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

Abstract

The application discloses a marker group for prostate 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.1-46.The marker group constructed by the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of prostate cancer.The auxiliary diagnosis result is superior to the PCa diagnosis mode recommended by the European Urology Society PCa diagnosis and treatment guideline and the prostate image report data system, i.e., the use of mp-MRI (sensitivity is 83.45%, and specificity is 92.94%).The early screening result is superior to the serum PSA examination which is currently the preferred method for prostate cancer screening, the critical value is 4.0 ng / mL (strongly recommended by the guideline, and the evidence classification is medium), the sensitivity is 72.84%, and the specificity is 81.10%.

Description

TECHNICAL FIELD

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

[0002] Prostate cancer is an epithelial malignant tumor originating from the prostate of men. According to the European Association of Urology (EAU) guidelines, the diagnosis and clinical staging of prostate cancer are mainly determined by prostate biopsy, and whether a patient needs to be biopsied is usually determined according to digital rectal examination or the level of prostate-specific antigen (PSA). The screening of PSA detection can benefit the screened population, but the false positives caused by a single indicator often lead to over-diagnosis and over-puncture. According to the data of the United States, which is the earliest country to start serum PSA screening, the false positive rate of serum PSA screening is 10% to 15%. In addition, tissue biopsy is the gold standard for the diagnosis of prostate cancer, but as an invasive examination, it also has certain risks. Another challenge of prostate cancer tissue biopsy is that when it is clinically metastatic, the main site of diffusion is the bone, which increases the difficulty of biopsy sampling, and it is technically difficult to obtain tumor tissue-based samples for genetic testing. The use of CTC and liquid biopsy based on free nucleic acids provides a potential solution to overcome the shortcomings of traditional tumor biopsy techniques.

[0003] Liquid biopsy, as an emerging diagnostic technology, can overcome the influence of tumor heterogeneity, provide more comprehensive tumor molecular information, and provide strong support for guiding clinical treatment and evaluating prognosis. CTC is a tumor cell that spontaneously or due to diagnostic and therapeutic procedures detaches from the primary tumor or metastatic tumor and enters the peripheral blood circulation. At present, the CTC count in metastatic castration-resistant prostate cancer (mCRPC) has been approved by the US Food and Drug Administration as a prognostic marker, and has been confirmed in a prospective trial. Due to the low detection rate of whole blood CTC in the early stage and non-metastatic state, the application value of CTC-based detection methods is limited in the early stage of the disease, and the detection platform needs to be further improved to improve the detection sensitivity.

[0004] As a biomarker of advanced prostate cancer, cfDNA can be detected not only by blood samples but also by urine. In a study of metastatic hormone-sensitive prostate cancer patients treated with androgen deprivation therapy and mCRPC patients treated with docetaxel chemotherapy, scholars performed whole genome sequencing on urine cfDNA to determine tumor-related copy number changes, and the results showed that copy number analysis could detect common genomic abnormalities and detect androgen receptor amplification in 50% of CRPC patients. Although cfDNA is an ideal biomarker for early screening of prostate cancer, the cfDNA released into the plasma is not uniformly covered by the genome due to its unique fragmentation pattern, and obtaining a baseline of cfDNA from healthy people to ensure sensitivity is a challenge. Whether prostate cancer-specific chromosomal structural variations can be detected and used as a molecular marker for tumor early screening requires more samples and big data to accumulate more evidence. SUMMARY

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

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application to solve its technical problems is:

[0007] A marker group for prostate cancer screening and diagnosis, comprising at least four of the polypeptides shown in sequences SEQ ID NO. 1-46, and the specific sequences are shown in Table 1.

[0008] Table 1 Polypeptide sequences

[0009]

[0010] Among them, the first amino acid Q in polypeptide 9 has Gln->pyro-Glu modification, and the sixth amino acid N has Dehydrated modification; the third amino acid G in polypeptide 11 has Phospho modification; the fourth amino acid G in polypeptide 21 has Dehydrated modification; the second amino acid K in polypeptide 26 has Acetyl modification; the fourth amino acid G in polypeptide 41 has Phospho modification.

[0011] Further, the marker group comprises polypeptides shown in sequences 1 and 2, and the following polypeptide combinations:

[0012] The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 15 and sequence 16; sequence 30 and sequence 46; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 15, sequence 16 and sequence 20; sequence 28, sequence 30 and sequence 46.

[0013] Further, the marker group comprises the polypeptides shown in sequence 7 and sequence 8, and the following polypeptide combinations:

[0014] 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.

[0015] Further, the marker group comprises the polypeptides shown in sequence 1, sequence 10 and sequence 30, and the polypeptides shown in sequence 40; sequence 46; sequence 40 and sequence 28; or sequence 28 and sequence 46.

[0016] Further, the marker group comprises the polypeptides shown in sequence 2 and sequence 5, and the following polypeptide combinations:

[0017] The polypeptide combination is one of sequence 18 and sequence 19; sequence 18, sequence 19 and sequence; or sequence 8 and sequence 9.

[0018] Further, the marker group comprises the polypeptides shown in sequence 4, sequence 8, sequence 24, sequence 25 and / or sequence 20.

[0019] Further, the marker group comprises the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 20.

[0020] Further, the marker group comprises the polypeptides shown in sequence 5, sequence 15, sequence 35, and sequence 46 or sequence 28 and sequence 46.

[0021] Further, the marker group comprises the polypeptides shown in sequence SEQ ID NO. 1~46.

[0022] The use of the above marker group in the preparation of a preparation for the screening and diagnosis of prostate cancer.

[0023] The use of the above marker group in medical basic research for non-diagnostic / therapeutic purposes.

[0024] Further, the medical basic research is Western Blot, immunohistochemistry or flow cytometry, etc.

[0025] The beneficial effects of the present application are:

[0026] The marker combination constructed in the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of prostate cancer. The auxiliary diagnosis result is better than the current European Urological Society PCa diagnosis and treatment guidelines and the prostate imaging reporting and data system (PI-RADS) recommended PCa diagnosis using mp-MRI. The sensitivity of the above-mentioned imaging MRI examination method is 83.45%, and the specificity is 92.94%. The early screening result is better than the serum PSA examination which is currently the preferred method for prostate cancer screening, and the critical value is 4.0 ng / mL (guideline strongly recommended, evidence classification: medium), the sensitivity is 72.84%, and the specificity is 81.10%. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 ROC curve diagram of the marker combination 1;

[0028] Figure 2 ROC curve diagram of the marker combination 2;

[0029] Figure 3 ROC curve diagram of the marker combination 3;

[0030] Figure 4 ROC curve diagram of the marker combination 4;

[0031] Figure 5 ROC curve diagram of the marker combination 5;

[0032] Figure 6 ROC curve diagram of the marker combination 6;

[0033] Figure 7 ROC curve diagram of the marker combination 7;

[0034] Figure 8 ROC curve diagram of the marker combination 8;

[0035] Figure 9 ROC curve diagram of the marker combination 9;

[0036] Figure 10 ROC curve diagram of the marker combination 10;

[0037] Figure 11 ROC curve diagram of the marker combination 11;

[0038] Figure 12 ROC curve diagram of the marker combination 12;

[0039] Figure 13 ROC curve diagram of the marker combination 13;

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

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

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

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

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

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

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

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

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

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

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

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

[0052] Figure 26 ROC curve plot for marker combination 26. DETAILED DESCRIPTION

[0053] The specific embodiments of the present application are described below to enable those skilled in the art to understand the present application, 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, any changes that are obvious within the spirit and scope of the present application as defined and determined by the appended claims are obvious, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0054] The patient samples used in the present application are from Zhongshan Hospital, Affiliated Hospital of Fudan University, and have passed the ethical review.

[0055] The experimental methods used in the present application are as follows:

[0056] I. Serum sample collection

[0057] 1) Sample type: Serum.

[0058] 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 cryogenic tube.

[0059] 3) Sample storage:

[0060] Used on the same day, the storage condition is 2-8°C;

[0061] If it cannot be used on the same day, it is stored at -20°C and can be stored for 30 days;

[0062] If stored for a long time (more than a month), it needs to be stored at -80°C.

[0063] The repeated freezing and thawing should not exceed 3 times.

[0064] II. Extraction of the analyte in serum

[0065] 1) After the mass spectrometer instrument is calibrated, open the solid-phase biological automatic sample analysis system instrument SPS1000 / SPS4000, put in consumables, matching reagent kits and samples to be tested;

[0066] Select the program method "solid-phase sample addition";

[0067] Run the program:

[0068] a. Open the hole;

[0069] b. Take not 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 later use;

[0070] c. Wash the customized suction head in cleaning reagent 1 and cleaning reagent 2 in order. When washing, take not less than 10 µL of liquid each time, and repeat the suction and beating not less than 3 times;

[0071] d. Use the cleaned customized suction head to process the serum mixture in the G row hole. When processing, take not less than 10 µL of solution each time, and repeat the suction and beating not less than 3 times;

[0072] e. Use cleaning reagent 3 to clean the customized suction head after adsorbing the serum mixture. When cleaning, take not less than 10 µL of liquid each time, and repeat the suction and beating not less than 3 times;

[0073] f. Transfer not less than 10 µL of buffer reagent to the H row reserved hole, and place the customized suction head after using cleaning reagent 3 in the liquid to take not less than 10 µL of liquid, and repeat the suction and beating not less than 3 times;

[0074] g. Transfer no less than 10 μL sample matrix solution to H row reserved hole, complete sample processing;

[0075] h. Absorb 2.0 μL solution in H hole to hydrophobic coating biochip (Pimo Technology Co., Ltd. Wuxi);

[0076] i. Vacuum dry for 240 s.

[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 uploading

[0081] Place the vacuum-dried hydrophobic coating biochip (Pimo Technology Co., Ltd. Wuxi) into the mass spectrometer;

[0082] Use 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 for data acquisition.

[0083] IV. Quality control

[0084] 1) After data acquisition, upload the data 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. Unqualified quality control may include various possibilities, including non-prostate lesion samples, signal spectrum intensity not meeting standards, etc.

[0086] 3) If the quality control is unqualified, according to the quality control results, correct the corresponding parameters and re-extract the analyte in serum;

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

[0088] V. Establishment of positive judgment value and result analysis

[0089] The research of positive judgment value adopts prostate cancer samples with clear diagnostic information and normal samples, covering benign non-prostate cancer samples such as acute and chronic prostatitis, atypical adenomatous hyperplasia, etc. The core algorithm is supervised learning based on known prostate 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 with 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.). Finally, the maximum Youden index method is used to determine the similarity score positive judgment value of the kit. When the similarity score < positive judgment value, the detection result of the sample is negative; when the similarity score ≥ positive judgment value, the detection result of the sample is positive. Taking the maximum similarity score as the positive judgment value, when assisting in the diagnosis of prostate cancer or conducting early screening of prostate cancer, the sensitivity = true positive number / (true positive number + false negative number) × 100%, and the specificity = true negative number / (true negative number + false positive number) × 100%.

[0090] Example 1 Screening and identification of markers

[0091] Through time-of-flight mass spectrometry testing on 400 normal samples (all male, age distribution range 25-70 years old, average age 46.3±11.8 years old. The specific age distribution is: 53 cases of 25-34 years old, 107 cases of 35-44 years old, 118 cases of 45-54 years old, 86 cases of 55-64 years old, and 36 cases of 65-70 years old), 400 cases of prostate cancer samples (all male, age distribution range 48-82 years old, average age 68.7±8.3 years old. The specific age distribution is: 43 cases of 48-55 years old, 97 cases of 56-63 years old, 142 cases of 64-71 years old, 98 cases of 72-79 years old, and 20 cases of 80-82 years old), through primary mass spectrometry testing, comprehensive consideration of the relative abundance difference of the characteristic peak data in normal people and prostate cancer patients, statistical difference of data (p<0.05, t test), influence factor sorting of feature screening by machine learning algorithm (random forest) and matching degree of data in database, 46 kinds of polypeptide substances in blood were found to have prostate 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 is normalized based on the normal sample, that is, ln (average signal intensity of cancer patient sample / average signal intensity of normal sample). The screening of characteristic peaks in machine learning is 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 is quantified. The feature importance score, i.e. influence factor, is obtained by calculating the average reduction of 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).

[0092] The specific primary mass spectrometry parameters are as follows:

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

[0094] Mass range: 100-4000 Da.

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

[0096] Laser energy: 30-40%.

[0097] Acquisition mode: positive ion mode.

[0098] Calibration: external mass calibration was performed using peptide standards (Bruker Peptide Calibration Standard).

[0099] Subsequently, the sequences of the 46 substances in the clinical serum were confirmed by secondary mass spectrometry (secondary mass spectrometry (MS / MS or TOF / TOF) is the recommended method for polypeptide identification by the China Food and Drug Administration and the U.S. Food and Drug Administration (FDA) guidelines). The secondary mass spectrometry data analysis process is as follows:

[0100] Data analysis method: Database search was performed using Mascot software (version 2.8), and the database was the UniProt human proteome database (released 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.

[0101] 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.

[0102] False positive exclusion: Specificity is verified by reverse database search, and the false positive rate is controlled 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: At least 1000 laser scans per sample were collected to improve the signal-to-noise ratio.

[0108] The sequences and specificities of the 46 markers were confirmed by secondary mass spectrometry, and the false positive problem was excluded. The specific sequences are shown in Table 1.

[0109] Example 2 Verification of marker combination

[0110] According to the 46 polypeptide markers identified and confirmed in Example 1 (see Table 1 for sequences and mass-to-charge ratios), 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 prostate cancer were verified. The analysis process was 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 mass spectrometric peak intensity data for all markers in Table 1. The entire detection process was carried out in a blind manner, i.e., the experimental operator was unaware of the grouping information of the samples. The raw mass spectrometric data were processed by baseline correction, smoothing and normalization (using 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 preprocessed marker intensity data were input into the logistic regression (Logistic Regression) model. For auxiliary diagnosis verification, ten-fold cross-validation was performed using all samples in 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 early screening verification, due to 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 referred to 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.). Sensitivity, Specificity and Area under the Receiver Operating Characteristic Curve (AUC) were calculated for each marker combination. The detailed calculation results of the performance indicators are shown in Table 3, and the corresponding ROC curves are shown in Figures 1-26 .

[0111] 1) 400 cases of prostate cancer patients (all male, age range from 49 to 81 years old, the average age is 67.8±8.1 years old. The specific distribution is: 49-56 years old 38 cases, 57-64 years old 92 cases, 65-72 years old 146 cases, 73-80 years old 108 cases, 81 years old 16 cases) and 400 cases of healthy people samples (all male, age range from 28 to 72 years old, the average age is 48.5±10.7 years old. The specific distribution is: 28-36 years old 53 cases, 37-44 years old 108 cases, 45-52 years old 119 cases, 53-60 years old 87 cases, 61-72 years old 33 cases) were used to verify the auxiliary diagnosis of prostate cancer by each marker combination.

[0112] 2) 300 cases of prostate cancer patients (all male, age range from 47 to 79 years old, the average age is 65.3±8.6 years old. The specific distribution is: 47-54 years old 42 cases, 55-62 years old 78 cases, 63-70 years old 113 cases, 71-79 years old 67 cases) and 2000 cases of healthy people samples (all male, age range from 30 to 75 years old, the average age is 49.2±11.3 years old. The specific distribution is: 30-38 years old 287 cases, 39-46 years old 412 cases, 47-54 years old 518 cases, 55-62 years old 423 cases, 63-70 years old 258 cases, 71-75 years old 102 cases) were used to verify the early screening of prostate cancer by each marker combination.

[0113] Table 3 Sensitivity and Specificity of Marker Combination for Auxiliary Diagnosis and Early Screening

[0114]

[0115] Continue the table above

[0116]

[0117] According to the detection results of Table 3 and Figures 1-26 The auxiliary diagnosis results of the selected marker combination of the present application are better than the mp-MRI recommended by the current European Urological Society PCa diagnosis and treatment guidelines and the prostate imaging reporting and data system (PI-RADS) for PCa diagnosis. The sensitivity of the above imaging MRI examination method is 83.45%, and the specificity is 92.94%. The early screening result is better than the serum PSA examination which is currently the preferred method for prostate cancer screening, and the critical value is 4.0 ng / mL (guideline strongly recommended, evidence classification: medium). The sensitivity is 72.84%, and the specificity is 81.10%.

[0118] 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 marker group for prostate cancer screening, diagnosis, characterized by, The marker group is selected from one of the following polypeptide combinations: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequence 1, Sequence 2, Sequence 5 and Sequence 6; C) Sequence 1, Sequence 2, Sequence 15 and Sequence 16; D) Sequence 1, Sequence 2, Sequence 30 and Sequence 46; 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 15, Sequence 16 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 28, Sequence 30 and Sequence 46; I) Sequence 2, Sequence 5, Sequence 18 and Sequence 19; J) Sequence 2, Sequence 5, Sequence 18, Sequence 19 and Sequence 20; K) Sequence 2, Sequence 5, Sequence 8 and Sequence 9 L) Sequence 1~Sequence 46; The amino acid sequences of the Sequence 1~Sequence 46 are shown in SEQ ID NO. 1~46; Wherein, the first amino acid Q in Sequence 9 has Gln->pyro-Glu modification, the sixth amino acid N has Dehydrated modification; the third amino acid G in Sequence 11 has Phospho modification; the fourth amino acid G in Sequence 21 has Dehydrated modification; the second amino acid K in Sequence 26 has Acetyl modification; the fourth amino acid G in Sequence 41 has Phospho modification.

2. Use of the reagent for detecting the marker group of claim 1 in the preparation of preparations for screening and diagnosing prostate cancer.

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