Metabolic marker combination for distinguishing kidney benign disease and kidney cancer and application thereof

Abstract

The invention provides a metabolic marker composition for distinguishing kidney benign diseases and kidney cancer and application of the metabolic marker composition. Based on the metabonomics data of the plasma sample, the diagnosis model capable of distinguishing the kidney benign disease and the kidney cancer is constructed through a machine learning algorithm, and powerful support is provided for early noninvasive diagnosis and individualized treatment strategy formulation of the kidney cancer. The method is simple and convenient in sample collection, small in wound, high in sample stability and easy in standardized collection, is suitable for early screening and risk stratification of kidney diseases of a large scale of people, particularly provides an effective kidney cancer auxiliary diagnosis tool for areas with relatively deficient medical resources, and realizes accurate distinguishing of kidney benign diseases and kidney cancer.

Description

Technical Field

[0001] This invention relates to the analysis and detection of metabolic markers, specifically to a combination of metabolic markers for differentiating between benign kidney diseases and kidney cancer, and their applications. Background Technology

[0002] Renal cell carcinoma (RCC), also known as kidney cancer, is the most common malignant tumor of the kidney. Originating in the renal parenchymal tubular epithelial system, it accounts for 2-3% of the global cancer burden. Its incidence and mortality rates have been rising significantly in recent years, becoming a serious health threat globally. Common causes of RCC include smoking, obesity, hypertension, long-term exposure to industrial chemicals (such as asbestos and cadmium), genetic factors (such as familial renal cancer caused by VHL gene mutations), and long-term dialysis in end-stage renal disease. Currently, clinical detection of renal cancer mainly relies on imaging examinations (ultrasound, CT scans), but these methods have limitations, such as the tendency to miss or misdiagnose early-stage or atypical tumors. Common symptoms of renal cancer include hematuria, lower back pain, and abdominal masses. However, because early-stage symptoms are often atypical, most patients are diagnosed at an advanced stage, significantly reducing the chance of a cure and the five-year survival rate. Therefore, accurate screening and diagnosis of precancerous lesions or early stages of renal cancer would greatly improve the severe situation of high mortality and low early diagnosis rates of renal cancer in my country, effectively improving treatment success rates and patients' quality of life.

[0003] The development of renal cell carcinoma is a multi-stage and complex biological process, typically evolving from renal tubular epithelial hyperplasia and benign adenoma to renal cell carcinoma. Benign renal diseases (BRDs) mainly include renal cysts, renal angiomyolipomas, and renal eosinophilic adenomas, some of which may be in a potential pre-cancerous state. Early-stage renal cell carcinoma often presents with no obvious symptoms, or only nonspecific symptoms such as microscopic hematuria and lower back pain, easily confused with benign diseases such as renal cysts and nephritis, leading to missed or misdiagnosis and a low early diagnosis rate in my country. Accurate differential diagnosis of benign renal diseases not only helps in the early detection and intervention of renal cell carcinoma, significantly improving patient survival and treatment outcomes, but also avoids overtreatment of benign lesions (such as unnecessary renal biopsy or surgery), reducing the physical and psychological burden on patients. Currently, the main methods for differentiation rely on imaging examinations (such as ultrasound and CT scans) and invasive puncture biopsies. However, the former has limited ability to distinguish atypical lesions, while the latter carries risks of trauma and bleeding and has low sampling accuracy for small lesions (such as <1 cm). Therefore, there is an urgent clinical need to develop a novel, non-invasive, and precise diagnostic technique that can effectively differentiate between benign renal diseases and renal cell carcinoma.

[0004] Metabolomics, as an emerging omics technology, is dedicated to the systematic analysis of the composition and dynamic changes of small molecule metabolites in organisms, and can reflect the metabolic characteristics of the body in real time and comprehensively under physiological and pathological states. By detecting changes in the metabolic profile in easily accessible biological samples such as urine and blood, metabolomics can reveal specific metabolic pathway abnormalities associated with benign and malignant kidney diseases. Furthermore, by leveraging artificial intelligence and deep learning algorithms, it can discover combinations of metabolic biomarkers with high discriminative value, thereby achieving accurate differentiation between benign kidney diseases and renal cell carcinoma. Metabolomics samples (such as blood and urine) are easy to collect and minimally invasive, making them particularly suitable for long-term dynamic monitoring and large-scale population screening. Based on this, this patent utilizes metabolomics data from plasma samples to construct a diagnostic model capable of distinguishing between benign kidney diseases and renal cell carcinoma. This method exhibits high sensitivity and specificity, providing a powerful tool for the early non-invasive diagnosis of renal cell carcinoma and the development of personalized treatment strategies. 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 a detection method based on a combination of plasma metabolic markers. This method can construct a diagnostic model that can distinguish between benign kidney diseases and kidney cancer, accurately screen for kidney cancer, and provide important assistance for the early screening, early diagnosis and early intervention of kidney cancer.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This invention discloses a metabolic marker composition for distinguishing between benign kidney diseases and kidney cancer, the metabolic marker composition comprising: uridine, threonine, O-acetylcarnitine and 1,5-dehydrated glucose alcohol.

[0007] Preferably, the composition further comprises: tridecanoic acid and 5,6-dihydrouracil.

[0008] Preferably, the composition further comprises isoleucine and aminomalonic acid.

[0009] Preferably, the composition further comprises: sucrose, mannose, and hydroxypyruvate. Preferably, the composition further comprises: trimethyllysine, glyoxycholic acid, carnitine, arabinitol, 3-indoleacetic acid, triglycerides 60:8, triglycerides 53:7, phosphatidylinositol 34:2, phosphatidylglycerol 34:0, and phosphatidylcholine 42:3.

[0010] This invention discloses a combination of metabolic markers for distinguishing between benign kidney diseases and kidney cancer, wherein the metabolites include: uridine, threonine, O-acetylcarnitine and 1,5-dehydroglucanol.

[0011] Preferably, the metabolite further comprises: tridecanoic acid and 5,6-dihydrouracil. Preferably, the metabolite further comprises: isoleucine and aminomalonic acid.

[0012] Preferably, the metabolites further include sucrose, mannose, and hydroxypyruvate.

[0013] Preferably, the metabolites further comprise: trimethyllysine, glyoxycholic acid, carnitine, arabinitol, 3-indoleacetic acid, triglycerides 60:8, triglycerides 53:7, phosphatidylinositol 34:2, phosphatidylglycerol 34:0, and phosphatidylcholine 42:3.

[0014] Preferably, the composition comprises the following metabolic markers: uridine, threonine, O-acetylcarnitine, and 1,5-dehydroglucan.

[0015] Preferably, the composition comprises the following metabolic markers: uridine, tridecanoic acid, threonic acid, O-acetylcarnitine, 5,6-dihydrouracil, and 1,5-dehydroglucanol.

[0016] Preferably, the composition comprises the following metabolic markers: uridine, tridecanoic acid, threonic acid, O-acetylcarnitine, isoleucine, aminomalonic acid, 5,6-dihydrouracil, and 1,5-dehydroglucanol.

[0017] Preferably, the composition comprises the following metabolic markers: uridine, tridecanoic acid, threonic acid, sucrose, O-acetylcarnitine, mannose, isoleucine, hydroxypyruvic acid, aminomalonic acid, 5,6-dihydrouracil, and 1,5-dehydroglucanol.

[0018] Preferably, the composition comprises the following metabolic markers: uridine, trimethyllysine, tridecanoic acid, threonic acid, sucrose, O-acetylcarnitine, mannose, isoleucine, glyoxycholic acid, carnitine, hydroxypyruvic acid, arabinitol, aminomalonic acid, 5,6-dihydrouracil, 3-indoleacetic acid, 1,5-dehydroglucanol, triglycerides 60:8, triglycerides 53:7, phosphatidylinositol 34:2, phosphatidylglycerol 34:0, and phosphatidylcholine 42:3.

[0019] This invention discloses the use of the described composition in the preparation of a kit for differentiating benign kidney diseases from kidney cancer.

[0020] Preferably, the samples used in the differentiation process are selected from serum, plasma, or blood.

[0021] This invention discloses the use of the described composition in the preparation of reagents for differentiating between benign kidney diseases and kidney cancer.

[0022] Preferably, the samples used in the differentiation process are selected from serum, plasma, or blood.

[0023] Preferably, the benign renal diseases include: renal cysts, renal angiomyolipoma, and renal eosinophilic adenoma.

[0024] Preferably, the benign renal disease is a renal cyst, a renal angiomyolipoma, or a renal eosinophilic adenoma.

[0025] This invention discloses a kit for differentiating benign kidney diseases from kidney cancer, the kit comprising the aforementioned metabolic marker composition.

[0026] Preferably, the kit also includes quality control products and standards.

[0027] Compared with existing technologies, this invention, based on metabolomics data from plasma samples and employing machine learning algorithms, constructs a diagnostic model capable of distinguishing between benign kidney diseases and renal cell carcinoma. This method is convenient, minimally invasive, and features high sample stability and easy standardized collection, making it suitable for early screening and risk stratification of kidney diseases in large populations. It particularly provides an effective auxiliary diagnostic tool for renal cell carcinoma in areas with relatively scarce medical resources. Attached Figure Description

[0028] Figure 1 In the modeling group, multivariate ROC curve analysis was performed on 21 important markers that distinguish between BRD and RCC.

[0029] Figure 2 In the validation group, multivariate ROC curve analysis was performed on 21 key biomarkers that distinguish between BRD and RCC. Detailed Implementation

[0030] 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.

[0031] Example 1: Subject Information and Sample Collection 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 informed consent form and be able to provide a blood sample for metabolomics testing; (3) All patients with renal cancer are diagnosed by the gold standard method of histopathological examination; (4) Patients with benign renal diseases, such as renal cysts, renal angiomyolipoma and renal eosinophilic adenoma, are included, either by biopsy / postoperative pathology or by comprehensive clinical assessment by a clinician.

[0032] 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.

[0033] 3) Subject information This study collected plasma samples from 272 subjects across two medical centers, including 64 subjects in the Benign Renal Disease (BRD) group (40 with renal cysts, 18 with renal angiomyolipoma, and 6 with renal eosinophilic adenoma) and 208 subjects in the Renal Cell Carcinoma (RCC) group. Specifically, the plasma samples used for the modeling group were from 48 subjects in the BRD group (30 with renal cysts, 14 with renal angiomyolipoma, and 4 with renal eosinophilic adenoma) and 156 subjects in the RCC group; the plasma samples used for the validation group were from 16 subjects in the BRD group (10 with renal cysts, 4 with renal angiomyolipoma, and 2 with renal eosinophilic adenoma) and 52 subjects in the RCC group (Table 1).

[0034] Table 1. Subject Information Benign Renal Disease Group (BRD) Renal cell carcinoma group (RCC) Number of people in the modeling team 48 156 Number of people in the verification group 16 52 total 64 208 Example 2 Detection of plasma 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.

[0035] 2) Sample preparation: Take 100 μL of plasma and place it in 1000 μL of pre-cooled solution (methyl tert-butyl ether: methanol, volume ratio 3:1). Vortex to mix the extracted blood sample and obtain the sample extract. Add 500 μL of solution (methanol: water, volume ratio 3:1) to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers. The upper layer is the organic phase and the lower layer is the aqueous phase.

[0036] - Organic phase: After the sample is separated into layers, take 500 μL of the upper organic phase into a centrifuge tube, dry it, add 200 μL of solution (acetonitrile:isopropanol, volume ratio 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm); take 180 μL of supernatant from the centrifuge tube into a 2 mL glass vial, which is the organic phase test solution, and perform LC-MS detection.

[0037] - Aqueous phase: After sample separation, transfer the lower 400 μL aqueous phase to a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate proteins. After protein precipitation, centrifuge the tube and transfer 1000 μL of the supernatant to a new centrifuge tube, then dry overnight. Add 200 μL of water to the dried centrifuge tube and incubate at room temperature for 15 minutes. After incubation, vortex the mixture, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm). Transfer 180 μL of the supernatant from the centrifuge tube to a 2 mL glass vial as the aqueous phase test solution. Analyze using LC-MS.

[0038] 3) Detection of small molecule metabolites: For small molecule separation, the organic phase was separated using a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase was separated using a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column. The liquid chromatography and mass spectrometry systems used were the ACQUITY UPLC I-Class liquid chromatography system (Waters) and the Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0039] The mobile phase parameters are as follows: Organic phase analyte mobile phase parameters – Mobile phase A is an aqueous solution containing 0.1% acetic acid and 0.1% ammonium acetate; Mobile phase B is an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 0.1% ammonium acetate. The separation elution gradient is as follows: 55%-89% mobile phase B for 0-12 minutes, and 100% mobile phase B for 12-19.5 minutes.

[0040] Mobile phase parameters for the aqueous test solution: 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 and elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, and 13-18 minutes is 99% mobile phase B.

[0041] 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 Collisional Dissociation (HCD) was 30 eV.

[0042] Example 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) 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.

[0043] 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), the Mass Spectrometry Database (www.massbank.jp), and the Lipid Map Database (Lipidmap, www.lipidmaps.org). Metabolites identified based on these databases are then finally validated using retention times, MS1, and MS2 mass spectrometry data obtained when separated from 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.

[0044] 3. Data Analysis 1) Biomarker screening Metabolite detection was performed on the above samples, and a total of 571 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, 21 differential metabolites were selected (Table 2) as important metabolic markers to distinguish between the BRD group and the RCC group.

[0045] Table 2. 21 Important Metabolic Markers for Differentiating BRD from RCC Logo - English Logo - Chinese HMDB ID 1 Uridine Ureidine HMDB0000296 2 Trimethyllysine Trimethyllysine HMDB0001325 3 Tridecanoic acid Tridecanoic acid HMDB0000910 4 Threonate Threonic acid HMDB0000943 5 Sucrose sucrose HMDB0000258 6 O-Acetylcarnitine O-acetylcarnitine HMDB0000201 7 Mannose Mannose HMDB0000169 8 Isoleucine Isoleucine HMDB0000172 9 Glycocholic acid Glycocholic acid HMDB0000138 10 Carnitine Carnitine HMDB0000062 11 Hydroxypyruvate Hydroxypyruvic acid HMDB0001352 12 Arabitol Arabitol HMDB0000568 13 Aminomalonic acid Aminomalonic acid HMDB0001147 14 5,6-Dihydrouracil 5,6-Dihydrouracil HMDB0000076 15 3-Indoleacetic acid 3-Indoleacetic acid HMDB0000197 16 1,5-Anhydroglucitol 1,5-Dehydrated Glucol HMDB0002712 17 TAG 60:8 Triglycerides 60:8 HMDB0049442 18 TAG 53:7 Triglycerides 53:7 - 19 PI 34:2 Phosphatidylinositol 34:2 HMDB0009784 20 PG 34:0 Phosphatidylglycerol 34:0 - 21 PC 42:3 Phosphatidylcholine 42:3 HMDB0008190 2) Construction of a diagnostic model to differentiate between benign kidney diseases and renal cell carcinoma To verify the discriminative effect of the 21 selected biomarkers in distinguishing between BRD and RCC, multivariate ROC curve analysis was performed on these 21 biomarkers in the modeling group. 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 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 for distinguishing between benign kidney diseases and renal cell carcinoma was constructed.

[0046] 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.

[0047] Sensitivity calculation is shown in Formula I: Formula I.

[0048] Specificity is calculated using Formula II: Formula II.

[0049] 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 1 As shown, AUC=0.891 (sensitivity=0.795, specificity=0.833), indicating that the constructed diagnostic model has high discriminative power.

[0050] In addition, ROC curve analysis was performed on diagnostic models with different combinations of metabolic markers in the modeling group. Eleven metabolic markers were used: uridine, tridecanoic acid, threonic acid, sucrose, O-acetylcarnitine, mannose, isoleucine, hydroxypyruvic acid, aminomalonic acid, 5,6-dihydrouracil, and 1,5-dehydroglucan. The combination of eight metabolic markers was used: uridine, tridecanoic acid, threonic acid, O-acetylcarnitine, isoleucine, aminomalonic acid, 5,6-dihydrouracil, and 1,5-dehydroglucol. A combination of six metabolic markers was used: uridine, tridecanoic acid, threonic acid, O-acetylcarnitine, 5,6-dihydrouracil, and 1,5-dehydroglucol. And the combination of four metabolic markers: uridine, threonine, O-acetylcarnitine and 1,5-dehydroglucol; The results showed that when using 11 metabolic biomarkers, the AUC was 0.829 (sensitivity = 0.718, specificity = 0.750); when using 8 metabolic biomarkers, the AUC was 0.878 (sensitivity = 0.769, specificity = 0.833); when using 6 metabolic biomarkers, the AUC was 0.870 (sensitivity = 0.718, specificity = 0.750); and when using 4 metabolic biomarkers, the AUC was 0.803 (sensitivity = 0.718, specificity = 0.750), all demonstrating stable discriminative ability.

[0051] 3) Validation of diagnostic models for differentiating between benign kidney diseases and renal cell carcinoma To further validate the diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma 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 input into the model constructed by the modeling group, a corresponding probability value was output for each sample based on the detection data of 21 important metabolic markers distinguishing BRD from RCC. 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 time is 0.7471.

[0052] As shown in Table 3, the confusion matrix results indicate that in the diagnostic model constructed based on the 21 metabolic biomarkers, a discrimination threshold of 0.7471 was used. When the output result was ≥ the threshold, the diagnosis was renal cell carcinoma; when the output result was < the threshold, the diagnosis was benign renal disease. Among the 16 subjects with benign renal disease, 12 were correctly diagnosed, and 4 were incorrectly diagnosed as renal cell carcinoma. Among the 52 subjects with renal cell carcinoma, 38 were correctly diagnosed, and 14 were incorrectly diagnosed as benign renal disease. 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 0.835 (sensitivity = 0.731, specificity = 0.750). These results indicate that the established diagnostic model for distinguishing between benign renal diseases and renal cell carcinoma also demonstrated good discriminative performance in the validation group.

[0053] Table 3. Confusion matrix of diagnostic models for distinguishing between benign renal diseases and renal cell carcinoma Types of diseases Kidney cancer Benign kidney diseases Kidney cancer, N=52 38 (TP) 14 (FN) Benign kidney disease, N=16 4 (FP) 12 (TN) Furthermore, 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 11 metabolic biomarkers—uridine, tridecanoic acid, threonic acid, sucrose, O-acetylcarnitine, mannose, isoleucine, hydroxypyruvic acid, aminomalonic acid, 5,6-dihydrouracil, and 1,5-dehydroglucan—achieved an AUC of 0.865 (sensitivity = 0.788, specificity = 0.750) in the validation group. The combination of the 8 metabolic biomarkers—uridine, tridecanoic acid, threonic acid, O-acetylcarnitine, isoleucine, aminomalonic acid, and 5,6-dihydrouracil—achieved an AUC of 0.865 (sensitivity = 0.788, specificity = 0.750). The combination of uridine and 1,5-dehydroglucol showed an AUC of 0.889 (sensitivity = 0.750, specificity = 0.875) in the validation group; the combination of the above six metabolic markers—uridine, tridecanoic acid, threonic acid, O-acetylcarnitine, 5,6-dihydrouracil, and 1,5-dehydroglucol—showed an AUC of 0.845 (sensitivity = 0.712, specificity = 0.938) in the validation group; and the combination of the above four metabolic markers—uridine, threonic acid, O-acetylcarnitine, and 1,5-dehydroglucol—showed an AUC of 0.802 (sensitivity = 0.750, specificity = 0.813) in the validation group. These results indicate that the established diagnostic model also exhibits good discriminative performance in the validation group.

[0054] 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 benign kidney diseases from renal cancer, characterized in that, The metabolic marker composition includes uridine, threonine, O-acetylcarnitine, and 1,5-dehydroglucol.

2. The composition according to claim 1, characterized in that, The composition further comprises: tridecanoic acid and 5,6-dihydrouracil.

3. The composition according to claim 2, characterized in that, The composition further comprises isoleucine and aminomalonic acid.

4. The composition according to claim 3, characterized in that, The composition further comprises: sucrose, mannose, and hydroxypyruvic acid.

5. The composition according to claim 4, characterized in that, The composition further comprises: trimethyllysine, glyoxycholic acid, carnitine, arabinitol, 3-indoleacetic acid, triglycerides 60:8, triglycerides 53:7, phosphatidylinositol 34:2, phosphatidylglycerol 34:0, and phosphatidylcholine 42:

3.

6. The composition according to claim 1, characterized in that, The composition comprises the following metabolic markers: uridine, trimethyllysine, tridecanoic acid, threonic acid, sucrose, O-acetylcarnitine, mannose, isoleucine, glyoxycholic acid, carnitine, hydroxypyruvic acid, arabinitol, aminomalonic acid, 5,6-dihydrouracil, 3-indoleacetic acid, 1,5-dehydroglucanol, triglycerides 60:8, triglycerides 53:7, phosphatidylinositol 34:2, phosphatidylglycerol 34:0, and phosphatidylcholine 42:

3.

7. Use of the composition according to any one of claims 1-6 in the preparation of reagents and / or kits for distinguishing between benign kidney diseases and renal cell carcinoma.

8. The use according to claim 7, characterized in that, The samples used in the differentiation process are selected from serum, plasma, or blood.

9. A reagent kit for differentiating between benign kidney diseases and kidney cancer, characterized in that, The kit comprises the metabolic biomarker composition according to any one of claims 1-6.

10. The reagent kit according to claim 9, characterized in that, The kit also includes quality control materials and standards.

Images