Primer group, probe group, kit and method for detecting prostatic cancer biomarkers

Abstract

The invention provides application of a reagent for detecting a biomarker in preparation of a detection product. The detection product is used for detecting prostatic cancer. The biomarker comprises a coding gene of an enzyme participating in oxidation of fatty acid and bile acid intermediate products, a coding gene of a transcription factor and a coding gene of a G protein coupled receptor. A urine sample is noninvasively collected, the exosome of the urine sample is detected, the expression condition of prostate cancer related RNA in the exosome is analyzed, a prostate cancer early prediction model is established through logistic regression, the prediction model converts the marker combination gene expression level into the prostate cancer risk level of a to-be-detected patient, and the prostate cancer early prediction model is used for predicting the prostate cancer risk level of the to-be-detected patient. The method has the characteristics of high sensitivity and high specificity, can be used for classifying and screening cancer and non-cancer samples of a single sample or a plurality of samples finally, and has the characteristics of noninvasive property, accuracy and rapidness.

Description

Technical Field

[0001] The present invention belongs to the field of biomolecule detection, and particularly relates to a primer set, a probe set, a kit and a method for detecting prostate cancer biomarkers. Background Art

[0002] Prostate cancer is one of the most common malignant tumors in the urogenital system of men. Most of the newly diagnosed cases of prostate cancer in China are in the middle and advanced clinical stages, and only 30% are clinically localized cases, resulting in a relatively poor overall prognosis for prostate cancer patients in China. The survival time of prostate cancer patients is closely related to the stage of malignant tumors at the time of clinical diagnosis. Therefore, "early screening, early diagnosis, and early treatment" of high-risk groups for prostate cancer is an effective means to improve the overall survival rate of Chinese prostate cancer patients.

[0003] Extracellular vesicles (EVs) are vesicles secreted by cells with a phospholipid bilayer structure, which can regulate the biological characteristics of recipient cells through the biomacromolecules they carry and participate in the physiological and pathological processes of cells. Exosomes are an important subset of EVs, which encapsulate substances such as proteins, mRNAs, and microRNAs. They are mediators of intercellular short-distance communication in health and disease and participate in multiple processes such as tumorigenesis, development, invasion, and metastasis. At the same time, compared with other body fluids, urine has the characteristics of safe sampling, large sample volume, and non-invasiveness. Studies have found that urine exosome RNA can be used as a biomarker to effectively detect urinary system cancers.

[0004] Currently, 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 cancer-specific, and PSA levels can increase under non-cancer conditions such as benign prostatic hyperplasia (BPH), prostatitis, or lower urinary tract infections. When PSA is between 4 and 10 ng / mL, the negative biopsy rate is only about 20%; when PSA is between 10 and 20 ng / mL, the negative biopsy rate is only about 30%. DRE is overly dependent on the doctor's experience and is an invasive and intrusive examination. How to improve the negative biopsy rate while avoiding overdiagnosis and overtreatment is a huge challenge in the early screening of prostate cancer. Summary of the Invention

[0005] To solve the deficiencies of the prior art, the present invention provides a primer set, a probe set, a kit and a method for detecting prostate cancer biomarkers. Urine samples are obtained by a non-invasive method. Before sample collection, there is no need for digital rectal examination, nor is it necessary to isolate exfoliated urinary cells from urine samples. By jointly detecting the expression of genes related to prostate cancer, the tumor detection rate can be significantly improved, and it has the characteristics of non-invasiveness, precision, and rapidity.

[0006] To achieve the above object, the technical solution adopted by the present invention includes:

[0007] The present invention discloses, in a first aspect, the use of a reagent for detecting a biomarker in the preparation of a detection product for detecting prostate cancer;

[0008] The biomarker includes the coding gene of an enzyme involved in the oxidation of intermediate products of fatty acids and bile acids, the coding gene of a transcription factor, and the coding gene of a G protein-coupled receptor.

[0009] Preferably, the enzyme involved in the oxidation of intermediate products of fatty acids and bile acids is a racemase, the transcription factor is a transcription factor of the homeobox gene family, 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 transcription factor of the homeobox gene family is selected from HOXB13, and the coding gene of the olfactory receptor protein is selected from PSGR.

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

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

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

[0014] The present invention discloses, in a second aspect, a primer set for detecting a biomarker, the biomarker including the coding gene of an enzyme involved in the oxidation of intermediate products of fatty acids and bile acids, the coding gene of a transcription factor, and the coding gene of a G protein-coupled receptor;

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

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

[0017] In the third aspect of the present invention, a probe set is disclosed, which is used to detect biomarkers, and the biomarkers include the coding genes of enzymes participating in the oxidation of intermediate products of fatty acids and bile acids, the coding genes of transcription factors, and the coding genes of G protein-coupled receptors; the probe set includes:

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

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

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

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

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

[0023] Preferably, the fluorescent reporter groups shown in SEQ ID NO: 31 to SEQ ID NO: 33 are FAM, and the fluorescent quenching group is BHQ1; the fluorescent reporter groups shown in SEQ ID NO: 34 to SEQ ID NO: 36 are HEX, and the fluorescent quenching group is BHQ1; the fluorescent reporter group shown in SEQ ID NO: 44 is CY5, and the fluorescent quenching group is BHQ2.

[0024] In the fourth aspect of the present invention, a kit is disclosed. The kit is used for detecting biomarkers, and the biomarkers include the coding genes of enzymes participating 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 urinary exosomes and reagents for extracting urinary exosomes.

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

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

[0028] Preferably, the final concentration of the primer in the RT-qPCR reaction system is 0.2 μM to 0.4 μM; the final concentration of the probe in the RT-qPCR reaction system is 0.1 μM to 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 invention discloses a non-diagnostic method for prostate cancer based on a combination of urinary exosome biomarkers. This method is used to convert the gene expression levels of the biomarker combination into the prostate cancer risk level of the patient to be tested. Its 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 all 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 each gene in the biomarker as described in any one of claims 1-6.

[0032] Preferably, the kit as described above is used. The kit is used to detect the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, and SPDEF in urinary exosomes, and then the output value is calculated using the model and compared with the positive judgment value to analyze the prostate cancer risk level of the subject.

[0033] The sixth aspect of the present invention discloses a device, which includes:

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

[0035] A calculation unit, which is used to calculate the output value of the model algorithm as described in claim 20;

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

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

[0038] The beneficial effects of the present invention are as follows:

[0039] (1) The output values of the biomarker combination of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, and SPDEF provided by the present invention show statistically significant differences in urinary exosomes between prostate cancer patients and control samples. A prediction model was constructed using this biomarker combination, trained with a training set, and optimized with a test validation set, ultimately obtaining an early diagnosis prediction model for prostate cancer;

[0040] (2) The exosome biomarker combination and prediction model provided by the present invention can be used more accurately for early detection of clinical samples of patients with clinically suspected prostate cancer or those recommended for prostate biopsy when PSA is between 4 - 20 ng / mL, especially 4 - 10 ng / mL. The best combination of biomarkers is: the AUC of HOXB13, AMACR, FOXA1, MALAT1, PCA3, and PSGR is 0.78, the sensitivity is 75%, and the specificity is 71%, having good clinical diagnostic value.

[0041] (3) The present invention non-invasively collects urine samples and detects their exosomes, analyzes the expression of prostate cancer-related RNA in exosomes, and establishes an early prediction model for prostate cancer through logistic regression. The constructed prostate cancer prediction model has the characteristics of high sensitivity and high specificity, and finally, this model can be used to classify and screen cancer and non-cancer samples for single or multiple samples. Description of the Drawings

[0042] Figure 1 . The NTA software quickly generates a high-resolution particle size distribution for each particle and the count of the observed vesicle particles.

[0043] Figure 2 . The morphology of exosomes under a transmission electron microscope.

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

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

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

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

[0048] Figure 7. The ROC curve of the training set with the true negative rate (sensitivity) as the ordinate and the false negative rate (1 - specificity) as the abscissa, and the AUC values of different exosome biomarker combinations in the training set.

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

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

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

[0052] Figure 7-4 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 is 0.78; Figure 7-5 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 + PSMA is 0.78;

[0053] Figure 7-6 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 + PSMA + PSCA is 0.78;

[0054] Figure 7-7 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 + PCA3 combined with age + tPSA + fPSA is 0.83;

[0055] Figure 8 . The ROC curve of the training set with the true negative rate (sensitivity) as the ordinate and the false negative rate (1 - specificity) as the abscissa, and the AUC values of different exosome biomarker combinations in the validation set.

[0056] Figure 8-1 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR is 0.73;

[0057] Figure 8-2 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR + MALAT1 is 0.75;

[0058] Figure 8-3 . The AUC of the biomarker combination AMACR + HOXB13 + PSGR + MALAT1 + FOXA1 is 0.75;

[0059] Figure 8-4 . The AUC of the biomarker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3 is 0.77; Figure 8-5 . The AUC of the biomarker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3+PSMA is 0.77;

[0060] Figure 8-6 . The AUC of the biomarker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3+PSMA+PSCA is 0.77;

[0061] Figure 8-7 . The AUC of the biomarker combination AMACR+HOXB13+PSGR+MALAT1+FOXA1+PCA3+age+tPSA+fPSA is 0.84;

[0062] Figure 9 . PCR curve graph of the reaction combination for amplifying positive prostate cancer samples

[0063] Figure 9-1 . Amplification curve graph of reaction combination 1 (HOXB13+PSGR+PSCA+ACP3) for detecting positive prostate cancer samples.

[0064] Figure 9-2 . Amplification curve graph of reaction combination 2 (AMACR+PCA3+TRPM8+NKX3-1) for detecting positive prostate cancer samples.

[0065] Figure 9-3 . Amplification curve graph of reaction combination 3 (FOXA1+MALAT1+PSMA+SPDEF) for detecting positive prostate cancer samples.

[0066] Figure 9-4 . Amplification curve graph of reaction combination 4 (ANO7+SLC45A3+KLK3) for detecting positive prostate cancer samples.

[0067] Figure 10 . PCR curve graph of the reaction combination for amplifying negative prostate cancer samples

[0068] Figure 10-1 . Amplification curve graph of reaction combination 1 HOXB13+PSGR+PSCA+ACP3 for detecting negative prostate cancer samples.

[0069] Figure 10-2 . Amplification curve graph of reaction combination 2 AMACR+PCA3+TRPM8+NKX3-1 for detecting negative prostate cancer samples.

[0070] Figure 10-3 . Amplification curve of reaction combination 3 FOXA1 + MALAT1 + PSMA + SPDEF for detecting negative samples of prostate cancer.

[0071] Figure 10-4 . Amplification curve of reaction combination 4 ANO7 + SLC45A3 + KLK3 for detecting negative samples of prostate cancer. Detailed implementation manners

[0072] The present invention will be further described below by way of examples, but the present invention is not limited to the scope of the described examples. The experimental methods in the following examples are all conventional methods unless otherwise specified, and are carried out according to the techniques or conditions described in the literature in this field or according to the product specifications. The materials, reagents, etc. used in the following examples can be obtained from commercial sources unless otherwise specified.

[0073] A urine exosome biomarker combination and kit for early detection of prostate cancer provided by the present invention, and its specific implementation manners include the following steps:

[0074] (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 30 - 50 mL of volume is collected starting from urination.

[0075] (2) Extraction and characteristics of exosomes in urine samples. In particular, the EXODUS exosome purification system (patent publication number: CN114616054A), a patented product of our company, is used to extract exosomes, and the obtained exosomes have the characteristics of high purity, high yield, and complete morphology.

[0076] 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 all feasible. Those skilled in the art can foresee that the obtained exosomes should have similar characteristics and there should be no differences due to the change of the extraction method.

[0077] In order to identify the characteristics of exosomes in the urine of prostate cancer patients and non - prostate cancer patients, the present invention uses nanoparticle tracking analysis (NTA), and obtains the particle size distribution and particle concentration of nano - particles in the liquid suspension by using the characteristics of light scattering and Brownian motion. The results are as Figure 1As shown. The morphological characteristics of the exosomes were analyzed using a transmission electron microscope (TEM), and the results are as Figure 2 shown.

[0078] (3) Extraction of exosomal RNA

[0079] In the present invention, a magnetic bead-based nucleic acid extraction reagent was used to extract the total nucleic acid of exosomes, and the quality of the extracted RNA was quality controlled. The quality of the extracted nucleic acid can be evaluated by comparing the ratio of the absorbance values at 260 nm and 280 nm (A260 / A280) of the nucleic acid sample. The A260 / A280 is preferably 1.8 - 2.2, more preferably 2.0.

[0080] (4) Detection of nucleic acid biomarkers

[0081] In the present invention, real-time fluorescence quantitative PCR was used to measure the expression levels of biomarkers in the above-mentioned extracted RNA. The biomarkers at least include HOXB13, AMACR, FOXA1, MALAT1, PCA3, and PSGR genes, as well as SPDEF and / or KLK3 genes.

[0082] (5) Analysis of the RNA expression levels of biomarkers

[0083] In the method provided herein, the expression levels of biomarker RNAs were determined by real-time fluorescence quantitative PCR analysis, and negative reactions were detected by the accumulation of fluorescence signals. The Ct value is defined as the number of cycles required for the fluorescence signal to exceed the threshold. The Ct value is inversely proportional to the amount of nucleic acid in the sample, that is, the smaller the Ct value, the greater the amount of nucleic acid in the sample.

[0084] In the method provided herein, the genes whose expression levels are used to calculate the relative expression levels are collectively referred to as reference genes, which are used to normalize the signal values of the detected genes to control the differences among the amounts of exosomes extracted between samples, the performance of reagent components, and the performance of the real-time fluorescence quantitative PCR instrument. Reference genes are usually present in urine exosomes. The internal reference genes for normalizing the biomarkers HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7, and SLC45A3 in the present invention are SPDEF or KLK3. The relative expression level analysis or normalization is completed by subtracting the Ct values of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7, and SLC45A3 from the Ct value of the reference gene (SPDEF or KLK3) respectively, and the resulting value is called ΔCt. For example:

[0085] ΔCt(HOXB13) = Ct(HOXB13) - Ct(SPDEF) or ΔCt(HOXB13) = Ct(HOXB13) - Ct(KLK3)

[0086] (6) Construct a prostate cancer prediction model

[0087] The receiver operating characteristic (ROC curve) is a widely used tool for evaluating the recognition and diagnostic ability of biomarkers or biomarker combinations. The area under the ROC curve (AUC) is established to evaluate the diagnostic value of each biomarker or biomarker combination. The biomarker or biomarker combination with the highest diagnostic value has an AUC value greater than 0.6, 0.7 or 0.8. Preferably, the performance of a single biomarker or biomarker combination has an AUC value greater than 0.7. As Figure 3 shown, when the reference gene is SPDEF, the AUC of the score created by logistic regression analysis of the combination of HOXB13, AMACR, FOXA1, MALAT1, PCA3, and PSGR measured together is 0.78, and the multi-target combination it detects also has the highest diagnostic accuracy.

[0088] Furthermore, the normalized expression levels of the above-mentioned HOXB13, AMACR, FOXA1, MALAT1, PCA3, and PSGR combination with higher diagnostic value are used to establish a mathematical expression through data analysis, and an early diagnosis model for prostate cancer is constructed by combining the sensitivity and specificity necessary for clinical application. The algorithm of this model is as follows:

[0089] Output value = {Ct(HOXB13) - Ct(SPDEF)} × a + {Ct(AMACR) - Ct(SPDEF)} × b + {Ct(FOXA1) + Ct(SPDEF)} × c + {Ct(MALAT1) + Ct(SPDEF)} × d + {Ct(PCA3) + Ct(SPDEF)} × e + {Ct(PSGR) + Ct(SPDEF)} × f + z.

[0090] Where a, b, c, and d are coefficients, and z is a constant. All of them can be determined by fitting the output value of the equation to the existing data set through logistic regression or linear regression. The coefficient a ranges from -1 to 1. Preferably, the coefficient a = -0.371; the coefficient b ranges from -1 to 1. Preferably, the coefficient b = 0.518; the coefficient c ranges from -1 to 1. Preferably, the coefficient c = -0.586; the coefficient d ranges from -1 to 1. Preferably, the coefficient d = -0.353; the coefficient e ranges from -1 to 1. Preferably, the coefficient d = 0.61; the coefficient f ranges from -1 to 1. Preferably, the coefficient d = -0.514; the constant z ranges from -1 to 1. Preferably, the constant z = 1.852.

[0091] The positive judgment value determined by the ROC curve of the output value is used to distinguish the risk of prostate cancer in the subject. Higher than the positive judgment value indicates a higher risk of prostate cancer, and lower than the positive judgment value indicates a lower risk of prostate cancer. The prostate cancer risk prompt obtained by this method helps doctors make decisions on the next diagnostic options for patients, as a supplement and assistance to the existing diagnostic methods, for clinical physicians' reference.

[0092] The present invention further discloses a method for using the above-mentioned kit in a kit for early diagnosis of prostate cancer, including the following steps:

[0093] (1) Collect a random urine sample from a suspected prostate cancer patient, starting from the first urine segment, and collect a volume of 30 - 50 mL;

[0094] (2) Isolate and purify exosomes from the urine sample;

[0095] (3) Extract RNA from the exosomes;

[0096] (4) Use the primer and probe compositions of the HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR, and SPDEF genes of the present invention and the kit to perform fluorescence quantitative RT-qPCR amplification on the nucleic acid;

[0097] (5) Obtain the expression values of the corresponding biomarkers, and use the prostate cancer prediction model of the present invention to evaluate the cancer risk of the subject.

[0098] The present invention discloses a device, the device comprising: a detection unit configured to detect the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urinary exosomes; a calculation unit configured to calculate the output value of the model algorithm as described in claim 20; and an analysis unit configured to compare the output value of the calculation unit with a positive judgment value to analyze the risk level of a subject suffering from prostate cancer.

[0099] The present invention discloses a computer-readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the method described above.

[0100] Some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including program code for performing the method shown in the flowchart. In such embodiments, the computer program may be downloaded and installed from a network via 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 functions defined in the method of some embodiments of the present disclosure are performed.

[0101] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, 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 may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0102] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed network.

[0103] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., connected through the Internet using an Internet service provider).

[0104] Example 1 Selection of the Optimal Exosome Purification Method

[0105] The yield and purity of exosome purification affect the efficiency of downstream nucleic acid extraction and gene detection, and thus affect the effect of constructing a diagnostic marker model. Therefore, it is crucial to select a suitable exosome purification method. In this example, exosomes from the urine of 20 patients with benign diseases and 20 patients with prostate cancer were purified using ultrafiltration centrifugation, PEG precipitation, and the EXODUS exosome purification system, respectively. The exosomes were subjected to nucleic acid extraction and fluorescence RT-qPCR detection, and a model was constructed for each method. The AUC values of the models were compared to confirm the feasibility and advantages of the EXODUS exosome purification system for the diagnosis of prostate cancer. The specific steps are as follows:

[0106] (1) Sample collection: Prepare urine samples from 20 patients with benign diseases and 20 patients with prostate cancer. Collect morning urine and collect a volume of 50 mL starting from the beginning of urination.

[0107] (2) Sample pretreatment: Centrifuge at 2000 g for 20 min at 4°C, retain the supernatant, and divide it into 3 equal parts of 15 mL each after filtration through a 0.8 μM or 0.22 μM filter membrane.

[0108] (3) Purify exosomes from the samples using the EXODUS exosome purification system, ultrafiltration centrifugation, and PEG precipitation, respectively. The purification volume is 15 mL for each method, and the final recovered volume of exosomes is 300 μL.

[0109] (4) Perform nucleic acid extraction using the Qiagen miRNeasy Mini Kit (cat.no.217004).

[0110] (5) Detect the expression values of AMACR, HOXB13, PSGR, and SPDEF genes by RT-qPCR method. The primer pair for amplifying the AMACR gene is SEQ ID NO:3, SEQ ID NO:4, and the probe is SEQ ID NO:32; the primer pair for amplifying the HOXB13 gene is SEQ ID NO:1, SEQ ID NO:2, and the probe is SEQ ID NO:31; the primer pair for amplifying the PSGR gene is SEQ ID NO:11, SEQ ID NO:12, and the probe is SEQ ID NO:36; the primer for amplifying the SPDEF gene is SEQ ID NO:27, SEQ ID NO:28, and the probe is SEQ ID NO:44. The results of the expression values of AMACR, HOXB13, PSGR, and SPDEF genes under different exosome purification methods are as follows:

[0111] Table 1 Mean values of AMACR, HOXB13, PSGR, and SPDEF genes under different exosome purification methods

[0112]

[0113]

[0114] (6) Analysis of normalized AMACR, HOXB13, and PSGR expression levels: Subtract the Ct values of the reference genes SPDEF or KLK3 from the Ct values of AMACR, HOXB13, and PSGR respectively to obtain the ΔCt values.

[0115] (7) Establish an ROC curve and analyze the area under the ROC curve (AUC): Use the SPSS binary logistic regression method to establish a mathematical expression for the combination of AMACR, HOXB13, and PSGR genes. Combine the output values with the pathological diagnosis results to establish an ROC and analyze the area under the curve (AUC) to evaluate the AUC values of the AMACR+HOXB13 combination, AMACR+PSGR combination, HOXB13+PSGR combination, and AMACR+HOXB13+PSGR combination. The results are as follows:

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

[0117]

[0118] As can be seen from the results in Table 1, the EXODUS method enriches exosome particles with a higher concentration than the PEG precipitation method and the ultrafiltration centrifugation method. The Ct values of the gene amplifications of AMACR, HOXB13, PSGR, and SPDEF are smaller, indicating that the nucleic acid content containing the AMACR, HOXB13, PSGR, and SPDEF genes enriched is higher. Therefore, it is easier to capture the gene detection signals from tumors, and thus a higher AUC value can be generated (Table 2, Figures 3 to 6 ), so the EXODUS exosome purification system is more suitable for the purification of urine exosomes in this project.

[0119] Example 2 Amplification of Urine Exosome Biomarkers by Prostate Cancer Detection Kit

[0120] Perform target combination screening of 13 tumor markers in exosomes of a prostate cancer detection kit, including a PCR reaction solution for RT-qPCR amplification. The PCR reaction solution includes a primer and probe combination for detecting 13 markers in urine exosomes of prostate cancer. The primer and probe combination includes: a primer pair for amplifying the HOXB13 gene at a concentration of 0.3 μM (SEQ ID NO:1, SEQ ID NO:2), a probe for amplifying the HOXB13 gene at a concentration of 0.1 μM (SEQ ID NO:31); a primer pair for amplifying the AMACR gene at a concentration of 0.4 μM (SEQ ID NO:3, SEQ ID NO:4), a probe for amplifying the AMACR gene at a concentration of 0.2 μM (SEQ ID NO:32); a primer pair for amplifying the FOXA1 gene at a concentration of 0.2 μM (SEQ ID NO:5, SEQ ID NO:6), a probe for amplifying the FOXA1 gene at a concentration of 0.1 μM (SEQ ID NO:33); a primer pair for amplifying the MALAT1 gene at a concentration of 0.2 μM (SEQ ID NO:7, SEQ ID NO:8), a probe for amplifying the FOXA1 gene at a concentration of 0.1 μM (SEQ ID NO:34); a primer pair for amplifying the PCA3 gene at a concentration of 0.4 μM (SEQ ID NO:9, SEQ ID NO:10), a probe for amplifying the PCA3 gene at a concentration of 0.2 μM (SEQ ID NO:35); a primer pair for amplifying the PSGR gene at a concentration of 0.3 μM (SEQ ID NO:11, SEQ ID NO:12), a probe for amplifying the PSGR gene at a concentration of 0.15 μM (SEQ ID NO:36); a primer pair for amplifying the PSMA gene at a concentration of 0.3 μM (SEQ ID NO:13, SEQ ID NO:14), a probe for amplifying the PSMA gene at a concentration of 0.15 μM (SEQ ID NO:37); a primer pair for amplifying the PSCA gene at a concentration of 0.2 μM (SEQ ID NO:15, SEQ ID NO:16), a probe for amplifying the PSCA gene at a concentration of 0.1 μM (SEQ ID NO:38); a primer pair for amplifying the ACP3 gene at a concentration of 0.4 μM (SEQ ID NO:17, SEQ ID NO:18), a probe for amplifying the ACP3 gene at a concentration of 0.2 μM (SEQ ID NO:39); a primer pair for amplifying the TRPM8 gene at a concentration of 0.3 μM (SEQ ID NO:19, SEQ ID NO:20), a probe for amplifying the TRPM8 gene at a concentration of 0.15 μM (SEQ ID NO:40); a concentration of 0.Primer pair for amplifying NKX3-1 gene at 3 μM (SEQ ID NO:21, SEQ ID NO:22), probe for amplifying NKX3-1 gene at 0.15 μM (SEQ ID NO:41); primer pair for amplifying ANO7 gene at 0.3 μM (SEQ ID NO:23, SEQ ID NO:24), probe for amplifying ANO7 gene at 0.15 μM (SEQ ID NO:42); primer pair for amplifying SLC45A3 gene at 0.4 μM (SEQ ID NO:25, SEQ ID NO:26), probe for amplifying SLC45A3 gene at 0.2 μM (SEQ ID NO:43); primer pair for amplifying SPDEF gene at 0.4 μM (SEQ ID NO:27, SEQ ID NO:28), probe for amplifying SPDEF gene at 0.2 μM (SEQ ID NO:44); primer pair for amplifying KLK3 gene at 0.4 μM (SEQ ID NO:29, SEQ ID NO:30), probe for amplifying KLK3 gene at 0.2 μM (SEQ ID NO:45).

[0121] In this embodiment, the fluorophore of the probe shown in SEQ ID NO:31 is FAM, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:32 is FAM, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:33 is FAM, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:34 is HEX, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:35 is HEX, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:36 is HEX, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:37 is ROX, and the quencher is BHQ2; the fluorophore of the probe shown in SEQ ID NO:38 is ROX, and the quencher is BHQ2; the fluorophore of the probe shown in SEQ ID NO:39 is CY5, and the quencher is BHQ2; the fluorophore of the probe shown in SEQ ID NO:40 is ROX, and the quencher is BHQ2; the fluorophore of the probe shown in SEQ ID NO:41 is CY5, and the quencher is BHQ2; the fluorophore of the probe shown in SEQ ID NO:42 is FAM, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:43 is HEX, and the quencher is BHQ1; the fluorophore of the probe shown in SEQ ID NO:44 is CY5, and the quencher is BHQ2; the fluorophore of the probe shown in SEQ ID NO:45 is ROX, and the quencher is BHQ2. See Table 3 for the PCR amplification system.

[0122] Table 3 PCR Amplification Reaction System

[0123]

[0124]

[0125] The PCR reaction solution is composed of a mixture of the above primers and probes, and a PCR buffer containing 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.

[0126] When preparing the PCR amplification reaction solution, the formula is: 18.5 μL of the PCR reaction solution containing the primer and probe combination of the above markers per test, 1.5 μL of the enzyme mixture per test, 10 μL of the test sample per test, and the total volume is 30 μL.

[0127] After the reagent preparation is completed, it is placed in an ABI 7500 fluorescence quantitative PCR instrument for reaction, and the PCR reaction conditions are set according to Table 4.

[0128] Table 4 PCR Amplification Reaction Conditions

[0129]

[0130] After the PCR reaction, the Ct value of each RNA marker was analyzed using the supporting software of ABI 7500. Figure 9-1 ~ Figure 9-4 They are the amplification curves of reaction combinations 1, 2, 3, and 4 for positive prostate cancer samples respectively; Figure 10-1 ~ Figure 10-4 They are the amplification curves of reaction combinations 1, 2, 3, and 4 for negative prostate cancer samples respectively.

[0131] Example 3 Screening of Urinary Exosome Marker Combinations for Prostate Cancer Detection and Model Construction

[0132] This example provides a method for screening urinary exosome marker combinations for prostate cancer detection and constructing a prediction model, as well as statistical verification of patient samples.

[0133] In this study, samples were used if the following criteria were met:

[0134] Inclusion criteria: 1) Biological males aged ≥ 50 years old; 2) Scheduled or already had serum PSA test in this hospital; 3) Scheduled to have prostate puncture / tissue biopsy; 4) Voluntarily participated in this trial, understood the research procedures and had signed the informed consent form.

[0135] Exclusion criteria: 1) History of previous prostate biopsy; 2) History of prostate cancer; 3) Using drugs or hormones known to affect serum prostate-specific antigen levels within 3 - 6 months at the time of study enrollment; 4) Patients in the acute stage of prostatitis undergoing antibiotic treatment; 5) History of invasive treatment for benign prostatic hypertrophy (benign prostatic hyperplasia) or lower urinary tract symptoms within 6 months at the time of study enrollment; 6) No record of known hepatitis (all types) and / or HIV in the patient's medical record.

[0136] According to the above criteria, a total of 280 patients were recruited in this study as the training set samples, and the sample statistical information is as follows:

[0137] PSA (ng / mL) Non-prostate cancer Prostate cancer Total 4～10 112 50 162 10～20 61 57 118 Total 173 107 280

[0138] The screening and prediction model construction method of the above biomarker combination is as follows:

[0139] (1) Enrich and purify exosomes from the above 280 samples using the method described in the patent application number (CN202280000660). Extract exosome biomarkers and perform RT-qPCR detection using well-known technical means in the art to obtain the Ct values of the detected exosome biomarkers. The biomarkers at least include HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR genes and SPDEF genes.

[0140] (2) Analyze the RNA expression levels of the biomarkers and normalize the gene expression levels.

[0141] (3) Use logistic regression to evaluate the diagnostic performance of different biomarker or biomarker combination models, and the results are shown in Table 5 below:

[0142] Table 5 Performance of Different Biomarkers or Biomarker Combinations

[0143]

[0144] In the logistic regression analysis, a prediction model constructed from 6 biomarkers HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and the internal reference SPDEF, as Figure 7 shown, has the largest AUC of 0.78, and its sensitivity and specificity for diagnosing prostate cancer are 75% and 71% respectively.

[0145] The present invention obtains urine samples through a non-invasive method. The urine samples do not require digital rectal examination before collection, nor is it necessary to isolate cell precipitates from the urine samples. By jointly detecting the expression of prostate cancer-related genes, the early tumor detection rate can be significantly improved. The detection of prostate cancer exosomes is non-invasive, accurate, and rapid.

[0146] Example 4 Application of the Prostate Cancer Prediction Model in the Validation Set

[0147] This example provides a method for using an early prostate cancer prediction model based on urine exosomes, and can also be used to verify the accuracy of the prostate cancer prediction model provided in Example 3 based on urine.

[0148] Select the above-mentioned kit, method and logistic regression formula to perform exosome nucleic acid detection and analysis on a total of 216 urine samples suspected of prostate cancer from outpatients in the Department of Urology of Tongji Hospital in Wuhan.

[0149] According to the clinical biopsy and pathological diagnosis results of the hospital, there were 97 urine specimens of prostate cancer and 119 urine specimens of non-prostate cancer. According to the obtained logistic regression calculation formula, a comprehensive score was calculated using the set proprietary algorithm to evaluate the risk of prostate cancer in 216 patients. The test results of the validation set are shown in Tables 6 and 7.

[0150] Table 6 Performance of the validation set for different biomarker combinations

[0151]

[0152] Table 7 Diagnostic performance of the target combination HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and the internal reference SPDEF compared with the pathological results (positive judgment value 0.4)

[0153]

[0154] Sensitivity = 71 / 97 = 73.20%;

[0155] Specificity = 84 / 119 = 70.59%;

[0156] Total coincidence rate = (71 + 84) / 216 = 71.76%;

[0157] As Figure 8 shown, the AUC of the exosomal RNA biomarker combination HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and the internal reference SPDEF is 0.77, and the exosomal biomarker combination has better diagnostic efficacy.

[0158] The accuracy of the early prediction model for prostate cancer was verified with clinical samples, and the results showed that it basically conformed to the previous data, could meet the requirements of clinical detection, and could increase the detection rate of early cancer in patients.

[0159] In addition, the biomarker 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) test value, serum free PSA (fPSA) test value, and digital rectal examination (DRE) to construct a comprehensive prediction model for prostate cancer. The AUCs of its training set and validation set are 0.83 and 0.84 respectively to further improve the accuracy of the diagnostic results.

[0160] The extraction of exosomal samples is convenient, fast, has the least risk, and the cost is also low, which can relieve the pain of patients; exosomes can be used to monitor the results of cancer treatment or monitor cancer recurrence, increase the detection rate of prostate cancer in high-risk populations, and detect early prostate cancer; reduce the mortality rate of prostate cancer in the screened population without affecting the quality of life of the screened population.

[0161] As described above, it is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered within the protection scope of the present invention.

Claims

1. Use of a reagent for detecting a biomarker in preparing a detection product, characterized in that: The detection product is used to detect prostate cancer; the biomarkers include genes encoding enzymes involved in the oxidation of fatty acids and bile acid intermediates, genes encoding transcription factors, and genes encoding G protein-coupled receptors.

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

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

4. The use according to claim 3, characterized in that: The biomarkers consist of HOXB13, AMACR, FOXA1, MALAT1, PCA3, and PSGR.

5. The use according to claim 3, characterized in that: The biomarkers also include FOXA1, PCA3, MALAT1, PSMA, PSCA, ACP3, TRPM8, NKX3-1, ANO7, and SLC45A3.

6. The use according to any one of claims 1 to 5, characterized in that: The biomarker also includes an internal reference gene, and the internal reference gene is selected from SPDEF or KLK3.

7. A primer set, characterized in that: The primer set is used to detect biomarkers, which include genes encoding enzymes involved in the oxidation of fatty acids and bile acid intermediates, genes encoding transcription factors, and genes encoding G protein-coupled receptors; the primer set includes: The primers for amplifying HOXB13 have an upstream primer sequence as shown in SEQ ID NO:1 and a downstream primer sequence as shown in SEQ ID NO:2; the primers for amplifying AMACR have an upstream primer sequence as shown in SEQ ID NO:3 and a downstream primer sequence as shown in SEQ ID NO:4; the primers for amplifying FOXA1 have an upstream primer sequence as shown in SEQ ID NO:5 and a downstream primer sequence as shown in SEQ ID NO:6; the primers for amplifying MALAT1 have an upstream primer sequence as shown in SEQ ID NO:7 and a downstream primer sequence as shown in SEQ ID NO:8; the primers for amplifying PCA3 have an upstream primer sequence as shown in SEQ ID NO:9 and a downstream primer sequence as shown in SEQ ID NO:10; the primers for amplifying PSGR have an upstream primer sequence as shown in SEQ ID NO:11 and a downstream primer sequence as shown in SEQ ID NO:12; the primers for amplifying PSMA have an upstream primer sequence as shown in SEQ ID NO:13 and a downstream primer sequence as shown in SEQ ID NO:

14. NO:14; the primers for amplifying PSCA, the upstream primer sequence of which is shown in SEQ ID NO:15, and the downstream primer sequence is shown in SEQ ID NO:16; the primers for amplifying ACP3, the upstream primer sequence of which is shown in SEQ ID NO:17, and the downstream primer sequence is shown in SEQ ID NO:18; the primers for amplifying TRPM8, the upstream primer sequence of which is shown in SEQ ID NO:19, and the downstream primer sequence is shown in SEQ ID NO:20; the primers for amplifying NKX3-1, the upstream primer sequence of which is shown in SEQ ID NO:21, and the downstream primer sequence is shown in SEQ ID NO:22; the primers for amplifying ANO7, the upstream primer sequence of which is shown in SEQ ID NO:23, and the downstream primer sequence is shown in SEQ ID NO:24; the primers for amplifying SLC45A3, the upstream primer sequence of which is shown in SEQ ID NO:25, and the downstream primer sequence is shown in SEQ ID NO:

26.

8. A primer set according to claim 7, characterized in that: The primer set further includes: a primer for amplifying the internal reference gene SPDEF, whose upstream primer sequence is shown in SEQ ID NO: 27, and whose downstream primer sequence is shown in SEQ ID NO: 28; a primer for amplifying the internal reference gene KLK3, whose upstream primer sequence is shown in SEQ ID NO: 29, and whose downstream primer sequence is shown in SEQ ID NO:

30.

9. A probe set, characterized in that: The probe set is used to detect biomarkers, which include genes encoding enzymes involved in the oxidation of fatty acids and bile acid intermediates, genes encoding transcription factors, and genes encoding G protein-coupled receptors; the probe set includes: The probe sequence for detecting HOXB13 is shown in SEQ ID NO:31, the probe sequence for detecting AMACR is shown in SEQ ID NO:32, the probe sequence for detecting FOXA1 is shown in SEQ ID NO:33, the probe sequence for detecting MALAT1 is shown in SEQ ID NO:34, the probe sequence for detecting PCA3 is shown in SEQ ID NO:35, the probe sequence for detecting PSGR is shown in SEQ ID NO:36, the probe sequence for detecting PSMA is shown in SEQ ID NO:37, the probe sequence for detecting PSCA is shown in SEQ ID NO:38, the probe sequence for detecting ACP3 is shown in SEQ ID NO:39, the probe sequence for detecting TRPM8 is shown in SEQ ID NO:40, the probe sequence for detecting NKX3-1 is shown in SEQ ID NO:41, the probe sequence for detecting ANO7 is shown in SEQ ID NO:42, the probe sequence for detecting SLC45A3 is shown in SEQ ID NO:43, the probe sequence for detecting SPDEF is shown in SEQ ID NO:44, the probe sequence for detecting KLK3 is shown in SEQ ID NO:45 NO:45 shown.

10. A probe set according to claim 9, characterized in that: The probes of the probe group are labeled with a fluorescent reporter group, and the fluorescent reporter group is selected from FAM, HEX, ROX, VIC, CY5, 5-TAMRA, TET, CY3 or JOE.

11. A probe set according to claim 10, characterized in that: The fluorescent reporter group of the probes shown in SEQ ID NO:31 to SEQ ID NO:33 is FAM; the fluorescent reporter group of the probes shown in SEQ ID NO:34 to SEQ ID NO:36 is HEX; and the fluorescent reporter group of the probe of SEQ ID NO:44 is CY5.

12. A probe set according to claim 9, characterized in that: The 3' end of the probe of the probe group further has a fluorescence quenching group, and the fluorescence quenching group is selected from BHQ1 or BHQ2.

13. A probe set according to claim 12, characterized in that: The fluorescence quenching group of the probes shown in SEQ ID NO:31 to SEQ ID NO:36 is BHQ1; the fluorescence quenching group of the probe shown in SEQ ID NO:44 is BHQ2.

14. A probe set according to claim 12, characterized in that: The fluorescent reporter group shown in SEQ ID NO:31 to SEQ ID NO:33 is FAM, and the fluorescent quencher group is BHQ1; the fluorescent reporter group shown in SEQ ID NO:34 to SEQ ID NO:36 is HEX, and the fluorescent quencher group is BHQ1; the fluorescent reporter group shown in SEQ ID NO:44 is CY5, and the fluorescent quencher group is BHQ2.

15. A kit, characterized in that: The kit is used to detect biomarkers, which include genes encoding enzymes involved in the oxidation of fatty acids and bile acid intermediates, genes encoding transcription factors, and genes encoding G protein-coupled receptors; the kit includes the primer set described in claim 7 or 8 and the probe set described in any one of claims 9 to 14.

16. A kit according to claim 15, characterized in that: The kit includes a reagent for detecting urine exosomes and a reagent for extracting urine exosomes.

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

18. A kit according to claim 17, characterized in that: The final concentration of the primers in the RT-qPCR reaction system is 0.2 μM to 0.4 μM; the final concentration of the probe in the RT-qPCR reaction system is 0.1 μM to 0.3 μM.

19. A kit according to claim 18, characterized in that: The RT-qPCR reaction system also includes Tris buffer, magnesium ions, and 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.

20. A non-diagnostic method for prostate cancer based on a combination of urinary exosome biomarkers, characterized in that: The method is used to convert 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; Wherein a, b, c...n are coefficients, z is a constant, and the coefficients are all between -1 and 1, Ct (internal reference) is the Ct value of the internal reference gene, and Ct (Target 1)...Ct (Target N) is the Ct value of each gene in the biomarker according to any one of claims 1 to 6.

21. The method according to claim 20, characterized in that Using the kit according to any one of claims 15 to 19, the kit is used to detect the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes, and then using the model to calculate the output value and compare it with the positive judgment value to analyze the risk level of prostate cancer in the subject.

22. A device, characterized in that: The device comprises: A detection unit, wherein the detection unit is used to detect the RNA expression levels of HOXB13, AMACR, FOXA1, MALAT1, PCA3, PSGR and SPDEF in urine exosomes; A calculation unit, the calculation unit being used to calculate an output value of the model algorithm as claimed in claim 20; An analysis unit is used to compare the output value of the calculation unit with the positive judgment value to analyze 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, and the one or more programs can be executed by one or more processors to implement the method according to claim 20 or 21.

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