism was associated with tyrosine and phenylalanine, and the influence coefficient based on metabolic pathway analysis was 0.119. Phenylalanine, tyrosine, and tryptophan biosynthesis were associated with tyrosine, phenylalanine, and tryptophan, and the influence coefficient was 0.008. The degradation and biosynthesis of valine, leucine and isoleucine were all associated with leucine and the influence coefficients based on metabolic pathway analysis were 0.022 and 0.013, respectively.

The tryptophan metabolism was associated with tryptophan, and the influence coefficient was 0.108. The glycerol phospholipid metabolism was associated with lysoPC (18: 1) and the influence coefficient was 0.104. Linoleic acid metabolism was associated with linoleic acid and gamma linoleic acid, and the coefficient of influence was 0.656. Arachidonic acid and hydroxyacocapentaenoic acid were associated with arachidonic acid metabolism and the coefficient of effect was 0.226. Early bile acid biosynthesis was associated with glycocholic acid, and the influence coefficient was 0.009. L-Palmitoyl carnitine and palmitic acid were associated with fatty acid metabolism and the coefficient of influence was 0.030.

Experimental Example 5: Relationship between the incidence of hepatocellular carcinoma and major metabolites

The correlation matrix including the major metabolites and the biochemical characteristics according to the frequency of liver cancer incidence is shown in FIG. 4 for the control group of 75 patients with hepatocellular carcinoma and the age and sex matched hepatocarcinoma group for 7 years .

To analyze the independent effects of different variables on the incidence of liver cancer, stepwise regression analysis was performed on the following variables: leukocyte count, AST level, ALT level; gamma] -GTP levels, and 37 metabolites (including isovaleraldehyde, 4-hydroxy nonenal, azelaic acid, 3-hydroxycabic acid, 3-hydroxydodecanediiodic acid, 3-hydroxytetradecanedioic acid L-palmitoylcarnitine, LPA (16: 0, 18: 1), lysoPC (14: 0, 15: 0, 16: 1), oleamide, phenylalanyl phenylalanine, dihydroepiandrosterone sulfate, androsterone sulfate, 20: 5, 20: 4, 20: 3, 22: 6, 22: 5), leucine, 5- Hydroxyhexane, phenylalanine, tyrosine, arachidonic acid, 8,11,14-eicosatrienoic acid, glycoordoxyocholic acid, glycolic acid, TUDCA).

The incidence of liver cancer in all patients was affected by AST, tyrosine, oleamide, lysoPC (16: 1), lysoPC (20: 3), 5-hydroxyhexane, androsterone sulfate and TUDCA. Based on the above results, a correlation matrix containing AST and 7 metabolites was prepared (FIG. 3).

Based on the results of the stepwise regression analysis, the optimal cut-off value of each variable was determined by ROC curve analysis.

Region (AUROC) of the lower end of the ROC curves for L- tyrosine 0.79 ± 0.03 (P <0.001) , when the AST level of 0.78 ± 0.04 (P <0.001) , the case of 5-hydroxy-hexane-level 0.61 ± 0.04 (P = 0.010) and 0.77 ± 0.04 ( P <0.001) for TUDCA, respectively. For the remaining variables, AUROC was less than 0.5. The sum of the predicted probabilities of the four variables (88%, P <0.001) was greater than when the variables were considered independently (FIG. 5).

The results show that tyrosine metabolism; Phenylalanine metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; Valine, leucine and isoleucine decomposition; Valine, leucine and isoleucine biosynthesis; Tryptophan metabolism; Glycerol phospholipid metabolism; Linoleic acid metabolism; Arachidonic acid metabolism; Early bile acid biosynthesis; And fatty acid metabolism suggest clinical relevance. These results suggest that the metabolic control disorder is a key mechanism of liver cancer development.

The results showed that both branched and aromatic amino acids were significantly higher in the HCC group than in the control group (baseline and 7 years). These results suggest that the biosynthesis of branched amino acids and aromatic amino acids is increased, the breakdown of branched amino acids is increased, and the pathological progress of liver cancer, which partially reflects the increase of serum carnitine, which oxidizes branched amino acids.

In addition, the above results suggest that oleamide is a distinct serum lipid of the HCC group and the normal control group. Specifically, the HCC group showed lower levels of oleamide than the control group, suggesting that oleamide is an independent influential factor in the incidence of liver cancer. In addition, the above results suggest that 5-hydroxyhexane and androsterone sulfate are independent influential factors of hepatocellular carcinoma incidence.

In conclusion, the results show that AST, an enzyme produced in the liver, tyrosine, lysoPC (16: 1) and lysoPC (20: 3) which are aromatic amino acids, 5-hydroxyhexane and androsterone sulfate, TUDCA, one of the bile acids, is an independent influential factor in the incidence of liver cancer.

In addition, in the above results, similar to previous studies, it is suggested that the regulation of bile acid biosynthesis and linoleic acid, arachidonic acid and fatty acid metabolism, as well as branched amino acids and aromatic amino acids, lysoPC, .

In addition, the above results suggest that the decrease in polyunsaturated lysoPC observed in the HCC group suggests that arachidonic acid levels may be involved in promoting inflammation leading to cancer and tumorigenesis.

These results showed that the level of serum ALT, a marker of hepatocyte apoptosis in the HCC group, was significantly increased compared to the control group. In addition, it is suggested that increased TUDCA levels in the HCC group before hepatocarcinogenesis may be a compensatory mechanism to reduce hepatocyte apoptosis by UPR.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included within the scope of the present invention.

Claims (8)

(a) obtaining a biological sample from a subject;
(b), wherein the biological sample contains tyrosine, aspartate aminotransferase (AST), lysoPC (16: 1), lysoPC (20: 3), oleamide, 5-hydroxyhexanoic acid, Measuring at least one metabolite concentration selected from the group consisting of androsterone sulfate, and tauroursodeoxycholic acid (TUDCA); And
(c) comparing the level of the measured metabolite to the measured value in a normal control sample. The method according to claim 1,
In step (b), the concentration of one or more metabolites selected from the group consisting of alanine aminotransferase (ALT),? -Glutamyl transpeptidase (? -GTP), leucine, phenylalanine and arachidonic acid / RTI &gt; The method according to claim 1,
Wherein the concentration of at least one metabolite selected from the group consisting of L-palmitoylcarnitine, LPA (16: 0) and LPA (18: 1) is further measured in the step (b). 4. The method according to any one of claims 1 to 3,
Diagnostic method to determine the likelihood of liver cancer after 7 years from baseline. It has been reported that tyrosine, aspartate aminotransferase (AST), lysoPC (16: 1), lysoPC (20: 3), oleamide, 5-hydroxyhexanoic acid, androsterone sulfate , And TUDCA (tauroursodeoxycholic acid). The kit for diagnosing the risk of liver cancer. 6. The method of claim 5,
Wherein the quantification device is a chromatography and mass spectrometer. 6. The method of claim 5,
Further comprises a quantification device for at least one metabolite selected from the group consisting of ALT (alanine aminotransferase), γ-GTP (γ-glutamyl transpeptidase), leucine, phenylalanine and arachidonic acid The kit. 6. The method of claim 5,
The kit further comprises a quantification device for at least one metabolite selected from the group consisting of L-palmitoylcarnitine, LPA (16: 0) and LPA (18: 1).

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