CN115128257A - Metabolic markers for predicting the risk of hepatocellular carcinoma and their applications - Google Patents

Abstract

The invention discloses a metabolic marker for liver cancer morbidity risk prediction and application thereof, and development and utilization of the metabolic marker can provide technical support for risk prediction and early diagnosis and treatment of liver cancer. The invention screens and verifies 44 plasma metabolites related to liver cancer morbidity risk by using a non-targeted metabonomics detection method, and determines a group of 18 plasma metabolites, so that the risk prediction level of liver cancer can be obviously improved.

Description

Metabolic markers for predicting the risk of hepatocellular carcinoma and their applications

technical field

The invention belongs to the technical field of biomedicine, and relates to a metabolic marker for predicting the risk of liver cancer and its application.

Background technique

Primary liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer death. The incidence of liver cancer in my country exceeds that of other countries in the world. The new and dead liver cancer cases account for more than 50% of the world's annual incidence. The incidence and mortality of liver cancer in my country rank second and fourth respectively among cancers, seriously threatening the lives and health of residents. The onset of liver cancer is insidious and the disease progresses rapidly. About 70% to 80% of the patients are in the middle and late stages at the time of diagnosis, losing the opportunity of surgery or other local treatments, and the recurrence rate is high, resulting in a 5-year survival rate of only 14%. Therefore, improving the prediction level of the risk of liver cancer 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.

Up to now, the screening or auxiliary diagnosis technology of liver cancer mainly includes serum alpha-fetoprotein detection and imaging technology. However, although alpha-fetoprotein is the most widely used serological marker in clinical practice, its sensitivity for detecting early-stage liver cancer is less than 40%, and 30% to 40% of liver cancer patients have no significant increase in alpha-fetoprotein. Therefore, alpha-fetoprotein has limited sensitivity and specificity in liver cancer screening. Imaging techniques include computed tomography, ultrasound, and magnetic resonance imaging, which are expensive, limited by the operator's skill level, and have low sensitivity to early-stage liver cancer. In addition, the above methods are mainly used for screening or auxiliary diagnosis, and cannot effectively evaluate the potential risk of liver cancer. The improvement of the early prevention of liver cancer requires new technical methods.

Metabolomics is a new omics technology emerging after genomics, transcriptomics and proteomics, which can quantitatively analyze thousands of intermediates and end products involved in biochemical reactions in organisms. It is widely used in the fields of diagnostics, biological function research and drug research and development. Compared with other omics research methods, metabolomics has the following advantages: (1) Measure the metabolic response of the body to changes in physiological and pathological conditions from an overall perspective, especially the metabolome is in the downstream regulation of life network, and the detection of metabolites is closer To reflect changes in biological phenotype; ② Minor 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, so the detection of metabolites is easier Discover the key events that change the biological phenotype; 3. The samples to be measured can be biological fluids (such as blood, urine, etc.), which are relatively easy to obtain, cause less damage to the human body, and are easy to popularize and apply. Therefore, metabolomic technology can help to discover early events in tumors, identify biomarkers with population application value, and improve the ability of tumor risk prediction and intervention.

Previous metabolomic studies of liver cancer have small sample sizes, a small number of metabolites detected, and lack of external validation. In particular, the predictive value of metabolic markers for future risk of disease has not been evaluated in prospective cohorts. Therefore, it is necessary to carry out cohort-based metabolomic studies of liver cancer and systematically screen metabolic biomarkers with application value, which is of great significance for realizing the advance and precise prevention of liver cancer prevention, and reducing the incidence and mortality of liver cancer in my country.

SUMMARY OF THE INVENTION

In view of the above deficiencies, the present invention provides a class of plasma metabolites for predicting the risk of liver cancer and applications thereof. This method can be applied to detect new biomarkers related to the pathogenesis of liver cancer.

The first object of the present invention is to provide a marker combination related to liver cancer, which is a combination of one or more of the following 44 compounds:

Copper quinoline

3-(4-Hydroxyphenyl)lactic acid

cystathionine

Glycocholate

Citrulline

Phenylalanine

Vitamin A

tyrosine

sarcosine

Glycinechenodeoxycholic acid

Taurochenodeoxycholate

Ribitol

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

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

Dehydroepiandrosterone Sulfate

N-Acetylglycine

androsterone sulfate

N-Acetyltyrosine

epiandrosterone sulfate

N1-Methyl-2-pyridone-5-carboxamide

1-Arachidinoyl-glycerophosphatidylcholine (20:4/0:0)

1-Arachididyl-glycerophosphorylethanolamine (20:4/0:0)

5α-Androstane-3β,17β-diol hydrogen sulfate

Taurocholenoic Acid Sulfate

Androstenediol Disulfate

5α-Pregnant-3β,20α-diol monosulfate

Androstenediol (3α,17α) Monosulfate

17-Androstenediol Sulfate(1)

17-Androstenediol Sulfate(2)

16α-Hydroxydehydroisoandrosterone 3-sulfate

androsterone glucuronide

Arginine

Glycine ursodeoxycholic acid

Citraconate

Glycinechenodeoxycholic acid 3-sulfate

1-(1-Enyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1/16:1)

1-(1-Enyl-palmitoyl)-2-palmitoyl-glycerophosphatidylcholine (P-16:0/16:0)

Glycinechenodeoxycholic acid glucuronide

Ceramides (d18:2/24:1, d18:1/24:2)

11β-Hydroxyandrosterone glucuronide

Glycinursodeoxycholate Sulfate

2,3-Dihydroxy-5-methylthio-4-pentenoate

Lactone Sulfate

Tetrahydrocortisol glucuronide.

Further, the marker combination is a combination of one or more of the following 33 compounds:

Copper quinoline

cystathionine

Citrulline

sarcosine

Ribitol

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

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

N-Acetylglycine

androsterone sulfate

N-Acetyltyrosine

epiandrosterone sulfate

N1-Methyl-2-pyridone-5-carboxamide

1-Arachididyl-glycerophosphorylethanolamine (20:4/0:0)

5α-Androstane-3β,17β-diol hydrogen sulfate

Taurocholenoic Acid Sulfate

Androstenediol Disulfate

5α-Pregnant-3β,20α-diol monosulfate

Androstenediol (3α,17α) Monosulfate

17-Androstenediol Sulfate(1)

17-Androstenediol Sulfate(2)

16α-Hydroxydehydroisoandrosterone 3-sulfate

androsterone glucuronide

Arginine

Citraconate

Glycinechenodeoxycholic acid 3-sulfate

1-(1-Enyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1/16:1)

Glycinechenodeoxycholic acid glucuronide

Ceramides (d18:2/24:1, d18:1/24:2)

11β-Hydroxyandrosterone glucuronide

Glycinursodeoxycholate Sulfate

2,3-Dihydroxy-5-methylthio-4-pentenoate

Lactone Sulfate

Tetrahydrocortisol glucuronide.

Further, the marker combination is a combination of one or more of the following 18 compounds:

Copper quinoline

3-(4-Hydroxyphenyl)lactic acid

cystathionine

Glycocholate

Citrulline

sarcosine

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

androsterone sulfate

1-Arachidinoyl-glycerophosphatidylcholine (20:4/0:0)

5α-Pregnant-3β,20α-diol monosulfate

17-Androstenediol Sulfate(1)

17-Androstenediol Sulfate(2)

16α-Hydroxydehydroisoandrosterone 3-sulfate

Arginine

Citraconate

Glycinechenodeoxycholic acid 3-sulfate

Glycinechenodeoxycholic acid glucuronide

Ceramides (d18:2/24:1, d18:1/24:2).

Further, the marker combination is a combination of one or more of the following 15 compounds:

Copper quinoline

cystathionine

Citrulline

sarcosine

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

androsterone sulfate

5α-Pregnant-3β,20α-diol monosulfate

17-Androstenediol Sulfate(1)

17-Androstenediol Sulfate(2)

16α-Hydroxydehydroisoandrosterone 3-sulfate

Arginine

Citraconate

Glycinechenodeoxycholic acid 3-sulfate

Glycinechenodeoxycholic acid glucuronide

Ceramides (d18:2/24:1, d18:1/24:2).

The second object of the present invention is to provide the application of the product for detecting the aforementioned combination of markers in the preparation of a product for diagnosis and/or risk prediction of liver cancer.

Further, the marker combination is derived from plasma.

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

Further, the marker combination is derived from plasma.

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

Further, the marker combination is derived from plasma.

Compared with the prior art, the beneficial effects of the present invention are:

(1) The present invention adopts a strict screening, verification and evaluation system to carry out a nested case-control study of liver cancer based on two prospective cohorts in China, and independently screened and verified that 44 metabolites are related to the risk of liver cancer, of which 33 The association of metabolites is the first international report;

(2) The present invention determines a group of 18 plasma metabolites, which are used to predict the occurrence risk of liver cancer, show good sensitivity and specificity, and provide new technical support for identifying high-risk groups of liver cancer and early diagnosis and treatment;

(3) The present invention shows that specific plasma metabolites can be used as a new type of minimally invasive biomarkers to improve the level of disease risk prediction, and the successful development of such biomarkers provides methods and strategies for the development of other disease biomarkers of reference.

Description of drawings

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

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

Detailed ways

experimental design:

(1) Establish a unified standard cohort specimen library and database: standard operating procedures (SOP) are used to collect blood samples that meet the standards, and complete demographic and clinical data are systematically collected.

(2) Metabolome detection: Two prospective cohorts of confirmed liver cancer cases and age- and sex-matched healthy controls were included, and non-target metabolomics technology was used to screen and verify the metabolic markers associated with the pathogenesis of liver cancer.

(3) For the screened positive associated metabolites, further use machine learning and other methods to identify metabolites with independent predictive value, and evaluate their combined predictive efficacy.

The inventors conducted a nested case-control study using 2 prospective Chinese population cohorts, and detected 612 named metabolites in baseline plasma by non-targeted metabolomics technology, and found that 44 metabolites were associated with the incidence of liver cancer. Significantly associated, 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 risk assessment of liver cancer and data support for the discovery of new small molecule drugs with potential intervention value.

Example 1 Collection of samples and arrangement of sample data

1. Selection of research samples:

A total of 163 new-onset liver cancer patients from two prospective cohorts in Nantong City and Changzhou City, Jiangsu Province, were matched 1:1 with healthy controls by age ± 2 years, gender and region.

A total of 326 eligible samples were included in this study, including 216 in Nantong and 110 in Changzhou.

2. Extraction of plasma samples:

From each research subject, a vacuum anticoagulation (EDTA) blood collection tube was used to collect 5ml of fasting venous blood in the morning, and the plasma was immediately separated according to standard methods and stored frozen at -80°C for future use; 100μL of plasma sample was transferred to an EP tube, and 300μL of plasma was added. Extract solution (methanol, containing isotope-labeled internal standard mixture), vortex and mix for 30s; ultrasonicate for 10min (ice-water bath); let stand at -40°C for 1h; centrifuge the sample at 4°C, 12000rpm for 15min; take the supernatant into the injection bottle On-board detection.

Example 2 Plasma metabolome detection

3. Metabolomics detection:

Detection samples: Ultra-high performance liquid chromatography (Waters ACQUITY) combined with quadrupole-orbitrap high-resolution mass spectrometry (Thermo Scientific Q Exactive) technology platform was used for detection.

(1) Use Waters' C18 column (UPLC BEH C18-2.1x100 mm, 1.7 μm), eluted with 80% mobile phase A (95:5:0.1vol/vol/vol 10mM ammonium acetate/methanol/formic acid) 1 minute, 80% mobile phase B (99.9:0.1vol/vol methanol/formic acid) eluted for 2 minutes, 100% mobile phase B eluted for 7 minutes; mass spectrometry analysis adopts electrospray ionization negative ion mode, in the range of 200-1000m/z Full scan analysis was carried out in the 70000 resolution, data acquisition rate was 3hz; other parameters: sheath gas flow rate 50, source CID 5ev, scavenging gas 5, spray voltage 3kv, capillary temperature 300℃, s-lens RF voltage 50v, heating The temperature of the device is 300°C.

(2) Use a HILIC column (UPLC BEH Amide 2.1x150 mm, 1.7 μm) from Waters, eluted with 5% mobile phase A (10 mM ammonium formate and 0.1% formic acid in water) for 0.5 minutes, and 40% mobile phase B ( Acetonitrile containing 0.1% formic acid) elution for 10 minutes; mass spectrometry analysis adopts electrospray ionization positive ion mode, full scan analysis in the range of 70-800m/z, resolution is 70000, data acquisition rate is 3hz; other parameters: sheath gas flow Speed 40, purge 2, spray voltage 3.5kV, capillary temperature 350°C, s-lens RF voltage 40, heater temperature 300°C.

4. Data processing:

Processes such as 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 taken as the minimum value of two 1/1 for padding; normalization is performed using the internal standard.

5. Statistical analysis:

Orthogonal-partial least squares discriminant analysis (variable importance projection, VIP) and paired t-test (P-value), after multiple correction (FDR) to avoid false positive results, found 44 metabolites between cases and controls There is a significant difference, meeting VIP>1 and P _FDR <0.05 (Table 1);

Lasso regression was used to further discover metabolic markers with independent predictive value for 18 metabolites; machine learning was used to establish a predictive model, and receiver operating characteristic (ROC) analysis found that the model had an 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.

From the ROC curves in Figure 1, the AUCs of the 44 metabolic markers in the two cohorts were 0.90 and 0.79, respectively, which had certain accuracy in the diagnosis of liver cancer; the AUCs of the 18 metabolic markers in the two cohorts in Figure 2 The areas under the ROC curve were all greater than 0.85, with high accuracy and clinical diagnostic significance.

Example 3 Data Analysis

Table 1 shows 44 significantly different metabolites between liver cancer cases and controls.

Table 1

The above embodiments are not used to limit the present invention, but are only used to illustrate the present invention. Unless otherwise specified, the experimental methods used in the above examples are conventional conditions and conventional methods, unless otherwise specified. The original reagent materials in the above examples can be obtained commercially. , are commercially available.

Claims (10)

1. A marker combination related to liver cancer, characterized in that, the marker combination is a combination of one or more of the following 44 compounds: Copper quinoline 3-(4-Hydroxyphenyl)lactic acid cystathionine Glycocholate Citrulline Phenylalanine Vitamin A tyrosine sarcosine Glycinechenodeoxycholic acid Taurochenodeoxycholate Ribitol 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0/16:0) 1-Myristoyl-2-palmitoyl-glycerophosphatidylcholine (14:0/16:0) Dehydroepiandrosterone Sulfate N-Acetylglycine androsterone sulfate N-Acetyltyrosine epiandrosterone sulfate N1-Methyl-2-pyridone-5-carboxamide 1-Arachidinoyl-glycerophosphatidylcholine (20:4/0:0) 1-Arachididyl-glycerophosphorylethanolamine (20:4/0:0) 5α-Androstane-3β,17β-diol hydrogen sulfate Taurocholenoic Acid Sulfate Androstenediol Disulfate 5α-Pregnant-3β,20α-diol monosulfate Androstenediol (3α,17α) Monosulfate 17-Androstenediol Sulfate(1) 17-Androstenediol Sulfate(2) 16α-Hydroxydehydroisoandrosterone 3-sulfate androsterone glucuronide Arginine Glycine ursodeoxycholic acid Citraconate Glycinechenodeoxycholic acid 3-sulfate 1-(1-Enyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1/16:1) 1-(1-Enyl-palmitoyl)-2-palmitoyl-glycerophosphatidylcholine (P-16:0/16:0) Glycinechenodeoxycholic acid glucuronide Ceramides (d18:2/24:1, d18:1/24:2) 11β-Hydroxyandrosterone glucuronide Glycinursodeoxycholate Sulfate 2,3-Dihydroxy-5-methylthio-4-pentenoate Lactone Sulfate Tetrahydrocortisol glucuronide. 2. The marker combination according to claim 1, wherein the marker combination is a combination of one or more of the following 33 compounds: Copper quinoline cystathionine Citrulline sarcosine Ribitol 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0/16:0) 1-Myristoyl-2-palmitoyl-glycerophosphatidylcholine (14:0/16:0) N-Acetylglycine androsterone sulfate N-Acetyltyrosine epiandrosterone sulfate N1-Methyl-2-pyridone-5-carboxamide 1-Arachididyl-glycerophosphorylethanolamine (20:4/0:0) 5α-Androstane-3β,17β-diol hydrogen sulfate Taurocholenoic Acid Sulfate Androstenediol Disulfate 5α-Pregnant-3β,20α-diol monosulfate Androstenediol (3α,17α) Monosulfate 17-Androstenediol Sulfate(1) 17-Androstenediol Sulfate(2) 16α-Hydroxydehydroisoandrosterone 3-sulfate androsterone glucuronide Arginine Citraconate Glycinechenodeoxycholic acid 3-sulfate 1-(1-Enyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1/16:1) Glycinechenodeoxycholic acid glucuronide Ceramides (d18:2/24:1, d18:1/24:2) 11β-Hydroxyandrosterone glucuronide Glycinursodeoxycholate Sulfate 2,3-Dihydroxy-5-methylthio-4-pentenoate Lactone Sulfate Tetrahydrocortisol glucuronide. 3. The marker combination according to claim 1, wherein the marker combination is a combination of one or more of the following 18 compounds: Copper quinoline 3-(4-Hydroxyphenyl)lactic acid cystathionine Glycocholate Citrulline sarcosine 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0/16:0) androsterone sulfate 1-Arachidinoyl-glycerophosphatidylcholine (20:4/0:0) 5α-Pregnant-3β,20α-diol monosulfate 17-Androstenediol Sulfate(1) 17-Androstenediol Sulfate(2) 16α-Hydroxydehydroisoandrosterone 3-sulfate Arginine Citraconate Glycinechenodeoxycholic acid 3-sulfate Glycinechenodeoxycholic acid glucuronide Ceramides (d18:2/24:1, d18:1/24:2). 4. The marker combination according to claim 1, wherein the marker combination is a combination of one or more of the following 15 compounds: Copper quinoline cystathionine Citrulline sarcosine 1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0/16:0) androsterone sulfate 5α-Pregnant-3β,20α-diol monosulfate 17-Androstenediol Sulfate(1) 17-Androstenediol Sulfate(2) 16α-Hydroxydehydroisoandrosterone 3-sulfate Arginine Citraconate Glycinechenodeoxycholic acid 3-sulfate Glycinechenodeoxycholic acid glucuronide Ceramides (d18:2/24:1, d18:1/24:2). 5. Use of a product for detecting the combination of markers according to any one of claims 1 to 4 in the preparation of a product for liver cancer diagnosis and/or risk prediction. 6. The use according to claim 5, wherein the marker combination is derived from plasma. 7. Use of the marker combination according to any one of claims 1 to 4 in screening drugs for treating and/or relieving liver cancer. 8. The use according to claim 7, wherein the marker combination is derived from plasma. 9. Use of the marker combination of any one of claims 1 to 4 in the preparation of a detection kit for liver cancer diagnosis and/or risk prediction. 10. The use according to claim 9, wherein the marker combination is derived from plasma.

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