Application of metabolic marker detection reagent in preparation of liver disease diagnosis product

Abstract

The invention belongs to the field of liver cirrhosis and hepatocellular carcinoma marker detection, and particularly relates to application of a metabolic marker detection reagent in preparation of liver disease diagnosis products. The metabolic marker is a combination of Nicotinamide N-oxide, Pregnandiol 3-O-glucose, Guanidoacetic acid, Glycine, Isomaltose, Picolinic acid, 2-Hydroxycyanic acid, Quinolinic acid, Carnosine, 3-Methyluridine, 3-Methyluridine and Formiminoglumic acid, and the metabolic marker is a combination of the Nicotinamide N-oxide, the Pregnandiol 3-O-glucose acid, the Guanidoacetic acid, the Glycine, the Isomaltose, the Picolinic acid, the 2-Hydroxycyanic acid, the Quinolinic acid, the Carnosine, the 3-Methyluridine, the 3-Methyluridine and the Formiminoglumic acid. The liver disease is liver cirrhosis or hepatocellular carcinoma. The 12 urine metabolism markers are combined to accurately distinguish normal people from patients with liver cirrhosis, and normal people from patients with hepatocellular carcinoma. The kit provides a brand-new and objective biological basis for early diagnosis, treatment and prognosis evaluation of liver cirrhosis and hepatocellular carcinoma.

Description

Technical Field

[0001] This invention belongs to the field of biomarker detection for cirrhosis and hepatocellular carcinoma, specifically involving the application of metabolic biomarker detection reagents in the preparation of liver disease diagnostic products. Background Technology

[0002] High-risk factors for hepatocellular carcinoma include viral hepatitis, alcoholic liver disease, and fatty liver.

[0003] Primary hepatocellular carcinoma (HCC) has an insidious onset, with most patients diagnosed at a locally advanced stage or with distant metastases. The 5-year survival rate for advanced HCC is approximately 15%, while that for early-stage HCC can reach 75%. Therefore, early diagnosis is crucial for improving patient prognosis. To date, HCC diagnosis primarily relies on imaging examinations and serum biomarker testing, such as alpha-fetoprotein (AFP), prothrombin-induced vitamin K absence or antagonist-II (PIVKA-II), and viral load. However, imaging has a sensitivity of only 47% for early-stage HCC, while AFP has a sensitivity of 52.9% and a specificity of 93.3%. Combining imaging can slightly improve the results, but approximately 30% of HCC patients are AFP negative, failing to effectively cover all early-stage cases. Therefore, novel diagnostic methods are urgently needed to improve the efficiency and accuracy of early HCC screening.

[0004] Metabolomics, an emerging discipline following genomics, proteomics, and transcriptomics, developed in the mid-1990s. It aims to quantitatively analyze all small-molecule metabolites of biological systems under specific time and conditions. Compared to genomics and proteomics, metabolomics more directly reflects physiological states. By comparing the metabolic profiles of HCC patients and healthy individuals, identifying characteristic metabolites, and elucidating their related metabolic pathways, the metabolic levels and interactions of HCC can be systematically revealed, providing important biological evidence for early diagnosis. Compared to blood samples, urine samples are easier to obtain, non-invasive, and can be collected repeatedly, thus offering significant advantages in early disease screening and dynamic monitoring. However, existing non-invasive detection methods for hepatocellular carcinoma (HCC) still suffer from insufficient specificity, limited sensitivity, and long detection cycles, failing to meet the urgent clinical need for rapid, accurate, and non-invasive early diagnosis. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides the application of metabolic biomarker detection reagents in the preparation of liver disease diagnostic products.

[0006] The present invention adopts the following technical solution: The application of metabolic marker detection reagents in the preparation of liver disease diagnostic products, wherein the metabolic markers are a combination of Nicotinamide N-oxide, Pregnanediol 3-O-glucuronide, Guanidoacetic acid, Glycoursodeoxycholic acid, Isomaltose, Picolinic acid, 2-Hydroxycinnamic acid, Quinolinic acid, Carnosine, 3-Methyluridine, 3-Methyladenine and Formiminoglutamic acid.

[0007] This invention utilizes metabolomics technology to screen a series of specific metabolic biomarkers in urine samples for the first time, enabling early and accurate identification and effective differentiation between hepatocellular carcinoma patients and healthy individuals, as well as cirrhosis patients and healthy individuals. Using these urinary metabolic biomarkers, only ordinary urine samples are required, without any invasive procedures, to achieve highly specific, rapid, and non-invasive detection of cirrhosis and early-stage hepatocellular carcinoma. This innovative method greatly improves the convenience, accuracy, and clinical applicability of detection, successfully overcoming a major challenge faced by existing detection technologies.

[0008] Furthermore, the metabolic markers are metabolic markers in urine.

[0009] Furthermore, the product is a reagent kit.

[0010] Furthermore, the kit includes the aforementioned metabolic markers, quality control materials, and chromatographic separation reagents.

[0011] Furthermore, the chromatographic separation reagent includes mobile phase A and mobile phase B, wherein mobile phase A is an aqueous phase containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and mobile phase B is acetonitrile.

[0012] Furthermore, the product is a diagnostic system, which includes: A detection device for detecting the levels of the metabolic markers; A reference device is used to receive the level of the metabolic marker output by the detection device; The comparison device is used to receive the levels of metabolic markers output by the detection device and the known grouping information output by the reference device, analyze and compare the detection results of the subject's metabolic marker levels with the known grouping information dataset, and determine whether the subject belongs to the liver disease group or the non-liver disease group.

[0013] Furthermore, the diagnostic system detects the levels of the metabolic markers in a sample using the following methods: chromatography, mass spectrometry, fluorescence assay, electrophoresis, immunoaffinity, immunohybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, or turbidimetry.

[0014] Furthermore, in the reference device, all patients with liver disease are set as the liver disease group, and all healthy individuals without liver disease are set as the non-liver disease group, with both groups set as known grouping information.

[0015] Furthermore, the diagnostic system also includes sample pretreatment reagents.

[0016] Furthermore, the sample pretreatment reagent is an extract consisting of an equal volume mixture of methanol and acetonitrile.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes metabolomics technology to conduct a comprehensive and in-depth analysis of characteristic metabolites in urine samples, successfully identifying for the first time a series of biomarkers of high value for the early diagnosis of cirrhosis (GH) and hepatocellular carcinoma (HCC). These biomarkers include Nicotinamide N-oxide, Pregnanediol 3-O-glucuronide, Guanidoaceticacid, Glycoursodeoxycholic acid, Isomaltose, Picolinic acid, 2-Hydroxycinnamicacid, Quinolinic acid, Carnosine, 3-Methyluridine, 3-Methyladenine, and Forminoglutamic acid. The discovery of these biomarkers provides novel and objective biological evidence for the early diagnosis, treatment, and prognostic assessment of GH and HCC, and is expected to significantly improve the limitations of existing technologies in terms of non-invasiveness, sensitivity, and specificity, bringing a major breakthrough to the clinical management of HCC. Attached Figure Description

[0018] Figure 1 This is the ROC analysis result of biomarker combinations and AFP based on the discovery cohort in this invention.

[0019] Figure 2The present invention presents the ROC analysis results of 12 metabolites as biomarker combinations using three models: LASSO, RF, and SVM. Among them, A: ROC analysis results for predicting hepatocellular carcinoma; B: ROC analysis results for predicting cirrhosis; C: ROC analysis results for predicting hepatocellular carcinoma with negative AFP or no AFP detected.

[0020] Figure 3 This is the ROC analysis result of a combination of 12 metabolites as biomarkers based on a validation cohort in this invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments, but this should not be construed as limiting the invention. Unless otherwise specified, the technical means used in the following embodiments are conventional means well known to those skilled in the art, and the materials, reagents, etc. used in the following embodiments are commercially available unless otherwise specified.

[0022] This study sample consisted of patients who visited Jiangxi Cancer Hospital from February 2023 to February 2025. The study included a healthy control group (CN), a hepatitis group (GY), a cirrhosis group (GH), and a hepatocellular carcinoma group (HCC). The CN group required no history of liver disease, normal liver function, and no active infection or major chronic diseases (such as kidney disease or diabetes). The GY group consisted of patients with clinically and laboratory-confirmed chronic viral hepatitis. The GH group consisted of patients with radiologically, laboratory-, or histologically confirmed cirrhosis, and no radiological or pathological evidence of hepatocellular carcinoma. The HCC group consisted of patients with radiologically or pathologically confirmed primary hepatocellular carcinoma who had not received or had not undergone systemic treatment / major surgery for ≥3 months at the time of sampling. Common exclusion criteria for all four groups included: severe renal insufficiency (eGFR <60), urinary tract infection or hematuria, recent (within 4 weeks) heavy alcohol consumption or use of antibiotics / hormones, pregnancy or lactation, and coexisting active malignancies. All urine samples were collected from the midstream of morning urine, aliquoted by low-temperature centrifugation, and stored at -80℃ for subsequent metabolomics analysis.

[0023] Example 1: Screening of urinary biomarkers associated with hepatocellular carcinoma (HCC) This embodiment used a Vanquish ultra-high performance liquid chromatograph (Thermo Fisher Scientific) combined with an Orbitrap Exploris 120 mass spectrometer to analyze the metabolic profiles of urine samples (screening cohort) from 133 patients with hepatocellular carcinoma, 38 patients with cirrhosis, 35 patients with hepatitis, and 124 healthy controls based on metabolomics analysis (see Table 1). Database matching, data standardization, quantitative and statistical analyses revealed that compared with healthy controls, 522 metabolites in the urine of hepatitis patients showed significant changes (OPLS-DA VIP>1 and...). p <0.05), 396 metabolites showed significant changes in patients with cirrhosis, and 622 metabolites showed significant changes in patients with hepatocellular carcinoma. Further metaboloanalysis showed that a total of 1108 metabolites showed significant changes in patients with hepatitis, cirrhosis, or hepatocellular carcinoma. To preliminarily screen potential biomarkers, 192 metabolites that showed significant changes in both patients with cirrhosis and hepatocellular carcinoma were selected. After excluding exogenous substances (non-human synthetic metabolites), 126 candidate metabolites were obtained and evaluated using the MetaboAnalyst 5.0 online tool combined with a Random Forest (RF) prediction model. 25 and 15 metabolite combinations were selected as urinary biomarkers for hepatocellular carcinoma and cirrhosis, respectively (Table 2), and their AUC values ​​were higher than the corresponding AFP detection results (see Figure 1).

[0024] Of the 25 and 15 metabolites mentioned above, 12 metabolites (see Table 3) showed significant changes in both hepatocellular carcinoma and cirrhosis patients. These metabolites were: Glycoursodeoxycholic acid, Isomaltose, Picolinicacid, 2-Hydroxycinnamic acid, Quinolinic acid, Pregnanediol 3-O-glucuronide, Carnosine, 3-Methyluridine, 3-Methyladenine, Formininoglutamic acid, Nicotinamide N-oxide, and Guanidoacetic acid. The predictive performance of this combination was evaluated using three models: LASSO, RF, and SVM. ROC analysis showed that the AUC values ​​for predicting both hepatocellular carcinoma and cirrhosis using this combination were all above 0.96. Especially in predicting hepatocellular carcinoma (HCC) patients, the AUC values ​​reached 0.9920 (LASSO model), 0.9843 (RF model), and 0.9826 (SVM model), corresponding to prediction accuracies of 99%, 93%, and 98%, respectively (see Figure 2). Notably, when this combination of 12 metabolites was used in HCC patients who were AFP-negative or had not been tested for AFP, the predictive AUC value remained as high as 0.9942 (see Figure 2), with an accuracy of 98.9%. This indicates that this biomarker group has extremely high sensitivity and specificity in the early diagnosis of HCC and can significantly compensate for the shortcomings of existing AFP detection methods.

[0025] Table 1. Basic information on the source of the 330 samples in the screening cohort. Note: Spaces indicate that the item is not included, and the same applies below.

[0026] Table 2. List of 25 metabolites for liver cancer and 15 metabolites for liver cirrhosis selected by the random forest model. Table 3. 12 Metabolites with Significant Changes The specific process for screening the above-mentioned hepatocellular carcinoma-related urinary biomarkers is as follows: 1. Urine metabolomics analysis (1) Sample pretreatment. Based on the osmotic pressure results, urine samples were mixed with pure water to a final volume of 100 μL, and 400 μL of extraction buffer (methanol:acetonitrile = 1:1 (V / V), containing isotope-labeled internal standard) was added. After vortexing for 30 s, the samples were sonicated for 10 min in an ice-water bath. After standing at -40 ℃ for 1 h, the samples were centrifuged at 4 ℃ and 12000 rpm (centrifugal force 13800 ×g, radius 8.6 cm) for 15 min. The supernatant was then transferred to a sample vial for analysis. Simultaneously, equal volumes of the supernatant from all samples were mixed to prepare a quality control sample (QC) for monitoring instrument stability and data repeatability.

[0027] (2) Chromatographic separation conditions. For the analysis of polar metabolites, a Vanquish ultra-high performance liquid chromatograph (Thermo Fisher Scientific) was used in conjunction with a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm). Mobile phase A was an aqueous phase containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and mobile phase B was acetonitrile. The sample pan temperature was set to 4 ℃, and the injection volume was 2 μL.

[0028] (3) Mass spectrometry detection conditions. An Orbitrap Exploris 120 mass spectrometer was used for mass spectrometry detection. First-stage (Full MS) and second-stage (MS / MS) data acquisition were performed under the control of Xcalibur software (version 4.4, Thermo). The main parameters are as follows: Sheath gas flow rate: 50 Arb, Aux gas flow rate: 15 Arb, Capillary temperature: 320 ℃, Full ms resolution: 60000, MS / MS resolution: 15000, Collision energy: SNCE 20 / 30 / 40, Spray voltage: 3.8 kV (positive) or -3.4 kV (negative).

[0029] (4) Data Processing and Analysis. The raw mass spectrometry data were converted to mzXML format using ProteoWizard software, and then metabolite structures were identified using R packages (database: BiotreeDB V3.0), followed by visualization analysis. The data processing workflow included: metabolite structure identification, data preprocessing, experimental data quality assessment, and statistical analysis. Statistical analysis covered univariate analysis, multidimensional statistical analysis, differential metabolite screening, and ROC (Receiver Operating Characteristic) analysis.

[0030] 2. Analysis Results In this embodiment, 2,238 metabolites were identified from 330 urine samples after merging positive and negative ion patterns. First, Principal Component Analysis (PCA) revealed that the urinary metabolic profiles of 133 hepatocellular carcinoma patients, 38 cirrhosis patients, and 35 hepatitis patients were significantly distinguishable from those of 124 healthy controls, indicating significant differences in metabolic characteristics across different disease states. Further verification of the model's discriminative ability using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) showed that the model could also clearly distinguish samples from the aforementioned different groups. (VIP>1 and...) p Using a strict screening criterion of <0.05, the results showed that 522 metabolites were significantly altered in hepatitis patients, 396 metabolites were significantly altered in cirrhosis patients, and 622 metabolites were significantly altered in hepatocellular carcinoma patients. Metabolic analysis indicated that a total of 1108 metabolites were significantly altered in patients with hepatitis, cirrhosis, or hepatocellular carcinoma, with 192 metabolites showing significant alterations in both cirrhosis and hepatocellular carcinoma patients, providing important candidate molecules for subsequent biomarker screening.

[0031] 3. Establishment and evaluation of the prediction model After excluding exogenous substances, a total of 126 candidate metabolites were obtained and evaluated using the MetaboAnalyst 5.0 online platform combined with a Random Forest (RF) prediction model. The results are shown in Table 2 and... Figure 1In hepatocellular carcinoma prediction, the AUC values ​​of 25 metabolites as a combination were 1.000 and 0.9882 in the training and test sets, respectively, significantly better than the AUC value of AFP (0.786). In cirrhosis prediction, the AUC values ​​of 15 metabolites as a combination were 1.000 and 0.9926 in the training and test sets, respectively, also higher than AFP (AUC = 0.6216). Further metaboloanalysis revealed that 12 metabolites (see Table 3) showed significant changes in both hepatocellular carcinoma and cirrhosis patients. The levels of Nicotinamide N-oxide, Pregnanediol 3-O-glucuronide, and Guanidoacetic acid were significantly decreased in the urine of both groups of patients; while the levels of the remaining nine metabolites—Glycoursodeoxycholic acid, Isomaltose, Picolinic acid, 2-Hydroxycinnamic acid, Quinolinic acid, Carnosine, 3-Methyluridine, 3-Methyladenine, and Formiminoglutamic acid—were significantly increased in the urine. The predictive performance of this combination of 12 metabolites was evaluated using three models: Least Absolute Shrinkage and Selection Operator (LASO), Random Forest (RF), and Support Vector Machine (SVM). Figure 2 A and 2B): Predictive AUC for hepatocellular carcinoma: LASSO 0.9920, RF 0.9843, SVM 0.9826; Predictive AUC for cirrhosis: LASSO 0.9758, RF 0.9716, SVM 0.9662. Further, it was confirmed that the combination of the above 12 metabolites as biomarkers was optimal (Table 4). Particularly noteworthy is that in 53 hepatocellular carcinoma patients who were AFP-negative or had not tested for AFP, the predictive AUC of this combination of 12 metabolites was as high as 0.9942, with a predictive accuracy of 98.9%, demonstrating significantly superior diagnostic potential compared to existing serological tests. Figure 2 C).

[0032] Table 4. ROC analysis results of different combinations of 12 biomarkers (metabolites) Example 2: Validation of a urinary biomarker set related to hepatocellular carcinoma (HCC) To further validate the robustness and generalization ability of the aforementioned 12 metabolite combinations in predicting hepatocellular carcinoma, we collected an additional cohort of 61 independent urine samples (validation cohort), including 31 hepatocellular carcinoma patients (see Table 5) and 30 healthy controls, strictly matched for sex and age. The samples were analyzed for metabolomics using a Vanquish ultra-high performance liquid chromatograph (Thermo Fisher Scientific) in conjunction with an Orbitrap Exploris 120 mass spectrometer to obtain their urinary metabolomics profiles. The results showed that the levels of Nicotinamide N-oxide, Pregnanediol 3-O-glucuronide, and Guanidoacetic acid were significantly reduced in the urine of patients with hepatocellular carcinoma, while the levels of Glycoursodeoxycholic acid, Isomaltose, Picolinic acid, 2-Hydroxycinnamic acid, Quinolinic acid, Carnosine, 3-Methyluridine, 3-Methyladenine, and Formiminoglutamic acid were significantly increased, consistent with previous analysis results. Similarly, the hepatocellular carcinoma prediction model based on this combination of 12 metabolites demonstrated extremely high diagnostic performance in ROC curve analysis, with an AUC of 0.9583 (Figure 3), corresponding to a diagnostic accuracy of 97.78%.

[0033] Table 5. Basic information of 34 patients with hepatocellular carcinoma The above results indicate that 12 metabolites—Glycoursodeoxycholic acid, Isomaltose, Picolinicacid, 2-Hydroxycinnamic acid, Quinolinic acid, Pregnanediol 3-O-glucuronide, Carnosine, 3-Methyluridine, 3-Methyladenine, Formininoglutamic acid, Nicotinamide N-oxide, and Guanidoacetic acid—can serve as a group of urinary diagnostic biomarkers for early-stage hepatocellular carcinoma, providing a specific, rapid, and non-invasive detection method for the early diagnosis of hepatocellular carcinoma.

[0034] It should be noted that when numerical ranges are mentioned in the claims of this invention, it should be understood that the two endpoints of each numerical range and any value between the two endpoints can be selected. To avoid redundancy, the present invention describes preferred embodiments.

[0035] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

Claims

1. The application of metabolic marker detection reagents in the preparation of liver disease diagnostic products, characterized in that, The metabolic markers are a combination of Nicotinamide N-oxide, Pregnanediol 3-O-glucuronide, Guanidoacetic acid, Glycoursodeoxycholic acid, Isomaltose, Picolinic acid, 2-Hydroxycinnamic acid, Quinolinic acid, Carnosine, 3-Methyluridine, 3-Methyladenine, and Formiminoglutamic acid; The liver disease mentioned is either cirrhosis or hepatocellular carcinoma.

2. The application of the detection reagent for metabolic markers according to claim 1 in the preparation of liver disease diagnostic products, characterized in that, The metabolic markers are metabolic markers in urine.

3. The application of the detection reagent for metabolic markers according to claim 2 in the preparation of liver disease diagnostic products, characterized in that, The product in question is a reagent kit.

4. The application of the detection reagent for metabolic markers according to claim 3 in the preparation of liver disease diagnostic products, characterized in that, The kit includes the aforementioned metabolic markers, quality control products, and chromatographic separation reagents.

5. The application of the detection reagent for metabolic markers according to claim 4 in the preparation of liver disease diagnostic products, characterized in that, The chromatographic separation reagent includes mobile phase A and mobile phase B. Mobile phase A is an aqueous phase containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia water, and mobile phase B is acetonitrile.

6. The application of the detection reagent for metabolic markers according to claim 2 in the preparation of liver disease diagnostic products, characterized in that, The product is a diagnostic system, which includes: A detection device for detecting the levels of the metabolic markers; A reference device is used to receive the level of the metabolic marker output by the detection device; The comparison device is used to receive the levels of metabolic markers output by the detection device and the known grouping information output by the reference device, analyze and compare the detection results of the subject's metabolic marker levels with the known grouping information dataset, and determine whether the subject belongs to the liver disease group or the non-liver disease group.

7. The application of the detection reagent for metabolic markers according to claim 6 in the preparation of liver disease diagnostic products, characterized in that, The diagnostic system detects the levels of the metabolic markers in a sample using the following methods: chromatography, mass spectrometry, fluorescence assay, electrophoresis, immunoaffinity, immunohybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, or turbidimetry.

8. The application of the detection reagent for metabolic markers according to claim 6 in the preparation of liver disease diagnostic products, characterized in that, In the reference device, all patients with liver disease are set as the liver disease group, and all healthy individuals without liver disease are set as the non-liver disease group. Both groups are set as known grouping information.

9. The application of the detection reagent for metabolic markers according to claim 6 in the preparation of liver disease diagnostic products, characterized in that, The diagnostic system also includes sample pretreatment reagents.

10. The application of the detection reagent for metabolic markers according to claim 9 in the preparation of liver disease diagnostic products, characterized in that, The sample pretreatment reagent is an extract consisting of an equal volume mixture of methanol and acetonitrile.

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