Blood metabolism marker combination for distinguishing healthy cancer from kidney cancer and application of blood metabolism marker combination

Abstract

The invention provides a blood metabolism marker combination for distinguishing health and kidney cancer and application of the blood metabolism marker combination. According to the metabolic marker combination for screening and diagnosing the kidney cancer, whether a patient suffers from the kidney cancer or not is judged by detecting the metabolic marker combination in a blood sample, the method has high sensitivity and specificity, accurate screening of the kidney cancer can be achieved, and important help is provided for prevention of the kidney cancer and reduction of the morbidity. The kidney cancer diagnosis model with high sensitivity and specificity is constructed based on the metabonomics data of plasma, the method is convenient and economical, samples are convenient to obtain, and the method is more suitable for large-scale population screening and regular tracking in areas with shortage of medical resources.

Description

Technical Field

[0001] The present invention relates to the field of metabolic biomarker analysis and detection, and specifically relates to a blood metabolic biomarker combination for distinguishing health from renal cancer and its application. Background Art

[0002] Renal cancer (RC), as one of the common malignant tumors in the urinary system, has an increasing incidence rate globally year by year. The high-incidence age of renal cancer is 40 - 55 years old, and the incidence rate in men is twice that in women. Moreover, the incidence rate increases with age. Early renal cancer usually has no obvious symptoms, resulting in most patients being in the middle and late stages at the time of diagnosis, and the prognosis is poor. Therefore, early screening and accurate diagnosis are of great significance for improving the survival rate and quality of life of renal cancer patients.

[0003] The existing clinically commonly used renal cancer screening and diagnosis techniques mainly include imaging examinations, such as CT imaging, MRI imaging, and PET-CT imaging, etc. As one of the main means for current renal cancer diagnosis, imaging examinations can clearly show the size and shape of kidney masses, but the examination cost is high, and it increases the patient's radiation exposure. It also requires combining with other diagnostic means (such as pathological examination) or further optimizing the technology to improve the accuracy and reliability of diagnosis. Pathological examination refers to obtaining kidney mass tissue through puncture for pathological examination, which is the "gold standard" for diagnosing renal cancer. However, this method has certain invasiveness and the risk of complications, and the patient compliance is low, which is not suitable for large-scale risk screening of the general population. Therefore, there is an urgent need to develop a renal cancer screening method with high sensitivity, high specificity, convenience, and economy for the screening and prevention of renal cancer.

[0004] The application of metabolomics technology in the clinical diagnosis of renal cancer has become a research hotspot in recent years. By analyzing metabolites in organisms, metabolomics can reveal the molecular characteristics and metabolic changes of renal cancer, providing important support for early diagnosis, disease staging, prognosis evaluation, and treatment response monitoring. Therefore, based on the metabolomics data of blood samples, this patent constructs a renal cancer diagnosis model with high sensitivity and high specificity to provide effective help for the diagnosis of renal cancer, achieving early screening, early detection, and early treatment.

[0005] The technical problem to be solved by this invention patent is to overcome the defects and deficiencies of the above-mentioned existing technologies, and provide a set of metabolic biomarker combinations for renal cancer screening and diagnosis. By detecting this metabolic biomarker combination in blood samples, it can be judged whether a patient has renal cancer. This method has high sensitivity and specificity, can achieve precise screening of renal cancer, and provides important help for the prevention of renal cancer and the reduction of its incidence rate. Summary of the Invention

[0006] The technical problem to be solved by this invention patent is to overcome the defects and deficiencies of the above-mentioned prior art, and provide a set of metabolic biomarker combinations for renal cancer screening and diagnosis. By detecting this metabolic biomarker combination in a blood sample, it can be determined whether a patient has renal cancer. This method has high sensitivity and specificity, can achieve precise screening of renal cancer, and provides important help for the prevention and reduction of the incidence rate of renal cancer.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention discloses a screening method for blood metabolic biomarkers for distinguishing health from renal cancer, comprising the following steps: 1) Collect plasma samples from renal cancer patients and healthy individuals, and prepare organic and aqueous phases for the samples respectively; 2) Use liquid chromatography and mass spectrometry to perform small molecule separation on the organic and aqueous phases. The organic phase uses a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase uses a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column; 3) After parsing the original data, obtain the spectral information of metabolites, and perform qualitative analysis on the metabolites after matching with common metabolite databases; 4) In the modeling group, perform univariate ROC analysis and OPLS-DA analysis on the metabolomics data of renal cancer patients and healthy individuals, and screen for differential metabolites according to the AUC value and VIP value.

[0008] Preferably, the mass spectrometry conditions are as follows: Mass spectrometry data is collected in the Full MS and Full MS / dd-MS2 modes. The parameters used for Q Exactive are as follows: In the Full MS mode, the resolution is 70,000, the scanning range is 100 - 1500 m / z, AGC is 3E+6, and the Maximum IT is 200 milliseconds; In the Full MS / dd-MS2 mode, the resolution of the secondary 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.

[0009] Preferably, the screening conditions in step 4) are AUC value > 0.8 and VIP value > 2.1.

[0010] Preferably, the common metabolite databases in step 3) are selected from the Human Metabolome Database, Metabolomics Database, Mass Spectrometry Database, and Lipid Metabolite Database.

[0011] The present invention discloses a metabolite marker composition obtained by screening with the said screening method, and the metabolite marker composition includes: L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, and arginine.

[0012] Preferably, the metabolite marker composition further includes: lysophosphatidylcholine 16:0e, arauridine, L-prolyl-L-aspartic acid, and L-threonyl-L-aspartic acid.

[0013] Preferably, the metabolite marker composition further includes: glycyl-L-glutamic acid, lysophosphatidylcholine 18:1e, glycolic acid, and lysophosphatidylcholine 20:1.

[0014] The present invention discloses a blood metabolite marker composition for distinguishing health from renal cancer, and the metabolite marker composition includes: L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, and arginine.

[0015] The present invention discloses a blood metabolite marker composition for distinguishing health from renal cancer, and the metabolite marker composition includes: L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, arginine, lysophosphatidylcholine 16:0e, arauridine, L-prolyl-L-aspartic acid, and L-threonyl-L-aspartic acid.

[0016] The present invention discloses a blood metabolite marker composition for distinguishing health from renal cancer, and the metabolite marker composition includes: L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, arginine, lysophosphatidylcholine 16:0e, arauridine, L-prolyl-L-aspartic acid, L-threonyl-L-aspartic acid, glycyl-L-glutamic acid, lysophosphatidylcholine 18:1e, glycolic acid, and lysophosphatidylcholine 20:1.

[0017] The present invention discloses a blood metabolite marker composition for distinguishing health from renal cancer, and the metabolite marker composition consists of the following metabolite markers: L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, arginine, lysophosphatidylcholine 16:0e, arauridine, L-prolyl-L-aspartic acid, L-threonyl-L-aspartic acid, glycyl-L-glutamic acid, lysophosphatidylcholine 18:1e, glycolic acid, and lysophosphatidylcholine 20:1.

[0018] The present invention discloses the use of the said metabolite marker composition in the preparation of a reagent / or kit for detecting renal cancer.

[0019] The present invention discloses a kit for detecting renal cancer, and the kit includes the said metabolite marker composition.

[0020] Preferably, the kit further includes a quality control product and / or a standard product.

[0021] Compared with the prior art, based on the plasma metabolomics data, the present invention constructs a renal cancer diagnosis model with high sensitivity and specificity. This method is convenient, economical, and the samples are easy to obtain, and it is more suitable for large-scale population screening and regular follow-up in areas with tight medical resources. Brief Description of the Drawings

[0022] Figure 1 . Multivariate ROC curve analysis of 12 important markers for differentiating HC vs RC in the modeling group.

[0023] Figure 2 . Multivariate ROC curve analysis of 12 important markers for differentiating HC vs RC in the validation group. Detailed Description of the Embodiments

[0024] The following further elaborates on the detailed embodiments of the present invention with reference to the accompanying drawings. It should be understood that the detailed embodiments described herein are only for explaining and illustrating the present invention and are not used to limit the present invention.

[0025] Example 1 Subject Conditions and Sample Collection 1) Sample inclusion criteria: Subjects must meet all of the following inclusion criteria to be eligible to participate in this study: (1) Males or females aged ≥ 18 years old; (2) Read and fully understand, sign the informed consent form, and be able to provide blood samples for metabolomics testing; (3) Renal cancer group: Patients diagnosed by biopsy / postoperative pathology or clinically diagnosed as primary renal malignancies by clinicians through comprehensive evaluation.

[0026] 2) Sample exclusion criteria: Subjects who meet any of the following exclusion criteria are ineligible to participate in this study: (1) Pregnancy or lactation; (2) Emergency or need for rescue; (3) History of blood transfusion within 7 days before sampling; (4) Those who have received organ transplantation or have previously received non-autologous (allogeneic) bone marrow or stem cell transplantation; (5) History of malignant tumor within 5 years or any anti-tumor treatment before sampling; (6) Concurrent multiple primary malignancies.

[0027] 3) Subject conditions In this study, plasma samples from 236 subjects were collected through two medical centers, including 100 samples from the Healthy Control (HC) group and 136 samples from the Renal Cancer (RC) group. Specifically, for the modeling group, there were 76 plasma samples from the healthy control (HC) group and 105 plasma samples from the renal cancer (RC) group; for the validation group, there were 24 plasma samples from the healthy control (HC) group and 31 plasma samples from the renal cancer (RC) group (Table 1).

[0028] Table 1: Subject information Healthy control group (HC) Renal cancer (RC) Number of people in the modeling group 76 105 Number of people in the validation group 24 31 Total 100 136 Example 2 Plasma metabolite detection 1) Reagents: 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 all purchased from Sigma-Aldrich, USA.

[0029] 2) Sample preparation: 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 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 it stand, vortex, and centrifuge to separate layers. The upper layer is the organic phase and the lower layer is the aqueous phase.

[0030] - Organic phase: After the sample is layered, take 500 μL of the upper organic phase into a centrifuge tube. After drying, add 200 μL of a solution (acetonitrile: isopropanol, volume ratio 3:1) and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube to mix evenly, sonicate for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes (12,000 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 measured and perform on-machine (LC-MS) detection.

[0031] - Aqueous phase: After the sample is layered, take 400 μL of the lower aqueous phase into a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate proteins; after the proteins precipitate in the centrifuge tube, centrifuge the centrifuge tube and 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 minutes; after incubation, vortex the centrifuge tube to mix evenly, sonicate for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes (12,000 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 measured and perform on-machine (LC-MS) detection.

[0032] 3) Detection of small molecule metabolites: For small molecule separation, an organic phase column Waters ACQUTTY UPLC ® BEH C8 1.7µm 2.1*100mm column and an aqueous phase column Waters ACQUTTY UPLC ® HSS T3 1.8µm 2.1*100mm column are used. The ACQUITY UPLC I-Class liquid chromatography system (Waters) and the Q-Exactive mass spectrometry system (Thermo Fisher Scientific) are used for both liquid chromatography and mass spectrometry.

[0033] The mobile phase parameters are as follows: Mobile phase parameters for the organic phase analyte solution – 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 are as follows: 55% - 89% of Mobile phase B from 0 - 12 minutes, and 100% of Mobile phase B from 12 - 19.5 minutes. Mobile phase parameters for the aqueous phase analyte 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 are as follows: 1% - 70% of Mobile phase B from 0 - 13 minutes, and 99% of Mobile phase B from 13 - 18 minutes.

[0034] The mass spectrometry parameters are as follows: Mass spectrometry data is collected in the Full MS and Full MS / dd-MS2 modes (both 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 the 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.

[0035] Example 3: Metabolomics data preprocessing and metabolite identification 1) Metabolomics data processing: (1) Extract peaks from the RAW format files output from the mass spectrometer to obtain FeatureXML format files, reducing the dimension of the original mass spectrometry data and improving the signal-to-noise ratio; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention times of the extracted peak format data among samples, thereby converting the mass spectrometry data into a data matrix; (3) Match and filter the isotope peaks in the data matrix obtained in step 2, and then replace abnormal data (0, negative values, background noise, etc.) with missing values; (4) Among all the characteristic peaks obtained in step 3, those with a detection rate <80% are excluded, and the characteristic peaks with a detection rate >80% are filled with the median value of the characteristic peak and added with 5% random noise (following the standard normal distribution); (5) In order to reduce the differences in metabolite concentrations among samples and make the data distribution more symmetric, use Normalization Autoencoder (NormAE) for normalization processing to remove systematic errors such as batch effects.

[0036] 2) Identification of metabolites: According to 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 primary mass spectrometry and the fragments of the secondary ions, match them with the spectral information of 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 metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrometry information during separation under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are within a 0.1 min difference in retention time and the theoretical value and measured value of the metabolite molecular weight being less than 10 ppm.

[0037] 3) Data analysis (1) Screening of metabolic markers for distinguishing healthy from renal cancer First, perform univariate ROC analysis and OPLS-DA analysis on the data of the modeling group, and screen for differential metabolites according to the AUC value and VIP value. Specifically, AUC > 0.8 and VIP > 2.1, and a total of 12 metabolites with significant differences between groups are screened out (Table 2), which are used as important metabolic markers for distinguishing healthy from renal cancer.

[0038] Table 2. Twelve important metabolic markers for distinguishing healthy from renal cancer Marker - English Marker - Chinese AUC VIP 1 Glycyl-L-glutamic acid Glycyl-L-glutamic acid 0.951 2.987 2 L-Glutamyl-L-Serine L-Glutamyl-L-Serine 0.921 2.727 3 D-Glyceric acid D-Glyceric acid 0.867 2.421 4 Iminodiacetic acid Iminodiacetic acid 0.863 2.412 5 LysoPC 16:0e Lysophosphatidylcholine 16:0e 0.860 2.337 6 LysoPC 18:1e Lysophosphatidylcholine 18:1e 0.853 2.305 7 Arabinofuranosyluracil Arabinofuranosyluracil 0.831 2.269 8 L-Prolyl-L-aspartic acid L-Prolyl-L-aspartic acid 0.829 2.258 9 L-Threonyl-L-aspartic acid L-Threonyl-L-aspartic acid 0.809 2.249 10 Glycolic acid Glycolic acid 0.833 2.211 11 Arginine Arginine 0.823 2.146 12 LysoPC 20:1 Lysophosphatidylcholine 20:1 0.818 2.115 (2)Construction of a diagnostic model for differentiating healthy individuals from those with renal cancer To verify the diagnostic efficacy of the 12 screened metabolic markers in differentiating healthy individuals from those with renal cancer, multivariate ROC curve analysis was performed on the above 12 metabolic markers in the modeling group. Specifically, 3 / 4 of the sample data of the HC group and the RC group in the modeling group were randomly used as the training set, and 1 / 4 as the test set for learning. The machine learning support vector machine (SVM) was used to randomly iterate 1000 times. By statistically averaging the accuracy of the final model, a diagnostic model for differentiating healthy individuals from those with renal cancer was constructed.

[0039] The ROC curve is a method for studying the relationship between the sensitivity and specificity of a model. With sensitivity as the ordinate and 1 - specificity as the abscissa, 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 and the better the diagnostic effect. If it is less than 0.5, it indicates poor model accuracy. In addition to common parameters such as the receiver operating characteristic curve (ROC) and the area under the curve (AUC), the ROC classification prediction model also includes sensitivity and specificity.

[0040] Sensitivity is:

[0041] Specificity is:

[0042] Among them, TP (True Positive): True positive, the number of samples that are actually positive and are correctly predicted as positive; TN (Ture Negative): True negative, the number of samples that are actually negative and are correctly predicted as negative; FP (False Positive): False positive, the number of samples that are actually negative but are wrongly predicted as positive; FN (False Negative): False negative, the number of samples that are actually positive but are wrongly predicted as negative.

[0043] The results are as Figure 1 shown, AUC = 0.996 (sensitivity = 0.962, specificity = 0.947), indicating that the constructed diagnostic model has high diagnostic efficacy.

[0044] In addition, diagnostic models for distinguishing between healthy and renal cancer were constructed for the combinations of 8 metabolic markers, namely L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, lysophosphatidylcholine 16:0e, arabinouridine, L-prolyl-L-aspartic acid, L-threonyl-L-aspartic acid, and arginine, and for the combinations of 4 metabolic markers, namely L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, and arginine, respectively. The results showed that for the diagnostic model constructed with the combination of 8 metabolic markers, AUC = 0.982 (sensitivity = 0.926, specificity = 0.947), and for the diagnostic model constructed with the combination of 4 metabolic markers, AUC = 0.976 (sensitivity = 0.923, specificity = 0.947). The results indicated that the diagnostic models constructed based on the above-mentioned combinations of 8 and 4 metabolic markers respectively had high diagnostic efficacy and clinical diagnostic significance.

[0045] (3) Validation of the diagnostic model for distinguishing between healthy and renal cancer To further validate the effectiveness of the diagnostic model for distinguishing between healthy and renal cancer 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 the unknown data set outside the modeling group data set. After putting the samples of the validation group into the diagnostic model constructed by the modeling group, according to the detection data of 12 important metabolic markers for distinguishing between healthy and renal cancer of each sample, the corresponding probability value (Probability) would be output. Taking 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) could be 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 would be 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 0.6039.

[0046] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above-mentioned 12 metabolic markers, with 0.6039 as the diagnostic threshold, among 31 renal cancer patients, 30 were judged to have renal cancer, and 1 was misjudged as a healthy person; among 24 healthy subjects, 22 were correctly judged, and 2 were misjudged as having renal cancer; the ROC analysis results of the diagnostic model in the validation group are as Figure 2As shown, the sensitivity and specificity were calculated based on the results of the confusion matrix, and the results showed that AUC = 0.985 (sensitivity = 0.968, specificity = 0.917). The above results indicate that the constructed diagnostic model for distinguishing healthy from renal cancer also has good diagnostic effects in the validation group.

[0047] Table 3. Confusion Matrix of the Diagnostic Model for Distinguishing Healthy from Renal Cancer Renal cancer Healthy subjects 31 cases of renal cancer 30 (TP) 1 (FN) 24 healthy subjects 2 (FP) 22 (TN) In addition, the diagnostic models of the combinations of 8 metabolic markers, L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, lysophosphatidylcholine 16:0e, arauridine, L-prolyl-L-aspartic acid, L-threonyl-L-aspartic acid, and arginine, and the combinations of 4 metabolic markers, L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, and arginine, were also verified in the validation group. The results showed that the results of the diagnostic model constructed by the combination of 8 metabolic markers in the validation group were AUC = 0.987 (sensitivity = 0.935, specificity = 0.917), and the results of the diagnostic model constructed by the combination of 4 metabolic markers were AUC = 0.988 (sensitivity = 0.935, specificity = 0.958). The above results indicate that the constructed diagnostic model for distinguishing healthy from renal cancer also has good diagnostic effects in the validation group.

[0048] The present invention uses the above embodiments to illustrate the process method of the present invention, 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 improvement of the present invention, the equivalent replacement of the raw materials selected by the present invention, the addition of auxiliary components, the selection of specific methods, etc. all fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A screening method for blood metabolic markers for differentiating health from renal cancer, characterized in that, It includes the following steps: 1) Collect plasma samples from renal cancer patients and healthy individuals, and prepare organic and aqueous phases for the samples respectively; 2) Use liquid chromatography and mass spectrometry to separate small molecules in the organic and aqueous phases. The organic phase uses a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase uses a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column; 3) Obtain the spectral information of metabolites after parsing the original data, and identify the metabolites after matching with common metabolite databases; 4) Perform univariate ROC analysis and OPLS-DA analysis on the metabolomics data of renal cancer patients and healthy individuals in the modeling group, and screen for differential metabolites according to the AUC value and VIP value.

2. The screening method according to claim 1, wherein The mass spectrometry conditions are as follows: Mass spectrometry data is collected in the Full MS and Full MS / dd-MS2 modes. The parameters used for Q Exactive are as follows: In the Full MS mode, the resolution is 70,000, the scanning range is 100 - 1500 m / z, AGC is 3E+6, and the Maximum IT is 200 milliseconds; in the Full MS / dd-MS2 mode, the resolution of the secondary 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 HCD relative collision energy is 30 eV.

3. The screening method according to claim 1, characterized in that In step 3), the common metabolite databases are selected from the Human Metabolome Database, Metabolomics Database, Mass Spectrometry Database, and Lipid Metabolite Database.

4. The screening method according to claim 1, characterized in that, The screening conditions in step 4) are AUC value > 0.8 and VIP value > 2.

1.

5. The metabolite marker composition obtained by screening with the screening method according to claim 1, characterized in that The metabolite marker composition includes: L-glutamyl-L-serine, D-glyceric acid, iminodiacetic acid, and arginine.

6. The metabolic marker composition according to claim 5, wherein The metabolite marker composition further includes: lysophosphatidylcholine 16:0e, arauridine, L-prolyl-L-aspartic acid, and L-threonyl-L-aspartic acid.

7. The metabolic marker composition according to claim 6, wherein The metabolite marker composition further includes: glycyl-L-glutamic acid, lysophosphatidylcholine 18:1e, glycolic acid, and lysophosphatidylcholine 20:

1.

8. Use of the metabolite marker composition according to any one of claims 5 - 7 in the preparation of a reagent and / or kit for detecting renal cancer.

9. A kit for detecting renal cancer, characterized in that, The kit includes the metabolite marker composition according to any one of claims 5 - 7.

10. The kit according to claim 9, wherein The kit further includes a quality control product and / or a standard product.

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