Methylation marker for risk assessment of prostatic cancer and application of methylation marker

Abstract

The invention provides a methylation marker for risk assessment of prostatic cancer and application of the methylation marker, and relates to the technical field of molecular biomedicine. The invention provides a methylation marker for early non-invasive identification of prostatic cancer and a combination thereof, and a new marker group related to identification of prostatic cancer is identified based on methylation sequencing data analysis of measured tissue interstitial fluid and urine EV DNA samples of prostatic cancer patients and cancer-free patients. The marker group can effectively distinguish benign and malignant prostate cancer or distinguish inert prostate cancer and highly invasive prostate cancer, a new biomarker is provided for prostate cancer identification, and compared with the previously reported and used technical means, the marker group can more sensitively detect the methylation change of related genes of prostate cancer, and has a good application prospect. More favorable evidence is provided for early diagnosis of the prostate cancer, and a new method is provided for early diagnosis of the prostate cancer.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of molecular biomedical technology, and particularly relates to a methylation marker for prostate cancer risk assessment and application thereof. BACKGROUND

[0003] At present, prostate specific antigen (PSA) is the most commonly used serum biomarker for detecting prostate cancer in high-risk groups due to its low cost and convenient detection, and its normal range is 0-4 ng / mL. Although it has prostate tissue specificity, it is not specific to prostate cancer and is difficult to distinguish between benign lesions (such as prostate hyperplasia) and malignant lesions (tumor lesions). Moreover, prostate inflammation, infection, trauma and benign hyperplasia can all cause PSA to rise, especially in the "gray zone" of 4-10 ng / mL, and its specificity is only about 25%, and these non-tumor-specific PSA elevations often lead to over-diagnosis and treatment, so there is an urgent need to explore new markers for individualized diagnosis of prostate cancer.

[0004] Extracellular vesicles (EVs) as one of the three major targets of liquid biopsy have very important significance in tumor diagnosis. Literature has reported that EV DNA is a very potential tumor marker. However, the current research on EV DNA is still very insufficient, and most of the research focuses on the detection of gene mutations, while DNA methylation is the most common driving molecular event of tumors, which can accurately determine the source and prognosis of tumors. However, the development of a product based on urine EV DNA methylation with high sensitivity and specificity requires a large amount of methylation sequencing data to screen for methylation markers with excellent performance, and a strict machine learning model construction and verification process, which is a challenging task.

[0005] In summary, there is an urgent need for a biomarker with high sensitivity and specificity for detecting the benignity and malignancy of prostate cancer. SUMMARY

[0006] The purpose of the present application is to screen prostate cancer-specific methylation markers from prostate tissue interstitial fluid and urine EV DNA methylation targeted NGS sequencing data, and to construct and verify machine learning models using the screened methylation markers, for early diagnosis of prostate cancer in urine detection, to achieve the purpose of early detection of prostate cancer; or to distinguish between prostate cancer indolent patients and high-invasive patients in urine detection, to provide guidance for clinical diagnosis and treatment.

[0007] In one aspect, the present application provides a methylation marker for detecting the malignancy of prostate cancer and / or distinguishing between high-invasive prostate cancer and indolent prostate cancer, characterized in that the methylation marker comprises any one or more of the following (a1)-(a5):

[0008] (a1), one or more sub-regional sequences in MACF1, LINC01359, ADCY4, GAPLINC, C19orf25;

[0009] (a2), a region of the sequence shown in (a1) after bisulfite, bisulfite and / or deaminase treatment;

[0010] (a3), a variant having at least 90% or more sequence identity with the sequence of (a1) or (a2) and having the same methylation site as (a1) or (a2);

[0011] (a4), a DNA methylation site in the region of the sequence shown in (a1) or (a2) or (a3);

[0012] (a5), a DNA methylation abundance covered in the region of the sequence shown in (a1) or (a2) or (a3) or (a4).

[0013] Preferably, the present application provides a methylation marker for simultaneously detecting the malignancy of prostate cancer and distinguishing between high-invasive prostate cancer and indolent prostate cancer.

[0014] Preferably, the prostate cancer is early-stage prostate cancer.

[0015] Preferably, the methylation marker is a non-invasive methylation marker for identifying prostate cancer, and more preferably, the methylation marker is a non-invasive methylation marker for identifying EV DNA.

[0016] Wherein, the effect of bisulfite, sulfite and / or deaminase treatment is to convert the unmethylated cytosine on the DNA into a base that does not bind to guanine. In the present application, the methylation marker takes the methylation state / level of the CpG island-containing region / fragment / methylation site involved in the present application as the basis for prostate cancer risk assessment. The methylation level of the methylation marker can be obtained by various detection means known in the art, including but not limited to obtaining the methylation level by second-generation sequencing.

[0017] The methylation marker in the present application can be used alone or in combination as a prostate cancer-related methylation molecular marker for detecting or assisting in detecting or identifying prostate cancer or prostate cancer-related diseases.

[0018] The MACF1, LINC01359, ADCY4, GAPLINC and C19orf25 are used for the first time in the present application as a methylation marker combination for prostate cancer risk assessment. It has been verified through experiments that the MACF1, LINC01359, ADCY4, GAPLINC and C19orf25 and part of the sub-regional sequences thereof have the characteristics of high sensitivity and good specificity, and provide a new marker combination for prostate cancer detection.

[0019] In addition, in one aspect, the MACF1, LINC01359, ADCY4, GAPLINC and C19orf25 marker combination provided in the present application can accurately detect benign and malignant prostate cancer, effectively distinguish benign prostate cancer (such as prostate hyperplasia) from malignant prostate cancer (tumor lesions), solve the problem of poor specificity in the prior art for prostate cancer detection, and achieve early diagnosis and painless detection of prostate cancer. On the other hand, the marker combination can also accurately distinguish high-invasive prostate cancer from indolent prostate cancer, and one sampling can achieve the simultaneous detection of benign and malignant prostate cancer and the differentiation of high-invasive prostate cancer from indolent prostate cancer, saving the detection process and cost. Moreover, the methylation marker is a non-invasive methylation marker combination for identifying EV DNA, which can be sampled and detected based on urine, and is non-invasive, painless and easy to collect.

[0020] Further, the Hg19 coordinates of the methylation marker MACF1 include chr1:39546570-39546970; preferably, the Hg19 coordinates of the methylation marker MACF1 include chr1:39546670-39546870;

[0021] and / or, the Hg19 coordinates of the methylation marker LINC01359 include chr1:65468139-65468680; preferably, the Hg19 coordinates of the methylation marker LINC01359 include chr1:65468239-65468580; more preferably, the Hg19 coordinates of the methylation marker LINC01359 include chr1:65468239-65468439 and / or chr1:65468380-65468580;

[0022] and / or, the Hg19 coordinates of the methylation marker ADCY4 include chr14:24804038-24804438; preferably, the Hg19 coordinates of the methylation marker ADCY4 include chr14:24804138-24804338;

[0023] and / or, the Hg 19 coordinate of the methylation marker GAPLINC comprises chr18: 3499015-3499215; preferably, the Hg 19 coordinate of the methylation marker GAPLINC comprises chr18: 3499015-3499215;

[0024] and / or, the Hg 19 coordinate of the methylation marker C19orf25 comprises chr19: 1466957-1467357; preferably, the Hg 19 coordinate of the methylation marker C19orf25 comprises chr19: 1467057-1467257.

[0025] More preferably, a methylation marker for detecting prostate cancer malignancy and / or distinguishing high-invasive prostate cancer from indolent prostate cancer comprises chr1: 39546670-39546870, chr1: 65468239-65468439, chr1: 65468380-65468580, chr14: 24804138-24804338, chr18: 3499015-3499215, chr19: 1467057-1467257.

[0026] The numbering of the position of the sequence in the genome in the present application corresponds to the position on the UCSC (http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / hg19.fa.gz) HG19 genome.

[0027] More preferably, the Hg 19 coordinate of the methylation marker MACF1 comprises chr1: 39546670-39546870, and the nucleotide sequence comprises the sequence set forth in SEQ ID No. 1 or a nucleotide sequence having at least 90% sequence identity to SEQ ID No. 1;

[0028] The Hg 19 coordinate of the methylation marker LINC01359 comprises chr1: 65468239-65468439, and the nucleotide sequence comprises the sequence set forth in SEQ ID No. 2 or a nucleotide sequence having at least 90% sequence identity to SEQ ID No. 2;

[0029] The Hg 19 coordinate of the methylation marker LINC01359 comprises chr1: 65468380-65468580, and the nucleotide sequence comprises the sequence set forth in SEQ ID No. 3 or a nucleotide sequence having at least 90% sequence identity to SEQ ID No. 3;

[0030] Hg19 coordinates of the methylation marker ADCY4 include chrl4: 24804138- 24804338, and the nucleotide sequence includes the sequence shown in SEQ ID No. 4 or a nucleotide sequence with at least 90% sequence identity to SEQ ID No. 4;

[0031] Hg19 coordinates of the methylation marker GAPLINC include chr18: 3499015- 3499215, and the nucleotide sequence includes the sequence shown in SEQ ID No. 5 or a nucleotide sequence with at least 90% sequence identity to SEQ ID No. 5;

[0032] Hg19 coordinates of the methylation marker C19orf25 include chrl9: 1467057- 1467257, and the nucleotide sequence includes the sequence shown in SEQ ID No. 6 or a nucleotide sequence with at least 90% sequence identity to SEQ ID No. 6.

[0033] For the first time in this application, the detection region of MACF1, LINC01359, ADCY4, GAPLINC, C19orf25 is limited, a functional sub-region in the methylation marker with good specificity and high sensitivity is obtained, the detection time is greatly shortened, and the detection cost is saved.

[0034] Further, for detecting the benign and malignant prostate cancer, the (a1) further comprises one or more sub-region sequences of CITED4, PTGER4, RASGEF1A, TBC1D30.

[0035] In a preferred embodiment, a methylation marker for detecting the benign and malignant prostate cancer, characterized in that the methylation marker comprises any one or more of the following (a1)-(a5):

[0036] (a1), one or more sub-region sequences of MACF1, LINC01359, ADCY4, GAPLINC, C19orf25, CITED4, PTGER4, RASGEF1A, TBC1D30;

[0037] (a2), a region of the sequence shown in (a1) after bisulfite, bisulfite and / or deaminase treatment;

[0038] (a3), a variant with at least 90% sequence identity to the sequence of (a1) or (a2) and with the same methylation site as (a1) or (a2);

[0039] (a4), a DNA methylation site in the region of the sequence shown in (a1) or (a2) or (a3).

[0040] (a5), (a1) or (a2) or (a3) or (a4) the DNA methylation abundance covered in the region of the sequence.

[0041] Preferably, the Hg 19 coordinates of the methylation marker CITED4 include chr1:41326857-41327257; preferably, the Hg 19 coordinates of the methylation marker CITED4 include chr1:41326957-41327157;

[0042] The Hg 19 coordinates of the methylation marker PTGER4 include chr5:40681041-40681494; preferably, the Hg 19 coordinates of the methylation marker PTGER4 include chr5:40681141-40681394;

[0043] The Hg 19 coordinates of the methylation marker RASGEF1A include chr10:43697709-43698258; preferably, the Hg 19 coordinates of the methylation marker RASGEF1A include chr10:43697809-43698158;

[0044] The Hg 19 coordinates of the methylation marker TBC1D30 include chr12:65218480-65218999; preferably, the Hg 19 coordinates of the methylation marker TBC1D30 include chr12:65218580-65218899.

[0045] More preferably, a methylation marker for detecting the benign or malignant prostate cancer includes chr1:39546670-39546870, chr1:65468239-65468439, chr1:65468380-65468580, chr14:24804138-24804338, chr18:3499015-3499215, chr19:1467057-1467257, chr1:41326957-41327157, chr5:40681141-40681394, chr10:43697809-43698158, chr12:65218580-65218899.

[0046] Further, for distinguishing the high-invasive prostate cancer from the indolent prostate cancer, the (a1) further includes EGFLAM, ASAP1, SLC43A3.

[0047] In a preferred embodiment, a methylation marker for distinguishing high-invasive prostate cancer from indolent prostate cancer, characterized in that the methylation marker comprises any one or more of (a1)-(a5) as follows:

[0048] (a1), one or more sub-regional sequences in MACF1, LINC01359, ADCY4, GAPLINC, C19orf25, EGFLAM, ASAP1, SLC43A3;

[0049] (a2), a region of the sequence of (a1) after bisulfite, hydroxybisulfite and / or deaminase treatment;

[0050] (a3), a variant having at least 90% or more sequence identity to the sequence of (a1) or (a2) and having the same methylation sites as (a1) or (a2);

[0051] (a4), a DNA methylation site in the region of the sequence of (a1) or (a2) or (a3);

[0052] (a5), a DNA methylation abundance covered in the region of the sequence of (a1) or (a2) or (a3) or (a4).

[0053] Preferably, the Hg19 coordinates of the methylation marker EGFLAM comprise chr5: 38257847-38258247; preferably, the Hg19 coordinates of the methylation marker EGFLAM comprise chr5: 38257947-38258147;

[0054] Preferably, the Hg19 coordinates of the methylation marker ASAP1 comprise chr8: 131455169-131455569; preferably, the Hg19 coordinates of the methylation marker ASAP1 comprise chr8: 131455269-131455469;

[0055] Preferably, the Hg19 coordinates of the methylation marker SLC43A3 comprise chr11: 57194409-57194809; preferably, the Hg19 coordinates of the methylation marker SLC43A3 comprise chr11: 57194509-57194709;

[0056] Preferably, the Hg19 coordinates of the methylation marker C19orf25 further comprise chr19: 1466994-1467194.

[0057] More preferably, a methylation marker for distinguishing high-invasive prostate cancer from indolent prostate cancer comprises chr1:39546670-39546870, chr1:65468239-65468439, chr1:65468380-65468580, chr14:24804138-24804338, chr18:3499015-3499215, chr19:1467057-1467257, chr5:38257947-38258147, chr8:131455269-131455469, chr11:57194509-57194709, chr19:1466994-1467194.

[0058] In another aspect, the present application also provides primers and / or probes for detecting the methylation markers, wherein the primers are used for specific amplification of the target sequence of the nucleotide sequence where the methylation marker locates; and the probes specifically capture the nucleotide sequence where the methylation marker locates.

[0059] The MACF1 methylation detection primer pair comprises a forward primer with a nucleotide sequence as shown in SEQ ID NO. 10, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 11, and a probe with a nucleotide sequence as shown in SEQ ID NO. 12; and / or,

[0060] The LINC01359 methylation detection primer pair comprises a forward primer with a nucleotide sequence as shown in SEQ ID NO. 13, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 14, and a probe with a nucleotide sequence as shown in SEQ ID NO. 15; and / or, a forward primer with a nucleotide sequence as shown in SEQ ID NO. 16, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 17, and a probe with a nucleotide sequence as shown in SEQ ID NO. 18; and / or,

[0061] The ADCY4 methylation detection primer pair comprises a forward primer with a nucleotide sequence as shown in SEQ ID NO. 19, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 20, and a probe with a nucleotide sequence as shown in SEQ ID NO. 21; and / or,

[0062] The GAPLINC methylation detection primer pair comprises a forward primer with a nucleotide sequence as shown in SEQ ID NO. 22, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 23, and a probe with a nucleotide sequence as shown in SEQ ID NO. 24; and / or,

[0063] The C19orf25 methylation detection primer pair comprises a forward primer with a nucleotide sequence as shown in SEQ ID NO. 25, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 26, and a probe with a nucleotide sequence as shown in SEQ ID NO. 27.

[0064] Preferably, the probe is a fluorescent probe.

[0065] In a preferred embodiment, the fluorescent probe is labeled with a fluorescent dye (such as FAM, HEX / VIC, TAMRA, Texas Red, or Cy5) at the 5' end and a quencher (such as BHQ1, BHQ2, BHQ3, DABCYL, or TAMRA) at the 3' end.

[0066] In another aspect, the present application also provides a kit for detecting the malignancy of prostate cancer and / or distinguishing high-invasive prostate cancer from indolent prostate cancer, which comprises reagents for specifically detecting the presence and / or abundance of the methylation state of the methylation marker.

[0067] Preferably, the prostate cancer is early-stage prostate cancer.

[0068] Further, the kit comprises primers and / or probes of the methylation marker.

[0069] Preferably, the kit further comprises primers and / or probes or sequences for detecting the internal reference β-actin gene.

[0070] Further, the kit further comprises reagents used in any one or a combination of PCR amplification, fluorescent quantitative PCR, digital PCR, liquid chip, first-generation sequencing, high-throughput sequencing, and third-generation sequencing.

[0071] Further, the reagents can be selected from the following: PCR buffer, polymerase, dNTP, primer, probe, methylation-sensitive or -insensitive restriction enzyme, enzyme digestion buffer, fluorescent dye, fluorescent quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control, etc.

[0072] Further, the kit further comprises bisulfite, heavy sulfite, and / or deaminase.

[0073] Preferably, the bisulfite is selected from one or more of calcium bisulfite, sodium bisulfite, potassium bisulfite, and ammonium bisulfite; and the heavy sulfite is selected from one or more of sodium pyrosulfite, potassium pyrosulfite, and ammonium pyrosulfite.

[0074] The skilled person in the art can understand that the bisulfite, the heavy sulfite and / or the deaminase can be selected according to the actual situation, as long as the effect of converting the unmethylated cytosine on the DNA into a base not combined with guanine can be achieved.

[0075] In another aspect, the application also provides a method for constructing a prostate cancer benignity evaluation model, which comprises the following steps:

[0076] Step one, collecting prostate cancer malignant samples and prostate cancer benign samples, and dividing them into a training set and a test set;

[0077] Step two, extracting EV DNA of the samples, and performing library construction and sequencing;

[0078] Step three, training using the samples of the training set, and constructing an algorithm model;

[0079] Step four, verifying the effect of the model using the sample data of the test set, and determining a final model.

[0080] In another aspect, the application also provides a method for constructing a model for distinguishing high-invasive prostate cancer from indolent prostate cancer, which comprises the following steps:

[0081] Step one, collecting high-invasive prostate cancer samples and indolent prostate cancer samples, and dividing them into a training set and a test set;

[0082] Step two, extracting EV DNA of the samples, and performing library construction and sequencing;

[0083] Step three, training using the samples of the training set, and constructing an algorithm model;

[0084] Step four, verifying the effect of the model using the sample data of the test set, and determining a final model.

[0085] Specifically, after detecting different clinical samples, a diagnostic model based on the methylation level of the methylation marker gene is constructed by using a statistical method. First, biomarkers with obvious differences in methylation expression level in different clinical stages are screened, and statistical test methods include F test, T test, Wald test, likelihood ratio test and ratio test. Then, the screened biomarkers are used to construct a diagnostic model, and the statistical method is selected from the following methods: random forest model, support vector machine, Probit regression, logistic regression, cluster analysis, neighborhood analysis, genetic algorithm, Bayesian and non-Bayesian methods, etc.

[0086] In another aspect, the application also provides a prostate cancer risk evaluation model, which comprises:

[0087] a sequencing module configured to obtain a methylation level of at least one of the methylation markers in a sample from the individual; and a data comparison module configured to compare the methylation level with a corresponding reference level, wherein the same or higher methylation level of one or more methylation markers relative to the corresponding reference level indicates that the individual has prostate cancer or is at risk of developing prostate cancer.

[0088] Preferably, the prostate cancer is early stage prostate cancer.

[0089] In a preferred embodiment, the sequencing is performed by polymerase chain reaction (e.g. real-time polymerase chain reaction, digital polymerase chain reaction), nucleic acid sequencing, mass-based separation (e.g. electrophoresis, mass spectrometry), or target capture (e.g. hybridization, microarray). Preferably, the sequencing is performed by real-time polymerase chain reaction, optionally multiplexed real-time polymerase chain reaction.

[0090] In a preferred embodiment, the same or higher methylation level of the target marker relative to the corresponding reference methylation level indicates that the individual has prostate cancer or is at risk of developing prostate cancer or has an increased likelihood of developing prostate cancer or has a poor prognosis or is at risk of having a poor prognosis for prostate cancer.

[0091] In a preferred embodiment, the sample is a liquid sample, which is a liquid sample from a mammal and other biological sources, such as urine, prostatic fluid, peripheral blood, serum, plasma, ascites, cerebrospinal fluid, sputum, saliva, etc. The mammal includes rat, mouse and human; preferably human. The liquid sample is preferably urine and / or prostatic fluid.

[0092] In an embodiment, the sample is urine and / or prostatic fluid. The sample includes extracellular vesicle DNA, genomic DNA. Preferably, extracellular vesicle DNA.

[0093] The skilled person can select the sample to be tested according to the actual situation. In a preferred embodiment, the amount of the biological sample is not less than 20 ng, or not less than 10 ng.

[0094] The prostate cancer risk assessment model includes: a prostate cancer benign and malignant detection model and / or a high-invasive prostate cancer and indolent prostate cancer differentiation model.

[0095] In a preferred embodiment, the prostate cancer benign and malignant detection model includes:

[0096] a sequencing module, configured to obtain the methylation level of at least one of the methylation markers in the sample to be tested; and a data comparison module, configured to compare the methylation level with the corresponding reference level, wherein the same or higher methylation level of one or more methylation markers relative to the corresponding reference level indicates that the individual has malignant prostate cancer or is at risk of developing malignant prostate cancer.

[0097] Preferably, the model can be:

[0098] wherein x is the methylation level of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score.

[0099] The threshold value is set to 0.6, and the prediction score greater than the threshold value is judged as positive, indicating the risk of malignant prostate cancer, and the prediction score less than the threshold value is judged as negative, indicating benign prostate cancer.

[0100] The model has a specificity of greater than or equal to 62.7% and a sensitivity of greater than or equal to 75.8%.

[0101] Preferably, the sequencing module can further comprise detecting serum PSA, and the data comparison module can further comprise comparing the serum PSA with the corresponding reference level.

[0102] In a preferred embodiment, the model for distinguishing the benign and malignant prostate cancer comprises:

[0103] a sequencing module, configured to obtain the methylation level of at least one of the methylation markers in the sample to be tested and the serum PSA level; and a data comparison module, configured to compare the methylation level and the serum PSA level with the corresponding reference level, wherein the same or higher level of one or more methylation markers and the serum PSA level relative to the corresponding reference level indicates that the individual has malignant prostate cancer or is at risk of developing malignant prostate cancer.

[0104] The model has a threshold value of 0.47, and the prediction score greater than the threshold value is judged as positive, indicating the risk of malignant prostate cancer, and the prediction score less than the threshold value is judged as negative, indicating benign prostate cancer. The specificity is greater than or equal to 66%, and the sensitivity is greater than or equal to 80.4%.

[0105] In a preferred embodiment, the model for distinguishing the high-invasive prostate cancer from the indolent prostate cancer comprises:

[0106] a sequencing module, configured to obtain the methylation level of at least one of the methylation markers in the sample to be tested; and a data comparison module, configured to compare the methylation level with a corresponding reference level, wherein the same or higher methylation level of one or more methylation markers relative to the corresponding reference level indicates that the individual has high-invasive prostate cancer or is at risk of developing high-invasive prostate cancer.

[0107] Preferably, the model can be:

[0108] wherein x is the methylation level of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score.

[0109] The threshold value is set to 0.4, and the prediction score greater than the threshold value is judged as positive, indicating the risk of high-invasive prostate cancer, and the prediction score less than the threshold value is judged as negative, indicating the inert prostate cancer.

[0110] The model has a specificity of greater than or equal to 82.7% and a sensitivity of greater than or equal to 65.0%.

[0111] Preferably, the sequencing module can further comprise detecting serum PSA, and the data comparison module can further comprise comparing the serum PSA with a corresponding reference level.

[0112] In a preferred embodiment, the model for distinguishing high-invasive prostate cancer from inert prostate cancer comprises:

[0113] a sequencing module, configured to obtain the methylation level of at least one of the methylation markers in the sample to be tested and the serum PSA level; and a data comparison module, configured to compare the methylation level and the serum PSA level with a corresponding reference level, wherein the same or higher level of one or more methylation markers and the serum PSA level relative to the corresponding reference level indicates that the individual has malignant prostate cancer or is at risk of developing malignant prostate cancer.

[0114] The threshold value is set to 0.3, and the prediction score greater than the threshold value is judged as positive, indicating the risk of high-invasive prostate cancer, and the prediction score less than the threshold value is judged as negative, indicating the inert prostate cancer. The model has a specificity of greater than or equal to 86.7% and a sensitivity of greater than or equal to 66%.

[0115] In another aspect, the present application also provides an information data processing terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0116] (a) obtaining the methylation level of at least one of the methylation markers in the sample to be tested of the individual; (b) comparing the methylation level in step (a) with the corresponding reference level, respectively, wherein one or more methylation markers having the same or higher methylation level relative to the corresponding reference level indicates that the individual has prostate cancer or is at risk of developing prostate cancer.

[0117] Preferably, the prostate cancer is early stage prostate cancer.

[0118] Preferably, the prostate cancer comprises malignant prostate cancer and / or high invasive prostate cancer.

[0119] In a preferred embodiment, the processor, when executing the computer program, implements the following steps:

[0120] (a) obtaining the methylation level of at least one of the methylation markers in the sample to be tested of the individual; (b) performing the prostate cancer risk assessment model; (c) determining whether the individual has prostate cancer or is at risk of developing prostate cancer.

[0121] In a preferred embodiment, an information data processing terminal comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the following steps:

[0122] (a) obtaining the methylation level of at least one of the methylation markers in the sample to be tested of the individual; (b) performing the detection of benign and malignant prostate cancer model; (c) determining the benign and malignant prostate cancer of the individual.

[0123] In a preferred embodiment, an information data processing terminal comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the following steps:

[0124] (a) obtaining the methylation level of at least one of the methylation markers in the sample to be tested of the individual; (b) performing the model for distinguishing high invasive prostate cancer from indolent prostate cancer; (c) distinguishing whether the individual has high invasive prostate cancer or indolent prostate cancer.

[0125] In another aspect, the present application also provides a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0126] (a) obtaining the methylation level of at least one of the methylation markers in a sample to be tested from the individual; and (b) comparing the methylation level with the corresponding reference level, respectively, wherein one or more of the methylation markers having the same or higher methylation level relative to the corresponding reference level indicates that the individual has prostate cancer or is at risk of developing prostate cancer.

[0127] Preferably, the prostate cancer is early stage prostate cancer.

[0128] Preferably, the prostate cancer comprises malignant prostate cancer and / or high invasive prostate cancer.

[0129] In one preferred embodiment, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, carries out the following steps:

[0130] (a) obtaining the methylation level of at least one of the methylation markers in a sample to be tested from the individual; and (b) performing the prostate cancer risk assessment model; and (c) determining whether the individual has prostate cancer or is at risk of developing prostate cancer.

[0131] In one preferred embodiment, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, carries out the following steps:

[0132] (a) obtaining the methylation level of at least one of the methylation markers in a sample to be tested from the individual; and (b) performing the detection of prostate cancer malignancy model; and (c) determining the malignancy of the prostate cancer in the individual.

[0133] In one preferred embodiment, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, carries out the following steps:

[0134] (a) obtaining the methylation level of at least one of the methylation markers in a sample to be tested from the individual; and (b) performing the differentiation of high invasive prostate cancer from indolent prostate cancer model; and (c) differentiating whether the individual has high invasive prostate cancer or indolent prostate cancer.

[0135] In another aspect, the present application also provides a method for detecting the risk of prostate cancer, comprising the following steps:

[0136] In one aspect, the present application provides a method for diagnosing the malignancy of prostate cancer in an individual, comprising the following steps:

[0137] Step one, treating the DNA obtained from the biological sample with a reagent capable of distinguishing between unmethylated sites and methylated sites in the DNA, thereby obtaining treated DNA;

[0138] Step two, quantitatively analyzing the methylation level of a set of target markers in the treated DNA of step one, respectively, wherein the target markers are selected from one or more of the sequences of MACF1, LINC01359, ADCY4, GAPLINC, C19orf25 or sub-regional sequences thereof.

[0139] Step three, comparing the methylation level of at least one of the target markers quantitatively analyzed in step two with the corresponding reference level, respectively, wherein the same or higher methylation level of one or more target markers relative to the corresponding reference level indicates that the individual has malignant prostate cancer, or the individual has the risk of forming or developing malignant prostate cancer, or the individual has an increased likelihood of developing malignant prostate cancer, or the individual has a poor prognosis or the risk of poor prognosis of malignant prostate cancer.

[0140] In one aspect, the present application provides a method for diagnosing high- invasive prostate cancer in an individual, the method comprising the following steps:

[0141] Step one, treating the DNA obtained from the biological sample with a reagent capable of distinguishing between unmethylated sites and methylated sites in the DNA, thereby obtaining treated DNA;

[0142] Step two, quantitatively analyzing the methylation level of a set of target markers in the treated DNA of step one, respectively, wherein the target markers are selected from one or more of the sequences of MACF1, LINC01359, ADCY4, GAPLINC, C19orf25 or sub-regional sequences thereof.

[0143] Step three, comparing the methylation level of at least one of the target markers quantitatively analyzed in step two with the corresponding reference level, respectively, wherein the same or higher methylation level of one or more target markers relative to the corresponding reference level indicates that the individual has high-invasive prostate cancer, or the individual has the risk of forming or developing high-invasive prostate cancer, or the individual has an increased likelihood of developing high-invasive prostate cancer, or the individual has a poor prognosis or the risk of poor prognosis of high-invasive prostate cancer.

[0144] Preferably, the prostate cancer risk model or the prostate cancer risk detection method can also be combined with existing diagnostic techniques for joint diagnosis. The existing diagnostic techniques include serum PSA technology.

[0145] The present application has the following beneficial effects:

[0146] The present application provides a methylation marker for early non-invasive identification of prostate cancer and a combination thereof, based on the analysis of methylation sequencing data of EV DNA samples in the interstitial fluid and urine of prostate cancer patients and non-cancer patients, a new marker group related to the identification of prostate cancer is identified, which can effectively distinguish between benign and malignant prostate cancer or between indolent prostate cancer and high-invasive prostate cancer, providing a new biomarker for the identification of prostate cancer, and compared with the previously reported and used technical means, the related gene methylation changes of prostate cancer can be more sensitively detected, providing more favorable evidence for the early diagnosis and accurate typing of prostate cancer, and providing a new method for the early diagnosis and accurate typing of prostate cancer.

[0147] The present application uses a machine learning-based method for modeling, and constructs two different prostate cancer diagnosis models, which can respectively realize the identification of benign and malignant prostate cancer and the identification of indolent prostate cancer and high-invasive prostate cancer, covering various clinical needs and achieving excellent performance. At the same time, the two models of the present application also show complementarity with existing serum tumor markers, and the combination of the two can achieve more excellent performance, which illustrates the broad application prospect and high industrial value of the present application.

[0148] Liquid biopsy based on urine EV DNA methylation is a new type of prostate cancer detection method, which has the following advantages compared with traditional detection methods: 1. Non-invasive: it detects through the urine sample of the patient, without the need for blood sampling or invasive examination, reducing the pain and discomfort of the patient; 2. High sensitivity and specificity: the biomarkers and related models described in the present application can detect changes in the methylation level of related markers in early prostate patients, not only can distinguish between prostate cancer patients and benign and malignant lesions, but also can distinguish between indolent prostate cancer patients and high-invasive patients; 3. Convenient and easy to operate: it is simple, fast and does not require special equipment and professional skills. In summary, the present application uses urine samples for detection, realizes truly non-invasive diagnosis, has high safety, is convenient for large-scale clinical application, and is convenient for disease monitoring, providing a new direction and possibility for early diagnosis and individualized treatment of prostate cancer. BRIEF DESCRIPTION OF DRAWINGS

[0149] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0150] Figure 1 ROC curve of five-fold cross-validation of NGS methylation marker prostate cancer benign and malignant model;

[0151] Figure 2 Figure 6 is a five-fold cross-validation ROC curve plot for the NGS methylation marker prostate cancer indolent / aggressive model;

[0152] Figure 3 Figure 7 is a five-fold cross-validation ROC curve plot for the PCR methylation marker prostate cancer benign / malignant model;

[0153] Figure 4 Figure 8 is a sample score distribution plot for the PCR methylation marker prostate cancer benign / malignant model;

[0154] Figure 5 Figure 9 is a five-fold cross-validation ROC curve plot for the PCR methylation marker prostate cancer indolent / aggressive model;

[0155] Figure 6 Figure 10 is a sample score distribution plot for the PCR methylation marker prostate cancer indolent / aggressive model;

[0156] Figure 7 Figure 11 is a methylation signature combined with serum PSA modeling plot. DETAILED DESCRIPTION

[0157] In order to more clearly illustrate the overall manner of the present application, the following detailed description is provided in connection with the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without one or more of these specific details. In other instances, well-known features have not been described in detail to avoid obscuring aspects of the present application.

[0158] It should be noted that the following detailed description is merely exemplary in nature intended to provide an overall understanding of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0159] It is also important to note that the term "or" as used herein is intended to mean an inclusive "or," such that "A or B" means any of the following: A; B; or A and B. Furthermore, as used herein, the common abbreviation "e.g.", which derives from the Latin phrase "exempli gratia," can be used to introduce examples of a thing as described, such as in a list of items. "E.g." should be interpreted, in context, as meaning only "one or more examples" or "one or more instances" of a thing.

[0160] Unless otherwise specified, in the following examples, the reagents or instruments used are not specified by the manufacturer, and are all conventional products that can be obtained by commercial purchase. The plasmids, endonucleases, PCR enzymes, column DNA extraction kits, and DNA gel recovery kits used in the following examples are commercial products, and the specific operations are performed according to the kit instructions.

[0161] Before further describing the specific embodiments of the present application, it should be understood that the scope of protection of the present application is not limited to the following specific embodiments; it should also be understood that the terms used in the embodiments of the present application are for the purpose of describing the specific embodiments, and are not intended to limit the scope of protection of the present application. Unless otherwise specified, the experimental methods, detection methods, and preparation methods disclosed in the present application all use conventional techniques in the fields of molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology, and related fields, which can be specifically performed according to Molecular Cloning: A Laboratory Manual (Fourth Edition).

[0162] In this application, all patients sign informed consent.

[0163] In the following examples, the extraction, quality control, and conversion of unmethylated cytosine on DNA to a base that does not bind to guanine. In one or more embodiments, the conversion is performed using an enzymatic method, preferably deaminase treatment, or the conversion is performed using a non-enzymatic method, preferably treatment with bisulfite or heavy sulfate, more preferably treatment with calcium bisulfite, sodium bisulfite, potassium bisulfite, ammonium bisulfite, sodium persulfate, potassium persulfate, and ammonium persulfate.

[0164] In addition, those skilled in the art can understand that, in the context, the diagnostic "sensitivity" is defined as the proportion of correct identification of positive results, that is, the percentage of individuals correctly identified as having the disease. And "specificity" is defined as the proportion of correct identification of negative results, that is, the percentage of individuals correctly identified as not having the disease.

[0165] Example 1 Extraction of extracellular vesicles

[0166] An extraction method of extracellular vesicles (EV) is given in this example, which comprises the following steps:

[0167] 1. The patient fasts and abstains from water the night before, and 100 mL of the patient's first morning urine is collected, which is processed within 6 hours;

[0168] 2. Add 4 mL of 0.5M EDTA and 10 mL of 20×PBS buffer and mix well;

[0169] 3. 3000g 4℃ centrifugation for 15min, take the supernatant and freeze or immediately proceed to the next step;

[0170] 4. Use 100KDa ultrafiltration membrane to perform ultrafiltration concentration in 100mL ultrafiltration cup;

[0171] 5. After ultrafiltration, add 50mL PBS, resuspend the EV by blowing the filter membrane, and perform ultrafiltration again;

[0172] 6. Resuspend the EV by blowing the filter membrane with PBS, freeze at -80℃ for standby or directly proceed to the next step.

[0173] Example 2 Extraction and transformation of extracellular vesicle DNA

[0174] (I) Column method DNA kit is used to extract EV DNA, and the steps are as follows:

[0175] 1. 600μL EV suspension is added with 30μL proteinase K, 5μL carrier RNA, 600μL lysis solution, vortexed thoroughly for 30s, and incubated at 60℃ for 20min;

[0176] 2. Add 300μL isopropanol, vortex thoroughly for 30s, and stand at room temperature for 3min;

[0177] 3. Add the mixture to HiPure Viral Mini Column adsorption column in two times, and centrifuge at 13000g for 1min;

[0178] 4. Put the column in a new collection tube, add 500μL Buffer GW1, and centrifuge at 13000g for 1min;

[0179] 5. Discard the filtrate, add 500μL Buffer GW2, centrifuge at 13000g for 1min, and repeat once;

[0180] 6. Change the new collection tube, and centrifuge at 13000g for 2min;

[0181] 7. Put the column in a new 1.5mL centrifuge tube, and further dry the column at 60℃ for 5min;

[0182] 8. Add 40μL TE buffer, stand at room temperature for 5min, and centrifuge at 13000g for 2min;

[0183] 9. The obtained EV DNA solution is used for DNA concentration determination by Qubit reagent, and the total amount of DNA should be≥20ng.

[0184] (II) EpiTect Fast DNA Bisulfite Kit (59824) kit is used for bisulfite transformation, and the steps are as follows:

[0185] 1. Add 85 μL of Bisulfite Solution and 15 μL of DNA Protect Buffer to the 40 μL of EV DNA, mix well, and centrifuge briefly;

[0186] 2. Incubate at 95°C for 5 min, 60°C for 10 min, repeat 2 times, and store at 20°C, process as soon as possible;

[0187] 3. Transfer the mixture to a clean 1.5 mL centrifuge tube, add 310 μL of freshly prepared Buffer BL, mix well, and centrifuge briefly;

[0188] 4. Add 250 μL of absolute ethanol, mix well, and centrifuge briefly;

[0189] 5. Add the mixture to a MinElute DNA spin column, centrifuge at 13000 g for 1 min;

[0190] 6. Discard the filtrate, add 500 μL of Buffer BW, centrifuge at 13000 g for 1 min;

[0191] 7. Discard the filtrate, add 500 μL of Buffer BD, incubate at room temperature for 15 min, centrifuge at 13000 g for 1 min;

[0192] 8. Discard the filtrate, add 500 μL of Buffer BW, centrifuge at 13000 g for 1 min, and repeat once;

[0193] 9. Discard the filtrate, add 250 μL of absolute ethanol, centrifuge at 13000 g for 1 min;

[0194] 10. Replace the collection tube, centrifuge at 13000 g for 2 min;

[0195] 11. Place the column in a new 1.5 mL centrifuge tube, further dry the column at 60°C for 5 min;

[0196] 12. Add 15 μL of TE buffer, stand at room temperature for 5 min, centrifuge at 13000 g for 2 min;

[0197] 13. The obtained EV DNA solution is used for DNA concentration determination using Qubit reagent, and the obtained DNA concentration is diluted to 1 ng / μL.

[0198] Example 3 Screening of methylation markers specific for prostate cancer by methylation targeted sequencing

[0199] (I) Sample collection

[0200] Urine samples were collected from 27 patients with benign prostatic hyperplasia and 30 patients with prostate cancer. All the prostate cancer patients were scored by Gleason score. Informed consent was obtained from all the patients. Urinary EV DNA was extracted and bisulfite converted as described in Example 1 and 2.

[0201] (ii) Library preparation

[0202] The library was constructed using MethylTitan method as follows:

[0203] Bisulfite converted DNA was dephosphorylated and ligated to universal Illumina sequencing adapters with molecular tag (UMI). After second strand synthesis and purification, a semi-targeted PCR reaction was performed on the converted DNA to target amplify the desired region. After another purification, sample specific barcodes and full length Illumina sequencing adapters were added to the target DNA molecules by PCR reaction. The final library was quantified using KAPA library quantification kit for Illumina (KK4844) and sequenced on Illumina sequencer. MethylTitan library construction method can effectively enrich the desired target fragments with less amount of DNA, and the method can well preserve the original DNA methylation status. The entire methylation pattern of a specific region can be used as a unique marker, rather than comparing the status of a single base.

[0204] (iii) Sequencing and data preprocessing

[0205] 1. Sequencing was performed on Illumina Hiseq 2500 sequencer with 25-35M reads per sample. Trim_galore v 0.6.0 and cutadapt v2.1 were used to remove the adapters from the paired-end 150bp sequencing data from Illumina Hiseq 2500 sequencer. The sequence "AGATCGGAAGAGCACACGTCTGAACTCCAGTC" was removed from the 3' end of Read 1 and the sequence "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" was removed from the 3' end of Read 2. Bases with quality score less than 20 at both ends were removed. Reads with 3bp adapter sequence at the 5' end were removed. Reads shorter than 30 bases were removed after adapter removal.

[0206] 2. Merge paired-end sequences into single-end sequences using Pear v0.9.6 software. Merge two-end reads with at least 20 overlapping bases, and discard the reads after merging if the length of the merged reads is shorter than 30 bases.

[0207] 3. Sequence data alignment:

[0208] The reference genome data used in the present application is from UCSC database (UCSC:hg19, http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / hg19.fa.gz).

[0209] 1) First, convert the hg19 to cytosine to thymine (CT) and adenine to guanine (GA) using Bismark software, and index the converted genome using Bowtie2 software respectively.

[0210] 2) Convert the pre-processed data to CT and GA as well.

[0211] 3) Align the converted sequences to the converted HG19 reference genome using Bowtie2 software respectively, with the minimum seed sequence length of 20, and no mismatch allowed for the seed sequence.

[0212] 4) Extract methylation information:

[0213] For each target region CpG site of hg19, according to the alignment results described above, obtain the methylation level corresponding to each site. The nucleotide number of the site involved in the present application corresponds to the nucleotide position number of hg19. The calculation of AMF, for each target region, calculates the sum of the number of reads methylated at each CpG site in the region and the ratio of the total number of reads in the region, as follows:

[0214]

[0215] Where i represents the CpG site in the target region, M is the total number of CpG sites in the region, N v,i represents the number of cytosine (C) observed at i site, and N T,i represents the number of thymine (T) observed at i site.

[0216] 4. Methylation data matrix:

[0217] 1) Combine the methylation AMF data of all samples into a data matrix, and make missing value processing for each region with a depth lower than 100.

[0218] 2) Remove sites with a missing value ratio higher than 10%.

[0219] 3) For missing values of data matrix, KNN algorithm is used for missing data imputation.

[0220] (IV) Screening of feature methylation region by five-fold cross-validation

[0221] (1) Constructing benign and malignant model of prostate cancer to screen methylation markers for prostate cancer identification. Leave out a portion of all samples as test data, and the rest as training set. In the training set, the best 10 methylation regions are screened to construct a logistic regression model, which is tested in the test set. Specifically: for the AMF data matrix of the training set, the SelectKBest function in the scikit-learn software package (0.24.2) of the python software (v3.6.9) is used to screen the top 10 best methylation regions, and then the LogisticRegression is used to construct a logistic regression model, the command line is:

[0222] from sklearn.linear_model import LogisticRegression

[0223] from sklearn.feature_selection import SelectKBest, f_classif

[0224] model = LogisticRegression()

[0225] feature_selector = SelectKBest(score_func=f_classif, k=10)

[0226] X_sel = feature_selector.fit_transform(X, Y)

[0227] model.fit(X_sel, Y)

[0228] Where X is the methylation feature matrix of the training set, and Y is the classification label of each sample.

[0229] The above steps are repeated five times to traverse all data, and the AUC of the test data is calculated each time and the top 10 methylation regions are recorded. After repeating five times, the average AUC of the five times is calculated. Figure 1). From all the best methylation regions obtained after repeating five times, the top 10 most frequently appearing ones were selected as the prostate cancer methylation marker group I (Table 1). The average methylation levels in the methylation marker regions of the prostate cancer and the prostate hyperplasia population in the data set are shown in Table 1. As can be seen from Table 1, the distribution of the average methylation levels in the methylation marker regions in the prostate cancer and the prostate hyperplasia population is significantly different, has a good discrimination effect, has a significant difference (P<0.05), and is a good methylation marker for prostate cancer.

[0230] Table 1 Prostate cancer methylation marker group I

[0231]

[0232] (2) Construct a prostate cancer indolent / highly invasive model to screen methylation markers that can distinguish between prostate cancer indolent and highly invasive patients. The prostate hyperplasia patients and the prostate cancer patients with Gleason score less than or equal to 7 are prostate cancer indolent patients (sample size = 40), and the prostate cancer patients with Gleason score higher than 7 are prostate cancer highly invasive patients (sample size = 17). Leave out a portion of all samples as test data, and the rest of the samples as the training set. In the training set, the best 10 methylation regions are screened to construct a logistic regression model, which is tested in the test set. Specifically, for the AMF data matrix of the training set, the SelectKBest function in the scikit-learn software package (0.24.2) of the python software (v3.6.9) is used to screen the top 10 best methylation regions, and then the LogisticRegression is used to construct a logistic regression model, the command line is:

[0233] from sklearn.linear_model import LogisticRegression

[0234] from sklearn.feature_selection import SelectKBest,f_classif

[0235] model = LogisticRegression()

[0236] feature_selector = SelectKBest(score_func = f_classif, k = 10)

[0237] X_sel = feature_selector.fit_transform(X, Y)

[0238] model.fit(X_sel, Y)

[0239] wherein X is the methylation feature matrix of the training set, and Y is the classification label of each sample.

[0240] The above steps are repeated five times to traverse all data, each time calculating the AUC of the test data and recording the top 10 methylation regions, and after five repetitions, the average AUC of the five times is calculated. Figure 2 From all the top methylation regions obtained after five repetitions, the top 10 regions with the highest frequency are selected as the prostate cancer methylation marker group II (Table 2). The average methylation levels of the methylation marker regions of the high-invasive prostate cancer patients and the indolent prostate cancer patients in the data set are shown in Table 2. As can be seen from Table 2, the average methylation levels in the methylation marker regions are significantly different between the high-invasive prostate cancer patients and the indolent prostate cancer patients, with a good discrimination effect and a significant difference (P<0.01), which is a good methylation marker for high-invasive prostate cancer.

[0241] Table 2 Prostate cancer methylation marker group II

[0242]

[0243]

[0244] Example 4 PCR probe design

[0245] The methylation markers common to the prostate cancer methylation marker group I and the prostate cancer methylation marker group II in Example 3 are taken as the final prostate cancer methylation marker group III (Table 3). The sequences of the 6 methylation markers obtained are SEQ ID NO: 1-6. The methylation levels of all CpG sites in each methylation marker can be obtained by the MethylTitan methylation sequencing method. The mean of the methylation levels of all CpG sites in each region, as well as the methylation level of a single CpG site, can be used as a marker for prostate cancer. As can be seen from Table 1 and Table 2, the average methylation levels in the methylation marker regions are significantly different between the prostate cancer and the benign prostatic hyperplasia population, and between the high-invasive prostate cancer patients and the indolent prostate cancer patients, with a good discrimination effect and a significant difference (P<0.05), which is a good methylation marker for prostate cancer and high-invasive prostate cancer.

[0246] Table 3 Prostate cancer methylation marker group III

[0247]

[0248]

[0249] The target region of prostate cancer methylation marker group III and the sequence within 100 bp upstream and downstream thereof were obtained. Then, the CpG island enrichment region was screened for primer probe design. According to the design principle of methylation-specific PCR primer and probe, the C in the CpG island was considered to be methylated, and the C in the non-CpG region was not methylated. After the simulation of bisulfite treatment sequence, the conventional primer and probe design tool was used for design and chemical synthesis. The methylation marker PCR region and specific primer and probe sequence information are shown in Table 4.

[0250] Table 4 Prostate cancer PCR methylation marker primer and probe sequence

[0251]

[0252] The amplification PCR reaction conditions are shown in Table 5, and β-actin is used as an internal reference. The PCR amplification is performed in a final volume of 20 μL. The relative methylation level is represented by 2 -ΔΔCt The data were analyzed as follows.

[0253] Table 5 PCR reaction conditions

[0254]

[0255] Example 5 Prostate cancer benign and malignant model

[0256] Urine samples of 29 patients with prostate hyperplasia and 46 patients with prostate cancer were collected, all the prostate cancer patients had Gleason score, and all the enrolled patients signed the informed consent form.

[0257] The urine EV DNA was extracted and subjected to bisulfite conversion according to the method described in Examples 1 and 2.

[0258] The methylation marker group III detected in Example 4 was used to build a machine learning model, and the modeling details are as follows: -ΔΔCt

[0259] A portion of all samples was set aside as test data, and the remaining samples were used as a training set. In the training set, the LogisticRegression of the scikit-learn software package (0.24.2) of the python software (v3.6.9) was used to build a logistic regression model, and the test was performed in the test set. Command line:

[0260] from sklearn.linear_model import LogisticRegression ​

[0261] model = LogisticRegression()

[0262] model.fit(X, Y)

[0263] Where X is the methylation level matrix of training set based on PCR detection, Y is the classification label of each sample.

[0264] Set the threshold value (such as 0.6), repeat the above steps five times to traverse all data, calculate the AUC and specificity and sensitivity of the test data each time, and calculate the average AUC (AUCavg), specificity and sensitivity of the five times after repeating five times. Figure 3

[0265] The final model can be:

[0266] Where x is the methylation level value of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score.

[0267] The average AUC of the model reaches 0.71. The distribution of the model score is shown in Figure 4 It can be seen that the score of the cancer sample is significantly higher than that of the benign control sample. The threshold value is set to 0.6, greater than the threshold value is judged as positive, less than the threshold value is judged as negative, then under the average specificity of 62.7%, the average sensitivity can reach 75.8%. Cancer patients and benign control samples can be better distinguished.

[0268] Example 6: Prostate cancer indolent / highly invasive differentiation model

[0269] Using the samples and methylation data in Example 5, the sample phenotype label is divided into prostate hyperplasia and prostate cancer Gleason score less than or equal to 7 indolent sample and prostate cancer Gleason score greater than 7 highly invasive sample.

[0270] Use the 2 -ΔΔCt value detected by the target target to build a machine learning model, and the modeling details are as follows:

[0271] Leave out a portion of all samples as test data, and the rest of the samples as training set. In the training set, use the LogisticRegression of the scikit-learn software package (0.24.2) of the python software (v3.6.9) to build a logistic regression model, and test in the test set. Command line:

[0272] from sklearn.linear_model import LogisticRegression​

[0273] model = LogisticRegression()

[0274] model.fit(X,Y)

[0275] Where X is the methylation level matrix of training set based on PCR detection, Y is the classification label of each sample.

[0276] Set threshold (such as 0.4), repeat the above steps five times to traverse all data, calculate the AUC and specificity and sensitivity of test data each time, and calculate the average AUC (AUCavg), specificity and sensitivity of five times after repeating five times. Figure 5

[0277] The final model can be:

[0278] Where x is the methylation level value of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score.

[0279] The average AUC of the model reaches 0.8. The score distribution of the model is shown in Figure 6 It can be seen that the score of the high-invasiveness sample is significantly higher than that of the indolent sample control. The threshold is set to 0.4, greater than the threshold is judged as positive, and less than the threshold is judged as negative, so that 65.0% of the sensitivity can be achieved under the specificity of 82.7%. The prostate cancer indolent patients and the prostate cancer high-invasiveness patients can be better distinguished.

[0280] Example 7: Methylation signature combined with serum PSA for diagnosis

[0281] The methylation signatures of Example 5 and Example 6 are combined with serum PSA values, respectively, and the serum PSA value is used as a feature, which is combined with the methylation level of the methylation marker to perform logistic regression modeling. The method is the same as in Example 5 or 6, and a methylation marker combined with serum PSA detection of prostate cancer benign and malignant model is obtained: threshold 0.47, specificity 66%, sensitivity 80.4%; methylation marker combined with serum PSA detection of prostate cancer indolent / high-invasiveness model: threshold 0.3, specificity 86.7%, sensitivity 66%. It can be seen that the combination of the obtained methylation biomarker group combined with the serum PSA value improves the ability to distinguish between prostate cancer benign and malignant and indolent prostate cancer and high-invasiveness prostate cancer (AUCavg=0.8, sensitivity=65.0%, specificity=82.7%) (AUCavg=0.8, sensitivity=66%, specificity=86.7%), which shows the complementarity of the present patent model and the existing serum tumor marker, and the combination of the two can achieve better diagnostic effect. Figure 7

[0282] ​​The above merely provides an example of the present application, but is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. A methylation marker for detecting prostate cancer malignancy and / or distinguishing between high aggressive and indolent prostate cancer, characterized in that, The methylation markers include any one or more of (a1)-(a5) as follows: (a1), one or more sub-regional sequences in MACF1, LINC01359, ADCY4, GAPLINC, C19orf25; (a2), a region of the sequence shown in (a1) after bisulfite, heavy metal bisulfite and / or deaminase treatment; (a3), a variant having at least 90% or more sequence identity to the sequence of (a1) or (a2) and having the same methylation sites as (a1) or (a2); (a4), a DNA methylation site in the region of the sequence shown in (a1) or (a2) or (a3); (a5), a DNA methylation abundance covered in the region of the sequence shown in (a1) or (a2) or (a3) or (a4).

2. The methylation marker of claim 1, wherein The Hg19 coordinates of the methylation marker MACF1 include chr1:39546570-39546970; preferably, the Hg19 coordinates of the methylation marker MACF1 include chr1:39546670-39546870; and / or, the Hg19 coordinates of the methylation marker LINC01359 include chr1:65468139-65468680; preferably, the Hg19 coordinates of the methylation marker LINC01359 include chr1:65468239-65468580; more preferably, the Hg19 coordinates of the methylation marker LINC01359 include chr1:65468239-65468439 and / or chr1:65468380-65468580; and / or, the Hg19 coordinates of the methylation marker ADCY4 include chr14:24804038-24804438; preferably, the Hg19 coordinates of the methylation marker ADCY4 include chr14:24804138-24804338; and / or, the Hg19 coordinates of the methylation marker GAPLINC include chr18:3498915-3499315; preferably, the Hg19 coordinates of the methylation marker GAPLINC include chr18:3499015-3499215; and / or, the Hg19 coordinates of the methylation marker C19orf25 include chr19:1466957-1467357; preferably, the Hg19 coordinates of the methylation marker C19orf25 include chr19:1467057-1467257.

3. The methylation marker of claim 1, wherein For detecting the benign and malignant prostate cancer, the (a1) further includes one or more sub-regional sequences in CITED4, PTGER4, RASGEF1A, TBC1D30; (a2), a region of the sequence shown in (a1) after bisulfite, heavy metal bisulfite and / or deaminase treatment; (a3), a variant having at least 90% or more sequence identity to the sequence of (a1) or (a2) and having the same methylation sites as (a1) or (a2); (a4), a DNA methylation site in the region of the sequence shown in (a1) or (a2) or (a3); (a5), a DNA methylation abundance covered in the region of the sequence shown in (a1) or (a2) or (a3) or (a4). Preferably, the Hg19 coordinates of the methylation marker CITED4 comprise chr1:41326857-41327257; preferably, the Hg19 coordinates of the methylation marker CITED4 comprise chr1:41326957-41327157. The Hg19 coordinates of the methylation marker PTGER4 comprise chr5:40681041-40681494; preferably, the Hg19 coordinates of the methylation marker PTGER4 comprise chr5:40681141-40681394. The Hg19 coordinates of the methylation marker RASGEF1A comprise chr10:43697709-43698258; preferably, the Hg19 coordinates of the methylation marker RASGEF1A comprise chr10:43697809-43698158. The Hg19 coordinates of the methylation marker TBC1D30 comprise chr12:65218480-65218999; preferably, the Hg19 coordinates of the methylation marker TBC1D30 comprise chr12:65218580-65218899.

4. The methylation marker of claim 1, wherein For distinguishing high-invasive prostate cancer from indolent prostate cancer, the (a1) further comprises EGFLAM, ASAP1, SLC43A3; Preferably, the Hg19 coordinates of the methylation marker EGFLAM comprise chr5:38257847-38258247; preferably, the Hg19 coordinates of the methylation marker EGFLAM comprise chr5:38257947-38258147. Preferably, the Hg19 coordinates of the methylation marker ASAP1 comprise chr8:131455169-131455569; preferably, the Hg19 coordinates of the methylation marker ASAP1 comprise chr8:131455269-131455469. Preferably, the Hg19 coordinates of the methylation marker SLC43A3 comprise chr11:57194409-57194809; preferably, the Hg19 coordinates of the methylation marker SLC43A3 comprise chr11:57194509-57194709. Preferably, the Hg19 coordinates of the methylation marker C19orf25 further comprise chr19:1466994-1467194.

5. Primer and / or probe for detecting the methylation marker according to any one of claims 1 to 4, characterized in that The primers are used for specific amplification of the nucleotide sequence where the methylation marker locates; the probes specifically capture the nucleotide sequence where the methylation marker locates.

6. A kit for detecting prostate cancer aggressiveness and / or discriminating between high- and low-invasive prostate cancer, characterized in that, The kit comprises reagents for specifically detecting the presence and / or abundance of the methylation state of the methylation marker according to any one of claims 1-4.

7. The kit of claim 6, wherein The kit comprises primers and / or probes of the methylation marker according to claim 5.

8. A model for detecting benign or malignant prostate cancer, characterized in that, The model for detecting the benignity or malignancy of prostate cancer comprises: a sequencing module for obtaining the methylation level of at least one of the methylation markers of any one of claims 1-4 in a sample to be tested from an individual; and a data comparison module for comparing the methylation level with a corresponding reference level, wherein the same or higher methylation level of one or more of the methylation markers relative to the corresponding reference level indicates that the individual has or is at risk of developing prostate cancer; preferably, the sequencing module further comprises detecting serum PSA; and the data comparison module further comprises comparing the serum PSA with a corresponding reference level.

9. A model for distinguishing between aggressive and indolent prostate cancer, characterized in that, The model for distinguishing high-invasive prostate cancer from indolent prostate cancer comprises: a sequencing module for obtaining the methylation level of at least one of the methylation markers of any one of claims 1-4 in a sample to be tested from an individual; and a data comparison module for comparing the methylation level with a corresponding reference level, wherein the same or higher methylation level of one or more of the methylation markers relative to the corresponding reference level indicates that the individual has or is at risk of developing high-invasive prostate cancer; preferably, the sequencing module further comprises detecting serum PSA; and the data comparison module further comprises comparing the serum PSA with a corresponding reference level.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that When the computer program is executed by a processor, the following steps are implemented: (a) obtaining the methylation level of at least one of the methylation markers of any one of claims 1-4 in a sample to be tested from an individual; and (b) comparing the methylation level with a corresponding reference level, respectively, wherein the same or higher methylation level of one or more of the methylation markers relative to the corresponding reference level indicates that the individual has or is at risk of developing prostate cancer.

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