Use of primers and probes for detecting prostate cancer specific methylation markers in preparation of prostate cancer detection reagent

Abstract

The present invention relates to the field of in vitro molecular biology diagnostic reagents, and provides a use of primers and probes for detecting prostate cancer specific methylation markers in preparation of a prostate cancer detection reagent. Primers and probes are designed on the basis of partial regions or the full length of positive strands of differentially methylated target regions, namely chr10:111767101-111767600, chr4:54965801-54966100, and chr16:88717311-88717610, of prostate cancer specific methylation markers; the combination of methylation markers allows for effective distinguishing of prostate cancer from benign prostatic hyperplasia or other cancers of the urinary system, thereby helping to address the issue of false positives in prostate cancer screening and significantly improving the detection sensitivity of non‑digital rectal examination urine. The present invention provides an important reference for clinicians in early diagnosis and differential diagnosis of prostate cancer.

Description

Application of primers and probes for detecting prostate cancer-specific methylation markers in the preparation of prostate cancer detection reagents Technical Field

[0001] This invention relates to the field of in vitro molecular biological diagnostic reagents, and more particularly to the application of primer probes for detecting prostate cancer-specific methylation markers in the preparation of prostate cancer detection reagents. Background Technology

[0002] Prostate cancer (PCa) is one of the most common malignant tumors of the male genitourinary system. Currently, the diagnostic process for prostate cancer involves a digital rectal examination, serum PSA testing, followed by imaging studies and a prostate biopsy. While serum PSA testing is currently the routine test for prostate cancer, its specificity and sensitivity are not high. Prostate biopsy is currently the gold standard, but its main limitations include its invasiveness, false negatives, potential for missing high-risk prostate cancer cases, and overdiagnosis.

[0003] DNA methylation falls under the category of epigenetics and can regulate gene expression without altering the base sequence. Studies have shown that changes in DNA methylation can be used for the diagnosis of prostate cancer, with methylation of genes such as GSTP1, APC, RASSF1, AR, and NEP reportedly associated with prostate cancer development. Currently, there are few non-invasive products for prostate cancer detection both domestically and internationally, and most are based on NGS testing platforms. This type of technology is not suitable for general hospital laboratories, and the testing process is complex, costly, and has a long reporting cycle. Research and products based on PCR technology for methylation detection have emerged, but because prostatic fluid is squeezed from the prostatic acini into the ejaculatory ducts and then into the urethra, prostate palpation is often required to stimulate its secretion before collecting urine samples for testing, thus reducing clinical compliance. Whether the need for prostate palpation before prostate testing can be effectively addressed by improving the sensitivity of detection technologies is a key technical challenge.

[0004] Furthermore, choosing the appropriate unit of analysis is crucial when assessing the impact of gene methylation on disease. Differential methylation analysis can be categorized into different levels, including DMP (differentiated CpG sites), DMR (differentiated CpG regions), and DMB (larger differentially methylated regions). DMP represents identifying a single differentially methylated CpG site, while DMR refers to a continuous, relatively long differentially methylated fragment. Scientists believe that such continuous differentially methylated fragments have a more significant impact on genes, improving the precision of computational biology. Therefore, analysis using DMR can more accurately reflect the dynamic changes in gene methylation status and how these changes are associated with gene expression and disease risk. Consequently, gene methylation analysis using DMR has greater value and practicality in studies assessing disease risk and disease mechanisms.

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

[0006] CN111154876B discloses a primer-probe composition, kit, and detection method for detecting methylation of human ADD3 and CDH23 genes. However, the embodiments of this patent are limited to testing small amounts of negative and positive control samples or negative and positive samples, and have not been tested on clinical samples. Therefore, its sensitivity and specificity for clinical auxiliary diagnostic purposes cannot be determined. Furthermore, the biological sample of this invention is cells in the initial urine stream after prostate massage, approximately 40-50 ml. This fails to effectively address the issue of clinical compliance.

[0007] CN116287227A discloses a reagent and kit for diagnosing prostate cancer. This reagent uses blood or urine samples and performs a combined diagnosis by detecting the methylation levels of target regions CHST11-R3, AOX1-R2, PRKCB-R2, and C2orf88-R1, achieving non-invasive prostate cancer detection. While this patent has used numerous clinical prostate cancer samples and healthy individuals for testing, achieving good detection sensitivity and specificity, it does not include samples from clinical benign prostatic hyperplasia (BPH) or other urinary tract cancers such as bladder cancer for specific testing. Clinical studies have found that BPH also leads to abnormally high PSA levels, resulting in numerous false positives. Therefore, there is an urgent need to discover new screening biomarkers to reduce false positives. Our research found that methylation in BPH and other urinary tract cancers significantly interferes with prostate cancer detection. Finding targets that can distinguish prostate cancer from healthy individuals is not difficult, but finding biomarkers that can effectively differentiate between prostate cancer and BPH or other urinary tract cancers is very challenging and is a truly pressing clinical problem.

[0008] CN117925845A discloses a highly sensitive method for diagnosing prostate cancer by detecting specific regions of the RARB gene, a key methylation marker, without the need for rectal massage; testing can be performed directly from urine samples. However, it still has limitations: 1. This method targets RARB gene methylation, and the sensitivity and specificity of single-gene methylation detection for early cancer screening are limited; 2. This method uses ≥2 of the 5 fragments of the RARB gene as the criterion for a positive result. While this can compensate to some extent for the loss of detection signals in some samples due to low methylation of one or more CpG sites, thus improving detection sensitivity, our previous research experience shows that the methylation level of the DMR region of the same gene has a certain linkage correlation. Most samples will show high methylation at CpG sites in this region simultaneously. 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 achieve prostate cancer detection in urine without prostate massage.

[0009] This invention achieves the auxiliary diagnosis of prostate cancer by directly collecting urine without prostate palpation and detecting multi-gene methylation levels. (1) Through special urine preservation and nucleic acid enrichment methods, nucleic acid materials from prostate-derived cells are effectively enriched; (2) Differentially methylated regions (DMRs) specific to prostate tissue are screened through MeDIP high-throughput sequencing, and the intersection is taken with prostate cancer methylation data mined from TCGA and GEO databases. Target gene methylation high-throughput sequencing is then performed on the intersecting DMRs regions. (3) By optimizing the number and position of CpG sites covered by primer and probe sequences, optimizing primer and probe concentration, and optimizing reaction procedures, a high-sensitivity and high-specificity multiplex PCR fluorescence detection system is obtained to realize the detection of DMR regions of multiple genes, accumulate multi-site methylation signals of each DMR region, and improve detection sensitivity; (4) For the training set samples, the methylation level of multiple DMR regions is detected, regression statistical analysis is used to determine the weight coefficient of each gene, establish the corresponding weighted scoring model, and verify it in the validation set samples to effectively improve the specificity and accuracy of detection, thereby realizing the detection of low-frequency prostate target nucleic acid without prostate palpation urine collection. Summary of the Invention

[0010] The purpose of this invention is to provide the application of a formulation for detecting prostate cancer-specific methylation markers in the preparation of prostate cancer detection reagents, as well as the detection reagent itself. The detection performance of this method was evaluated through validation using a large number of clinical samples.

[0011] This invention is achieved using the following technical solution:

[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 differentially methylated target regions of the prostate cancer-specific methylation markers, with reference to GRCh37.p13, include the following three regions: a partial or full-length region of the positive chain of chr10: 111767101-111767600; a partial or full-length region of the positive chain of chr4: 54965801-54966100; and a partial or full-length region of the positive chain of chr16: 88717311-88717610; each region covers no fewer than 8 CpG sites.

[0013] Furthermore, the differential methylation 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_chr4:54965883

[0023] GSX2_chr4:54966019

[0024] GSX2_chr4:54966058

[0025] CYBA_chr16:88717489

[0026] CYBA_chr16:88717571

[0027] CYBA_chr16:88717602.

[0028] The detection reagent includes primers and probes designed based on differential methylation sites, with the following sequences:

[0029] (1)

[0030] Two F primers and the CYBA-Q-R1-1 primer, used with the P probe, amplified the target sequence covering the CpG site CYBA_chr16:88717489; two F primers and the CYBA-Q-R1-2 primer, used with the P probe, amplified the target sequence covering the CpG site CYBA_chr16:88717571.

[0031] (2)

[0032] Two F primers and two R primers can be used in combination with the P probe to amplify the target sequence covering the CpG site CYBA_chr16:88717489.

[0033] (3)

[0034] Two F primers and two R primers can be used in combination with the P probe to amplify the target sequence covering the CpG site CYBA_chr16:88717602.

[0035] (4)

[0036] Two F primers and two R primers can be used interchangeably with the P probe to amplify the target sequence, which covers the CpG site ADD3_chr10:111767228.

[0037] (5)

[0038] Any one of the three F primers can be used with the R primer and P probe to amplify the target sequence that covers the CpG site ADD3_chr10:111767249;

[0039] (6)

[0040] Two F primers and two R primers can be used in combination with the P probe to amplify target sequences that cover the CpG sites ADD3_chr10:111767345 and ADD3_chr10:111767350.

[0041] (7)

[0042] Two F primers and two R primers can be used interchangeably with the P probe to amplify target sequences that cover the CpG sites ADD3_chr10:111767479, ADD3_chr10:111767481, ADD3_chr10:111767493 and ADD3_chr10:111767530.

[0043] (8)

[0044] Three F primers and two R primers can be used in combination with the P probe to amplify the target sequence covering the CpG site GSX2_chr4:54965883;

[0045] (9)

[0046] Two F primers and two R primers can be used interchangeably with the P probe to amplify target sequences that cover the CpG sites GSX2_chr4:54966019 and GSX2_chr4:54966058.

[0047] The above applications also include internal reference gene primers and probes, with the following sequences: mACTB-F GGTGTTTAAGATAGTGTTGTGG mACTB-R CTACTTAATACACACTCCAAAACC mACTB-P CTTTACACCAACCTCATAACCTTATC.

[0048] Furthermore, the aforementioned applications,

[0049] Furthermore,

[0050] Single-gene testing and diagnostic analysis:

[0051] The formulas for the probabilities (P) of each combination used in dual-gene or multi-gene combined diagnostic analysis are as follows:

[0052] The test kit also includes a urine preservation solution.

[0053] The formulation is as follows: 4M guanidine isothiocyanate, 50mM EDTA, 200mM TCEP, 20% PEG, 500mM sulfosalicylic acid, 20% isopropanol, 10% Tween 20, 0.1M citrate-sodium citrate buffer at pH 4.5.

[0054] This invention also provides a prostate cancer-specific methylation detection reagent, comprising primers and probes designed based on the differentially methylated target region or specific differentially methylated site of the prostate cancer-specific methylation marker.

[0055] The research and development concept of this invention is as follows:

[0056] 1. Select the Cancer group (prostate cancer tissue samples) and the Control group (prostate hyperplasia tissue and bladder cancer tissue samples). Use MeDIP (Methylated DNA immunoprecipitation) to obtain prostate cancer-specific differentially methylated regions. Simultaneously, mine differentially methylated genes in the TCGA database. Take the intersection of the two to obtain the differentially methylated gene set panel of the tissue samples.

[0057] 2. Based on the obtained differentially methylated gene set panels of tissue samples, the Cancer group (urine samples from prostate cancer) and the Control group (urine samples from benign prostatic hyperplasia, bladder cancer, and normal individuals) were selected. Target Bisulfite sequencing (Target-BS) technology was used, which involves using methylation capture sequencing technology on existing target gene panel combinations to capture the target genes after sulfite conversion, followed by precise detection of ultra-high depth (above 1000X) methylation to obtain the differentially methylated regions and sites in the urine samples.

[0058] The differentially methylated target regions obtained, with GRCh37.p13 as a reference, are the following regions: chr10: 111767101-111767600 (partial or full-length region on the human ADD3 gene); chr4: 54965801-54966100 (partial or full-length region on the human GSX2 gene); chr16: 88717311-88717610 (partial or full-length region on the human CYBA gene promoter).

[0059] Multiple primer probes were designed for the DNA methylation sites of the three gene regions, namely ADD3, GSX2, and CYBA, and tested. The optimal primer probe combination was selected and combined with the optimal PCR reaction solution to form a qPCR detection system.

[0060] The above detection system used 120 clinically derived urine samples (30 from prostate cancer, 60 from benign prostatic hyperplasia, 10 from bladder cancer, and 20 from normal individuals) as the training set. The methylation status of these three genes in the samples was detected by an optimized methylation-specific qPCR detection system for the ADD3, GSX2, and CYBA genes, and the difference in ct values ​​between the target and the internal control was calculated (Δct = ct target - ct internal control).

[0061] Using 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 ΔCt for each gene. For dual-gene or multi-gene combined diagnostic analysis, a logistic regression analysis model was trained and tested to obtain the model parameters with the best utility, establish scores for each gene, and then determine the optimal threshold based on the principle of maximizing the Youden index (sensitivity + specificity - 1) to obtain the positive judgment value under different gene combinations. By comparing the detection performance of single-gene and multi-gene combination analysis, we found that the performance was best when three genes were used for combined diagnosis. That is, when three genes were used for combined diagnosis, the area under the AUC curve was the largest, and the accuracy was the highest.

[0062] Thus, we have obtained the optimal target combination for qPCR detection of methylation in urinary prostate cancer. This test kit has high specificity and sensitivity when used to detect prostate cancer, and it is a non-invasive method.

[0063] Beneficial effects of this invention:

[0064] 1. This invention uses urine as a sample for detection, providing a truly non-invasive method for the early diagnosis of prostate cancer that does not require prostate palpation for urine collection.

[0065] 2. Prostatic fluid entering urine differs 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 this invention can simultaneously preserve complete genomic nucleic acids and free nucleic acids at room temperature for 15 days, effectively avoiding the missed detection of free nucleic acids caused by common sampling methods that rely on centrifugation to enrich exfoliated cells. This preservation solution can be stored and transported at room temperature, making it highly clinically applicable.

[0066] 3. The combination of methylation biomarkers and their detection methods in this invention is innovative and can significantly improve the detection sensitivity of non-touch urine. Validated with a large number of clinical samples, its sensitivity and specificity meet the needs of clinical auxiliary diagnosis of prostate cancer. This combination of methylation biomarkers can effectively distinguish prostate cancer from benign prostatic hyperplasia or other cancers of the urinary tract, helping to solve the false-positive problem of clinical PSA testing in prostate cancer screening. It provides important reference for clinicians in the early diagnosis and differential diagnosis of prostate cancer. Attached Figure Description

[0067] Figure 1: Panel of differentially methylated genes in tissue samples obtained by intersecting the differentially methylated regions specific to prostate cancer obtained from the samples with differentially methylated genes mined from the TCGA database.

[0068] Figure 2: ROC curves of different gene combinations detected in Example 5.

[0069] Figure 3: ROC curve of the diagnostic model in Example 7. Detailed Implementation

[0070] The following examples are intended to further illustrate the present invention, but do not constitute a limitation thereof.

[0071] Example 1: Obtaining differentially methylated regions in tissue samples using MeDIP (Methylated DNA immunoprecipitation).

[0072] The Cancer group (28 prostate cancer tissue samples) and the Control group (30 benign prostatic 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. Differentially methylated genes were simultaneously mined from the TCGA database. The intersection of the two was used to obtain the differentially methylated gene set panel of the tissue samples. The results are shown in Figure 1.

[0073] Example 2: Target gene DNA methylation sequencing

[0074] For the obtained differentially methylated gene panel from tissue samples, two groups were selected: the Cancer group (20 prostate cancer tissue samples and 20 prostate cancer urine samples) and the Control group (20 benign prostatic hyperplasia tissue samples, 20 benign prostatic hyperplasia urine samples, 20 bladder cancer tissue samples, and 20 bladder cancer urine samples). Target bisulfite sequencing (Target-BS) technology was used to amplify and capture the target genes after sulfite conversion, followed by ultra-high depth (above 1000X) methylation sequencing to obtain the differentially methylated target regions and methylation sites (CpG sites) consistent between tissue and urine samples. Variables were screened using lasso regression, and a set of candidate differentially methylated sites was obtained through random forest modeling. Using GRCh37.p13 as a reference, sites with a value of 1 in the Predictor column of Table 1 are those selected by the model, while sites with a value of 0 are those not selected by the model. As can be seen from the table, the CpG sites within the target region chr10:111767372-11767390 disclosed in patent CN111154876B are not among the model screening sites. Similarly, the CpG sites within the target regions chr16:88717676-88717702 and chr16:88717737-88717760 disclosed in patent CN107988365A are also not among the model screening sites. No published patents have been found reporting the use of this gene methylation for tumor detection or screening for the GSX2 gene. Therefore, the set of candidate differentially methylated sites screened by the model in this invention is inconsistent with the published patent sites, demonstrating its innovation.

[0075] Table 1

[0076] Example 3: Establishment and optimization of qPCR detection system

[0077] 1. Multiple sets of qPCR detection primers and probes were designed for the differential methylation sites (CpG sites) of the tissue samples and urine samples obtained in Example 2, as shown in Tables 2-11.

[0078] Table 2

[0079] Table 3

[0080] Table 4

[0081] Table 5

[0082] Table 6

[0083] Table 7

[0084] Table 8

[0085] Table 9

[0086] Table 10

[0087] Table 11

[0088] Internal reference gene primers and probes:

[0089] The fluorescent group attached to the detection probe can be selected from any one of FAM, HEX, VIC, CY5, ROX, Texsa Red, JOE, and Quasar 705. In this embodiment, multiple probes on the same gene use the same fluorescent group, with MGB as the quencher group; probes for different genes use different fluorescent groups. The internal reference gene uses VIC as the fluorescent group and MGB as the quencher group. The fluorescent groups attached to the detection probes of the target region and the internal reference gene are not limited to the above and can also be other fluorescent groups.

[0090] 2. Screening of PCR reaction solutions

[0091] After nucleic acid extraction, the sample undergoes bisulfite conversion. 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-specific quantitative PCR.

[0092] This embodiment screened five methylation-specific PCR reaction solutions. Specific reaction systems and detection procedures were described in the respective reagent instructions. The sample input was 100 ng / reaction. See Table 12 for details.

[0093] Table 12 Note: The positive control is a 10% methylation positive control prepared by mixing DNA (2.5 ng / μL) extracted from methylation-positive PC-3 prostate cancer cell line and DNA (2.5 ng / μL) extracted from normal human leukocytes at a ratio of 1:10. The negative control is DNA (25 ng / μL) extracted from normal human leukocytes. The loading volume is 4 μL.

[0094] For each of the three genes, one set of primers and probes was selected from the designed multiple sets of qPCR detection primers and probes as the primers and probes used for testing, as shown in Table 13 below:

[0095] Table 13

[0096] The test results are shown in Table 14.

[0097] Table 14 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 detecting methylation-positive controls, Novizan EM701, Yisheng 11211, and Tiangen FP206 have good sensitivity; for detecting methylation-negative controls, Novizan EM701 and Baorui M2161 have good specificity. In summary, the optimal reaction solution is BioSmart MethyLight qPCR Mix and Novizan EM701.

[0098] 3. Primer and probe screening and concentration gradient testing

[0099] Using the optimal reaction solution, this example followed the standard primer / probe concentrations (F / R / P: 0.25 μM / 0.25 μM / 0.125 μM) and standard reaction procedure recommended in the Novizan EM701 reaction kit instructions to screen primer / probe combinations for various gene methylation. Samples were used as positive and negative controls, with each test performed in triplicate. The test conditions and results are shown in Table 15 below.

[0100] Table 15

[0101] Based on this, the better gene primer combinations are shown in Table 16:

[0102] Table 16

[0103] For the primer combinations mentioned above, using the optimal reaction solution, this embodiment screens and optimizes the primer combinations and concentrations used for each gene methylation, and performs primer set screening and concentration optimization tests according to Table 17 below:

[0104] Table 17

[0105] Primer-probe concentration gradient test results:

[0106] (1) ct value of ADD3 gene target

[0107] Table 18 Note: The 0.1 μM system represents an upstream primer concentration of 0.1 μM, a downstream primer concentration of 0.1 μM, and a probe concentration of 0.05 μM; 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; 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; 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; 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; and the 2 μM system represents an upstream primer concentration of 2 μM, a downstream primer concentration of 2 μM, and a probe concentration of 1 μM.

[0108] The results are shown in Table 19:

[0109] Table 19

[0110] The results in the table show that the optimal primer and probe concentration for the ADD3 gene is 1 μM.

[0111] (2) ct value of GSX2 gene target

[0112] Table 20

[0113] The results are shown in Table 21:

[0114] Table 21

[0115] The results in the table show that although the GSX2 gene primer concentration of 2.0 μM has the best R value and efficiency, non-specific amplification with ct value <40 occurs in the NC template. Therefore, the optimal primer and probe concentration is selected as 1.5 μM.

[0116] (3) CT value of CYBA gene target

[0117] Table 22

[0118] The results are shown in Table 23:

[0119] Table 23

[0120] The results in the table show that the optimal primer and probe concentration for the CYBA gene is 0.5 μM.

[0121] (4) ct value of internal reference gene

[0122] Table 24

[0123] The results are shown in Table 25.

[0124] Table 25

[0125] The results in the table show that the optimal primer and probe concentration for the internal reference gene is 0.1 μM.

[0126] 4. Reaction program optimization

[0127] Using the optimal reaction solution and primer / probe concentrations, positive and negative control samples were used as test samples. The experiment was repeated 10 times, and the results were obtained on both the standard and optimized reaction procedures. The mean values ​​were then calculated.

[0128] The specific reaction system is shown in Table 26 below:

[0129] Table 26

[0130] Primer & Probe Mix

[0131] Table 27

[0132] The standard reaction procedure is as follows:

[0133] Table 28

[0134] The reaction procedure of this invention is as follows:

[0135] Table 29

[0136] Test results:

[0137] Table 30

[0138] In summary, the optimal reaction detection procedure is the detection procedure of this invention.

[0139] 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.

[0140] Example 4: Preliminary test of qPCR detection in urine samples

[0141] 30 mL of urine was collected from the clinical Cancer group (10 cases of prostate cancer) and the Control group (10 cases of benign prostatic hyperplasia, 10 cases of bladder cancer, and 10 normal individuals). The urine was preserved using a self-developed urine preservation solution. The urine preservation solution formula was as follows: 4M guanidine isothiocyanate, 50 mM EDTA, 200 mM TCEP, 20% PEG, 500 mM sulfosalicylic acid, 20% isopropanol, 10% Tween 20, and 0.1 M citrate-sodium citrate buffer (pH 4.5). The volume ratio of preservation solution to urine was 1:4.

[0142] A 4ml sample of urine and preservation solution was extracted using a nucleic acid extraction reagent (Tiangen, catalog number DP710). The obtained nucleic acids were detected using primers and probes targeting three genes: ADD3, GSX2, and CYBA. The detection system is as follows:

[0143] Table 31

[0144] The procedure is the same as the optimized reaction procedure in Example 3.

[0145] Table 32

[0146] Calculate the ΔCt value (Ct value) for each gene. 靶标基因 -Ct value 内参基因 The test results are shown in the table below:

[0147] Table 33

[0148] The results showed that the three genes ADD3, GSX2, and CYBA could effectively distinguish between the Cancer group and the Control group.

[0149] Example 5: Obtaining a qPCR detection and diagnostic model using training set samples

[0150] 120 clinically derived urine samples were collected (30 from prostate cancer patients, 60 from patients with benign prostatic hyperplasia, 10 from patients with bladder cancer, and 20 from healthy individuals). Urine samples were collected and preserved according to the collection method described in Example 4. Nucleic acid was then extracted from the urine samples using a nucleic acid extraction kit. Simultaneously, 100 ng of nucleic acid was converted to double salt (see the instruction manual for double salt conversion procedures). The samples were then analyzed according to the detection system and procedure described in Example 4.

[0151] The ct values ​​of the three genes ADD3, GSX2, and CYBA in the above samples were statistically analyzed, and the ct value of the internal reference gene was calculated. The difference between the ct values ​​of the target and the internal reference was calculated (Δct = ct target - ct internal reference). Using 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 ΔCt for each gene. For dual-gene or multi-gene combined diagnostic analysis, a 10-fold cross-validation method was used. The sample set was divided into training and testing sets, and a logistic regression analysis model was used for training and testing to obtain the model parameters with the best utility. Scores for each gene were established, and then the optimal threshold was determined based on the principle of maximizing the Youden index (sensitivity + specificity - 1). The positive judgment values ​​for different gene combinations were obtained, and the results are shown in the table below.

[0152] Table 34

[0153] The ROC curve is shown in Figure 2.

[0154] The thresholds and result interpretation rules for single-gene analysis, and the formulas and result interpretation rules for the threshold probabilities (P) of each combination in dual-gene or multi-gene combined diagnostic analysis are as follows:

[0155] Table 35

[0156] The results showed that the combined diagnostic performance of the three genes ADD3, GSX2, and CYBA was optimal. The formula for predicting the probability (P) of a sample as a positive prostate cancer sample was ln(P / (1-P))=4.153+(-0.006)×ΔCt(ADD3)+(-0.132)×ΔCt(GSX2)+(-0.229)×ΔCt(CYBA); the threshold was 0.155. When the P of a sample was greater than or equal to 0.155, the sample was considered a positive prostate cancer sample; when the P of a sample was less than 0.155, the sample was considered a negative prostate cancer sample. By comparing the detection performance of single-gene and multi-gene combination analysis, it was finally determined that when the three genes were used in combination for diagnosis, the kit had the largest area under the AUC curve (0.946) and the highest accuracy (0.932) (see Table 34).

[0157] Example 6: Performance differences of diagnostic models applied to prostate palpation urine samples and morning urine samples

[0158] The Clinical Cancer group (6 cases of prostate cancer urine samples) and the Control group (6 cases of benign prostatic hyperplasia urine samples) were selected. Morning urine and the first urine after prostate palpation were collected and mixed in a self-developed urine preservation solution. After mixing, 8 ml of the urine mixture was taken and nucleic acid was extracted using a large-volume magnetic bead method urine free DNA extraction kit (Jifan Biotechnology, M117-02). The nucleic acid was converted with bisulfite conversion reagent (Zymo Research, catalog number D5031). The converted nucleic acid was used for routine single-gene detection using the ADD3 gene, which was evaluated as having the best single-gene performance in Example 5. On the other hand, it was also detected using the multi-gene joint diagnostic model detection system constructed in Example 5.

[0159] First, routine testing was performed based on the single ADD3 gene target, forming the single-gene routine testing group. The system and procedure for the single-gene testing group are as follows:

[0160] Table 36

[0161] Table 37

[0162] The ct values ​​of the ADD3 gene and the internal reference gene were statistically analyzed, and the difference between the ct values ​​of the target and the internal reference (Δct = ct target - ct internal reference) was calculated. The results were interpreted according to the positive judgment value of ADD3 in Example 5 (when the ΔCt value (ctADD3 - ct internal reference) is greater than 12.949, it is considered negative for prostate cancer; when it is less than or equal to 12.949, it is considered positive for prostate cancer).

[0163] In addition, a multi-gene joint diagnostic model constructed based on Embodiment 5 of the present invention was simultaneously set up for detection as the multi-gene joint diagnostic model detection group.

[0164] The system and procedure for multi-gene detection groups are as follows:

[0165] Table 38

[0166] The ct values ​​of the three genes ADD3, GSX2, and CYBA were statistically analyzed and compared with the ct value of the internal reference gene. The difference between the ct values ​​of the target and the internal reference (Δct = ct target - ct internal reference) and the ΔCt value (Ct value target gene - Ct value internal reference gene) were calculated. The results were interpreted based on the positive judgment value of the combined diagnosis of ADD3_GSX2_CYBA in Example 5.

[0167] The formula for predicting the probability (P) of a test sample being a positive prostate cancer sample is 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 of the test sample is greater than or equal to 0.155, the test sample is positive for prostate cancer; when the P of the test sample is less than 0.155, the test sample is negative for prostate cancer.

[0168] Results of ADD3 single gene routine detection system

[0169] Table 39

[0170] Detection results of the ADD3_GSX2_CYBA three-gene combined diagnostic model

[0171] Table 40 Note: BPH stands for benign prostatic hyperplasia, PCA stands for prostate cancer; CN stands for non-touch morning urine sample, and DRE stands for first urine sample after touch.

[0172] The results of this embodiment show that positive signals can be detected in morning urine samples, but the positive rate is lower than that of touch-based urine. The three-gene combined diagnostic system established in Example 5 effectively improves detection sensitivity. In 6 PCA urine samples from the Cancer group, the positive rate of the three-gene combined diagnostic system in detecting morning urine samples (4 / 6) was consistent with the positive rate of the ADD3 conventional detection system in detecting touch-based urine 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, and the urine collection method can be changed from prostate touch-based collection to morning urine.

[0173] Example 7: Validating the performance of the diagnostic model using a large number of clinical samples

[0174] 170 clinically derived urine samples were collected (the Cancer group included 76 urine samples from prostate cancer patients; the Control group included 94 samples from individuals with benign prostatic hyperplasia, 15 urine samples from individuals with bladder cancer, and 15 urine samples from healthy individuals). Nucleic acid was extracted from the urine samples using a nucleic acid extraction kit. Simultaneously, 100 ng of nucleic acid was converted to double salt (see the instruction manual for double salt conversion procedures). The samples were then analyzed according to the detection system and procedure described in Example 4.

[0175] The ct values ​​of the three genes ADD3, GSX2, and CYBA in the above samples were statistically analyzed, and the ct values ​​of the internal reference gene were compared with those in Table 35 of Example 5 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.

[0176] Table 41

[0177] Thus far, through modeling experiments on the training set and validation experiments on the validation set, this invention has obtained various diagnostic models for prostate cancer detection, including single-gene, double-gene, and triple-gene models for methylation of the three genes ADD3, GSX2, and CYBA. The best-performing diagnostic model is the triple-gene combined diagnostic model, and its clinical performance on the validation set is summarized in Table 42 below:

[0178] Table 42 Sensitivity = 69 / (69+7)×100% = 90.8% Specificity = 90 / (4+90)×100% = 95.7% Positive predictive value = 69 / (69+4)×100% = 94.5% Negative predictive value = 90 / (7+90)×100% = 92.8% Overall accuracy = (69+90) / (69+4+7+90)×100% = 93.5%

Claims

1. The application of primers and probes for detecting prostate cancer-specific methylation markers in the preparation of prostate cancer detection reagents; characterized in that, The differentially methylated target region of the prostate cancer-specific methylation markers is referenced to 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-FAMACGACAATACGCGCCTAAC (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 application according to claim 1, characterized in that, The detection reagents also include internal reference gene primers and probes. The sequence is as follows: mACTB-FGGTGTTTAAGATAGTGTTGTGG mACTB-R CTACTTAATACACACTCCAAAACC mACTB-P CTTTACACCAACCTCATAACCTTATC.

3. The application according to claim 1, characterized in that, The test kit also includes a urine preservation solution. The formulation is as follows: 4M guanidine isothiocyanate, 50mM EDTA, 200mM TCEP, 20% PEG, 500mM sulfosalicylic acid, 20% isopropanol, 10% Tween 20, 0.1M citrate-sodium citrate buffer at pH 4.

5.

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

5. The detection reagent according to claim 4, characterized in that, It also includes urine preservation solution, The formulation is as follows: 4M guanidine isothiocyanate, 50mM EDTA, 200mM TCEP, 20% PEG, 500mM sulfosalicylic acid, 20% isopropanol, 10% Tween 20, 0.1M citrate-sodium citrate buffer at pH 4.5.

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