Application and detection reagents of prostate cancer-specific methylation markers

Abstract

The present invention relates to the field of in vitro molecular biology diagnostic reagents, and specifically discloses the application and detection reagent of a prostate cancer-specific methylation marker. The present invention designs primers and probes based on the differentially methylated target regions of the prostate cancer-specific methylation marker: partial regions or the full length of the positive strands of chr10:111767101-111767300, chr11:54965801-54966100, and chr16:88717311-88717610. This methylation marker combination can effectively distinguish prostate cancer from benign prostatic hyperplasia or other cancers of the urinary tract system, helps to solve the problem of false positives in prostate cancer screening, and can significantly improve the detection sensitivity of non-touch urine. It provides an important reference for clinicians in the early diagnosis and differential diagnosis of prostate cancer.

Description

Technical Field

[0001] The present invention relates to the field of in vitro molecular biology diagnostic reagents, and in particular to the application and detection reagents of prostate cancer specific methylation markers. Background Art

[0002] Prostate cancer (PCa) is one of the most common malignancies of the male reproductive system. Currently, the diagnostic process for prostate cancer involves a digital rectal examination, serum PSA testing, followed by imaging studies and prostate biopsy. Serum PSA testing is currently a routine test for prostate cancer, but its specificity and sensitivity are low. Prostate biopsy, the current gold standard, has major limitations, including invasiveness, false negatives, missed diagnosis of high-risk prostate cancer, and overdiagnosis.

[0003] DNA methylation, a form of epigenetic research, can regulate gene expression without altering base sequence. Studies have shown that changes in DNA methylation can be used to diagnose prostate cancer. Methylation of genes including GSTP1, APC, RASSF1, AR, and NEP has been associated with prostate cancer. Currently, there are few noninvasive prostate cancer testing products available domestically and internationally, and most are based on NGS platforms. This technology is not suitable for general hospital laboratory departments, and the testing process is complex, costly, and requires a long turnaround time. Research and products based on PCR-based platforms for methylation testing are emerging. However, because prostatic fluid is extruded from the prostate acini, enters the ejaculatory duct, and then drains into the urethra, prostate massage is often required to stimulate secretion, followed by urine collection for testing, which reduces clinical compliance. Improving the sensitivity of detection technology to effectively address the need for prostate massage before prostate testing remains a key technical challenge.

[0004] In addition, when evaluating the impact of gene methylation on disease, it is very important to choose the right analysis unit. Differential methylation analysis can be divided into different levels, including DMP (differential CpG site), DMR (differential CpG region) and DMB (differential region of a larger range). DMP represents the identification of a single differentially methylated CpG site, while DMR refers to a continuous and relatively long differential fragment. Scientists believe that such continuous differential fragments have a more obvious impact on genes and can improve the accuracy of computational biology. Therefore, analysis through DMR can more accurately reflect the dynamic changes in gene methylation status and how these changes are associated with gene expression and disease risk. Therefore, using DMR for gene methylation analysis has higher value and practicality in the study of assessing disease risk and disease mechanism.

[0005] Therefore, it is very necessary to develop a method for detecting multi-gene methylation levels by directly collecting urine without prostate palpation for auxiliary diagnosis of clinical prostate cancer.

[0006] CN111154876B discloses a primer-probe combination, kit, and detection method for detecting methylation of the human ADD3 and CDH23 genes. However, the patent's examples are limited to testing a small number of negative and positive quality control samples or negative and positive samples. Clinical samples were not tested, making it impossible to determine the sensitivity and specificity of the invention for clinical auxiliary diagnosis. Furthermore, the biological sample used in this invention is cells from first-pass urine after prostate massage, which is approximately 40-50 ml. This method fails to effectively address clinical compliance issues.

[0007] CN116287227A discloses a reagent and kit for diagnosing prostate cancer. This reagent uses blood or urine as a sample and performs a combined diagnosis by detecting methylation levels in the target regions of CHST11-R3, AOX1-R2, PRKCB-R2, and C2orf88-R1, enabling non-invasive detection of prostate cancer. While this patent uses a large number of clinical prostate cancer samples and healthy subjects for testing, achieving good sensitivity and specificity, it does not include clinical prostate hyperplasia samples or other urinary tract cancers, such as bladder cancer, for specific testing. Clinical studies have found that prostate hyperplasia can also lead to abnormally elevated PSA levels, resulting in a large number of false positives. Therefore, the discovery of new screening markers to reduce the incidence of false positives is urgently needed. Our research has found that methylation in prostate hyperplasia and other urinary tract cancers can significantly interfere with prostate cancer detection. While finding targets that can distinguish prostate cancer from healthy subjects is not difficult, finding markers that can effectively distinguish prostate cancer from prostate hyperplasia or other urinary tract cancers is much more challenging and a truly urgent clinical problem.

[0008] CN117925845A discloses that high-sensitivity prostate cancer diagnosis can be achieved by detecting specific regions of the RARB gene, a specific methylation molecular marker. This method can be performed directly on a urine sample without rectal massage. However, limitations exist: 1. This method specifically detects methylation of the RARB gene, and the sensitivity and specificity of single-gene methylation testing for early cancer screening are limited. 2. This method uses ≥2 of the five RARB gene segments as the criterion for a positive result. While this method can somewhat compensate for the loss of detection signal in some samples due to hypomethylation of one or several CpG sites, thereby improving detection sensitivity, previous research has shown that the methylation levels in the DMR region of the same gene exhibit a certain degree of linkage association, and most samples will simultaneously show hypermethylation at all CpG sites in this region. For these samples, when the number of prostate-derived cells in urine without prostate massage is extremely low, this patented method, equivalent to conventional qPCR detection, still cannot effectively improve detection sensitivity. Theoretically, it should be difficult to detect prostate cancer in urine without prostate massage.

[0009] The present invention mainly realizes the auxiliary diagnosis of prostate cancer by directly collecting urine for multi-gene methylation level detection without prostate massage through the following four aspects. (1) Through a special urine preservation and nucleic acid enrichment method, the nucleic acid substances of prostate-derived cells are effectively enriched; (2) Differential methylation regions (DMRs) specific to prostate tissues are screened by MeDIP high-throughput sequencing, and the intersection is obtained with the prostate cancer methylation data mined from the TCGA and GEO databases, and the target gene methylation high-throughput sequencing (Target Bisulfite (1) Target-BS) was used to obtain tissue and urine consistent prostate cancer-specific methylation detection target sites; (2) the number and position of CpG sites covered by primer probe sequences were optimized, the concentration of primer probes was optimized, the reaction procedure was optimized, etc., to obtain a high-sensitivity and high-specificity multiplex PCR fluorescence detection system, thereby realizing the detection of DMR regions of multiple genes, accumulating multi-site methylation signals in each DMR region, and improving the detection sensitivity; (3) the number and position of CpG sites covered by primer probe sequences, the concentration of primer probes, and the reaction procedure were optimized, etc., to obtain a high-sensitivity and high-specificity multiplex PCR fluorescence detection system, thereby realizing the detection of DMR regions of multiple genes, accumulating multi-site methylation signals in each DMR region, and improving the detection sensitivity; (4) the methylation levels of multiple methylation sites in multiple DMR regions were detected for training set samples, and the weight coefficients of each gene were determined by regression statistical analysis. The corresponding weighted scoring model was established and verified in the validation set samples, effectively improving the specificity and accuracy of the detection, thereby realizing the detection of low-frequency prostate target nucleic acids without prostate touch urine sampling. Summary of the Invention

[0010] The present invention aims to provide a method for detecting prostate cancer-specific methylation markers, the use of a preparation for the preparation of a prostate cancer detection reagent, and the detection reagent. The detection performance of the method was evaluated using a large number of clinical samples.

[0011] The present invention is implemented by the following technical solutions:

[0012] The application of prostate cancer-specific methylation markers, and the application of preparations for detecting prostate cancer-specific methylation markers in the preparation of prostate cancer detection reagents; the differential methylation target regions of the prostate cancer-specific methylation markers are based on GRCh37.p13 and include the following three regions: a partial region or the full length of the positive chain of chr10:111767101-111767300; a partial region or the full length of the positive chain of chr11:54965801-54966100; a partial region or the full length of the positive chain of chr16:88717311-88717610; each region covers no less than 8 CpG sites.

[0013] Furthermore, differentially methylated sites include:

[0014] ADD3_chr10:111767228

[0015] ADD3_chr10:111767249

[0016] ADD3_chr10:111767345

[0017] ADD3_chr10:111767350

[0018] ADD3_chr10:111767479

[0019] ADD3_chr10:111767481

[0020] ADD3_chr10:111767493

[0021] ADD3_chr10:111767530

[0022] GSX2_chr11:54965883

[0023] GSX2_chr11:54966019

[0024] GSX2_chr11:54966058

[0025] CYBA_chr16:88717489

[0026] CYBA_chr16:88717571

[0027] CYBA_chr16:88717602.

[0028] The detection reagent includes primers and probes designed based on the differential methylation sites, and the sequences are as follows: (1)

[0030]

[0031] Two F primers and two R primers can be used in conjunction with the P probe to amplify target sequences covering the CpG site CYBA_chr16:88717571; (2)

[0033]

[0034] Two F primers and two R primers can be cross-matched with the P probe to amplify the target sequence covering the CpG site CYBA_chr16:88717489; (3)

[0036]

[0037] Two F primers and two R primers can be cross-matched with the P probe to amplify the target sequence covering the CpG site CYBA_chr16:88717602; (4)

[0039]

[0040] Two F primers and two R primers can be cross-matched with the P probe to amplify the target sequence covering the CpG site ADD3_chr10:111767228; (5)

[0042]

[0043]

[0044] Any one of the three F primers can be used with the R primer and P probe to amplify the target sequence covering the CpG site ADD3_chr10:111767249; (6)

[0046]

[0047] Two F primers and two R primers can be used in conjunction with the P probe to amplify target sequences covering the CpG sites ADD3_chr10:111767345 and ADD3_chr10:111767350; (7)

[0049]

[0050] Two F primers and two R primers can be cross-matched with the P probe to amplify target sequences covering the CpG sites ADD3_chr10:111767479, ADD3_chr10:111767481, ADD3_chr10:111767493 and ADD3_chr10:111767530; (8)

[0052]

[0053] The three F primers and two R primers can be cross-matched with the P probe, and the amplified target sequences all cover the CpG site GSX2_chr11:54965883; (9)

[0055]

[0056] Two F primers and two R primers can be cross-matched with the P probe, and the amplified target sequences cover the CpG sites GSX2_chr11:54966019 and GSX2_chr11:54966058.

[0057] The above application also includes an internal reference gene primer probe, the sequence of which is as follows: mACTB-FGGTGTTTAAGATAGTGTTGTGG

[0058] mACTB-R CTACTTAATACACACTCCAAAACC

[0059] mACTB-P CTTTACACCAACCTCATAACCTTATC.

[0060] Furthermore, the application,

[0061]

[0062] Furthermore,

[0063] Single gene testing diagnostic analysis:

[0064]

[0065]

[0066] The formula for each combination probability (P) used for two-gene or multi-gene combined diagnostic analysis is as follows:

[0067]

[0068] The test reagents also include urine preservation solution,

[0069] The formulation is as follows: 4 M guanidine thiocyanate, 50 mM EDTA, 200 mM TCEP, 20% PEG, 500 mM sulfosalicylic acid, 20% isopropanol, 10% Tween 20, 0.1 M citric acid-sodium citrate buffer at pH 4.5.

[0070] The present invention also provides a prostate cancer-specific methylation detection reagent, comprising primers and probes designed according to the differential methylation target region or specific differential site of the prostate cancer-specific methylation marker.

[0071] The research and development ideas of the present invention are as follows:

[0072] 1. Cancer (prostate cancer tissue samples) and control (prostate hyperplasia and bladder cancer tissue samples) groups were selected. Methylated DNA immunoprecipitation (MeDIP) was used to identify prostate cancer-specific differentially methylated regions. Simultaneously, differentially methylated genes were mined in the TCGA database. The intersection of the two groups was taken to obtain a panel of differentially methylated genes in the tissue samples.

[0073] 2. For the obtained differentially methylated gene panel of tissue samples, a Cancer group (prostate cancer urine samples) and a Control group (prostate hyperplasia urine samples, bladder cancer urine samples, and normal human urine samples) were selected. Target bisulfite sequencing (Target-BS) technology was used. Specifically, methylation capture sequencing technology was used to capture probes of target genes after sulfite conversion based on the existing target gene panel combination. This was followed by ultra-high-depth (above 1000X) precise methylation detection to identify differentially methylated regions and sites in the urine samples.

[0074] The differentially methylated target regions obtained are based on GRCh37.p13 and are the following regions: partial region or full length of chr10:111767101-111767300 (partial region on the human ADD3 gene); partial region or full length of chr11:54965801-54966100 (partial region on the human GSX2 gene); partial region or full length of chr16:88717311-88717610 (partial region on the human CYBA gene promoter).

[0075] Multiple pairs of primers and probes were designed for the DNA methylation sites in the three gene regions of ADD3, GSX2, and CYBA, and tested. The optimal primer and probe combination was combined with the preferred PCR reaction solution to form a qPCR detection system.

[0076] The above detection system uses 120 clinically derived urine samples (including 30 prostate cancer urine samples, 60 prostate hyperplasia urine samples, 10 bladder cancer urine samples, and 20 normal human urine samples) as training set samples. The methylation status of these three genes in the above samples is detected by the optimized methylation-specific qPCR detection system of the ADD3, GSX2, and CYBA genes, and the ct value difference between the target and the internal standard is calculated (Δct = ct target - ct internal reference).

[0077] Using the clinical diagnostic gold standard (pathological test results) as the gold standard, for single-gene diagnostic analysis, the receiver operating characteristic (ROC) curve method was used to determine the positive judgment value of each gene's ΔCt value. For dual-gene or multi-gene combined diagnostic analysis, a logistic regression analysis model was trained and tested to obtain the most effective model parameters. Scores for each gene were then established. The optimal threshold was determined based on the principle of maximizing the Youden index (sensitivity + specificity - 1) to obtain the positive judgment value for different gene combinations. By comparing the performance of single-gene and multi-gene combined analysis, we found that the combined diagnosis of three genes performed best. Specifically, the area under the curve (AUC) was the largest when the three genes were used for combined diagnosis, indicating the highest accuracy.

[0078] Thus, we have obtained the optimal target combination for urine prostate cancer methylation qPCR detection. This detection reagent has good specificity and high sensitivity for detecting prostate cancer in a non-invasive manner.

[0079] Beneficial effects of the present invention:

[0080] 1. The present invention uses urine as a sample for detection, providing a truly non-invasive method for early diagnosis of prostate cancer without the need for prostate massage and urine collection.

[0081] 2. Prostatic fluid enters urine differently from bladder exfoliated cells; it may enter urine not only as exfoliated cells but also as free nucleic acids. The urine preservation solution used in the present invention can preserve both intact genomic nucleic acids and free nucleic acids for 15 days at room temperature, effectively avoiding the omission of free nucleic acids that occurs with the common commercial sampling method of centrifugation to enrich exfoliated cells. This preservation solution can be stored and transported at room temperature, demonstrating high clinical applicability.

[0082] 3. The combination of the methylation markers and their detection methods of the present invention is innovative and can significantly improve the sensitivity of non-touch urine detection. After verification by a large number of clinical samples, its sensitivity and specificity can meet the needs of clinical auxiliary diagnosis of prostate cancer. This methylation marker combination can effectively distinguish prostate cancer from benign prostatic hyperplasia or other cancers of the urinary tract system, and can help solve the problem of false positives in clinical PSA testing in prostate cancer screening. It provides an important reference for clinicians in the early diagnosis and differential diagnosis of prostate cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 : A panel of differentially methylated gene sets of tissue samples was obtained by intersecting prostate cancer-specific differentially methylated regions with differentially methylated genes mined in the TCGA database.

[0084] Figure 2 : ROC curves of different gene combination detection in Example 5.

[0085] Figure 3 : ROC curve of the diagnostic model in Example 7. DETAILED DESCRIPTION

[0086] The following examples are intended to further illustrate the present invention but are not intended to limit the present invention.

[0087] Example 1: MeDIP (Methylated DNA immunoprecipitation) to obtain differentially methylated regions in tissue samples

[0088] A Cancer group (28 prostate cancer tissue samples) and a Control group (30 prostate hyperplasia tissue samples and 20 bladder cancer tissue samples) were selected. MeDIP (Methylated DNA immunoprecipitation) was used to obtain prostate cancer-specific differentially methylated regions. The differentially methylated genes were simultaneously mined in the TCGA database. The intersection of the two was taken to obtain a panel of differentially methylated genes in the tissue samples. The results are shown below. Figure 1 .

[0089] Example 2: Target gene DNA methylation sequencing

[0090] For the obtained differentially methylated gene panel for tissue samples, a Cancer group (20 prostate cancer tissues and 20 prostate cancer urine samples) and a Control group (20 prostate hyperplasia tissues, 20 prostate hyperplasia urine samples, 20 bladder cancer tissues, and 20 bladder cancer urine samples) were selected. Target bisulfite sequencing (Target-BS) technology was used to perform multiplex amplification and capture of target genes after sulfite conversion. Ultra-high-depth (1000X or higher) methylation sequencing was then performed to identify differentially methylated target regions and methylation sites (CpG sites) that were consistent across tissue and urine samples. Lasso regression was used to select variables, and random forest modeling was used to identify candidate differentially methylated sites, using GRCh37.p13 as a reference. Sites listed as 1 in the Predictor column of Table 1 are sites selected by the model, while sites listed as 0 are sites not selected by the model. As can be seen from the table, the CpG sites within the target region chr10:111767372-11767390 published in patent CN111154876B are not among the model screening sites. The CpG sites within the target regions chr16:88717676-88717702 and chr16:88717737-88717760 published in patent CN107988365A are also not among the model screening sites. No published patents have yet been found for the GSX2 gene reporting that methylation of this gene is used for tumor detection or screening. Therefore, the candidate differential methylation site set screened by the model of the present invention is inconsistent with the disclosed patent sites, which is innovative.

[0091] Table 1

[0092]

[0093]

[0094] Example 3: Establishment and optimization of qPCR detection system

[0095] 1. Multiple sets of qPCR detection primers and probes were designed for the consistent differential methylation sites (CpG sites) of the tissue samples and urine samples obtained in Example 2, as shown in Tables 2 to 11.

[0096] Table 2

[0097]

[0098] Table 3

[0099]

[0100] Table 4

[0101]

[0102] Table 5

[0103]

[0104] Table 6

[0105]

[0106] Table 7

[0107]

[0108] Table 8

[0109]

[0110] Table 9

[0111]

[0112] Table 10

[0113]

[0114] Table 11 Internal reference gene primers and probes:

[0115] mACTB-F GGTGTTTAAGATAGTGTTGTGG mACTB-R CTACTTAATACACACTCCAAAACC mACTB-P CTTTACACCAACCTCATAACCTTATC

[0116] The fluorescent group attached to the detection probe can be selected from any of FAM, HEX, VIC, CY5, ROX, Texsa Red, JOE, and Quasar 705. In this example, multiple probes for the same gene use the same fluorescent group, with MGB serving as a quencher; probes for different genes use different fluorescent groups. The internal reference gene uses VIC as the fluorescent group and MGB as the quencher. The fluorescent groups attached to the detection probes for the target region and the internal reference gene are not limited to those listed above and can also be other fluorescent groups.

[0117] 2. PCR reaction solution screening

[0118] After nucleic acid extraction, the sample is subjected to bisulfite conversion, and the methylation status of the target gene region can be achieved by one or more of the following methods: methylation-specific PCR, bisulfite sequencing, methylation-specific microarray, whole-genome methylation sequencing, pyrosequencing, methylation-specific high-performance liquid chromatography, digital PCR, methylation-specific high-resolution melting curve method, methylation-sensitive restriction endonuclease method and methylation fluorescence quantitative PCR method.

[0119] This example screened five methylation-specific PCR reaction solutions. The specific reaction systems and detection procedures were referred to the instructions for each reagent. The sample input amount was 100 ng / reaction, as shown in Table 12:

[0120] Table 12

[0121]

[0122] Note: The positive control is a 10% methylation-positive control prepared by mixing DNA extracted from the methylation-positive PC-3 prostate cancer cell line (2.5 ng / μL) with DNA extracted from normal human leukocytes (2.5 ng / μL) at a ratio of 1:10. The negative control is DNA extracted from normal human leukocytes (25 ng / μL). The sample volume is 4 μL.

[0123] For each of the three genes, one set of primer probes was selected from the multiple sets of designed qPCR detection primer probes as the primer probes for testing, as shown in Table 13 below:

[0124] Table 13

[0125] FAM channel ROX Channel CY5 channel CYBA-Q-F1-1 GSX2-Q-F2-1 ADD3-Q-F1-1 CYBA-Q-R1-1 GSX2-Q-R2-1 ADD3-Q-R1-1 CYBA-Q-P1 GSX2-Q-P2 ADD3-Q-P1-1

[0126] The test results are shown in Table 14.

[0127] Table 14

[0128]

[0129] Note: The FAM channel detects the CYBA gene, the ROX channel detects the GSX2 gene, and the CY5 channel detects the ADD3 gene. The results in the table above show that for methylation-positive controls, Novozymes EM701, Yisheng 11211, and Tiangen FP206 have good sensitivity; for methylation-negative controls, Novozymes EM701 and Baorui M2161 have good specificity. In summary, the optimal reaction mixture is BioSmartMethyLight qPCR Mix and Novozymes EM701.

[0130] 3. Primer probe screening and concentration gradient testing

[0131] Using the optimal reaction solution, this example screened the primer and probe combinations for each gene methylation according to the standard primer and probe concentrations (F / R / P: 0.25 μM / 0.25 μM / 0.125 μM) and standard reaction procedures recommended in the instructions of the Novozymes EM701 reaction solution kit. The samples were positive and negative controls, and each was tested in triplicate. The test conditions and results are shown in Table 15 below:

[0132] Table 15

[0133]

[0134]

[0135]

[0136]

[0137] Based on this, the better gene primer combinations were screened out and shown in Table 16:

[0138] Table 16

[0139]

[0140] For the above primer combinations, using the optimal reaction solution, this example screened and optimized the primer combinations and primer concentrations for each gene methylation, and performed primer group screening and concentration optimization tests according to Table 17 below:

[0141] Table 17

[0142]

[0143] Primer probe concentration gradient test results:

[0144] (1) ADD3 gene target ct value

[0145] Table 18

[0146]

[0147] Note: 0.1μM system means upstream primer concentration is 0.1μM, downstream primer concentration is 0.1μM, and probe concentration is 0.05μM;

[0148] The 0.25 μM system represents an upstream primer concentration of 0.25 μM, a downstream primer concentration of 0.25 μM, and a probe concentration of 0.125 μM;

[0149] The 0.5 μM system represents an upstream primer concentration of 0.5 μM, a downstream primer concentration of 0.5 μM, and a probe concentration of 0.25 μM;

[0150] The 1 μM system represents an upstream primer concentration of 1 μM, a downstream primer concentration of 1 μM, and a probe concentration of 0.5 μM;

[0151] The 1.5 μM system represents an upstream primer concentration of 1.5 μM, a downstream primer concentration of 1.5 μM, and a probe concentration of 0.75 μM;

[0152] The 2 μM system means that the upstream primer concentration is 2 μM, the downstream primer concentration is 2 μM, and the probe concentration is 1 μM.

[0153] The results are shown in Table 19:

[0154] Table 19

[0155] 0.1μM system y=-3.9332x+34.377 R2＝0.9993 0.25μM system y=-4.2487x+34.518 R2＝0.997 0.5μM system y=-4.0893x+34.36 R2＝0.9848 1μM system y=-3.7571x+34.243 R2＝0.9996 1.5μM system y=-3.6176x+34.187 R2＝0.9936 2μM system y=-3.7106x+34.348 R2＝0.9976

[0156] The results in the table show that the optimal primer and probe concentration for the ADD3 gene is 1 μM.

[0157] (2) GSX2 gene target ct value

[0158] Table 20

[0159]

[0160] The results are shown in Table 21:

[0161] Table 21

[0162] 0.1μM system y=-3.5179x+34.502 R2＝0.9937 0.25μM system y=-3.8401x+34.721 R2＝0.9864 0.5μM system y=-4.1225x+35.269 R2＝0.9932 1μM system y=-4.1591x+35.281 R2＝0.9949 1.5μM system y=-4.0096x+35.327 R2＝0.9952 2μM system y=-3.4349x+34.974 R2＝0.9957

[0163] The results in the table show that although the R value and efficiency are the best when the GSX2 gene primer concentration is 2.0 μM, non-specific amplification with a ct value of less than 40 will occur in the NC template. Therefore, the optimal primer probe concentration is selected to be 1.5 μM. (3) ct value of CYBA gene target

[0164] Table 22

[0165]

[0166] The results are shown in Table 23:

[0167] Table 23

[0168] 0.1μM system y=-4.0328x+34.412 R2＝0.9971 0.25μM system y=-4.3517x+34.702 R2＝0.9985 0.5μM system y=-3.7073x+33.933 R2＝0.9993 1μM system y=-3.8069x+34.478 R2＝0.9978 1.5μM system y=-3.6176x+34.187 R2＝0.9936 2μM system y=-4.0494x+34.783 R2＝0.9946

[0169] The results in the table show that the optimal primer probe concentration for CYBA gene is 0.5 μM.

[0170] (4) ct value of internal reference gene

[0171] Table 24

[0172]

[0173] The results are shown in Table 25

[0174] Table 25

[0175]

[0176]

[0177] The results in the table show that the optimal primer probe concentration for the internal reference gene is 0.1 μM.

[0178] 4. Reaction program optimization

[0179] The optimal reaction solution and primer probe concentrations were used, and positive quality control products and negative quality control products were used as test samples. The test was repeated 10 times, and the conventional reaction procedure and the optimized reaction procedure were used for detection, and the mean value was calculated.

[0180] The specific reaction system is shown in Table 26:

[0181] Table 26

[0182] Reagent name Volume (uL) 2×BioSmart MethyLight qPCR Mix 10 Primer & Probe Mix 2 Heavy salt conversion of purified nucleic acids 2 <![CDATA[NF-H2O]]> Up to 20μl

[0183] Primer & Probe Mix

[0184] Table 27

[0185]

[0186]

[0187] The general reaction procedure is as follows:

[0188] Table 28

[0189]

[0190] The reaction procedure of the present invention is as follows:

[0191] Table 29

[0192]

[0193] Test results:

[0194] Table 30

[0195] Test procedure type Detection rate of methylation-positive quality control products Number of false negatives for negative control products Standard qPCR detection procedure 90％ 1 Detection procedure of the present invention 100％ 0

[0196] In summary, the optimal reaction detection procedure is the detection procedure of the present invention.

[0197] Example 3 of the present invention is merely a further optimization of the reaction system conditions, but the purpose of the present invention can not be achieved only under the above-mentioned optimized conditions.

[0198] Example 4: Preliminary testing of urine sample qPCR detection

[0199] 30 mL of urine was collected from the clinical cancer group (10 urine samples from prostate cancer patients) and the control group (10 urine samples from prostate hyperplasia patients, 10 urine samples from bladder cancer patients, and 10 urine samples from normal subjects). The urine was preserved using a self-developed urine preservation solution. The urine preservation solution formula is as follows: 4 M guanidine isothiocyanate, 50 mM EDTA, 200 mM TCEP, 20% PEG, 500 mM sulfosalicylic acid, 20% isopropanol, 10% Tween 20, 0.1 M citric acid-sodium citrate buffer (pH 4.5). The volume ratio of preservation solution to urine was 1:4.

[0200] A nucleic acid extraction reagent (Tian Gen, Cat. No. DP710) was used to extract a 4 ml urine and preservation solution mixture. The resulting nucleic acid was detected using primer probes for three target genes: ADD3, GSX2, and CYBA. The detection system is as follows:

[0201] Table 31

[0202]

[0203]

[0204] The procedure is the same as the optimized reaction procedure in Example 3.

[0205] Table 32

[0206]

[0207] Calculate the ΔCt value (Ct value) of each gene 靶标基因 -Ct value 内参基因 ) The test results are shown in the following table:

[0208] Table 33

[0209]

[0210]

[0211] The results showed that the three genes ADD3, GSX2, and CYBA could well distinguish the Cancer group from the Control group.

[0212] Example 5: Obtaining a qPCR diagnostic model using training set samples

[0213] 120 clinical urine samples (30 prostate cancer urine samples, 60 prostate hyperplasia urine samples, 10 bladder cancer urine samples, and 20 normal human urine samples) were collected and stored according to the collection method described in Example 4. Nucleic acid was then extracted from the urine samples using a nucleic acid extraction kit. 100 ng of nucleic acid was then converted to a heavy salt (see the instructions for the heavy salt conversion procedure) and tested according to the detection system and procedure described in Example 4.

[0214] The ct values ​​of the three genes ADD3, GSX2, and CYBA in the above samples were compared with the ct values ​​of the internal reference genes, and the ct value difference between the target and the internal reference was calculated (Δct = ct target - ct internal reference). Taking the clinical diagnostic gold standard (pathological test results) as the gold standard, for single-gene diagnostic analysis, the ROC curve method was used to determine the positive judgment value of the ΔCt value of each gene; for dual-gene or multi-gene combined diagnostic analysis, a ten-fold cross-validation method was used to divide the sample set into a training set and a test set. The logistic regression analysis model was then trained and tested to obtain the most effective model parameters. The scores for each gene were then established. The optimal threshold was then determined based on the principle of maximizing the Youden index (sensitivity + specificity - 1) to obtain the positive judgment value under different gene combinations. The results are shown in the following table:

[0215] Table 34

[0216] threshold specificity sensitivity accuracy Gene 12.949 0.912 0.743 0.872 ADD3 12.409 0.894 0.771 0.865 GSX2 16.306 0.862 0.971 0.886 CYBA 0.500 0.925 0.771 0.887 ADD3_GSX2 0.205 0.894 0.971 0.914 ADD3_CYBA 0.313 0.924 0.971 0.914 GSX2_CYBA 0.155 0.918 0.971 0.932 ADD3_GSX2_CYBA

[0217] ROC curve diagram Figure 2 .

[0218] The thresholds and result interpretation rules for single genes, and the formulas for the threshold probabilities (P) of each combination of two-gene or multi-gene combined diagnostic analysis and the result interpretation rules are as follows:

[0219] Table 35

[0220]

[0221]

[0222] The results showed that the performance was best when the three genes ADD3, GSX2, and CYBA were used for combined diagnosis. The formula for predicting the probability (P) of the sample being tested being a prostate cancer-positive sample was ln(P / (1-P)) = 4.153 + (-0.006) * ΔCt(ADD3) + (-0.132) * ΔCt(GSX2) + (-0.229) * ΔCt(CYBA); the threshold value was 0.155. When the P value of the sample being tested was greater than or equal to 0.155, the sample was positive for prostate cancer; when the P value of the sample being tested was less than 0.155, the sample was negative for prostate cancer. By comparing the detection performance of single-gene and multi-gene combination analysis, it was finally determined that the area under the curve of this kit was the largest at 0.946 when the three genes were used for combined diagnosis (see Figure 2 ), with the highest accuracy of 0.932 (see Table 34).

[0223] Example 6: Performance Differences of the Diagnostic Model Applied to Prostate Massage Urine and Morning Urine Samples

[0224] A clinical Cancer group (6 urine samples from prostate cancer) and a Control group (6 urine samples from prostate hyperplasia) were selected. Morning urine and the first urine after prostate massage were collected in a self-developed urine preservation solution. After mixing, 8 ml of the urine mixture was taken and the nucleic acid was extracted using a large-volume magnetic bead method urine free DNA extraction kit (Jifan Biological, M117-02). The nucleic acid was converted to a bisulfite conversion reagent (Zymo Research, Product No. D5031). The converted nucleic acid was used, on the one hand, for conventional single-gene detection using the ADD3 gene with the best single-gene performance evaluated in Example 5, and on the other hand, for detection using the multi-gene joint diagnostic model detection system constructed in Example 5.

[0225] First, routine testing was performed based on the single ADD3 gene target, as a single gene routine testing panel. The system and procedures of the single gene testing panel are as follows:

[0226] Table 36

[0227] Reagent name Volume (uL) 2×BioSmart MethyLight qPCR Mix 10 ADD3-Q-F1-1 (100 μM) 0.04 ADD3-Q-R1-1 (100 μM) 0.04 ADD3-Q-P1-1 (100 μM) 0.02 Heavy salt conversion of purified nucleic acids 8 <![CDATA[NF-H2O]]> Up to 20μl

[0228] Table 37

[0229]

[0230] The ct values ​​of the ADD3 gene and the internal reference gene in the test results were statistically analyzed, and the ct value difference between the target and internal reference genes was calculated (Δct = ct target - ct internal reference). The results were interpreted according to the positive judgment value of ADD3 in Example 5 (when the ΔCt value (ct ADD3 - ct internal reference) is greater than 12.949, it is negative for prostate cancer, and when it is less than or equal to 12.949, it is positive for prostate cancer.).

[0231] In addition, a multi-gene joint diagnosis model constructed based on Example 5 of the present invention is simultaneously set up for detection as a multi-gene joint diagnosis model detection group.

[0232] The system and procedures of the multi-gene panel are as follows:

[0233] Table 38

[0234]

[0235]

[0236] The ct values ​​of the three genes ADD3, GSX2, and CYBA and the ct value of the internal reference gene were counted, and the ct value difference between the target and the internal standard (Δct = ct target - ct internal reference) ΔCt value (Ct value target gene - Ct value internal reference gene) was calculated. The results were interpreted according to the positive judgment value of the ADD3_GSX2_CYBA combined diagnosis in Example 5.

[0237] Formula for predicting the probability (P) that the sample to be tested is a prostate cancer positive sample

[0238] ln(P / (1-P)) = 4.153 + (-0.006) * ΔCt(ADD3) + (-0.132) * ΔCt(GSX2) + (-0.229) * ΔCt(CYBA); the threshold is 0.155. When the P value of the sample is greater than or equal to 0.155, the sample is positive for prostate cancer; when the P value of the sample is less than 0.155, the sample is negative for prostate cancer.

[0239] Detection results of ADD3_F1-1 / R1-1 / P1-1 single gene conventional detection system

[0240] Table 39

[0241] Serial number Clinical diagnosis serial number Test results serial number Test results 1 BPH 176(CN) Negative 176(DRE) Negative 2 BPH 177(CN) Negative 177(DRE) Negative 3 BPH 178(CN) Negative 178(DRE) Negative 4 BPH 179(CN) Negative 179(DRE) Negative 5 BPH 180(CN) Negative 180(DRE) Negative 6 BPH 181(CN) Negative 181(DRE) Negative 7 PCA 175(CN) Negative 175(DRE) Negative 8 PCA 182(CN) Positive 182(DRE) Positive 9 PCA 183(CN) Negative 183(DRE) Positive 10 PCA 184(CN) Negative 184(DRE) Negative 11 PCA 185(CN) Negative 185(DRE) Positive 12 PCA 190(CN) Positive 190(DRE) Positive

[0242] Detection results of the ADD3_GSX2_CYBA three-gene combined diagnostic model

[0243] Table 40

[0244] Serial number Clinical diagnosis serial number Interpretation of results serial number Interpretation of results 1 BPH 176(CN) Negative 176(DRE) Negative 2 BPH 177(CN) Negative 177(DRE) Negative 3 BPH 178(CN) Negative 178(DRE) Negative 4 BPH 179(CN) Negative 179(DRE) Negative 5 BPH 180(CN) Negative 180(DRE) Negative 6 BPH 181(CN) Negative 181(DRE) Negative 7 PCA 175(CN) Negative 175(DRE) Positive 8 PCA 182(CN) Positive 182(DRE) Positive 9 PCA 183(CN) Positive 183(DRE) Positive 10 PCA 184(CN) Negative 184(DRE) Negative 11 PCA 185(CN) Positive 185(DRE) Positive 12 PCA 190(CN) Positive 190(DRE) Positive

[0245] Note: BPH is benign prostatic hyperplasia, PCA is prostate cancer; CN is non-stroked first urine sample, and DRE is first urine sample after stroke.

[0246] The results of this example show that positive sample signals can be detected in morning urine samples, but the positive rate is not as high as that of urine massage. The three-gene combined diagnostic system established in Example 5 can effectively improve detection sensitivity. For six PCA urine samples from the Cancer group, the positive rate of the three-gene combined diagnostic system for morning urine samples (4 / 6) was consistent with the positive rate of the ADD3_F1-1 / R1-1 / P1-1 conventional detection system for urine massage samples (4 / 6). By optimizing the detection system and constructing a multi-gene combined diagnostic model, the sensitivity of methylation detection can be effectively improved, replacing the urine collection method from prostate massage with morning urine collection.

[0247] Example 7: Validating the performance of the diagnostic model using a large number of clinical samples

[0248] 170 clinically derived urine samples (76 prostate cancer samples in the Cancer group; 94 control samples, including 64 prostate hyperplasia samples, 15 bladder cancer samples, and 15 normal subjects) were collected. Nucleic acid was extracted from each urine sample using a nucleic acid extraction kit. 100 ng of nucleic acid was then converted to a heavy salt (see the instructions for the heavy salt conversion procedure) and tested using the detection system and procedures described in Example 4.

[0249] The ct values ​​of the three genes ADD3, GSX2, and CYBA in the above samples and the ct values ​​of the internal reference genes were counted respectively, and the results were judged according to the thresholds and result interpretation rules in Table 35 in Example 5. The results were compared with the clinical diagnosis gold standard (pathological test results) to evaluate the detection performance of each gene or combination model. The results are shown in Table 41 and the ROC curve is shown in Figure 3 :

[0250] Table 41

[0251] Gene sensitivity specificity accuracy ADD3 0.846 0.849 0.848 GSX2 0.655 0.891 0.823 CYBA 0.821 0.857 0.848 ADD3_GSX2 0.846 0.861 0.857 ADD3_CYBA 0.821 0.928 0.897 GSX2_CYBA 0.846 0.889 0.878 ADD3_GSX2_CYBA 0.908 0.957 0.935

[0252] Thus, through modeling experiments on the training set and validation experiments on the validation set, the present invention has obtained multiple diagnostic models, including single-gene, double-gene, and triple-gene models, for the methylation of the three genes ADD3, GSX2, and CYBA for the detection of prostate cancer. Among them, the best performing diagnostic model is the three-gene combined diagnostic model. Its clinical performance in the validation set is summarized in Table 42 below:

[0253] Table 42

[0254]

[0255] Sensitivity = 69 / (69+7)×100% = 90.8%

[0256] Specificity = 90 / (4+90) × 100% = 95.7%

[0257] Positive predictive value = 69 / (69+4) × 100% = 94.5%

[0258] Negative predictive value = 90 / (7 ​​+ 90) × 100% = 92.8%

[0259] Total compliance rate = (69 + 90) / (69 + 4 + 7 + 90) × 100% = 93.5%.

Claims

1. Use of a primer probe for detecting a prostate cancer-specific methylation marker in the preparation of a prostate cancer detection reagent; characterized in that: The differentially methylated target region of the prostate cancer-specific methylation marker is based on GRCh37.p13, and the primer and probe sequences are as follows: （1） CYBA-Q-F1-1 ATTGTTAGGCGCGTATTGTCG CYBA-Q-R1-1 CAACCCTACACCCTACAAATACG CYBA-Q-P1-FAM CCAACCGCGATCACCTA （2） CYBA-Q-F2-2TCGGACGTTAGCGTTTGTTC CYBA-Q-R2-2 ACGAAATTCGACCGAAAACG CYBA-Q-P2-FAM ACGACAATACGCGCCTAAC （3） ADD3-Q-F1-1 AGTAGTTAGCGTGGGCGGTC ADD3-Q-R1-1 AAAAACGCCTCGAAAATATCTC ADD3-Q-P1-1-CY5 ACTCCCGAAACTAAACCGCCGCTT （4） ADD3-Q-F1-2TTTTAGTAGTTAGCGTGGGC ADD3-Q-R1-1 AAAAACGCCTCGAAAATATCTC ADD3-Q-P1-2-CY5CGCCCTAAATAAACGAACCC （5） GSX2-Q-F2-2 GAGTTGCGTTTAGGGATTGGAC GSX2-Q-R2-2 CTCTATAATAAAAATAACTACGACGCG GSX2-Q-P2-ROXAAACAAAATTATACGAACGACGAACT （6） GSX2-Q-F3-1AAGTTTTTATTTGGGTACGTTTTGC GSX2-Q-R3-1CGCTTTACTAAAAAATCACAACG GSX2-Q-P3-ROX CTCTCGCTACCCTAACCGCAA.

2. The use according to claim 1, characterized in that The detection reagents also include internal reference gene primer probes, The sequence is as follows: mACTB-FGGTGTTTAAGATAGTGTTGTGG mACTB-R CTACTTAATACACACTCCAAAACC mACTB-P CTTTACACCAACCTCATAACCTTATC.

3. The use according to claim 1, characterized in that The test reagent also includes urine preservation solution, The formula is as follows: 4M guanidine thiocyanate, 50mM EDTA, 200mM TCEP, 20% PEG, 500mM sulfonyl Salicylic acid, 20% isopropanol, 10% Tween 20, 0.1 M citric acid-sodium citrate buffer, pH 4.

5.

4. A prostate cancer-specific methylation detection reagent, characterized in that: include: The primers and probes according to claim 1.

5. The detection reagent according to claim 4, characterized in that Also includes urine preservation solution, The formula is as follows: 4M guanidine thiocyanate, 50mM EDTA, 200mM TCEP, 20% PEG, 500mM sulfonyl Salicylic acid, 20% isopropanol, 10% Tween 20, 0.1 M citric acid-sodium citrate buffer, pH 4.5.

Images