Marker for evaluating therapeutic effect of hydroxychloroquine on IgA nephropathy and application thereof

Abstract

The application belongs to the technical field of biological medicine, and particularly relates to a marker for evaluating the curative effect of hydroxychloroquine on IgA nephropathy and application. The marker for evaluating the curative effect of hydroxychloroquine on IgA nephropathy is any one or a combination of two or three or four or five markers of D-ornithine, L-arginine, L-cysteine, L-cystine and D-cysteine. Based on the above-mentioned five markers, a joint prediction model is constructed, which exhibits high prediction performance and can provide an early and non-invasive screening method for the clinic to identify IgAN patients who are potentially ineffective for HCQ treatment.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and specifically relates to a biomarker for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy and its application. Background Technology

[0002] Immunoglobulin A (IgA) nephropathy is one of the most common primary glomerular diseases worldwide, characterized by IgA deposition in the glomerular mesangial area, leading to inflammation and progressive renal impairment. The clinical presentation is diverse, ranging from asymptomatic hematuria to rapidly progressive renal failure. Although the disease progresses slowly in some patients, approximately 30%–40% will develop end-stage renal disease within 20–30 years, requiring dialysis or kidney transplantation.

[0003] Currently, treatment strategies for IgA nephropathy mainly include blood pressure control, proteinuria management, and immunosuppressive therapy. However, the efficacy of traditional treatments is limited and there are significant individual variability. Hydroxychloroquine, as an immunomodulator, has been used to treat IgA nephropathy in recent years and has shown some clinical efficacy. However, there is significant individual variability in patient response to hydroxychloroquine; some patients may achieve complete remission, while others show no significant benefit or even experience side effects. Currently, there is a lack of effective methods and biomarkers to predict patient response to hydroxychloroquine, leading to a lack of precision in treatment selection.

[0004] Metabolomics, a novel omics technology developed after proteomics, utilizes advanced analytical instruments with high separation efficiency, high sensitivity, and low detection limits to elucidate the overall physiological and pathological trends of organisms by analyzing the dynamic changes of small-molecule endogenous metabolites. It has wide applications in qualitative and quantitative analysis of disease progression and drug-efficacy relationships. Therefore, metabolomics-based analytical methods provide a new approach for screening IgA nephropathy patients sensitive to hydroxychloroquine treatment.

[0005] However, existing screening techniques still have the following shortcomings and deficiencies: 1) Lack of specific biomarkers: Current research focuses on single types of biomarkers, lacking systematic studies on small molecule biomarkers such as amino acid metabolites. 2) Technical limitations: Traditional detection methods, such as enzyme-linked immunosorbent assay (ELISA), have low accuracy and sensitivity, making it difficult to detect low-abundance amino acid metabolites; moreover, the results are affected by many factors, potentially leading to false positives or false negatives; furthermore, ELISA can only detect specific antigens or antibodies, and cannot detect complex antigen-antibody complexes; ELISA is complex to operate, requires strict control during the process, and is easily affected by external environmental factors, leading to errors. 3) Insufficient clinical application: Existing biomarker screening methods are mostly confined to the laboratory stage, lacking standardized and automated detection systems.

[0006] Therefore, developing a metabolomics-based biomarker and screening method for predicting the efficacy of hydroxychloroquine treatment is of great significance for realizing personalized treatment of IgA nephropathy, improving treatment outcomes, and reducing unnecessary drug exposure. Summary of the Invention

[0007] The purpose of this invention is to provide a biomarker and its application for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy, so as to enable individualized treatment of IgA nephropathy, improve treatment efficacy, and reduce unnecessary drug exposure.

[0008] Therefore, the present invention provides the following technical solution.

[0009] The first aspect of the present invention provides a biomarker for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy, said biomarker being any one, two, three, four, or five of the following biomarkers: D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine.

[0010] In a preferred embodiment of the present invention, the marker is a combination of five markers: D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine.

[0011] In a preferred embodiment of the invention, the biomarker is derived from the subject's urine.

[0012] A second aspect of the invention provides the use of the biomarker as described above in studies of hydroxychloroquine use for non-therapeutic purposes.

[0013] A third aspect of the present invention provides the use of a reagent for detecting the expression level of the aforementioned biomarkers in the preparation of reagents for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy or for drug sensitivity testing.

[0014] In a preferred embodiment of the present invention, the reagent for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy or the drug sensitivity test reagent is a reagent for evaluating the efficacy or detecting the drug sensitivity of hydroxychloroquine in the process of treating IgA nephropathy.

[0015] In a preferred embodiment of the present invention, the sample for efficacy evaluation or drug sensitivity testing is the subject's urine.

[0016] In a preferred embodiment of the present invention, the reagent determines the level of the marker in the sample by one or more of 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, and turbidimetry.

[0017] A fourth aspect of the present invention provides a kit for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy, comprising reagents for detecting the expression levels of biomarkers for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy as described above.

[0018] In a preferred embodiment of the present invention, the detection is a quantitative detection of the level of a marker in the urine of the subject.

[0019] In a preferred embodiment of the present invention, the reagent determines the level of the marker in the sample by one or more of 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, and turbidimetry.

[0020] In a preferred embodiment of the present invention, the kit is an ELISA kit.

[0021] The fifth aspect of the present invention provides a method for assessing whether hydroxychloroquine can prevent or treat IgA nephropathy, comprising using any one, two, three, four, or five of D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine ​​as biomarkers to assess the effect of hydroxychloroquine.

[0022] A sixth aspect of the invention provides a method for screening compounds that can prevent or treat IgA nephropathy, comprising using any one, two, three, four, or five of the following markers selected from D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine ​​as markers to evaluate the efficacy of the compounds.

[0023] A seventh aspect of the present invention provides a system for analyzing the efficacy of hydroxychloroquine in IgA nephropathy, comprising: Target expression level detection device: used to detect the expression level of the biomarkers as described above in the sample; The sample was urine from the subject. Drug efficacy analysis device: Determine the efficacy of medication for IgA nephropathy based on the expression level of biomarkers; Result output device: Used to output the results obtained from the pharmacological analysis device.

[0024] In a preferred embodiment of the present invention, the target expression level detection device uses reagents to determine the level of the biomarker in the sample by one or more of 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, and turbidimetry.

[0025] An eighth aspect of the invention provides the use of a drug that inhibits the expression of the aforementioned markers in the preparation of a pharmaceutical composition for treating IgA nephropathy, the pharmaceutical composition comprising hydroxychloroquine.

[0026] A ninth aspect of the present invention provides a pharmaceutical composition for treating IgA nephropathy, comprising hydroxychloroquine and a drug for inhibiting the expression of the aforementioned biomarkers.

[0027] By employing the above technical solution, the present invention has at least the following advantages: This invention combines metabolomics data to screen for combinations of biomarkers associated with hydroxychloroquine treatment response. By detecting the levels of these biomarkers, the efficacy of hydroxychloroquine in treating IgA nephropathy can be accurately assessed. In one aspect, this invention utilizes the high sensitivity and specificity of LC-MS / MS technology to achieve precise detection of low-abundance biomarkers. In another aspect, this invention employs advanced machine learning algorithms to construct efficient predictive models, improving the accuracy of biomarker screening and efficacy prediction. Finally, this invention also develops a standardized detection system, promoting the translation of biomarker screening methods from laboratory research to clinical application. This invention holds promise for providing IgA nephropathy patients with a non-invasive, early, and accurate method for predicting the efficacy of hydroxychloroquine treatment, thus advancing the development of personalized medicine.

[0028] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below. Attached Figure Description

[0029] Figure 1 The flowchart illustrates a method for screening biomarkers based on metabolomics analysis to evaluate the efficacy of hydroxychloroquine in treating IgA nephropathy, as provided in an embodiment of the present invention. Figure 2 The changes in 24-hour urinary protein and eGFR before and after treatment are shown in the effective and ineffective groups of hydroxychloroquine treatment; where A represents the changes in 24-hour urinary protein before and after treatment in the effective group of hydroxychloroquine treatment, B represents the changes in 24-hour urinary protein before and after treatment in the ineffective group of hydroxychloroquine treatment, C represents the changes in eGFR before and after treatment in the effective group of hydroxychloroquine treatment, and D represents the changes in eGFR before and after treatment in the ineffective group of hydroxychloroquine treatment. Figure 3 The ion chromatograms of standard solution and urine sample extraction are shown; where A is the ion chromatogram of standard solution extraction and B is the ion chromatogram of sample extraction. Figure 4 The PCA and OPLS-DA analysis plots of urinary amino acid metabolism profiles in the effective and ineffective hydroxychloroquine treatment groups are shown; where A is the PCA score plot; B is the OPLS-DA score plot; and C is the OPLS-DA permutation test plot. Figure 5 The diagram shows volcano plots and heatmaps of differentially expressed amino acids in urine from the hydroxychloroquine-treated and ineffective groups; where A is the volcano plot analysis and B is the hierarchical cluster analysis heatmap. Figure 6 The KEGG enrichment analysis of differentially expressed amino acids in urine from the hydroxychloroquine-treated and ineffective groups is shown. In the figure, A is the differential amino acid enrichment analysis; B is the differential abundance score analysis. Figure 7 The diagram shows the metabolic pathway analysis of differentially expressed amino acids in urine from the hydroxychloroquine treatment effective and ineffective groups; where A is a bubble diagram analysis and B is a pathway enrichment tree diagram analysis. Figure 8 The ROC curves for predictive performance evaluation of single amino acids are shown; where A is the ROC curve of D-ornithine; B is the ROC curve of L-arginine; C is the ROC curve of L-cysteine; D is the ROC curve of L-cysteine; and E is the ROC curve of D-cysteine. Figure 9 The ROC curves for the performance evaluation of the five amino acid joint prediction models are shown; where A is the ROC curve of Model I; B is the performance of Model I in the training set and validation set; C is the ROC curve of Model II; D is the performance of Model II in the training set and validation set; E is the ROC curve of Model III; and F is the performance of Model III in the training set and validation set. Detailed Implementation

[0030] To make the technical means, creative features, achieved objectives, and effects of this invention readily understandable, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0031] Example 1: Screening of biomarkers 1. Experimental Methods like Figure 1 As shown in this embodiment, the marker screening process includes: S1: Acquisition of samples for the collection, treatment, grouping, and testing of patients with primary IgA nephropathy; S2: Sample processing and acquisition of LC-MS / MS spectral data; S3: Processing LC-MS / MS data, screening differentially expressed amino acids and identifying biomarkers; S4: Utilize metabolomics-related databases and online software to construct biomarker metabolic pathways and perform predictive analysis; S5: Use ROC curves to assess the ability of biomarkers to predict the efficacy of hydroxychloroquine.

[0032] The following is a detailed explanation: 1.1 Collection, treatment, grouping, and acquisition of test samples for patients with primary IgA nephropathy The study included patients with primary IgA nephropathy collected at Nanjing Drum Tower Hospital from June 1, 2023 to May 31, 2024. A total of 60 patients met the inclusion and exclusion criteria and were included in the study. 1) Inclusion criteria are: A. Age 18-75 years, pathologically diagnosed (light microscopy + electron microscopy) as primary IgA nephropathy; B. Estimated glomerular filtration rate (eGFR) > 30 mL / (min•1.73 m 2 ); C. Despite receiving the maximum dose of a renin-angiotensin-aldosterone system inhibitor (RAASi) for at least 3 months, 24-hour urinary protein remained between 0.75 and 3.5 g; D. Treatment with hydroxychloroquine alone.

[0033] 2) Exclusion criteria are: A. Secondary IgA nephropathy; B. Use of glucocorticoids or other immunosuppressants within the past 3 months; C. Currently or planning to become pregnant or breastfeed; D. Hydroxychloroquine is contraindicated.

[0034] Subsequently, 60 subjects were treated with hydroxychloroquine for at least 6 months. During the treatment follow-up, patients who dropped out were excluded. Among them, 1 decided to discontinue hydroxychloroquine, 3 switched to hormones and other immunosuppressants, 2 experienced diarrhea, and 3 had poor treatment adherence. Ultimately, 51 subjects were included in the final data analysis cohort for biomarker discovery. Statistically, among these 51 subjects, 27 were in the treatment-responsive group and 24 were in the treatment-ineffective group. Their clinical information is shown in Table 1.

[0035] The dosage of hydroxychloroquine used in the above subjects was: for eGFR ≥ 60 mL / (min•1.73m 2 The dosage is 0.2g, twice daily; for eGFR 45~59mL / (min•1.73m), 2 The dosage is 0.1g, three times daily; for eGFR 30–44 mL / (min•1.73m), 2 The dosage is 0.1g, twice daily; if eGFR decreases by 25% or falls below 30 mL / (min•1.73m), 2 The dosage was reduced to 0.1g daily. Efficacy was assessed after 6 months of treatment, with participants divided into effective and ineffective groups. The effective group was defined as: 24-hour urinary protein quantification <500mg, or a ≥50% decrease in 24-hour urinary protein quantification from baseline after 6 months of treatment.

[0036] Table 1. Baseline clinical data of patients in the hydroxychloroquine treatment effective and ineffective groups. Urine samples were collected from the 51 subjects, including baseline urine before drug intervention and urine after 6 months of hydroxychloroquine treatment. All urine samples were morning midstream urine, with a sample volume of approximately 5 mL. The collected urine samples were centrifuged at 1000 rpm and 4°C for 5 minutes. After centrifugation, the samples were filtered through a 0.22 μm filter membrane, and the supernatant was aliquoted into centrifuge tubes, 2 mL per tube, and stored at -80°C for subsequent analysis.

[0037] 1.2 Sample processing and acquisition of LC-MS / MS spectral data Thaw the collected frozen urine samples at room temperature or 4°C. After thawing, take an appropriate amount of urine (e.g., 1 mL) into a centrifuge tube and centrifuge at 10,000 × g for 10 min to remove insoluble particles. Dilute the urine sample with ultrapure water or mobile phase (e.g., 0.1% formic acid aqueous solution) according to the concentration of the target analyte. Finally, filter the sample through a 0.22 μm microporous membrane and perform LC-MS / MS analysis using an ultra-high performance liquid chromatography system (Thermo vanquish UHPLC System, Thermo Fisher) and a triple quadrupole mass spectrometer (Thermo Altis TSQ Plus, Thermo Fisher) to obtain the corresponding LC-MS / MS spectral data.

[0038] LC-MS / MS conditions: Liquid chromatography conditions: C18 reversed-phase column (e.g., 2.1 × 50 mm, 1.8 µm), injection volume 5-10 μL, column temperature 40℃, run for 30 min; mobile phase A: 0.1% formic acid aqueous solution, mobile phase B: 0.1% formic acid acetonitrile solution, flow rate 0.3 mL / min, elution conditions shown in Table 2; Mass spectrometry conditions: ion source: electrospray ionization, alternating positive and negative ion modes to obtain more comprehensive metabolite information. Ion source temperature: 500℃, spray voltage: 5500 V (positive ion mode) or -4500 V (negative ion mode), curtain gas 30 psi, nebulizer gas 50 psi, auxiliary gas 50 psi.

[0039] Table 2 Chromatographic gradient conditions 1.3 Processing of LC-MS / MS data, screening of differentially expressed amino acids and identification of biomarkers Statistical methods were used to process and identify patterns in the LC-MS / MS data obtained above, and urinary metabolomics biomarkers in IgA nephropathy patients sensitive to HCQ treatment were screened out, specifically including: Data acquisition was performed using Xcalibur software (version 4.4.16.14, Thermo Fisher), and quantitative analysis was conducted using Skyline software. Data preprocessing and statistical analysis primarily employed R language, SPSS (version 26.0), and SIMCA (V16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden) software. Logarithmic transformation and centering (CTR) formatting were performed using SIMCA software, followed by principal component analysis. Principal component analysis (PCA) for unsupervised pattern recognition and orthogonal partial least squares discriminant analysis (OPLS-DA) for supervised pattern recognition were employed. To avoid overfitting and assess statistical significance, model quality was evaluated using parameters such as R²X, R²Y, and Q². R²X and R²Y values ​​closer to 1 indicate greater model stability, while Q² > 0.5 indicates high predictive accuracy. Based on the variable weights (VIPs) obtained from the OPLS-DA model, variables with VIP values ​​greater than 1 were selected as candidate biomarkers. This was done to validate the multidimensionality... To determine whether the candidate variables identified in the statistics showed statistically significant differences, a t-test was used in the experiment, where p < 0.05 was considered statistically significant. Combining heatmaps and candidate variable screening, the mass spectrometry information of the compounds represented by these variables was used to search, match, and infer potential biomarkers in databases such as HMDB, KEGG, and PubChem. All data were statistically analyzed using SPSS (version 26.0), and data from each group are expressed as (Mean ± SD). Two-sample t-tests or Mann-Whitney U tests were used to determine differences between groups.

[0040] 1.4 Using metabolomics-related databases and online software, construct metabolic pathways for biomarkers and perform predictive analysis. The selected urinary biomarker metabolomics profiles were input into the MetaboAnalyst 5.0 dialog box. The metabolite names were selected under the "Iuput" tab, and "Submit" was clicked. The species selected was *Homo sapiens* (human). A comprehensive analysis of the pathways containing differentially expressed metabolites was performed (including enrichment and topological analysis). Signal pathway analysis was conducted using the KEGG (Kyoto Encyclopedia of Genes and Genomes; http: / / www.kegg.jp / ) database, and the HMDB (The Human Metabolome Database; http: / / www.hmdb.ca / ) database was used to analyze metabolite molecular annotations, related enzymes or transport proteins, and their properties. The MetaboAnalyst 5.0 (Met PA) network software visualized the metabolite pathways, for example, using bubble diagrams. Furthermore, to facilitate a more diverse representation of changes in sample metabolic levels, the pathway enrichment bubble diagram could also be converted into a pathway enrichment tree diagram. Calculate the pathway impact and enrichment p-value (Fisher exact test) to screen pathways with impact > 0.1 and p < 0.05.

[0041] 1.5 Using ROC curves to assess the ability of biomarkers to predict the efficacy of hydroxychloroquine Differentially matched amino acids were selected as candidate biomarkers through metabolic pathway enrichment analysis. Due to the large variations in amino acid metabolite concentrations, each metabolite value was transformed before constructing a separate Logistic Regression model to assess the correlation between amino acids and the efficacy of hydroxychloroquine. The Hosmer-Lemeshow test and calibration plot were used to evaluate the goodness of fit of the models. ROC curves were plotted to evaluate the predictive performance of the univariate models. Simultaneously, combined predictive models were constructed by combining different amino acids, and the area under the ROC curve (AUC) was calculated to compare the sensitivity, specificity, and overall accuracy of the univariate and combined models. k-fold cross-validation was used to evaluate the stability and generalization ability of the models. Finally, based on statistical indicators and biological significance, the single metabolite or combination of metabolites with the best predictive effect of hydroxychloroquine were selected. The ROC curve is plotted using a series of different binary classification methods (score = cutoff value or decision threshold), with the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa. The AUC is between 1.0 and 0.5. When AUC > 0.5, the closer the AUC is to 1, the better the diagnostic effect. AUC between 0.5 and 0.7 indicates low accuracy, AUC between 0.7 and 0.9 indicates some accuracy, and AUC above 0.9 indicates high accuracy. AUC ≤ 0.5 indicates no diagnostic value.

[0042] 2. Results and Analysis like Figure 2 As shown, compared with the ineffective group, patients in the effective group showed a significant decrease in 24-hour urinary protein levels after 6 months of hydroxychloroquine treatment, while eGFR (estimated glomerular filtration rate) remained unchanged. These results indicate that throughout the treatment process, the effective group experienced a decrease in 24-hour urinary protein levels, and there were no significant changes in renal function between the two groups.

[0043] like Figure 3 As shown, urine samples from two groups of patients were analyzed, and ion chromatograms were extracted from standard solutions and urine samples from both groups (effective and ineffective groups). The standard solution used was Sigma-Aldrich amino acid standard (catalog number: A6407). A mixed standard solution was prepared to contain amino acids within the physiological concentration range (e.g., 0.1-100 μM), with concentration gradients of 0.1, 1, 5, 10, 50, and 100 μM (diluted with 0.1% formic acid aqueous solution). Simultaneously, the amino acid isotope internal standard L-leucine from MCE was selected. 13 C (Catalog No.: HY-N0486S1), with added ¹³C-labeled amino acids to a final concentration of 5 μM. From Figure 3As can be seen from the results: 1. The analytical method used in this invention produces symmetrical chromatographic peaks for all target compounds; 2. It achieves excellent chromatographic separation of each target compound; 3. There are no significant differences in retention time and peak shape between the target compounds in biological samples and standard solutions.

[0044] PCA analysis is an unsupervised multivariate statistical analysis method that can reflect metabolic differences and inter-group differences in samples from multiple dimensions, representing a basic, unfiltered state of the original data. This invention uses baseline urine samples from patients in the treatment-responsive and treatment-ineffective groups for PCA analysis. The results are shown below. Figure 4 PCA score chart (e.g.) Figure 4 As shown in Figure A, there were some differences in the metabolic profiles of the two groups of urine samples, but some samples showed overlapping clustering. Subsequently, supervised OPLS-DA analysis was used to better distinguish between the two groups of patients. The OPLS-DA model can exclude some variables irrelevant to grouping, more accurately screening out valuable differential variables, thus improving discriminative ability. By filtering out irrelevant orthogonal signals through OPLS-DA analysis and combining the weights (VIPs) of differential variables, the obtained differential metabolites are more reliable. Figure 4 As shown in Figure B, the OPLS-DA model in this invention makes the separation between the effective and ineffective treatment groups more obvious. The effective group's sample points are mainly distributed in the second and third quadrants and are relatively concentrated, exhibiting a certain degree of clustering, indicating that the differences in endogenous metabolites among patients within the group are small, and their metabolic state and trends are relatively stable. The ineffective group's sample points are mainly distributed in the first and fourth quadrants and are relatively dispersed, with no significant overlap or intersection with the effective group's sample points, indicating significant differences in endogenous metabolites in the urine of the two groups. Furthermore, the score plot shows obvious clustering separation (R2Y=0.664, Q2=0.584), both greater than 0.5, indicating that the model was successfully constructed and has good fit and predictive ability. In addition, the model permutation test results R2Y = (0.0, -0.06), Q2Y = (0.0, -1.08), indicate that the model does not exhibit overfitting. Figure 4 As shown in C.

[0045] Through the identification and analysis of the above models, significant metabolic differences in urinary endogenous metabolites were determined between the two groups of patients in this invention. In the OPLS-DA model, the contribution of evaluation variables is generally represented by the VIP value; the larger the VIP value, the greater its contribution. Since the average VIP value of the variables is close to 1, variables with VIP > 1 are often considered to be of significant importance to the model. Therefore, this invention uses variables with VIP values ​​greater than 1 as candidate variables for biomarkers, and finally screens out 28 variables with VIP values ​​greater than 1, as shown in Table 3. To visually demonstrate the distribution of these differentially expressed amino acids, volcano plots were drawn (…). Figure 5 A) and hierarchical clustering heatmap ( Figure 5 B).

[0046] Table 3. Differentially expressed amino acids in urine between the hydroxychloroquine treatment effective and ineffective groups. To explore the potential roles of the differentially expressed amino acids screened above in metabolism, this invention further utilized the KEGG database to perform pathway enrichment and metabolic pathway analysis on the candidate amino acids, and evaluated the overall abundance changes. The results are shown in […]. Figure 6 .like Figure 6 The results showed that differentially expressed metabolites were mainly enriched in the following pathways: D-amino acid metabolism (56.25%), protein digestion and absorption (45.45%), amino acid biosynthesis (31.25%), cysteine ​​and methionine metabolism (31.25%), and aminoacyl-tRNA biosynthesis (31.25%), as shown in 6A. Simultaneously, the differential abundance score (DAS) in each pathway was analyzed to assess the overall expression changes of differentially expressed metabolites within the same pathway. The results showed that in the IgA nephropathy ineffective group, the metabolism of amino acids such as cysteine, methionine, and D-amino acids in urine was significantly upregulated, as shown in 6B.

[0047] Based on matching information obtained from the KEGG database, metabolic pathways corresponding to Homo sapiens (Human) were further retrieved, identifying a total of 22 pathways related to amino acid metabolism, as shown in Table 4. The results of the metabolic pathway analysis are presented in a bubble diagram (…). Figure 7 A) and pathway enrichment rectangular tree diagram ( Figure 7B) The enrichment of each pathway was visualized to intuitively reflect its characteristics. Using the screening criteria of Impact > 0.1 and P < 0.05, the results showed that two of the 22 pathways were statistically significant: D-Arginine and D-ornithine metabolism, and Cysteine ​​and methionine metabolism. The differentially expressed amino acids involved in these two pathways were D-Ornithine, L-Arginine, L-Cysteine, L-Cystine, and D-Cysteine, respectively. These five amino acids were considered potential biomarkers for predicting the efficacy of HCQ treatment.

[0048] Table 4. Results of unique pathway analysis obtained through metabolic pathway analysis (MetPA). Note: Total: The total number of compounds in the pathway; Hits: The number of uploaded biomarker data that exactly match the metabolome library; P: The raw p-value obtained through pathway analysis; Impact: The pathway impact value obtained through topological analysis.

[0049] Example 2: Construction of the prediction model and further validation of biomarkers In this embodiment, the selected markers are modeled and further validated: 1. Construction and internal validation of the prediction model First, a single-quantity prediction model was constructed. Considering the significant variations in the values ​​of each amino acid, the values ​​of each biomarker selected in the above examples were transformed (each unit change in D-Ornithine and D-Cysteine ​​was multiplied by 100; each unit change in L-Arginine, L-Cysteine, and L-Cystine was multiplied by 10000). Then, a univariate logistic regression model was constructed. The results showed that all five amino acids were significant predictors of the efficacy of hydroxychloroquine (P < 0.05), and the Hosmer-Lemeshow test results showed no overfitting (P > 0.05), as shown in Table 5. The predictive ability of each univariate model was evaluated by plotting ROC curves, and the results are shown in Table 5. Figure 8 .like Figure 8 As shown, all five amino acids exhibited good predictive performance (AUC > 0.7), with L-cysteine ​​showing the highest AUC (0.924).

[0050] Table 5 Results of univariate logistic regression analysis Subsequently, three joint prediction models were constructed for the five selected biomarkers: Model I: Arginine and Ornithine Metabolism Model (Combination of D-Ornithine and L-Arginine); Model II: Cysteine ​​and methionine metabolism model (composed of L-cysteine, L-cysteine ​​and D-cysteine). Model III: A combined model of all five amino acids.

[0051] The predictive performance of each model was evaluated by plotting ROC curves; the results are shown below. Figure 9 .like Figure 9 The results showed that all three models had good predictive ability (AUC > 0.7), with Model III (combined all 5 amino acids) showing the best performance (AUC = 0.979). Figure 9 (As shown in A, C, and E).

[0052] 2. Sensitivity and specificity of external validation markers The dataset used for external validation consisted of patients with primary IgA nephropathy collected from Nanjing Gulou Hospital between June 1, 2024, and February 28, 2025. A total of 70 patients met the inclusion and exclusion criteria and were included in the study. The inclusion and exclusion criteria were the same as in Example 1. Subsequently, these 70 subjects were treated with hydroxychloroquine for at least 6 months according to the method described in Example 1. During the treatment follow-up, 2 patients decided to discontinue hydroxychloroquine, 3 switched to hormones and other immunosuppressants, 1 developed diarrhea, and 2 had poor treatment adherence, resulting in a final inclusion of 62 patients. These patients were then divided into an effective group (n=30) and an ineffective group (n=32) according to the same criteria as in Example 1, and urine samples were detected and analyzed using the same LC-MS / MS method as in Example 1.

[0053] The collected dataset was randomly divided into training and validation sets in a 7:3 ratio for cross-validation. The results showed that the prediction performance of each joint model remained stable on both the training and validation sets (AUC > 0.7). Figure 9 As shown in B, D, and F.

[0054] The above results suggest that the combination of five amino acids has high sensitivity and specificity and can be used as a biomarker to evaluate the efficacy of hydroxychloroquine in treating IgA nephropathy.

[0055] Example 3: Application of biomarkers in studies of hydroxychloroquine use for non-therapeutic purposes This example studies the activity of hydroxychloroquine: specific urine samples were selected, and different batches of hydroxychloroquine were reacted with them. The quantitative expression of one or more of the five markers (D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine) screened in Example 1 was measured, and the expression levels were compared to determine the biological activity of each batch of hydroxychloroquine.

[0056] Example 4: Kit for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy This embodiment provides a kit for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy, comprising the detection of any one of the five biomarkers screened in Example 1: D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine. This embodiment selects quantitative reagents for detecting the expression levels of D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine. Specific detection of the expression levels of D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine ​​in samples helps to determine the sensitivity of IgA nephropathy patients to hydroxychloroquine in advance. The kit can be tested immediately after obtaining urine samples from IgA nephropathy patients to assess the efficacy of hydroxychloroquine and provide patients with personalized and precise treatment plans.

[0057] Example 5: Hydroxychloroquine Efficacy Analysis System for IgA Nephropathy This embodiment provides a system for analyzing the efficacy of hydroxychloroquine in IgA nephropathy, including: Target expression level detection device: used to detect the expression level of markers in a sample; the sample is patient urine; Drug efficacy analysis device: Determine the efficacy of medication for IgA nephropathy based on the expression level of biomarkers; Result output device: Used to output the results obtained from the pharmacological analysis device.

[0058] The efficacy of hydroxychloroquine can be assessed before patients use it, allowing for personalized and precise treatment plans.

[0059] Example 6: Pharmaceutical composition for treating IgA nephropathy A pharmaceutical composition for treating IgA nephropathy, comprising hydroxychloroquine and drugs that inhibit five biomarkers.

[0060] For patients with IgA nephropathy who were found to be unresponsive to hydroxychloroquine treatment in Example 5, treatment with the drug composition described in this example can improve efficacy.

[0061] In summary, this invention utilizes LC-MS / MS metabolomics technology to screen a group of highly sensitive biomarkers from baseline urine of IgAN patients, including D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine. These metabolites can effectively predict the efficacy of HCQ in IgAN patients. Further metabolic pathway enrichment analysis revealed that patients with poor HCQ response exhibited disturbances in arginine and ornithine metabolism, as well as cysteine ​​and methionine metabolism. Finally, based on these five differentially expressed amino acids, a joint predictive model was constructed. This model demonstrated high predictive performance and can provide clinicians with an early, non-invasive screening method for identifying IgAN patients who may be unresponsive to HCQ treatment.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the methods and techniques disclosed above without departing from the scope of the present invention to create equivalent embodiments. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A biomarker for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy, characterized in that, The biomarkers are a combination of five biomarkers: D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine.

2. The marker according to claim 1, characterized in that, The biomarker was derived from the subject's urine.

3. The use of a reagent for detecting the expression level of the biomarker as described in claim 1 in the preparation of a reagent for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy or a drug sensitivity test reagent.

4. The application according to claim 3, characterized in that, The reagents or drug sensitivity testing reagents for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy are reagents for evaluating the efficacy or testing the drug sensitivity of hydroxychloroquine during the treatment of IgA nephropathy. The sample used for efficacy evaluation or drug sensitivity testing is the subject's urine; The reagent determines the level of the marker in a sample by one or more of the following methods: chromatography, mass spectrometry, electrophoresis, immunoaffinity, immunohybridization, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, and turbidimetry.

5. A kit for evaluating the efficacy of hydroxychloroquine in treating IgA nephropathy, characterized in that, Includes reagents for detecting the expression levels of biomarkers used to assess the efficacy of hydroxychloroquine in treating IgA nephropathy as described in claim 1.

6. The reagent kit according to claim 5, characterized in that, The detection involves the quantitative measurement of marker levels in the subject's urine; The reagent determines the level of the marker in a sample by one or more of the following methods: chromatography, mass spectrometry, electrophoresis, immunoaffinity, immunohybridization, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, and turbidimetry.

7. A method for screening compounds capable of preventing or treating IgA nephropathy, characterized in that, This includes using five biomarkers from D-ornithine, L-arginine, L-cysteine, L-cysteine, and D-cysteine ​​as biomarkers to evaluate the efficacy of compounds.

8. A system for analyzing the efficacy of hydroxychloroquine in IgA nephropathy, characterized in that, include: Target expression level detection device: used to detect the expression level of the biomarker described in claim 1 in a sample; The sample was urine from the subject. Drug efficacy analysis device: Determine the efficacy of medication for IgA nephropathy based on the expression level of biomarkers; Result output device: Used to output the results obtained from the pharmacological analysis device; The target expression level detection device uses reagents to determine the level of the biomarker in a sample by one or more of the following methods: chromatography, mass spectrometry, electrophoresis, immunoaffinity, immunohybridization, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, and turbidimetry.

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