Metabolic markers for predicting the risk of liver cancer and their application

Abstract

The present invention discloses metabolic markers for predicting the risk of liver cancer and their applications. The development and utilization of such metabolic markers will provide technical support for risk prediction and early diagnosis and treatment of liver cancer. The present invention uses a non-targeted metabolomics detection method to screen and verify 44 plasma metabolites associated with the risk of liver cancer, and identifies a group of 18 plasma metabolites that can significantly improve the risk prediction level of liver cancer.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology and relates to metabolic markers for liver cancer risk prediction and applications thereof. Background Art

[0002] Primary liver cancer is the sixth most common cancer in the world and the third leading cause of cancer death. The incidence of liver cancer in my country exceeds that of other countries in the world. New cases and deaths of liver cancer each year account for more than 50% of the world's total. The incidence and mortality of liver cancer in my country rank second and fourth among cancers, respectively, seriously threatening the lives and health of residents. Liver cancer has an insidious onset and progresses rapidly. About 70%-80% of patients are already in the middle and late stages when diagnosed, and have lost the opportunity for surgery or other local treatments. The recurrence rate is high, resulting in a 5-year survival rate of only 14%. Therefore, improving the prediction level of liver cancer risk and identifying high-risk groups for early intervention and diagnosis and treatment are of great public health significance for reducing the incidence and mortality of liver cancer.

[0003] To date, the screening or auxiliary diagnosis technologies for liver cancer mainly include serum alpha-fetoprotein detection and imaging technology. However, although alpha-fetoprotein is the most widely used serological indicator in clinical practice, its sensitivity for detecting early liver cancer is less than 40%, and 30% to 40% of liver cancer patients do not have significantly elevated alpha-fetoprotein. Therefore, alpha-fetoprotein has limited sensitivity and specificity in the screening of liver cancer. Imaging technologies include computed tomography, ultrasound, and magnetic resonance imaging, which are expensive, limited by the operator's technical level, and have low sensitivity for early liver cancer. In addition, the above methods are mainly used for screening or auxiliary diagnosis, and cannot effectively assess the potential risk of liver cancer. Improving the level of early prevention of liver cancer urgently requires new technical methods.

[0004] Metabolomics is a new omics technology that has emerged after genomics, transcriptomics and proteomics. It can quantitatively analyze thousands of intermediates and end products involved in biochemical reactions in organisms. It is widely used in etiology, diagnostics, biological function research and drug development. Compared with other omics research methods, metabolomics has the following advantages: ① It measures the metabolic response of the body to changes in physiological and pathological conditions from a holistic perspective. In particular, the metabolome is at the downstream of the regulation of the life network, and the detection of metabolites is closer to reflecting the changes in biological phenotypes; ② Small changes in gene and protein expression at the functional level can be amplified by metabolites, while their non-functional changes will not be reflected at the metabolic level. Therefore, the detection of metabolites is easier to detect key events that change biological phenotypes; ③ The samples measured can be biological fluids (such as blood, urine, etc.), which are relatively easy to obtain, less harmful to the human body, and easy to promote and apply. Therefore, metabolomics technology helps to discover early tumor events and identify biomarkers with population application value, so as to improve the risk prediction and intervention capabilities of tumors.

[0005] Previous metabolomics studies on liver cancer had small sample sizes, detected a small number of metabolites, lacked external validation, and especially failed to evaluate the predictive value of metabolite markers for future risk of disease in prospective cohorts. Therefore, it is necessary to conduct cohort-based metabolomics studies on liver cancer and systematically screen metabolic biomarkers with application value, which is of great significance for achieving the advancement of liver cancer prevention and precise prevention, and reducing the incidence and mortality of liver cancer in my country. Summary of the invention

[0006] In view of the above shortcomings, the present invention provides a class of plasma metabolites for liver cancer risk prediction and its application. The method can be applied to detect new biomarkers related to liver cancer.

[0007] The first object of the present invention is to provide a marker combination associated with liver cancer, wherein the marker combination is a combination of one or more of the following 44 compounds:

[0008] Copper Hydroxyquinoline

[0009] 3-(4-Hydroxyphenyl)lactic acid

[0010] Cystathionine

[0011] Glycocholate

[0012] Citrulline

[0013] Phenylalanine

[0014] Vitamin A

[0015] Tyrosine

[0016] Creatine

[0017] Glycochenodeoxycholic acid

[0018] Taurochenodeoxycholate

[0019] Ribitol

[0020] 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0 / 16:0)

[0021] 1-Myristoyl-2-palmitoyl-glycerophosphatidylcholine (14:0 / 16:0)

[0022] Dehydroepiandrosterone Sulfate

[0023] N-acetyl glycine

[0024] Androsterone Sulfate

[0025] N-Acetyl Tyrosine

[0026] Epiandrosterone Sulfate

[0027] N1-Methyl-2-pyridone-5-carboxamide

[0028] 1-Arachidonic acid-glycerophosphatidylcholine (20:4 / 0:0)

[0029] 1-Arachidonic acid-glycerophosphoethanolamine (20:4 / 0:0)

[0030] 5α-Androstane-3β,17β-diol hydrogen sulfate

[0031] Taurocholine Sulfate

[0032] Androstenediol disulfate

[0033] 5α-Pregnane-3β,20α-diol monosulfate

[0034] Androstenediol (3α,17α) monosulfate

[0035] 17-Androstenediol sulfate (1)

[0036] 17-Androstenediol sulfate (2)

[0037] 16α-Hydroxydehydroisoandrosterone 3-sulfate

[0038] Androsterone glucuronide

[0039] Arginine

[0040] Glycine ursodeoxycholic acid

[0041] Citrate

[0042] Glycochenodeoxycholic acid 3-sulfate

[0043] 1-(1-Alkenyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1 / 16:1)

[0044] 1-(1-Alkenyl-palmitoyl)-2-palmitoyl-glycerophosphatidylcholine (P-16:0 / 16:0)

[0045] Glycochenodeoxycholic acid glucuronide

[0046] Ceramide (d18:2 / 24:1, d18:1 / 24:2)

[0047] 11β-Hydroxyandrosterone glucuronide

[0048] Glycoursodeoxycholic acid sulfate

[0049] 2,3-Dihydroxy-5-methylthio-4-pentenoate

[0050] Lactone sulfuric acid

[0051] Tetrahydrocortisol glucuronide.

[0052] Furthermore, the marker combination is a combination of one or more of the following 33 compounds:

[0053] Copper Hydroxyquinoline

[0054] Cystathionine

[0055] Citrulline

[0056] Creatine

[0057] Ribitol

[0058] 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0 / 16:0)

[0059] 1-Myristoyl-2-palmitoyl-glycerophosphatidylcholine (14:0 / 16:0)

[0060] N-acetyl glycine

[0061] Androsterone Sulfate

[0062] N-Acetyl Tyrosine

[0063] Epiandrosterone Sulfate

[0064] N1-Methyl-2-pyridone-5-carboxamide

[0065] 1-Arachidonic acid-glycerophosphoethanolamine (20:4 / 0:0)

[0066] 5α-Androstane-3β,17β-diol hydrogen sulfate

[0067] Taurocholine Sulfate

[0068] Androstenediol disulfate

[0069] 5α-Pregnane-3β,20α-diol monosulfate

[0070] Androstenediol (3α,17α) monosulfate

[0071] 17-Androstenediol sulfate (1)

[0072] 17-Androstenediol sulfate (2)

[0073] 16α-Hydroxydehydroisoandrosterone 3-sulfate

[0074] Androsterone glucuronide

[0075] Arginine

[0076] Citrate

[0077] Glycochenodeoxycholic acid 3-sulfate

[0078] 1-(1-Alkenyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1 / 16:1)

[0079] Glycochenodeoxycholic acid glucuronide

[0080] Ceramide (d18:2 / 24:1, d18:1 / 24:2)

[0081] 11β-Hydroxyandrosterone glucuronide

[0082] Glycoursodeoxycholic acid sulfate

[0083] 2,3-Dihydroxy-5-methylthio-4-pentenoate

[0084] Lactone sulfuric acid

[0085] Tetrahydrocortisol glucuronide.

[0086] Furthermore, the marker combination is a combination of one or more of the following 18 compounds:

[0087] Copper Hydroxyquinoline

[0088] 3-(4-Hydroxyphenyl)lactic acid

[0089] Cystathionine

[0090] Glycocholate

[0091] Citrulline

[0092] Creatine

[0093] 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0 / 16:0)

[0094] Androsterone Sulfate

[0095] 1-Arachidonic acid-glycerophosphatidylcholine (20:4 / 0:0)

[0096] 5α-Pregnane-3β,20α-diol monosulfate

[0097] 17-Androstenediol sulfate (1)

[0098] 17-Androstenediol sulfate (2)

[0099] 16α-Hydroxydehydroisoandrosterone 3-sulfate

[0100] Arginine

[0101] Citrate

[0102] Glycochenodeoxycholic acid 3-sulfate

[0103] Glycochenodeoxycholic acid glucuronide

[0104] Ceramide (d18:2 / 24:1, d18:1 / 24:2).

[0105] Furthermore, the marker combination is a combination of one or more of the following 15 compounds:

[0106] Copper Hydroxyquinoline

[0107] Cystathionine

[0108] Citrulline

[0109] Creatine

[0110] 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0 / 16:0)

[0111] Androsterone Sulfate

[0112] 5α-Pregnane-3β,20α-diol monosulfate

[0113] 17-Androstenediol sulfate (1)

[0114] 17-Androstenediol sulfate (2)

[0115] 16α-Hydroxydehydroisoandrosterone 3-sulfate

[0116] Arginine

[0117] Citrate

[0118] Glycochenodeoxycholic acid 3-sulfate

[0119] Glycochenodeoxycholic acid glucuronide

[0120] Ceramide (d18:2 / 24:1, d18:1 / 24:2).

[0121] The second object of the present invention is to provide a product for detecting the aforementioned marker combination for use in preparing liver cancer diagnosis and / or risk prediction products.

[0122] Furthermore, the marker combination is derived from plasma.

[0123] The third object of the present invention is to provide the use of the aforementioned marker combination in screening drugs for treating and / or alleviating liver cancer.

[0124] Furthermore, the marker combination is derived from plasma.

[0125] The fourth object of the present invention is to provide the use of the aforementioned marker combination in the preparation of a detection kit for liver cancer diagnosis and / or risk prediction.

[0126] Furthermore, the marker combination is derived from plasma.

[0127] Compared with the prior art, the present invention has the following beneficial effects:

[0128] (1) This paper adopts a rigorous screening, verification and evaluation system, and conducts a nested case-control study of liver cancer based on two prospective cohorts in China. It independently screens and verifies that 44 metabolites are associated with the risk of liver cancer, of which 33 metabolites are reported for the first time internationally.

[0129] (2) The present invention identified a group of 18 plasma metabolites for predicting the risk of liver cancer, showing good sensitivity and specificity, providing new technical support for identifying high-risk groups for liver cancer and early diagnosis and treatment;

[0130] (3) The present invention demonstrates that specific plasma metabolites can be used as a new type of minimally invasive biomarker to improve the level of disease risk prediction. The successful development of this type of biomarker provides a reference for the development of other disease biomarkers in terms of methods and strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0131] Figure 1 Results of receiver operating characteristic analysis for 44 metabolites. A is the result of the Nantong cohort screening set, B is the result of the Changzhou cohort validation set, and the C index calculated by logistic regression in the validation set was 0.79 (95% CI: 0.70-0.88).

[0132] Figure 2 Results of receiver operating characteristic analysis for 18 metabolites. A is the result of the Nantong cohort screening set, B is the result of the Changzhou cohort validation set, and the C index calculated by logistic regression in the validation set was 0.86 (95% CI: 0.80-0.93). DETAILED DESCRIPTION

[0133] Experimental design:

[0134] (1) Establish a unified standard cohort specimen library and database: collect standard blood samples according to standard operating procedures (SOPs) and systematically collect complete demographic and clinical data.

[0135] (2) Metabolomics testing: Confirmed HCC cases and age- and sex-matched healthy controls from two prospective cohorts were included, and non-targeted metabolomics technology was used to screen and verify metabolic markers associated with HCC.

[0136] (3) For the positively correlated metabolites screened out, machine learning and other methods are further used to identify metabolites with independent predictive value and evaluate their joint predictive efficacy.

[0137] The inventors conducted a nested case-control study using two prospective Chinese population cohorts, and detected 612 named metabolites in baseline plasma using non-targeted metabolomics technology, and found that 44 of them were significantly associated with the incidence of liver cancer, including 12 androgens / progestins, 8 bile acids, 10 amino acids, 6 phospholipids and 8 other metabolites. Machine learning technology was used to further identify 18 metabolic markers with predictive value, providing technical support for liver cancer risk assessment and data support for the discovery of new small molecule drugs with potential intervention value.

[0138] Example 1 Collection of samples and organization of sample data

[0139] 1. Selection of research samples:

[0140] A total of 163 newly diagnosed HCC patients from two prospective cohorts in Nantong and Changzhou, Jiangsu Province were matched 1:1 with healthy controls by age ± 2 years, sex, and region.

[0141] A total of 326 samples that met the criteria were included in this study, including 216 cases in Nantong and 110 cases in Changzhou.

[0142] 2. Extraction of plasma samples:

[0143] From each research subject, 5 ml of fasting venous blood was collected in the morning using a vacuum anticoagulation (EDTA) blood collection tube. The plasma was immediately separated according to the standard method and frozen at -80°C for later use. 100 μL of plasma sample was transferred to an EP tube, 300 μL of extraction solution (methanol, containing an isotope-labeled internal standard mixture) was added, and the mixture was vortexed for 30 seconds; ultrasonicated for 10 minutes (ice-water bath); allowed to stand at -40°C for 1 hour; the sample was centrifuged at 4°C, 12000 rpm for 15 minutes; the supernatant was taken into a sampling bottle and tested on the machine.

[0144] Example 2 Plasma Metabolome Detection

[0145] 3. Metabolomics testing:

[0146] Sample detection: Ultra-high performance liquid chromatography (Waters ACQUITY) and quadrupole-orbitrap high-resolution mass spectrometry (Thermo Scientific Q Exactive) were used for detection.

[0147] (1) A Waters C18 column (UPLC BEH C18-2.1x100 mm, 1.7 μm) was used, and eluted with 80% mobile phase A (95:5:0.1 vol / vol / vol 10 mM ammonium acetate / methanol / formic acid) for 1 min, 80% mobile phase B (99.9:0.1 vol / vol methanol / formic acid) for 2 min, and 100% mobile phase B for 7 min; mass spectrometry analysis was performed in electrospray ionization negative ion mode, full scan analysis was performed in the range of 200-1000 m / z, the resolution was 70000, and the data acquisition rate was 3 Hz; other parameters: sheath gas flow rate 50, in-source CID 5ev, sweep gas 5, spray voltage 3kV, capillary temperature 300°C, s-lens RF voltage 50V, heater temperature 300°C.

[0148] (2) A Waters HILIC column (UPLC BEH Amide 2.1x150 mm, 1.7 μm) was used, and eluted with 5% mobile phase A (aqueous solution of 10 mM ammonium formate and 0.1% formic acid) for 0.5 min and 40% mobile phase B (acetonitrile containing 0.1% formic acid) for 10 min; mass spectrometry analysis was performed in electrospray ionization positive ion mode, with full scan analysis in the range of 70-800 m / z, a resolution of 70,000, and a data acquisition rate of 3 Hz; other parameters: sheath gas flow rate 40, sweep gas 2, spray voltage 3.5 kV, capillary temperature 350°C, s-lens RF voltage 40, and heater temperature 300°C.

[0149] 4. Data processing:

[0150] Peak identification, peak extraction, peak alignment and integration were performed, and substance annotation was performed to determine 612 named metabolites in baseline plasma; outliers were filtered based on relative standard deviation; missing data were filled with half of the minimum value; and normalization was performed using an internal standard.

[0151] 5. Statistical analysis:

[0152] Orthogonal partial least squares discriminant analysis (variable importance projection, VIP) and paired t-test (P value) were performed, and after multiple correction (FDR) to avoid false positive results, 44 metabolites were found to be significantly different between cases and controls, satisfying VIP>1 and P FDR <0.05 (Table 1);

[0153] Lasso regression was used to further identify 18 metabolites with independent predictive value as metabolic markers. Machine learning was used to establish a prediction model, and receiver operating characteristic (ROC) analysis revealed that the model had excellent predictive effect. The area under the ROC curve (AUC) calculated by logistic regression in the validation cohort was 0.86 (95% CI: 0.80-0.93), with a sensitivity and specificity of 81.8% and 74.5%, respectively.

[0154] from Figure 1 The ROC curves in the figure show that the AUCs of the 44 metabolic markers in the two cohorts were 0.90 and 0.79, respectively, which have a certain degree of accuracy in the diagnosis of liver cancer; Figure 2 The areas under the ROC curves of the 18 metabolic markers in both cohorts were greater than 0.85, which showed high accuracy and clinical diagnostic significance.

[0155] Example 3 Data Analysis

[0156] The 44 significantly different metabolites between HCC cases and controls are shown in Table 1 .

[0157] Table 1

[0158]

[0159]

[0160]

[0161]

[0162] The above examples are not intended to limit the present invention, but are only intended to illustrate the present invention. The experimental methods used in the above examples are conventional conditions and conventional methods unless otherwise specified, and the raw materials in the above examples are all commercially available and can be obtained from commercial sources unless otherwise specified.

Claims

1. A marker combination, characterized in that: The marker combination is a combination of the following 18 compounds: Copper Hydroxyquinoline 3-(4-Hydroxyphenyl)lactic acid Cystathionine Glycocholate Citrulline Creatine 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0 / 16:0) Androsterone Sulfate 1-Arachidonic acid-glycerophosphatidylcholine (20:4 / 0:0) 5α-Pregnane-3β,20α-diol monosulfate 17-Androstenediol sulfate (1) 17-Androstenediol sulfate (2) 16α-Hydroxydehydroisoandrosterone 3-sulfate Arginine Citrate Glycochenodeoxycholic acid 3-sulfate Glycochenodeoxycholic acid glucuronide Ceramide (d18:2 / 24:1, d18:1 / 24:2).

2. The combination of markers associated with liver cancer according to claim 1, characterized in that: In addition to the combination of 18 compounds described in claim 1, the marker combination further includes one or more of the following compounds: Phenylalanine Vitamin A Tyrosine Glycochenodeoxycholic acid Taurochenodeoxycholate Ribitol 1-Myristoyl-2-palmitoyl-glycerophosphatidylcholine (14:0 / 16:0) Dehydroepiandrosterone Sulfate N-acetyl glycine N-Acetyl Tyrosine Epiandrosterone Sulfate N1-Methyl-2-pyridone-5-carboxamide 1-Arachidonic acid-glycerophosphoethanolamine (20:4 / 0:0) Taurocholine Sulfate Androstenediol disulfate 5α-Pregnane-3β,20α-diol monosulfate Androstenediol (3α,17α) monosulfate Androsterone glucuronide Glycine ursodeoxycholic acid 1-(1-Alkenyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1 / 16:1) 1-(1-Alkenyl-palmitoyl)-2-palmitoyl-glycerophosphatidylcholine (P-16:0 / 16:0) 11β-Hydroxyandrosterone glucuronide Glycoursodeoxycholic acid sulfate 2,3-Dihydroxy-5-methylthio-4-pentenoate Lactone sulfuric acid Tetrahydrocortisol glucuronide.

3. Use of a product for detecting the marker combination according to claim 1 or 2 in the preparation of liver cancer diagnosis and / or risk prediction products.

4. The use according to claim 3, characterized in that: The marker combination is derived from plasma.

5. Use of the marker combination according to claim 1 or 2 in screening drugs for treating and / or alleviating liver cancer.

6. The use according to claim 5, characterized in that: The marker combination is derived from plasma.

7. Use of the marker combination according to claim 1 or 2 in the preparation of a detection kit for liver cancer diagnosis and / or risk prediction.

8. The use according to claim 7, characterized in that: The marker combination is derived from plasma.

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