Primer set, probe set, and kit for detecting prostate cancer biomarkers, and method

Abstract

A use of a reagent for detecting biomarkers in the preparation of a detection product, the detection product being used for detecting prostate cancer. The biomarkers comprise genes encoding enzymes involved in the oxidation of intermediate products of fatty acids and bile acids, genes encoding transcription factors, and genes encoding G protein-coupled receptors. By means of non-invasively collecting a urine sample and testing exosomes therein, the expression of prostate cancer-related RNA in the exosomes is analyzed. An early prediction model for prostate cancer is established by means of logistic regression, and the early prediction model converts the combined gene expression levels of the biomarkers into a prostate cancer risk level for a patient to be examined. The model has the features of high sensitivity and high specificity, and can ultimately be used for classification and screening of cancer and non-cancer samples from a single sample or multiple samples, and has the features of being non-invasive, accurate, and rapid.

Description

Primer set, probe set, kit and method for detecting prostate cancer biomarker TECHNICAL FIELD

[0001] The present application belongs to the field of biomolecular detection, and particularly relates to a primer set, probe set, kit and method for detecting a prostate cancer biomarker. BACKGROUND

[0002] Prostate cancer is one of the most common malignant tumors of the male urogenital system. In China, most of the initial cases of prostate cancer are in the middle and late stages of clinical diagnosis, and only 30% of the cases are clinically localized, resulting in poor overall prognosis of prostate cancer patients in China. The survival time of prostate cancer patients is closely related to the stage of malignant tumor at the time of clinical diagnosis, so early screening, early diagnosis and early treatment of high-risk groups of prostate cancer is an effective means to improve the overall survival rate of prostate cancer patients in China.

[0003] Extracellular vesicles (EVs) are vesicles with a phospholipid bilayer structure secreted by cells, which can regulate the biological characteristics of recipient cells through the biological macromolecules they carry, and participate in physiological and pathological processes of cells. Exosomes are an important subpopulation of EVs, which encapsulate proteins, mRNAs and microRNAs, etc. inside, and they are the medium of intercellular short-distance communication in health and disease, and participate in multiple processes such as tumor occurrence, development, invasion and metastasis. At the same time, compared with other body fluids, urine has the characteristics of safe sampling, large sample size and non-invasive, and research has found that urinary exosome RNA can be used as a marker to effectively detect urinary system cancer.

[0004] At present, prostate specific antigen (PSA) and digital rectal examination (DRE) are the main tools for screening prostate cancer in men, but both have inherent limitations. PSA is not specific to cancer, and PSA levels can be elevated in non-cancer conditions such as benign prostatic hyperplasia (BPH), prostatitis or lower urinary tract infection. When PSA is 4-10 ng / mL, the negative puncture rate is only about 20%; when PSA is 10-20 ng / ml, the negative puncture rate is only about 30%. DRE is overly dependent on the experience of doctors, and is an invasive and invasive examination. How to improve the negative puncture rate while avoiding over-diagnosis and treatment is a great challenge in the early screening of prostate cancer. SUMMARY

[0005] In order to solve the problems in the prior art, the application provides a primer set, a probe set, a kit and a method for detecting a prostate cancer biomarker, which obtains a urine sample by a non-invasive method, does not need a rectal examination before the urine sample is collected, and does not need to separate and obtain urine exfoliated cells from the urine sample. By jointly detecting the expression of prostate cancer related genes, the tumor detection rate can be obviously improved, and the method has the characteristics of non-invasiveness, precision and rapidness.

[0006] In order to achieve the above object, the technical scheme adopted by the application comprises:

[0007] The application discloses a first aspect of the application, which discloses an application of a reagent for detecting a biomarker in the preparation of a detection product, wherein the detection product is used for detecting prostate cancer.

[0008] The biomarker comprises a coding gene of an enzyme involved in the oxidation of fatty acid and bile acid intermediates, a coding gene of a transcription factor, and a coding gene of a G protein-coupled receptor.

[0009] Preferably, the enzyme involved in the oxidation of fatty acid and bile acid intermediates is a racemase, the transcription factor is a homeobox gene family transcription factor, and the G protein-coupled receptor is an olfactory receptor protein.

[0010] Preferably, the coding gene of the racemase is selected from AMACR, the coding gene of the homeobox gene family transcription factor is selected from HOXB13, and the coding gene of the olfactory receptor protein is selected from PSGR.

[0011] Preferably, the biomarker is composed of HOXB13, AMACR, FOXA1, MALAT1, PCA3 and PSGR.

[0012] Preferably, the biomarker further comprises FOXA1, PCA3, MALAT1, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7 and SLC45A3.

[0013] Preferably, the biomarker further comprises a reference gene, and the reference gene is selected from SPDEF or KLK3.

[0014] The application discloses a second aspect of the application, which discloses a primer set, wherein the primer set is used for detecting a biomarker, and the biomarker comprises a coding gene of an enzyme involved in the oxidation of fatty acid and bile acid intermediates, a coding gene of a transcription factor, and a coding gene of a G protein-coupled receptor.

[0015] The primer set comprises: primers for amplifying HOXB13, the upstream primer sequence of which is shown as SEQ ID NO: 1, and the downstream primer sequence of which is shown as SEQ ID NO: 2; primers for amplifying AMACR, the upstream primer sequence of which is shown as SEQ ID NO: 3, and the downstream primer sequence of which is shown as SEQ ID NO: 4; primers for amplifying FOXA1, the upstream primer sequence of which is shown as SEQ ID NO: 5, and the downstream primer sequence of which is shown as SEQ ID NO: 6; primers for amplifying MALAT1, the upstream primer sequence of which is shown as SEQ ID NO: 7, and the downstream primer sequence of which is shown as SEQ ID NO: 8; primers for amplifying PCA3, the upstream primer sequence of which is shown as SEQ ID NO: 9, and the downstream primer sequence of which is shown as SEQ ID NO: 10; primers for amplifying PSGR, the upstream primer sequence of which is shown as SEQ ID NO: 11, and the downstream primer sequence of which is shown as SEQ ID NO: 12; primers for amplifying PSMA, the upstream primer sequence of which is shown as SEQ ID NO: 13, and the downstream primer sequence of which is shown as SEQ ID NO: 14; primers for amplifying PSCA, the upstream primer sequence of which is shown as SEQ ID NO: 15, and the downstream primer sequence of which is shown as SEQ ID NO: 16; primers for amplifying ACP3, the upstream primer sequence of which is shown as SEQ ID NO: 17, and the downstream primer sequence of which is shown as SEQ ID NO: 18; primers for amplifying TRPM8, the upstream primer sequence of which is shown as SEQ ID NO: 19, and the downstream primer sequence of which is shown as SEQ ID NO: 20; primers for amplifying NKX3-1, the upstream primer sequence of which is shown as SEQ ID NO: 21, and the downstream primer sequence of which is shown as SEQ ID NO: 22; primers for amplifying ANO7, the upstream primer sequence of which is shown as SEQ ID NO: 23, and the downstream primer sequence of which is shown as SEQ ID NO: 24; primers for amplifying SLC45A3, the upstream primer sequence of which is shown as SEQ ID NO: 25, and the downstream primer sequence of which is shown as SEQ ID NO: 26.

[0016] Preferably, the primer set further comprises: primers for amplifying the reference gene SPDEF, the upstream primer sequence of which is shown as SEQ ID NO: 27, and the downstream primer sequence of which is shown as SEQ ID NO: 28; primers for amplifying the reference gene KLK3, the upstream primer sequence of which is shown as SEQ ID NO: 29, and the downstream primer sequence of which is shown as SEQ ID NO: 30.

[0017] The third aspect of the present application discloses a probe set for detecting biomarkers, wherein the biomarkers comprise coding genes of enzymes involved in oxidation of fatty acid and bile acid intermediate products, coding genes of transcription factors, and coding genes of G protein-coupled receptors; the probe set comprises:

[0018] The probe sequence for detecting HOXB13 is shown as SEQ ID NO: 31, the probe sequence for detecting AMACR is shown as SEQ ID NO: 32, the probe sequence for detecting FOXA1 is shown as SEQ ID NO: 33, the probe sequence for detecting MALAT1 is shown as SEQ ID NO: 34, the probe sequence for detecting PCA3 is shown as SEQ ID NO: 35, the probe sequence for detecting PSGR is shown as SEQ ID NO: 36, the probe sequence for detecting PSMA is shown as SEQ ID NO: 37, the probe sequence for detecting PSCA is shown as SEQ ID NO: 38, the probe sequence for detecting ACP3 is shown as SEQ ID NO: 39, the probe sequence for detecting TRPM8 is shown as SEQ ID NO: 40, the probe sequence for detecting NKX3-1 is shown as SEQ ID NO: 41, the probe sequence for detecting ANO7 is shown as SEQ ID NO: 42, the probe sequence for detecting SLC45A3 is shown as SEQ ID NO: 43, the probe sequence for detecting SPDEF is shown as SEQ ID NO: 44, and the probe sequence for detecting KLK3 is shown as SEQ ID NO: 45.

[0019] Preferably, the probes of the probe set are labeled with fluorescent reporter groups selected from FAM, HEX, ROX, VIC, CY5, 5-TAMRA, TET, CY3 or JOE.

[0020] Preferably, the fluorescent reporter groups of the probes shown as SEQ ID NO: 31-SEQ ID NO: 33 are FAM; the fluorescent reporter groups of the probes shown as SEQ ID NO: 34-SEQ ID NO: 36 are HEX; and the fluorescent reporter group of the probe shown as SEQ ID NO: 44 is CY5.

[0021] Preferably, the 3' end of the probes of the probe set further has a fluorescent quenching group selected from BHQ1 or BHQ2.

[0022] Preferably, the fluorescent quenching group of the probes shown as SEQ ID NO: 31-SEQ ID NO: 36 is BHQ1; and the fluorescent quenching group of the probe shown as SEQ ID NO: 44 is BHQ2.

[0023] Preferably, the fluorescent reporter group of SEQ ID NO: 31-33 is FAM, and the fluorescent quencher group is BHQ1; the fluorescent reporter group of SEQ ID NO: 34-36 is HEX, and the fluorescent quencher group is BHQ1; the fluorescent reporter group of SEQ ID NO: 44 is CY5, and the fluorescent quencher group is BHQ2.

[0024] The fourth aspect of the present application discloses a kit for detecting biomarkers, wherein the biomarkers include the coding genes of enzymes involved in the oxidation of fatty acid and bile acid intermediates, the coding genes of transcription factors, and the coding genes of G protein-coupled receptors; the kit includes the primer set and the probe set.

[0025] Preferably, the kit includes reagents for detecting urine exosomes and reagents for extracting urine exosomes.

[0026] Preferably, the kit includes: an RT-qPCR reaction solution containing primers and probes that specifically recognize the RNA sequences of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, and SPDEF; an enzyme mixture containing reverse transcriptase, DNA polymerase, and UDG enzyme; positive quality control; and negative quality control.

[0027] Preferably, the positive quality control is an artificially synthesized pseudovirus particle containing the gene fragments to be detected, and the negative quality control is deionized water without DNase and RNase.

[0028] Preferably, the final concentration of the primers in the RT-qPCR reaction system is 0.2-0.4 μM, and the final concentration of the probes in the RT-qPCR reaction system is 0.1-0.3 μM.

[0029] Preferably, the RT-qPCR reaction system further includes Tris buffer, magnesium ions, dA / G / C / UTPs; the final concentration of magnesium ions is 5 mM, the final concentration of dATP is 0.4 mM, the final concentration of dCTP is 0.4 mM, the final concentration of dGTP is 0.4 mM, and the final concentration of dUTP is 0.8 mM.

[0030] The fifth aspect of the present application discloses a non-diagnostic method for prostate cancer based on a combination of urine exosome biomarkers, which is used for converting the marker combination gene expression level into the prostate cancer risk level of a patient to be tested, and the model algorithm is: output value = {Ct(Target 1)-Ct(internal reference)}*a+{Ct(Target 2)-Ct(internal reference)}*b+{Ct(Target 3)-Ct(internal reference)}*c+{Ct(Target 4)-Ct(internal reference)}*d+…+{Ct(Target N)-Ct(internal reference)}*n+z.

[0031] In the formula, a, b, c…n are coefficients, z is a constant, the coefficients are between-1 and 1, Ct(internal reference) is the Ct value of the internal reference gene, and Ct(Target 1)…Ct(Target N) are the Ct values of the genes in the biomarkers according to any one of claims 1-6.

[0032] Preferably, the kit is used to detect the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes, then the output value is calculated by the model and compared with the positive judgment value, and the risk level of the subject suffering from prostate cancer is analyzed.

[0033] The sixth aspect of the present application discloses a device, which comprises:

[0034] A detection unit is used to detect the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes.

[0035] A calculation unit is used to calculate the output value of the model algorithm according to claim 20.

[0036] An analysis unit is used to compare the output value of the calculation unit with the positive judgment value, and analyze the risk level of the subject suffering from prostate cancer.

[0037] The seventh aspect of the present application discloses a computer readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method.

[0038] The present application has the following beneficial effects:

[0039] (1) The output value of the HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF biomarker combination provided by the application has a statistically significant difference in the urine exosomes of the prostate cancer patients and the control group samples. The marker combination is used to construct a prediction model, the model is trained by a training set, and the model is optimized by a test validation set, and finally an early diagnosis prediction model for prostate cancer is obtained;

[0040] (2) The exosome marker combination and the prediction model provided by the application can be used for early detection of the clinical samples of the patients suspected to have prostate cancer or recommended to have prostate biopsy in the clinic with PSA of 4-20 ng / mL, especially 4-10 ng / mL, wherein the best combination marker is: HOXB13, AMACR, FOXA1, MALAT1, PCA3 and PSGR, AUC=0.78, sensitivity 75%, specificity 71%, and has good clinical diagnostic value.

[0041] (3) The application collects urine samples non-invasively and detects the exosomes thereof, analyzes the expression of prostate cancer related RNA in the exosomes, and establishes an early prediction model for prostate cancer by logistic regression. The prostate cancer prediction model has the characteristics of high sensitivity and high specificity, and finally the model can be used for classification and screening of cancer and non-cancer samples for a single sample or multiple samples. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1. NTA software rapidly generates high-resolution particle size distribution of individual particles and count of observed vesicle particles.

[0043] Figure 2. Exosome morphology under transmission electron microscope.

[0044] Figure 3. AUC value of AMACR+HOXB13 under different exosome purification methods.

[0045] Figure 4. AUC value of AMACR+PSGR under different exosome purification methods.

[0046] Figure 5. AUC value of HOXB13+PSGR under different exosome purification methods.

[0047] Figure 6. AUC value of AMACR+HOXB13+PSGR under different exosome purification methods.

[0048] Figure 7. ROC curve of training set with true negative rate (sensitivity) as the vertical coordinate and false negative rate (1-specificity) as the horizontal coordinate, AUC value of training set of different exosome marker combinations.

[0049] Figure 7-1. The AUC of the marker combination AMACR+HOXB13+PSGR is 0.72;

[0050] Figure 7-2. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1 is 0.74;

[0051] Figure 7-3. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1 is 0.75;

[0052] Figure 7-4. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3 is 0.78;

[0053] Figure 7-5. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3+PSMA is 0.78;

[0054] Figure 7-6. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3+PSMA+PSCA is 0.78;

[0055] Figure 7-7. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3 combined with age+tPSA+fPSA is 0.83;

[0056] Figure 8. ROC curve of the training set plotted with true negative rate (sensitivity) as the vertical coordinate and false negative rate (1-specificity) as the horizontal coordinate, AUC values of the validation set for different exosome marker combinations.

[0057] Figure 8-1. The AUC of the marker combination AMACR+HOXB13+PSGR is 0.73;

[0058] Figure 8-2. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1 is 0.75;

[0059] Figure 8-3. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1 is 0.75;

[0060] Figure 8-4. The AUC of the marker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3 is 0.77;

[0061] Figure 8-5. AUC of 0.77 for the marker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 + PSMA.

[0062] Figure 8-6. AUC of 0.77 for the marker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 + PSMA + PSCA.

[0063] Figure 8-7. AUC of 0.84 for the marker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 + age + tPSA + fPSA.

[0064] Figure 9. PCR curve plots of reaction combinations amplifying prostate cancer positive samples

[0065] Figure 9-1. Amplification curve plot of reaction combination 1 (HOXB13 + PSGR + PSCA + ACP3) detecting prostate cancer positive samples.

[0066] Figure 9-2. Amplification curve plot of reaction combination 2 (AMACR + PCA3 + TRPM8 + NKX3-1) detecting prostate cancer positive samples.

[0067] Figure 9-3. Amplification curve plot of reaction combination 3 (FOXA1 + MALAT1 + PSMA + SPDEF) detecting prostate cancer positive samples.

[0068] Figure 9-4. Amplification curve plot of reaction combination 4 (ANO7 + SLC45A3 + KLK3) detecting prostate cancer positive samples.

[0069] Figure 10. PCR curve plots of reaction combinations amplifying prostate cancer negative samples

[0070] Figure 10-1. Amplification curve plot of reaction combination 1 HOXB13 + PSGR + PSCA + ACP3 detecting prostate cancer negative samples.

[0071] Figure 10-2. Amplification curve plot of reaction combination 2 AMACR + PCA3 + TRPM8 + NKX3-1 detecting prostate cancer negative samples.

[0072] Figure 10-3. Amplification curve plot of reaction combination 3 FOXA1 + MALAT1 + PSMA + SPDEF detecting prostate cancer negative samples.

[0073] Figure 10-4. Amplification curve plot of reaction combination 4 ANO7 + SLC45A3 + KLK3 detecting prostate cancer negative samples. DETAILED DESCRIPTION

[0074] The application will be further described in the following by way of examples without limiting the application to the examples described. The experimental methods used in the following examples are conventional methods, unless otherwise specified, and are performed according to the techniques or conditions described in the literature in the art or according to the instructions of the products. The materials, reagents, etc. used in the following examples, unless otherwise specified, can be obtained commercially.

[0075] The application provides a urine exosome marker combination and kit for early detection of prostate cancer, and the specific implementation manner comprises the following steps:

[0076] (1) Obtain urine samples from different subject groups. The different subject groups include patients diagnosed with prostate cancer and patients without prostate cancer. The urine sample is the first urine discharged from the bladder on the same day, also known as "morning urine", and the volume collected from the beginning of urination is 30-50 mL.

[0077] (2) Extraction and characteristics of exosomes in urine samples. In particular, the exosomes are extracted in cooperation with the EXODUS exosome purification system (patent publication number: CN114616054A) of the company. The obtained exosomes have the characteristics of high purity, high yield, and complete morphology.

[0078] For those skilled in the art, the extraction method of exosomes is not limited to the above operation steps. Suitable methods in the prior art such as ultracentrifugation, gradient density centrifugation, ultrafiltration centrifugation, magnetic bead immunization, etc. and the use of other commercial exosome precipitants are feasible. Those skilled in the art can predict that the obtained exosomes should have similar characteristics and there should be no difference due to the change of the extraction method.

[0079] In order to identify the characteristics of exosomes in the urine of prostate cancer patients and non-prostate cancer patients, the application uses Nanoparticle Tracking Analysis (NTA) to obtain the nanoparticle size distribution and particle concentration in liquid suspension by using the characteristics of light scattering and Brownian motion, as shown in Figure 1. Transmission electron microscope (TEM) is used to analyze the morphological characteristics of the extracted exosomes, as shown in Figure 2.

[0080] (3) Exosome RNA extraction

[0081] The present application extracts total nucleic acid of exosomes by using magnetic bead method nucleic acid extraction reagent, and controls the quality of extracted RNA. The quality of extracted nucleic acid can be evaluated by comparing the ratio of absorbance values of nucleic acid sample at 260 nm and 280 nm (A260 / A280). A260 / A280 is preferably 1.8-2.2, and more preferably 2.0.

[0082] (4) Detection of nucleic acid biomarkers

[0083] The present application measures the expression level of biomarkers in the above extracted RNA by using real-time fluorescent quantitative PCR method. The biomarkers at least include HOXB13, AMACR, FOXA1, MALAT1, PCA3 and PSGR genes, and SPDEF and / or KLK3 genes.

[0084] (5) Analysis of biomarker RNA expression level

[0085] In the method provided herein, the expression level of biomarker RNA is determined by real-time fluorescent quantitative PCR analysis, and the negative reaction is detected by the accumulation of fluorescent signal. Ct value is defined as the number of cycles required for the fluorescent signal to exceed the threshold value. Ct value is inversely proportional to the amount of nucleic acid in the sample, i.e. the smaller the Ct value, the greater the amount of nucleic acid in the sample.

[0086] In the method provided herein, the genes whose expression levels are detected for calculating the relative expression level are collectively referred to as reference genes, which are used to normalize the signal value of the detected genes to control the differences between the amount of extracted exosomes, the performance of reagent components, and the performance of fluorescent quantitative PCR instrument between samples. The reference gene is usually present in urine exosomes, and the present application uses SPDEF or KLK3 as the internal reference gene for normalizing the markers HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7 and SLC45A3. The relative expression level analysis or normalization is completed by subtracting the Ct value of the reference gene (SPDEF or KLK3) from the Ct value of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7 and SLC45A3, respectively, and the result is referred to as ΔCt, for example:

[0087] ΔCt(HOXB13) = Ct(HOXB13) - Ct(SPDEF) or ΔCt(HOXB13) = Ct(HOXB13) - Ct(KLK3)

[0088] (6) Construction of prostate cancer prediction model

[0089] Receiver operating characteristic (ROC curve) is a widely used tool to assess the recognition and diagnostic capability of a biomarker or a combination of biomarkers. Area under the curve (AUC) is established to evaluate the diagnostic value of each biomarker or marker combination. The biomarker or marker combination with the highest diagnostic value has an AUC value greater than 0.6, 0.7 or 0.8. Preferably, the individual marker or marker combination performance has an AUC value greater than 0.7. As shown in Figure 3, when the score created by logistic regression analysis of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR combination together is determined, the AUC = 0.78, and the multi-target combination also has the highest diagnostic accuracy.

[0090] Further, the normalized expression levels of the above-mentioned HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR combination with higher diagnostic value are established by data analysis to establish a mathematical expression, and by combining the necessary sensitivity and specificity of clinical application, a prostate cancer early diagnosis model is constructed. The algorithm of the model is:

[0091] Output value = {Ct(HOXB13)-Ct(SPDEF)}x a + {Ct(AMACR)-Ct(SPDEF)}x b + {Ct(FOXA1)+Ct(SPDEF)}x c + {Ct(MALAT1)+Ct(SPDEF)}x d + {Ct(PCA3)+Ct(SPDEF)}x e + {Ct(PSGR)+Ct(SPDEF)}x f + z.

[0092] In the formula, a, b, c, d are coefficients, and z is a constant, which can be determined by fitting the output value of the equation to the existing data set by logistic regression or linear regression. The coefficient a is between -1 and 1, preferably the coefficient a = -0.371; the coefficient b is between -1 and 1, preferably the coefficient b = 0.518; the coefficient c is between -1 and 1, preferably the coefficient c = -0.586; the coefficient d is between -1 and 1, preferably the coefficient d = -0.353; the coefficient e is between -1 and 1, preferably the coefficient d = 0.61; the coefficient f is between -1 and 1, preferably the coefficient d = -0.514; and the constant z is between -1 and 1, preferably the constant z = 1.852.

[0093] The output value determined by the ROC curve is used as a positive judgment value for distinguishing the risk of the subject suffering from prostate cancer, and a higher positive judgment value indicates a higher risk of prostate cancer, and a lower positive judgment value indicates a lower risk of prostate cancer. The prostate cancer risk prompt obtained by the method helps doctors make decisions on the next diagnosis of patients, serves as a supplement and auxiliary to existing diagnostic methods, and is used as a reference for clinicians.

[0094] The application further discloses a use method of the kit in a kit for early diagnosis of prostate cancer, and the use method comprises the following steps:

[0095] (1) collecting a random urine sample of a suspected prostate cancer patient, collecting 30-50 mL of urine sample from the first urine;

[0096] (2) separating and purifying exosomes from the urine sample;

[0097] (3) extracting RNA in the exosomes;

[0098] (4) performing fluorescence quantitative RT-qPCR amplification on the nucleic acid by using the primer and probe composition and the kit of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF genes of the application;

[0099] (5) obtaining the expression value of the corresponding biomarker, and evaluating the risk of the subject suffering from cancer by using the prostate cancer prediction model of the application.

[0100] The application discloses a device, which comprises: a detection unit for detecting the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes; a calculation unit for calculating the output value of the model algorithm as described in claim 20; and an analysis unit for comparing the output value of the calculation unit with a positive judgment value and analyzing the risk level of the subject suffering from prostate cancer.

[0101] The application discloses a computer readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method.

[0102] Some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for executing the methods illustrated in the flowcharts. In some embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the methods of some embodiments of the present disclosure are performed.

[0103] It should be noted that the computer readable medium recorded with the computer program of some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In some embodiments of the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that can send, propagate or transfer a program for use by or in connection with an instruction execution system, device or apparatus. The program code contained on the computer readable medium can be transmitted in any suitable medium, including but not limited to, a wire, an optical fiber, an RF (radio frequency) or the like, or any suitable combination of the above.

[0104] In some embodiments, the client, server, can communicate using any known or future developed network protocols, such as the HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current or future developed networks.

[0105] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any one or combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network ("LAN") or a wide area network ("WAN"), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0106] Example 1: Selection of optimal exosome purification method

[0107] The yield and purity of exosome purification affect the efficiency of downstream nucleic acid extraction and gene detection, which in turn affects the effectiveness of diagnostic marker model construction, so it is crucial to select the appropriate exosome purification method. In this embodiment, 20 cases of benign disease patients and 20 cases of prostate cancer patients' urine exosomes were purified by ultrafiltration centrifugation method, PEG precipitation method and EXODUS exosome purification system respectively. The exosomes were subjected to nucleic acid extraction and fluorescent RT-qPCR detection, and the models were constructed respectively. The AUC values of the models were compared to confirm the feasibility and advantages of EXODUS exosome purification system in the diagnosis of prostate cancer. The specific steps are as follows:

[0108] (1) Sample collection: Prepare 20 cases of benign disease patients and 20 cases of prostate cancer patients' urine samples, collect morning urine, and collect 50 mL of urine from the beginning of urination.

[0109] (2) Sample pretreatment: centrifuge at 2000g for 20 min at 4°C, reserve the supernatant, filter through 0.8 μM or 0.22 μM filter membrane, and then divide into 3 equal parts, each part being 15 mL.

[0110] (3) The samples were purified by EXODUS exosome purification system, ultrafiltration centrifugation method and PEG precipitation method respectively, and the purified volume was 15 mL, and the final recovery volume of exosomes was 300 μL.

[0111] (4) Nucleic acid extraction was performed using Qiagen miRNeasy Mini Kit (cat. no. 217004).

[0112] (5) The expression values of AMACR, HOXB13, PSGR and SPDEF genes were detected by RT-qPCR method. The primer pair for amplifying AMACR gene was SEQ ID NO: 3, SEQ ID NO: 4, and the probe was SEQ ID NO: 32; the primer pair for amplifying HOXB13 gene was SEQ ID NO: 1, SEQ ID NO: 2, and the probe was SEQ ID NO: 31; the primer pair for amplifying PSGR gene was SEQ ID NO: 11, SEQ ID NO: 12, and the probe was SEQ ID NO: 36; the primer for amplifying SPDEF gene was SEQ ID NO: 27, SEQ ID NO: 28, and the probe was SEQ ID NO: 44. The expression values of AMACR, HOXB13, PSGR and SPDEF genes under different exosome purification methods were as follows:

[0113] Table 1 Average values of AMACR, HOXB13, PSGR and SPDEF genes under different exosome purification methods

[0114] (6) Normalization of AMACR, HOXB13, PSGR expression level analysis: the Ct values of AMACR, HOXB13, PSGR were subtracted by the Ct values of reference genes SPDEF or KLK3 respectively to obtain ΔCt values.

[0115] (7) Establishment of ROC curve and analysis of area under ROC curve (AUC): the mathematical expression of AMACR, HOXB13, PSGR multi-gene combination was established by SPSS binary logistic regression method, and the output value was combined with the pathological diagnosis results to establish ROC and analyze the area under the curve (AUC), which was used to evaluate the AUC values of AMACR+HOXB13 combination, AMACR+PSGR combination, HOXB13+PSGR combination and AMACR+HOXB13+PSGR combination, and the results were as follows:

[0116] Table 2 AUC values of different marker combinations under different exosome purification methods

[0117] From the results of Table 1, it can be seen that the EXODUS method enriches exosome particles with higher concentration, smaller Ct values of AMACR, HOXB13, PSGR and SPDEF gene amplification, indicating that the enriched nucleic acid containing AMACR, HOXB13, PSGR and SPDEF genes has higher content, and thus it is easier to capture the tumor-derived gene detection signal, thus producing a higher AUC value (Table 2, Figures 3-6), so the EXODUS exosome purification system is more suitable for the purification of urine exosomes in this project.

[0118] Example 2 Prostate cancer detection kit amplifies urine exosome biomarkers

[0119] The target combination screening of 13 tumor markers of prostate cancer detection kit exosomes is carried out, and a PCR reaction solution for RT-qPCR amplification is included, and the PCR reaction solution includes a set of primer and probe combination for detecting 13 markers of prostate cancer urine exosomes, and the primer and probe combination includes: the primer pair (SEQ ID NO: 1, SEQ ID NO: 2) for amplifying HOXB13 gene with a concentration of 0.3 μM, and the probe (SEQ ID NO: 31) for amplifying HOXB13 gene with a concentration of 0.1 μM; the primer pair (SEQ ID NO: 3, SEQ ID NO: 4) for amplifying AMACR gene with a concentration of 0.4 μM, and the probe (SEQ ID NO: 32) for amplifying AMACR gene with a concentration of 0.2 μM; the primer pair (SEQ ID NO: 5, SEQ ID NO: 6) for amplifying FOXA1 gene with a concentration of 0.2 μM, and the probe (SEQ ID NO: 33) for amplifying FOXA1 gene with a concentration of 0.1 μM; the primer pair (SEQ ID NO: 7, SEQ ID NO: 8) for amplifying MALAT1 gene with a concentration of 0.2 μM, and the probe (SEQ ID NO: 34) for amplifying FOXA1 gene with a concentration of 0.1 μM; the primer pair (SEQ ID NO: 9, SEQ ID NO: 10) for amplifying PCA3 gene with a concentration of 0.4 μM, and the probe (SEQ ID NO: 35) for amplifying PCA3 gene with a concentration of 0.2 μM; the primer pair (SEQ ID NO: 11, SEQ ID NO: 12) for amplifying PSGR gene with a concentration of 0.3 μM, and the probe (SEQ ID NO: 36) for amplifying PSGR gene with a concentration of 0.15 μM; the primer pair (SEQ ID NO: 13, SEQ ID NO: 14) for amplifying PSMA gene with a concentration of 0.3 μM, and the probe (SEQ ID NO: 37) for amplifying PSMA gene with a concentration of 0.15 μM; the primer pair (SEQ ID NO: 15, SEQ ID NO: 16) for amplifying PSCA gene with a concentration of 0.2 μM, and the probe (SEQ ID NO: 38) for amplifying PSCA gene with a concentration of 0.1 μM; the primer pair (SEQ ID NO: 17, SEQ ID NO: 18) for amplifying ACP3 gene with a concentration of 0.4 μM, and the probe (SEQ ID NO: 39) for amplifying ACP3 gene with a concentration of 0.2 μM; the primer pair (SEQ ID NO: 19, SEQ ID NO: 20) for amplifying TRPM8 gene with a concentration of 0.3 μM, and the probe (SEQ ID NO: 40) for amplifying TRPM8 gene with a concentration of 0.15 μM;3 μM of primer pair for amplifying NKX3-1 gene (SEQ ID NO: 21, SEQ ID NO: 22), 0.15 μM of probe for amplifying NKX3-1 gene (SEQ ID NO: 41) at a concentration of 0.15 μM; 0.3 μM of primer pair for amplifying AN07 gene (SEQ ID NO: 23, SEQ ID NO: 24) at a concentration of 0.15 μM, 0.15 μM of probe for amplifying AN07 gene (SEQ ID NO: 42); 0.4 μM of primer pair for amplifying SLC45A3 gene (SEQ ID NO: 25, SEQ ID NO: 26) at a concentration of 0.2 μM, 0.2 μM of probe for amplifying SLC45A3 gene (SEQ ID NO: 43); 0.4 μM of primer pair for amplifying SPDEF gene (SEQ ID NO: 27, SEQ ID NO: 28) at a concentration of 0.2 μM, 0.2 μM of probe for amplifying SPDEF gene (SEQ ID NO: 44); 0.4 μM of primer pair for amplifying KLK3 gene (SEQ ID NO: 29, SEQ ID NO: 30) at a concentration of 0.2 μM, 0.2 μM of probe for amplifying KLK3 gene (SEQ ID NO: 45).

[0120] In the present embodiment, the fluorescent group of the probe represented by SEQ ID NO: 31 is FAM, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 32 is FAM, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 33 is FAM, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 34 is HEX, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 35 is HEX, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 36 is HEX, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 37 is ROX, and the quenching group is BHQ2; the fluorescent group of the probe represented by SEQ ID NO: 38 is ROX, and the quenching group is BHQ2; the fluorescent group of the probe represented by SEQ ID NO: 39 is CY5, and the quenching group is BHQ2; the fluorescent group of the probe represented by SEQ ID NO: 40 is ROX, and the quenching group is BHQ2; the fluorescent group of the probe represented by SEQ ID NO: 41 is CY5, and the quenching group is BHQ2; the fluorescent group of the probe represented by SEQ ID NO: 42 is FAM, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 43 is HEX, and the quenching group is BHQ1; the fluorescent group of the probe represented by SEQ ID NO: 44 is CY5, and the quenching group is BHQ2; the fluorescent group of the probe represented by SEQ ID NO: 45 is ROX, and the quenching group is BHQ2. The PCR amplification system is shown in Table 3.

[0121] Table 3 PCR amplification reaction system

[0122] The PCR reaction solution is composed of the mixture of the above-mentioned primer and probe combination, and the PCR buffer solution containing the magnesium ions, dA / G / C / UTPs necessary for the PCR reaction. In addition, the kit also includes an enzyme mixture composed of reverse transcriptase, DNA polymerase and UNG enzyme.

[0123] When preparing the PCR amplification reaction solution, the formula is as follows: 18.5 microliters of the PCR reaction solution containing the above-mentioned primer and probe combination per test, 1.5 microliters of the enzyme mixture per test, 10 microliters of the sample to be tested per test, and the total volume is 30 microliters.

[0124] After the reagent is prepared, it is placed in an ABI 7500 fluorescent quantitative PCR instrument for reaction, and the PCR reaction conditions are set according to Table 4.

[0125] Table 4 PCR amplification reaction conditions

[0126] After the PCR reaction, the Ct value of each RNA marker was analyzed using the ABI 7500 matching software. FIGS. 9-1 to 9-4 are amplification curve graphs of reaction combination 1, reaction combination 2, reaction combination 3, and reaction combination 4 on prostate cancer positive samples, respectively; FIGS. 10-1 to 10-4 are amplification curve graphs of reaction combination 1, reaction combination 2, reaction combination 3, and reaction combination 4 on prostate cancer negative samples, respectively.

[0127] Example 3 Screening of urine exosome marker combination for prostate cancer detection and model construction

[0128] The present embodiment provides a method for screening of a urine exosome marker combination for prostate cancer detection and construction of a prediction model, and statistical verification of patient samples.

[0129] In the present study, samples were used if the following criteria were met:

[0130] Inclusion criteria: 1) Biological male aged 50 years or older; 2) Planned or already in our hospital for serum PSA detection; 3) Planned for prostate puncture / tissue biopsy; 4) Voluntarily participate in the present trial, understand the research procedure and have signed the informed consent form.

[0131] Exclusion criteria: 1) History of prostate biopsy; 2) History of prostate cancer; 3) Use of drugs or hormones known to affect serum prostate specific antigen levels within 3-6 months of study entry; 4) Patients undergoing antibiotic treatment during the acute stage of prostatitis; 5) History of invasive treatment for benign prostatic hypertrophy (benign prostatic hyperplasia) or lower urinary tract symptoms within 6 months of study entry; 6) No known hepatitis (all types) and / or HIV record in the patient's medical history.

[0132] According to the above criteria, a total of 280 patients were recruited in the present study as training set samples, and the sample statistical information is as follows:

[0133] The screening and prediction model construction method of the above biomarker combination is as follows:

[0134] (1) The above 280 samples were subjected to exosome enrichment and purification using the method described in the patent application number (CN202280000660). Exosome biomarker extraction and RT-qPCR detection were performed using techniques well known in the art to obtain the detection Ct value of the exosome biomarker. The biomarker at least comprises HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR gene and SPDEF gene.

[0135] (2) Analysis of biomarker RNA expression level, and normalization processing of gene expression level.

[0136] (3) The diagnostic performance of different markers or marker combination models was evaluated using logistic regression, and the results are shown in Table 5:

[0137] Table 5 Performance of different markers or marker combinations

[0138] In the logistic regression analysis, the prediction model constructed by 6 markers HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and the internal reference SPDEF, as shown in Figure 7, has the maximum AUC of 0.78, and the sensitivity and specificity for diagnosing prostate cancer are 75% and 71%, respectively.

[0139] The present application obtains a urine sample by a non-invasive method, without the need for rectal examination before collection, and without the need for separating and obtaining a cell precipitate from the urine sample. By combined detection of prostate cancer-related gene expression detection, the early tumor detection rate can be significantly improved, and the prostate cancer exosome detection has the characteristics of non-invasiveness, precision and speed.

[0140] Example 4 Application of prostate cancer prediction model in validation set

[0141] The present embodiment provides a use method of a prostate cancer early prediction model based on urine exosomes, which can also be used to verify the accuracy of the prostate cancer prediction model based on urine provided in Example 3.

[0142] The above kit, method and logistic regression formula were selected to perform exosome nucleic acid detection analysis on 216 urine samples of suspected prostate cancer from outpatients of the Department of Urology of Wuhan Tongji Hospital.

[0143] According to the results of clinical biopsy and pathological diagnosis of the hospital, 97 cases of prostate cancer urine specimens and 119 cases of non-prostate cancer urine specimens were obtained. According to the obtained logistic regression calculation formula, the comprehensive score was calculated using the set proprietary algorithm to evaluate the risk of prostate cancer in 216 patients. The detection results of the validation set are shown in Tables 6 and 7.

[0144] Table 6 Performance of different marker combinations in validation set

[0145] Table 7 Diagnostic performance of target combination HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and internal reference SPDEF compared with pathological results (positive judgment value 0.4)

[0146] Sensitivity = 71 / 97 = 73.20%;

[0147] Specificity = 84 / 119 = 70.59%;

[0148] Total coincidence rate = (71+84) / 216 = 71.76%;

[0149] As shown in Figure 8, the AUC of the exosome RNA marker combination HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and the internal reference SPDEF is 0.77, and the exosome marker combination has better diagnostic performance.

[0150] The accuracy of the early prostate cancer prediction model is verified by using clinical samples, and the results show that the data basically conforms to the previous data, which can meet the requirements of clinical detection, and can increase the detection rate of early cancer for patients.

[0151] In addition, the marker combination HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and the internal reference SPDEF can also be combined with known clinical information such as patient age, serum total PSA (tPSA) detection value, serum free PSA (fPSA) detection value, rectal examination (DRE) to construct a comprehensive prostate cancer prediction model, and the AUC of the training set and the validation set is 0.83 and 0.84, respectively, to further improve the accuracy of the diagnostic results.

[0152] Exosome samples are convenient, fast, minimal risk and low cost, which can reduce the pain of patients; exosomes can be used to monitor cancer treatment results or monitor cancer recurrence, increase the detection rate of prostate cancer in high-risk populations, and find early prostate cancer; reduce the mortality rate of prostate cancer in the screening population, and do not affect the quality of life of the screening population.

[0153] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. Use of a reagent for detecting a biomarker in the manufacture of a test product, characterized in that, The detection product is used for detecting prostate cancer; the biomarker comprises a coding gene of an enzyme involved in oxidation of fatty acid and bile acid intermediate product, a coding gene of a transcription factor, and a coding gene of a G protein-coupled receptor.

2. Use according to claim 1, characterized in that, The enzyme involved in oxidation of fatty acid and bile acid intermediate product is a racemase, the transcription factor is a transcription factor of a homeobox gene family, and the G protein-coupled receptor is an olfactory receptor protein.

3. Use according to claim 2, characterized in that, The coding gene of the racemase is selected from AMACR, the coding gene of the transcription factor of the homeobox gene family is selected from HOXB13, and the coding gene of the olfactory receptor protein is selected from PSGR.

4. Use according to claim 3, characterized in that, The biomarker consists of HOXB13, AMACR, FOXA1, MALAT1, PCA3, and PSGR.

5. Use according to claim 3, characterized in that, The biomarker further comprises FOXA1, PCA3, MALAT1, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7, and SLC45A3.

6. Use according to any one of claims 1 to 5, characterized in that, The biomarker further comprises a reference gene selected from SPDEF or KLK3.

7. A primer set, characterized in that, The primer set is used for detecting a biomarker comprising a coding gene of an enzyme involved in oxidation of fatty acid and bile acid intermediate product, a coding gene of a transcription factor, and a coding gene of a G protein-coupled receptor; the primer set comprises: The primer for amplifying HOXB13 has an upstream primer sequence as shown in SEQ ID NO: 1 and a downstream primer sequence as shown in SEQ ID NO: 2; the primer for amplifying AMACR has an upstream primer sequence as shown in SEQ ID NO: 3 and a downstream primer sequence as shown in SEQ ID NO: 4; the primer for amplifying FOXA1 has an upstream primer sequence as shown in SEQ ID NO: 5 and a downstream primer sequence as shown in SEQ ID NO: 6; the primer for amplifying MALAT1 has an upstream primer sequence as shown in SEQ ID NO: 7 and a downstream primer sequence as shown in SEQ ID NO: 8; the primer for amplifying PCA3 has an upstream primer sequence as shown in SEQ ID NO: 9 and a downstream primer sequence as shown in SEQ ID NO: 10; the primer for amplifying PSGR has an upstream primer sequence as shown in SEQ ID NO: 11 and a downstream primer sequence as shown in SEQ ID NO: 12; the primer for amplifying PSMA has an upstream primer sequence as shown in SEQ ID NO: 13 and a downstream primer sequence as shown in SEQ ID NO: 14; the primer for amplifying PSCA has an upstream primer sequence as shown in SEQ ID NO: 15 and a downstream primer sequence as shown in SEQ ID NO: 16; the primer for amplifying ACP3 has an upstream primer sequence as shown in SEQ ID NO: 17 and a downstream primer sequence as shown in SEQ ID NO: 18; the primer for amplifying TRPM8 has an upstream primer sequence as shown in SEQ ID NO: 19 and a downstream primer sequence as shown in SEQ ID NO: 20; the primer for amplifying NKX3-1 has an upstream primer sequence as shown in SEQ ID NO: 21 and a downstream primer sequence as shown in SEQ ID NO: 22; the primer for amplifying ANO7 has an upstream primer sequence as shown in SEQ ID NO: 23 and a downstream primer sequence as shown in SEQ ID NO: 24; the primer for amplifying SLC45A3 has an upstream primer sequence as shown in SEQ ID NO: 25 and a downstream primer sequence as shown in SEQ ID NO:

26.

8. The primer set according to claim 7, wherein The primer set further comprises: a primer for amplifying the reference gene SPDEF, wherein the upstream primer sequence is as shown in SEQ ID NO: 27 and the downstream primer sequence is as shown in SEQ ID NO: 28; a primer for amplifying the reference gene KLK3, wherein the upstream primer sequence is as shown in SEQ ID NO: 29 and the downstream primer sequence is as shown in SEQ ID NO:

30.

9. A probe set, characterized by The probe set comprises: The probe set comprises: The probe sequence for detecting HOXB13 is shown as SEQ ID NO: 31, the probe sequence for detecting AMACR is shown as SEQ ID NO: 32, the probe sequence for detecting FOXA1 is shown as SEQ ID NO: 33, the probe sequence for detecting MALAT1 is shown as SEQ ID NO: 34, the probe sequence for detecting PCA3 is shown as SEQ ID NO: 35, the probe sequence for detecting PSGR is shown as SEQ ID NO: 36, the probe sequence for detecting PSMA is shown as SEQ ID NO: 37, the probe sequence for detecting PSCA is shown as SEQ ID NO: 38, the probe sequence for detecting ACP3 is shown as SEQ ID NO: 39, the probe sequence for detecting TRPM8 is shown as SEQ ID NO: 40, the probe sequence for detecting NKX3-1 is shown as SEQ ID NO: 41, the probe sequence for detecting ANO7 is shown as SEQ ID NO: 42, the probe sequence for detecting SLC45A3 is shown as SEQ ID NO: 43, the probe sequence for detecting SPDEF is shown as SEQ ID NO: 44, and the probe sequence for detecting KLK3 is shown as SEQ ID NO:

45.

10. The probe set of claim 9, wherein The probes of the probe set are labeled with fluorescent reporter groups selected from FAM, HEX, ROX, VIC, CY5, 5-TAMRA, TET, CY3 or JOE.

11. The probe set of claim 10, wherein The fluorescent reporter groups of the probes shown as SEQ ID NO: 31-SEQ ID NO: 33 are FAM; the fluorescent reporter groups of the probes shown as SEQ ID NO: 34-SEQ ID NO: 36 are HEX; and the fluorescent reporter group of the probe shown as SEQ ID NO: 44 is CY5.

12. The probe set of claim 9, wherein The 3' end of the probes of the probe set further has a fluorescent quenching group selected from BHQ1 or BHQ2.

13. The probe set of claim 12, wherein The fluorescent quenching group of the probes shown as SEQ ID NO: 31-SEQ ID NO: 36 is BHQ1; and the fluorescent quenching group of the probe shown as SEQ ID NO: 44 is BHQ2.

14. The probe set of claim 12, wherein The fluorescent reporter group of the probes shown as SEQ ID NO: 31-SEQ ID NO: 33 is FAM, and the fluorescent quenching group is BHQ1; the fluorescent reporter group of the probes shown as SEQ ID NO: 34-SEQ ID NO: 36 is HEX, and the fluorescent quenching group is BHQ1; and the fluorescent reporter group of the probe shown as SEQ ID NO: 44 is CY5, and the fluorescent quenching group is BHQ2.

15. A kit comprising, The kit is used for detecting biomarkers including coding genes of enzymes involved in oxidation of fatty acid and bile acid intermediates, coding genes of transcription factors, and coding genes of G protein-coupled receptors; the kit comprises the primer set of claim 7 or 8 and the probe set of any one of claims 9-14.

16. The kit of claim 15, wherein The kit comprises reagents for detecting urine exosomes and extracting urine exosomes.

17. A kit according to claim 16, wherein The kit comprises: The RT-qPCR reaction solution containing primers and probes specifically recognizing RNA sequences of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF, an enzyme mixture containing reverse transcriptase, DNA polymerase and UDG enzyme, positive quality control and negative quality control.

18. A kit according to claim 17, wherein The final concentration of the primers in the RT-qPCR reaction system is 0.2-0.4 μM, and the final concentration of the probes in the RT-qPCR reaction system is 0.1-0.3 μM.

19. A kit according to claim 18, wherein The RT-qPCR reaction system further comprises Tris buffer, magnesium ions, dA / G / C / UTPs; the final concentration of the magnesium ions is 5 mM, the final concentration of the dATP is 0.4 mM, the final concentration of the dCTP is 0.4 mM, the final concentration of the dGTP is 0.4 mM, and the final concentration of the dUTP is 0.8 mM.

20. A non-diagnostic method of prostate cancer based on a combination of urinary exosomal biomarkers, characterized in that, The method is used for converting the marker combination gene expression level into the prostate cancer risk level of the patient to be tested, and the model algorithm is: output value = {Ct(Target 1)-Ct(internal reference)}×a+{Ct(Target 2)-Ct(internal reference)}×b+{Ct(Target 3)-Ct(internal reference)}×c+{Ct(Target 4)-Ct(internal reference)}×d+…+{Ct(Target N)-Ct(internal reference)}×n+z; In the formula, a, b, c, …, n are coefficients, z is a constant, the coefficients are between -1 and 1, Ct(internal reference) is the Ct value of the internal reference gene, and Ct(Target 1)…Ct(Target N) are the Ct values of the genes in the biomarkers according to any one of claims 1-6.

21. The method of claim 20, wherein, The kit according to any one of claims 15-19 is used for detecting the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes, then the model is used to calculate the output value and compare it with the positive judgment value, and the risk level of the subject suffering from prostate cancer is analyzed.

22. An apparatus comprising: The device comprises: a detection unit for detecting the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes; a calculation unit for calculating the output value of the model algorithm according to claim 20; an analysis unit for comparing the output value of the calculation unit with the positive judgment value and analyzing the risk level of the subject suffering from prostate cancer.

23. A computer-readable storage medium, characterized in that, The computer readable storage medium stores one or more programs which can be executed by one or more processors to implement the method according to claim 20 or 21.

Images