Marker group for screening and diagnosing bladder cancer and application of marker group

Abstract

The invention discloses a marker group for screening and diagnosing bladder cancer and application of the marker group, and belongs to the technical field of molecular biology. The marker group comprises at least four of polypeptides as shown in sequences SEQ ID NO. 1 to 35. The marker combination constructed by the invention has more excellent sensitivity and specificity when being used for auxiliary diagnosis and early screening of bladder cancer. The auxiliary diagnosis results of the selected marker combination are partially or completely superior to the urine cytology examination (the sensitivity is 62.5% and the specificity is 87.5%) recommended by the current bladder cancer clinical guide and the biomarker NMP22 (the sensitivity is 85.4% and the specificity is 76.5%) approved by FDA. The early screening results are partially or completely superior to the mass spectrum metabonomics OPLS-DA model (the sensitivity is 91.2%, and the specificity is 86.8%) recommended by the current bladder cancer early screening expert consensus.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of molecular biology technology, and particularly relates to a marker group for bladder cancer screening and diagnosis and use thereof. BACKGROUND

[0002] Bladder cancer is one of the common malignant tumors of the urinary system, and muscle invasion and distant metastasis are the main factors for the death or poor prognosis of bladder cancer patients. Studies have shown that most muscle invasive bladder cancer (MIBC) patients are found at the time of diagnosis, rather than after progression of NMIBC patients, suggesting that early diagnosis can effectively reduce the incidence of muscle invasion and metastatic bladder cancer.

[0003] Cystoscopy and urine exfoliative cytology are the gold standard for diagnosing BC; however, both methods have limitations. Cystoscopy is expensive and invasive, and it ignores non-invasive tumors (such as carcinoma in situ, which can be more likely to cause invasive BC than many other tumors). Although cytology is highly specific, it requires the isolation of a large number of cells from urine and is not sensitive enough in detecting low-grade cancer. In addition, the interpretation of cytology results varies depending on sample collection conditions, therapeutic interventions, and user subjective judgment. Currently, the monitoring of BC recurrence is mainly done through 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 can provide early adjuvant therapy when recurrent tumors cannot be detected by imaging, improving patient survival. Cystoscopy is still the main way for the diagnosis and follow-up of bladder cancer. However, cystoscopy is a traumatic examination that causes pain to patients and can even lead to complications such as infection, dysuria, hematuria, etc., in addition, cystoscopy is difficult to identify some flat or small tumors.

[0004] Nuclear matrix protein 22 (NMP22) is a new type of tumor marker existing in urine, which has been approved by the US Food and Drug Administration for the early diagnosis and postoperative monitoring of clinical bladder cancer. Its function is related to DNA replication, RNA synthesis and gene expression regulation. Studies have shown that the expression level of NMP22 in the urine of bladder cancer patients can reach more than twenty times that of normal people, and the expression level in the urine of patients with high grade, high stage, lymph node metastasis, and tumor diameter greater than or equal to 3cm is higher. Its sensitivity is high and increases with the increase of grade and stage, but the specificity is low, and it is easily interfered by other factors. Fibrin degradation products (FDP) are degradation products produced by fibrin or fibrinogen by fibrinolysin, which can inhibit platelet aggregation and release, and is an important indicator of the coagulation system. The content of FDP in the normal human body is very small, but when the bladder cancer changes, the fibrinolysin is activated, which will decompose the fibrinogen exuded outside the blood vessels into FDP and be excreted through urine, so the FDP level in the urine of bladder cancer patients is significantly higher than that of normal people. However, the sensitivity of FDP in diagnosing bladder cancer is 64%, and the specificity is 77%, which still cannot meet the requirements of clinical diagnosis. SUMMARY

[0005] In view of the above problems in the prior art, the present application provides a marker group for bladder cancer screening and diagnosis and its use. In the auxiliary diagnosis and early screening of bladder cancer, it has excellent sensitivity and specificity, and is expected to be applied to the diagnosis and treatment of bladder cancer.

[0006] To achieve the above object, the technical scheme adopted by the present application to solve its technical problems is: A marker group for bladder cancer screening and diagnosis, comprising at least four of the polypeptides shown in SEQ ID NO. 1-35, and the specific sequence is shown in Table 1.

[0007] Table 1 Polypeptide sequence

[0008] Among them, the third amino acid G of polypeptide 3 has Phospho modification, the fourth amino acid G of polypeptide 4 has Phospho modification, the second amino acid K of polypeptide 16 has Acetyl modification, the first amino acid Q of polypeptide 21 has Gln->pyro-Glu modification, the sixth amino acid N has Dehydrated modification, and the fourth amino acid G of polypeptide 28 has Dehydrated modification.

[0009] Further, the marker group comprises the polypeptides shown in Sequence 1 and Sequence 2, and the following polypeptide combinations: 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.

[0010] Further, the marker group comprises the polypeptides shown in Sequence 7 and Sequence 8, and the following polypeptide combinations: The polypeptide combination is one of Sequence 3 and Sequence 4; Sequence 9 and Sequence 10; Sequence 3, Sequence 4 and Sequence 9; Sequence 9, Sequence 10 and Sequence 11.

[0011] Further, the marker group comprises the polypeptides shown in Sequence 1, Sequence 10 and Sequence 30, and the polypeptides shown in Sequence 35; Sequence 20; Sequence 35 and Sequence 9 or Sequence 20 and Sequence 22.

[0012] Further, the marker group comprises the polypeptides shown in Sequence 5 and Sequence 15, and the following polypeptide combinations: The polypeptide combination is one of Sequence 25 and Sequence 35; Sequence 2 and Sequence 16; Sequence 9, Sequence 25 and Sequence 35; Sequence 2, Sequence 16 and 20.

[0013] Further, the marker group comprises the polypeptides shown in Sequence 4 and Sequence 8, and the following polypeptide combinations: The polypeptide combination is one of Sequence 19 and Sequence 20; Sequence 2 and Sequence 6; or Sequence 19, Sequence 20 and Sequence 22.

[0014] Further, the marker group comprises the polypeptides shown in Sequence 7, Sequence 10, Sequence 17, Sequence 18 and / or Sequence 22.

[0015] Further, the marker group comprises the polypeptides shown in Sequence SEQ ID NO. 1~35.

[0016] The use of the above marker group in the preparation of a preparation for the screening and diagnosis of bladder cancer.

[0017] The use of the above marker group in medical basic research for non-diagnostic / therapeutic purposes.

[0018] Further, the medical basic research is Western Blot, immunohistochemistry or flow cytometry, etc.

[0019] The beneficial effects of the present application: The marker combination constructed in the application has more 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. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 ROC curve diagram of the marker combination 1; Figure 2 ROC curve diagram of the marker combination 2; Figure 3 ROC curve diagram of the marker combination 3; Figure 4 ROC curve diagram of the marker combination 4; Figure 5 ROC curve diagram of the marker combination 5; Figure 6 ROC curve diagram of the marker combination 6; Figure 7 ROC curve diagram of the marker combination 7; Figure 8 ROC curve diagram of the marker combination 8; Figure 9 ROC curve diagram of the marker combination 9; Figure 10 ROC curve diagram of the marker combination 10; Figure 11 ROC curve diagram of the marker combination 11; Figure 12 ROC curve diagram of the marker combination 12; Figure 13 ROC curve diagram of the marker combination 13; Figure 14 ROC curve diagram of the marker combination 14; Figure 15 ROC curve diagram of the marker combination 15; Figure 16 ROC curve diagram of the marker combination 16; Figure 17 ROC curve diagram of the marker combination 17; Figure 18 ROC curve diagram of the marker combination 18; Figure 19 ROC curve diagram of the marker combination 19; Figure 20 ROC curve diagram of the marker combination 20; Figure 21 ROC curve diagram of the marker combination 21; Figure 22ROC curve chart of the marker combination 22; Figure 23 ROC curve chart of the marker combination 23; Figure 24 ROC curve chart of the marker combination 24; Figure 25 ROC curve chart of the marker combination 25; Figure 26 ROC curve chart of the marker combination 26. DETAILED DESCRIPTION

[0021] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0022] The patient samples used in the present application are from Zhongshan Hospital Affiliated to Fudan University, and have passed the ethical review.

[0023] The experimental methods used in the present application are as follows: I. Serum sample collection 1) Sample type: serum.

[0024] 2) Collection requirements: fasting collection, 5 mL of venous blood is drawn using a coagulation tube, and after standing for 30 min, 3000 rpm centrifugation for 15 min, about 1 mL of serum is taken out and placed in a cryopreservation tube.

[0025] 3) Sample storage: Used on the same day, the storage condition is 2-8℃; If it cannot be used on the same day, it is stored at-20℃, and can be stored for 30 days; If stored for a long time (more than one month), it needs to be stored at-80℃.

[0026] Repeated freezing and thawing should not exceed 3 times.

[0027] II. Extraction of the analyte in serum 1) After calibrating the mass spectrometer instrument, open the solid-phase biosystem full-automatic sample analysis system instrument SPS1000 / SPS4000, put in consumables, matched reagent kit and the sample to be tested; Select the program method "solid-phase sample addition"; Run the program: a. Open the hole; b. Take no less than 10 µL of serum sample and activated reagent, mix them in a ratio of 1:1, and then place them in the G row reserved hole for use; c. The customized pipette tip is sequentially washed in washing reagent 1 and washing reagent 2. Each time, at least 10 μL of liquid is sucked, and the process is repeated at least 3 times; d. The customized pipette tip is used to process the serum mixture in the G row of holes. Each time, at least 10 μL of solution is sucked, and the process is repeated at least 3 times; e. The customized pipette tip after adsorbing the serum mixture is washed using washing reagent 3. Each time, at least 10 μL of liquid is sucked, and the process is repeated at least 3 times; f. At least 10 μL of buffer reagent is transferred to the H row of reserved holes, and the customized pipette tip after using washing reagent 3 is placed in the liquid to suck at least 10 μL of liquid, and the process is repeated at least 3 times; g. At least 10 μL of sample matrix solution is transferred to the H row of reserved holes, and the sample processing is completed; h. 2.0 μL of the solution in the H hole is spotted on the hydrophobic coated biochip (Pimcore Technology Co., Ltd.); i. Vacuum drying for 240 s.

[0028] The main components of each reagent are shown in Table 2.

[0029] Table 2. Reagent composition

[0030] III. Mass spectrometry data acquisition and uploading The vacuum-dried hydrophobic coated biochip (Pimcore Technology Co., Ltd.) is placed in the mass spectrometer; The data acquisition is performed using the set 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 point diameter, laser frequency, calibration method, and laser intensity.

[0031] IV. Quality control 1) After data acquisition, the data is uploaded to the mass spectrometry data analysis software; 2) The software reads the sample information and signal spectrum, and determines whether the sample and sample pretreatment are qualified according to the quality control model. Unqualified quality control may include various possibilities, including non-bladder lesion samples, and signal spectrum intensity not meeting the standard.

[0032] 3) If the quality control is unqualified, the corresponding parameters are corrected according to the quality control results, and the serum analyte extraction process is performed again; 4) If the quality control is qualified, the next process is entered.

[0033] V. Establishment of positive judgment value and result analysis The study of positive judgment value uses bladder cancer samples with clear diagnostic information and normal samples, covering benign urological disease populations such as bladder stones and chronic cystitis. The core algorithm is based on supervised learning of known bladder cancer sample atlas. Through a series of processes such as smoothing denoising baseline, screening characteristic peaks, constructing classification model, and calculating the similarity of hormone signal atlas and the known hormone signal atlas stored in the software (Cannataro M, Guzzi P H, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data [J]. 2005.), the similarity score positive judgment value of the kit is finally determined by the maximum Youden index method. When the similarity score < positive judgment value, the sample test result is negative; when the similarity score ≥ positive judgment value, the sample test result is positive. Taking the maximum similarity score as the positive judgment value, the sensitivity = true positive number / (true positive number + false negative number) x 100%, and the specificity = true negative number / (true negative number + false positive number) x 100% when assisting in the diagnosis of bladder cancer or early screening of bladder cancer.

[0034] Example 1 Screening and identification of markers The time-of-flight mass spectrometry was performed on 350 normal samples (175 males (50.0%) and 175 females (50.0%), with an age distribution range of 25 to 75 years old and an average age of 48.5 ± 10.8 years old. The specific age distribution was as follows: 62 cases of 25-34 years old, 78 cases of 35-44 years old, 85 cases of 45-54 years old, 75 cases of 55-64 years old, and 50 cases of 65-75 years old) and 350 bladder cancer samples (259 males (74.0%) and 91 females (26.0%). The gender ratio was consistent with the epidemiological characteristics of bladder cancer, which is more common in males. The age distribution range was 30 to 85 years old, with an average age of 65.2 ± 11.3 years old. The specific age distribution was as follows: 28 cases of 30-39 years old, 45 cases of 40-49 years old, 72 cases of 50-59 years old, 105 cases of 60-69 years old, 75 cases of 70-79 years old, and 25 cases of 80-85 years old). Through primary mass spectrometry testing, the relative abundance differences of characteristic peak data in normal people and bladder cancer patients were considered, the statistical differences of the data (p < 0.05, t test), the influence factor sorting of the machine learning algorithm (random forest) for feature selection, and the matching degree of the data in the database, 35 polypeptide substances in the blood were found to have bladder cancer diagnostic ability. The mass-to-charge ratio (m / z) of these polypeptides, relative abundance, and influence factor are shown in Table 1. The relative abundance was normalized based on the normal sample, that is, ln (average signal intensity of cancer patient sample / average signal intensity of normal sample). The feature peak selection in machine learning was based on the feature importance evaluation of ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction was quantified. The feature importance score, that is, the influence factor, was obtained by calculating the average reduction in impurity brought by the feature at all tree node splits (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197-227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0035] The specific primary mass spectrometry parameters are as follows: Ionization method: matrix-assisted laser desorption ionization (MALDI), with CHCA as the matrix.

[0036] Mass range: 100-4000 Da.

[0037] Resolution: 20000 (full mass range).

[0038] Laser energy: 30-40%.

[0039] Acquisition mode: positive ion mode.

[0040] Calibration: External mass calibration was performed using peptide standards (Bruker Peptide Calibration Standard).

[0041] Subsequently, the sequences of the 35 substances in the clinical serum were confirmed by secondary mass spectrometry (secondary mass spectrometry (MS / MS or TOF / TOF) is recommended by the China Food and Drug Administration and the U.S. Food and Drug Administration (FDA) guidelines for polypeptide identification). The secondary mass spectrometry data analysis process is as follows: Data analysis method: Database search was performed using Mascot software (version 2.8), and the database was the UniProt human proteome database (published in 2023). Search parameters: enzyme setting "no enzyme digestion", parent ion mass error allowed ± 0.5 Da, fragment ion mass error allowed ± 0.3 Da, fixed modification cysteine urea methylation, variable modification methionine oxidation.

[0042] Sequence confirmation standard: The confirmation of polypeptide sequence is based on the matching of fragment ion spectrum (b- and y-ions) with theoretical spectrum, and Mascot score higher than 30 (p<0.05) is considered significant.

[0043] False positive exclusion: Specificity is verified by reverse database search, and the false positive rate is controlled below 1%.

[0044] Secondary mass spectrometry parameters: Collision-induced dissociation (CID).

[0045] Collision energy: 30 eV.

[0046] Fragment ion mass range: 100-3500 Da.

[0047] Data acquisition: At least 1000 laser scans per sample were collected to improve signal-to-noise ratio.

[0048] The sequences and specificities of the 35 markers were confirmed by secondary mass spectrometry, and the false positive problem was excluded. The specific sequences are shown in Table 1.

[0049] Example 2 Verification of marker combination According to the 35 polypeptide markers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different marker combinations shown in Table 3 were formed, and the sensitivity and specificity of the marker combinations in the auxiliary diagnosis and early screening of bladder cancer 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 spectral peak intensity data for all markers in Table 1. The whole detection process was performed in a blinded manner, i.e., the experiment operator was unaware of the grouping information of the samples. The raw mass spectral data were processed by baseline correction, smoothing and normalization (with the internal standard peak intensity as the reference), and then the peak area or intensity value of each marker was extracted. Statistical analysis was performed using R software (version 4.0.2). The pre-processed marker intensity data were input into the Logistic Regression model. For the auxiliary diagnosis validation, ten-fold cross-validation was performed using all samples of the 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 the early screening validation, given the uneven sample size, the SMOTE (Synthetic Minority Over-sampling Technique) technique 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 risk prediction models: illustration and simulation using logistic regression. J Am Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093. PMID: 35686364; PMCID: PMC9382395.).The data analysis procedure refers to the general guidelines for clinical prediction model construction (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.). The sensitivity (Sensitivity), specificity (Specificity) and area under the receiver operating characteristic curve (AUC) of each marker combination were calculated. The specific calculation results of performance indicators are shown in Table 3, and the corresponding ROC curves are shown in. Figures 1-26 .

[0050] 1) 500 cases of bladder cancer patients (male 374 cases (74.8%), female 126 cases (25.2%), 40-49 years old 48 cases, 50-59 years old 83 cases, 60-69 years old 157 cases, 70-79 years old 162 cases, 80-85 years old 50 cases.) And 500 cases of healthy people samples (male 247 cases (49.4%), female 253 cases (50.6%), 25-29 years old 32 cases, 30-39 years old 107 cases, 40-49 years old 118 cases, 50-59 years old 113 cases, 60-69 years old 72 cases, 70-75 years old 58 cases), based on each marker combination to assist in the diagnosis of bladder cancer verification.

[0051] 2) 300 cases of bladder cancer patients (male 228 cases (76.0%), female 72 cases (24.0%), 40-49 years old 43 cases, 50-59 years old 62 cases, 60-69 years old 108 cases, 70-79 years old 73 cases, 80-85 years old 14 cases) and 3000 cases of healthy people samples (male 1487 cases (49.6%), female 1513 cases (50.4%), 20-29 years old 447 cases, 30-39 years old 603 cases, 40-49 years old 595 cases, 50-59 years old 528 cases, 60-69 years old 452 cases, 70-80 years old 375 cases), based on each marker combination to assist in the early screening of bladder cancer verification.

[0052] Table 3 Sensitivity and specificity of marker combination for auxiliary diagnosis and early screening

[0053] Continue the table above

[0054] Continue the table above

[0055] According to the detection results of Table 3 and Figures 1-26 The auxiliary diagnosis results of the selected discriminant peak set (marker combination) of the present application are all partially or completely superior to the urinary cytology examination (sensitivity 62.5%, specificity 87.5%) recommended by the current clinical guidelines for 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 for early screening of bladder cancer.

[0056] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to examples, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A biomarker set for bladder cancer screening and diagnosis, characterized in that, The biomarker group includes at least four of the polypeptides represented by sequences SEQ ID NO. 1 to 35.

2. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in Sequence 1 and Sequence 2, as well as the following combinations of peptides: 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.

3. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides: 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; sequence 9, sequence 10 and sequence 11.

4. The marker set according to claim 1, characterized in that, The biomarker set includes polypeptides represented by sequences 1, 10, and 30, as well as polypeptides represented by sequences 35, 20, 35, and 9, or sequences 20 and 22.

5. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 5 and 15, as well as the following combinations of peptides: 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.

6. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 4 and 8, as well as the following combinations of peptides: The polypeptide combination is sequence 19 and sequence 20; sequence 2 and sequence 6; or sequence 19, sequence 20 and sequence 22.

7. The set of markers according to claim 1, characterized in that, The biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 22.

8. The set of markers according to claim 1, characterized in that, The biomarker group includes polypeptides as shown in SEQ ID NO. 1~35.

9. Use of the biomarker group according to any one of claims 1 to 8 in the preparation of a formulation for bladder cancer screening and diagnosis.

10. Use of the biomarker group according to any one of claims 1 to 8 or the formulation according to claim 9 in basic medical research for non-diagnostic / therapeutic purposes.

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