AAGN disease blood or urine metabolism marker, kit and application of AAGN disease blood or urine metabolism marker

Abstract

The invention relates to the technical field of nephritis medicine, in particular to an AAGN disease blood or urine metabolism marker, a kit and application of the AAGN disease blood or urine metabolism marker. In the blood or urine metabolism markers, the blood metabolism marker comprises one or more of 2-hydroxyphenylacetic acid, galacturonic acid, D-mannose-6-phosphoric acid, 1-methyluric acid and L-rhamnose acid; the urine metabolism marker comprises one or more of phosphatidylserine PS (20: 020: 4), lysophosphatidylcholine LPC (0: 0 / 20: 2), phosphatidylcholine PC (20: 122: 6), phosphatidylcholine PC (O-18: 116: 0) and phosphatidylcholine PC (O-18: 120: 4). The metabolic marker combination and the kit are used for predicting treatment response of ANCA-related glomerulonephritis (AAGN) patients, and have good repeatability, stability and biological correlation.

Description

TECHNICAL FIELD

[0001] The present application relates to the medical technical field of nephritis, in particular to an AAGN disease blood or urine metabolic marker, a kit and application thereof. BACKGROUND

[0002] Anti-neutrophil cytoplasmic antibodies (ANCA) associated vasculitis (AAV) is a kind of autoimmune disease characterized by ANCA-mediated necrotizing inflammation of blood vessels. AAV can cause damage and dysfunction of multiple organs and tissues, most commonly involving the kidneys and lungs. The kidney damage caused by AAV is called ANCA-associated glomerulonephritis (AAGN). AAGN is the leading cause of acute kidney injury in elderly patients in China. If not intervened in time, it is extremely easy to progress to end-stage renal disease, and the possibility of spontaneous remission is extremely low.

[0003] At present, glucocorticoids combined with immunosuppressive agents (cyclophosphamide or rituximab) is the standard induction therapy for AAV recommended by international guidelines. However, according to literature reports, about 10%-40% of patients with refractory AAV develop treatment resistance after using this regimen, and the renal survival rate of AAV patients with treatment resistance is very low. Studies have shown that for AAV patients who do not respond to glucocorticoids combined with cyclophosphamide therapy, especially PR3-AAV patients, rituximab therapy can make some patients achieve disease remission. In addition, intravenous immunoglobulin can also help improve the remission rate of patients with refractory AAGN. Therefore, early prediction of AAGN patient treatment response and timely adjustment of treatment regimen are crucial to improve the prognosis of such patients. However, there is still a lack of effective biomarkers for predicting AAGN patient treatment resistance in clinical practice.

[0004] Metabolomics is a high-throughput technology that can simultaneously detect and quantify a large number of small molecule metabolites in biological samples, and has the potential to reflect changes in physiological and pathological states. However, at present, there is still a lack of research programs on AAGN disease metabolic markers in academia and practice. SUMMARY

[0005] In order to solve the problem that there is still a lack of effective biomarkers for predicting AAGN patient treatment resistance in clinical practice, the present application compares and analyzes the plasma and urine samples of the treatment response group and the treatment resistance group of AAGN patients by metabolomics method, and screens out a group of differentially expressed metabolites closely related to treatment response as non-invasive predictive biomarkers for predicting AAGN patient treatment response.

[0006] In order to achieve the above-mentioned purpose, the present application provides an application of a reagent for detecting a metabolic marker in blood or urine in the preparation of a product for predicting the treatment response of AAGN patients, wherein the blood metabolic marker comprises one or more of 2-hydroxyphenylacetic acid, galacturonic acid, D-mannose-6-phosphate, 1-methyluric acid and L-rhamnose acid; and the urine metabolic marker comprises one or more of phosphatidylserine PS (20:0 / 20:4), lysophosphatidylcholine LPC (0:0 / 20:2), phosphatidylcholine PC (20:1 / 22:6), phosphatidylcholine PC (O-18:1 / 16:0) and phosphatidylcholine PC (O-18:1 / 20:4).

[0007] The full name of the above-mentioned urine metabolic marker in IUPAC standard nomenclature is: Phosphatidylserine PS (20:0 / 20:4): 1-eicosanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraenoyl)-glycero-3-phosphoserine; 1-eicosanoyl-2-(5Z,8Z,11Z,14Z)-eicosatetraenoyl-sn-glycero-3-phospho-L-serine; Lysophosphatidylcholine LPC (0:0 / 20:2): 1-(11Z,14Z-eicosadienoyl)-glycero-3-phosphocholine; 1-(11Z,14Z-eicosadienoyl)-glycero-3-phosphocholine; Phosphatidylcholine PC (20:1 / 22:6): 1-(11Z-eicosenoyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-glycero-3-phosphocholine; 1-(11Z-eicosenoyl)-2-(4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoyl)-glycero-3-phosphocholine; Phosphatidylcholine PC (O-18:1 / 16:0): [2-hexadecanoyloxy-3-[(Z)-octadec-9-enoxy]propyl] 2-(trimethylazaniumyl)ethyl phosphate; 1-O-(Z)-octadec-9-enyl-2-palmitoyl-sn-glycero-3-phosphocholine; Phosphatidylcholine PC (O-18:1 20:4): [2-[(8Z,11Z,14Z,17Z)-icosa-8,11,14,17-tetraenoyl]oxy-3-[(Z)-octadec-9-enoxy]propyl] 2-(trimethylazaniumyl)ethyl phosphate; 1-O-(Z)-octadec-9-enyl-2-palmitoyl-sn-glycero-3-phosphocholine or octadecenyl-palmitoyl phosphatidylcholine.

[0008] Preferably, the product comprises a kit and / or a chip.

[0009] Preferably, the product is used for predicting the response of AAGN patients to treatment by detecting the relative content of blood or urine metabolic markers in blood or urine.

[0010] Preferably, the product is used for predicting the response of AAGN patients to treatment by detecting the relative content of blood or urine metabolic markers in blood or urine.

[0011] Preferably, when the blood metabolic marker reagent is used in the product, the product detects a significant increase in one or more of 2-hydroxyphenylacetic acid, galacturonic acid, D-mannose-6-phosphate, 1-methyluric acid and L-rhamnose acid in blood, indicating that the AAGN patient is at risk of treatment resistance; When the urine metabolic marker reagent is used in the product, the product detects a significant increase in one or more of phosphatidylserine PS (20:0 20:4), lysophosphatidylcholine LPC (0:0 / 20:2), phosphatidylcholine PC (20:1 22:6), phosphatidylcholine PC (O-18:1 16:0) and phosphatidylcholine PC (O-18:1 20:4) in urine, indicating that the AAGN patient is at risk of treatment resistance; Preferably, the detection method uses one or more of ultra-high performance liquid chromatography-mass spectrometry, high performance liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry or liquid chromatography-mass spectrometry. The above detection techniques need to have the detection sensitivity, repeatability and adaptability equivalent to the ultra-high performance liquid chromatography-mass spectrometry in the present application.

[0012] Preferably, the detection uses ultra-high performance liquid chromatography-mass spectrometry.

[0013] Preferably, the screening method for detecting the metabolic markers in blood or urine comprises the following steps: The integral value of the peak area of the metabolic markers in blood or urine obtained by the detection method is taken as a relative quantitative index, and the variable importance on projection value, statistical test P value and abundance change multiple are statistically analyzed to screen the metabolic markers in blood or urine.

[0014] Preferably, when the metabolic markers in blood or urine satisfy the variable importance on projection value ≥ 1, the statistical test P value < 0.05, and the abundance change multiple ≥ 1.5, it indicates a significant increase.

[0015] Under the same technical concept, the present application also provides a kit comprising metabolic markers in blood or urine, wherein the kit contains reagents for detecting the metabolic markers in blood or urine.

[0016] The above-mentioned scheme of the present application has the following beneficial effects: (1) The present application provides a blood or urine metabolic marker combination based on metabolomics screening for predicting the response of ANCA-associated glomerulonephritis (AAGN) patients to treatment; the marker combination can be detected by a non-invasive method (such as detecting plasma or urine samples), has good repeatability, stability and biological correlation, and can more accurately, dynamically and low-riskly assist in judging whether the disease has a treatment resistance risk, thereby improving the scientificity and timeliness of clinical diagnosis and treatment decision-making; (2) The blood or urine metabolic marker kit of the present application is simple to operate, low in cost and high in responsiveness, and provides a technical basis for accurately identifying AAGN disease activity.

[0017] Other beneficial effects of the present application will be described in detail in the subsequent specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figures 1-2 is the total ion current chromatogram of the plasma sample of the discovery cohort under positive ion and negative ion modes; Figures 3-4 is the total ion current chromatogram of the urine sample of the discovery cohort under positive ion and negative ion modes; Figures 5-6 is the total ion current chromatogram of the plasma sample of the verification cohort under positive ion and negative ion modes; Figures 7-8 is the total ion current chromatogram of the urine sample of the verification cohort under positive ion and negative ion modes; Figure 9 is the OPLS-DA score plot of the plasma sample of the discovery cohort; Figure 10This is an OPLS-DA score graph of plasma samples from the validation cohort of this invention; Figure 11 This invention discovers the OPLS-DA score map of urine samples from the cohort.

[0019] Figure 12 This is an OPLS-DA score graph of urine samples from the verification cohort of this invention. Detailed Implementation

[0020] To make the technical problems, solutions, and advantages of this invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] Example 1: Screening of metabolic markers in blood or urine for predicting treatment response in AAGN. 1. Research Subjects 1.1 Inclusion and exclusion criteria for research subjects With the approval of the hospital's ethics committee, this study included 63 patients with acute kidney injury (AAGN) in the cohort and 27 patients in the validation cohort. Patients in the cohort were newly diagnosed between December 2019 and June 2022, while patients in the validation cohort were diagnosed between July 2022 and December 2023. The two cohorts were completely independent with no duplicates. All AAGN patients underwent renal biopsy. The inclusion and exclusion criteria for AAGN patients are as follows: Inclusion criteria: (1) The diagnosis is in line with the 2012 Chapel Hill consensus on vasculitis; (2) Patients with AAGN confirmed by renal biopsy; Exclusion criteria: (1) Suffering from a critical illness or having concurrent active infection (various types of hepatitis, tuberculosis, AIDS, etc.); (2) During the period of trying to conceive, pregnancy, or breastfeeding; (3) History of malignant tumors; (4) The presence of any other multisystem autoimmune disease, such as systemic lupus erythematosus, anti-glomerular basement membrane, etc.; (5) Comorbid diabetes mellitus and hyperthyroidism; (6) Had undergone hemodialysis or plasma exchange within two weeks prior to sampling; (7) The patient had received immunosuppressants, hormones and lipid-lowering drugs before entering this study; (8) EGPA patients.

[0022] 2 Sample collection Collection and storage methods Blood and urine samples of all patients were collected before the first diagnosis and the start of immunosuppressive therapy. The samples of all study subjects were collected in the morning on an empty stomach. After sample collection, centrifugation was performed immediately, and then the supernatant was stored in a -80°C refrigerator. Before detecting metabolites in the test samples, all samples were kept in the -80°C refrigerator to avoid repeated freezing and thawing.

[0023] 3 Standards and reagents The standards and reagents used in the experiment are shown in Table 1.

[0024] Table 1 Standards and reagents

[0025] 4 Sample extraction 4.1 Plasma sample extraction method Extraction of hydrophilic substances in plasma samples ① After thawing the plasma sample on ice, vortex for 10 seconds and mix well, take 50µL into a centrifuge tube; ② Add 20% acetonitrile methanol internal standard extraction solution (300μL) to the centrifuge tube of the previous step; ③ Vortex for 3 minutes to mix the sample well; ④ Centrifuge for 10 minutes (4℃, 13523g / min); ⑤ Take 200µL of supernatant into a new centrifuge tube; ⑥ Place the centrifuge tube in a -20℃ refrigerator for 30 minutes; ⑦ Take out the sample and centrifuge for 3 minutes (4℃, 13523g / min); ⑧ The supernatant obtained after centrifugation is the hydrophilic substance in the plasma sample; ⑨ Take 180µL for subsequent detection and analysis.

[0026] Extraction of hydrophobic substances in plasma samples ① After thawing the plasma sample on ice, vortex for 10 seconds and mix well, take 50µL into a centrifuge tube; ② Add 1mL of internal standard-containing lipid extraction solution to the centrifuge tube of the previous step, and the lipid extraction solution is prepared by mixing methyl tert-butyl ether and methanol at a ratio of 3:1; ③ Vortex for 15 minutes to mix the sample well; ④ Mix well after adding ultrapure water (200μL) to the sample (vortex for 1 minute); ⑤ Centrifuge for 10 minutes (4℃, 13523g / min); ⑥ Take 200µL of supernatant into a new centrifuge tube; ⑦ Concentrate the sample; ⑧ After the sample is completely dried, add a mixture of acetonitrile and isopropanol containing 0.1% formic acid (200μL) and mix well (vortex for 3 minutes); ⑨ Centrifuge for 3 minutes (4℃, 13523g / min); ⑩ The supernatant obtained after centrifugation is the extracted hydrophobic substance.

[0027] 4.2 Urine sample extraction method Extraction of hydrophilic substances in urine samples ​

[0028] Extraction of hydrophilic substances from urine samples

[0029] 5. Sample detection Plasma and urine samples were detected separately. All plasma samples were detected in the same time period and batch, and all urine samples were detected in the same time period and batch. The instrument used for detection was an ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) purchased from SCIEX Company.

[0030] 5.1 Liquid phase conditions for hydrophilic substances ​​​100 mm; ② mobile phase: including phase A and phase B, phase A is ultrapure water (0.1% formic acid), phase B is acetonitrile (0.1% formic acid); ③ elution gradient: 0 min water / acetonitrile (95:5 V / V), 11.0 min is 10:90 V / V, 12.0 min is 10:90 V / V, 12.1 min is 95:5 V / V, 14.0 min is 95:5 V / V; ④ flow rate 0.4 ml / min; column temperature 40 ℃; injection volume 2 μL.

[0031] 5.2 Hydrophobic substance liquid phase condition ① column: Thermo Accucore™ C30 column, i.d. 2.1x100 mm, 2.6 um; ② mobile phase: phase A: acetonitrile / water (60 / 40, V / V) (containing 0.1% formic acid, 10 mmol / L ammonium formate); phase B: acetonitrile / isopropanol (10 / 90, V / V) (containing 0.1% formic acid, 10 mmol / L ammonium formate); ③ mobile phase gradient: 0 min A / B (80:20, V / V), 2 min (70:30, V / V), 4 min (40:60, V / V), 9 min (15:85, V / V), 14 min (10:90, V / V), 15.5 min (5:95, V / V), 17.3 min (5:95, V / V), 17.5 min (80:20, V / V), 20 min (80:20, V / V); ④ flow rate 0.35 ml / min; column temperature 45 ℃; injection volume 2 μL.

[0032] 5.3 Hydrophilic substance mass spectrometry condition Electrospray ion source (ESI) temperature 500 ℃, mass spectrometry voltage 5500 V (positive), -4500 V (negative), ion source gas I (GSI) 55 psi, gas II (GS II) 60 psi, curtain gas (CUR) 25 psi, collision-activated dissociation (CAD) parameter setting is high. In the triple quadrupole (Qtrap), each ion pair is scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0033] 5.4 Mass spectrometry conditions for hydrophobic substances Electrospray ionization (ESI) temperature 500 °C, mass spectrometry voltage 5500 V in positive ion mode, -4500 V in negative ion mode, ion source gas 1 (GS1) 45 psi, gas 2 (GS2) 55 psi, curtain gas (CUR) 35 psi, collision-activated dissociation (CAD) parameters set to Medium. In the triple quadrupole, each ion pair is scanned and detected according to the optimized declustering potential (DP) and collision energy (CE).

[0034] 6 Qualitative and quantitative analysis of metabolites The present application provides a metabolite qualitative and quantitative analysis method based on triple quadrupole mass spectrometry, which combines a self-built targeted metabolite standard database (Metware Database, MWDB) and a multiple reaction monitoring mode (Multiple Reaction Monitoring, MRM) to achieve high specificity and high sensitivity detection of metabolites.

[0035] The qualitative analysis of metabolites is based on the self-built targeted metabolite database MWDB, and the identification of target metabolites is achieved by comparing the retention time, parent / daughter ion pairs and secondary spectrum data.

[0036] The quantitative analysis of metabolites is performed using the MRM mode: the first quadrupole selects the parent ion, the parent ion is collision-induced dissociated in the collision chamber to form multiple daughter ions, and the third quadrupole selects the characteristic daughter ion for quantification. This mode can significantly reduce background noise and improve specificity and sensitivity of detection.

[0037] Figures 1-8 The total ion chromatograms of the mixed quality control sample in positive ion mode and negative ion mode are shown, which are used to evaluate the overall ion response and chromatographic separation effect. Figures 1-8 In the figure, the abscissa is the retention time of metabolite detection, in minutes; the ordinate is the ion flow intensity, in cps (counts per second).

[0038] Figure 1 The total ion chromatogram of the discovery cohort plasma sample in positive ion mode is shown; Figure 2 The total ion chromatogram of the discovery cohort plasma sample in negative ion mode is shown; Figure 3Total ion current chromatograms of urine samples in the discovery cohort in positive ion mode; Figure 4 Total ion current chromatograms of urine samples in the discovery cohort in negative ion mode; Figure 5 Total ion current chromatograms of plasma samples in the validation cohort in positive ion mode; Figure 6 Total ion current chromatograms of plasma samples in the validation cohort in negative ion mode; Figure 7 Total ion current chromatograms of urine samples in the validation cohort in positive ion mode; Figure 8 Total ion current chromatograms of urine samples in the validation cohort in negative ion mode; Under the premise that the quality control of total ion current chromatograms is normal, the original mass spectrum data is collected and processed by using Analyst 1.6.3 software, the extracted ion current chromatograms are generated by extracting characteristic ions, the chromatographic peaks are identified and the peak areas are calculated to reflect the relative abundance of the corresponding metabolites in the samples, which can be used for quantitative comparison analysis between samples.

[0039] 7 Screening of differential metabolites 7.1 Sample grouping mode and research design The AAGN patients involved in the present application all received standardized induction and maintenance period treatments. The treatment regimens adopted are based on the 2024 Kidney disease: improving global outcomes (KDIGO) guidelines, the 2021 Chinese guidelines for the diagnosis and treatment of anti-neutrophil cytoplasmic antibody-associated glomerulonephritis, and the European league against rheumatism (EULAR) related recommendations, so as to ensure that the clinical treatment path is widely comparable and consistent in the international range.

[0040] In order to screen out a combination of metabolite markers that can predict the treatment response and long-term prognosis of AAGN patients, the patients included are systematically followed up, and are divided into two subgroups according to the treatment effect, as follows: Treatment response group: refers to patients who achieve remission after induction treatment, defined as no disease activity, and the Birmingham vasculitis activity score (BVAS) score is 0; Treatment-resistant group: refers to patients meeting any of the following conditions: ① Patients with acute AAV who receive standard induction therapy for 4 weeks, and the disease activity does not improve or worsens; ② The disease activity score decreases by less than 50% after 6 weeks of treatment; ③ Chronic persistent disease, defined as at least 1 major or 3 minor items in the disease activity score list after 12 weeks of treatment.

[0041] To ensure the objectivity and accuracy of metabolite analysis, all patient biological samples were collected at the initial diagnosis stage, i.e., before starting immunosuppressive therapy, to avoid the interference of treatment factors on the metabolic profile. Each patient was collected with one plasma sample and one urine sample to realize the joint screening of metabolic markers in multiple body fluid systems.

[0042] The research samples in the present application are divided into two completely independent cohorts for the discovery and verification of differential metabolites. Specifically as follows:

[0043] Discovery cohort: sample collection time is from December 2019 to June 2022, and the patients contained are new cases of AAGN diagnosed in this stage, which are used for preliminary screening and identification of differential metabolites;

[0044] Verification cohort: sample collection time is from July 2022 to December 2023, and the patients contained are independent cases, and there is no overlap between individuals and the discovery cohort, which are used to verify the stability and clinical applicability of the screened markers

[0045] The clinical information collection, processing method, storage condition and analysis process of all samples in the two cohorts are consistent, and each includes one plasma sample and one urine sample. The distribution of sample types in each cohort is shown in Table 2 as follows:

[0046] Table 2 Sample composition in the two research cohorts (plasma and urine)

[0047] 7.2 Screening criteria for differential metabolites between groups After completing the qualitative and quantitative analysis of metabolites, the chromatographic peak area data of all samples are derived for subsequent statistical analysis and differential metabolite screening. In order to extract effective information from high-dimensional metabolic data, this study uses multiple multivariate statistical analysis methods for dimensionality reduction modeling and visual recognition.

[0048] Firstly, unsupervised principal component analysis (PCA) is used to preliminarily evaluate the metabolic differences between samples. PCA can effectively reveal the metabolic profile differences between groups and the variability of samples within the group. The analysis results show that there is an obvious metabolic separation trend between different experimental groups Subsequently, supervised modeling was performed using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). This method maximizes the differences between groups and removes non-relevant variations, enhancing the identification of differential metabolites. The results (e.g. Figures 9-12 ), showing a clear metabolic separation trend between the treatment-responsive and treatment-resistant groups. After log2 transformation and centralization of the raw data, the OPLS-DA model was constructed using the MetaboAnalystR package in R software, which showed good fitting and prediction performance.

[0049] Based on the establishment and verification of the OPLS-DA model, the following three statistical indicators were used to screen differential metabolites: (1) Variable Importance on Projection (VIP) ≥ 1 The VIP value is calculated by the OPLS-DA model and reflects the contribution of each metabolite to the discrimination between groups. VIP ≥ 1 indicates that the metabolite has a significant effect in the model.

[0050] (2) Statistical test P-value < 0.05 Hypothesis testing was performed on the relative abundance of each metabolite between the two groups of samples to screen variables with statistically significant differences.

[0051] (3) Fold Change (FC) ≥ 1.5 or ≤ 0.67 FC represents the average abundance ratio between the treatment group and the control group and is commonly used to screen metabolites with significantly up-regulated or down-regulated expression. The threshold is generally set as FC ≥ 1.5 or FC ≤ 0.67, where: FC ≥ 1.5 indicates that the metabolite is significantly up-regulated in the treatment group (e.g., AAGN group) relative to the control group; FC ≤ 0.67 indicates that the metabolite is significantly down-regulated in the treatment group relative to the control group.

[0052] Figure 9 The OPLS-DA score plot for plasma samples from the discovery cohort is shown in Figure 1; The horizontal axis represents the predictive principal component, which reflects the difference between groups, and the vertical axis represents the orthogonal principal component, which represents the intra-group difference. The percentage in parentheses represents the explanatory rate of the component for data variance. Each point in the figure represents a sample, and samples in the same group are represented by the same symbol: circles represent the treatment-responsive group, and squares represent the treatment-resistant group.

[0053] Figure 10 The OPLS-DA score plot for urine samples from the discovery cohort is shown in Figure 2; The horizontal axis is the predictive principal component, which can reflect the difference between groups; the vertical axis is the orthogonal principal component, which represents the difference within the group. The percentage in the brackets represents the explanatory rate of the component to the data variance. Each point in the figure represents a sample, and samples in the same group are represented by the same symbol: circles represent the treatment-resistant group, and squares represent the treatment-responsive group.

[0054] Figure 11 OPLS-DA score plot of the validation cohort plasma samples; The horizontal axis is the predictive principal component, which can reflect the difference between groups; the vertical axis is the orthogonal principal component, which represents the difference within the group. The percentage in the brackets represents the explanatory rate of the component to the data variance. Each point in the figure represents a sample, and samples in the same group are represented by the same symbol: circles represent the treatment-resistant group, and squares represent the treatment-responsive group.

[0055] Figure 12 OPLS-DA score plot of the validation cohort urine samples; The horizontal axis is the predictive principal component, which can reflect the difference between groups; the vertical axis is the orthogonal principal component, which represents the difference within the group. The percentage in the brackets represents the explanatory rate of the component to the data variance. Each point in the figure represents a sample, and samples in the same group are represented by the same symbol: circles represent the treatment-resistant group, and squares represent the treatment-responsive group.

[0056] 7.3 Screening of differential metabolites in plasma samples and validation analysis According to the metabolite screening criteria (VIP≥1, P<0.05, FC≥1.5 or FC≤0.67) set by the present application, a total of 119 differential metabolites were screened in the discovery cohort plasma samples, and 236 differential metabolites were further screened in the validation cohort. Cross comparison of the differential metabolite results of the two cohorts obtained 33 overlapping metabolites that showed stable differential trends in both cohorts, with good reproducibility and consistency.

[0057] To evaluate the discriminant ability of the above metabolites, ROC curve analysis was performed on the 33 overlapping metabolites, and the AUC values were calculated. The analysis results are shown in Table 3. Most of the metabolites have strong discrimination ability, among which the following five metabolites showed high diagnostic performance (AUC values in the validation cohort were greater than 0.83) in both independent cohorts, and the expression levels in the treatment-resistant group continued to rise: 2-hydroxyphenylacetic acid D-Galacturonic Acid D-Mannose 6-phosphate 1-Methyluric Acid L-rhamnonic acid (L- rhamnose acid) The above 5 metabolites have high VIP values and significant abundance changes in the discovery cohort, and also show stable expression trends and good discrimination ability in the validation cohort. Among them, the AUC values of 2-hydroxyphenylacetic acid, galacturonic acid and D-mannose-6-phosphate are all greater than 0.87, which are particularly outstanding, indicating that they have high consistency and stable clinical discrimination in different cohorts.

[0058] The above metabolites can be used alone or in combination to form a metabolite combination for predicting the treatment response state of AAGN patients. They show stable expression trends and high classification performance in different cohorts and different sample sources, have good application universality and clinical transformation prospects, and are suitable for developing in vitro diagnostic products for assisting in judging the treatment response of patients.

[0059] Table 3. Plasma metabolites screened by the application for predicting the treatment response of AAGN and their statistical indicators

[0060] 7.4 Screening of differential metabolites in urine samples and verification analysis

[0061] According to the metabolite screening criteria (VIP ≥ 1, P < 0.05, FC ≥ 1.5 or FC ≤ 0.67) set by the application, 68 differential metabolites were screened in the urine samples of the discovery cohort, and 244 differential metabolites were screened in the validation cohort. Cross comparison of the results of the two cohorts obtained 9 overlapping metabolites that showed stable differential trends in both cohorts, all of which were consistently increased in the treatment-resistant group, and had good reproducibility and consistency.

[0062] To evaluate the discrimination ability of the above metabolites, ROC curve analysis was performed on the 9 overlapping metabolites, and the AUC values were calculated. The analysis results are shown in Table 4, and most of the metabolites showed good diagnostic performance in both cohorts. Among them, the AUC values of the following 5 metabolites in the validation cohort were all greater than 0.74, and the Log2FC values were all greater than 0.95, showing significant expression differences and discrimination ability. These metabolites are mainly phospholipid compounds, including: PC(O-18:1_20:4) PC(20:1_22:6) LPC(0:0 / 20:2) PS(20:0 / 20:4) PC(O-18:1_16:0) The five metabolites all have high VIP values (range: 2.207-2.560) and statistical significance (P values are all <0.05) in the discovery cohort, and also show good consistency in the validation cohort, with the highest AUC value of 0.788, a narrow range of confidence interval, and stable discrimination ability. Among them, PC(20:1_22:6) performs best in the validation cohort (AUC = 0.788, CI = 0.640-0.916) and has strong discrimination ability.

[0063] These metabolites can be used alone or in combination as candidate urine markers for predicting the treatment response status of AAGN patients, providing the possibility of non-invasive body fluid detection, and having good clinical transformation potential and product development value.

[0064] Table 4. Urine metabolites screened by the application for predicting the treatment response of AAGN and statistical indicators thereof

[0065] 8. Conclusion

[0066] In summary, by performing systematic metabolomics analysis on the plasma and urine samples collected from AAGN patients before treatment, based on strict screening criteria (VIP ≥ 1, P < 0.05, FC ≥ 1.5 or FC ≤ 0.67), a series of differential metabolites closely related to treatment response were identified in two completely independent sample cohorts. Through cross-validation, 33 metabolites with stable differences were finally screened in the plasma samples, and 9 overlapping metabolites were screened in the urine samples, showing good reproducibility and consistency between samples.

[0067] Further ROC curve analysis shows that metabolites such as 2-hydroxyphenylacetic acid, galacturonic acid, D-mannose-6-phosphate, 1-methyluric acid and L-rhamnose acid in plasma have high AUC values in the two cohorts, and have excellent diagnostic performance; and phospholipid metabolites such as PC(O-18:1_20:4), PC(20:1_22:6), LPC(0:0 / 20:2), PS(20:0 / 20:4) and PC(O-18:1_16:0) in urine also show strong discrimination ability and stable expression trend.

[0068] The plasma and urine metabolites screened by the application can be used alone or in combination to form a metabolite combination for judging the treatment response status of AAGN patients, and have significant predictive value and transformation potential. In particular, the use of urine samples provides a more convenient and non-invasive detection method, which is expected to provide auxiliary basis for individualized treatment regimen formulation and dynamic efficacy monitoring of AAGN, and promote the development and clinical application of related in vitro diagnostic products.

[0069] The application screens a group of small molecule metabolites closely related to treatment response in the plasma and urine samples of AAGN patients in two completely independent cohorts by metabolomics method, and constructs a candidate marker combination, which has the following technical advantages: 1. Non-invasive sampling method suitable for dynamic monitoring: The metabolites screened by the application are all derived from body fluid samples such as plasma and urine, and the collection process is safe, convenient and highly repeatable, avoiding the additional burden on patients caused by invasive operation, and being suitable for multiple monitoring and efficacy tracking in clinical practice.

[0070] 2. Significant differential expression and good discrimination ability: The selected metabolites show statistically significant differences (P < 0.05, VIP > 2) in the treatment-resistant and treatment-responsive groups, and the Log2FC values of some metabolites are more than 1.8, maintaining consistent expression trends in the two cohorts. In the verification cohort, the AUC values are mostly higher than 0.83, and the highest can reach 0.894, having strong discrimination performance.

[0071] 3. Stable screening results and strong cross-cohort repeatability: All candidate metabolites are cross-validated by independent discovery cohort and verification cohort, and a total of 33 metabolites in plasma and 9 metabolites in urine show consistent expression trends in the two cohorts, indicating that the model has good stability and data repeatability, reducing the risk of false positive interference.

[0072] 4. Wide distribution of metabolic pathways and strong biological interpretability: The marker combination covers multiple functional metabolic pathways, including aromatic compounds (such as 2-hydroxyphenylacetic acid), sugar metabolites (such as D-mannose-6-phosphate), purine degradation products (such as 1-methyluric acid) and phospholipid compounds (such as PC(20:1_22:6)), which reflect the immune activity status and treatment response from multiple dimensions, facilitating the construction of a mechanism-based, complementary combined detection model.

[0073] 5. Platform adaptability and standardized detection potential: The screened metabolites are small molecule compounds with clear structure and stable physicochemical properties, having a clear chemical structure, which can be quantitatively detected by platforms such as ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS), facilitating the development of standardized and automated in vitro diagnostic kits, improving detection efficiency and operability.

[0074] In summary, the present application provides a non-invasive prediction strategy based on metabolite combination, which can earlier and more accurately evaluate the response state of AAGN patients to treatment, has good practicability and conversion prospect, and is expected to replace or supplement traditional biological indicators to help optimize individualized precision treatment plan.

[0075] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. Use of a metabolic marker in blood or urine for the manufacture of a product for predicting the therapeutic response of a patient with AAGN, characterized in that, The blood metabolic markers include one or more of 2-hydroxyphenylacetic acid, galacturonic acid, D-mannose-6-phosphate, 1-methyluric acid and L-rhamnose acid; and the urine metabolic markers include one or more of phosphatidylserine_PS(20:0_20:4), lysophosphatidylcholine_LPC(0:0 / 20:2), phosphatidylcholine_PC(20:1_22:6), phosphatidylcholine_PC(O-18:1_16:0) and phosphatidylcholine_PC(O-18:1_20:4).

2. Use according to claim 1, wherein The product includes a kit and / or a chip.

3. The use according to claim 1, wherein The product is used for predicting the response of an AAGN patient to treatment by detecting the relative content of blood or urine metabolic markers in blood or urine.

4. The use according to claim 1, wherein The product is used for predicting the response of an AAGN patient to treatment by detecting the level change of blood or urine metabolic markers alone or jointly.

5. The use according to claim 1, wherein the compound is ###0002### When the blood metabolic marker reagent is used in the product, the product detects a significant increase in one or more of 2-hydroxyphenylacetic acid, galacturonic acid, D-mannose-6-phosphate, 1-methyluric acid and L-rhamnose acid in blood, indicating that the AAGN patient has a risk of treatment resistance; When the urine metabolic marker reagent is used in the product, the product detects a significant increase in one or more of phosphatidylserine_PS(20:0_20:4), lysophosphatidylcholine_LPC(0:0 / 20:2), phosphatidylcholine_PC(20:1_22:6), phosphatidylcholine_PC(O-18:1_16:0) and phosphatidylcholine_PC(O-18:1_20:4) in urine, indicating that the AAGN patient has a risk of treatment resistance.

6. Use according to any one of claims 1 to 5, wherein The detection method adopts one or more of ultra-high performance liquid chromatography-mass spectrometry, high performance liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry or liquid chromatography-mass spectrometry.

7. Use according to claim 6, wherein The detection adopts ultra-high performance liquid chromatography-mass spectrometry.

8. The use according to claim 6, wherein the compound is ###0002### The screening method for detecting metabolic markers in blood or urine includes the following steps: The integral value of the peak area of the metabolic markers in blood or urine obtained by the detection method is taken as a relative quantitative index, and the variable importance on projection value, statistical test P value and abundance change fold are statistically analyzed to screen the metabolic markers in blood or urine.

9. Use according to claim 8, wherein the compound is ###0002### When the metabolic markers in blood or urine meet the variable importance on projection value ≥ 1, the statistical test P value < 0.05 and the abundance change fold ≥ 1.5, it indicates a significant increase.

10. A kit comprising reagents for detecting a metabolic marker in blood or urine, characterized in that, The kit contains reagents for detecting the blood or urine metabolic markers according to any one of claims 1-9.

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