Metabolic marker combination for early kidney cancer diagnosis and application thereof

Abstract

The invention 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, wherein the first combination comprises glycyl-L-glutamic acid, L-glutamyl-L-serine, methylphosphatidylcholine 33: 2e and triglyceride 54: 7, and the second combination comprises glycyl-L-glutamic acid, L-glutamyl-L-serine, methylphosphatidylcholine 33: 2e and triglyceride 54: 7; and the second combination is prepared from glycyl-L-glutamic acid, L-glutamyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56: 8, O-phosphoethanolamine, methyl phosphatidylcholine 33: 2e and triglyceride 54: 7. By detecting the metabolic marker combination in a blood sample, whether a patient suffers from the early kidney cancer or not can be judged, high sensitivity and specificity are achieved, accurate screening of the early kidney cancer can be achieved, and important help is provided for prevention of the kidney cancer and reduction of the morbidity.

Description

Technical Field

[0001] The present invention belongs to the technical field of in vitro diagnosis, and particularly relates to a metabolic marker combination for early renal cancer diagnosis and its application. Background Art

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

[0003] Early diagnosis of renal cancer is often difficult because the vast majority of renal cancers usually have no obvious symptoms in the early stage and only have obvious symptoms in the late stage. There are fewer treatment methods available for late-stage renal cancer and the curative effect after surgery is not good. Therefore, the screening of early renal cancer becomes particularly important.

[0004] Currently, the main methods for early renal cancer screening in clinical practice include abdominal ultrasound examination, CT examination, and magnetic resonance imaging. These detection techniques have the advantages of being non-invasive and simple, and can detect 80%-90% of early renal cancers clinically. However, these methods usually have a certain radiation harm to patients, and require professional equipment and skilled and experienced medical staff, and are not suitable for the screening of large-scale high-risk populations. Therefore, there is an urgent need to develop a screening method for early renal cancer with high sensitivity and high specificity for the screening and prevention of early renal cancer. Summary of the Invention

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

[0006] The present invention provides a metabolic marker combination for early renal cancer diagnosis, including a first combination or a second combination: the first combination includes: glycyl-L-glutamic acid, L-glutamyl-L-serine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7; the second combination includes: glycyl-L-glutamic acid, L-glutamyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56:8, O-phosphoethanolamine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7.

[0007] The present invention also provides the application of a substance for detecting the above-mentioned metabolic marker combination in the preparation of a product for early renal cancer diagnosis or prognosis.

[0008] In the specific implementation process of the present invention, the early renal cancer is stage I or stage II renal cancer.

[0009] The present invention also provides the application of a substance for detecting the above-mentioned metabolic marker combination in the preparation of a product for distinguishing healthy subjects from early renal cancer patients.

[0010] In the specific implementation process of the present invention, the substance for detecting the metabolite marker combination described in the above solution includes a liquid chromatography-mass spectrometry detection reagent.

[0011] The present invention also provides a reagent or kit for the diagnosis of early renal cancer, and the reagent or kit includes a substance for detecting the metabolite marker combination described in the above solution.

[0012] In the specific implementation process of the present invention, the early renal cancer is stage I or stage II renal cancer.

[0013] The present invention also provides a system for the diagnosis of early renal cancer, including a reagent and / or instrument for detecting the metabolite marker combination described in the above solution.

[0014] The present invention also provides a method for constructing a diagnostic model for early renal cancer, including the following steps:

[0015] Perform small molecule metabolite detection and identification on the plasma samples of the healthy population and the early renal cancer population in the modeling group respectively, and obtain the healthy population data and early renal cancer population data in the modeling group respectively;

[0016] Perform significant difference metabolite analysis on the healthy population data and early renal cancer population data in the modeling group, screen out small molecule metabolites with significant differences between groups, and obtain a metabolite marker combination;

[0017] Perform multivariate ROC curve analysis on the metabolite marker combination.

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

[0019] The present invention provides a combination of metabolic markers for the diagnosis of early renal cancer, including a first combination or a second combination: the first combination includes: glycyl-L-glutamic acid, L-glutamyl-L-serine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7; the second combination includes: glycyl-L-glutamic acid, L-glutamyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56:8, O-phosphoethanolamine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7. By detecting the combination of metabolic markers in a blood sample, the present invention can determine whether a patient has early renal cancer, has high sensitivity and specificity, can achieve precise screening of early renal cancer, provides important help for the prevention and reduction of the incidence of renal cancer, and enables early screening, early detection, and early treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 Multivariate ROC curve analysis chart of 8 important markers for differentiating HC vs eRC in the modeling group;

[0022] Figure 2 Multivariate ROC curve analysis chart of 8 important markers for differentiating HC vs eRC in the validation group;

[0023] Figure 3 Multivariate ROC curve analysis chart of 4 important markers for differentiating HC vs eRC in the validation group. DETAILED DESCRIPTION OF THE INVENTION

[0024] The present invention provides a combination of metabolic markers for the diagnosis of early renal cancer, including a first combination or a second combination: the first combination includes: glycyl-L-glutamic acid, L-glutamyl-L-serine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7; the second combination includes: glycyl-L-glutamic acid, L-glutamyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56:8, O-phosphoethanolamine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7.

[0025] In the specific implementation process of the present invention, the first combination is composed of glycyl-L-glutamic acid, L-glutamyl-L-serine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7.

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

[0027] The metabolic marker combination of the present invention is obtained by using metabolomics technology to analyze plasma samples of healthy subjects and early renal cancer patients, and screening out a group of metabolic markers with significant differences. The metabolic marker combination of the present invention is a non-invasive, non-invasive, and highly accurate metabolic marker combination for distinguishing healthy subjects from early renal cancer patients, and can be used to construct a diagnostic model with high sensitivity and specificity for distinguishing healthy subjects from early renal cancer patients. Early diagnosis of early renal cancer based on the metabolic marker combination of the present invention has the advantages of non-invasiveness, non-invasiveness, convenient sample acquisition, high accuracy, high sensitivity, and high specificity, can improve the diagnostic evaluation ability for early renal cancer patients, so as to achieve early detection, early diagnosis, and early intervention of early renal cancer clinically, thereby improving the survival rate of early renal cancer patients, and has the advantages of convenience, economy, and convenient sample acquisition, and is more suitable for large-scale population screening and regular follow-up in areas with tight medical resources.

[0028] The present invention also provides the use of a substance for detecting the metabolic marker combination described in the above solution in the preparation of a product for early renal cancer diagnosis or prognosis.

[0029] In the specific implementation process of the present invention, the early renal cancer is stage I or stage II renal cancer; the early renal cancer includes primary renal malignancies.

[0030] The present invention also provides the use of a substance for detecting the metabolic marker combination described in the above solution in the preparation of a product for distinguishing healthy subjects from early renal cancer patients.

[0031] In the specific implementation process of the present invention, the substance for detecting the metabolic marker combination described in the above solution includes a liquid chromatography-mass spectrometry detection reagent.

[0032] The present invention also provides a reagent or kit for early renal cancer diagnosis, and the reagent or kit includes a substance for detecting the metabolic marker combination described in the above solution.

[0033] The present invention also provides a system for early renal cancer diagnosis, including a reagent and / or instrument for detecting the metabolic marker combination described in the above solution.

[0034] The present invention also provides a method for constructing a diagnostic model for early renal cancer, including the following steps:

[0035] Small molecule metabolite detection and identification were respectively carried out on the plasma samples of the healthy group and the early renal cancer group in the modeling group, and the data of the healthy group and the early renal cancer group in the modeling group were obtained respectively.

[0036] Significant difference metabolite analysis was carried out on the data of the healthy group and the early renal cancer group in the modeling group, and small molecule metabolites with significant differences between groups were screened to obtain a metabolite marker combination.

[0037] Multivariate ROC curve analysis was carried out on the metabolite marker combination.

[0038] In the present invention, small molecule metabolite detection and identification were first respectively carried out on the plasma samples of the healthy group and the early renal cancer group in the modeling group, and the data of the healthy group and the early renal cancer group in the modeling group were obtained respectively.

[0039] In the specific implementation process of the present invention, the detection includes liquid chromatography-mass spectrometry combined detection.

[0040] Before the identification, the present invention also includes processing the metabolomics data obtained by detection, including the following steps: 1) Performing peak extraction on the RAW format file downloaded from the mass spectrometer to obtain a FeatureXML format file, reducing the dimension 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 times of the peak format data in the FeatureXML format file after peak extraction among samples, so as to convert the mass spectrometry data into a data matrix; (3) Matching and filtering the isotope peaks in the data matrix, and replacing abnormal data with missing values; the abnormal data includes 0, negative values, and background noise; (4) Removing the characteristic peaks with a detection rate <80% among all the characteristic peaks obtained in step (3), filling the median value of the characteristic peaks with a detection rate >80%, and adding 5% random noise (subject to the standard normal distribution); (5) Performing normalization processing using NormalizationAutoencoder (NormAE), aiming to reduce the differences in metabolite concentrations between samples, make the data distribution more symmetric, and remove systematic errors such as batch effects for processing.

[0041] In the specific implementation process of the present invention, the criteria for identification are within 0.1 min difference in retention time, and the theoretical value and measured value 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 carried out significant difference metabolite analysis on the data of the healthy group and the early renal cancer group in the modeling group, and screened small molecule metabolites with significant differences between groups to obtain a metabolite marker combination.

[0043] In the present invention, the significant differential metabolite analysis includes: screening according to the fold change (FC) and the significance of the difference (p-value), where FC > 1.5 or FC < 0.67, and p < 0.05.

[0044] After obtaining the metabolite biomarker combination, the present invention performs a multivariate ROC curve analysis on the metabolite biomarker combination.

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

[0046] The diagnostic model for early renal cancer constructed using the metabolite biomarker combination of the present invention has high diagnostic efficacy and has clinical diagnostic significance.

[0047] In the present invention, it has been verified that for the diagnostic model for early renal cancer constructed by the first combination, AUC = 0.967 (sensitivity = 0.862, specificity = 0.905), and the diagnostic threshold is 0.6899; for the diagnostic model for early renal cancer constructed by the second combination, AUC = 0.970 (sensitivity = 0.862, specificity = 0.905), and the diagnostic threshold is 0.7011.

[0048] To further illustrate the present invention, the following will describe in detail a metabolite biomarker combination for early renal cancer diagnosis and its application provided by the present invention in conjunction with the accompanying drawings and embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0049] Example 1

[0050] 1. Subject situation

[0051] 1) Sample inclusion criteria:

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

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

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

[0055] (3) Early renal cancer group: Patients diagnosed by biopsy / postoperative pathology or clinically diagnosed as primary renal malignancies by clinicians through comprehensive evaluation, and according to the clinical staging information, patients with stage I and stage II renal cancer were included and combined into the early renal cancer group (eRC).

[0056] 2) Sample exclusion criteria:

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

[0058] (1) Pregnancy or lactation;

[0059] (2) Emergency or need for rescue;

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

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

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

[0063] (6) Concurrent multiple primary malignancies.

[0064] 1) Subject situation

[0065] A total of 167 plasma samples from subjects were collected through two medical centers in this study, including 72 samples from the healthy control group (HC) and 95 samples from the early renal cancer group (eRC, only including stage I and stage II samples). Specifically, for the plasma samples used in the modeling group, there were 51 samples in the healthy control (HC) group and 66 samples in the early renal cancer (eRC) group; for the plasma samples used in the validation group, there were 21 samples in the healthy control (HC) group and 29 samples in the early renal cancer (eRC) group (Table 1).

[0066] Table 1 Subject situation

[0067] Grouping Healthy control group (HC) Early renal carcinoma (eRC) Number of people in the modeling group 51 66 Number of people in the validation group 21 29 Total 72 95

[0068] 2. Plasma metabolite detection

[0069] 1) Reagents:

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

[0071] 2) Sample preparation:

[0072] Take 100 μL of plasma and place it in 1000 μL of a pre-cooled solution (methyl tert-butyl ether: methanol, volume ratio 3:1). Vortex the extracted blood sample to obtain a sample extract; add 500 μL of a solution (methanol: water, volume ratio 3:1) to the sample extract, sonicate, let stand, vortex, and centrifuge for layering. The upper layer is the organic phase and the lower layer is the aqueous phase.

[0073] - Organic phase: After sample layering, take 500 μL of the upper organic phase into a centrifuge tube. After drying, add 200 μL of (acetonitrile: isopropanol, volume ratio 3:1) and incubate at room temperature for 15 min; after incubation, vortex the centrifuge tube to mix evenly, perform ultrasonic-assisted treatment for 5 min, and then centrifuge the centrifuge tube at room temperature for 5 min (12000 rpm); take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass injection vial as the organic phase sample to be tested and perform on-machine (LC-MS) detection.

[0074] - Aqueous phase: After sample layering, 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 in the centrifuge tube, centrifuge the centrifuge tube, transfer 1000 μL of the supernatant to a new centrifuge tube, and 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 to mix evenly, perform ultrasonic-assisted treatment for 5 min, and then centrifuge the centrifuge tube at room temperature for 5 min (12000 rpm); take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass injection vial as the aqueous phase sample to be tested and perform on-machine (LC-MS) detection.

[0075] 3) Small molecule metabolite detection:

[0076] The organic phase uses a Waters ACQUTTY BEH C8 1.7 μm 2.1*100 mm column, and the aqueous phase uses a Waters ACQUTTY HSS T3 1.8 μm 2.1*100 mm column for small molecule separation; both the liquid chromatography and the mass spectrometer use an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0077] The mobile phase parameters are as follows:

[0078] Mobile phase parameters for organic phase samples to be measured: 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% of Mobile phase B from 0 to 12 min, and 100% of Mobile phase B from 12 to 19.5 min.

[0079] Mobile phase parameters for aqueous phase samples to be measured: 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: 1% - 70% of Mobile phase B from 0 to 13 min, and 99% of Mobile phase B from 13 to 18 min.

[0080] The mass spectrometry parameters are as follows:

[0081] Mass spectrometry data is collected in the Full MS and Full MS / dd-MS2 modes (each containing positive and negative modes). The parameters used for QExactive are as follows: In the Full MS mode, the resolution is 70,000, the scan range is 100 - 1500 m / z, AGC is 3E+6, and Maximum IT is 200 milliseconds; in the Full MS / dd-MS2 mode, the resolution of the second - stage mass spectrometry is 17,500, the quadrupole window is 1.5 m / z, AGC is 1E+5, the maximum ion injection time is 50 ms, and the relative collision energy of HCD is 30 eV.

[0082] 3. Pretreatment of metabolomics data and identification of metabolites

[0083] 1) Processing of metabolomics data:

[0084] (1) Extract peaks from the RAW - format files downloaded from the mass spectrometer to obtain FeatureXML - format files; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention times of the peak - format data in the FeatureXML - format files after peak extraction among samples, thus 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 abnormal data (0, negative values, background noise, etc.) with missing values; (4) Among all the characteristic peaks obtained in step (3), remove the characteristic peaks with a detection rate <80%, fill the median value of the characteristic peaks with a detection rate >80%, and add 5% random noise (following a standard normal distribution); (5) Use NormalizationAutoencoder (NormAE) for normalization to remove systematic errors such as batch effects.

[0085] 2) Identification of metabolites:

[0086] Based on the spectral information of the compound's primary parent ion (MS1) and secondary fragment ion (MS2) obtained after software parsing of the original data, such as the mass-to-charge ratio (m / z) of the first-order mass spectrum, the fragments of the secondary ions are matched with the spectral information of the primary and secondary metabolites in the public database to qualitatively analyze the metabolites. Commonly used metabolite databases include the Human Metabolome Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), the Mass Spectrometry Database (www.massbank.jp), and the Lipid Metabolite Database (Lipidmap, www.lipidmaps.org ). Based on the metabolites identified from the relevant databases, the retention time, MS1, and MS2 mass spectrometry information of the standards under the same chromatographic column and mass spectrometry conditions are used to finally verify the metabolites. The criteria for metabolite identification are that the retention time difference is within 0.1 min, and the theoretical value and measured value of the metabolite molecular weight are less than 10 ppm.

[0087] 4. Data Analysis

[0088] 1) Screening of metabolic markers for distinguishing healthy from early renal cancer

[0089] First, perform a significant differential metabolite analysis on the data of the modeling group, and screen according to 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 groups are obtained (Table 2), which are used as important metabolic markers for distinguishing healthy from early renal cancer.

[0090] Table 2 8 important metabolic markers for distinguishing healthy from early renal cancer

[0091] Marker - English Marker - 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 - aspartic acid L - Threonyl - L - aspartic acid 2.268 2.5E-19 4 Iminodiacetic acid Iminodiacetic acid 1.772 1.932E-18 5 TAG56:8 Triglyceride 56:8 2.435 3.1414E-11 6 O - Phosphorylethanolamine O - Phosphorylethanolamine 1.515 3.8614E-11 7 MePC33:2e Methylphosphatidylcholine 33:2e 0.624 7.5693E-09 8 TAG54:7 Triglyceride 54:7 1.950 2.5975E-06

[0092] 2) Construction of a diagnostic model for distinguishing healthy from early renal cancer

[0093] To verify the diagnostic efficacy of the 8 screened metabolic markers in distinguishing healthy from early renal cancer, a multivariate ROC curve analysis was performed on the above 8 metabolic markers in the modeling group. Specifically, 3 / 4 of the sample data of the HC group and the eRC group in the modeling group were randomly used as the training set (training), and 1 / 4 as the test set (test) for learning. The machine learning support vector machine (SVM) was used to randomly iterate 1000 times, and a diagnostic model for distinguishing healthy from early renal cancer was constructed by statistically averaging the final model accuracy.

[0094] The ROC curve is a method for studying the relationship between the sensitivity and specificity of a model. With sensitivity as the vertical axis and 1 - specificity as the horizontal axis, the evaluation is based on comparing the area under the curve (AUC). When AUC is greater than 0.5, the closer AUC is to 1, the better the model performance, indicating a better diagnostic effect. If it is less than 0.5, it means the model has poor accuracy. In addition to common parameters such as the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC), the ROC classification prediction model also includes sensitivity and specificity.

[0095] Sensitivity is:

[0096]

[0097] Specificity is:

[0098]

[0099] Among them,

[0100] TP (True Positive): True positive, which is the number of samples that are actually positive cases and are correctly predicted as positive cases;

[0101] TN (True Negative): True negative, which is the number of samples that are actually negative cases and are correctly predicted as negative cases;

[0102] FP (False Positive): False positive, which is the number of samples that are actually negative cases but are wrongly predicted as positive cases;

[0103] FN (False Negative): False negative, which is the number of samples that are actually positive cases but are wrongly predicted as negative cases.

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

[0105] In addition, a diagnostic model for distinguishing healthy from early renal cancer was constructed using a combination of 4 metabolic markers, glycyl - L - glutamate, L - glutamyl - L - serine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7. The results showed that AUC = 0.981 (sensitivity = 0.875, specificity = 0.846). The results indicate that the diagnostic model constructed based on the above - mentioned combination of 4 metabolic markers has high diagnostic efficacy and has clinical diagnostic significance.

[0106] 3) Validation of the diagnostic model for differentiating healthy individuals from those with early-stage renal cancer

[0107] To further validate the effectiveness of the diagnostic model for differentiating healthy individuals from those with early-stage renal cancer, which was constructed based on the data of the modeling group, the above-mentioned diagnostic model was validated using the data of the validation group. Specifically, multivariate ROC curve analysis was performed to evaluate the independent validation effect of the diagnostic model on an unknown dataset other than the modeling group dataset. After putting the samples of the validation group into the diagnostic model constructed by the modeling group, according to the detection data of 8 important metabolic markers for differentiating healthy individuals from those with early-stage renal cancer for each sample, a corresponding probability value (Probability) would be output. Using the probability value of each sample as the diagnostic threshold, a set of confusion matrices (including true positives, true negatives, false positives, and false negatives) was obtained. According to the formula, the sensitivity and specificity could be calculated. In the ROC analysis graph with sensitivity as the ordinate and 1 - specificity as the abscissa, a point could be marked. Similarly, when the probability value of each sample was used as the diagnostic threshold, multiple different points were obtained in the ROC analysis graph. Connecting these points would draw an ROC curve ( Figure 2 ). Among them, the point composed of the best-performing sensitivity and specificity was selected, and the diagnostic threshold at this time was 0.7011.

[0108] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 8 metabolic markers, with 0.7011 as the diagnostic threshold, among 29 patients with early-stage renal cancer, 25 were judged as having early-stage renal cancer, and 4 were misjudged as healthy individuals; among 21 healthy subjects, 19 were correctly judged, and 2 were misjudged as having early-stage renal cancer; the ROC analysis results of the diagnostic model in the validation group are as Figure 2 shown. Based on the results of the confusion matrix, the sensitivity and specificity were calculated. The results showed that AUC = 0.970 (sensitivity = 0.862, specificity = 0.905). The above results indicate that the constructed diagnostic model for differentiating healthy individuals from those with early-stage renal cancer also has good diagnostic effects in the validation group.

[0109] Table 3 Confusion matrix of the diagnostic model for differentiating healthy individuals from those with early-stage renal cancer

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

[0111] In addition, a diagnostic model combining four metabolic markers, glycyl-L-glutamic acid, L-glutamyl-L-serine, methyl phosphatidylcholine 33:2e, and triglyceride 54:7, was also verified in the validation group. The results showed that in the diagnostic model constructed based on the above four metabolic markers, with a diagnostic threshold of 0.6899, among 29 early-stage renal cancer patients, 25 were judged to have early-stage renal cancer, and 4 were misjudged as healthy individuals; among 21 healthy subjects, 19 were correctly judged, and 2 were misjudged as having early-stage renal cancer; the ROC analysis results of the diagnostic model in the validation group are as Figure 3 shown, and the results showed that AUC = 0.967 (sensitivity = 0.862, specificity = 0.905). The above results indicate that the constructed diagnostic model for distinguishing between health and early-stage renal cancer also has good diagnostic effects in the validation group. Although the above embodiments have described the present invention in detail, they are only a part of the embodiments of the present invention, rather than all embodiments. People can also obtain other embodiments based on this embodiment without creative efforts, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A metabolic marker combination for early diagnosis of renal cancer, characterized in that: The method comprises the first combination or the second combination: the first combination comprises: glycyl-L-glutamic acid, L-glutamyl-L-serine, methylphosphatidylcholine 33:2e and triglyceride 54:7; The second combination includes: glycyl-L-glutamate, L-glutamyl-L-serine, L-threonyl-L-aspartic acid, iminodiacetic acid, triglyceride 56:8, O-phosphoethanolamine, methylphosphatidylcholine 33:2e and triglyceride 54:

7.

2. Use of a substance for detecting the metabolic marker combination according to claim 1 in the preparation of a product for early renal cancer diagnosis or prognosis.

3. The use according to claim 2, characterized in that: The early renal cancer is stage I or stage II renal cancer.

4. Use of a substance for detecting the combination of metabolic markers according to claim 1 in the preparation of a product for distinguishing healthy subjects from early renal cancer patients.

5. The use according to any one of claims 2 to 4, characterized in that: The substance used to detect the metabolic marker combination according to claim 1 includes a liquid chromatography-mass spectrometry detection reagent.

6. A reagent or kit for diagnosing early renal cancer, characterized in that: The reagent or kit comprises substances for detecting the metabolic marker combination according to claim 1.

7. The reagent or kit according to claim 6, characterized in that The early renal cancer is stage I or stage II renal cancer.

8. A system for early diagnosis of renal cancer, characterized in that: Comprising reagents and / or instruments for detecting the metabolic marker combination according to claim 1.

9. A method for constructing a diagnostic model for early renal cancer, characterized in that: The following steps are involved: Small molecule metabolites were detected and identified in the plasma samples of the healthy group and the early renal cancer group in the modeling group, respectively, to obtain the healthy group data and the early renal cancer group data in the modeling group; Performing a significant difference metabolite analysis on the healthy population data and the early renal cancer population data in the modeling group, screening small molecule metabolites with significant differences between the groups, and obtaining a metabolic marker combination; Multivariate ROC curve analysis was performed on the metabolic marker combination.

10. The method according to claim 9, characterized in that The multivariate ROC curve analysis includes: selecting 3 / 4 samples from the healthy group data and early renal cancer group data in the modeling group as a training set, and the remaining 1 / 4 samples as a test set; using a support vector machine to randomly iterate 1000 times, and constructing a diagnostic model for early renal cancer by statistically calculating the average value of the final model accuracy.

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