Metabolic marker combination for early diagnosis of kidney cancer and application thereof

Abstract

The application provides a metabolic marker combination for early kidney cancer diagnosis and application thereof, and belongs to the technical field of in vitro diagnosis. The metabolic marker combination for early kidney cancer diagnosis comprises a first combination or a second combination. The first combination comprises glycyl-L-glutamic acid, L-glutamyl-L-serine, methylphosphatidylcholine 33:2e and triglyceride 54:7. The second combination comprises glycyl-L-glutamic acid, L-glutamyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e and triglyceride 54:7. By detecting the metabolic marker combination in a blood sample, it can be determined whether a patient has early kidney cancer, has high sensitivity and specificity, can realize precise screening of early kidney cancer, and provides important help for prevention and reduction of the incidence of kidney cancer.

Description

Technical Field

[0001] This invention belongs to the field of in vitro diagnostic technology, and particularly relates to a combination of metabolic biomarkers for the diagnosis of early renal cell carcinoma and their application. Background Technology

[0002] Renal cancer (RC), also known as kidney cell carcinoma, originates from renal tubular epithelial cells and is the most common malignant tumor of the kidney parenchyma.

[0003] Early diagnosis of kidney cancer is often difficult because most kidney cancers do not present obvious symptoms in their early stages, and only show obvious symptoms in the later stages. There are fewer treatment options available for late-stage kidney cancer, and the cure rate after surgery is poor. Therefore, early screening for kidney cancer is particularly important.

[0004] Currently, early renal cell carcinoma screening in clinical practice mainly involves techniques such as abdominal ultrasound, CT scans, and magnetic resonance imaging. These detection techniques have the advantages of being non-invasive and simple, and can detect 80%-90% of early renal cell carcinomas in clinical practice. However, these methods usually cause certain radiation damage to patients and require specialized equipment and skilled and experienced medical personnel. They are not suitable for screening large-scale high-risk populations. Therefore, there is an urgent need to develop a screening method for early renal cell carcinoma with high sensitivity and specificity for the screening and prevention of early renal cell carcinoma. Summary of the Invention

[0005] The purpose of this invention is to provide a combination of metabolic biomarkers for the diagnosis of early renal cell carcinoma and its application, which has high sensitivity and high specificity and can be used for the screening and prevention of early renal cell carcinoma.

[0006] This invention provides a combination of metabolic biomarkers for the early diagnosis of renal cell carcinoma, comprising a first combination or a second combination: the first combination comprises: glycyl-L-glutamate, L-glutyl-L-serine, methylphosphatidylcholine 33:2e, and triglycerides 54:7; the second combination comprises: glycyl-L-glutamate, L-glutyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglycerides 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e, and triglycerides 54:7.

[0007] The present invention also provides the use of substances for detecting the combination of metabolic markers described in the above scheme in the preparation of products for the diagnosis or prognosis of early renal cell carcinoma.

[0008] In the specific implementation of this invention, the early renal cell carcinoma is stage I or stage II renal cell carcinoma.

[0009] The present invention also provides the use of substances for detecting the combination of metabolic markers described in the above scheme in the preparation of products that distinguish between healthy subjects and patients with early-stage renal cell carcinoma.

[0010] In the specific implementation of this invention, the substance used to detect the combination of metabolic markers described in the above scheme includes a liquid chromatography-mass spectrometry detection reagent.

[0011] The present invention also provides reagents or kits for the early diagnosis of renal cell carcinoma, the reagents or kits comprising substances for detecting the combination of metabolic markers described in the above-described scheme.

[0012] In the specific implementation of this invention, the early renal cell carcinoma is stage I or stage II renal cell carcinoma.

[0013] The present invention also provides a system for the early diagnosis of renal cell carcinoma, comprising reagents and / or instruments for detecting the combination of metabolic markers described in the above-described scheme.

[0014] This invention also provides a method for constructing a diagnostic model for early-stage renal cell carcinoma, comprising the following steps:

[0015] Small molecule metabolites were detected and identified in plasma samples from healthy individuals and early-stage renal cell carcinoma individuals in the modeling group, respectively, to obtain data from healthy individuals and early-stage renal cell carcinoma individuals in the modeling group.

[0016] Significantly different metabolite analyses were performed on the healthy population data and early renal cancer population data in the modeling group to screen small molecule metabolites that showed significant differences between the groups, thus obtaining a combination of metabolic biomarkers.

[0017] Multivariate ROC curve analysis was performed on the combination of metabolic biomarkers.

[0018] In the specific implementation of this invention, the multivariate ROC curve analysis includes: selecting 3 / 4 of the samples from the healthy group data and the early renal cancer group data in the modeling group as the training set, and the remaining 1 / 4 of the samples as the test set, and using a support vector machine to randomly iterate 1000 times. By statistically calculating the average accuracy of the final model, a diagnostic model for early renal cancer is constructed.

[0019] This invention provides a combination of metabolic biomarkers for the diagnosis of early renal cell carcinoma, comprising a first combination or a second combination: the first combination includes: glycyl-L-glutamate, L-glutyl-L-serine, methylphosphatidylcholine 33:2e, and triglycerides 54:7; the second combination includes: glycyl-L-glutamate, L-glutyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglycerides 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e, and triglycerides 54:7. This invention, by detecting the aforementioned combination of metabolic biomarkers in blood samples, can determine whether a patient has early renal cell carcinoma, exhibiting high sensitivity and specificity. It enables accurate screening for early renal cell carcinoma, providing significant assistance in the prevention and reduction of its incidence, achieving early screening, early detection, and early treatment. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 Multivariate ROC curve analysis of eight important markers distinguishing HC vs eRC in the modeling group;

[0022] Figure 2 Multivariate ROC curve analysis of eight key biomarkers that distinguish HC vs eRC in the validation group;

[0023] Figure 3 Multivariate ROC curve analysis of four important markers that distinguish HC vs eRC in the validation group. Detailed Implementation

[0024] This invention provides a combination of metabolic biomarkers for the early diagnosis of renal cell carcinoma, comprising a first combination or a second combination: the first combination comprises: glycyl-L-glutamate, L-glutyl-L-serine, methylphosphatidylcholine 33:2e, and triglycerides 54:7; the second combination comprises: glycyl-L-glutamate, L-glutyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglycerides 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e, and triglycerides 54:7.

[0025] In a specific implementation of the present invention, the first combination consists of glycyl-L-glutamic acid, L-glutyl-L-serine, methylphosphatidylcholine 33:2e and triglycerides 54:7.

[0026] In the specific implementation of this invention, the second combination is composed of glycyl-L-glutamic acid, L-glutyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e and triglyceride 54:7.

[0027] The metabolic biomarker combination of this invention utilizes metabolomics technology to analyze plasma samples from healthy subjects and patients with early-stage renal cell carcinoma, screening out a set of metabolic biomarkers with significant differences. This metabolic biomarker combination is a non-invasive, non-surgical, and highly accurate combination for distinguishing between healthy subjects and patients with early-stage renal cell carcinoma. It can be used to construct diagnostic models with high sensitivity and specificity for differentiating between healthy subjects and patients with early-stage renal cell carcinoma. Based on this metabolic biomarker combination, early diagnosis of early-stage renal cell carcinoma has the advantages of being non-invasive, non-surgical, easy to obtain samples, and highly accurate, sensitive, and specific. It can improve the diagnostic assessment ability for patients with early-stage renal cell carcinoma, enabling early detection, early diagnosis, and early intervention in clinical practice, thereby improving the survival rate of patients with early-stage renal cell carcinoma. Furthermore, it has the advantages of convenience, economy, and easy sample acquisition, making it more suitable for large-scale population screening and regular follow-up in areas with limited medical resources.

[0028] The present invention also provides the use of substances for detecting the combination of metabolic markers described in the above scheme in the preparation of products for the diagnosis or prognosis of early renal cell carcinoma.

[0029] In the specific implementation of this invention, the early renal cell carcinoma is stage I or stage II renal cell carcinoma; the early renal cell carcinoma includes primary malignant renal tumors.

[0030] The present invention also provides the use of substances for detecting the combination of metabolic markers described in the above scheme in the preparation of products that distinguish between healthy subjects and patients with early-stage renal cell carcinoma.

[0031] In the specific implementation of this invention, the substance used to detect the combination of metabolic markers described in the above scheme includes a liquid chromatography-mass spectrometry detection reagent.

[0032] The present invention also provides reagents or kits for the early diagnosis of renal cell carcinoma, the reagents or kits comprising substances for detecting the combination of metabolic markers described in the above-described scheme.

[0033] The present invention also provides a system for the early diagnosis of renal cell carcinoma, comprising reagents and / or instruments for detecting the combination of metabolic markers described in the above-described scheme.

[0034] This invention also provides a method for constructing a diagnostic model for early-stage renal cell carcinoma, comprising the following steps:

[0035] Small molecule metabolites were detected and identified in plasma samples from healthy individuals and early-stage renal cell carcinoma individuals in the modeling group, respectively, to obtain data from healthy individuals and early-stage renal cell carcinoma individuals in the modeling group.

[0036] Significantly different metabolite analyses were performed on the healthy population data and early renal cancer population data in the modeling group to screen small molecule metabolites that showed significant differences between the groups, thus obtaining a combination of metabolic biomarkers.

[0037] Multivariate ROC curve analysis was performed on the combination of metabolic biomarkers.

[0038] This invention first performs small molecule metabolite detection and identification on plasma samples from healthy individuals and early-stage renal cancer individuals in the modeling group, respectively, to obtain data from healthy individuals and early-stage renal cancer individuals in the modeling group.

[0039] In the specific implementation of this invention, the detection includes detection using liquid chromatography-mass spectrometry.

[0040] Before the identification, the present invention also includes processing the metabolomics data obtained from the detection, including the following steps: (1) extracting peaks from the RAW format file of the mass spectrometer to obtain a FeatureXML format file, reducing the dimensionality of the original mass spectrometry data and improving the signal-to-noise ratio; (2) using the peak alignment algorithm of OpenMS software to correct and align the retention time of the peak format data in the FeatureXML format file after peak extraction between samples, thereby converting the mass spectrometry data into a data matrix; (3) matching and filtering the isotope peaks in the data matrix, and then replacing the abnormal data with missing values; the abnormal data includes 0, negative values ​​and background noise; (4) removing the characteristic peaks with a detection rate of <80% from all the characteristic peaks obtained in step (3), filling the median value of the characteristic peak with the characteristic peaks with a detection rate of >80%, and adding 5% random noise (following a standard normal distribution); (5) using NormalizationAutoencoder (NormAE) for homogenization processing, the purpose of which is to reduce the difference in metabolite concentration between samples, make the data distribution more symmetrical, and remove systematic errors such as batch effects.

[0041] In the specific implementation of this invention, the criteria for identification are that the retention time differs by 0.1 min, and the theoretical and measured values ​​of the metabolite molecular weight are less than 10 ppm.

[0042] After obtaining the data of the healthy group and the early renal cancer group in the modeling group, the present invention performs significant difference metabolite analysis on the data of the healthy group and the early renal cancer group in the modeling group, and screens out small molecule metabolites that show significant differences between the groups to obtain a combination of metabolic markers.

[0043] In this invention, the analysis of significantly different metabolites includes screening based on the fold change (FC) and the significance of the difference (p value), wherein the FC > 1.5 or FC < 0.67, and p < 0.05.

[0044] After obtaining the combination of metabolic biomarkers, the present invention performs multivariate ROC curve analysis on the combination of metabolic biomarkers.

[0045] In the specific implementation of this invention, the multivariate ROC curve analysis includes: selecting 3 / 4 of the samples from the healthy group data and the early renal cancer group data in the modeling group as the training set, and the remaining 1 / 4 of the samples as the test set, and using a support vector machine to randomly iterate 1000 times. By statistically calculating the average accuracy of the final model, a diagnostic model for early renal cancer is constructed.

[0046] The early renal cell carcinoma diagnostic model constructed using the metabolic biomarker combination of the present invention has high diagnostic efficacy and clinical diagnostic significance.

[0047] In this invention, it was verified that the diagnostic model for early renal cell carcinoma constructed by the first combination had an AUC of 0.967 (sensitivity = 0.862, specificity = 0.905) and a diagnostic threshold of 0.6899; the diagnostic model for early renal cell carcinoma constructed by the second combination had an AUC of 0.970 (sensitivity = 0.862, specificity = 0.905) and a diagnostic threshold of 0.7011.

[0048] To further illustrate the present invention, a combination of metabolic biomarkers for early diagnosis of renal cell carcinoma and its application are described in detail below with reference to the accompanying drawings and embodiments. However, these descriptions should not be construed as limiting the scope of protection of the present invention.

[0049] Example 1

[0050] 1. Subject Information

[0051] 1) Sample inclusion criteria:

[0052] Participants must meet all of the following inclusion criteria to be eligible to participate in this study:

[0053] (1) Males or females aged 18 years or older;

[0054] (2) Read and fully understand the information, sign the informed consent form, and be able to provide a blood sample for metabolomics testing;

[0055] (3) Early Renal Cancer Group: Patients diagnosed with primary renal malignancy by biopsy / postoperative pathology or by comprehensive clinical assessment by clinicians, and included in the early renal cancer group (eRC) based on clinical staging information, are included in the group of patients with stage I and stage II renal cancer.

[0056] 2) Sample exclusion criteria:

[0057] Subjects who meet any of the following exclusion criteria are ineligible to participate in this study:

[0058] (1) During pregnancy or lactation;

[0059] (2) Emergency room visit or resuscitation required;

[0060] (3) History of blood transfusion within 7 days prior to sampling;

[0061] (4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants;

[0062] (5) History of malignant tumor within 5 years or any anti-tumor treatment before sampling;

[0063] (6) Multiple primary malignant tumors coexist.

[0064] 1) Subject information

[0065] This study collected plasma samples from 167 participants across two medical centers, including 72 healthy controls (HC) and 95 early renal cancer (eRC) samples (stage I and II only). Specifically, the plasma samples used for the modeling group consisted of 51 healthy controls (HC) and 66 early renal cancer (eRC) samples; the plasma samples used for the validation group consisted of 21 healthy controls (HC) and 29 early renal cancer (eRC) samples (Table 1).

[0066] Table 1 Subject Information

[0067] Grouping Healthy control group (HC) Early-stage renal cell carcinoma (eRC) Number of people in the modeling team 51 66 Number of people in the verification group 21 29 total 72 95

[0068] 2. Plasma metabolite detection

[0069] 1) Reagents:

[0070] Methanol, acetonitrile, water, acetic acid, and isopropanol of mass spectrometry grade purity, and formic acid, ammonium acetate, and methyl tert-butyl ether of chromatographic (HPLC) grade purity were purchased from Sigma-Aldrich, USA.

[0071] 2) Sample preparation:

[0072] Take 100 μL of plasma and place it in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio 3:1) solution. Vortex to mix the extracted blood sample and obtain the sample extract. Add 500 μL of (methanol: water, volume ratio 3:1) solution 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.

[0073] - 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 (acetonitrile:isopropanol, volume ratio 3:1), and incubate at room temperature for 15 min. After incubation, vortex the centrifuge tube, sonicate for 5 min, and then centrifuge at room temperature for 5 min (12000 rpm). Take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass vial, which is the organic phase test solution, and perform LC-MS analysis.

[0074] - Aqueous phase: After sample separation, take 400 μL of the lower aqueous phase into 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 min. After incubation, vortex the centrifuge tube, sonicate for 5 min, and then centrifuge at room temperature for 5 min (12000 rpm). Take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass vial as the aqueous phase test solution, and perform LC-MS analysis.

[0075] 3) Detection of small molecule metabolites:

[0076] Organic phase using Waters ACQUTTY BEH C8 1.7μm 2.1*100mm column, with WatersACQUTTY water phase. HSS T3 1.8μm 2.1*100mm column was used for small molecule separation; ACQUITYUPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific) were used for both liquid chromatography and mass spectrometry.

[0077] The mobile phase parameters are as follows:

[0078] The parameters of the mobile phase of the organic phase test solution are as follows: Mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate; Mobile phase B is an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 1% ammonium acetate. The separation and elution gradient is as follows: 55%-89% mobile phase B from 0 to 12 min, and 100% mobile phase B from 12 to 19.5 min.

[0079] The mobile phase parameters of the aqueous test solution 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 and elution gradient is as follows: 0-13 min is 1%-70% mobile phase B, and 13-18 min is 99% mobile phase B.

[0080] The mass spectrometry parameters are as follows:

[0081] 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 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 HCD relative collision energy was 30 eV.

[0082] 3. Metabolomics data preprocessing and metabolite identification

[0083] 1) Metabolomics data processing:

[0084] (1) Extract peaks from the RAW format file of the mass spectrometer to obtain a FeatureXML format file; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention time of the peak format data in the FeatureXML format file after peak extraction 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 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) Use NormalizationAutoencoder (NormAE) to perform normalization processing to remove systematic errors such as batch effects.

[0085] 2) Identification of metabolites:

[0086] 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 Lipidmap Database. www.lipidmaps.org Based on metabolites identified from relevant databases, final validation was performed 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 were retention times within 0.1 min and a difference between the theoretical and measured molecular weight of the metabolite of less than 10 ppm.

[0087] 4. Data Analysis

[0088] 1) Screening for metabolic biomarkers to differentiate between healthy individuals and early-stage renal cell carcinoma

[0089] First, a significant difference metabolite analysis was performed on the data of the modeling group. The metabolites were screened based on the fold change (FC) and the significance of the difference (p value), specifically FC>1.5 or FC<0.67 and p<0.05. A total of 8 metabolites with significant differences between the groups were selected (Table 2) as important metabolic markers to distinguish between healthy individuals and early renal cell carcinoma.

[0090] Table 28 Important Metabolic Markers for Differentiating Between Healthy Individuals and Early-Stage Kidney Cancer

[0091] Logo - English Logo - Chinese FC p-value 1 Glycyl-L-glutamic acid Glycyl-L-glutamic acid 2.045 6.1583E-30 2 L-Glutamyl-L-Serine L-glutamyl-L-serine 1.637 4.1535E-26 3 L-Threonyl-L-asparticacid L-Threonyl-L-aspartic acid 2.268 2.5E-19 4 Iminodiaceticacid Iminodiacetic acid 1.772 1.932E-18 5 TAG56:8 Triglycerides 56:8 2.435 3.1414E-11 6 O-Phosphorylethanolamine O-phosphoethanolamine 1.515 3.8614E-11 7 MePC33:2e Methylphosphatidylcholine 33:2e 0.624 7.5693E-09 8 TAG54:7 Triglycerides 54:7 1.950 2.5975E-06

[0092] 2) Construction of a diagnostic model to distinguish between healthy individuals and early-stage renal cell carcinoma

[0093] To validate the diagnostic efficacy of the eight selected metabolic biomarkers in distinguishing between healthy individuals and early-stage renal cell carcinoma, multivariate ROC curve analysis was performed on these eight biomarkers in the modeling group. Specifically, three-quarters of the sample data from the HC and eRC 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, and the diagnostic model for distinguishing between healthy individuals and early-stage renal cell carcinoma was constructed by statistically analyzing the average accuracy of the final model.

[0094] 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.

[0095] Sensitivity is:

[0096]

[0097] Specificity is:

[0098]

[0099] in,

[0100] TP (True Positive): The number of samples that are actually positive but were correctly predicted as positive.

[0101] TN (True Negative): The number of samples that are actually negative but were correctly predicted as negative.

[0102] FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive.

[0103] FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative.

[0104] The results are as follows Figure 1 As shown, AUC = 0.982 (sensitivity = 0.941, specificity = 0.923), indicating that the constructed diagnostic model has high diagnostic efficacy.

[0105] In addition, a diagnostic model for differentiating between healthy individuals and early-stage renal cell carcinoma was constructed using a combination of four metabolic markers: glycyl-L-glutamate, L-glutyl-L-serine, methylphosphatidylcholine 33:2e, and triglycerides 54:7. The results showed that the AUC was 0.981 (sensitivity = 0.875, specificity = 0.846), indicating that the diagnostic model constructed based on the combination of the above four metabolic markers has high diagnostic efficacy and clinical diagnostic significance.

[0106] 3) Validation of diagnostic models for distinguishing between healthy individuals and early-stage renal cell carcinoma

[0107] To further validate the effectiveness of the diagnostic model for distinguishing between healthy individuals and early-stage 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 eight important metabolic markers distinguishing between healthy individuals and early-stage 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 positives, true negatives, false positives, and false negatives) 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 will generate an ROC curve. Figure 2 Among them, the point with the best sensitivity and specificity was selected, and the diagnostic threshold at this point was 0.7011.

[0108] As shown in Table 3, the confusion matrix results indicate that, based on the eight metabolic biomarkers, the diagnostic model, with a diagnostic threshold of 0.7011, resulted in 25 out of 29 patients with early-stage renal cell carcinoma being correctly diagnosed, while 4 were misdiagnosed as healthy individuals. Among 21 healthy subjects, 19 were correctly diagnosed, and 2 were misdiagnosed as having early-stage 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 0.970 (sensitivity = 0.862, specificity = 0.905). These results indicate that the constructed diagnostic model for distinguishing between healthy individuals and early-stage renal cell carcinoma also demonstrated good diagnostic performance in the validation group.

[0109] Table 3 Confusion matrix of diagnostic models used to distinguish between healthy individuals and early-stage renal cell carcinoma.

[0110] Grouping Early-stage renal cell carcinoma healthy subjects 29 cases of early-stage renal cell carcinoma 25(TP) 4(FN) 21 healthy subjects 2(FP) 19(TN)

[0111] In addition, the diagnostic model based on the combination of four metabolic biomarkers—glycyl-L-glutamate, L-glutyl-L-serine, methylphosphatidylcholine 33:2e, and triglycerides 54:7—was validated in the validation group. The results showed that, using a diagnostic threshold of 0.6899, the diagnostic model based on these four metabolic biomarkers correctly diagnosed 25 out of 29 patients with early-stage renal cell carcinoma, while misdiagnosing 4 as healthy individuals. Among 21 healthy subjects, 19 were correctly diagnosed, and 2 were misdiagnosed as having early-stage renal cell carcinoma. The ROC analysis results of the diagnostic model in the validation group are as follows: Figure 3 As shown, the results indicate that the AUC = 0.967 (sensitivity = 0.862, specificity = 0.905). These results demonstrate that the constructed diagnostic model for distinguishing between healthy individuals and early-stage renal cell carcinoma also exhibits good diagnostic performance in the validation group. Although the above embodiments provide a detailed description of the present invention, they are merely some embodiments, not all embodiments. Other embodiments can be obtained based on these embodiments without inventive step, and all such embodiments fall within the scope of protection of this invention.

Claims

1. A combination of metabolic biomarkers for the early diagnosis of renal cell carcinoma, characterized in that, The combination of metabolic markers is either the first combination or the second combination; the first combination consists of glycyl-L-glutamate, L-glutyl-L-serine, methylphosphatidylcholine 33:2e and triglycerides 54:

7. The second combination consists of glycyl-L-glutamic acid, L-glutyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglycerides 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e, and triglycerides 54:

7.

2. The use of the combination of metabolic markers according to claim 1 in the preparation of products for early renal cell carcinoma diagnosis.

3. The application according to claim 2, characterized in that, The early-stage renal cell carcinoma refers to stage I or stage II renal cell carcinoma.

4. The use of the combination of metabolic markers of claim 1 in the preparation of a product that distinguishes between healthy subjects and patients with early-stage renal cell carcinoma.

5. A reagent or kit for the diagnosis of early renal cell carcinoma, characterized in that, The reagent or kit comprises the combination of metabolic biomarkers as described in claim 1.

6. The reagent or kit according to claim 5, characterized in that, The early-stage renal cell carcinoma refers to stage I or stage II renal cell carcinoma.

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