A marker group for kidney cancer screening and diagnosis and use thereof

Abstract

The application discloses a marker group for kidney cancer screening and diagnosis and application thereof, and belongs to the field of molecular biological technology.The marker group comprises at least four polypeptides shown in sequences of SEQ ID NO.1-38.The marker group constructed by the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of kidney cancer.The auxiliary diagnosis result is better than that of one of main current clinical diagnosis schemes for kidney tumors, i.e., imaging examination, and enhanced CT (sensitivity and specificity are 78.67% and 87.88%, respectively).The early screening result is better than that of a high-frequency color Doppler ultrasound instrument (diagnostic sensitivity is 94.87%, and specificity is 80%) applied to the routine examination of the abdomen in a health examination; and multi-gene methylation combined analysis (sensitivity is 62.9%, and specificity is 87.0%).

Description

Technical Field

[0001] This invention belongs to the field of molecular biotechnology, specifically relating to a biomarker group for kidney cancer screening and diagnosis and its applications. Background Technology

[0002] Renal cell carcinoma, commonly known as kidney cancer, is one of the most common malignant tumors of the urinary system. Early-stage kidney cancer often presents with no obvious symptoms, and more than 20% of patients are diagnosed with locally advanced or metastatic kidney cancer, resulting in a poor prognosis. Because kidney cancer is not sensitive to radiotherapy and chemotherapy, early diagnosis and early surgical treatment are particularly important.

[0003] Imaging examinations are currently one of the main methods for diagnosing renal tumors in clinical practice. The typical presentation of renal cell carcinoma on CT or MRI is significant enhancement of the mass in the arterial phase, followed by significant decrease in enhancement in the venous and excretory phases. CT scans involve radiation exposure, and the use of contrast agents may impair renal function. Contrast-enhanced ultrasound (CEUS) has a significantly higher accuracy (83.33%) in diagnosing cystic renal cell carcinoma than CT (68.42%). However, it still faces challenges such as the inability to obtain detailed objective imaging data, operator-dependent diagnostic efficacy, and contraindications for patients with allergies to contrast agents. In recent years, the role of nuclear medicine in improving the accuracy of renal tumor diagnosis has gradually gained attention. Various positron emission tomography (PET) tracers are considered as biomarkers for the diagnosis and prognosis of renal cell carcinoma. A study comparing the diagnostic efficacy of the novel tracer 124I-cG250-PET with enhanced CT showed that 124I-cG250-PET had a sensitivity and specificity of 86.2% and 85.9% for diagnosing ccRCC, respectively, significantly higher than enhanced CT's 75.5% and 46.8%, and better inter-observer consistency. However, the radiation dose of a single PET scan is approximately 7-25 mSv (equivalent to multiple CT scans), which may pose a cumulative risk to patients requiring multiple follow-ups. Furthermore, although histopathological examination is the gold standard for diagnosing renal cell carcinoma, ultrasound-guided renal biopsy is currently commonly used with a high success rate. However, obtaining clinical specimens is often difficult, hindering early diagnosis and screening of renal cell carcinoma.

[0004] With the development of molecular biology, some tumor markers can now indicate the presence of tumors. However, there is currently no highly specific tumor marker that can diagnose renal cell carcinoma alone; clinically, it is often diagnosed in combination with other examinations. Unlike traditional tumor markers which are protein molecules, emerging tumor markers include cell tumor cells (CTCs), ctDNA, DNA methylation, and ncRNA. The genomic profiles of liquid biopsies have been shown to be very similar to those of the corresponding tumors. Ivonne et al. developed a CTCs detection technique based on multiparameter immunofluorescence microscopy, which can detect epithelial markers such as EpCAM and cells with anaplastic and stem cell-like properties. Furthermore, a new combination of cell surface markers, consisting of CA9 and CD147, has been developed to replace CTCs as an antigen for renal cell carcinoma, showing significantly improved efficiency compared to traditional EpCAM-based methods. It is noteworthy that CTCs can be apoptotic or viable, but only viable CTCs play a crucial role in tumor metastasis, and CTCs are technically difficult to isolate. Free nucleic acids have a half-life of 15 minutes to several hours in circulation, making them more stable than cells and RNA. ctDNA has higher diagnostic specificity than cfDNA, while cfDNA detection technology is relatively simple, usually requiring only quantitative analysis, and can be used as a monitoring indicator for renal cell carcinoma. Although liquid biopsy is increasingly used in clinical practice, current ctDNA detection methods have limitations, including a significant inconsistency rate between ctDNA detection and tissue analysis. In some cases, this non-invasive detection technology cannot be beneficial, such as in tumor screening and detection of minimal residual disease after treatment. In particular, differentiating between cfDNA and ctDNA requires highly sensitive technology and low-cost detection methods. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the prior art, the present invention provides a biomarker set for kidney cancer screening and diagnosis and its uses. It has excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of kidney cancer, and is expected to be applied to the diagnosis and treatment of kidney cancer.

[0006] To achieve the above objectives, the technical solution adopted by the present invention to solve its technical problem is as follows:

[0007] A biomarker group for the screening and diagnosis of renal cell carcinoma includes at least four of the polypeptides shown in SEQ ID NO. 1-38, the specific sequences of which are shown in Table 1.

[0008] Table 1. Peptide Sequences

[0009]

[0010] Among them, the third amino acid G in peptide 12 is modified with Phospho; the fourth amino acid G in peptide 20 is modified with Dehydrated; the second amino acid K in peptide 28 is modified with Acetyl; the first amino acid Q in peptide 32 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated.

[0011] Furthermore, the biomarker set includes the peptides shown in sequences 1 and 2, as well as the following combinations of peptides:

[0012] The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 11 and sequence 12; sequence 30 and sequence 38; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 11, sequence 12 and sequence 20; sequence 9, sequence 30 and sequence 38.

[0013] Furthermore, the biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides:

[0014] The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; or sequence 9, sequence 10 and sequence 11.

[0015] Furthermore, the biomarker group includes peptides shown in sequences 1, 10, and 30, as well as peptides shown in sequences 38; 20; 38 and 9 or 20 and 22.

[0016] Furthermore, the biomarker set includes the peptides shown in sequences 5 and 15, as well as the following combinations of peptides:

[0017] The polypeptide combination is one of the following: sequence 25 and sequence 38; sequence 2 and sequence 16; sequence 9, sequence 25 and sequence 38; sequence 2, sequence 16 and 20.

[0018] Furthermore, the biomarker set includes the peptides shown in sequences 4 and 8, as well as the following combinations of peptides:

[0019] The polypeptide combination is sequence 19 and sequence 20; sequence 2 and sequence 6; or sequence 19, sequence 20 and sequence 22.

[0020] Furthermore, the biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 22.

[0021] Furthermore, the biomarker group includes polypeptides as shown in SEQ ID NO. 1~38.

[0022] The use of the above biomarkers in the preparation of formulations for kidney cancer screening and diagnosis.

[0023] The above biomarkers may be used in basic medical research for non-diagnostic / therapeutic purposes.

[0024] Further, basic medical research includes Western blotting, immunohistochemistry, or flow cytometry.

[0025] The beneficial effects of this invention are:

[0026] The biomarker combination constructed in this invention has superior sensitivity and specificity when used for the auxiliary diagnosis and early screening of renal cell carcinoma, and is expected to be applied to the diagnosis and treatment of renal cell carcinoma. Attached Figure Description

[0027] Figure 1 ROC curve for marker combination 1;

[0028] Figure 2 ROC curve for marker combination 2;

[0029] Figure 3 ROC curve for marker combination 3;

[0030] Figure 4 ROC curve for marker combination 4;

[0031] Figure 5 ROC curve for marker combination 5;

[0032] Figure 6 ROC curve for marker combination 6;

[0033] Figure 7 ROC curve for marker combination 7;

[0034] Figure 8 ROC curve for marker combination 8;

[0035] Figure 9 ROC curve for marker combination 9;

[0036] Figure 10 ROC curve for marker combination 10;

[0037] Figure 11 ROC curve for marker combination 11;

[0038] Figure 12 ROC curve for marker combination 12;

[0039] Figure 13 ROC curve for marker combination 13;

[0040] Figure 14 ROC curve for marker combination 14;

[0041] Figure 15 ROC curve for marker combination 15;

[0042] Figure 16 ROC curve for marker combination 16;

[0043] Figure 17 ROC curve for marker combination 17;

[0044] Figure 18 ROC curve for marker combination 18;

[0045] Figure 19 ROC curve for marker combination 19;

[0046] Figure 20 ROC curve for marker combination 20;

[0047] Figure 21 ROC curve for marker combination 21;

[0048] Figure 22 ROC curve for marker combination 22;

[0049] Figure 23 ROC curve for marker combination 23;

[0050] Figure 24 ROC curve for marker combination 24;

[0051] Figure 25 ROC curve for marker combination 25;

[0052] Figure 26 ROC curve for marker combination 26. Detailed Implementation

[0053] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0054] The patient samples used in this invention are all from Zhongshan Hospital affiliated with Fudan University and have passed ethical review.

[0055] The experimental methods used in this invention are as follows:

[0056] I. Serum Sample Collection

[0057] 1) Sample type: serum.

[0058] 2) Collection requirements: Fasting is required. Use a coagulation tube to draw 5 mL of venous blood, let it stand for 30 min, centrifuge at 3000 rpm for 15 min, and take out about 1 mL of serum and put it into a cryopreservation tube.

[0059] 3) Sample storage:

[0060] Use on the same day; store at 2-8℃.

[0061] If not used on the same day, store at -20℃ for up to 30 days;

[0062] If stored for an extended period (more than one month), it should be kept at -80°C.

[0063] The freeze-thaw cycle should not exceed 3 times.

[0064] II. Extraction of analytes from serum

[0065] 1) After calibrating the mass spectrometer, turn on the Solid Bio Fully Automated Sample Analysis System SPS1000 / SPS4000, and put in the consumables, matching reagent kits and the sample to be tested;

[0066] Select the procedure method "Concentrated loading";

[0067] Run the program:

[0068] a. Opening a hole;

[0069] b. Take at least 10µL of serum sample and activation reagent, mix them in a 1:1 ratio, and place them in the G-row pre-reserved well for later use;

[0070] c. Clean the custom pipette tip in cleaning reagent 1 and cleaning reagent 2 in sequence. Each time, aspirate at least 10µL of liquid and repeat the aspiration and dispensing process at least 3 times.

[0071] d. Process the serum mixture in the G-row wells using the cleaned custom pipette tips. Aspirate at least 10 µL of solution each time, repeating the process at least three times.

[0072] e. Clean the custom pipette tip after adsorbing the serum mixture using cleaning reagent 3. During cleaning, aspirate at least 10µL of liquid each time, repeating the aspiration and dispensing process at least 3 times.

[0073] f. Transfer no less than 10µL of buffer reagent into the H-row pre-reserved hole, and place the customized pipette tip after using cleaning reagent 3 into the liquid to draw no less than 10µL of liquid. Repeat the suction and aspiration at least 3 times.

[0074] g. Transfer at least 10µL of sample matrix solution into the H-row pre-reserved well to complete sample processing;

[0075] h. Spot 2.0 µL of the solution from well H onto the hydrophobic-coated biochip (Wuxi Pimo Technology Co., Ltd.).

[0076] i. Vacuum drying for 240 seconds.

[0077] The main components of each reagent are shown in Table 2.

[0078] Table 2 Reagent Composition

[0079]

[0080] III. Mass Spectrometry Data Acquisition and Upload

[0081] The hydrophobic coated biochip (Wuxi Pimo Technology Co., Ltd.) was vacuum dried and placed into a mass spectrometer;

[0082] Data acquisition is performed using the pre-defined SP1 voltage (target high voltage), SP2 voltage (pulse high voltage), focusing voltage (lens high voltage), detector voltage (MCP voltage), pulse delay time, acquisition card range, target diameter, laser frequency, calibration method, and laser intensity.

[0083] IV. Quality Control

[0084] 1) After data collection, the data will be uploaded to the "Mass Spectrometry Data Analysis Software";

[0085] 2) The software reads the sample information and signal spectrum, and judges whether the sample and sample pretreatment are qualified according to the quality control model; quality control failure may include a variety of possibilities, including the sample is not a kidney disease sample, the signal spectrum intensity is not up to standard, etc.

[0086] 3) If the quality control fails, adjust the corresponding parameters according to the quality control results and repeat the serum analyte extraction process;

[0087] 4) If the quality control is qualified, proceed to the next process.

[0088] V. Establishment of Positive Criterion Value and Result Analysis

[0089] The study on positive cutoff values ​​used renal cell carcinoma samples with clear diagnostic information and normal human samples, covering patients with benign kidney diseases such as renal cysts and renal angiomyolipoma. The core algorithm is based on supervised learning of known renal cell carcinoma sample atlases. Through a series of processes such as smoothing, noise reduction, and baseline removal, characteristic peaks are screened, a classification model is constructed, and the similarity between hormone signal atlases and known hormone signal atlases stored in the software is calculated (Cannataro M, Guzzi PH, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data[J]. 2005.). Finally, the similarity score positive cutoff value of the kit is determined by the Youden exponent maximum method. When the similarity score < positive cutoff value, the sample test result is negative; when the similarity score ≥ positive cutoff value, the sample test result is positive. When using the maximum similarity score as the positive cutoff value to assist in the diagnosis of renal cell carcinoma or to perform early screening for renal cell carcinoma, the sensitivity is calculated as: Sensitivity = (Number of true positives / (Number of true positives + Number of false negatives)) × 100%, and Specificity is calculated as: (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0090] Example 1: Screening and Identification of Biomarkers

[0091] The study analyzed 400 healthy individuals (206 males (51.5%) and 194 females (48.5%), aged 25 to 70 years, with a mean age of 47.2 ± 12.1 years; specifically, the age distribution was as follows: 25-34 years (58 cases), 35-44 years (103 cases), 45-54 years (116 cases), 55-64 years (88 cases), and 65-70 years (35 cases)) and 400 renal cell carcinoma samples (262 males (65.5%) and 138 females (34.5%), aged 38 to 78 years, with a mean age of 59). The age distribution was 0.3 ± 10.8 years. Specifically, there were 52 cases aged 38-47, 128 cases aged 48-57, 143 cases aged 58-67, and 77 cases aged 68-78. Time-of-flight mass spectrometry (TOF-MS) was performed. Through first-level mass spectrometry testing, the relative abundance differences of characteristic peak data in normal individuals and renal cell carcinoma patients were considered, along with statistical differences (p < 0.05, t-test). A machine learning algorithm (random forest) was used to rank the influence factors for feature selection and to assess the matching degree of data in the database. This identified 38 blood peptides with diagnostic capabilities for renal cell carcinoma. The mass-to-charge ratio (m / z), relative abundance, and influence factors of these peptides are shown in Table 1. The relative abundance was normalized to ln(mean signal intensity of cancer patient samples / mean signal intensity of normal samples) based on the normal human sample. In machine learning, the selection of characteristic peaks was based on feature importance assessment using ensemble learning. Multiple decision trees were constructed to quantify the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction. Feature importance score, or influence factor, is obtained by calculating the mean reduction in impurity caused by the feature when splitting across all tree nodes (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197–227 (2016). (https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0092] The specific parameters for first-order mass spectrometry are as follows:

[0093] Ionization method: Matrix-assisted laser desorption / ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0094] Quality range: 100-4000 Da.

[0095] Resolution: 20000 (full quality range).

[0096] Laser energy: 30-40%.

[0097] Acquisition mode: Positive ion mode.

[0098] Calibration: External quality calibration was performed using the Bruker Peptide Calibration Standard.

[0099] Subsequently, the sequences of these 38 substances in the clinical serum were confirmed using secondary mass spectrometry (MS / MS or TOF / TOF is a peptide identification method recommended by the guidelines of the China Food and Drug Administration and the U.S. Food and Drug Administration). The secondary mass spectrometry data analysis process is as follows:

[0100] Data analysis methods: Mascot software (version 2.8) was used for database searching, with the UniProt Human Proteome Database (released in 2023) as the target database. Search parameters: Enzyme was set to "no digestion", parent ion mass error was allowed ±0.5 Da, fragment ion mass error was allowed ±0.3 Da, fixed modification was cysteine ​​urea methylation, and variable modification was methionine oxidation.

[0101] Sequence confirmation criteria: The confirmation of peptide sequences is based on the matching of fragment ion spectra (b- and y- ions) with theoretical spectra. A Mascot score higher than 30 (p<0.05) is considered significant.

[0102] False positive exclusion: Specificity was verified through reverse database search, and the false positive rate was controlled to below 1%.

[0103] Secondary mass spectrometry parameters:

[0104] Collision-induced dissociation (CID).

[0105] Collision energy: 30 eV.

[0106] Fragment ion mass range: 100-3500 Da.

[0107] Data acquisition: Each sample is scanned at least 1000 times with lasers to improve the signal-to-noise ratio.

[0108] The sequences and specificity of these 38 biomarkers were confirmed by secondary mass spectrometry, ruling out false positives. The specific sequences are shown in Table 1.

[0109] Example 2: Validation of Marker Combinations

[0110] Based on the 38 polypeptide biomarkers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different biomarker combinations were formed as shown in Table 3, and the sensitivity and specificity of the biomarker combinations in the auxiliary diagnosis and early screening of renal cell carcinoma were verified. The analysis process is as follows: For all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain the mass spectrometry peak intensity data of all biomarkers in Table 1. The entire detection process was blinded, meaning that the experimental operators were unaware of the sample grouping information. The raw mass spectrometry data were processed by baseline correction, smoothing, and normalization (based on the internal standard peak intensity), and then the peak area or intensity value of each biomarker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed biomarker intensity data were input into a logistic regression model. For auxiliary diagnostic validation, ten-fold cross-validation was performed using all samples from this cohort (Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024 Aug 9;14:1403392. doi: 10.3389 / fonc.2024.1403392. PMID:39184040; PMCID: PMC11341396.). For early screening validation, given the imbalanced sample size, oversampling (SMOTE) was used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for riskprediction models: illustration and simulation using logistic regression. JAm Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093.PMID: 35686364; PMCID: PMC9382395.).The data analysis process referenced general guidelines for constructing clinical prediction models (Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993 Apr;39(4):561-77. Erratum in: Clin Chem 1993 Aug;39(8):1589. PMID: 8472349.). Specific calculation results for performance indicators are shown in Table 3, and the corresponding ROC curves are shown below. Figures 1-26 .

[0111] 1) The use of various biomarker combinations for the auxiliary diagnosis of renal cell carcinoma was validated in 400 patients (263 males (65.8%), 137 females (34.2%), aged 36 to 79 years, with a mean age of 58.7 ± 10.5 years. Specifically, the distribution was as follows: 48 patients aged 36-45, 126 patients aged 46-55, 143 patients aged 56-65, 68 patients aged 66-75, and 15 patients aged 76-79) and 400 healthy individuals (203 males (50.8%), 197 females (49.2%), aged 26 to 72 years, with a mean age of 46.8 ± 11.3 years. Specifically, the distribution was as follows: 62 patients aged 26-35, 108 patients aged 36-44, 116 patients aged 45-53, 78 patients aged 54-62, and 36 patients aged 63-72).

[0112] 2) A sample of 400 patients with renal cell carcinoma (261 males (65.3%) and 139 females (34.7%), aged 38 to 77 years, with a mean age of 57.9 ± 10.8 years. The specific distribution was: 53 patients aged 38-47, 132 patients aged 48-57, 141 patients aged 58-67, and 74 patients aged 68-77) and 3000 healthy individuals (1523 males, ...) were compared with a sample of 3000 healthy individuals. 50.8% were female and 1477 were male (49.2%), with an age range of 25 to 75 years and a mean age of 47.5 ± 12.2 years. The specific distribution was as follows: 412 cases aged 25-33, 588 cases aged 34-42, 623 cases aged 43-51, 587 cases aged 52-60, 518 cases aged 61-69, and 272 cases aged 70-75. The combination of various biomarkers was validated for early screening of renal cell carcinoma.

[0113] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0114]

[0115] (Continued from the table above)

[0116]

[0117] (Continued from the table above)

[0118]

[0119] According to Table 3 and Figures 1-26 The test results show that the auxiliary diagnostic results of the combination of biomarkers selected in this invention are superior to imaging examinations, one of the main clinical diagnostic methods for renal tumors, enhanced CT (sensitivity and specificity are 78.67% and 87.88%, respectively).

[0120] Early screening results are superior to those of high-frequency color Doppler ultrasound (diagnostic sensitivity 94.87%, specificity 80%) used in routine abdominal examinations during health checkups, as well as multi-gene methylation combined analysis (sensitivity 62.9%, specificity 87.0%).

[0121] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A biomarker set for kidney cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequences 1, 2, 5, and 6; C) Sequence 1, Sequence 2, Sequence 11 and Sequence 12; D) Sequences 1, 2, 30, and 38; E) Sequence 1, Sequence 2, Sequence 3, Sequence 4 and Sequence 5; F) Sequence 1, Sequence 2, Sequence 5, Sequence 6 and Sequence 7; G) Sequence 1, Sequence 2, Sequence 11, Sequence 12 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 38; I) Sequence 2, Sequence 5, Sequence 15, Sequence 16 and Sequence 20; J) Sequences 2, 6, 4 and 8 K) Sequence 1~Sequence 38; J) Sequences 2, 5, 15, and 16; The amino acid sequences of sequences 1 to 38 are shown in SEQ ID NO. 1 to 38; Specifically, the third amino acid G in sequence 12 has a Phospho modification; the fourth amino acid G in sequence 20 has a Dehydrated modification; the second amino acid K in sequence 28 has an Acetyl modification; the first amino acid Q in sequence 32 has a Gln->pyro-Glu modification, and the sixth amino acid N has a Dehydrated modification.

2. Use of the reagent for detecting the biomarker group of claim 1 in the preparation of preparations for renal cancer screening and diagnosis.

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