Biomarker for diagnosing idiopathic membranous nephropathy and application
By detecting biomarkers such as D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol in urine, non-invasive diagnostic products are provided, solving the problem of early diagnosis of idiopathic membranous nephropathy and achieving non-invasive, accurate disease monitoring and risk reduction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-12
- Publication Date
- 2026-04-14
AI Technical Summary
In the current technology, the diagnosis of idiopathic membranous nephropathy relies on invasive renal biopsy, which is economical, carries the risk of bleeding and infection, and is difficult to perform frequently. It is especially difficult to operate on children or obese patients, and there is a lack of simple and accurate non-invasive screening methods.
Using biomarkers such as D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol, and employing techniques such as mass spectrometry and chromatography, we can detect urine samples and provide non-invasive diagnostic products such as reagents, chips, and test strips for the early diagnosis and monitoring of idiopathic membranous nephropathy.
It enables non-invasive and accurate diagnosis of idiopathic membranous nephropathy, avoiding the risks of traditional kidney biopsy, allowing for early detection of disease activity, reducing the risk of irreversible damage, and improving diagnostic accuracy through the combination of multiple metabolic markers.
Smart Images
Figure CN121856448A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of biomedical detection technology, specifically to a biomarker for diagnosing idiopathic membranous nephropathy and its application. Background Technology
[0002] Idiopathic membranous nephropathy (MN) is a common glomerular disease characterized by the deposition of immune complexes (mainly IgG and C3) in the lateral and subepithelial regions of the glomerular basement membrane, accompanied by diffuse thickening of the glomerular basement membrane (GBM). MN is a chronic, progressive disease, and early diagnosis is crucial for slowing its progression. Studies have shown that timely treatment can slow the progression of kidney disease and reduce the risk of renal failure. Early intervention in kidney disease helps protect renal function and delay disease progression.
[0003] Currently, renal biopsy remains the gold standard for diagnosis. However, this invasive procedure is not only limited by economic constraints and cannot be performed frequently, but more importantly, it requires puncture to obtain kidney tissue, which carries risks of bleeding, infection, and serious complications, potentially causing significant harm to patients. This is especially true for children or obese patients, requiring a high level of surgical skill from the physician. Therefore, there is an urgent need for a simple and accurate non-invasive screening method to enhance the ability to predict MN patients in clinical treatment. Summary of the Invention
[0004] In view of the deficiencies of the prior art, the purpose of this invention is to provide a biomarker for diagnosing idiopathic membranous nephropathy and its application, and to provide a product for diagnosing idiopathic membranous nephropathy to solve the problems mentioned in the background art.
[0005] The present invention solves the technical problem by adopting the following technical solution: 1. This invention provides a biomarker for diagnosing idiopathic membranous nephropathy, wherein the biomarker is at least one of D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol.
[0006] Preferably, the biomarkers are: D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol.
[0007] 2. The use of the reagent for detecting the biomarker described in claim 1 in the preparation of products for the prevention or diagnosis of idiopathic membranous nephropathy.
[0008] Furthermore, in the aforementioned application, compared with normal controls, the expression levels of D-2-Hydroxyglutaric acid and Geranyl Diphosphate were downregulated in patients with idiopathic membranous nephropathy, while the expression level of Mucronulatol was upregulated in patients with idiopathic membranous nephropathy.
[0009] 3. A product for diagnosing idiopathic membranous nephropathy, the product comprising a reagent for detecting a biomarker in a sample to be tested, said biomarker being at least one of D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol.
[0010] Furthermore, the product can be a reagent, chip, test strip, kit, or high-throughput screening platform that can detect the expression level of biomarkers in a sample.
[0011] Furthermore, the product can be any one of the following: mass spectrometry, chromatography, chromatography-mass spectrometry, gene chip, transcriptome sequencing, PCR, and immunohistochemistry-related reagents.
[0012] Furthermore, the product is a chromatography-mass spectrometry (GC-MS) product.
[0013] Furthermore, the sample to be tested is urine.
[0014] Furthermore, the product also includes extraction reagents and / or internal standards.
[0015] Furthermore, the internal standard is L-2-chlorophenylalanine, Palmitoyl-L-carnitine-(N-methyl-d3), cholic acid-2,2,4,4-d4 and / or DL-Phenylalanine-d5.
[0016] Compared with existing technologies, the present invention has the following beneficial effects: The non-invasive biomarkers D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol provided by the present invention can be used to diagnose idiopathic membranous nephropathy. While ensuring accurate diagnosis of idiopathic membranous nephropathy, it avoids the risks associated with traditional invasive renal biopsies, facilitating early diagnosis and long-term monitoring. The performance of combinations of multiple metabolic biomarkers is significantly superior to that of single metabolic biomarkers. The biomarkers D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol may appear earlier than clinical symptoms in the pathological progression of idiopathic membranous nephropathy, and their level changes can reflect disease activity. This is particularly important for this progressive disease, idiopathic membranous nephropathy, as it can prevent irreversible damage caused by delayed diagnosis. Attached Figure Description
[0017] Figure 1 This is a QC sample evaluation chart.
[0018] Figure 2 This is a heatmap of sample correlation.
[0019] Figure 3 This is a graph of PCA scores.
[0020] Figure 4 This is the PLS-DA score graph.
[0021] Figure 5 A bar chart for classifying and statistically analyzing KEGG compounds.
[0022] Figure 6 A boxed bar chart for KEGG pathway statistics.
[0023] Figure 7 This is a bar chart showing the differential metabolites between the normal control group and the idiopathic membranous nephropathy group in Example 1 of the present invention.
[0024] Figure 8 Volcano plot showing the differences in metabolite expression between the two groups.
[0025] Figure 9 Venn diagram for metabolic set analysis. Detailed Implementation
[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0027] Example 1 Sample collection Twelve subjects were recruited from the First Affiliated Hospital of China Medical University in Shenyang. All clinical studies were conducted with informed consent and on a voluntary basis. Six healthy subjects (normal control group, N group) and six patients with idiopathic membranous nephropathy (patient group, NO group) were selected based on age and sex matching according to strict screening and exclusion criteria. All patients meeting the criteria for this study were accurately diagnosed with the following conditions: proteinuria, hypoalbuminemia, and hyperlipidemia. The healthy controls had the following characteristics: (1) normal renal tubular function and no kidney disease, abdominal disease, etc.; (2) patients who had never received antibiotics, immunosuppressants, or functional foods (probiotics) within the past month.
[0028] 2. Liquid sampling: Aspirate 100 μL of centrifuged urine supernatant into a 1.5 mL centrifuge tube, add 400 μL of extraction buffer (volume ratio, acetonitrile:methanol = 1:1) containing four internal standards (Palmitoyl-L-carnitine-(N-methyl-d3), cholic acid-2,2,4,4-d4, DL-Phenylalanine-d5, L-2-chlorophenylalanine (0.02 mg / mL)), vortex for 30 s, and then sonicate at low temperature for 30 min (5℃, 40 kHz). Place the sample at -20℃ for 30 min. Centrifuge at 4℃ (1300-3000 rpm) for 15 min, transfer the supernatant, dry with nitrogen, reconstitute with 100 μL of reconstituted solution (volume ratio, acetonitrile:water = 1:1), perform low-temperature ultrasonic extraction for 5 min (5℃, 40 kHz), centrifuge at 4℃ (1300-3000 rpm) for 10 min, transfer the supernatant to a vial with an inner tube for analysis.
[0029] 3. QC Samples: Take equal volumes of all sample metabolites and mix them to prepare a quality control (QC) sample. During the instrumental analysis, insert one QC sample every 5-10 samples to examine the repeatability of the entire analysis process and ensure that the relative standard deviation (RSD) of the QC sample is ≤30%.
[0030] LC-MS / MS analysis: The instrument platform used for this LC-MS analysis was the Thermo Fisher Scientific UHPLC-Q Exactive HF-X ultra-high performance liquid chromatography-tandem Fourier transform mass spectrometry system.
[0031] 5. Chromatographic conditions: A 3 μL sample was separated using an HSS T3 column (100 mm × 2.1 mm id, 1.8 μm) and then analyzed by mass spectrometry. Mobile phase A was a 95 / 5 (v / v) solution of water / acetonitrile containing 0.1% formic acid, and mobile phase B was a 47.5 / 47.5 / 5 (v / v / v) solution of acetonitrile / isopropanol / water containing 0.1% formic acid. The flow rate was 0.40 mL / min, and the column temperature was 40 °C.
[0032] 6. Mass spectrometry conditions: Sample mass spectrometry signal acquisition adopts positive and negative ion scanning mode, with a mass scanning range of m / z: 70-1050; ion spray voltage: positive ion voltage 3500V, negative ion voltage -3000V, sheath gas 50 arb, auxiliary heating gas 13 arb, ion source heating temperature 450℃, and cyclic collision energy 20-40-60 V.
[0033] 7. Data Analysis After the LC-MS was completed, the raw LC-MS data were imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. Finally, a data matrix of retention time, mass-to-charge ratio, and peak intensity was obtained. At the same time, the MS and MSMS mass spectrometry information was matched with the public metabolic databases HMDB (http: / / www.hmdb.ca / ) and Metlin (https: / / metlin.scripps.edu / ) as well as the self-built library of Metlin to obtain metabolite information.
[0034] The data matrix after the database search is uploaded to the Meiji Cloud Platform (cloud.majorbio.com) for analysis.
[0035] First, the data matrix is preprocessed. The original data includes quality control (QC) samples and test samples. To better analyze the data, a series of preprocessing steps are required, mainly including missing value recoding and data normalization. Preprocessing reduces the impact of data variations irrelevant to the research objective on data analysis, facilitating the screening and analysis of potential target differential metabolites.
[0036] The data preprocessing process includes: filtering low-quality peaks, filling missing values, data normalization, QC sample RSD evaluation, and data transformation.
[0037] Missing value filtering Samples may have one or more missing values due to various reasons (a. the signal is too low to be detected; b. detection errors, such as ion suppression or instrument instability; c. limitations of the peak extraction algorithm, which cannot extract low signals from the background; d. inability to resolve all overlapping peaks during deconvolution). Missing values in the table are usually presented as null values or NA (Not A Number). Data filtering based on the proportion of missing values within a sample or group is a common method in metabolomics analysis.
[0038] Missing value imputation If unfiltered missing values are ignored, the resulting data matrix may affect subsequent algorithm calculations and trigger anomalies. Therefore, simulated imputation is necessary. The main methods include minima, median (suitable for skewed distributions), mean (suitable for normal distributions), random forest, expected value, and zero padding.
[0039] Data normalization Data normalization maps data to a specific range before processing, facilitating faster and more convenient calculations. Data normalization is a crucial step in data preprocessing, eliminating statistical errors such as sample processing variations, concentration differences, and instrument bias. Commonly used data normalization methods in metabolomics include: median, mean, sum, specified sample, and internal reference.
[0040] QC verification Calculate the RSD (standard deviation / mean) of a certain ion in the QC sample. The smaller the value, the smaller the bias. Metabolomics requires removing variables with RSD exceeding this threshold. Generally, variables with RSD > 30% fluctuate greatly during the experiment and are not included in differential quantitative analysis.
[0041] Data transformation for metabolomics analysis generally requires data to be normally or Gaussian distributed, often necessitating log transformation. Log transformation corrects for heteroscedasticity, reduces or eliminates data asymmetry, and improves the normality of the data distribution. This satisfies the assumptions of common statistical analysis methods such as the student's test, linear regression, and correlation analysis, thereby reducing analytical errors.
[0042] The data matrix preprocessing is as follows: Missing values are removed using the 80% rule, meaning variables with at least 80% or more non-zero values in at least one sample are retained. Then, missing values are filled (the minimum value in the original matrix is used to fill the gaps). To reduce errors caused by sample preparation and instrument instability, the response intensity of the sample mass spectrometry peaks is normalized using summation normalization, resulting in a normalized data matrix. Simultaneously, variables with a relative standard deviation (RSD) > 30% for QC samples are removed, and logarithmic transformation (log10) is performed to obtain the final data matrix used for subsequent analysis. See details... Figure 1 In the figure, the horizontal axis represents the RSD (%) value, i.e., standard deviation / mean, and the vertical axis represents the cumulative proportion of ion peaks. For the overall data, if RSD < 30% and the cumulative proportion of peaks > 0.7, then the overall data is acceptable (dashed lines represent before preprocessing, solid lines represent after preprocessing, and raw data has only one solid line).
[0043] Example 2: Sample Comparison Analysis Based on the expression of metabolites among different samples, correlation heatmap analysis and principal component analysis (PCA) were performed on the samples to evaluate the similarity of samples within groups and the differences between samples between groups.
[0044] Correlation analysis between samples serves two purposes: firstly, it verifies whether the variation among biological replicates meets the expectations of the experimental design; secondly, it provides a basic reference for differential metabolite analysis. A correlation coefficient closer to 1 indicates a higher similarity in metabolite expression levels among samples, meaning a better correlation. The degree of difference in metabolite expression among samples can be quantified using statistical distance analysis. Statistical algorithms are used to calculate the distance between each pair of samples, obtaining a distance matrix and visual statistical analysis.
[0045] PCA analysis Principal Component Analysis (PCA) is a technique for simplifying data analysis. This method effectively identifies the most "primary" elements and structures in the data, removes noise and redundancy, reduces the dimensionality of complex data, and reveals the simple structure hidden behind the complexity. Its advantages include simplicity and no parameter limitations. In practical projects, we can use PCA to identify outliers and distinguish clusters of highly similar samples.
[0046] PCA analysis is essentially an unsupervised multivariate statistical analysis method that reflects the overall differences between groups and the magnitude of variability within groups. The more similar the metabolite expression patterns of samples, the closer they are reflected in the PCA plot. The basic principle is to use mathematical methods to recombine the original variables into several new, mutually independent composite variables (i.e., principal components), rank all factors by importance, and typically ignore minor factors at the lower end of the hierarchy, thus simplifying the data through dimensionality reduction. The differences between multiple groups of data are then reflected on a two-dimensional coordinate graph, with the axes representing the two characteristic values that best reflect the differences between samples.
[0047] Table 1 shows the sample correlation data. The first row and first column are the sample names, and the values in the table are the correlation coefficients or p-values between each pair of samples. A larger absolute value of the correlation coefficient indicates a stronger correlation between the two samples; a smaller p-value indicates a more significant correlation. A sample correlation heatmap is shown below. Figure 2 As shown in the figure, the right and bottom sides display the sample names. Each cell represents the correlation between two samples, different colors represent the relative magnitude of the correlation coefficient, and the length of the clustering branches indicates the relative distance between samples; samples on the same branch are more similar. The degree of variation in metabolite composition and abundance among samples can be quantified using the correlation data. The closer the correlation is to 1, the higher the similarity in metabolite composition and abundance among the samples. This section can determine whether the sample repeatability meets expectations. If there are samples that deviate from the expected values, they can be removed during the analysis phase before proceeding to subsequent analyses.
[0048] Table 1. Sample Correlation Data Figure 3 The PCA score plot shows the relative coordinates of each sample on principal components p1 and p2 after dimensionality reduction analysis. The distance between these coordinates represents the degree of clustering and dispersion among the samples; closer distances indicate higher similarity, while greater distances indicate greater dissimilarity. PCA analysis allows observation of the separation trend between groups in the experimental model, the presence of outliers, and reflects the variability between and within groups from the original data. The confidence ellipse represents the distribution of the "true" samples in this group within this region at a 95% confidence level.
[0049] PLS-DA (Partial Least Squares Discriminant Analysis) is a supervised discriminant analysis method and a multivariate statistical analysis method. This method uses partial least squares regression to establish a regression model between metabolite expression levels and sample categories to predict sample categories. PLS-DA models are established for each comparison group, and cross-validation is performed to obtain the model evaluation parameters R² (model interpretability) and Q² (model predictability). The closer R² and Q² are to 1, the more stable and reliable the model. The PLS-DA score graph is shown below. Figure 4 As shown, the classification effect of the model can be intuitively displayed. The greater the separation between the two groups of samples in the figure, the more significant the classification effect. Component1 explains the first principal component, and Component2 explains the second principal component.
[0050] The parameters of the sample PLS-DA model are shown in Table 2. 2Y represents the explanatory power of the constructed model for the Y matrix, R 2 Y(cum) represents the cumulative explained rate; Q2 represents the predictive power of the model. The closer this index is to 1, the more stable and reliable the model is. Q2 > 0.5 indicates good predictive power, and Q2 < 0.5 indicates poor predictive power. p1 and p2 represent the first and second principal components, respectively.
[0051] Table 2 Sample PLS-DA Model Parameters Example 3 Metabolite annotation information All metabolites identified by mass spectrometry were compared with the KEGG and HMDB databases to obtain annotation information for each metabolite in the databases, and the annotation status of each metabolite in the databases was statistically analyzed.
[0052] KEGG compound classification KEGG Compounds are collections of small molecules, biopolymers, and other chemical substances related to biological systems. KEGG Compound classification is based on the hierarchical level of biological functions involved in metabolites, with main categories including: Compounds with biological roles, Bioactive peptides, Endocrine disrupting compounds, Pesticides, Phytochemical compounds, and Lipids. Identified metabolites are compared to the KEGG Compound database to obtain an overview of metabolite classifications and statistical graphs are generated. A KEGG compound classification statistical bar chart is shown below. Figure 5 As shown.
[0053] The KEGGPATHWAY database is a collection of manually mapped metabolic pathways, primarily describing information such as intermolecular interactions, physiological and biochemical reactions, and relationships between gene products. By matching metabolites to their KEGG compound IDs, information about the metabolic pathways in which they participate can be obtained, thereby evaluating their impact on biological metabolic processes. Figure 6A boxed bar chart is used to statistically analyze KEGG pathways. The horizontal axis represents the secondary classification of KEGG metabolic pathways, and the vertical axis represents the number of compounds annotated under that pathway. KEGG metabolic pathways can be divided into seven categories: Metabolism, Genetic Information Processing, Environmental Information Processing, Cellular Processes, Organismal Systems, Human Diseases, and Drug Development. Different colors represent different metabolic pathway categories. This analysis uses the KEGG database to annotate all identified metabolites and statistically analyzes pathway level 2 annotation. Annotated metabolites are marked in red on the pathway chart.
[0054] Example 4 After preprocessing, the data were then screened for differentially expressed metabolites between the two biological groups using a combination of univariate and multivariate statistical analyses. First, orthogonal partial least squares discriminant analysis (OPLS-DA) was used for modeling. To validate the model's performance, cross-validation was employed to assess its reliability. Finally, 200 permutation tests were conducted to evaluate the model's reliability and goodness of fit. Significantly differentiating metabolites were identified based on the variable weights (VIPs) obtained from the OPLS-DA model and the p-values obtained from Student's t-test, using opls_vip > 1 and p_value < 0.05 as screening criteria. The number of differentially expressed metabolites screened in each comparison group in this project is as follows: Figure 7 As shown in the figure (the horizontal axis represents different control groups; the vertical axis represents the number of metabolites; red in the figure represents upregulation of differentially regulated metabolites, and blue represents downregulation of differentially regulated metabolites).
[0055] Compared to the NO group, group N had 430 upregulated metabolites and group NO had 691 downregulated metabolites. These differentially expressed metabolites mainly included lipids and lipid-like molecules, organic acids and derivatives, organic heterocyclic compounds, organic oxygen compounds, and benzene compounds (or aromatic benzene compounds).
[0056] Figure 8This is a volcano plot illustrating the differences in metabolite expression between the two groups. The x-axis represents the fold change in metabolite expression between the two groups (log2FC), and the y-axis represents the statistical test value of the difference in metabolite expression (-log10(p_value)). Higher values indicate more significant expression differences. Both axes have been logarithmically normalized. Each point in the plot represents a specific metabolite, and the point size represents the Vip value. By default, red points indicate significantly upregulated metabolites, blue points indicate significantly downregulated metabolites, and gray points indicate metabolites with no significant difference. See the difference details table for corresponding data. After mapping all metabolites, it can be seen that points on the left represent downregulated metabolites, and points on the right represent upregulated metabolites. The further to the left, right, and top of the points, the more significant the expression difference.
[0057] Table 3 shows some statistical results of differentially expressed metabolites between the normal control group and the patients with idiopathic membranous nephropathy.
[0058] Table 3 shows data on some differentially expressed metabolites. The following are important differential metabolites identified through analysis and screening: (1) D-2-Hydroxyglutaric acid (D-2-HG) was significantly downregulated in the group (P = 0.0104, Log2FC = 0.9586). Under normal physiological conditions, the content of D-2-HG is extremely low. Its metabolism is closely related to the mutation of isocitrate dehydrogenase (IDH) and is a key biomarker of mIDH tumors.
[0059] (2) Geranyl Diphosphate: The highest VIP value was 3.83299 (P=3887e-05, Log: FC=-2.8397), which was significantly downregulated. It may be involved in metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of cofactors, biosynthesis of ubiquinone and other terpenoid quinones, biosynthesis of monoterpenes, biosynthesis of terpenoid skeletons, biosynthesis of terpenoids and polyketide alkaloids, biosynthesis of plant hormones, biosynthesis of terpenoids and steroidal compounds, and biosynthesis of plant secondary metabolites.
[0060] (3) Amantadine: The P value was the smallest at 1.186E-14, with the most significant difference.
[0061] (4) (+ / -)-Mucronulatol: FC value was 28.0218, with the largest upward adjustment factor (P=4.738e-07, VP=3.6397).
[0062] The aforementioned differentially expressed metabolites can serve as biomarkers for diagnosing idiopathic membranous nephropathy. Their accuracy is enhanced when used alone or in combination.
[0063] Example 5 Metabolic set analysis: Obtaining a metabolic set based on certain screening criteria (such as function, expression level, and expression differences) and then analyzing it. Figure 9 A Venn plot can be used to display common or unique metabolites across different metabolite sets. If 2 ≤ control groups ≤ 5, the graph will display a Venn plot or Upset plot, with different colors representing different groups (or samples). The numbers in the overlapping areas represent the number of metabolites common to multiple control groups, and the numbers in the non-overlapping areas represent the number of metabolites unique to the corresponding control group. If there are ≥ 6 control groups, the graph will display a petal Venn plot, with the petals showing the number of metabolites unique to the corresponding group and the center showing the number of metabolites common to all groups. A bar chart represents the number of metabolites contained in each metabolite set. On the interactive page of the cloud platform, clicking on the numbers in the Venn plot allows you to create metabolite sets for differentially expressed metabolites of interest for subsequent analysis.
[0064] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.
[0065] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.
Claims
1. A biomarker for diagnosing idiopathic membranous nephropathy, characterized in that, The biomarker is at least one of D-2-Hydroxyglutaric acid, Geranyl Diphosphate, and Mucronulatol.
2. The use of the reagent for detecting the biomarker described in claim 1 in the preparation of products for the prevention or diagnosis of idiopathic membranous nephropathy.
3. The application according to claim 2, characterized in that, Compared with normal controls, the expression levels of D-2-Hydroxyglutaric acid and Geranyl Diphosphate were downregulated in patients with idiopathic membranous nephropathy, while the expression level of Mucronulatol was upregulated in patients with idiopathic membranous nephropathy.
4. A product for diagnosing idiopathic membranous nephropathy, characterized in that, The product includes a reagent for detecting the biomarker of claim 1 in a sample to be tested.
5. The product for diagnosing idiopathic membranous nephropathy according to claim 4, characterized in that, The products mentioned are reagents, chips, test strips, kits, or high-throughput screening platforms.
6. The product for diagnosing idiopathic membranous nephropathy according to claim 5, characterized in that, The product can be any one of the following: mass spectrometry, chromatography, chromatography-mass spectrometry, gene chip, transcriptome sequencing, PCR, and immunohistochemistry-related reagents.
7. The product for diagnosing idiopathic membranous nephropathy according to claim 6, characterized in that, The product is a chromatography-mass spectrometry (GC-MS) product.
8. The product for diagnosing idiopathic membranous nephropathy according to claim 4, characterized in that, The sample to be tested was urine.
9. The product for diagnosing idiopathic membranous nephropathy according to claim 4, characterized in that, The product also includes extraction reagents and / or internal standards.
10. The product for diagnosing idiopathic membranous nephropathy according to claim 9, characterized in that, The internal standard is L-2-chlorophenylalanine, Palmitoyl-L-carnitine-(N-methyl-d3), cholic acid-2,2,4,4-d4 and / or DL-Phenylalanine-d5.