A marker panel for bladder cancer screening, diagnosis and uses thereof

Abstract

The application discloses a marker group for bladder 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.1-35.The marker group constructed by the application has more excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of bladder cancer.The auxiliary diagnosis results of the marker group selected by the application are all partially or completely superior to the urinary cytology examination (sensitivity 62.5%, specificity 87.5%) recommended by the current clinical guideline of bladder cancer and the biomarker NMP22 (sensitivity 85.4%, specificity 76.5%) approved by FDA.The early screening results are all partially or completely superior to the mass spectrometry metabolomics OPLS-DA model (sensitivity 91.2%, specificity 86.8%) recommended by the current expert consensus of bladder cancer early screening.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology, specifically relating to a biomarker group for bladder cancer screening and diagnosis and its uses. Background Technology

[0002] Bladder cancer is one of the most common malignant tumors of the urinary system. Muscle invasion and distant metastasis are major factors contributing to death or poor prognosis in bladder cancer patients. Studies show that most patients with muscle-invasive bladder cancer (MIBC) are diagnosed at the time of diagnosis, rather than after progression in patients with non-MIBC, suggesting that early diagnosis can effectively reduce the incidence of muscle-invasive and metastatic bladder cancer.

[0003] Cystoscopy and urine cytology are the gold standard for diagnosing bladder cancer (BC); however, both methods have limitations. Cystoscopy is expensive and invasive, and it misses non-invasive tumors (such as carcinoma in situ, which may be more likely to cause invasive BC than many other tumors). While cytology is highly specific, it requires the isolation of a large number of cells from the urine and is not sensitive enough to detect low-grade cancers. Furthermore, the interpretation of cytological results varies depending on sample collection conditions, treatment interventions, and the user's subjective judgment. Currently, monitoring for BC recurrence is primarily done via standard computed tomography (CT). Although imaging techniques can estimate tumor burden, their monitoring potential is limited by suboptimal detection limits and inconsistent measurements. Early detection of metastasis or BC recurrence after cystectomy, and early adjuvant therapy when recurrent tumors cannot be detected by imaging, can improve patient survival. Cystoscopy remains the primary method for bladder cancer diagnosis and follow-up. However, cystoscopy is an invasive procedure that can cause pain and even complications such as infection, difficulty urinating, and hematuria. Additionally, cystoscopy is difficult to identify some flat or small tumors.

[0004] Nuclear matrix protein 22 (NMP22) is a novel tumor marker found in urine and has been approved by the U.S. Food and Drug Administration for early diagnosis and postoperative monitoring of bladder cancer. Its function is related to DNA replication, RNA synthesis, and gene expression regulation. Studies have shown that NMP22 expression levels in the urine of bladder cancer patients can be more than twenty times higher than in normal individuals, with even higher expression levels in high-grade, high-stage, lymph node metastatic, and tumor diameter ≥3 cm patients. Its sensitivity is high and increases with increasing grade and stage, but it suffers from low specificity and is easily interfered with by other factors. Fibrin degradation products (FDPs) are degradation products of fibrin or fibrinogen produced by plasminogen lysis. They inhibit platelet aggregation and release and are important indicators of the coagulation system. Normal human bodies contain very little fibrinogen (FDP). However, when bladder cancer develops, plasmin is activated, breaking down fibrinogen that has leaked out of blood vessels into FDP, which is then excreted in the urine. Therefore, the FDP level in the urine of bladder cancer patients is significantly higher than that of normal individuals. However, the sensitivity of FDP in diagnosing bladder cancer is only 64%, and its specificity is 77%, which is still insufficient for clinical diagnosis. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the prior art, the present invention provides a set of biomarkers for bladder cancer screening and diagnosis and their uses. It has excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of bladder cancer, and is expected to be applied to the diagnosis and treatment of bladder 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 set for bladder cancer screening and diagnosis includes at least four of the polypeptides shown in SEQ ID NO. 1-35, with specific sequences shown in Table 1.

[0008] Table 1. Peptide Sequences

[0009]

[0010] Among them, the third amino acid G of peptide 3 is modified with Phospho, the fourth amino acid G of peptide 4 is modified with Phospho, the second amino acid K of peptide 16 is modified with Acetyl, the first amino acid Q of peptide 21 is modified with Gln->pyro-Glu, the sixth amino acid N is modified with Dehydrated, and the fourth amino acid G of peptide 28 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 35; 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 35.

[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 35, 20, 35, and 9, or sequences 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 35; sequence 2 and sequence 16; sequence 9, sequence 25 and sequence 35; sequence 2, sequence 16 and sequence 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 sequences SEQ ID NO.1~35.

[0022] The use of the above biomarkers in the preparation of formulations for bladder 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 bladder cancer, and is expected to be applied to the diagnosis and treatment of bladder cancer. 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 preprocessing are qualified according to the quality control model; quality control failure may include a variety of possibilities, including the sample is not a bladder 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 bladder cancer samples with clear diagnostic information and normal human samples, covering populations with benign urinary system diseases such as bladder stones and chronic cystitis. The core algorithm is based on supervised learning of known bladder cancer 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 index maximization 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 bladder cancer or to perform early screening for bladder cancer, 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] This study analyzed 350 healthy individuals (175 males (50.0%) and 175 females (50.0%), aged 25 to 75 years with a mean age of 48.5 ± 10.8 years. The specific age distribution was as follows: 25-34 years (62 cases), 35-44 years (78 cases), 45-54 years (85 cases), 55-64 years (75 cases), and 65-75 years (50 cases)). It also analyzed 350 bladder cancer samples (259 males (74.0%) and 91 females (26.0%). This gender ratio is consistent with the epidemiological characteristic of bladder cancer being more prevalent in men. The age distribution ranged from 30 to 85 years with a mean age of 6...). The mean age was 5.2 ± 11.3 years. The specific age distribution was as follows: 28 cases aged 30-39, 45 cases aged 40-49, 72 cases aged 50-59, 105 cases aged 60-69, 75 cases aged 70-79, and 25 cases aged 80-85. 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 bladder cancer patients were comprehensively 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 the matching degree of data in the database was also considered. This revealed 35 blood peptides with diagnostic capabilities for bladder cancer. 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, feature peak selection is based on feature importance assessment using ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction is quantified. The feature importance score, or influence factor, is obtained by calculating the average 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 35 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 35 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 35 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 bladder cancer were verified. The analysis process is as follows:

[0111] For all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain mass spectrometry peak intensity data for all biomarkers in Table 1. The entire detection process was blinded, meaning the experimenters were unaware of the sample grouping information. The raw mass spectrometry data underwent baseline correction, smoothing, and normalization (based on the internal standard peak intensity), and then the peak area or intensity value for 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) technology is used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, VanCalster B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J Am MedInform 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 predictive 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.). Sensitivity, specificity, and area under the receiver operating characteristic (AUC) curve were calculated for each biomarker combination. The specific calculation results for the performance indicators are shown in Table 3, and the corresponding ROC curves are shown in [Table data missing]. Figures 1-26 .

[0112] 1) The auxiliary diagnosis of bladder cancer was validated based on various biomarker combinations using a sample of 500 patients (374 males (74.8%), 126 females (25.2%), aged 40-49 (48 cases), 50-59 (83 cases), 60-69 (157 cases), 70-79 (162 cases), and 80-85 (50 cases)) and 500 healthy individuals (247 males (49.4%), 253 females (50.6%), aged 25-29 (32 cases), 30-39 (107 cases), 40-49 (118 cases), 50-59 (113 cases), 60-69 (72 cases), and 70-75 (58 cases).

[0113] 2) The early screening of bladder cancer was validated based on various biomarker combinations using a sample of 300 bladder cancer patients (228 males (76.0%), 72 females (24.0%), 43 aged 40-49, 62 aged 50-59, 108 aged 60-69, 73 aged 70-79, and 14 aged 80-85) and 3000 healthy individuals (1487 males (49.6%), 1513 females (50.4%), 447 aged 20-29, 603 aged 30-39, 595 aged 40-49, 528 aged 50-59, 452 aged 60-69, and 375 aged 70-80).

[0114] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0115]

[0116] (Continued from the table above)

[0117]

[0118] (Continued from the table above)

[0119]

[0120] According to Table 3 and Figures 1-26 The test results show that the auxiliary diagnostic results of the discriminant peak set (biomarker combination) selected in this invention are partially or completely superior to the urine cytology test recommended in current clinical guidelines for bladder cancer (sensitivity 62.5%, specificity 87.5%) and the FDA-approved biomarker NMP22 (sensitivity 85.4%, specificity 76.5%). The early screening results are partially or completely superior to the OPLS-DA mass spectrometry metabolomics model recommended in current expert consensus on early bladder cancer screening (sensitivity 91.2%, specificity 86.8%).

[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 bladder cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 4, Sequence 2 and Sequence 3; B) Sequence 1, Sequence 2, Sequence 3, Sequence 4, and Sequence 5; C) Sequence 4, Sequence 7, Sequence 8 and Sequence 3; D) Sequence 4, Sequence 7, Sequence 8, Sequence 3 and Sequence 9; E) Sequence 4, Sequence 8, Sequence 19 and Sequence 20; F) Sequence 4, Sequence 8, Sequence 2 and Sequence 6; G) Sequence 4, Sequence 8, Sequence 19, Sequence 20 and Sequence 22; H) The polypeptides shown in sequences 1 to 35; The amino acid sequences of sequences 1 to 35 are shown in SEQ ID NO. 1 to 35; Specifically, amino acid G at position 3 of sequence 3 is modified with Phospho, amino acid G at position 4 of sequence 4 is modified with Phospho, amino acid K at position 16 of sequence 16 is modified with Acetyl, amino acid Q at position 21 of sequence 21 is modified with Gln->pyro-Glu, amino acid N at position 6 of sequence 21 is modified with Dehydrated, and amino acid G at position 4 of sequence 28 is modified with Dehydrated.

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

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