Urine metabolism marker combination for distinguishing healthy kidney cancer from early kidney cancer and application of urine metabolism marker combination

Abstract

The invention provides a metabolic marker composition for distinguishing healthy kidney cancer from early kidney cancer. According to an analysis method based on urine metabonomics, a diagnosis model capable of accurately distinguishing healthy people from early-stage kidney cancer patients is constructed by detecting a specific metabolic marker combination and combining a machine learning algorithm. The model can noninvasively discriminate the early-stage kidney cancer in a high-sensitivity and high-specificity manner, and breaks through the limitation of an existing clinical detection method. The method disclosed by the invention has the advantages of noninvasive property, simple and convenient sampling and the like, is suitable for large-scale population screening, is particularly beneficial to popularization of early-stage kidney cancer risk screening in remote areas deficient in medical resources, and provides support for early-stage noninvasive diagnosis and effective prevention and control of kidney cancer.

Description

Technical Field

[0001] This invention relates to the field of metabolite analysis and application, specifically to a combination of urinary metabolic markers for distinguishing between healthy individuals and early-stage renal cell carcinoma and their applications. Background Technology

[0002] Kidney cancer is one of the most common malignant tumors of the urinary system, and its incidence is increasing globally. Due to the deep location of the kidneys and the often subtle nature of early symptoms, most patients are diagnosed at an advanced stage when the tumor has already progressed locally or metastasized to distant sites. Treatment for advanced kidney cancer is limited, the prognosis is poor, and the five-year survival rate is significantly reduced, posing a serious threat to patients' lives and health. Therefore, early detection and accurate diagnosis of kidney cancer are crucial for improving patient outcomes.

[0003] Currently, clinical diagnosis of renal cell carcinoma mainly relies on imaging examinations (CT / MRI) and biopsy. However, these methods have significant limitations: imaging examinations are the primary means of detecting renal space-occupying lesions, but their differential diagnostic ability is limited, making it difficult to reliably distinguish between benign tumors (such as renal cysts) and early-stage malignant renal cell carcinoma, especially for small renal masses smaller than 2 cm, where determining their nature is particularly challenging. Furthermore, CT scans involve radiation exposure and are relatively expensive, making them unsuitable for routine screening in large populations. Histopathological biopsy is the gold standard for diagnosis, but it is an invasive procedure with potential risks such as bleeding, hematuria, and tumor implantation via the needle tract, resulting in low patient acceptance and making it less than ideal for early screening. Therefore, developing non-invasive detection technologies that can accurately identify early-stage renal cell carcinoma is of urgent significance in breaking the current clinical deadlock of "detection at an advanced stage."

[0004] Metabolomics, as a cutting-edge technology in systems biology, focuses on the systematic qualitative and quantitative analysis of endogenous small-molecule metabolites in organisms. Urine, as the body fluid directly in contact with the kidneys, allows changes in its metabolite profile to reflect the malignant transformation process of the renal parenchyma in real time, providing a unique biological window for establishing novel early diagnostic systems. Furthermore, urine offers advantages such as being non-invasive, continuously collectable, and having direct contact with kidney tissue, protecting metabolomics signals from systemic metabolic noise interference. Therefore, this invention, based on metabolomics data from urine samples, constructs a diagnostic model capable of distinguishing between healthy controls and early-stage renal cancer. This model can non-invasively, with high sensitivity and high specificity, identify early-stage renal cancer, overcoming the limitations of existing clinical detection methods and providing effective assistance for early screening and personalized treatment of renal cancer. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the defects and deficiencies of the prior art and provide an analysis method based on urine metabolomics. By detecting specific combinations of metabolic markers, a diagnostic model that can accurately distinguish between healthy people and patients with early-stage renal cell carcinoma can be constructed, providing support for the early non-invasive diagnosis and effective prevention and control of renal cell carcinoma.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This invention discloses a metabolic biomarker composition for distinguishing between healthy individuals and early-stage renal cancer, the composition comprising: O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0007] Preferably, the composition comprises: O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0008] Preferably, the composition comprises: arginine succinic acid, cystathionine, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0009] Preferably, the composition comprises: arginine succinic acid, cystathionine, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0010] Preferably, the composition comprises: arginine succinic acid, cystathionine, hydantoin-5-propionic acid, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0011] Preferably, the composition comprises: arginine succinic acid, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0012] Preferably, the composition comprises: arginine succinic acid, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-alanine, L-prolyl-L-valine, L-valine-glycine, high carnosine, asparagine, and aspartic acid.

[0013] Preferably, the composition comprises the following metabolic markers: O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0014] Preferably, the composition comprises the following metabolic markers: O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0015] Preferably, the composition comprises the following metabolic markers: arginine succinate, cystathionine, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0016] Preferably, the composition comprises the following metabolic markers: argininosuccinic acid, cystathionine, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0017] Preferably, the composition comprises the following metabolic markers: arginine succinate, cystathionine, hydantoin-5-propionic acid, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0018] Preferably, the composition comprises the following metabolic markers: arginine succinate, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0019] Preferably, the composition comprises the following metabolic markers: arginine succinate, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-alanine, L-prolyl-L-valine, L-valine-glycine, high carnosine, asparagine, and aspartic acid.

[0020] This invention discloses a combination of metabolic biomarkers for distinguishing between healthy individuals and early-stage renal cancer, the combination comprising: O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0021] Preferably, the combination comprises: O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0022] Preferably, the combination comprises: arginine succinic acid, cystathionine, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0023] Preferably, the combination comprises: arginine succinic acid, cystathionine, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0024] Preferably, the combination comprises: arginine succinic acid, cystathionine, hydantoin-5-propionic acid, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0025] Preferably, the combination comprises: arginine succinic acid, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0026] Preferably, the combination comprises: arginine succinic acid, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-alanine, L-prolyl-L-valine, L-valine-glycine, high carnosine, asparagine, and aspartic acid.

[0027] Preferably, the combination consists of the following metabolic markers: O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0028] Preferably, the combination consists of the following metabolic markers: O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0029] Preferably, the combination consists of the following metabolic markers: arginine succinate, cystathionine, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0030] Preferably, the combination consists of the following metabolic markers: argininosuccinic acid, cystathionine, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0031] Preferably, the combination consists of the following metabolic markers: arginine succinate, cystathionine, hydantoin-5-propionic acid, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0032] Preferably, the combination consists of the following metabolic markers: arginine succinate, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine.

[0033] Preferably, the combination comprises the following metabolic markers: arginine succinate, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-alanine, L-prolyl-L-valine, L-valine-glycine, high carnosine, asparagine, and aspartic acid.

[0034] This invention discloses the use of the described composition in the preparation of reagents and / or kits for differentiating healthy individuals from early-stage renal cell carcinoma.

[0035] This invention discloses the use of the described combination in the preparation of reagents and / or kits for differentiating healthy individuals from early-stage renal cell carcinoma.

[0036] Preferably, the samples used in the differentiation process are selected from urine, body fluids, or tissue fluids.

[0037] This invention discloses a kit for distinguishing between healthy individuals and early-stage renal cell carcinoma, the kit comprising the aforementioned metabolic biomarker composition.

[0038] Preferably, the kit also includes quality control products and standards.

[0039] Compared with existing technologies, this invention constructs a diagnostic model to distinguish between healthy individuals and patients with early-stage renal cell carcinoma based on metabolomics data from urine samples and combined with machine learning algorithms. This method has advantages such as being non-invasive and simple to sample, making it suitable for large-scale population screening, and particularly beneficial for promoting early-stage renal cell carcinoma risk screening in remote areas with limited medical resources. Attached Figure Description

[0040] Figure 1 In the modeling group, multivariate ROC curve analysis was performed on 18 key biomarkers that distinguish between HC and eRCC.

[0041] Figure 2 Multivariate ROC curve analysis of 18 key biomarkers distinguishing HC vs eRCC in the validation group. Detailed Implementation

[0042] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0043] Example 1: Subject Information and Sample Grouping 1. Subject Information 1) Inclusion criteria: Participants must meet all of the following inclusion criteria to be eligible to participate in this study: (1) Males or females aged ≥18 years; (2) Read and fully understand the information, sign the informed consent form, and be able to provide a urine sample for metabolomics testing; (3) Patients diagnosed with primary renal cell carcinoma by biopsy / postoperative pathology or by comprehensive clinical assessment by clinicians, and included in the early renal cell carcinoma group based on pathological staging information, are classified as patients with stage I and stage II renal cell carcinoma.

[0044] 2) Exclusion criteria: Subjects who meet any of the following exclusion criteria are ineligible to participate in this study: (1) During pregnancy or lactation; (2) Emergency room visit or resuscitation required; (3) History of blood transfusion within 7 days prior to sampling; (4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants; (5) History of malignant tumor within 5 years or any anti-tumor treatment before sampling; (6) Simultaneous co-occurrence of multiple primary malignant tumors.

[0045] 3) Subject information This study collected urine samples from 251 participants across two medical centers, including 116 healthy controls (HC) and 135 early renal cell carcinoma (eRCC) samples (94 stage I and 41 stage II). Specifically, the urine samples used for the modeling group consisted of 87 healthy controls (HC) and 101 early renal cell carcinoma (eRCC) participants (70 stage I and 31 stage II). The urine samples used for the validation group consisted of 29 healthy controls (HC) and 34 early renal cell carcinoma (eRCC) participants (24 stage I and 10 stage II) (Table 1).

[0046] Table 1. Subject Information Healthy control group (HC) Early-stage renal cell carcinoma group (eRCC) Number of people in the modeling team 87 101 Number of people in the verification group 29 34 total 116 135 Example 2: Detection of urinary metabolites 1) Test reagents: Methanol, acetonitrile, water, acetic acid, methyl tert-butyl ether of mass spectrometry grade, and formic acid of chromatographic (HPLC) grade were all purchased from Sigma-Aldrich, USA.

[0047] 2) Sample preparation: After thawing the urine sample from the -80℃ freezer, take 40 μL of the urine sample into an extraction tube, add 400 μL of a pre-chilled mixture of methyl tert-butyl ether and methanol (methyl tert-butyl ether: methanol, volume ratio 3:1), vortex and sonicate to mix, then add 360 μL of a methanol-water mixture, vortex and centrifuge to separate the layers; take 300 μL of the lower aqueous phase solution from the extraction tube, add 900 μL of pre-chilled methanol solution to it, after protein precipitation, take 1000 μL of the supernatant and evaporate to dryness, add 200 μL of water to reconstitute, and use the reconstituted solution for LC-MS detection of polar substances in aqueous phase.

[0048] 3) Detection of water-soluble small molecule metabolites: Small molecule separation was performed using a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column; the liquid chromatography and mass spectrometry systems used were the ACQUITYUPLC I-Class liquid chromatography system (Waters) and the Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0049] The mobile phase parameters are as follows: Mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid. The separation elution gradient is as follows: 0-13 minutes 1%-70% mobile phase B, 13-18 minutes 99% mobile phase B; The mass spectrometry parameters are as follows: Mass spectrometry data were acquired using Full MS and Full MS / dd-MS2 (each with both positive and negative modes). The parameters used by QExactive were as follows: Full MS mode had a resolution of 70,000, a scan range of 100-1500 m / z, an Automatic Gain Control (AGC) of 3E+6, and a Maximum IT of 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer was 17,500, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the Higher Energy Collision Dissociation (HCD) was 30 eV.

[0050] Example 3: Metabolomics Data Processing 1. Metabolomics data preprocessing and metabolite identification 1) Metabolomics data processing: (1) Extract peaks from the RAW format file of the mass spectrometer and convert it into a FeatureXML format file to reduce the dimensionality of the original mass spectrometry data and improve the signal-to-noise ratio; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention time of the extracted peak format data between samples, thereby converting the mass spectrometry data into a data matrix; (3) Match and filter the isotope peaks in the data matrix obtained in step 2, and replace abnormal data (0, negative values, background noise, etc.) with missing values; (4) Remove the characteristic peaks with a detection rate of <80% from all the characteristic peaks obtained in step 3, fill the median value of the characteristic peak with the characteristic peaks with a detection rate of >80%, and add 5% random noise (following a standard normal distribution); (5) In order to reduce the difference in metabolite concentration between samples and make the data distribution more symmetrical, use Normalization Autoencoder (NormAE) to perform normalization processing to remove systematic errors such as batch effects.

[0051] 2) Identification of metabolites: After analyzing the raw data using software, the spectral information of the primary precursor ion (MS1) and secondary fragment ion (MS2) of the compound is obtained. This includes matching the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment ion information with the spectral information of primary and secondary metabolites in public databases to qualitatively identify the metabolites. Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), and the Mass Spectrometry Database (www.massbank.jp). Metabolites identified based on these databases are then finally validated using retention times, MS1, and MS2 mass spectrometry information obtained from separation of standards under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are a retention time difference within 0.1 min and a theoretical and measured molecular weight difference of less than 10 ppm.

[0052] 2. Data Analysis 1) Biomarker screening Metabolite detection was performed on the above samples, and a total of 497 metabolites were obtained after annotation. LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis was performed on the data of the modeling group. The average error corresponding to each regularization parameter alpha was calculated using 5-fold cross-validation. The optimal alpha with the smallest error was found to be 0.0286. Metabolites with non-zero regression coefficients in their corresponding models were retained. Finally, 18 differential metabolites were selected (Table 2) as important metabolic markers to distinguish between the HC group and the eRCC group.

[0053] Table 2. 18 Important Metabolic Markers for Differentiating HC from eRCC Logo - English Logo - Chinese HMDB ID 1 Argininosuccinate Arginine succinic acid HMDB0000052 2 Cystathionine Cystine HMDB0000099 3 2-Methylcitric acid 2-Methylcitric acid HMDB0000379 4 Hydantoin-5-propionate Hydantoin-5-propionic acid HMDB0001212 5 Hypoxanthine hypoxanthine HMDB0000157 6 L-Methionine sulfoxide L-methionine sulfoxide HMDB0002005 7 Dimethylglycine dimethylglycine HMDB0000092 8 Ureidosuccinic acid Ureosuccinic acid HMDB0000828 9 O-Hydroxyhippuric acid O-hydroxyhippuric acid HMDB0000840 10 7-Methyluric acid 7-Methyluric acid HMDB0011107 11 L-Phenylalanyl-L-threonine L-phenylalanyl-L-threonine HMDB0029005 12 L-Methionyl-L-aspartic acid L-methionyl-L-aspartic acid HMDB0028969 13 L-Prolyl-L-alanine L-prolyl-L-alanine HMDB0029010 14 L-Prolyl-L-valine L-prolyl-L-valine HMDB0029030 15 L-Valylglycine L-valine glycine HMDB0029127 16 Homocarnosine High carnosine HMDB0000745 17 Asparagine Asparagine HMDB0251512 18 Aspartic acid Aspartic acid HMDB0000191 2) Construction of a diagnostic model to distinguish between healthy controls and early-stage renal cell carcinoma To verify the discriminative effect of the 18 selected biomarkers in distinguishing between HC and eRCC, multivariate ROC curve analysis was performed on these 18 biomarkers in the modeling group. Three-quarters of the sample data from the HC and eRCC groups in the modeling group were randomly used as the training set and one-quarter as the test set for training. The model was then iterated 1000 times using a support vector machine (SVM) machine learning method. By statistically analyzing the average accuracy of the final model, a diagnostic model that distinguishes between healthy controls and early-stage renal cell carcinoma was constructed.

[0054] ROC curves are a method for studying the relationship between model sensitivity and specificity. Sensitivity is plotted on the ordinate, and 1-specificity on the x-axis. The evaluation criterion is the area under the curve (AUC). An AUC greater than 0.5, and closer to 1, indicates better model performance and better discrimination. An AUC less than 0.5 indicates poor model accuracy. ROC classification prediction models, in addition to common parameters such as the receiver operating characteristic (ROC) curve and AUC, also include sensitivity and specificity.

[0055] Sensitivity calculation is shown in Formula I: Formula I.

[0056] Specificity is calculated using Formula II: Formula II.

[0057] Among them, TP (True Positive): True positive, the number of samples that are actually positive but were correctly predicted as positive; TN (True Negative): The number of samples that are actually negative but were correctly predicted as negative. FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive. FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative. The results are as follows Figure 1As shown, AUC=0.918 (sensitivity=0.720, specificity=0.955), indicating that the constructed diagnostic model has high discriminative power.

[0058] In addition, ROC curve analysis was performed on diagnostic models with different combinations of metabolic markers in the modeling group. Fifteen metabolic markers were used: arginine succinate, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine. The study used 13 metabolic markers: argininosuccinic acid, cystathionine, hydantoin-5-propionic acid, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and a combination of high carnosine. The study used 11 metabolic markers: argininosuccinic acid, cystathionine, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and a combination of high carnosine. The study used eight metabolic markers: arginine succinate, cystathionine, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and a combination of high carnosine. Using a combination of six metabolic markers: O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine; It also uses a combination of four metabolic markers: O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine.

[0059] The results showed that the AUC was 0.890 (sensitivity = 0.808, specificity = 0.857) when using 15 metabolic biomarkers; 0.893 (sensitivity = 0.880, specificity = 0.727) when using 13 metabolic biomarkers; 0.891 (sensitivity = 0.880, specificity = 0.727) when using 11 metabolic biomarkers; 0.925 (sensitivity = 0.846, specificity = 0.857) when using 8 metabolic biomarkers; 0.890 (sensitivity = 0.808, specificity = 0.762) when using 6 metabolic biomarkers; and 0.951 (sensitivity = 0.880, specificity = 0.864) when using 4 metabolic biomarkers, all demonstrating stable discrimination ability.

[0060] 3) Validation of the diagnostic model used to distinguish between healthy controls and early-stage renal cell carcinoma group To further validate the diagnostic model for distinguishing between healthy controls and early-stage renal cell carcinoma (eRCC) based on the modeling group data, validation group data was used to validate the model. Multivariate ROC curve analysis was performed to evaluate the model's independent validation performance on unknown datasets outside the modeling group dataset. After the validation group samples were fed into the model constructed by the modeling group, the probability value was output for each sample based on the detection data of 18 important metabolic biomarkers distinguishing HC and eRCC. Using the probability value of each sample as the discrimination threshold, a confusion matrix (including true positive, true negative, false positive, and false negative) was obtained. Sensitivity and specificity could be calculated using formulas. A point could be marked on the ROC analysis graph with sensitivity on the ordinate and 1-specificity on the abscissa. Similarly, when the probability value of each sample was used as the discrimination threshold, multiple different points were obtained in the ROC analysis graph. Connecting these points would produce an ROC curve. Figure 2 Among them, the point with the best sensitivity and specificity is selected, and the discrimination threshold at this point is 0.5765.

[0061] As shown in Table 3, the confusion matrix results indicate that in the prediction model constructed based on the 18 metabolic biomarkers, a discrimination threshold of 0.5765 was used. When the output result was ≥ the threshold, the patient was identified as having early-stage renal cell carcinoma; when the output result was < the threshold, the patient was identified as a healthy control. Among the 34 patients with early-stage renal cell carcinoma, 28 were correctly identified, and 6 were incorrectly identified as healthy controls. Among the 29 healthy controls, 22 were correctly identified, and 7 were incorrectly identified as having early-stage renal cell carcinoma. The ROC analysis results of the diagnostic model in the validation group are as follows: Figure 2As shown, sensitivity and specificity were calculated based on the confusion matrix results, with an AUC of 0.882 (sensitivity = 0.824, specificity = 0.759). These results indicate that the established diagnostic model for distinguishing between healthy controls and early-stage renal cell carcinoma also demonstrated good discriminative performance in the validation group.

[0062] Table 3. Confusion matrix for distinguishing between healthy controls and early renal cell carcinoma diagnostic models Types of diseases Early-stage renal cell carcinoma Health comparison Early-stage renal cell carcinoma, N=34 28 (TP) 6 (FN) Healthy control group, N=29 7 (FP) 22 (TN) In addition, diagnostic models based on different combinations of metabolic biomarkers were validated in the validation group. Multivariate ROC curve analysis showed that the combination of the above 15 metabolic biomarkers—argininosuccinic acid, cystathionine, 2-methylcitric acid, hydantoin-5-propionic acid, hypoxanthine, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionine-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine—achieved an AUC of 0.871 (sensitivity = 0.794, specificity = 0.793) in the validation group. The combination of 13 metabolic biomarkers—argininosuccinic acid, cystathionine, hydantoin-5-propionic acid, L-methionine sulfoxide, dimethylglycine, ureosuccinic acid, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine ... L-methionine sulfoxide, L-hydroxyhippuric acid, L-methionine sulfoxide, The combination of threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine showed an AUC of 0.881 (sensitivity = 0.794, specificity = 0.828) in the validation group; 11 metabolic markers, including argininosuccinate, cystathionine, dimethylglycine, ureosuccinate, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl- The combination of L-aspartic acid, L-prolyl-L-valine, L-valine-glycine, and high carnosine showed an AUC of 0.911 (sensitivity = 0.920, specificity = 0.773) in the validation group; 8 metabolic markers, including argininosuccinate, cystathionine, O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine, were also analyzed. In the validation group, the AUC was 0.885 (sensitivity = 0.765, specificity = 0.793); the combination of six metabolic markers—O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine—achieved an AUC of 0.891 (sensitivity = 0.794, specificity = 0.862) in the validation group; and the combination of four metabolic markers—O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine, and high carnosine—achieved an AUC of 0.851 (sensitivity = 0.824, specificity = 0.793) in the validation group. These results indicate that the established diagnostic model also demonstrated good discriminative efficacy in the validation group.

[0063] The present invention has been illustrated through the above embodiments, but the present invention is not limited to the above process steps, that is, it does not mean that the present invention must rely on the above process steps to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials used in the present invention, additions of auxiliary components, and selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A metabolic marker composition for differentiating between healthy and early stage kidney cancer, characterized in that, The composition comprises O-hydroxyhippuric acid, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

2. The composition of claim 1, wherein, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

3. The composition of claim 2, wherein, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

4. The composition of claim 3, wherein, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

5. The composition of claim 4, wherein, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

6. The composition of claim 5, wherein, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine. The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

7. The composition of claim 6, wherein, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine. The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine. The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

9. Use according to claim 8, characterized in that, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

10. A kit for differentiating between healthy and early stage kidney cancer, characterized in that, The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

8. Use of a composition according to any one of claims 1 to 7 for the manufacture of a reagent and / or a kit for differentiating between healthy and early stage kidney cancer. The sample employed in the differentiation process is selected from urine, body fluid or tissue fluid. The kit comprises a composition of metabolic markers according to any one of claims 1 to 7. The composition comprises O-hydroxyhippuric acid, 7-methyluric acid, L-phenylalanyl-L-threonine, L-methionyl-L-aspartic acid, L-prolyl-L-valine and homocarnosine.

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