Urine metabolism marker for kidney cancer diagnosis and application thereof

Abstract

The invention belongs to the technical field of biomedicine, and particularly relates to a urine metabolism marker for kidney cancer diagnosis and application of the urine metabolism marker. According to the embodiment of the application, proper metabolic markers are screened by a metabonomics method, and the metabolic markers can establish a kidney cancer diagnosis model with high sensitivity and specificity, so that a more reliable and convenient diagnosis method is provided for kidney cancer diagnosis, and the application prospect in the field of kidney cancer diagnosis is expected to be wide.

Description

Technical Field

[0001] The present application belongs to the field of biomedical technology, and specifically relates to a urine metabolic marker for the diagnosis of renal cancer and its application. Background Art

[0002] Renal cell carcinoma (RCC), a common malignant tumor of the urinary system, originates in the renal parenchyma, accounting for 2%-3% of all malignant tumors. It is most common in people aged 50-70 years, with a slightly higher incidence in men than in women. Common types include clear cell carcinoma (accounting for 70%-80%), papillary renal cell carcinoma, and chromophobe renal cell carcinoma. Early-stage RCC often presents with no obvious symptoms. As the tumor progresses, symptoms such as hematuria, flank pain or a mass, and systemic symptoms such as weight loss, fever, and anemia may appear. Consequently, most patients are diagnosed in the advanced stage, resulting in a poor prognosis. Therefore, early screening and accurate diagnosis are crucial for improving the survival rate and quality of life of RCC patients.

[0003] The existing commonly used clinical screening and diagnostic technologies for kidney cancer mainly include imaging examinations, such as CT imaging, MRI imaging, and PET-CT imaging. As one of the main means of diagnosing kidney cancer, imaging examinations can provide information such as its size, location, and the presence of metastasis. However, there are still some limitations. For example, some benign lesions may have similar imaging manifestations to kidney cancer, making it difficult to distinguish between benign and malignant lesions; tiny metastases may not be detected by conventional imaging, resulting in missed diagnosis of early metastases. When necessary, a puncture biopsy is still required to confirm the diagnosis. Pathological examination, as the "gold standard" for diagnosing kidney cancer, has certain invasiveness and complications risks, and patient compliance is low. It is not suitable for large-scale risk screening of the general population.

[0004] Tumor markers are molecules that appear abnormally in body fluids during the development, progression, or persistence of a tumor and can be used for tumor screening, diagnosis, prognosis assessment, and treatment monitoring. These markers can be found in various body fluids, including blood, urine, and tissues. Currently, some common tumor markers are widely used in physical examinations. However, current clinically used tumor markers suffer from insufficient sensitivity and specificity.

[0005] Therefore, there is an urgent need to develop markers with high sensitivity and specificity for early screening and prevention of renal cancer. Summary of the Invention

[0006] Based on this, Example 1 of the present application provides a urine metabolic marker for diagnosing renal cancer and its application. The renal cancer diagnostic biomarker has high sensitivity and specificity.

[0007] On the one hand, the present application provides the use of a detection reagent for a renal cancer diagnostic biomarker in the preparation of a renal cancer diagnostic kit.

[0008] The renal cancer diagnostic biomarkers include one or more of allantoic acid, cystathionine, hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, O-hydroxyhippuric acid, uracil, 7-methyluric acid, L-prolyl-L-valine, homocarnosine and aspartic acid.

[0009] On the other hand, the present application provides the use of a detection reagent for a renal cancer diagnostic biomarker in the preparation of a renal cancer diagnostic kit.

[0010] The renal cancer diagnostic biomarkers include one or more of 5-hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, uracil, 7-methyluric acid, L-prolyl-L-valine, and aspartic acid.

[0011] On the other hand, the present application provides the use of a detection reagent for a renal cancer diagnostic biomarker in the preparation of a renal cancer diagnostic kit.

[0012] The renal cancer diagnostic biomarkers include one or more of hypoxanthine, L-prolyl-L-valine and aspartic acid.

[0013] In some embodiments, the sample detected by the detection reagent includes a urine sample.

[0014] In some embodiments, the detection reagent detects the renal cancer diagnostic biomarker by high performance liquid chromatography-mass spectrometry.

[0015] In some embodiments, the liquid chromatography conditions of the HPLC-MS method include: the stationary phase is a T3 chromatographic column.

[0016] The mobile phase includes mobile phase A and mobile phase B; the mobile phase A includes formic acid and water, and the volume proportion of formic acid in the mobile phase A is 0.08%-0.12%; the mobile phase B includes formic acid and acetonitrile, and the volume proportion of formic acid in the mobile phase B is 0.08%-0.12%.

[0017] The elution method includes gradient elution, and the gradient elution procedure includes:

[0018] From 0 min to 13 min, the volume proportion of the mobile phase B increased from 1% to 70%;

[0019] From 13 min to 18 min, the volume proportion of the mobile phase B increased from 70% to 99%.

[0020] In some embodiments, the mass spectrometry conditions of the HPLC-MS method include: acquisition in Full MS and Full MS / dd-MS2 modes; both the Full MS and the Full MS / dd-MS2 include positive and negative modes.

[0021] In some embodiments, the resolution is 60,000-80,000.

[0022] In some embodiments, the scan range is 100 m / z-1500 m / z.

[0023] In some embodiments, the automatic gain control is 2.5×10 6 -3.5×10 6 .

[0024] In some embodiments, Maximum IT is 180 ms-220 ms.

[0025] In some embodiments, the HCD relative collision energy is 10 eV-30 eV.

[0026] In some embodiments, the maximum ion injection time is 30 ms-50 ms.

[0027] In some embodiments, the kit further comprises a reagent for extracting the renal cancer diagnostic biomarker from a sample.

[0028] In some embodiments, the reagent used to extract the renal cancer diagnostic biomarker from the sample includes one or both of methyl tert-butyl ether and methanol.

[0029] In some embodiments, the volume ratio of the methyl tert-butyl ether to the methanol is (2-4):1.

[0030] Another aspect of the present application provides a method for constructing a diagnostic model for renal cancer, characterized by comprising:

[0031] Small molecule metabolite detection and identification were performed on urine samples of the healthy group and the renal cancer group in the modeling group, respectively, to obtain the healthy group data and the renal cancer group data in the modeling group;

[0032] Performing a significant difference metabolite analysis on the healthy group data and the renal cancer group data in the modeling group, screening small molecule metabolites with significant differences between the groups, and obtaining a metabolic marker combination;

[0033] Multivariate ROC curve analysis was performed on the metabolic marker combination.

[0034] The embodiments of the present application screened suitable metabolite markers through metabolomics methods. These metabolite markers can establish a highly sensitive and specific kidney cancer diagnostic model, providing a more reliable and convenient diagnostic method for kidney cancer diagnosis, and are expected to have broad application prospects in the field of kidney cancer diagnosis.

[0035] This application uses a non-invasive method to detect this metabolite marker combination in urine samples to determine whether a patient has kidney cancer. This allows for accurate screening of kidney cancer, providing important support for preventing and reducing the incidence of kidney cancer. Compared to some traditional kidney cancer diagnostic methods, using metabolite markers in urine is more cost-effective and reduces some expensive and complex steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application and to more fully understand the present application and its beneficial effects, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0037] Figure 1 Multivariate ROC curve analysis of 11 markers for distinguishing HC from RCC in the modeling panel provided in Example 1 of the present application;

[0038] Figure 2 Multivariate ROC curve analysis of 11 markers for distinguishing HC from RCC in the validation group provided in Example 1 of the present application;

[0039] Figure 3 This is a multivariate ROC curve analysis of the seven markers used to distinguish HC from RCC in the modeling panel provided in Example 1 of the present application;

[0040] Figure 4 Multivariate ROC curve analysis of 7 markers for distinguishing HC from RCC in the validation group provided in Example 1 of the present application;

[0041] Figure 5 This is a multivariate ROC curve analysis of the three markers for distinguishing HC from RCC in the modeling group provided in Example 1 of the present application;

[0042] Figure 6 This is a multivariate ROC curve analysis of the three markers for distinguishing HC from RCC in the validation group provided in Example 1 of the present application. DETAILED DESCRIPTION

[0043] Below in conjunction with embodiment and example, the application is described in further detail.Should be understood that these embodiment and example are only used to illustrate the application and are not used to limit the scope of the application, and the purpose of providing these embodiment and example is to make the understanding of the disclosure of the application more thorough and comprehensive.It should also be understood that the application can be implemented in many different forms, is not limited to the embodiment and example described herein, and those skilled in the art can make various changes or modifications without violating the connotation of the application, and the equivalent form obtained also falls within the protection scope of the application.In addition, in the description hereinafter, a large amount of specific details are given in order to provide a more complete understanding of the application, and it should be understood that the application can be implemented without one or more of these details.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0045] the term

[0046] Unless otherwise specified or incompatible herewith, the terms and phrases used herein shall have the following meanings:

[0047] The terms "and / or", "or / and", and "and / or" used herein include any one of two or more related listed items, and also include any and all combinations of the related listed items, wherein the any and all combinations include any combination of two related listed items, any more related listed items, or all related listed items. It should be noted that when at least three items are connected by at least two conjunctions selected from "and / or", "or / and", and "and / or", it should be understood that in this application, the technical solution undoubtedly includes technical solutions connected by "logical AND" and technical solutions connected by "logical OR".

[0048] In this application, "plurality", "multiple", "multiple times", "multiples", etc., unless otherwise specified, refer to a quantity greater than or equal to 2. For example, "one or more" means one or more than or equal to two.

[0049] In this application, the terms "optionally," "optional," and "optional" mean optional or dispensable, i.e., they refer to either option being selected from two parallel options: "with" or "without." If a technical solution contains multiple "optional" clauses, each "optional" clause is independent unless otherwise specified and there are no contradictions or constraints.

[0050] In this application, when referring to a numerical interval (i.e., a numerical range), unless otherwise specified, the distribution of the optional values ​​within the numerical interval is considered continuous and includes the two numerical endpoints of the numerical range (i.e., the minimum and maximum values), as well as every numerical value between these two numerical endpoints. Unless otherwise specified, when a numerical interval refers only to integers within the numerical interval, it includes the two numerical endpoints of the numerical range, as well as every integer between the two numerical endpoints. In this document, this is equivalent to directly listing each integer. For example, "t is an integer selected from 1 to 10" means that t is any integer selected from the group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. In addition, when multiple ranges are provided to describe a feature or characteristic, these ranges may be combined. In other words, unless otherwise specified, ranges disclosed herein should be understood to include any and all subranges subsumed therein.

[0051] Unless otherwise specified, the temperature parameters in this application allow for both constant temperature treatment and temperature fluctuations within a certain temperature range. It should be understood that the constant temperature treatment allows for temperature fluctuations within the accuracy range of instrument control. Fluctuations within ranges such as ±5°C, ±4°C, ±3°C, ±2°C, and ±1°C are permitted.

[0052] All documents mentioned in this application are cited as references in this application, just as each document is cited as a reference individually. Unless they conflict with the invention purpose and / or technical solution of this application, the cited documents involved in this application are cited in their entirety and for all purposes. When cited documents are involved in this application, the definitions of relevant technical features, terms, nouns, phrases, etc. in the cited documents are also cited. When cited documents are involved in this application, the examples and preferred embodiments of the cited relevant technical features may also be incorporated into this application as references, but are limited to the ability to implement this application. It should be understood that when the cited content conflicts with the description in this application, the present application shall prevail or be adaptively amended according to the description in this application.

[0053] In this application, "metabolite marker" and "biomarker" are synonymous.

[0054] The application of metabolomics technology in the clinical diagnosis of renal cancer has become a research hotspot in recent years. Metabolomics analyzes changes in small molecule metabolites (such as amino acids, lipids, and carbohydrates) within an organism to reveal abnormalities in disease-related metabolic pathways. It has shown significant potential in the early diagnosis, classification, prognosis, and discovery of therapeutic targets for renal cancer. Therefore, this application constructs a highly sensitive and specific renal cancer diagnostic model based on metabolomics data from urine samples, providing effective assistance for the diagnosis of renal cancer and enabling early screening, detection, and treatment.

[0055] On the one hand, the present application provides the use of a detection reagent for a renal cancer diagnostic biomarker in the preparation of a renal cancer diagnostic kit;

[0056] The diagnostic biomarkers for kidney cancer include one or more of allantoic acid, cystathionine, hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, O-hydroxyhippuric acid, uracil, 7-methyluric acid, L-prolyl-L-valine, homocarnosine, and aspartic acid.

[0057] In one embodiment, the 11 renal cancer diagnostic biomarkers described above are detected in urine samples, which can effectively distinguish between healthy people and renal cancer patients.

[0058] On the other hand, the present application provides the use of a detection reagent for a renal cancer diagnostic biomarker in the preparation of a renal cancer diagnostic kit.

[0059] The biomarkers for kidney cancer diagnosis include one or more of 5-hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, uracil, 7-methyluric acid, L-prolyl-L-valine, and aspartic acid.

[0060] In one embodiment, the seven renal cancer diagnostic biomarkers described above in urine samples are used as detection targets, which can effectively distinguish healthy individuals from renal cancer patients, with an AUC of approximately 0.854, a sensitivity of approximately 72.2%, and a specificity of approximately 77.3%.

[0061] On the other hand, the present application provides the use of a detection reagent for a renal cancer diagnostic biomarker in the preparation of a renal cancer diagnostic kit.

[0062] The diagnostic biomarkers for kidney cancer include one or more of hypoxanthine, L-prolyl-L-valine, and aspartic acid.

[0063] In one embodiment, the above three kidney cancer diagnostic biomarkers in urine samples are used as detection objects, which can effectively distinguish healthy people from kidney cancer patients, with an AUC of about 0.852, a sensitivity of about 83.3%, and a specificity of about 72.7%.

[0064] In some embodiments, the sample detected by the detection reagent includes a urine sample. Urine is easier to collect than other body fluids, and using metabolite markers in urine is more cost-effective and reduces some expensive and complicated steps.

[0065] In some embodiments, the detection reagent detects renal cancer diagnostic biomarkers by high performance liquid chromatography-mass spectrometry.

[0066] In some embodiments, the liquid chromatography conditions of the high performance liquid chromatography-mass spectrometry method include:

[0067] The stationary phase was a T3 column;

[0068] The mobile phase includes mobile phase A and mobile phase B; mobile phase A includes formic acid and water, and the volume proportion of formic acid in mobile phase A is 0.08%-0.12%; for example, 0.08%, 0.085%, 0.09%, 0.095%, 0.1%, 0.105%, 0.11%, 0.115% or 0.12% and any value therebetween.

[0069] Mobile phase B includes formic acid and acetonitrile, and the volume proportion of formic acid in mobile phase B is 0.08%-0.12%; for example, 0.08%, 0.085%, 0.09%, 0.095%, 0.1%, 0.105%, 0.11%, 0.115% or 0.12% and any value therebetween.

[0070] The elution method includes gradient elution, and the gradient elution procedure includes:

[0071] From 0 min to 13 min, the volume proportion of mobile phase B increased from 1% to 70%;

[0072] From 13min to 18min, the volume proportion of mobile phase B increased from 70% to 99%.

[0073] In some embodiments, Full MS and Full MS / dd-MS2 modes are used for acquisition, wherein both Full MS and Full MS / dd-MS2 modes include both positive and negative modes. In some embodiments, the resolution is 60,000-80,000, for example, 60,000, 70,000, or 80,000.

[0074] In some embodiments, the scan range is 100 m / z-1500 m / z, for example, the scan range is 100 m / z, 200 m / z, 300 m / z, 400 m / z, 500 m / z, 600 m / z, 700 m / z, 800 m / z, 900 m / z, 1000 m / z, 1100 m / z, 1200 m / z, 1300 m / z, 1400 m / z or 1500 m / z and any values ​​therebetween.

[0075] In some embodiments, the AGC automatic gain control is 2.5×10 6 -3.5×10 6 ; For example, the AGC automatic gain control is 2.5×10 6 , 2.6×10 6 , 2.7×10 6 , 2.8×10 6 , 2.9×10 6 , 3.0×10 6 , 3.1×106 , 3.2×10 6 , 3.3×10 6 or 3.4×10 6 , 3.5×10 6 and any values ​​in between.

[0076] In some embodiments, the Maximum IT is between 180 ms and 220 ms, for example, 180 ms, 185 ms, 190 ms, 195 ms, 200 ms, 205 ms, 210 ms, 215 ms, or 220 ms, and any value therebetween.

[0077] In some embodiments, the HCD relative collision energy is 10 eV-30 eV, for example, the HCD relative collision energy is 10 eV, 11 eV, 12 eV, 13 eV, 14 eV, 15 eV, 16 eV, 17 eV, 18 eV, 19 eV, 20 eV, 21 eV, 22 eV, 23 eV, 24 eV, 25 eV, 26 eV, 27 eV, 28 eV, 29 eV or 30 eV, and any values ​​therebetween.

[0078] In some embodiments, the maximum ion injection time is 30 ms-50 ms, for example, 30 ms, 31 ms, 32 ms, 33 ms, 34 ms, 35 ms, 36 ms, 37 ms, 38 ms, 39 ms, 40 ms, 41 ms, 42 ms, 43 ms, 44 ms, 45 ms, 46 ms, 47 ms, 48 ​​ms, 49 ms, or 50 ms, or any value therebetween.

[0079] For example, mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each including positive and negative modes). The parameters used by Q Exactive were as follows: Full MS mode resolution of 70,000, scan range of 100-1500 m / z, AGC (automatic gain control) of 3E+6, and Maximum IT of 200 milliseconds; in Full MS / dd-MS2 mode, the secondary mass spectrometry resolution was 17,500, the quadrupole window was 1.5 m / z, AGC of 1E+5, and the maximum ion injection time was 50 ms.

[0080] In some embodiments, the kit further comprises reagents for extracting renal cancer diagnostic biomarkers from the sample.

[0081] In some embodiments, the reagent for extracting a renal cancer diagnostic biomarker from a sample includes one or both of methyl tert-butyl ether and methanol;

[0082] In some embodiments, the volume ratio of methyl tert-butyl ether to methanol is (2-4): 1. For example, the volume ratio of methyl tert-butyl ether to methanol is 2:1, 3:1 or 4:1 and any value therebetween.

[0083] Another aspect of the present application provides a method for constructing a diagnostic model for renal cancer, comprising:

[0084] Small molecule metabolite detection and identification were performed on urine samples of the healthy group and the renal cancer group in the modeling group, respectively, to obtain the healthy group data and the renal cancer group data in the modeling group;

[0085] Perform significant differential metabolite analysis on the healthy group data and the renal cancer group data in the modeling group, screen for small molecule metabolites with significant differences between the groups, and obtain a metabolic marker combination;

[0086] Multivariate ROC curve analysis was performed for the combination of metabolic markers.

[0087] The multivariate ROC curve analysis included selecting 3 / 4 of the samples from the healthy group data and the renal cancer group data in the modeling group as the training set, and the remaining 1 / 4 of the samples as the test set. The support vector machine was used to randomly iterate 1,000 times, and the average value of the final model accuracy was statistically calculated to construct a diagnostic model for renal cancer.

[0088] The embodiments of the present application will be described in detail below with reference to the examples. It should be understood that these examples are intended to illustrate the present application only and are not intended to limit the scope of the present application. The experimental methods for which specific conditions are not specified in the following examples are preferably referred to the guidance provided in the present application, and can also be based on the experimental manuals or conventional conditions in this area, or according to the conditions recommended by the manufacturer, or with reference to experimental methods known in the art.

[0089] In the following specific examples, the measured parameters of raw material components may have slight deviations within the range of weighing accuracy unless otherwise specified. For temperature and time parameters, acceptable deviations caused by instrument testing accuracy or operational accuracy are allowed.

[0090] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0091] Example 1

[0092] Step 1: Obtain the sample to be tested

[0093] 1. Subjects

[0094] 1) Sample inclusion criteria: Subjects must meet all of the following inclusion criteria to be eligible for this study:

[0095] (1) Male or female aged ≥18 years; (2) Read and fully understand, sign the informed consent form, and be able to provide urine samples for metabolomics testing; (3) Renal cancer group: patients diagnosed with primary renal malignancy by biopsy / postoperative pathology or through comprehensive evaluation by clinicians.

[0096] 2) Sample exclusion criteria:

[0097] Subjects who meet any of the following exclusion criteria are not eligible to participate in this study:

[0098] (1) Pregnancy or lactation; (2) Emergency or emergency treatment; (3) History of blood transfusion within 7 days before sampling; (4) Persons who have received organ transplantation or non-autologous (allogeneic) bone marrow or stem cell transplantation; (5) Persons with a history of malignant tumor within 5 years or who have received any anti-tumor treatment before sampling; (6) Persons with multiple primary malignant tumors at the same time.

[0099] 3) Subject information:

[0100] This study collected urine samples from 310 subjects at two medical centers, including 116 healthy control (HC) samples and 194 renal cell carcinoma (RCC) samples. The urine samples used for modeling were 87 from the HC group and 145 from the RCC group; the urine samples used for validation were 29 from the HC group and 49 from the RCC group (Table 1).

[0101] Table 1: Subjects’ information

[0102]

[0103] 2. Urine metabolomics analysis

[0104] 1) Reagents:

[0105] Methanol, acetonitrile, water, acetic acid, and methyl tert-butyl ether of mass spectrometry grade, and formic acid of HPLC grade were purchased from Sigma-Aldrich, USA.

[0106] 2) Urine pretreatment:

[0107] After taking out the urine sample from the -80℃ refrigerator and thawing, take 40μL of urine sample and put it into the extraction tube, add 400μL of pre-cooled methyl tert-butyl ether and methanol mixed extract (the volume ratio of methyl tert-butyl ether and methanol is 3:1), vortex and ultrasonically mix, then add 360μL of methanol and water mixed solution, vortex and centrifuge to separate the layers; take 300μL of the lower aqueous phase solution in the extraction tube, add 900μL of pre-cooled methanol solution, take 1000μL of the supernatant after protein precipitation, spin dry, add 200μL of water to reconstitute, and use the reconstituted aqueous phase as the test solution for machine (LC-MS) detection.

[0108] 3) Small molecule metabolite detection:

[0109] A Waters ACQUTTY UPLC ® HSS T3 1.8µm 2.1*100mm column was used for small molecule separation; the liquid chromatography and mass spectrometry used were ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively.

[0110] The mobile phase parameters are as follows:

[0111] Mobile phase A was an aqueous solution containing 0.1% formic acid; mobile phase B was an acetonitrile solution containing 0.1% formic acid; the separation elution gradient was as follows: 1%-70% mobile phase B from 0 to 13 minutes, and 99% mobile phase B from 13 to 18 minutes.

[0112] The mass spectrometry parameters are as follows:

[0113] Mass spectrometric data were acquired using Full MS and Full MS / dd-MS2 modes (each in positive and negative modes). The QExactive parameters used were as follows: Full MS mode with a resolution of 70,000 s, a scan range of 100–1500 m / z, an automatic gain control (AGC) of 3E+6, and a maximum time interval (IT) of 200 ms. In Full MS / dd-MS2 mode, the secondary mass spectrometer had a resolution of 17,500 s, a quadrupole window of 1.5 m / z, an AGC of 1E+5, a maximum ion injection time of 50 ms, and an HCD relative collision energy of 30 eV.

[0114] 3. Metabolomics data preprocessing and metabolite identification

[0115] 1) Metabolomics data processing:

[0116] (1) Extract peaks from the RAW format files of mass spectrometry data into Feature XML format files, reduce the dimension of the original mass spectrometry data, and improve the signal-to-noise ratio.

[0117] (2) Using the peak alignment algorithm of OpenMS software, the retention time of the extracted peak format data is corrected and aligned between samples, thereby converting the mass spectrometry data into a data matrix.

[0118] (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 blank values.

[0119] (4) Among all the characteristic peaks obtained in step (3), those with a detection rate of <80% are eliminated, and those with a detection rate of >80% are filled with the median value of the characteristic peak and 5% random noise (obeying the standard normal distribution) is added.

[0120] (5) In order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, Normalization Autoencoder (NormAE) was used for normalization processing to remove systematic errors such as batch effects.

[0121] 2) Identification of metabolites:

[0122] The software analyzes the raw data to obtain the spectral information of the compound's primary parent ion (MS1) and secondary fragment ion (MS2), such as the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragments of the secondary ion, and matches them with the spectral information of the primary and secondary metabolites in the public database to qualitatively identify the metabolites.

[0123] Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), and the Mass Spectrometry Database (www.massbank.jp). Metabolites identified based on these databases are ultimately verified based on retention times, MS1, and MS2 mass spectra of standards separated using the same chromatographic column and mass spectrometry conditions. Metabolite identification criteria include retention times within 0.1 minutes and a molecular weight difference between the theoretical and measured values ​​of the metabolites less than 10 ppm.

[0124] 4. Data Analysis

[0125] 1) Screening of metabolic markers for distinguishing healthy individuals from renal cancer

[0126] First, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis was performed on the data of the modeling group, and a total of 11 differential metabolites were screened (Table 2) as important metabolic markers for distinguishing between health and renal cancer.

[0127] Table 2: 11 important metabolic markers that distinguish healthy individuals from kidney cancer

[0128]

[0129] 2) Construction of a diagnostic model for distinguishing healthy and renal cancer patients

[0130] In order to verify the diagnostic effect of the 11 screened metabolic markers in distinguishing health from renal cancer, multivariate ROC curve analysis and area under the curve (AUC) were performed on the above 11 metabolic markers in the modeling group.

[0131] Specifically, 3 / 4 of the sample data from the HC and RCC groups in the modeling group were randomly used as the training set (training), and 1 / 4 was used as the test set (test). The machine learning support vector machine (SVM) was used to randomly iterate 1,000 times. By statistically calculating the average accuracy of the final model, a diagnostic model for distinguishing between healthy and renal cancer was constructed.

[0132] The ROC curve is a method for studying the relationship between model sensitivity and specificity. It uses sensitivity as the vertical axis and 1-specificity as the horizontal axis. Evaluation is based on comparing the area under the curve (AUC). When the AUC is greater than 0.5, the closer the AUC is to 1, the better the model performance and the better the diagnostic effect. A value less than 0.5 indicates poor model 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.

[0133] The sensitivity calculation formula is:

[0134] The specificity calculation formula is:

[0135] TP (True Positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples.

[0136] TN (Ture Negative): True negative, the number of samples that are actually negative examples that are correctly predicted as negative examples.

[0137] FP (False Positive): False positives, the number of samples that are actually negative examples that are mistakenly predicted as positive examples.

[0138] FN (False Negative): False negatives, the number of samples that are actually positive examples but are mistakenly predicted as negative examples.

[0139] The results are as follows Figure 1 As shown in the figure, AUC = 0.867 (sensitivity = 0.806, specificity = 0.773), which shows that the constructed diagnostic model has high diagnostic efficacy.

[0140] In addition, diagnostic models for distinguishing between healthy and renal cancer were constructed for seven metabolic markers: hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, uracil, 7-methyluric acid, L-prolyl-L-valine and aspartic acid combination; and three metabolic markers: hypoxanthine, L-prolyl-L-valine and aspartic acid combination.

[0141] The results showed that the diagnostic model constructed by combining 7 metabolic markers had an AUC of 0.854 (sensitivity = 0.722, specificity = 0.773) ( Figure 3 ), the diagnostic model constructed by combining three metabolic markers, AUC=0.852 ( Figure 5 ) (sensitivity = 0.833, specificity = 0.727). The results showed that the diagnostic models constructed based on the combination of the above seven and three metabolite markers, respectively, had high diagnostic efficacy and clinical diagnostic significance.

[0142] 3) Validation of a diagnostic model for distinguishing healthy individuals from kidney cancer

[0143] In order to further verify the effectiveness of the diagnostic model for distinguishing between healthy and renal cancer based on the modeling group data, the validation group data was used to verify the above diagnostic model. A multivariate ROC curve analysis was specifically performed to evaluate the independent verification effect of the diagnostic model on unknown data sets other than the modeling group data set. After the validation group samples were placed in the diagnostic model constructed by the modeling group, the corresponding probability value (Probability) was output based on the detection data of the 11 important metabolic markers for distinguishing between healthy and renal cancer in each sample. Based on the probability value of each sample as the diagnostic threshold, a set of confusion matrices (including true positive, true negative, false positive and false negative) was obtained. The sensitivity and specificity can be calculated according to the formula, and a point can be marked in the ROC analysis graph with sensitivity (sensitivity) as the vertical coordinate and 1-specificity (1-specificity) as the horizontal coordinate. Similarly, when the probability value of each sample is used as the diagnostic threshold, multiple different points are obtained in the ROC analysis graph. These points are linked to draw the ROC curve graph ( Figure 2 ). Among them, the point with the best sensitivity and specificity is selected, and the diagnostic threshold at this time is 0.6016.

[0144] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 11 metabolic markers, with a diagnostic threshold of 0.6016, 37 of the 49 renal cancer patients were diagnosed as renal cancer, and 12 were misclassified as healthy subjects; among the 29 healthy subjects, 24 were correctly diagnosed, and 5 were misclassified as renal cancer. The ROC analysis results of the diagnostic model in the validation group are shown in Figure 3. Figure 2 As shown in the figure, based on the confusion matrix, sensitivity and specificity were calculated, and the results showed an AUC of 0.849 (sensitivity = 0.755, specificity = 0.828). These results indicate that the diagnostic model constructed for distinguishing healthy from renal cancer also has a good diagnostic effect in the validation group.

[0145] Table 3: Confusion matrix of the diagnostic model for distinguishing healthy from kidney cancer

[0146]

[0147] In addition, the diagnostic model of 7 metabolite markers, hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, uracil, 7-methyluric acid, L-prolyl-L-valine and aspartic acid, and 3 metabolite markers, hypoxanthine, L-prolyl-L-valine and aspartic acid, were validated in the validation group. The results showed that the diagnostic model constructed by the combination of 7 metabolite markers had an AUC of 0.868 (sensitivity = 0.837, specificity = 0.828) in the validation group ( Figure 4 ), the diagnostic model constructed by combining the three metabolite markers had an AUC of 0.865 (sensitivity = 0.816, specificity = 0.828) ( Figure 6 ).

[0148] The above results show that the diagnostic model constructed to distinguish between healthy and renal cancer also has a good diagnostic effect in the validation group.

[0149] The embodiments described above only express several implementation methods of the present application, which are convenient for understanding the technical solutions of the present application in a specific and detailed manner, but they cannot be understood as limiting the scope of protection of the patent application. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all fall within the scope of protection of the present application. In addition, it should be understood that after reading the above-mentioned teaching content of the present application, those skilled in the art can make various changes or modifications to the present application, and the equivalent forms obtained also fall within the scope of protection of the present application. It should also be understood that the technical solutions obtained by those skilled in the art through logical analysis, reasoning or limited experiments on the basis of the technical solutions provided in the present application are all within the scope of protection of the claims attached to the present application. Therefore, the scope of protection of the patent application of this application shall be based on the content of the attached claims, and the description can be used to interpret the content of the claims.

Claims

1. Application of detection reagents for renal cancer diagnostic biomarkers in the preparation of renal cancer diagnostic kits; The renal cancer diagnostic biomarkers include one or more of allantoic acid, cystathionine, hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, O-hydroxyhippuric acid, uracil, 7-methyluric acid, L-prolyl-L-valine, homocarnosine and aspartic acid.

2. Application of detection reagents for renal cancer diagnostic biomarkers in the preparation of renal cancer diagnostic kits; The renal cancer diagnostic biomarkers include one or more of 5-hydantoin-5-propionic acid, hypoxanthine, L-kynurenine, uracil, 7-methyluric acid, L-prolyl-L-valine, and aspartic acid.

3. Application of detection reagents for renal cancer diagnostic biomarkers in the preparation of renal cancer diagnostic kits; The renal cancer diagnostic biomarkers include one or more of hypoxanthine, L-prolyl-L-valine and aspartic acid.

4. The use according to any one of claims 1 to 3, characterized in that The sample detected by the detection reagent includes a urine sample.

5. The use according to any one of claims 1 to 3, characterized in that The detection reagent detects the renal cancer diagnostic biomarker by high performance liquid chromatography-mass spectrometry.

6. The use according to claim 5, characterized in that The liquid chromatography conditions of the high performance liquid chromatography-mass spectrometry method include: The stationary phase was a T3 column; The mobile phase includes a mobile phase A and a mobile phase B; the mobile phase A includes formic acid and water, and the volume proportion of formic acid in the mobile phase A is 0.08%-0.12%; the mobile phase B includes formic acid and acetonitrile, and the volume proportion of formic acid in the mobile phase B is 0.08%-0.12%; The elution method includes gradient elution, and the gradient elution procedure includes: From 0 min to 13 min, the volume proportion of the mobile phase B increased from 1% to 70%; From 13 min to 18 min, the volume proportion of the mobile phase B increased from 70% to 99%.

7. The use according to claim 6, characterized in that The mass spectrometry conditions of the high performance liquid chromatography-mass spectrometry method meet one or more of the following conditions: (1) Acquiring in Full MS and Full MS / dd-MS2 modes; both Full MS and Full MS / dd-MS2 include positive and negative modes; (2) Resolution is 60,000-80,000; (3) Scanning range: 100 m / z-1500 m / z; (4) Automatic gain control is 2.5×10 6 -3.5×10 6 ; (5) Maximum IT is 180 ms-220 ms (6) HCD relative collision energy is 10ev-30ev; and (7) The maximum ion injection time is 30ms-50ms.

8. The use according to any one of claims 1 to 3 and 6 to 7, characterized in that The kit also includes reagents for extracting the renal cancer diagnostic biomarkers from a sample.

9. The use according to claim 8, characterized in that The reagent for extracting the renal cancer diagnostic biomarker from the sample includes one or both of methyl tert-butyl ether and methanol; Optionally, the volume ratio of the methyl tert-butyl ether to the methanol is (2-4):

1.

10. A method for constructing a diagnostic model for renal cancer, characterized in that: include: Small molecule metabolite detection and identification were performed on urine samples of the healthy group and the renal cancer group in the modeling group, respectively, to obtain the healthy group data and the renal cancer group data in the modeling group; Performing a significant difference metabolite analysis on the healthy group data and the renal cancer group 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.

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