Urine metabolism marker combination for distinguishing kidney benign disease and kidney cancer and application of urine metabolism marker combination

Abstract

The invention provides a urine metabolism marker combination for distinguishing kidney benign diseases and kidney cancer and application of the urine metabolism marker combination, and belongs to the technical field of biomarkers. The urine metabolism marker combination for distinguishing the kidney benign disease from the kidney cancer comprises at least two of the following components: (S)-2-piperidine-6-carboxylic acid, 2 '-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-L-proline, glycyl-gamma-aminobutyric acid, L-cysteine sulfinic acid and N, N-dimethyl formamide. The amino acid sequence is shown in the specification, and the amino acid sequence is shown in the specification. The amino acid sequence is shown in the specification, namely, N, N-dimethyl glycine, N-acetyl-L-glutamic acid, L-methionine-L-aspartic acid, 2-oxoglutaric acid and glycine chenodeoxycholic acid. Model construction and verification analysis show that the AUC values of diagnosis models constructed by combining different quantities of metabolite markers are all greater than 0.8, the sensitivity is greater than 0.7, and the specificity is greater than 0.7. Therefore, based on the urine metabolism marker combination screened by the invention, the kidney benign disease and the kidney cancer can be efficiently and accurately distinguished.

Description

Technical Field

[0001] This application belongs to the field of biomarker technology, specifically relating to a combination of urinary metabolic markers for distinguishing between benign kidney diseases and kidney cancer, and their application. Background Technology

[0002] Kidney diseases are broadly classified into benign and malignant categories. Benign kidney diseases include renal cysts, renal hamartomas, and chronic nephritis. Malignant kidney diseases include renal cell carcinoma (RCC). Common types of RCC include clear cell carcinoma (accounting for 70%–80%), papillary renal cell carcinoma, and chromophobe carcinoma. Early-stage RCC often presents with no obvious symptoms. As the tumor progresses, symptoms may appear such as hematuria, lower back pain or a palpable mass, and systemic symptoms like weight loss, fever, and anemia. This often leads to many patients being diagnosed at an advanced stage, resulting in a poor prognosis. Therefore, early screening and accurate differentiation between benign kidney diseases and RCC are crucial for improving precision medicine and preventing both overtreatment and undertreatment, which can delay diagnosis and increase the psychological burden on patients.

[0003] The application of metabolomics technology in the clinical diagnosis of renal cell carcinoma has become a research hotspot in recent years. Metabolomics reveals abnormalities in disease-related metabolic pathways by analyzing changes in small molecule metabolites (such as amino acids, lipids, and carbohydrates) in the body. However, there are currently no reported metabolic biomarkers for differentiating between benign kidney diseases and renal cell carcinoma. Summary of the Invention

[0004] This invention provides a combination of urinary metabolic markers for differentiating between benign kidney diseases and kidney cancer. It features high sensitivity and specificity, enabling accurate screening for kidney cancer and greatly improving the efficiency of kidney cancer diagnosis.

[0005] This invention provides a combination of urinary metabolic markers for differentiating between benign kidney diseases and renal cancer, comprising at least two of the following metabolites: (S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid.

[0006] Preferably, the combination comprises at least one group of three metabolites: a first combination formed by 2'-deoxyguanosine, L-cysteine ​​sulfinic acid and glycine chenodeoxycholic acid; a second combination formed by (S)-2-piperidinone-6-carboxylic acid, 6-aminohexanoic acid and N,N-dimethylglycine; a third combination formed by 3-hydroxybutyric acid, glycyl-L-proline and N-acetyl-L-glutamic acid; a fourth combination formed by 6-aminohexanoic acid, glycyl-L-proline and L-methionyl-L-aspartic acid; and a fifth combination formed by L-methionyl-L-aspartic acid, 2-oxoglutarate and glycine chenodeoxycholic acid.

[0007] Preferably, the combination comprises at least one group of five metabolites: a sixth combination consisting of 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, L-cysteine ​​sulfinic acid, and glycine chenodeoxycholic acid; a seventh combination consisting of (S)-2-piperidinone-6-carboxylic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, N,N-dimethylglycine, and N-acetyl-L-glutamic acid; an eighth combination consisting of N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid; and a ninth combination consisting of L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, and 2-oxoglutarate.

[0008] Preferably, the combination comprises at least one group of seven metabolites: a tenth combination consisting of 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N-acetyl-L-glutamic acid, and glycine-chenodeoxycholic acid; an eleventh combination consisting of (S)-2-piperidinone-6-carboxylic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, N,N-dimethylglycine, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine-chenodeoxycholic acid; and a twelfth combination consisting of (S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, N-acetyl-L-glutamic acid, and L-methionyl-L-aspartic acid.

[0009] Preferably, it includes a thirteenth combination formed by 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, and glycine chenodeoxycholic acid, or a fourteenth combination formed by (S)-2-piperidinone-6-carboxylic acid, glycyl-L-proline, L-methionyl-L-aspartic acid, 6-aminohexanoic acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid.

[0010] Preferably, the fifteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid, or the sixteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-L-proline, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid, is also included.

[0011] This invention provides the application of the combination of urinary metabolic markers in constructing a diagnostic model that distinguishes between benign kidney diseases and kidney cancer.

[0012] Preferably, the diagnostic model is constructed using machine learning support vector machines.

[0013] This invention provides the application of the detection reagent of the combination of urine metabolic markers in the preparation of a diagnostic kit to distinguish between benign kidney diseases and kidney cancer.

[0014] Preferably, the detection reagents include formic acid, acetonitrile, methyl tert-butyl ether, and methanol.

[0015] This invention provides a combination of urinary metabolic biomarkers for differentiating between benign renal diseases and renal cell carcinoma, comprising at least two of the following metabolites: (S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, L-cysteine, N,N-dimethylglycine, N-acetyl-L-glutamate, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycinechenodeoxycholic acid. Based on urinary metabolomics data from patients with benign renal diseases and renal cell carcinoma, this invention used the Least Absolute Contraction and Selection (LASSO) regression analysis to screen for 12 differentially expressed metabolites. Model construction and validation analyses showed that the diagnostic models constructed from different combinations of metabolite biomarkers all had AUC values ​​greater than 0.8, sensitivity greater than 0.7, and specificity greater than 0.7. It is evident that the combination of urinary metabolic markers screened based on this invention can efficiently and accurately distinguish between benign kidney diseases and kidney cancer. Furthermore, a diagnostic model with high sensitivity and specificity for distinguishing between benign kidney diseases and kidney cancer is constructed based on the combination of urinary metabolic markers. This method is convenient, economical, easy to obtain samples, non-invasive, and has higher subject compliance, making it more suitable for large-scale population screening and long-term follow-up in areas with limited medical resources. Attached Figure Description

[0016] Figure 1 The results of multivariate ROC curve analysis for 12 important markers that distinguish BRD and RCC in the modeling group; Figure 2 To validate the results of multivariate ROC curve analysis of 12 key markers that distinguish BRD and RCC in the group. Detailed Implementation

[0017] This invention provides a combination of urinary metabolic markers for differentiating between benign kidney diseases and renal cancer, comprising at least two of the following metabolites: (S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid.

[0018] In this invention, the urine metabolic marker combination preferably includes at least one combination of three metabolites: a first combination formed by 2'-deoxyguanosine, L-cysteine ​​sulfinic acid and glycine chenodeoxycholic acid; a second combination formed by (S)-2-piperidinone-6-carboxylic acid, 6-aminohexanoic acid and N,N-dimethylglycine; a third combination formed by 3-hydroxybutyric acid, glycyl-L-proline and N-acetyl-L-glutamic acid; a fourth combination formed by 6-aminohexanoic acid, glycyl-L-proline and L-methionyl-L-aspartic acid; and a fifth combination formed by L-methionyl-L-aspartic acid, 2-oxoglutarate and glycine chenodeoxycholic acid.

[0019] In this embodiment of the invention, a diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the first combination as a representative. ROC curves were plotted, and the AUC value was used to evaluate the model's diagnostic efficacy. The modeling group had an AUC of 0.87 (sensitivity = 0.784, specificity = 0.727), indicating that the constructed diagnostic model has high diagnostic efficacy. The constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also showed good diagnostic efficacy in the validation group, with an AUC of 0.867 (sensitivity = 0.75, specificity = 0.813).

[0020] In this invention, the urine metabolic marker combination preferably includes at least one combination of five metabolites: a sixth combination formed by 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, L-cysteine ​​sulfinic acid, and glycine chenodeoxycholic acid; a seventh combination formed by (S)-2-piperidinone-6-carboxylic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, N,N-dimethylglycine, and N-acetyl-L-glutamic acid; an eighth combination formed by N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid; and a ninth combination formed by L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, and 2-oxoglutarate.

[0021] In this embodiment of the invention, a diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the sixth combination as a representative. ROC curves were plotted, and the AUC value was used to evaluate the model's diagnostic efficacy. The modeling group had an AUC of 0.898 (sensitivity = 0.806, specificity = 0.917), indicating that the constructed diagnostic model has high diagnostic efficacy. The constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also showed good diagnostic efficacy in the validation group, with an AUC of 0.875 (sensitivity = 0.708, specificity = 0.875).

[0022] In this invention, the urine metabolic marker combination preferably includes at least one combination of seven metabolites: a tenth combination formed by 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N-acetyl-L-glutamic acid, and glycine-chenodeoxycholic acid; an eleventh combination formed by (S)-2-piperidinone-6-carboxylic acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, N,N-dimethylglycine, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine-chenodeoxycholic acid; and a twelfth combination formed by (S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, glycyl-L-proline, glycyl-γ-aminobutyric acid, N-acetyl-L-glutamic acid, and L-methionyl-L-aspartic acid.

[0023] In this embodiment of the invention, a diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the tenth group as a representative. ROC curves were plotted, and the AUC value was used to evaluate the model's diagnostic efficacy. The modeling group had an AUC of 0.808 (sensitivity = 0.75, specificity = 0.75), indicating that the constructed diagnostic model has high diagnostic efficacy. The constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also showed good diagnostic efficacy in the validation group, with an AUC of 0.863 (sensitivity = 0.729, specificity = 0.875).

[0024] In this invention, the combination of urinary metabolic markers preferably includes a thirteenth combination formed by 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, and glycine chenodeoxycholic acid, or a fourteenth combination formed by (S)-2-piperidinone-6-carboxylic acid, glycyl-L-proline, L-methionyl-L-aspartic acid, 6-aminohexanoic acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid.

[0025] In this embodiment of the invention, a diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the thirteenth combination as a representative. ROC curves were plotted, and the AUC value was used to evaluate the model's diagnostic efficacy. The modeling group had an AUC of 0.873 (sensitivity = 0.722, specificity = 0.833), indicating that the constructed diagnostic model has high diagnostic efficacy. The constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also showed good diagnostic efficacy in the validation group, with an AUC of 0.855 (sensitivity = 0.708, specificity = 0.813).

[0026] In this invention, the combination of urinary metabolic markers preferably includes a fifteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid, or a sixteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-L-proline, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine chenodeoxycholic acid.

[0027] In this embodiment of the invention, a diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the fifteenth combination as a representative. ROC curves were plotted, and the AUC value was used to evaluate the model's diagnostic efficacy. The modeling group had an AUC of 0.801 (sensitivity = 0.75, specificity = 0.917), indicating that the constructed diagnostic model has high diagnostic efficacy. The constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also showed good diagnostic efficacy in the validation group, with an AUC of 0.857 (sensitivity = 0.729, specificity = 0.875).

[0028] In this embodiment of the invention, a diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using a combination of urinary metabolic markers formed from the aforementioned 12 metabolites. ROC curves were plotted, and the AUC value was used to evaluate the diagnostic efficacy of the model. The modeling group had an AUC of 0.87 (sensitivity = 0.784, specificity = 0.727), indicating that the constructed diagnostic model has high diagnostic efficacy. The constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also showed good diagnostic efficacy in the validation group, with an AUC of 0.833 (sensitivity = 0.792, specificity = 0.75).

[0029] In this invention, the screening method for the combination of urine metabolic markers preferably involves detecting metabolites in urine samples from patients with benign kidney disease and patients with kidney cancer, and screening for differential metabolites to obtain a combination of urine metabolic markers.

[0030] In this invention, the number of patients with benign renal diseases is no less than 50, and the number of patients with renal cancer is no less than 100. In this embodiment of the invention, the number of patients with benign renal diseases is 62, and the number of patients with renal cancer is 194. The patients with benign renal diseases and patients with renal cancer are qualified as subjects through strict screening criteria to ensure the accuracy of the test results. The screening criteria preferably include the following exclusion criteria: 1) pregnancy or lactation; 2) emergency or requiring resuscitation; 3) history of blood transfusion within 7 days prior to sampling; 4) individuals who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants; 5) history of malignant tumors within 5 years or having undergone any anti-tumor treatment before sampling; 6) concurrent multiple primary malignant tumors.

[0031] In this invention, the method for metabolite detection preferably includes urine sample pretreatment and the use of liquid chromatography-mass spectrometry (LC-MS) to detect small molecule metabolites. The urine sample pretreatment method preferably includes mixing urine and an extract homogenize, treating the resulting mixture with a methanol-water solution, centrifuging, collecting the aqueous phase, treating it with a methanol solution, drying the supernatant by rotary evaporation, and redissolving the resulting solid phase in water to obtain the aqueous test solution. The extract is preferably a mixture of methyl tert-butyl ether and methanol at a volume ratio of 3:1. Homogenization is preferably performed using vortex sonication. The volume ratio of methanol-water solution to the mixture is preferably 1:1 to 2. The volume ratio of methanol solution to the aqueous phase is preferably 3:1. The LC analyzer preferably includes an ACQUITY UPLC I-Class system (Waters). The mass spectrometer preferably includes a Q-Exactive mass spectrometry system (Thermo Fisher Scientific). The LC column is preferably a Waters ACQUTTY UPLC. ®The HSS T3 1.8µm 2.1×100mm column was used. The preferred mobile phase parameters for the liquid chromatography detection were: mobile phase A was an aqueous solution containing 0.1% formic acid; mobile phase B was an acetonitrile solution containing 0.1% formic acid; the separation and elution gradient was as follows: 1%~70% mobile phase B for 0~13 minutes, and 99% mobile phase B for 13~18 minutes. The preferred mass spectrometry detection parameters are as follows: mass spectrometry data are acquired using Full MS and Full MS / dd-MS2 (each with both positive and negative modes). The parameters used for Q Exactive are as follows: Full MS mode resolution is 70,000 m / s, scan range is 100–1500 m / s, AGC (automatic gain control) is 3E+6, and Maximum IT is 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500 m / s, quadrupole window is 1.5 m / s, AGC is 1E+5, maximum ion implantation time is 50 ms, and HCD relative collision energy is 30%. After obtaining the metabolomics data, data preprocessing and metabolite identification are preferred. This invention does not impose any special limitations on the data preprocessing method; conventional methods for metabolomics data preprocessing well-known in the art can be used. The method for metabolite identification preferably involves comparing and matching the spectral information of the primary precursor ion (MS1) and secondary fragment ion (MS2) of the compound obtained after analysis with spectral information in a public database, and then verifying the mass spectrometry information using matched standards. The spectral information preferably includes the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragments of the secondary ion. The public database preferably includes the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), and the Mass Spectrometry Database (www.massbank.jp).

[0032] This invention provides the application of the combination of urinary metabolic markers in constructing a diagnostic model that distinguishes between benign kidney diseases and kidney cancer.

[0033] In this invention, the diagnostic model preferably includes construction using a machine learning support vector machine (SVM). The SVM is preferably iterated 1000 times in a random loop. The diagnostic threshold of the diagnostic model is preferably 0.739. The diagnostic model preferably also includes validation. Samples from the validation group are used for independent validation of the diagnostic model. The AUC value, sensitivity, and specificity in the ROC curve are used as indicators to evaluate the model's diagnostic effectiveness. A value closer to 1 indicates better model performance and a better diagnostic effect; conversely, a value less than 1 indicates a poor diagnostic effect. Results show that the diagnostic models constructed using different combinations of metabolite biomarkers all have AUC values ​​greater than 0.8, sensitivity greater than 0.7, and specificity greater than 0.7. This indicates that the diagnostic models constructed using combinations of metabolic biomarkers all have high diagnostic efficacy and clinical diagnostic significance.

[0034] This invention provides the application of the detection reagent of the combination of urine metabolic markers in the preparation of a diagnostic kit to distinguish between benign kidney diseases and kidney cancer.

[0035] In this invention, the detection reagent preferably includes formic acid, acetonitrile, methyl tert-butyl ether, and methanol. The formic acid, acetonitrile, and methyl tert-butyl ether are preferably of mass spectrometry grade purity. The methanol is preferably of chromatographic grade purity.

[0036] In this invention, the diagnostic method of the diagnostic kit preferably uses the detection reagent to detect the combination of urinary metabolic markers in the sample to be tested, and uses a diagnostic model to output the diagnostic results. With 0.739 as the diagnostic threshold, when the output data is ≥0.739, it indicates that the sample is a high-risk sample for renal cancer, and when the output data is less than 0.739, it indicates that the sample has benign kidney disease.

[0037] The following detailed description, in conjunction with embodiments, of a combination of urinary metabolic markers for distinguishing between benign kidney diseases and kidney cancer, and their application, should not be construed as limiting the scope of protection of this invention.

[0038] Example 1 A set of screening methods for urinary metabolic biomarkers for kidney cancer screening and diagnosis 1. Subject Information 1) Sample 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 or older; (2) Read and fully understand the information, sign the informed consent form, and be able to provide a urine sample for metabolomics testing; (3) Renal cancer group: Patients diagnosed with primary malignant renal tumor by biopsy / postoperative pathology or by comprehensive clinical evaluation by clinicians.

[0039] (4) Benign renal disease group: patients diagnosed with benign renal diseases by biopsy / postoperative pathology or by comprehensive clinical evaluation by clinicians, including patients with benign renal diseases such as renal cysts, renal angiomyolipoma and renal eosinophilic adenoma.

[0040] 2) Sample 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.

[0041] 3) Subject information: This study collected urine samples from 256 subjects across two medical centers, including 62 samples from benign renal disease (BRD) and 194 samples from renal cell carcinoma (RCC). Specifically, the urine samples used for the modeling group consisted of 46 cases in the BRD group and 146 cases in the RCC group; the urine samples used for the validation group consisted of 16 cases in the BRD group and 48 cases in the RCC group (Table 1).

[0042] Table 1 Subject Information

[0043] 2. Urine metabolite detection 1) Reagents: Methanol, acetonitrile, water, methyl tert-butyl ether of mass spectrometry grade, and formic acid of chromatographic (HPLC) grade were all purchased from Sigma-Aldrich, USA.

[0044] 2) Urine pretreatment: After removing the urine sample from the -80℃ freezer and thawing it, take 40 μL of the urine sample into an extraction tube, add 700 μL of pre-cooled methyl tert-butyl ether and methanol mixed extraction solution (the volume ratio of methyl tert-butyl ether and methanol is 3:1), vortex and sonicate to mix well, then add 350 μL of methanol-water mixed solution, 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-cooled methanol solution to it, after protein precipitation, take 1000 μL of supernatant and evaporate to dryness, add 200 μL of water to reconstitute, and use the reconstituted solution as the aqueous phase test solution for analysis (LC-MS).

[0045] 3) Detection of 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 an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively.

[0046] 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 is 1%~70% mobile phase B, 13~18 minutes is 99% mobile phase B.

[0047] 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 m / z, a scan range of 100-1500 m / z, an AGC (automatic gain control) 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 m / z, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the HCD relative collision energy was 30%.

[0048] 3. 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) Using the peak alignment algorithm of OpenMS software, the retention time of the extracted peak format data is corrected and aligned 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 then replace the abnormal data (0, negative values, background noise, etc.) with missing values; (4) Remove the feature peaks with a detection rate of <80% from all the feature peaks obtained in step (3), fill the median value of the feature 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 concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used for normalization to remove systematic errors such as batch effects.

[0049] 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 information, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment ion data, is matched 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 data 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.

[0050] 4. Data Analysis 1) Screening of metabolic markers to differentiate between benign kidney diseases and kidney cancer First, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis was performed on the data from the modeling group, and a total of 12 differential metabolites were screened out (Table 2) as important metabolic markers to distinguish between benign renal diseases and renal cell carcinoma.

[0051] Table 2. 12 Important Metabolic Markers for Differentiating Benign Kidney Diseases from Kidney Cancer

[0052] 2) Construction of a diagnostic model to differentiate between benign kidney diseases and renal cell carcinoma To validate the diagnostic efficacy of the 12 selected metabolic biomarkers in distinguishing between benign renal diseases and renal cell carcinoma, multivariate ROC curve analysis was performed on these 12 biomarkers in the modeling group. Specifically, three-quarters of the sample data from the BRD and RCC groups in the modeling group were randomly used as the training set, and one-quarter as the test set. A support vector machine (SVM) was used for randomized iterations of 1000 times. The diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed by statistically analyzing the average accuracy of the final model.

[0053] 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 diagnostic effectiveness. 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.

[0054] Sensitivity is calculated according to Formula I: Formula I Specificity is calculated according to Formula II: Formula II.

[0055] Wherein, TP (TruePositive): True Positive, the number of samples that are actually positive but were correctly predicted as positive; TN (TrueNegative): 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.

[0056] The results are as follows Figure 1 As shown, AUC=0.87 (sensitivity=0.784, specificity=0.727), indicating that the constructed diagnostic model has high diagnostic efficacy.

[0057] 3) Validation of diagnostic models for differentiating between benign kidney diseases and renal cell carcinoma To further validate the effectiveness of the diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma, built based on the modeling group data, the model was validated using validation group data. Specifically, multivariate ROC curve analysis was performed to evaluate the independent validation performance of the diagnostic model on unknown datasets outside the modeling group dataset. After the validation group samples were placed into the diagnostic model constructed by the modeling group, the detection data of 12 important metabolic markers distinguishing between benign renal diseases and renal cell carcinoma for each sample generated corresponding probability values. Using the probability value of each sample as the diagnostic threshold, a confusion matrix (including true positive, true negative, false positive, and false negative) was obtained. Sensitivity and specificity can be calculated using formulas, and a point can be marked on the ROC analysis graph with sensitivity as the ordinate and 1-specificity as the abscissa. Similarly, when the probability value of each sample is used as the diagnostic threshold, multiple different points are obtained in the ROC analysis graph. Connecting these points, an ROC curve is plotted. The point with the best-performing sensitivity and specificity is selected, and the diagnostic threshold at this point is 0.739.

[0058] As shown in Table 3, the confusion matrix results indicate that, based on the 12 metabolic biomarkers, the diagnostic model with a diagnostic threshold of 0.739 resulted in 38 cases of renal cell carcinoma being diagnosed as renal cell carcinoma and 10 cases being misdiagnosed as having benign renal diseases among the 48 patients. Conversely, among the 16 subjects with benign renal diseases, 12 cases were correctly diagnosed and 4 cases were misdiagnosed as having renal cell carcinoma. The ROC analysis results of the diagnostic model in the validation group are as follows: Figure 2 As shown, sensitivity and specificity were calculated based on the confusion matrix results, with an AUC of 0.833 (sensitivity = 0.792, specificity = 0.75). These results indicate that the constructed diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also demonstrated good diagnostic performance in the validation group.

[0059] Table 3 Confusion matrix of diagnostic models used to distinguish between benign kidney diseases and kidney cancer

[0060] Example 2 A diagnostic model for differentiating benign renal diseases from renal cell carcinoma was constructed using the method described in Example 1, employing the following 11 metabolic biomarkers: (S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamate, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine-chenodeoxycholic acid. The results showed that the diagnostic model constructed using the combination of these 11 metabolic biomarkers had an AUC of 0.801 (sensitivity = 0.75, specificity = 0.917).

[0061] Furthermore, a diagnostic model based on 11 metabolic biomarkers ((S)-2-piperidinone-6-carboxylic acid, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamate, L-methionyl-L-aspartic acid, 2-oxoglutarate, and glycine-chenodeoxycholic acid combination) was validated in the validation group. The results showed that the diagnostic model constructed from the combination of 11 metabolic biomarkers had an AUC of 0.857 (sensitivity = 0.729, specificity = 0.875) in the validation group. The diagnostic model constructed from the combination of 11 metabolic biomarkers exhibits high diagnostic efficacy and has clinical diagnostic significance.

[0062] Example 3 A diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the method described in Example 1, employing the following nine metabolic biomarkers: 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamate, L-methionyl-L-aspartic acid, and glycine-chenodeoxycholic acid. The results showed that the diagnostic model constructed using the combination of these nine metabolic biomarkers had an AUC of 0.873 (sensitivity = 0.722, specificity = 0.833).

[0063] The diagnostic model based on a combination of nine metabolic biomarkers (2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N,N-dimethylglycine, N-acetyl-L-glutamate, L-methionyl-L-aspartic acid, and glycine-chenodeoxycholic acid) was validated in the validation group. The results showed that the diagnostic model constructed from the combination of the nine metabolic biomarkers had an AUC of 0.855 (sensitivity = 0.708, specificity = 0.813). The diagnostic model constructed from the combination of the nine metabolic biomarkers exhibits high diagnostic efficacy and has clinical diagnostic significance.

[0064] Example 4 A diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the method described in Example 1, employing the following seven metabolic biomarkers: 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N-acetyl-L-glutamate, and glycine chenodeoxycholic acid. The results showed that the diagnostic model constructed using the combination of these seven metabolic biomarkers had an AUC of 0.808 (sensitivity = 0.75, specificity = 0.75).

[0065] The diagnostic models constructed using seven metabolic biomarkers (a combination of 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, glycyl-γ-aminobutyric acid, L-cysteine ​​sulfinic acid, N-acetyl-L-glutamate, and glycine-chenodeoxycholic acid; and a combination of five metabolic biomarkers, 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, L-cysteine ​​sulfinic acid, and glycine-chenodeoxycholic acid) were validated in the validation group. The results showed that the diagnostic model constructed using the seven metabolic biomarkers had an AUC of 0.863 (sensitivity = 0.729, specificity = 0.875). The diagnostic model constructed using the seven metabolic biomarkers exhibits high diagnostic efficacy and has clinical diagnostic significance.

[0066] Example 5 A diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma was constructed using the method described in Example 1, employing a combination of five metabolic biomarkers: 2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, L-cysteine ​​sulfinic acid, and glycochenodeoxycholic acid. The results showed that the diagnostic model constructed using the combination of these five metabolic biomarkers had an AUC of 0.898 (sensitivity = 0.806, specificity = 0.917).

[0067] The diagnostic model based on a combination of five metabolic biomarkers (2'-deoxyguanosine, 3-hydroxybutyric acid, 6-aminocaproic acid, L-cysteine ​​sulfinic acid, and glycochenodeoxycholic acid) was validated in the validation group. The results showed that the diagnostic model constructed using the combination of these five metabolic biomarkers had an AUC of 0.875 (sensitivity = 0.708, specificity = 0.875). The diagnostic model based on this combination of five metabolic biomarkers exhibits high diagnostic efficacy and has clinical diagnostic significance.

[0068] Example 6 A diagnostic model for differentiating benign renal diseases from renal cell carcinoma was constructed using the method described in Example 1, employing a combination of three metabolic biomarkers: 2'-deoxyguanosine, L-cysteine ​​sulfinic acid, and glycochenodeoxycholic acid. The results showed that the diagnostic model constructed using the combination of these three metabolic biomarkers had an AUC of 0.856 (sensitivity = 0.806, specificity = 0.75).

[0069] The diagnostic model based on a combination of three metabolic biomarkers (2'-deoxyguanosine, L-cysteine ​​sulfinic acid, and glycochenodeoxycholic acid) was validated in the validation group. The results showed that the diagnostic model constructed using the combination of these three metabolic biomarkers had an AUC of 0.867 (sensitivity = 0.75, specificity = 0.813). The diagnostic model constructed using this combination of three metabolic biomarkers exhibits high diagnostic efficacy and has clinical diagnostic significance.

[0070] The results of the above examples show that the constructed diagnostic model for distinguishing between benign kidney diseases and renal cell carcinoma also has good diagnostic performance in the validation group.

[0071] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A combination of urinary metabolic markers for differentiating between benign renal disease and renal cancer, characterized in that, comprises at least one of the following combinations of three metabolites: a first combination of 2'-deoxyguanosine, L-cysteic acid, and glycochenocholic acid, a second combination of (S)-2-piperidinone-6-carboxylic acid, 6-aminohexanoic acid, and N,N-dimethylglycine, a third combination of 3-hydroxybutyric acid, glycyl-L-proline, and N-acetyl-L-glutamic acid, a fourth combination of 6-aminohexanoic acid, glycyl-L-proline, and L-methionyl-L-aspartic acid, and a fifth combination of L-methionyl-L-aspartic acid, 2-oxoglutaric acid, and glycochenocholic acid.

2. The combination of urinary metabolic markers according to claim 1, characterized in that, comprises at least one of the following combinations of three metabolites: a first combination of 2'-deoxyguanosine, L-cysteic acid, and glycochenocholic acid, a second combination of (S)-2-piperidinone-6-carboxylic acid, 6-aminohexanoic acid, and N,N-dimethylglycine, a third combination of 3-hydroxybutyric acid, glycyl-L-proline, and N-acetyl-L-glutamic acid, a fourth combination of 6-aminohexanoic acid, glycyl-L-proline, and L-methionyl-L-aspartic acid, and a fifth combination of L-methionyl-L-aspartic acid, 2-oxoglutaric acid, and glycochenocholic acid.

3. The combination of claim 1, wherein the combination comprises: comprises at least one of the following combinations of three metabolites: a first combination of 2'-deoxyguanosine, L-cysteic acid, and glycochenocholic acid, a second combination of (S)-2-piperidinone-6-carboxylic acid, 6-aminohexanoic acid, and N,N-dimethylglycine, a third combination of 3-hydroxybutyric acid, glycyl-L-proline, and N-acetyl-L-glutamic acid, a fourth combination of 6-aminohexanoic acid, glycyl-L-proline, and L-methionyl-L-aspartic acid, and a fifth combination of L-methionyl-L-aspartic acid, 2-oxoglutaric acid, and glycochenocholic acid.

4. The combination of claim 1, wherein the combination comprises: comprises at least one of the following combinations of three metabolites: a first combination of 2'-deoxyguanosine, L-cysteic acid, and glycochenocholic acid, a second combination of (S)-2-piperidinone-6-carboxylic acid, 6-aminohexanoic acid, and N,N-dimethylglycine, a third combination of 3-hydroxybutyric acid, glycyl-L-proline, and N-acetyl-L-glutamic acid, a fourth combination of 6-aminohexanoic acid, glycyl-L-proline, and L-methionyl-L-aspartic acid, and a fifth combination of L-methionyl-L-aspartic acid, 2-oxoglutaric acid, and glycochenocholic acid.

5. The combination of claim 1, wherein the combination comprises: a thirteenth combination formed by 2'-deoxyguanosine, 3-hydroxybutyric acid, 6- aminohexanoic acid, glycyl-gamma-aminobutyric acid, L-cysteine sulfinate, N,N- dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, glycochenocholic acid or a fourteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'- deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-L-proline, L- cysteine sulfinate, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L- aspartic acid, 2-oxoglutaric acid and glycochenocholic acid.

6. The combination of claim 1, wherein the combination comprises: a fifteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'- deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-gamma- aminobutyric acid, L-cysteine sulfinate, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L-aspartic acid, 2-oxoglutaric acid, glycochenocholic acid or a sixteenth combination formed by (S)-2-piperidone-6-carboxylic acid, 2'- deoxyguanosine, 3-hydroxybutyric acid, 6-aminohexanoic acid, glycyl-L-proline, L- cysteine sulfinate, N,N-dimethylglycine, N-acetyl-L-glutamic acid, L-methionyl-L- aspartic acid, 2-oxoglutaric acid and glycochenocholic acid.

7. Use of the urine metabolic marker combination of any one of claims 1-6 in constructing a diagnostic model for differentiating renal benign disease from renal cancer.

8. Use according to claim 7, characterized in that, The diagnostic model is constructed using a machine learning support vector machine.

9. Use of a detection reagent of the urine metabolic marker combination of any one of claims 1-6 in preparing a diagnostic kit for differentiating renal benign disease from renal cancer.

10. Use according to claim 9, characterized in that, The detection reagent comprises formic acid, acetonitrile, methyl tert-butyl ether and methanol.

Images