A biomarker and kit for distinguishing chronic hepatitis B, liver cancer and liver cirrhosis

Abstract

The present invention discloses a biomarker and a kit for distinguishing chronic hepatitis B from liver cancer and liver cirrhosis. The marker is one or any combination of more than one of 15 metabolites. The marker has high specificity and sensitivity, and can be used to distinguish patients with chronic hepatitis B, liver cirrhosis and liver cancer, and is used for early detection of liver cancer, so as to buy time for early intervention and treatment for patients.

Description

Technical Field

[0001] The present invention relates to the field of markers, and in particular to a biomarker and a kit for distinguishing chronic hepatitis B from liver cancer and liver cirrhosis. Background Art

[0002] The occurrence process of HCC in my country mostly follows the "three-step" model of hepatitis B-cirrhosis-liver cancer. Atypical hyperplastic nodules in the state of cirrhosis have a strong potential for malignant transformation, especially high-grade atypical hyperplastic nodules have a very high rate of canceration. Due to the hidden onset of the disease, most cases are already in the late stage when they are discovered, and the opportunity for surgical radical cure is lost. Therefore, the five-year overall survival rate of liver cancer is currently only 12.1%, ranking second to last among the overall survival rates of 17 common malignant tumors (Villanueva A, Hepatocellular Carcinoma. N Engl J Med. 2019; 380(15): 1450-1462, incorporated herein by reference). Statistical data show that the annual incidence and mortality rates of HCC in my country are very similar, which shows that the existing diagnosis and treatment strategies and measures are very limited in reducing the five-year total mortality rate of HCC, so it is extremely urgent to explore new HCC diagnosis and treatment strategies.

[0003] Metabolomics is the science of studying the endogenous metabolites produced by biological systems after external stimulation or disturbance and their changing rules, and is a research method for exploring the metabolic pathways of biological systems. At present, metabolomics has been widely used to study the mechanism of disease and discover new diagnostic biomarkers. Among the analytical techniques in metabolomics research, high performance liquid chromatography-mass spectrometry (HPLC-MS) is considered to be one of the best analytical techniques for metabolite spectrum and metabolomics research, with high selectivity, sensitivity and repeatability. In addition, the liquid chromatography-mass spectrometry metabolomics method based on efficient isotope labeling (HP-CIL) has higher analytical efficiency and higher precision qualitative and quantitative results (Xu et al., Development of High-Performance Chemical Isotope Labeling LC-MSfor Profiling the Human Fecal Metabolome. Anal. Chem. 87, 829-836 (2015), incorporated herein by reference), which has opened up a new path for the study of metabolomics.

[0004] The liver is the largest metabolic organ in the human body, and metabolic abnormalities are closely related to the occurrence and development of liver cancer. In patients with liver cancer, the balance of the body's metabolic network may be disrupted, and may ultimately lead to changes in terminal metabolites. Therefore, it is very promising to use HP-CIL-based metabolomics technology to study endogenous metabolic changes in patients with liver cancer and to understand the pathogenesis of liver cancer through the identified potential small molecule biomarkers, thereby establishing a highly sensitive and specific non-invasive liver cancer screening method. Summary of the invention

[0005] The purpose of the present invention is to provide a biomarker and a kit for distinguishing chronic hepatitis B from liver cancer and liver cirrhosis.

[0006] To achieve the above objectives, the present invention provides a biomarker for distinguishing chronic hepatitis B, liver cirrhosis and liver cancer, characterized in that the marker is one or any combination of more than one of the following 15 metabolites, and the 15 metabolites are Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde.

[0007] The present invention also provides a kit, characterized in that it contains the diagnostic markers for distinguishing chronic hepatitis B, cirrhosis and liver cancer.

[0008] The sample used for detecting biomarkers in the present invention is serum.

[0009] Compared with the prior art, the technical solution of the present invention proposes for the first time that any combination of Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde can be used as a biomarker for distinguishing chronic hepatitis B, cirrhosis, and liver cancer. The diagnostic marker has high specificity and sensitivity, can be used to distinguish patients with chronic hepatitis B, cirrhosis, and liver cancer, and is used for early detection of liver cancer, so as to buy time for patients to carry out early intervention and treatment.

[0010] The determination method of the present invention can comprehensively and comprehensively reflect the variation status of metabolites among chronic hepatitis B patients, cirrhosis patients and liver cancer patients, find diagnostic markers to distinguish chronic hepatitis B, cirrhosis and liver cancer, and provide favorable technical support for the early diagnosis and prognosis of liver cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a diagram of the distribution of identified metabolites in databases at different levels (discovery set).

[0012] Figure 2 It is a diagram of the distribution of identified metabolites in databases at different levels (validation set).

[0013] Figure 3 It is the ROC analysis chart.

[0014] Figure 4 It is the ROC analysis (discovery set) plot.

[0015] Figure 5 It is the ROC analysis (validation set) graph.

[0016] Figure 6 It is a pathway analysis bubble chart (discovery set) chart.

[0017] Figure 7 It is a pathway analysis bubble chart (validation set) chart.

[0018] Note: Figure 3-7 The HB group represents the chronic hepatitis B group, the SQ group represents the liver cancer group, and the LC group represents the liver cirrhosis group. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, and the examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by reference to the accompanying drawings are exemplary, are intended to be used to explain the present invention, and are not to be construed as limitations of the present invention. Those who do not indicate specific techniques or conditions in the embodiments are carried out according to the techniques or conditions described in the documents in this area or according to the product specification. Those who do not indicate the manufacturer of reagents or instruments used are all conventional products that can be obtained commercially.

[0020] The embodiment of the present invention uses liquid chromatography-mass spectrometry technology based on efficient chemical isotope labeling to detect serum metabolites of patients with chronic hepatitis B, cirrhosis and liver cancer, and finds diagnostic markers for distinguishing chronic hepatitis B, cirrhosis and liver cancer. The specific method includes: collecting serum from patients with chronic hepatitis B, cirrhosis and liver cancer, analyzing it by chromatography-mass spectrometry after processing, using the intersection of metabolites identified by the discovery set and validation set level 1 and level 2, performing LASSO analysis on the two batches of data to screen the intersection metabolites, and using the receiver operating curve (ROC) analysis for verification, and finally obtaining metabolites that can reliably and effectively distinguish chronic hepatitis B, cirrhosis and liver cancer. The determination method of the present invention can comprehensively and comprehensively reflect the variation of metabolites between patients with chronic hepatitis B, patients with cirrhosis and patients with liver cancer, find diagnostic markers for distinguishing chronic hepatitis B, cirrhosis and liver cancer, and provide favorable technical support for the early diagnosis and prognosis of liver cancer.

[0021] The method for determining a combination of diagnostic markers for distinguishing chronic hepatitis B, liver cirrhosis and liver cancer comprises the following steps.

[0022] Step 1, obtaining clinical serum samples from patients with chronic hepatitis B, patients with liver cirrhosis and patients with liver cancer;

[0023] Step 2, using HP-CIL liquid chromatography-mass spectrometry coupled metabolomics to analyze and identify serum metabolites of chronic hepatitis B patients, cirrhosis patients, and liver cancer patients;

[0024] Step 3, using the intersection of metabolites identified in the discovery set and validation set at level 1 and level 2, LASSO analysis was performed on the two batches of data to screen for intersection metabolites;

[0025] In step 4, the metabolites identified in the discovery set and validation set tier 1 and tier 2 were used to perform receiver operating characteristic (ROC) analysis to extract metabolites that contribute to the classification.

[0026] The chromatographic and mass spectrometric conditions of the HP-CIL liquid chromatography-mass spectrometry metabolomics method in step 2 include:

[0027] (1) Chromatographic column: Agilent eclipse plus reversed-phase C18 column (150 x 2.1 mm, 1.8 μm particle size);

[0028] (2) Mobile phase: A: water (0.1% formic acid), B: acetonitrile (0.1% formic acid)

[0029] (3) Elution gradient (phase B): 0-10 min (25-99%), 10-15 min (99%), 15-15.1 min (99-25%), 15.1-18 min (25%)

[0030] (4) Column temperature: 40°C

[0031] (5) Flow rate: 400 μL / min

[0032] (6) Mass spectrometry scanning range: 220-1000Da

[0033] The serum sample pretreatment method for the HP-CIL liquid chromatography-mass spectrometry metabolomics method in step 2 includes: (1) After the serum sample is packaged and precipitated with mass spectrometry-grade methanol protein, the supernatant is blown dry with nitrogen. (2) The single-channel sub-metabolome labeling (amines / phenols) is strictly carried out in accordance with the kit and standard operating procedures. (3) The samples are mixed and equal amounts of 13 The C-labeled mixed sample was added to 12 C-labeled samples for LC-MS analysis.

[0034] Embodiment 1:

[0035] 1. Experimental materials and test conditions

[0036] 1. Serum sample status

[0037] A total of sera from 200 patients with chronic hepatitis B, 200 patients with cirrhosis, and 199 patients with liver cancer were recruited as the discovery set; sera from 36 patients with chronic hepatitis B, 36 patients with cirrhosis, and 36 patients with liver cancer were recruited as the validation set.

[0038] 2. Sample Pretreatment

[0039] (1) After serum samples were aliquoted and protein precipitated using mass spectrometry grade methanol, the supernatant was dried using nitrogen gas.

[0040] (2) Single-channel sub-metabolome labeling (amines / phenols) Sample labeling was performed strictly according to the HP-CIL kit I (amines / phenols) and standard operating procedures.

[0041] (3) Sample mixing, equal volumes of13 The C-labeled mixed sample was added to 12 C-labeled samples for LC-MS analysis.

[0042] 3. Liquid chromatography mass spectrometry test

[0043] The samples were analyzed using an Agilent ultra-high performance liquid chromatography time-of-flight mass spectrometer. Chromatographic column: Agilent eclipse plus reversed-phase C18 column (150x 2.1mm, 1.8μm particle size). Mobile phase: A: water (0.1% formic acid), B: acetonitrile (0.1% formic acid). Elution gradient (phase B): 0-10min (25-99%), 10-15min (99%), 15-15.1min (99-25%), 15.1-18min (25%). Column temperature: 40°C. Flow rate: 400μL / min. Mass spectrometry scanning range: 220-1000Da. Data analysis and processing used IsoMS Pro software and NovaMT metabolomics database.

[0044] (II) Results

[0045] 1. Identification results

[0046] 1.1. Discovery set (599 sera)

[0047] After the above serum samples were analyzed by ultra-high performance liquid chromatography time-of-flight mass spectrometry, the test data results were analyzed and processed using IsoMS Pro software.

[0048] First, a three-level database was used (Level 1: According to the precise molecular weight and retention time, the derivatization standard database (CIL Library) was matched. The CIL Library contains experimental information of more than 1,500 metabolites. Among them, 230 metabolites can be identified in this database. Level 2: The associated metabolite database (LI Library) was used to identify the remaining metabolites. LI The library includes more than 9,000 metabolites associated with metabolic pathways, and can provide highly reliable inferred results based on precise molecular weight and predicted retention time. Among them, 386 metabolites can be identified in this database. Level 3: According to the precise molecular weight, the remaining metabolites are matched in the MyCompoundID (MCID) database. The database includes 8,021 known endogenous metabolites (0-level reaction database), 375,809 metabolites predicted by primary metabolic reactions (1-level reaction database), and 10,583,901 predicted metabolites generated by two-step metabolic reactions (2-level reaction database). In this database, 397, 952 and 344 metabolites were matched in the level 0, level 1 and level 2 databases, respectively. The metabolites detected in the above serum samples were identified, and 92.7% of the metabolites were identified or inferred. The test results are as follows: Figure 1 As shown (the distribution of identified metabolites in databases at different levels). 1.2. Validation set (108 serum samples)

[0049] After the above serum samples were analyzed by ultra-high performance liquid chromatography time-of-flight mass spectrometry, the test data results were analyzed and processed using IsoMS Pro software.

[0050] First, a three-level database was used (Level 1: According to the precise molecular weight and retention time, the derivatization standard database (CIL Library) was matched. The CIL Library contains experimental information of more than 1,500 metabolites. Among them, 255 metabolites can be identified in this database. Level 2: The associated metabolite database (LI Library) was used to identify the remaining metabolites. LI The library includes more than 9,000 metabolites associated with metabolic pathways, and can provide highly reliable inferred results based on precise molecular weight and predicted retention time. Among them, 425 metabolites can be identified in this database. Level 3: According to the precise molecular weight, the remaining metabolites are matched in the MyCompoundID (MCID) database. The database includes 8,021 known endogenous metabolites (0-level reaction database), 375,809 metabolites predicted by primary metabolic reactions (1-level reaction database), and 10,583,901 predicted metabolites generated by two-step metabolic reactions (2-level reaction database). In this database, 405,868 and 337 metabolites were matched in the 0-level, 1-level and 2-level databases, respectively. The metabolites detected in the above serum samples were identified, and 91.3% of the metabolites were identified or inferred. The test results are as follows Figure 2 As shown (the distribution of identified metabolites in databases at different levels).

[0051] 2. Statistical analysis results

[0052] By intersecting the metabolites identified in levels 1 and 2 of the discovery set and validation set, an intersection of 265 metabolites was obtained. The 265 metabolites screened out were subjected to LASSO analysis, and 15 metabolites were screened out, including Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde.

[0053] At the same time, the Biomarker Analysis module on the MetaboAnalyst website (www.metaboanalyst.ca) was used to perform receiver operating curve (ROC) analysis on the 15 screened metabolites. That is, after comparing the chronic hepatitis B, liver cirrhosis and liver cancer groups in pairs, ROC analysis was performed separately. The results of the chronic hepatitis B and liver cancer groups are as follows: Figure 3 As shown in a, the chronic hepatitis B and liver cirrhosis groups Figure 3 As shown in b, the results of liver cancer and liver cirrhosis groups are as follows Figure 3 The area under the curve (AUC) was further used to find that 15 metabolites with higher AUCs, including Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3ofOrnithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde, could better distinguish patients with chronic hepatitis B, cirrhosis, and liver cancer.

[0054] To determine the sensitivity and specificity of the 15 metabolites, after pairwise comparisons between the chronic hepatitis B, cirrhosis, and liver cancer groups, the binary logistic regression algorithm of Metaboanalyst was used to construct a diagnostic model, and the following logistic regression models for the three comparison groups were obtained:

[0055] 1) Chronic hepatitis B and liver cancer group

[0056] logit(P)=log(P / (1-P))=-0.405-0.291Taurine-1.277L-Histidinol+1.3182-Amino-3-Oxobutanoic Acid+0.486Isomer 3of Ornithine-0.254Serotonin-0.283Glycyl-Glycine-0.159Asparaginyl-Valine-1.67Xanthine-1.944Histidinyl-Glutamate-1.709Aspara ginyl-Asparagine+0.374Asparaginyl-Proline-0.933Prolyl-Methionine-0.246Methionyl-Isoleucine+0.433Tyrosyl-Glutamate-0.063 3-Methylsalicylaldehyde (model 1) was tested for its accuracy in distinguishing patients with chronic hepatitis B and liver cancer. The results were AUC: 0.953; Sensitivity: 0.910; Specificity: 0.865. Figure 4 a is the ROC curve of 15 metabolites screened by LASSO analysis.

[0057] 2) Chronic hepatitis B and liver cirrhosis group

[0058] logit(P)=log(P / (1-P))=0.27-0.633Taurine-2.675L-Histidinol+1.5562-Amino-3-Oxobutanoic Acid+3.044Isomer 3of Ornithine-0.711Serotonin-0.607Glycyl-Glycine+0.326Asparaginyl-Valine-0.34Xanthine-0.458Histidinyl-Glutamate-0.805Asparaginyl-Asparagine-0.051Asparaginyl-Proline-0.251Prolyl-Methionine-0.1Methionyl-Isoleucine+0.408Tyrosyl-Glutamate+0.3073-Methylsalicylaldehyde (Model 2) was tested for its accuracy in distinguishing patients with chronic hepatitis B and cirrhosis. The results were AUC: 0.883; Sensitivity: 0.779; Specificity: 0.850. Figure 4b is the ROC curve of 15 metabolites screened by LASSO analysis.

[0059] 3) Liver cancer and cirrhosis group

[0060] logit(P)=log(P / (1-P))=-0.313+0.407Taurine-0.812L-Histidinol+0.1252-Amino-3-Oxobutanoic Acid-0.289Isomer 3of Ornithine+0.546Serotonin+0.028Glycyl-Glycine-0.682Asparaginyl-Valine-0.715Xanthine-1.014Histidinyl-Glutamate-0.414Aspar aginyl-Asparagine+0.315Asparaginyl-Proline-0.877Prolyl-Methionine-0.545Methionyl-Isoleucine-0.081Tyrosyl-Glutamate-0.313 3-Methylsalicylaldehyde (Model 3) was tested for its accuracy in distinguishing patients with liver cancer and cirrhosis. The results were AUC: 0.922; Sensitivity: 0.845; Specificity: 0.844. Figure 4 c is the ROC curve of 15 metabolites screened by LASSO analysis.

[0061] The joint model was validated based on the validation set. The serum sample data of 36 patients with chronic hepatitis B, 36 patients with liver cirrhosis, and 36 patients with liver cancer in the validation set were validated using the serum marker group constructed by the present invention: Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, 3-Methylsalicylaldehyde. Pairwise comparisons were made between chronic hepatitis B, liver cirrhosis, and liver cancer, and ROC curves of the three comparison groups were drawn:

[0062] 1) Chronic hepatitis B and liver cancer group

[0063] The results are as follows Figure 5 As shown in a, the diagnostic model (model 1) constructed using the binary logistic regression algorithm of the above-mentioned Metaboanalyst was tested for its accuracy in distinguishing patients with chronic hepatitis B and liver cancer, and the result was AUC: 0.94.

[0064] 2) Chronic hepatitis B and liver cirrhosis group

[0065] The results are as follows Figure 5 As shown in b, the diagnostic model (model 2) constructed using the binary logistic regression algorithm of the above-mentioned Metaboanalyst was tested for its accuracy in distinguishing patients with chronic hepatitis B and cirrhosis. The results were AUC: 0.758; Sensitivity: 0.694; Specificity: 0.889.

[0066] 3) Liver cancer and cirrhosis group

[0067] The results are as follows Figure 5 As shown in c, the diagnostic model (model 3) constructed using the binary logistic regression algorithm of the above-mentioned Metaboanalyst was tested for its accuracy in distinguishing patients with liver cancer and cirrhosis, and the result was AUC: 0.877.

[0068] This shows that the combined model constructed by the serum marker group of the present invention: Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, 3-Methylsalicylaldehyde has a high value in distinguishing patients with chronic hepatitis B, cirrhosis and liver cancer.

[0069] 3. Metabolic pathway analysis results

[0070] 3.1 Discovery set (599 sera)

[0071] The Pathway Analysis module on the MetaboAnalyst website (www.metaboanalyst.ca) was used, Global Test was used as the enrichment analysis method, Relative-betweeness Centrality was used as the topological analysis method, and Homo sapiens (KEGG) was used to perform metabolic pathway analysis on 616 metabolites (230+386=616) with high credibility in level 1 and level 2. The comparisons between chronic hepatitis B, cirrhosis and liver cancer were made pairwise, and the results of the pathway analysis are as follows:

[0072] The results are as follows Figure 6 As shown in a, the main affected pathways in the serum of patients with chronic hepatitis B and liver cancer include tryptophan metabolism, taurine and hypotaurine metabolism, arginine and proline metabolism. The analysis results showed that significant changes occurred in amino acid metabolism and synthesis, leading to changes in the content of serum endogenous substances.

[0073] The results are as follows Figure 6 As shown in b, the main affected pathways in the serum of patients with chronic hepatitis B and cirrhosis include taurine and hypotaurine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism. The analysis results showed that significant changes occurred in amino acid metabolism and synthesis, leading to changes in the content of serum endogenous substances.

[0074] The results are as follows Figure 6 As shown in Figure c, the main affected pathways in the serum of patients with liver cancer and cirrhosis include arginine and proline metabolism, tyrosine metabolism, and the biosynthesis of phenylalanine, tyrosine, and tryptophan. The analysis results showed that significant changes had occurred in amino acid metabolism and synthesis, leading to changes in the content of serum endogenous substances.

[0075] In summary, the reliability of selecting Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde as a combination of biomarkers for distinguishing chronic hepatitis B, cirrhosis, and liver cancer is also confirmed from the side.

[0076] 3.2 Validation set (108 sera)

[0077] The Pathway Analysis module on the MetaboAnalyst website (www.metaboanalyst.ca) was used. Global Test was used as the enrichment analysis method, Relative-betweeness Centrality was used as the topological analysis method, and Homo sapiens (KEGG) was used to perform metabolic pathway analysis on 680 metabolites with high credibility in level 1 and level 2. The validation set chronic hepatitis B, cirrhosis and liver cancer groups were compared pairwise. The results of the pathway analysis are as follows:

[0078] The results are as follows Figure 7 As shown in a, the main influencing pathways between the serum of patients with chronic hepatitis B and liver cancer include tryptophan, arginine, proline and histidine metabolism. The analysis results show that significant changes have occurred in amino acid metabolism and synthesis, leading to changes in the content of serum endogenous substances.

[0079] The results are as follows Figure 7 As shown in b, the main influencing pathways between the serum of patients with chronic hepatitis B and cirrhosis include tryptophan, taurine and hypotaurine, cysteine ​​and methionine metabolism. The analysis results showed that significant changes occurred in amino acid metabolism and synthesis, leading to changes in the content of serum endogenous substances.

[0080] The results are as follows Figure 7 As shown in Figure c, the main affected pathways in the serum of patients with liver cancer and cirrhosis include tryptophan metabolism, taurine and hypotaurine metabolism, arginine and proline metabolism. The analysis results showed that significant changes occurred in amino acid metabolism and synthesis, leading to changes in the content of serum endogenous substances.

[0081] In summary, the reliability of selecting Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde as a combination of biomarkers for distinguishing chronic hepatitis B, cirrhosis, and liver cancer is also confirmed from the side.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A biomarker for distinguishing chronic hepatitis B, liver cirrhosis and liver cancer, characterized in that: The marker is one or any combination of more than one of the following 15 metabolites, and the 15 metabolites are Taurine, Glycyl-Glycine, Asparaginyl-Valine, Xanthine, Histidinyl-Glutamate, Asparaginyl-Asparagine, L-Histidinol, 2-Amino-3-Oxobutanoic Acid, Isomer 3 of Ornithine, Serotonin, Asparaginyl-Proline, Prolyl-Methionine, Methionyl-Isoleucine, Tyrosyl-Glutamate, and 3-Methylsalicylaldehyde.

2. A kit, characterized in that: Contains the diagnostic marker for distinguishing chronic hepatitis B, cirrhosis and liver cancer as described in claim 1.

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