Mass spectrometry method for detecting endometrial cancer based on plasma exosomes

Abstract

The application discloses a mass spectrum analysis method for detecting endometrial carcinoma based on plasma exosomes, and belongs to the technical field of medical detection. The exosome polypeptide group of an endometrial carcinoma patient has specific changes. The method provided by the application comprises three steps of plasma exosome extraction, exosome polypeptide fingerprint analysis, machine learning modeling and screening. Through characteristic peak screening and machine learning modeling, accurate screening can be performed in the early stage of endometrial carcinoma, and the early diagnosis rate is improved. Compared with CA125 and ultrasonic screening, the application has obvious advantages in accuracy, non-invasiveness and operability, and can be popularized to physical examination centers, gynecological clinics and high-risk population follow-up, thereby providing a reliable tool for early discovery and precision medicine of endometrial carcinoma.

Description

Technical Field

[0001] This invention relates to the field of medical testing technology, specifically to a mass spectrometry analysis method for detecting endometrial cancer based on plasma exosomes. Background Technology

[0002] Endometrial cancer (EC) is one of the most common gynecological malignancies, and its incidence has been rising steadily in recent years, seriously endangering women's lives and health. Although most patients experience symptoms such as abnormal uterine bleeding in the early stages, the current lack of effective early screening methods means that some patients fail to receive timely diagnosis and intervention. Therefore, developing rapid and accurate endometrial cancer screening methods has become a key scientific issue for improving patient prognosis.

[0003] Currently, commonly used clinical methods for endometrial cancer screening mainly include cancer antigen 125 (CA125) testing, gynecological ultrasound, and endometrial biopsy. However, these methods have certain limitations: the accuracy of CA125 and ultrasound examinations is relatively low, making it difficult to effectively distinguish between malignant lesions and benign hyperplasia; endometrial biopsy is the gold standard for diagnosis, but this method is highly invasive, and over 90% of patients ultimately have normal endometrium or benign lesions in their pathology results, leading to overdiagnosis and increased patient discomfort and psychological burden. Therefore, there is an urgent clinical need for a simple, efficient, and non-invasive new endometrial cancer screening technology to improve the early diagnosis rate and reduce unnecessary invasive examinations.

[0004] Studies have shown that tumor cells can release a large number of bioactive substances into the bloodstream. Further analysis of plasma components holds promise for identifying highly specific biomarkers, providing new insights for non-invasive early screening. Exosomes, as carriers of intercellular communication, carry various nucleic acids, proteins, and lipids, playing a crucial role in cell communication, signal transduction, and immune regulation. Numerous studies have demonstrated that tumor cells secrete an exceptionally large number of exosomes; therefore, exosomes have significant clinical application value in non-invasive cancer screening and prognostic assessment.

[0005] The molecular composition of exosomes can be detected using various mass spectrometry techniques, among which liquid chromatography-mass spectrometry (LC-MS) and matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS) are currently the two most commonly used methods. Compared to LC-MS, MALDI-TOF MS has advantages such as label-free analysis, fast analysis speed, small sample requirements, and high throughput, making it more suitable for large-scale clinical screening applications.

[0006] Although some studies have explored non-invasive screening methods for endometrial cancer, these methods remain in the early stages due to limitations in experimental techniques, racial differences, and insufficient sample sizes. No widely validated and clinically applicable screening protocols have yet been developed. Therefore, we developed a highly efficient and non-invasive endometrial cancer screening method based on plasma exosome MALDI-TOF mass spectrometry analysis, providing a new solution for early diagnosis and precision medicine. Summary of the Invention

[0007] This invention provides a non-invasive screening method for endometrial cancer based on plasma exosomes. It analyzes the polypeptide fingerprint profile of plasma exosomes using matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS) to screen for characteristic peaks significantly associated with endometrial cancer. A classification model is then constructed using machine learning algorithms to accurately distinguish endometrial cancer patients from normal individuals. This method is non-invasive, efficient, accurate, and has great clinical application potential, making it suitable for early screening and auxiliary diagnosis of endometrial cancer.

[0008] The method of the present invention includes the following steps: Plasma exosome extraction: Peripheral blood samples were collected, and exosomes were separated and extracted using ultracentrifugation.

[0009] Exosome characterization: Transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA) were used to identify the morphology, particle size and concentration of exosomes to ensure sample quality.

[0010] Peptide fingerprint analysis of plasma exosomes: Peptide fingerprint analysis of plasma exosomes was performed using MALDI-TOF MS, and the quality of mass spectrometry data was optimized through data preprocessing (such as normalization, baseline correction, signal smoothing, etc.).

[0011] Peak detection and feature extraction: Screening out biomarker peaks associated with endometrial cancer.

[0012] Machine learning model construction: LASSO regression, PLS-DA partial least squares discriminant analysis, and volcano plot analysis were used for feature selection, ultimately identifying 12 significantly different feature peaks. Classification models were constructed using machine learning methods such as Linear Support Vector Machine (Linear-SVM), Random Forest (RF), and PLS-DA, and their performance was evaluated.

[0013] Model validation: The accuracy of the classification model is evaluated through cross-validation on the training and test sets. The diagnostic efficacy of the feature peaks is validated in an independent test cohort, and the ROC curve and area under the AUC curve are calculated to assess the screening effect.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention utilizes plasma exosomes for detection, requiring only peripheral blood samples. It is non-invasive and employs MALDI-TOF MS technology. A machine learning algorithm is used to screen features from the plasma exosome peptide fingerprint profile, establishing a classification model with an AUC value as high as 1.0, demonstrating superior diagnostic performance. This allows for accurate screening in the early stages of endometrial cancer. This invention offers significant advantages in accuracy, non-invasiveness, and operability, and can be widely applied to health check-up centers, gynecological clinics, and follow-up of high-risk groups, providing a reliable tool for the early detection and precision medicine of endometrial cancer. Attached Figure Description

[0015] Figure 1 High-contrast TEM image of exosomes; Figure 2 The exosome size distribution in the endometrial cancer group and the normal endometrial control group; Figure 3 MALDI-TOF MS spectra of exosomes from plasma samples of endometrial cancer group and normal endometrial control group in the scanning range of m / z 2–20 kDa; Figure 4 Endometrial cancer samples and normal endometrial samples were effectively distinguished using partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Figure 5 When using 12 fingerprint feature peaks to construct a diagnostic model for endometrial cancer, the area under the curve (AUC) of partial least squares discriminant analysis, linear support vector machine (Linear-SVM), and random forest (RF) classifiers on the training and test sets are compared. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to specific embodiments. The embodiments given are only for illustrating the present invention and are not intended to limit the scope of the present invention. Unless otherwise specified, the experimental methods in the following embodiments are conventional methods. Unless otherwise specified, the materials, reagents, instruments, etc. used in the following embodiments are all commercially available.

[0017] The study protocol was approved by the Research Ethics Committee of the Obstetrics and Gynecology Hospital affiliated with Fudan University, and the consent of each patient was obtained. Peripheral plasma samples were collected from 20 patients who underwent total hysterectomy for endometrial cancer and 20 patients who underwent total hysterectomy for benign endometrial diseases.

[0018] Example 1 Exosomes extracted from plasma samples (1) Blood sample processing: Before total hysterectomy, 8 mL of peripheral blood was collected from each patient using a 10 mL EDTA anticoagulant tube. The blood samples were centrifuged at 2000 rpm for 10 min at 4°C, and the supernatant plasma was separated and aliquoted into 1.5 mL sterile cryovials and immediately stored in liquid nitrogen.

[0019] (2) Exosome isolation by ultracentrifugation: First, the frozen plasma sample was slowly thawed in an ice bath at 4°C to avoid repeated freeze-thaw cycles and reduce exosome damage. 1 mL of plasma was diluted with 1 mL of pre-chilled 1× phosphate-buffered saline (PBS), mixed well, and centrifuged at 12000 g for 30 min at 4°C to remove cell debris and large particulate impurities. The precipitate was discarded. The supernatant was transferred to an ultracentrifuge tube and centrifuged at 120000 g for 3 h at 4°C in an ultracentrifuge (Beckman Coulter Optima XPN-100, rotor Type 70 Ti). After discarding the supernatant, the precipitate was resuspended in 3 mL of pre-chilled PBS, and the ultracentrifugation step was repeated once. The final precipitate was resuspended in 100 μL of sterile deionized water, aliquoted, and stored at -80°C for subsequent analysis.

[0020] Example 2 Characterization of exosome morphology and particle size (1) Morphological characterization of exosomes using transmission electron microscopy: After thawing the exosome samples on ice, they were vortexed for 5 seconds. The total protein concentration of exosomes was determined using the BCA protein quantification method, and the concentration was adjusted to 1.1E+11 Particles / mL. 10 μL of sample was dropped onto the surface of a 200-mesh carbon support membrane copper grid, allowed to stand for 10 minutes to absorb excess liquid, and dried at room temperature. The sample was negatively stained with 20 μL of 2% uranyl acetate for 5 minutes, the dye was blotted dry with filter paper, and dried under an incandescent lamp. The morphology of exosomes was observed and photographed using a transmission electron microscope (TEM, NEC Corporation, Japan) at an accelerating voltage of 200 kV.

[0021] Electron micrograph of exosomes as shown Figure 1 As shown, the exosomes exhibit typical cup-shaped or spherical morphology, with a relatively regular appearance and clear boundaries. These vesicle-like structures are dispersed in the field of view, and no obvious aggregation was observed, indicating that the sample maintained a good dispersion state during exosome isolation and sample preparation, and was not severely damaged.

[0022] (2) Nanoparticle tracking analysis (NTA) to measure exosome size: The exosome sample was diluted to 1×10⁻⁶ with 1×PBS (Beyotime, Shanghai, China). 8Particles / mL were injected into a ZetaView PMX120-Z system (version 8.05.14 SP7, Particle Metrix, Germany) and calibrated using 100 nm polystyrene standard particles. The temperature was maintained between 23 and 30 °C, and each sample was measured three times at 11 locations.

[0023] Through the measurement and statistical analysis of a large number of exosomes, such as Figure 2 As shown, the diameter of the exosomes mainly ranges from 50 to 300 nm, with a peak particle size of about 150 nm, exhibiting a relatively narrow particle size distribution, indicating that the exosomes in this sample have relatively uniform size characteristics.

[0024] Example 3 A Mass Spectrometry Method for Detecting Endometrial Cancer Based on Plasma Exosomes (1) Preparation of matrix solution: Prepare 10 mg / mL α-cyano-4-hydroxycinnamic acid (CHCA) matrix in a solvent of 50% ACN + 49.9% H2O + 0.1% TFA (volume ratio).

[0025] (2) Exosome lysis: Take 2 μL of the exosome sample prepared in Example 1, add 1 μL of 70% formic acid (FA), and vortex mix at room temperature for 3 min to disrupt the membrane structure. Then add 1 μL of acetonitrile to the mixture and mix thoroughly for 3 min as well.

[0026] (3) Spotting: Take 1 μL of the mixture and spot it onto the MALDI target plate (Bruker, Germany). After drying at room temperature, cover it with 1 μL of CHCA matrix solution and dry it again.

[0027] (4) MALDI-TOF mass spectrometry analysis: A MALDI-TOF mass spectrometer (Bruker, Germany) was used to acquire data in positive ion linear mode, with a mass range of 2000-20000 Da. The instrument was calibrated using a mixed standard of cytochrome C (m / z 12361) and myoglobin (m / z 16952) (2 mg / mL each).

[0028] (5) Mass spectrometry data processing: The raw data was processed using FlexAnalysis 3.4 software, including square root transformation, smoothing (Savitzky-Golay, window width 5), baseline correction, and peak extraction (S / N≥6, half-maximum width 0.05-0.5 Da). The detected mass-to-charge ratio (m / z) values ​​were calibrated between each mass spectrum to create a characteristic peak matrix containing the m / z values ​​of the peaks and their corresponding signal intensity information. Peak alignment was performed using ClinProTools 3.0 software (mass tolerance 0.005 Da), finally generating a matrix containing 150 characteristic peaks (m / z 2000-20000).

[0029] The results are as follows Figure 3 As shown, most mass spectrometry peaks are concentrated in the m / z range of 2000–20000, with prominent peaks observed at m / z = 2433, 3294, 4511, 12369, 16964, and 17035, which are significantly downregulated in the endometrial cancer group. Conversely, peaks at m / z = 2184, 2226, 2311, 2354, 2951, and 4407 are significantly upregulated in the endometrial cancer group. These characteristic peaks can be used to distinguish endometrial cancer patients from healthy controls.

[0030] Example 4 Validating the sensitivity and specificity of plasma exosome fingerprinting for the early diagnosis of endometrial cancer. (1) Exosomes were extracted from plasma samples using the method described in Example 1, and MALDI-TOF mass spectrometry analysis was performed using the method described in Example 3 to obtain the fingerprint spectrum of plasma exosomes.

[0031] (2) Dataset construction: The raw mass spectrometry data were subjected to summation normalization, logarithmic transformation to base 10, and automatic scaling preprocessing in sequence. The preprocessed data were then input into MetaboAnalyst 6.0 software to construct partial least squares discriminant analysis (PLS-DA) models and orthogonal partial least squares discriminant analysis (OPLS-DA) models, respectively.

[0032] The results are as follows Figure 4 As shown, the PLS-DA score map shows that the endometrial cancer group (red scatter) and the normal endometrial control group (blue scatter) show a significant separation trend on the LV1 axis, with no overlapping area; the OPLS-DA score map shows that the endometrial cancer group and the healthy control group are completely separated along the predicted direction, indicating that the exosome fingerprint has a clear disease differentiation ability.

[0033] (3) Machine Learning Modeling: Endometrial cancer sample data and normal endometrial sample data were randomly divided into training and test sets with a ratio of 3:1. Feature selection was performed using three different methods: Least Absolute Contraction and Selection Operator (LASSO) regression, Partial Least Squares Discriminant Analysis (PLS-DA), and Volcano Plot Analysis. Twelve overlapping features were ultimately selected: m / z values ​​were 4511, 17035, 12369, 2184, 2433, 2311, 3294, 2951, 4407, 2226, 16964, and 2354, respectively. Based on these 12 feature peaks, a screening model for endometrial cancer was developed using Partial Least Squares Discriminant Analysis, Linear Support Vector Machine (Linear-SVM), and Random Forest (RF).

[0034] The results are as follows Figure 5 As shown, in the training set, the area under the receiver operating characteristic (AUC) of the three models are 0.969, 0.955, and 0.953, respectively. In the independent test cohort (n=10), the AUC of all three models reaches 1.0, indicating that the selected feature peak combination has excellent classification performance.

[0035] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A mass spectrometry analysis method for detecting endometrial cancer based on plasma exosomes, the method comprising the following steps: (1) Extraction of plasma exosomes: Peripheral blood samples were collected from the subjects, plasma was obtained by centrifugation, and then plasma exosomes were extracted by ultracentrifugation; (2) Exosomal peptide fingerprint analysis: MALDI-TOF MS was used to detect exosomal peptides and screen for characteristic peaks related to endometrial cancer; (3) Machine learning modeling and screening: Construct a dataset by combining endometrial cancer sample data and normal endometrial sample data, screen out differential features, and develop a screening model for endometrial cancer.

2. The method according to claim 1, characterized in that, (2) The specific steps of the exosome polypeptide fingerprint analysis include: (1) Co-crystallization of exosomal polypeptide samples was prepared using α-cyano-4-hydroxycinnamic acid matrix; (2) Mass spectrometry data in the range of 2000-20000 Daltons were acquired using MALDI-TOF mass spectrometry in positive ion linear mode; (3) After baseline correction, smoothing and peak extraction, the characteristic peaks that are significantly upregulated and downregulated in the endometrial cancer group in the mass spectrometry data are used as diagnostic markers to distinguish endometrial cancer patients from healthy control groups.

3. The method according to claim 2, characterized in that, The α-cyano-4-hydroxycinnamic acid matrix solution has a concentration of 10 mg / mL and uses 50% acetonitrile, 49.9% water, and 0.1% trifluoroacetic acid as solvents.

4. The endometrial cancer screening method according to claim 2, characterized in that, The diagnostic markers are at least six characteristic peaks selected from m / z values ​​of 2184, 2226, 2311, 2354, 2951, 4407, 4511, 12369, 16964, 17035, 2433, and 3294.

5. The method according to claim 1, characterized in that, (3) The machine learning modeling includes: (1) The endometrial cancer sample data and normal endometrial sample data were randomly divided into training set and test set; (2) Perform summation normalization, logarithmic transformation to base 10, and automatic scaling preprocessing on the raw mass spectrometry data in sequence; (3) Feature selection was performed using three different methods: least absolute shrinkage and selection operator LASSO regression, partial least squares discriminant analysis (PLS-DA), and volcano plot analysis. (3) A screening model for endometrial cancer was developed using partial least squares discriminant analysis, linear support vector machine (Linear-SVM), and random forest (RF).

6. The method according to claim 1, characterized in that, This method is suitable for early screening of endometrial cancer, detection of high-risk groups, and disease monitoring.

7. A plasma exosome extraction kit for carrying out the method according to any one of claims 1-5, characterized in that, Includes: EDTA anticoagulant tubes, phosphate buffer, 70% formic acid solution, α-cyano-4-hydroxycinnamic acid matrix, and operating instructions.

8. A diagnostic system for endometrial cancer based on plasma exosome analysis mass spectrometry, characterized in that, include: (1) Mass spectrometry data acquisition module, configured to acquire mass spectrometry data in the range of 2000-20000 Daltons; (2) Feature extraction module, configured to detect peak intensity at m / z 2184, 2226, 2311, 2354, 2951, 4407, 4511, 12369, 16964, 17035, 2433, 3294; (3) Classification model module, including trained PLS-DA, Linear-SVM or random forest classifier; (4) Results output module, outputs diagnostic suggestions based on classification results.

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