Metabolic marker combination, kit for detecting metabolic marker combination and application of metabolic marker combination

Abstract

The invention discloses a metabolic marker combination, a kit for detecting the metabolic marker combination and application of the metabolic marker combination, and particularly relates to a metabolic marker combination for auxiliary diagnosis of liver cancer, a kit for detecting the metabolic marker combination and application of the metabolic marker combination. The metabolic marker combination comprises glycocholic acid, sebacic acid, phenylalanyl tryptophan, arachidonic acid and alpha-linolenic acid. The metabolic marker combination has high accuracy, sensitivity and specificity when used for detecting the liver cancer or predicting the liver cancer risk, can be used for developing a rapid liver cancer detection kit, and provides a new early risk assessment scheme for patients or susceptible people.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of biological medicine, and particularly relates to a metabolic marker combination, a kit for detecting the same, and application thereof. BACKGROUND

[0002] Globally, liver cancer is one of the most common malignant tumors, and 95% of all primary liver cancer patients are hepatocellular carcinoma. At present, liver cancer is the fifth most common malignant tumor in terms of incidence rate and the third most common malignant tumor in terms of mortality rate. The pathological type of primary liver cancer is mainly hepatocellular carcinoma (HCC), accounting for 75% to 85%; a small number of patients are intrahepatic cholangiocarcinoma (ICC) and mixed hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA), and the three have large differences in pathogenesis, biological behavior, molecular characteristics, clinical manifestations, histopathological morphology, treatment methods, and prognosis. Due to the characteristics of liver cancer, such as insidious onset and no obvious early symptoms, most patients are in the middle and advanced stages at the time of diagnosis, thus missing the best treatment opportunity. Therefore, it is particularly important to develop an early diagnostic index for liver cancer with high sensitivity and accuracy for the cure of liver cancer. SUMMARY

[0003] The present application solves the technical problem of the prior art that there is a lack of early diagnostic means for liver cancer with high sensitivity and accuracy, and provides a metabolic marker combination for auxiliary diagnosis of liver cancer, a kit for detecting the same, and application thereof. The metabolic marker combination of the present application can quickly and accurately predict the risk of liver cancer, and has good application prospects.

[0004] The present application solves the above technical problems through the following technical solutions.

[0005] The first aspect of the present application provides a metabolic marker combination for diagnosis or risk prediction of liver cancer, which comprises glycocholic acid, sebacic acid, phenylalanyltryptophan, arachidonic acid, and alpha-linolenic acid.

[0006] In some embodiments, the metabolic marker combination consists of glycocholic acid, sebacic acid, phenylalanyltryptophan, arachidonic acid, and alpha-linolenic acid.

[0007] In some embodiments, the metabolic marker combination further comprises taurochenodeoxycholic acid, aspartic acid, butyric acid, indolepyruvic acid, isocitric acid, pyruvic acid, threonic acid, and phenylalanylphenylalanine.

[0008] In some embodiments, the metabolic marker combination consists of glycocholic acid, decanoic acid, phenylalanyltryptophan, arachidonic acid, a-linolenic acid, taurochenodeoxycholic acid, aspartic acid, butyric acid, indolepyruvic acid, isocitric acid, pyruvic acid, threonic acid, and phenylalanylphenylalanine.

[0009] A second aspect of the present application provides a liver cancer detection or diagnosis kit comprising the metabolic marker combination according to the first aspect, a reagent for detecting the metabolic marker combination, and / or a reagent for extracting the metabolic marker combination.

[0010] In some embodiments, the liver cancer detection or diagnosis kit further comprises an internal standard.

[0011] In some preferred embodiments, the internal standard is deuterated a-linolenic acid, deuterated glycocholic acid, deuterated decanoic acid, deuterated phenylalanyltryptophan, deuterated arachidonic acid.

[0012] A third aspect of the present application provides use of the metabolic marker combination according to the first aspect, a reagent for detecting the metabolic marker combination, or a reagent for extracting the metabolic marker combination in the manufacture of a product for the diagnosis or risk prediction of liver cancer.

[0013] In some embodiments, the product is selected from a reagent, a test paper, a kit, or an instrument.

[0014] A fourth aspect of the present application provides use of the metabolic marker combination according to the first aspect, or a reagent for detecting the metabolic marker combination in the screening of a drug for preventing and / or treating liver cancer.

[0015] A fifth aspect of the present application provides a method for screening the metabolic marker combination according to the first aspect, the method comprising the following steps:

[0016] The metabolic marker combination is obtained by analyzing and detecting metabolites in serum samples collected from healthy controls, patients with liver fibrosis, patients with liver cirrhosis, and patients with liver cancer, and performing multi-dimensional statistical analysis, single-dimensional statistical analysis, and pathway analysis on the metabolite content to obtain metabolites with specifically increased or decreased content in patients with liver cancer.

[0017] In some embodiments, the metabolite content is determined by liquid chromatography mass spectrometry.

[0018] The sixth aspect of the present application provides a liver cancer auxiliary diagnosis system, the liver cancer auxiliary diagnosis system includes detection analysis module, input module and judgment module; the detection analysis module carries out metabolite detection analysis to the collected serum, obtains the content of glycocholic acid, sebacic acid, arachidonic acid, phenylalanine tryptophan and alpha-linolenic acid, the input module inputs the content of the above metabolites detected, and the judgment module evaluates the liver cancer risk according to the content of glycocholic acid, sebacic acid, arachidonic acid, phenylalanine tryptophan and alpha-linolenic acid.

[0019] The present application (1) expands from the transverse direction, collects patients of different stages, obtains blood samples of patients, uses liquid chromatography-mass spectrometry and other modern analysis instruments based on mass spectrometry, combines bioinformatics and data analysis and other calculation technologies, summarizes the characteristic metabolic patterns of malignant tumor patients, finds metabolic marker peaks or characteristic metabolic patterns, and verifies the discovered diagnostic markers using a verification set, so that a set of liver cancer diagnostic marker combinations with high accuracy, sensitivity and specificity are obtained. (2) Development of liver cancer rapid detection kit: based on clinical and basic research, screening and determining metabolic markers that can be used for liver cancer risk prediction and diagnosis, converting into an in vitro diagnostic kit that can be used for liver cancer diagnosis, and constructing a system and software that can be used for liver cancer risk analysis, obtaining a liver cancer metabolite diagnostic ecosystem with independent intellectual property rights and applying it to clinical research, realizing the industrialization and localization of liver cancer detection kits.

[0020] On the basis of common sense in the art, the above-mentioned preferred conditions can be combined arbitrarily, i.e., to obtain each preferred example of the present application.

[0021] The reagents and raw materials used in the present application are commercially available.

[0022] The positive progress effect of the present application is that:

[0023] The metabolic marker combination of the present application has high accuracy, sensitivity and specificity in detecting liver cancer or predicting liver cancer risk, and can be used for the development of a liver cancer rapid detection kit, providing a new risk assessment method for patients or susceptible populations. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 To find the multivariate quality control chart of serum metabolites of the population;

[0025] Figure 2 To find the classification overview of serum metabolites of the population;

[0026] Figure 3 To find the multivariate quality control chart of serum metabolites of the verification population;

[0027] Figure 4 To find the classification overview of serum metabolites of the verification population;

[0028] Figure 5 PCA plot of serum metabolites for discovery set population;

[0029] Figure 6 PLS-DA plot of serum metabolome for discovery set population;

[0030] Figure 7 PCA plot of serum metabolome for validation set population;

[0031] Figure 8 PLS-DA plot of serum metabolome for validation set population;

[0032] Figure 9 Box plot of serum differential metabolites for discovery set population (μM);

[0033] Figure 10 Box plot of serum differential metabolites for validation set population (μmol / L);

[0034] Figure 11 Pathway enrichment analysis of differential metabolites for discovery set samples (HSA library);

[0035] Figure 12 Pathway enrichment analysis of differential metabolites for validation set samples (HSA library);

[0036] Figure 13 Schematic diagram of ROC results for external validation. DETAILED DESCRIPTION

[0037] The application will be further described by the following examples, but the application is not limited to the examples. The experimental methods in the following examples, if not otherwise specified, are carried out according to the conventional methods and conditions, or according to the instructions of the commercial products.

[0038] Example 1

[0039] The discovery set and independent validation set samples were collected, the discovery set samples included 564 liver cancer patients, 20 bile duct cancer patients, 388 liver fibrosis and cirrhosis patients, and 358 healthy controls from Shanghai Shuguang Hospital and Nanjing Gulou Hospital; the independent validation set included 79 liver cancer patients, 334 liver fibrosis and cirrhosis patients, and 305 healthy controls from Guangdong Provincial Hospital of Chinese Medicine, Zhongshan Third Hospital, Shanghai Shuguang Hospital, and Anhui Second Hospital. The Q300 metabolic chip technology was used to detect the serum metabolite samples of the discovery set and independent validation set by liquid chromatography-mass spectrometry, and then the TMBQ automatic quantification software was used to quantify the data. After quantification, IP4M analysis was performed on the discovery set and independent validation set, including PCA analysis, PLS-DA analysis or OPLS-DA analysis, univariate statistical analysis, and pathway enrichment analysis to screen reliable differential metabolites; then a diagnostic model was constructed in the discovery set using bioinformatics methods, and verified in the independent validation set.

[0040] Quality control

[0041] The sample detection quality was analyzed by multivariate quality control chart (PC1 score ≤1 SD) and quality control sample correlation (correlation coefficient r ≥0.99).

[0042] I. Test scheme

[0043] (I) Metabolite classification overview

[0044] The average abundance composition of each type of metabolite in all samples was analyzed, including amino acids, carbohydrates, short-chain fatty acids, organic acids, fatty acids, bile acids, carnitines, indoles, and phenylalanines, as shown in Table 1.

[0045] Table 1 Overview of detected metabolites

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] (II) Multivariate statistical analysis

[0055] Differential analysis of serum metabolome between different groups by principal component analysis and orthogonal partial least squares analysis, and finding key differential metabolites.

[0056] (Three) single dimension statistical analysis

[0057] Differential analysis of serum metabolome between different groups by T Test or Mann-Whitney U Test, and finding key differential metabolites; and drawing box plot of differential metabolites.

[0058] (Four) pathway analysis

[0059] Selecting differential metabolites by multi-dimensional statistical analysis and single-dimensional statistical analysis, and performing pathway enrichment analysis, including (iPath pathway analysis and hsa library pathway analysis).

[0060] (Five) diagnostic analysis

[0061] Selecting differential metabolites by multi-dimensional statistical analysis and single-dimensional statistical analysis, and using bioinformatics methods to construct a suitable diagnostic model, and verifying the effectiveness of the model in the independent validation set.

[0062] II. Statistical analysis results

[0063] Sample basic information

[0064] Discovery set: including 564 cases of liver cancer patients, 20 cases of cholangiocarcinoma patients, 388 cases of liver fibrosis and cirrhosis patients, and 358 cases of healthy controls from Shanghai Shuguang Hospital and Nanjing Drum Tower Hospital.

[0065] Independent validation set: including 79 cases of liver cancer patients, 334 cases of liver fibrosis and cirrhosis patients, and 305 cases of healthy controls from Guangdong Provincial Hospital of Traditional Chinese Medicine, Zhongshan Third Hospital, Shanghai Shuguang Hospital, and Anhui Second Hospital.

[0066] (I) Data overview and quality control

[0067] 1. Basic information of serum metabolome data in the discovery set

[0068] Through metabolomic quantitative detection analysis of metabolites in serum samples in the discovery set, metabolite content data was obtained.

[0069] Quality control of metabolome data in the discovery set:

[0070] Multivariate quality control chart (PC1 score ≤1SD) and sample correlation (correlation coefficient r ≥0.95) were drawn to analyze the quality of sample detection. Multivariate quality control chart showed that the sample detection quality was good. Figure 1), the sample distribution was uniform in the serum metabolite detection of the discovery set population, most samples were within 2 standard deviations, and QC samples were within 1 standard deviation, indicating that the sample processing and instrument detection process were under control, and the detection quality was good. The correlation heat map between QC samples showed that the correlation coefficient between all QC samples was greater than 0.97, indicating that the difference between QC samples was very small, the detection data was stable, and the experimental quality control effect was good.

[0071] Overview of discovery set metabolome data:

[0072] A total of 176 metabolites were detected in the serum of the discovery set population, including carbohydrates, organic acids, amino acids, fatty acids, carnitines, short-chain fatty acids, benzene rings, benzoic acids, bile acids, indoles, phenylacetic acids, pyridines, phenols, phenylpropanoids, imidazoles, etc. The classification overview of metabolites in each group of samples is shown in Table 1. Figure 2 .

[0073] 2. Basic situation of serum metabolome data of independent verification set

[0074] Metabolomic quantitative detection and analysis of metabolites in the serum of the independent verification set population were performed to obtain metabolite content data.

[0075] Quality control of metabolome data of verification set:

[0076] Multivariate quality control chart (PC1 score ≤1SD) and correlation of quality control samples (correlation coefficient r ≥0.95) were drawn to analyze the sample detection quality. Multivariate quality control chart (PC1 score ≤1SD) and correlation of quality control samples (correlation coefficient r ≥0.95) were drawn to analyze the sample detection quality. Figure 3 ), the sample distribution was uniform in the serum metabolite detection of the verification set population, most samples were within 2 standard deviations, and QC samples were within 1 standard deviation, indicating that the sample processing and instrument detection process were under control, and the detection quality was good. The correlation heat map between QC samples showed that the correlation coefficient between most QC samples was greater than 0.99, indicating that the difference between QC samples was very small, the detection data was stable, and the experimental quality control effect was good.

[0077] Overview of metabolome data of verification set:

[0078] A total of 186 metabolites were detected in the serum of the verification set population, including short-chain fatty acids, organic acids, amino acids, fatty acids, bile acids, benzene rings, carbohydrates, phenylacetic acids, phenylpropanoids, phenols, benzoic acids, carnitines, indoles, imidazoles, pyridines, etc. The classification overview of metabolites in each group of samples at three time points is shown in Table 2. Figure 4 .

[0079] (2) Multivariate statistical analysis

[0080] Multivariate statistical analysis of discovery set serum metabolome data

[0081] The serum metabolic profiles of liver cancer, advanced liver fibrosis, early liver fibrosis, hepatitis and healthy control in the discovery set were analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively, and the key differential metabolites were found.

[0082] The results of PCA analysis ( Figure 5 ) showed that the liver cancer group was obviously separated from the other four groups (advanced liver fibrosis, early liver fibrosis, hepatitis and healthy control) (PC2, P = 7.41 x 10 -199 ). The PLS-DA analysis ( Figure 6 ) further showed that there were obvious differences among the five groups (liver cancer, advanced liver fibrosis, early liver fibrosis, hepatitis and healthy control) (PC1, P = 2.7 x 10 -209 ; PC2, P = 6.55 x 10 -160 ). The PLS-DA analysis showed that 27 substances with VIP > 1.1 were shown in Table 2, which would be further analyzed in combination with the results of univariate statistics to find the differential metabolites.

[0083] Table 2. Serum metabolite PLS-DA analysis VIP value of the discovery set

[0084]

[0085] Multidimensional statistical analysis of the metabolome data of the validation set

[0086] The serum metabolic profiles of liver cancer, advanced liver fibrosis, early liver fibrosis, hepatitis and healthy control in the validation set were analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively, and the key differential metabolites were found.

[0087] The results of PCA analysis ( Figure 7 ) showed that the liver cancer group, advanced liver fibrosis group, early liver fibrosis group, hepatitis group and healthy control group were obviously separated (PC1, P = 1.02 x 10 -14 ; PC2, P = 3.02 x 10 -36 ). The PLS-DA analysis ( Figure 8 ) further showed that there were obvious differences among the five groups (liver cancer, advanced liver fibrosis, early liver fibrosis, hepatitis and healthy control) (PC1, P = 2.4 x 10 -62 ; PC2, P = 3.9 x 10 -64 ). The PLS-DA analysis showed that 30 substances with VIP > 1.1 were shown in Table 3, which would be further analyzed in combination with the results of univariate statistics to find the differential metabolites.

[0088] Table 3. Serum metabolite PLS-DA analysis VIP value of the validation set

[0089]

[0090] (III) Univariate statistical analysis

[0091] Univariate statistical analysis of discovery set serum metabolome data

[0092] Univariate Kruskal.test analysis was performed according to normality and homogeneity of variance of each substance to find serum differential metabolites in the discovery set population. The 9 substances with the smallest P values are shown in Table 2. The median values of the 9 most significant differential metabolites in each group are shown in Table 3. The 9 significantly changed differential metabolites are PheTRP (phenylalanyltryptophan), Decanoylcarnitine (decanoylcarnitine), Sebacic acid (sebacic acid), Tryptophan (tryptophan), alpha-Linolenic acid (alpha-linolenic acid), 2-Hydroxybutyric acid (2-hydroxybutyric acid), TCA (taurocholic acid), Octanoylcarnitine (octanoylcarnitine), and PhePhe (phenylalanylphenylalanine). Figure 9

[0093] Table 3. Median values, FDR values, and P values of serum differential metabolites in the discovery set population.

[0094]

[0095] Univariate statistical analysis of validation set serum metabolome data

[0096] Univariate Kruskal.test analysis was performed according to normality and homogeneity of variance of each substance to find serum differential metabolites in the validation set population. The 9 substances with the smallest P values are shown in Table 5. The median values of the 9 most significant differential metabolites in each group are shown in Table 6. The 9 significantly changed differential metabolites are Glyceric acid (glyceric acid), TCA (taurocholic acid), PhePhe (phenylalanylphenylalanine), PheTrp (phenylalanyltryptophan), Glutamic acid (glutamic acid), Isocitric acid (isocitric acid), Aspartic acid (aspartic acid), TCDCA (taurochenodeoxycholic acid), and GCA (glycocholic acid). Figure 10

[0097] Table 5. FDR values, VIP values, and FC values of serum differential metabolites in the validation set population.

[0098]

[0099] ​​The serum differential metabolites found by the above-mentioned discovery set and validation set were taken and the metabolites consistent in the discovery set and the validation set were obtained, and a total of 13 differential metabolites were obtained, which were arachidonic acid, alpha-linolenic acid, glycocholic acid, tauroursodeoxycholic acid, aspartic acid, butyric acid, indolepyruvic acid, isocitric acid, pyruvic acid, sebacic acid, threonic acid, phenylalanylphenylalanine, and phenylalanyltryptophan. Then, the bioinformatics method was used to construct a logistic regression model, which had the best diagnostic effect in the discovery set and the independent validation set (Table 6, Table 7). The cutoff value used was 0, and the correct diagnosis rate of the model in the validation set was 100% (305 / 305) for healthy controls, 95.4% (394 / 952) for liver diseases, and 88.6% (70 / 79) for liver cancer; the AUC of the combined marker was 0.946; the sensitivity and specificity were also relatively high, which were 93.0% and 95.6%, respectively, and the ROC results were as shown in Figure 13

[0100] Table 6. Diagnostic effect of the model in the discovery set

[0101]

[0102] Table 7. Diagnostic effect of the model in the validation set

[0103]

[0104] (Four) Pathway analysis

[0105] Pathway analysis of differential metabolites in the discovery set

[0106] After taking the union of the serum differential metabolites obtained by the single-dimensional statistical analysis and the multi-dimensional statistical analysis of the discovery set samples, the metabolic software IP4M developed by the company was used for pathway enrichment analysis, including iPath pathway analysis and hsa library pathway analysis.

[0107] The results of iPath pathway analysis of the differential metabolites in the discovery set samples showed that the differential metabolites were mainly distributed in the pathways of amino acid metabolism, citric acid cycle, fatty acid metabolism, and bile acid metabolism.

[0108] Further hsa library pathway enrichment analysis of the differential metabolites in the discovery set Figure 11 ​), it was found that the significantly enriched differential metabolic pathways included: Glyoxylate and dicarboxylate metabolism (FDR = 0.00015), Alanine, aspartate and glutamate metabolism (FDR = 0.00020), Valine, leucine and isoleucine biosynthesis (FDR = 0.00042), Butanoate metabolism (FDR = 0.0011), Citrate cycle (FDR = 0.0044), Phenylalanine metabolism (FDR = 0.0044), Arginine biosynthesis (FDR = 0.0044), Phenylalanine, tyrosine and tryptophan biosynthesis (FDR = 0.0045), Glycine, serine and threonine metabolism (FDR = 0.0085).

[0109] Verification set differential metabolite pathway analysis

[0110] After selecting the serum differential metabolites obtained by univariate statistical analysis and multivariate statistical analysis of the verification set sample, the company independently developed metabolomics software IP4M was used for pathway enrichment analysis, including iPath pathway analysis and hsa library pathway analysis.

[0111] After iPath pathway analysis of the differential metabolites of the verification set sample, the results showed that the differential metabolites were mainly distributed in the pathways of amino acid metabolism, citric acid cycle, fatty acid metabolism, bile acid metabolism, etc.

[0112] Further hsa library pathway enrichment analysis of the differential metabolites of the verification set Figure 12), we found that significantly enriched differential metabolic pathways included Glyoxylate and dicarboxylate metabolism (FDR = 0.00011), Alanine, aspartate and glutamate metabolism (FDR = 0.00014), Valine, leucine and isoleucine biosynthesis (FDR = 0.00035), Butanoate metabolism (FDR = 0.00085), Citrate cycle (FDR = 0.0043), Arginine biosynthesis (FDR = 0.0049), Phenylalanine, tyrosine and tryptophan biosynthesis (FDR = 0.0052), Glycine, serine and threonine metabolism (FDR = 0.0084), Biosynthesis of unsaturated fatty acids (FDR = 0.013), Phenylalanine metabolism (FDR = 0.043), and Primary bile acid biosynthesis (FDR = 0.045).

[0113] Conclusion

[0114] Through the analysis of serum metabolomics of the discovery set and the validation set samples, we obtained a combination of metabolites, including glycocholic acid, sebacic acid, phenylalanyltryptophan, arachidonic acid and a-linolenic acid, which can be used to distinguish liver cancer patients from liver fibrosis and healthy controls, and can be used for the auxiliary diagnosis of liver cancer. In the discovery set and the validation set, the differential metabolic pathways of liver cancer patients and other populations included Glyoxylate and dicarboxylate metabolism, Alanine, aspartate and glutamate metabolism, etc.

Claims

1. A combination of metabolic biomarkers for the diagnosis or risk prediction of liver cancer, characterized in that, The combination of metabolic markers includes glycocholic acid, sebacic acid, phenylalanine tryptophan, arachidonic acid, and alpha-linolenic acid.

2. The metabolic biomarker combination as described in claim 1, characterized in that, The combination of metabolic markers also includes taurine chenodeoxycholic acid, aspartic acid, butyric acid, indolepyruvate, isocitrate, pyruvate, threonine, and phenylalanine.

3. A liver cancer detection or diagnostic kit, characterized in that, The liver cancer detection or diagnostic kit includes the combination of metabolic biomarkers as described in claim 1 or 2, reagents for detecting the combination of metabolic biomarkers, and / or reagents for extracting the combination of metabolic biomarkers.

4. The liver cancer detection or diagnostic kit as described in claim 3, characterized in that, The liver cancer detection or diagnostic kit also includes an internal standard.

5. The liver cancer detection or diagnostic kit as described in claim 4, characterized in that, The internal standards are deuterated α-linolenic acid, deuterated glycinecholic acid, deuterated sebacic acid, deuterated phenylalanine, and deuterated arachidonic acid.

6. Use of a combination of metabolic biomarkers as described in claim 1 or 2, a reagent for detecting the combination of said metabolic biomarkers, or a reagent for extracting said metabolic biomarkers in the preparation of a product for the diagnosis or risk prediction of liver cancer.

7. The use as described in claim 6, characterized in that, The products are selected from reagents, test strips, kits, or instruments.

8. Use of a combination of metabolic markers as described in claim 1 or 2, a reagent for detecting the combination of said metabolic markers, or a reagent for extracting the combination of said metabolic markers in screening drugs for the prevention and / or treatment of liver cancer.

9. A method for screening combinations of metabolic biomarkers as described in claim 1 or 2, the method comprising the following steps: Serum samples collected from healthy controls, patients with liver fibrosis, cirrhosis, and liver cancer were analyzed to detect metabolite levels. Multidimensional statistical analysis, unidimensional statistical analysis, and pathway analysis were performed on the metabolite levels to identify metabolites whose levels were specifically elevated or decreased in liver cancer patients.

10. The method as described in claim 9, characterized in that, The content of the metabolites was determined by liquid chromatography-mass spectrometry.

11. A liver cancer auxiliary diagnostic system, characterized in that, The liver cancer auxiliary diagnostic system includes a detection and analysis module, an input module, and a judgment module. The detection and analysis module performs metabolite detection and analysis on the collected serum to obtain the contents of glycocholic acid, sebacic acid, arachidonic acid, phenylalanine tryptophan, and α-linolenic acid. The input module inputs the contents of the above-mentioned metabolites obtained from the detection. The judgment module assesses the risk of liver cancer based on the contents of glycocholic acid, sebacic acid, arachidonic acid, phenylalanine tryptophan, and α-linolenic acid.

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