Urine exosome mass spectrum metabolic fingerprint screened by combining SEC-LDI method and application

Abstract

The invention belongs to the technical field of biomedical detection and tumor molecular diagnosis, particularly relates to a urine exosome mass spectrum metabolic fingerprint, and further discloses a method for separating and purifying urine exosome based on exclusion chromatography and screening prostate malignant tumor biomarkers in combination with solid-phase mass spectrum metabonomics. The invention also discloses application of the compound in preparation of a prostatic cancer biomarker. According to the invention, urine mass spectrum metabolism fingerprint detection is carried out on the basis of an SEC + LDI-MS platform, a metabolism marker with stable diagnosis performance is screened out, six urine exosome metabolism markers for prostatic cancer are screened out, the difference between a healthy group and an experimental group is obvious (p is less than 0.05), and the sensitivity is high. Discovery of the prostate cancer marker is crucial to disease diagnosis and disease progress monitoring, and the defect that high-accuracy diagnosis performance cannot be achieved through an existing metabolic marker is effectively overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical detection and tumor molecular diagnosis, and specifically relates to a mass spectrometry metabolic fingerprint of urine exosomes. It further discloses a method for separating and purifying urine exosomes based on size exclusion chromatography combined with solid-phase mass spectrometry metabolomics to screen biomarkers of prostate malignant tumors, as well as the use of the method for preparing prostate cancer biomarkers. Background Art

[0002] In recent years, with the increasing incidence of prostate cancer and the urgent need for early treatment among patients, traditional prostate-specific antigen (PSA) testing and imaging examinations have gradually exposed problems such as insufficient sensitivity, limited specificity, and high invasiveness. These limitations have prompted researchers to continuously explore more accurate, non-invasive detection methods suitable for large-scale clinical application. In recent years, extracellular vesicles (EVs), as important carriers of cellular metabolic information, have gradually become an important direction for tumor biomarker development due to their high stability in biological fluids and their ability to reflect the intrinsic state of tumor cells.

[0003] Currently, the main methods for isolating exosomes include ultracentrifugation and pre-precipitation, but these methods have limitations in terms of separation efficiency, purity, and sample size requirements. In contrast, size exclusion chromatography (SEC) has become a key breakthrough in optimizing exosome separation technology due to its ease of operation, rapid separation speed, low sample size requirement, and high-purity separation. The SEC method utilizes the differences in the flow rates of molecules of different particle sizes within a fixed medium to achieve separation. This method not only effectively removes proteins and other impurities, but also maximizes the preservation of the original biological activity of exosomes, providing a stable and reliable sample foundation for subsequent metabolomics analysis.

[0004] Laser desorption / ionization-solid-phase mass spectrometry (LDI-MS), a novel technology platform in metabolomics analysis, offers unique advantages, including broad mass spectrometric metabolic fingerprint coverage, high sensitivity, excellent resolution, and rapid detection efficiency. These features make it highly effective for analyzing a variety of biofluid samples, including serum, urine, and cerebrospinal fluid, and better suited for clinical applications, providing strong technical support for the early diagnosis of diseases such as prostate cancer.

[0005] Although exosome isolation and metabolomics detection have each made progress in the field of tumor diagnosis, their combined application is still in its early stages of exploration, and a systematic and standardized detection process has yet to be established. This field awaits further exploration and research into this approach. Summary of the Invention

[0006] In order to overcome the shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide a urine exosome mass spectrometry metabolic fingerprint related to the diagnosis of prostate cancer and its use as a biomarker for the diagnosis of prostate cancer. The urine exosome mass spectrometry metabolic fingerprint related to the diagnosis of prostate cancer has good sensitivity and specificity when used for auxiliary diagnosis of prostate cancer, can be used to prepare reagents for the diagnosis of prostate cancer, and has the advantages of easy sample acquisition, non-invasiveness, simplicity and speed.

[0007] In order to solve the above problems, the present invention provides a urine exosome mass spectrometry metabolic fingerprint, including one or more mixtures having the following mass-to-charge ratios: that is, m / z ratios: F163.949, F164.974, F186.96, F197.921, F202.939, and F98.943.

[0008] The present invention also provides a use of the urine exosome mass spectrometry metabolic fingerprint for preparing a prostate cancer biomarker.

[0009] The present invention also provides a method for screening the mass spectrometry metabolic fingerprint of urine exosomes as described above by combining SEC-LDI method, comprising the following steps:

[0010] (1) Separation of urine samples

[0011] Urine samples were collected and stored at -80°C;

[0012] (2) SEC method for extracting exosomes

[0013] The urine is centrifuged at 2500-3500×g, the supernatant is collected, and the supernatant is further centrifuged at 8000-12000×g to collect the supernatant as a pretreatment sample; the pretreatment sample is added to the column filter plate of the equilibrium exclusion chromatography column, and when the sample just enters the column and the liquid outflow stops, the elution buffer is added, and the eluted sample is collected to obtain a sample to be tested for later use;

[0014] (3) Metabolic fingerprint detection

[0015] MeOH:ACN was added to the sample to be tested and the mixture was incubated and precipitated; the incubated reaction product was centrifuged, the supernatant extract was collected, evaporated to dryness, and then resuspended in ACN:H2O, centrifuged again, and the supernatant was collected for LDI-MS detection;

[0016] (4) Establishment of key metabolic fingerprints

[0017] Combining the mean ion intensity, t-test (P<0.05) and VIP score, we preliminarily screened the candidate metabolic fingerprints between prostate cancer and benign hyperplasia groups. We further extracted the most predictive variables using the Lasso regression method. Finally, we screened out key metabolic fingerprints with significant differences and clinical application value by intersecting the above key metabolic fingerprints.

[0018] Specifically, the method for screening the urine exosome mass spectrometry metabolic fingerprint by the combined SEC-LDI method further includes: (5) constructing a prostate cancer screening model based on machine learning, specifically comprising dividing the urine sample data collected from prostate cancer and prostate hyperplasia patients into a training set and a test set, and performing machine learning according to the following process:

[0019] Data collection and processing: urine mass spectrometry data is cleaned, feature extracted, normalized, and missing value filled;

[0020] Data segmentation: Divide the dataset into training set and validation set according to 8:2;

[0021] Model selection: Choose from ten machine learning algorithms including Adaboost, GBM, Glmnet, kknn, LogitBoost, MLP, NB, RegLogistic, RF, and svmRadiaWeights;

[0022] Modeling: Based on the selected model, use the training set to train the model;

[0023] Hyperparameter optimization: Automatically optimize the model's hyperparameters through algorithms to improve model performance;

[0024] Model Validation: Use the validation set to evaluate the performance of the model.

[0025] Specifically, in the method for screening the urine exosome mass spectrometry metabolic fingerprint by the combined SEC-LDI method, in step (3), the LDI-MS detection step includes:

[0026] A Brucker Autoflex time-of-flight mass spectrometer (TOF-MS) was used in positive ion mode with a laser pulse frequency of 1 kHz, an acceleration voltage of 20 kV, 2000 laser shots, and a delay time of 200 ns.

[0027] Specifically, in the method for screening the urine exosome mass spectrometry metabolic fingerprint by the combined SEC-LDI method, in step (3), the LC-MS detection step further comprises:

[0028] The sample to be tested uses iron oxide nanoparticles as a matrix, and before performing the LDI-MS analysis, the analyte of the standard small molecule or EV sample is added to the plate, and then the matrix is ​​added and dried at room temperature.

[0029] The present invention also provides an in vitro diagnostic marker model for prostate cancer, wherein the biological biomarkers of the diagnostic model are identified and constructed based on the urine exosome mass spectrometry metabolic fingerprint.

[0030] The present invention also provides the use of the urine exosome mass spectrometry metabolic fingerprint for preparing a prostate cancer diagnostic product.

[0031] Specifically, the product includes a reagent for detecting the content of the urine exosome mass spectrometry metabolic fingerprint.

[0032] The present invention also provides a kit for diagnosing prostate cancer, which comprises a reagent for detecting the content of the urine exosome mass spectrometry metabolic fingerprint in a sample.

[0033] This study, based on the extraction of exosomes by size exclusion chromatography and the screening of urine exosome mass spectrometry metabolomics using solid-phase mass spectrometry, constructs a biomarker for prostate malignancy. This organically combines SEC extraction of exosomes with LDI-MS metabolomics analysis. This not only leverages the high efficiency of SEC in exosome purification, but also enables the precise detection and quantitative analysis of trace mass spectrometry metabolic fingerprints within exosomes using the LDI-MS platform, thereby constructing a characteristic metabolomics profile. This powerful combination of technologies not only meets the needs of non-invasive, rapid, and high-precision prostate cancer detection, but also has the potential to effectively distinguish prostate cancer from benign hyperplasia in actual clinical applications.

[0034] The method for screening and constructing a urinary exosome mass spectrometry metabolic fingerprint described in this invention provides a noninvasive method for detecting prostate cancer based on extracellular vesicle metabolomics. To address the technical limitations of existing prostate-specific antigen (PSA) tests, which lack specificity, and the highly invasive nature of imaging examinations, urinary exosomes were isolated and purified using size exclusion chromatography, and exosome metabolomics profiles were constructed using laser desorption / ionization-solid phase mass spectrometry. Six characteristic mass spectrometry metabolic fingerprints were screened based on 44 clinical samples, and a machine learning diagnostic model was established, achieving an area under the curve of 0.86 for the validation set. By combining a standardized exosome isolation process with metabolic markers, this invention achieves high-precision differentiation between prostate cancer and benign hyperplasia.

[0035] In the method for screening and constructing a urinary exosome mass spectrometry metabolic fingerprint described in this paper, based on the aforementioned technological development status and clinical needs, researchers in this field are committed to exploring a urine-based exosome metabolomics detection method coupled with size exclusion chromatography and solid-phase mass spectrometry, with the goal of establishing a standardized, rapid, and high-throughput detection platform. This platform can not only improve the accuracy of early prostate cancer diagnosis and reduce the misdiagnosis rate, but also provide patients with a more convenient non-invasive detection method, effectively promoting the development of precision medicine for prostate cancer.

[0036] The method for screening and constructing a urinary exosome mass spectrometry metabolic fingerprint described in this invention provides a non-invasive cancer screening method based on body fluid metabolomics. Specifically, it integrates urinary exosome isolation and purification technologies, small molecule mass spectrometry metabolic fingerprinting detection and analysis technologies, and artificial intelligence-assisted diagnostic models. Through standardized exosome isolation procedures and combined identification of metabolic markers, molecular differential diagnosis of prostate cancer and benign prostate diseases can be achieved. This technical solution encompasses collaborative innovation across multiple technical branches, including clinical laboratory medicine, nanovesicle separation technology, mass spectrometry analysis, and medical big data processing.

[0037] In the method for screening and constructing a urinary exosome mass spectrometry metabolic fingerprint described in the present invention, based on urine samples from 44 prostate tumor groups and benign hyperplasia controls, exosomes were standardizedly extracted using the SEC method (fast, low sample requirement, and high extract purity). Combined with LDI-MS metabolomics (fast speed, high resolution, and wide coverage), an AI-assisted diagnosis model was constructed. Data analysis results showed that the urine + exosome + metabolic process can provide a more comprehensive analytical perspective (AUC = 0.86), effectively overcoming the insufficient sensitivity, low specificity, and highly invasive operation of traditional prostate cancer detection methods (such as PSA testing and imaging examinations), which lead to high misdiagnosis rates and poor patient compliance.

[0038] In the method for screening and constructing urine exosome mass spectrometry metabolic fingerprints described in the present invention, urine mass spectrometry metabolic fingerprint detection was performed based on the SEC+LDI-MS platform, and metabolic markers with stable diagnostic performance were screened. Six urine exosome metabolic markers for prostate cancer were screened, and the differences between the healthy group and the experimental group were significant (p less than 0.05). The discovery of prostate cancer markers is crucial for disease diagnosis and disease progression monitoring, and effectively overcomes the inability of existing metabolic markers to achieve high-accuracy diagnostic performance.

[0039] In the urine exosome mass spectrometry metabolic fingerprint screening and construction method described in the present invention, the efficiency of model construction is effectively improved by rationally introducing multi-type machine learning, which can quickly find the optimal model, greatly shorten the model construction time, reduce the probability of misjudgment, and quickly improve the model training speed; there is no need for manual parameter adjustment, and when selecting the most suitable machine learning model, it effectively overcomes the problem of manual screening and parameter adjustment requiring a certain amount of time cost and debugging difficulty.

[0040] This invention is based on the extraction of exosomes by size exclusion chromatography combined with solid-phase mass spectrometry metabolomics to screen biomarkers of prostate malignant tumors. To address the problems of insufficient specificity of PSA detection and limited sensitivity of imaging examinations in traditional prostate cancer detection, exosome metabolomics detection is used to construct a prostate cancer metabolic signature spectrum using exosome metabolomics. This can accurately distinguish between prostate cancer and benign hyperplasia, thereby greatly improving diagnostic accuracy and having higher clinical value than traditional PSA testing and imaging examinations.

[0041] This invention is based on the extraction of exosomes by size exclusion chromatography combined with solid-phase mass spectrometry metabolomics to screen biomarkers for prostate malignant tumors. To address the problems of existing detection methods, such as high invasiveness, low patient acceptance, and complex operation, urine samples are used for non-invasive testing, avoiding invasive operations such as tissue biopsy. At the same time, SEC is used to extract exosomes and LDI-MS detection technology is used to achieve a rapid and standardized detection process, which not only ensures the detection effect but also improves clinical operability, significantly surpassing the convenience and patient compliance of traditional methods in clinical applications.

[0042] This invention is based on the extraction of exosomes by size exclusion chromatography combined with solid-phase mass spectrometry metabolomics to screen biomarkers of prostate malignant tumors. To address the shortcomings of existing exosome extraction methods, such as large sample requirements, slow separation speed, and insufficient purity, the SEC method is used to extract exosomes. Taking advantage of its fast speed, low sample requirements, and high separation purity, compared with traditional methods such as ultrahigh-speed centrifugation, the SEC method has higher extraction efficiency and sample purity, providing a more stable and reliable foundation for subsequent metabolomics analysis and reducing detection errors.

[0043] This invention is based on the extraction of exosomes by size exclusion chromatography combined with solid-phase mass spectrometry metabolomics to screen biomarkers of prostate malignant tumors. To overcome the limitations of traditional mass spectrometry metabolic fingerprint detection methods in sensitivity and resolution, LDI-MS technology is used to achieve accurate detection and quantitative analysis of trace mass spectrometry metabolic fingerprints in exosomes. The established standardized detection process not only ensures the repeatability and stability of the test results, but also facilitates large-scale clinical screening and promotion and application, can effectively reduce detection costs and operational difficulty, and produce metabolic profiles with clinical diagnostic significance.

[0044] This invention uses size exclusion chromatography to extract exosomes and solid-phase mass spectrometry metabolomics to screen biomarkers for prostate malignancy. To address the lack of a unified standard detection process in existing technologies, a standardized exosome separation and metabolomics detection process was established based on preliminary experimental data and theoretical analysis. This method demonstrated higher sensitivity and specificity (AUC: 0.86) in distinguishing prostate cancer from benign diseases, ensuring the stability and repeatability of the test results. This detection method has shown significant improvements in practical applications, providing reliable data support for accurate diagnosis and facilitating its promotion to large-scale clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 Schematic diagram of the comparison of exosome characteristic marker detection using different exosome extraction methods in the present invention; PCa: prostate cancer; BPH: prostate hyperplasia; U: ultracentrifugation; P: precipitation; K: kit method; PS: precipitation plus size exclusion chromatography; S: size exclusion chromatography;

[0047] Figure 2 The key metabolic fingerprints identified in the present invention show differential patterns in prostate cancer (PCa) patients and benign prostatic hyperplasia (BPH) patients. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention.

[0049] To fully disclose the entire technical solution of the present invention, this embodiment describes in detail the various operational steps and corresponding effects of the present invention from sample processing to data analysis and model construction, and illustrates the significant advantages brought about by the technical improvements through comparative experiments.

[0050] Example 1

[0051] This embodiment performs test sample processing and exosome separation.

[0052] In this example, a urine sample collected clinically was pretreated. Immediately after sample collection, the urine was centrifuged to obtain the fluid, which was then stored at low temperatures to ensure stability. Subsequently, the exosomes in the urine were extracted using SEC.

[0053] After the urine sample is collected, it is quickly transferred to a -80°C refrigerator for storage to provide a stable sample basis for subsequent testing.

[0054] In this example, SEC was used for exosome extraction: first, centrifuge at 3000 × g for 10 minutes at 4°C, and transfer the supernatant. Subsequently, centrifuge at 10,000 × g for 30 minutes at 4°C, and collect the supernatant as the pretreatment sample. After cleaning and equilibrating the size exclusion chromatography column, 1-2 mL of the supernatant was added to the column filter. As soon as the sample entered the column and liquid flow ceased, a predetermined volume of elution buffer was added to exclude fractions containing no exosomes. A new EP tube was placed under the column, and 0.9 mL of elution buffer was added to collect the sample. Finally, this sample was stored at -80°C until further use.

[0055] In this embodiment, the sample is pretreated by using a suitable organic solvent to perform protein precipitation, ultrasonic treatment and concentration on the sample, so that the mass spectrometry metabolic fingerprint components can be fully released and remain stable.

[0056] In this embodiment, Figure 1 The results shown in Figure 2 show that exosomes extracted using SEC exhibit lower background interference and higher signal consistency in the detection of exosome signature markers, thus ensuring the acquisition of high-precision metabolic fingerprints. Compared with traditional ultracentrifugation methods, SEC is simpler to operate and significantly shortens processing time, while significantly improving the recovery rate and purity of exosomes.

[0057] Example 2

[0058] This example is based on the sample obtained in the above-mentioned Example 1, and performs metabolic fingerprint acquisition and data processing.

[0059] Sample pretreatment

[0060] Add 240 μL of MeOH:ACN (v:v, 1:1) to the sample, vortex for 30 seconds, and sonicate for 10 minutes. Incubate at 20°C for 1 hour to facilitate protein precipitation. Then, centrifuge the sample at 13,000 rpm for 15 minutes at 4°C, collect the supernatant, and evaporate to dryness in a vacuum concentrator. Resuspend the dried extract in 50 μL of 1:1 ACN:H2O, sonicate for 10 minutes, and centrifuge at 13,000 rpm for 15 minutes at 4°C. Collect the supernatant and store at -80°C.

[0061] LDI-MS detection process

[0062] This embodiment uses a novel LDI-MS system to perform mass spectrometry metabolic fingerprint analysis on the processed exosome samples.

[0063] This example uses a Bruker system to analyze standard mass spectrometry metabolic fingerprints and samples. Nd:YAG laser (355nm) is used for Autoflex (TOF-MS). All samples are based on conventional iron oxide nanoparticles (1.0mg / mL) and are detected using the LDI-MS platform. Before LDI-MS analysis, the analytes of standard small molecules (1mM, 1.5μL) or EV samples (1.5μL) are added to the plate, followed by the matrix (1.5μL) and dried at room temperature. All LDI-MS experiments were performed using a Bruker Autoflex time-of-flight mass spectrometer (TOF-MS) in positive ion mode, with a laser pulse frequency of 1kHz, an acceleration voltage of 20kV, 2000 laser shots, and a delay time of 200ns.

[0064] Data processing

[0065] Raw data files are converted to different formats, peak detected, aligned, and normalized to ensure data reproducibility and accuracy.

[0066] Several quality control and preprocessing steps were applied before data processing to ensure the accuracy and reliability of the metabolomics data. Missing values ​​were first addressed using the k-nearest neighbor (KNN) method. Subsequently, the data were normalized using the MetaboAnalystR package (version 4.0.0), employing MedianNorm, LogNorm, and MeanCenter normalization strategies.

[0067] In this embodiment, under the aforementioned pre-treatment and detection parameters, after a rigorous data processing process, the metabolic fingerprint spectrum obtained has high resolution and high stability, providing a solid data foundation for the subsequent screening of key metabolic markers.

[0068] Example 3

[0069] This embodiment is based on the method of the aforementioned Example 2 to establish key metabolic fingerprints.

[0070] In this example, a preliminary screening of candidate metabolic fingerprints was conducted between prostate cancer and benign hyperplasia groups using a combination of mean ion intensity, t-test (P < 0.05), and VIP score. Lasso regression was then used to further identify the most predictive variables. Finally, by intersecting these key metabolic fingerprints, key metabolic fingerprints with significant differences and clinical application value were identified.

[0071] Based on the metabolic fingerprint data initially obtained in Example 2, this embodiment uses multiple statistical methods (such as univariate correlation analysis, t-test, VIP score, and Lasso regression, each using R language) to screen for differences in mass spectrometry metabolic fingerprints between prostate cancer and benign prostatic hyperplasia samples. The specific steps are as follows:

[0072] (1) Using statistical analysis software, preliminary difference analysis was performed on the sample data of each group, i.e., t-test was performed, and P < 0.05, VIP > 1, |Log2FC| > 0.5 were used to preliminarily select candidate mass spectrometry metabolic fingerprints;

[0073] (2) Correlation analysis is performed in combination with clinical indicators to ensure that the screened mass spectrometry metabolic fingerprint has practical diagnostic significance;

[0074] (3) The Lasso regression method was used to further narrow the candidate range and finally determine the key metabolic fingerprints with significant differences and predictive capabilities, namely the theoretical mass-to-charge ratios: F163.949, F164.974, F186.96, F197.921, F202.939, and F98.943.

[0075] In this example, the differences in the metabolic fingerprints screened out between the groups are shown in the attached figure. Figure 2 .

[0076] It can be seen that the screened metabolic fingerprints (theoretical mass-to-charge ratio: F163.949, F164.974, F186.96, F197.921, F202.939, F98.943) showed significant differences in the inter-group comparison, proving its clinical feasibility as a non-invasive detection indicator.

[0077] Example 4

[0078] This example builds a prostate cancer screening model based on machine learning based on the results of the aforementioned Example 3.

[0079] In this example, the collected urine sample data (17 cases of prostate cancer and 19 cases of benign prostatic hyperplasia) were divided into a training set and a test set, and machine learning was performed according to the following six main processes:

[0080] 1) Data collection and processing: First, the urine mass spectrometry data is cleaned, feature extracted, normalized, and missing value filled;

[0081] 2) Data segmentation: The dataset is divided into training set and validation set according to the ratio of 8:2;

[0082] 3) Model selection: Ten machine learning algorithms, including Adaboost, GBM, Glmnet, kknn, LogitBoost, MLP, NB, RegLogistic, RF, and svmRadiaWeights, were selected using the screened key metabolic fingerprints. Models were trained using the training set data, and 10-fold cross-validation was applied to optimize model parameters.

[0083] 4) Modeling: Based on the selected model, use the training set to train the model;

[0084] 5) Hyperparameter optimization: Automatically optimize the model's hyperparameters through algorithms to improve model performance;

[0085] 6) Model Validation: Use an independent validation set to evaluate model performance and compare it using multiple metrics (such as AUC and accuracy). The results are shown in Table 1 below. The "training" in the "type" field is the basic model validation set, while the "validation" field is the validation data set used to test the model's performance.

[0086] Table 1 Classification performance of different prostate cancer screening models based on urine metabolic markers

[0087]

[0088]

[0089] In this example, the constructed screening model showed high stability and high accuracy (AUC was 0.86) on the validation set, and the multi-model evaluation indicators were better than the traditional methods, further confirming the practical application value of the method of the present invention in the early diagnosis of prostate cancer.

[0090] It can be seen that the biomarker screening method described in the present invention has various steps that are interconnected, forming a complete detection process from urine sample acquisition to metabolic fingerprint data analysis to machine learning screening model construction, ensuring the efficiency, accuracy and clinical applicability of the present invention in the non-invasive detection of early prostate cancer.

[0091] In summary, the present invention provides a comprehensive process from urine sample collection, exosome extraction, metabolic fingerprint acquisition, key metabolic fingerprint screening, to machine learning screening model construction. Each step, validated by experimental data, demonstrates significant technical advantages: improved detection accuracy and specificity, shortened detection time, and reduced sample requirements and operational complexity. Compared with existing technologies, this invention offers significant advantages, providing an efficient, standardized, and scalable technical solution for the noninvasive detection of early prostate cancer.

[0092] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A urine exosome mass spectrometry metabolic fingerprint, characterized in that: It includes one or more mixtures having the following mass-to-charge ratios: that is, m / z ratios: F163.949, F164.974, F186.96, F197.921, F202.939, F98.

943.

2. A use of the urine exosome mass spectrometry metabolic fingerprint as claimed in claim 1 for preparing a prostate cancer biomarker.

3. A method for screening the urine exosome mass spectrometry metabolic fingerprint according to claim 1 by combining SEC-LDI method, characterized in that: The steps include: (1) Separation of urine samples Urine samples were collected and stored at -80°C; (2) SEC method for extracting exosomes The urine is centrifuged at 2500-3500×g, the supernatant is collected, and the supernatant is further centrifuged at 8000-12000×g to collect the supernatant as a pretreatment sample; the pretreatment sample is added to the column filter plate of the equilibrium exclusion chromatography column, and when the sample just enters the column and the liquid outflow stops, the elution buffer is added, and the eluted sample is collected to obtain a sample to be tested for later use; (3) Metabolic fingerprint detection MeOH:ACN was added to the sample to be tested and the mixture was incubated and precipitated; the incubated reaction product was centrifuged, the supernatant extract was collected, evaporated to dryness, and then resuspended in ACN:H2O, centrifuged again, and the supernatant was collected for LDI-MS detection; (4) Establishment of key metabolic fingerprints The mean ion intensity, t-test (P<0.05), and VIP score were combined to preliminarily screen candidate metabolic fingerprints between prostate cancer and benign hyperplasia groups. The Lasso regression method was then used to further extract the most predictive variables. In addition, by intersecting the above key metabolic fingerprints, key metabolic fingerprints with significant differences and clinical application value are screened out.

4. The method for screening the urine exosome mass spectrometry metabolic fingerprint by combining SEC-LDI method according to claim 3, characterized in that: The method further includes: (5) constructing a prostate cancer screening model based on machine learning, specifically dividing the urine sample data collected from patients with prostate cancer and benign prostatic hyperplasia into a training set and a test set, and performing machine learning according to the following process: Data collection and processing: urine mass spectrometry data is cleaned, feature extracted, normalized, and missing value filled; Data segmentation: Divide the dataset into training set and validation set according to 8:2; Model selection: Choose from ten machine learning algorithms including Adaboost, GBM, Glmnet, kknn, LogitBoost, MLP, NB, RegLogistic, RF, and svmRadiaWeights; Modeling: Based on the selected model, use the training set to train the model; Hyperparameter optimization: Automatically optimize the model's hyperparameters through algorithms to improve model performance; Model Validation: Use the validation set to evaluate the performance of the model.

5. The method for screening the urine exosome mass spectrometry metabolic fingerprint by combining SEC-LDI method according to claim 3 or 4, characterized in that: In the step (3), the LDI-MS detection step includes: A Brucker Autoflex time-of-flight mass spectrometer (TOF-MS) was used in positive ion mode with a laser pulse frequency of 1 kHz, an acceleration voltage of 20 kV, 2000 laser shots, and a delay time of 200 ns.

6. The method for screening the urine exosome mass spectrometry metabolic fingerprint by combining SEC-LDI method according to claim 5, characterized in that: In the step (3), the LC-MS detection step further comprises: The sample to be tested uses iron oxide nanoparticles as a matrix, and before performing the LDI-MS analysis, the analyte of the standard small molecule or EV sample is added to the plate, and then the matrix is ​​added and dried at room temperature.

7. An in vitro diagnostic marker model for prostate cancer, characterized in that: The biological biomarkers of the diagnostic model are identified and constructed based on the urine exosome mass spectrometry metabolic fingerprint of claim 1.

8. Use of the urine exosome mass spectrometry metabolic fingerprint according to claim 1 for preparing a prostate cancer diagnostic product.

9. The use according to claim 8, characterized in that The product includes a reagent for detecting the content of the urine exosome mass spectrometry metabolic fingerprint.

10. A kit for diagnosing prostate cancer, characterized in that: The kit comprises a reagent for detecting the content of the urine exosome mass spectrometry metabolic fingerprint as claimed in claim 1 in a sample.

Images