Plasma exosome markers for early diagnosis of pancreatic cancer and application

Abstract

The application provides a plasma exosome marker and application for early diagnosis of pancreatic cancer, utilizes a metabolomics and proteomics method, analyzes metabolites and proteins with significant differences in exosomes separated from plasma of pancreatic cancer patients, pancreatitis patients and normal persons, screens a series of biomarkers capable of early indicating a pancreatic cancer occurrence risk, and further screens a group of biomarkers to construct a pancreatic cancer diagnosis model, so that the pancreatic cancer diagnosis model can be used for conveniently and efficiently predicting whether an individual is suffering from pancreatic cancer, and meets clinical requirements.

Description

Technical Field

[0001] This invention relates to the medical field, and more specifically, to the use of metabolomics to screen for biomarkers of pancreatic cancer and to their use in the diagnosis of pancreatic cancer. In particular, it relates to a predictive system for predicting the risk of pancreatic cancer by detecting the abundance of exosomes containing metabolites in plasma samples and its application. Background Technology

[0002] Metabolomics is a discipline that involves the qualitative and quantitative analysis of small molecule metabolites with a relative molecular weight of less than 1000 in the body. Metabolomics analysis can reflect the physiological and pathological conditions of the body and distinguish differences between individuals. With the development of mass spectrometry, liquid chromatography-mass spectrometry (LC-MS) has become the most important research tool in metabolomics research. Currently, metabolomics is widely used in clinical diagnostics, primarily to discover metabolic biomarkers related to disease diagnosis and treatment.

[0003] Currently, clinical diagnostic methods for pancreatic cancer mainly include imaging, histopathological, and blood immunobiochemical diagnostics. However, due to the low specificity of imaging examinations and the difficulty in performing biopsies at the lesion site for histopathological diagnosis, the expression level of tumor markers in the blood has become the primary detection indicator. Since its discovery in 1979, carbohydrate antigen CA19-9 remains the most commonly used tumor marker in clinical practice and is the only FDA-approved biomarker for the diagnosis of pancreatic cancer. However, approximately 25% of pancreatic cancer patients have normal CA19-9 levels. Therefore, the development of new tumor markers for the early diagnosis of pancreatic cancer with both high sensitivity and high specificity is urgently needed.

[0004] Exosomes are extracellular vesicles, ranging in size from 50 to 150 nm, containing DNA, microRNA, proteins, or other signaling molecules, playing a crucial role in intercellular communication. Analysis of abnormal plasma exosomes and their encapsulated molecules has been shown to indicate tumor development and progression, and exosomes are receiving increasing attention as a source for monitoring disease progression and identifying biomarkers. For example, the abundance of phosphatidylinositol proteoglycan-1 (GPC-1) in the serum of pancreatic cancer patients is significantly higher than in the normal population in early-stage pancreatic cancer patients, enabling accurate and sensitive early diagnosis of pancreatic cancer. A combined model including five exosome-based protein biomarkers (EGRF, EPCAM, MUC1, GPC1, and WNT2) showed higher sensitivity and specificity than the existing serum biomarker CA19-9. These studies confirm the superiority and potential of serum-derived exosomes as early diagnostic biomarkers for pancreatic cancer, while also highlighting the necessity for further in-depth research and validation of potential biomarkers.

[0005] Therefore, there is an urgent need to find a biomarker that can be conveniently and quickly sampled and can predict an individual's risk of pancreatic cancer at an early stage, so as to achieve more efficient assessment of pancreatic cancer risk. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a biomarker for pancreatic cancer detection. Utilizing metabolomics, it analyzes metabolites in exosomes in the blood of pancreatic cancer patients and healthy individuals that show significant differences, thereby screening out a series of biomarkers that can predict the early risk of pancreatic cancer. From these, a further set of biomarkers is selected to construct a diagnostic model for pancreatic cancer, which can be used to conveniently and efficiently predict whether an individual will develop pancreatic cancer, meeting clinical needs.

[0007] On one hand, the present invention provides the use of a biomarker in the preparation of a reagent for predicting whether an individual has pancreatic cancer, said biomarker being selected from one or more combinations of the following: 3-amino-2-piperidinone, trans-uric acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonicylglycerol phosphatidylserine, 1-stearoyl-2-arachidonicylglycerol phosphate ethanolamine, and N-acetylneuraminic acid.

[0008] This invention utilizes non-targeted metabolomics research, employing UPLC-MS / MS (high performance liquid chromatography-tandem mass spectrometry) to analyze exosome samples from three groups: a healthy group, a patient with chronic pancreatitis, and a patient with pancreatic cancer. Four statistical methods—randomforest, sPLS-DA, difference test, and SVM—are used to screen for metabolites showing significant differences between pancreatic cancer samples and non-pancreatic cancer control samples (healthy individuals). These metabolites are then quantified and validated using targeted metabolomics, ultimately yielding 10 plasma exosome metabolites that can serve as biomarkers for efficiently predicting whether an individual has pancreatic cancer.

[0009] In some approaches, the biomarker that can be used to predict whether an individual has pancreatic cancer can be used to prepare detection reagents with the biomarker as the detection target, such as sample pretreatment reagents, antigens or antibodies, and other biological reagents and kits suitable for the detection of the biomarker; or it can be developed into standardized reagents or kits suitable for the LC-UV or LC-MS detection of the biomarker.

[0010] In some embodiments, the biomarkers of the present invention are obtained through screening of plasma samples, and are particularly suitable for development into plasma diagnostic reagents or kits for pancreatic cancer prediction.

[0011] Furthermore, the biomarkers detected in the plasma sample refer to the abundance or concentration of exosome biomarkers in the plasma sample of the individual.

[0012] Further, the biomarker is selected from one or more of the following: 3-amino-2-piperidinone, trans-uric acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearic acid-2-arachidonic acid diacylglycerol phosphatidylserine, 1-stearic acid-2-arachidonic acid diacylglycerol phosphate ethanolamine, and N-acetylneuraminic acid.

[0013] By examining the concentration differences of biomarkers in plasma exosomes of pancreatic cancer patients and normal individuals, and based on the validation results of targeted metabolomics, three biomarkers with stable changes between pancreatic cancer patients and pancreatitis patients or normal controls were further selected from the 10 biomarkers. These biomarkers can be used to more effectively distinguish or predict the risk of pancreatic cancer, or to construct diagnostic models for pancreatic cancer.

[0014] Furthermore, the reagent is used to detect biomarkers in plasma exosomes.

[0015] This invention screens out biomarkers for pancreatic cancer from plasma exosomes. These biomarkers show significant differences in plasma exosomes between pancreatic cancer patients and non-pancreatic cancer patients (healthy individuals and patients with pancreatitis). By collecting plasma exosome samples, these biomarkers in an individual's plasma exosomes can be detected to predict or assist in the diagnosis of whether an individual has pancreatic cancer or the likelihood of having pancreatic cancer. Alternatively, these biomarkers in the plasma exosomes of a group can be detected, thereby dividing the group into a pancreatic cancer group or a non-pancreatic cancer group.

[0016] On the other hand, the present invention provides a kit or chip for predicting whether an individual has pancreatic cancer, the kit or chip including detection reagents for biomarkers as described above.

[0017] Furthermore, the reagent is used to detect biomarkers in plasma exosomes.

[0018] In another aspect, the present invention provides a combination of biomarkers for predicting whether an individual has pancreatic cancer, the combination of biomarkers comprising the following: 3-amino-2-piperidinone, trans-uric acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonicylglycerol phosphatidylserine, 1-stearoyl-2-arachidonicylglycerol phosphate ethanolamine, and N-acetylneuraminic acid.

[0019] Furthermore, the biomarker combination includes the following biomarker combination: adenosine, adenine, and N-acetylneuraminic acid.

[0020] In another aspect, the present invention provides a system for predicting whether an individual has pancreatic cancer, the system including a data analysis module; the data analysis module is used to analyze the detection values ​​of biomarkers, the biomarkers being selected from one or more of the following: 3-amino-2-piperidinone, trans-uric acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonicylglycerol phosphatidylserine, 1-stearoyl-2-arachidonicylglycerol phosphate ethanolamine, and N-acetylneuraminic acid.

[0021] Furthermore, the biomarker is selected from one or more of the following: 3-amino-2-piperidinone, trans-uric acid, 4-cholesten-3-one, adenosine, adenine, and N-acetylneuraminic acid.

[0022] Furthermore, the biomarker is selected from one or more of the following: adenosine, adenine, and N-acetylneuraminic acid.

[0023] Furthermore, the detection value of the biomarker is the detection value of the biomarker in plasma exosomes.

[0024] Furthermore, the detection value of the biomarker is the abundance or concentration of the biomarker in the plasma exosome sample of the individual being detected.

[0025] Furthermore, the data analysis module uses random forest or logistic regression equations to construct models for analysis.

[0026] Furthermore, the data analysis module calculates a predictive value for whether an individual has pancreatic cancer by substituting the detection values ​​of biomarkers into a logistic regression equation, thereby assessing whether an individual has pancreatic cancer.

[0027] Furthermore, the logistic regression equation is:

[0028] Z = 45.4514 * adenosine - 71.4211 * adenine - 0.2959 * N-acetylneuraminic acid - 3.1162;

[0029] The biomarker name represents the concentration (ng / mL) of the corresponding biomarker in the plasma exosome sample.

[0030] Furthermore, when Z is greater than -0.697, the probability of an individual having pancreatic cancer is high; when Z is less than -0.697, the probability of an individual having pancreatic cancer is low.

[0031] In another aspect, the present invention provides the use of the system described above for constructing a detection model that predicts the probability of whether an individual has pancreatic cancer.

[0032] The beneficial effects of this invention are as follows:

[0033] 1. Ten novel plasma exosome biomarkers were identified that can predict the early risk of pancreatic cancer (PC).

[0034] 2. Random forest diagnostic models for pancreatic cancer were selected using 1, 3, 6, and 10 biomarkers, and it was found that the model using 3 biomarkers was the optimal one.

[0035] 3. Comparing the generalized linear regression model and the random forest model constructed using three biomarkers, it was found that the generalized linear regression model can further improve the detection accuracy and can be used to more efficiently predict whether an individual has pancreatic cancer, with an AUC value of 0.968.

[0036] 4. The method only requires collecting plasma samples for testing, which is convenient and has great advantages and prospects in clinical practice. Attached Figure Description

[0037] Figure 1 This is a flowchart of the process for screening biomarkers in plasma exosomes using metabolomics in Example 1;

[0038] Figure 2 The structural formula of 3-amino-2-piperidinone in Example 1 is shown.

[0039] Figure 3 The structural formula of trans-uric acid in Example 1;

[0040] Figure 4 The structural formula of 4-cholesten-3-one in Example 1;

[0041] Figure 5 The structural formula of adenosine in Example 1;

[0042] Figure 6 The structural formula of adenine in Example 1;

[0043] Figure 7 The structural formula of N-acetylneuraminic acid in Example 1;

[0044] Figure 8 The structural formula of 1-stearoyl-2-arachidonicylglycerol phosphatidylserine in Example 1 is shown below.

[0045] Figure 9 The structural formula of 1-stearoyl-2-arachidonicylglycerol phosphate ethanolamine in Example 1 is shown below.

[0046] Figure 10 These are the 10 metabolites with the greatest predicted impact, constructed in Example 2;

[0047] Figure 11The ROC curve for the pancreatic cancer prediction model constructed with adenosine in Example 2;

[0048] Figure 12 The ROC curve for the pancreatic cancer prediction model constructed using adenine in Example 2 is shown.

[0049] Figure 13 The ROC curve for the N-acetylneuraminic acid prediction model of pancreatic cancer in Example 2 is shown.

[0050] Figure 14 The ROC curve for predicting whether a pancreatic cancer model exists using adenosine, adenine, and N-acetylneuraminic acid in Example 2 is shown. Detailed Implementation

[0051] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate understanding of the present invention and are not intended to limit it in any way. The reagents used in this embodiment are all known products and were obtained by purchasing commercially available products.

[0052] Example 1: Screening for biomarkers of pancreatic cancer in plasma exosomes using metabolomics

[0053] This embodiment first employs non-targeted metabolomics to analyze plasma exosome samples from three groups: a healthy group, a pancreatitis patient group, and a pancreatic cancer patient group, using UPLC-MS / MS. Secondly, models were constructed using random forest or logistic regression equations to screen for metabolites showing significant differences between pancreatic cancer samples and control samples. Targeted metabolomics quantification and validation were then performed. The screened significantly different metabolites were selected, ultimately yielding three plasma exosome metabolites as biomarkers. The role of these biomarkers in the diagnosis or differentiation of pancreatic cancer was then validated (see flowchart). Figure 1 ).

[0054] The specific steps are as follows:

[0055] 1. Experimental Methods

[0056] ① Sample collection

[0057] Patients with pancreatic cancer, patients with chronic pancreatitis, and healthy controls were recruited. The control group included age-matched healthy individuals or individuals without pancreatic disease (e.g., patients with indirect inguinal hernia). Blood samples of 8–12 mL were collected from these three groups of patients, centrifuged at 1600×g for 15 minutes at 4°C, then at 3000×g for 15 minutes to obtain 5–7 mL of plasma samples, which were stored at -80°C for further processing.

[0058] ② Sample processing

[0059] Exosomes were isolated from plasma samples using the classic ultracentrifugation method. Cells and debris in plasma samples were centrifuged at 2000×g for 30 min and then at 10000×g for 45 min at 4°C. The supernatant was filtered through a 0.45 μm filter. Exosomes in the plasma were then ultracentrifuged at 100,000×g at 4°C for 70 min using a TI70 rotor. After discarding the supernatant, the exosomes were resuspended in pre-chilled 1x PBS and ultracentrifuged again at 100,000×g at 4°C for 70 min. The exosomes were resuspended in an appropriate amount of PBS and sent for protein concentration determination and exosome characterization. The remaining exosomes were stored at -80°C.

[0060] ③LC-MS / MS detection and data processing

[0061] m / z ions were extracted from the raw mass spectrometry data obtained by LC-MS / MS detection. Metabolites were identified by searching the database. The peak areas of the metabolite chromatographic peaks were examined and normalized and missing values ​​were filled. The resulting data matrix was then subjected to subsequent bioinformatics analysis, including statistical methods such as sPLS-DA (sparse partial least squares discriminant analysis), volcano plot, generalized linear regression model, and random forest. The most effective differential metabolites for grouping pancreatic cancer samples and control samples were ranked.

[0062] 2. Experimental Results

[0063] Using sPLS-DA, difference test and volcano plot, 10 metabolites with the greatest differences between groups were screened, namely 10 biomarkers, as shown in Table 1.

[0064] Table 1. Ten biomarkers for pancreatic cancer

[0065]

[0066] Example 2: Pancreatic Cancer Prediction Model

[0067] This embodiment utilizes single biomarkers or combinations of multiple biomarkers screened in Example 1 to establish predictive or diagnostic models for pancreatic cancer. These models are used to distinguish between pancreatic cancer and non-pancreatic cancer, to screen pancreatic cancer patients from a population, or to predict whether an individual is a pancreatic cancer patient or the likelihood of an individual developing colorectal cancer. The specific models are as follows.

[0068] 1. Single biomarker

[0069] Data was processed using R software. Based on the grouping of pancreatic cancer patients and non-pancreatic cancer individuals, the concentration changes of metabolites in plasma exosome samples from these two groups were determined. The 10 metabolites with the greatest impact on grouping were screened using sPLS-DA. Figure 8In conjunction with clinical practice, the regression model efficacy of six metabolites was further evaluated using calibration curves and ROC curves.

[0070] The analysis results showed that the six biomarkers were significantly correlated with pancreatic cancer, as shown in Tables 2 and 3.

[0071] Table 2. Results of ROC analysis for single biomarkers

[0072]

[0073] The correlation between changes in the concentrations of the six biomarkers and the presence or absence of pancreatic cancer can be differentiated using the AUC values ​​shown in Table 2. A higher AUC value indicates that the biomarker is more accurate in distinguishing between individuals with pancreatic cancer and those without.

[0074] As shown in Table 2, when the concentration change of any one of the six biomarkers is used to distinguish between pancreatic cancer patients and non-pancreatic cancer patients, the AUC value can reach above 0.78, indicating high accuracy. Among them, the highest AUC values ​​are adenosine, 3-amino-2-piperidinone, and trans-uric acid, with an AUC value of 0.911.

[0075] The six biomarkers listed in Table 2 were further quantified and validated using targeted metabolomics. It was determined that adenosine, adenine, and N-acetylneuraminic acid were clearly and stably associated with pancreatic cancer patients. After optimizing the regression model, the AUC value of adenosine was further increased to 0.952. Figure 9 ).

[0076] 2. Combination of multiple biomarkers

[0077] While single biomarkers can differentiate between plasma exosome samples from pancreatic cancer and those from non-pancreatic cancer, or predict pancreatic cancer, their accuracy and inter-individual stability may be low. Therefore, a multivariate regression analysis was performed on the three metabolites—adenosine, adenine, and N-acetylneuraminic acid—to establish a generalized linear regression assessment model for predicting whether an individual has pancreatic cancer.

[0078] Z = 45.4514 * adenosine - 71.4211 * adenine - 0.2959 * N-acetylneuraminic acid - 3.1162;

[0079] The biomarker name represents the concentration (ng / mL) of the corresponding biomarker in the plasma exosome sample.

[0080] Furthermore, when Z is greater than -0.697, the probability of an individual having pancreatic cancer is high; when Z is less than -0.697, the probability of an individual having pancreatic cancer is low.

[0081] The ROC curve of the logistic regression model for predicting whether an individual has pancreatic cancer provided in this embodiment is as follows: Figure 12 As shown, the AUC value reached 0.957, which is a significant improvement compared to the individual regression models for the three biomarkers.

[0082] The generalized linear regression model used to predict whether an individual has pancreatic cancer was analyzed using a dataset of 20 clinically known pancreatic cancer patients and 31 non-pancreatic cancer patients (including 14 with chronic pancreatitis and 17 healthy controls). The results are shown in Table 3.

[0083] Table 3. Analysis Results of the Model for Predicting Individual Pancreatic Cancer

[0084]

[0085] As shown in Table 3, the generalized linear regression assessment model for predicting pancreatic cancer, constructed using three biomarkers individually and in combination, detected 19 out of 20 pancreatic cancer patients, achieving a sensitivity of 95%. Among 31 non-pancreatic cancer patients, one patient with pancreatitis was classified as a pancreatic cancer patient, achieving a specificity of over 96%. Compared to CA19-9 (70% accuracy), the most widely used pancreatic cancer biomarker in clinical practice, its predictive efficacy has been effectively improved.

[0086] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. The use of plasma exosome biomarkers in the preparation of reagents for diagnosing whether an individual has pancreatic cancer, characterized in that, The biomarkers are a combination of the following markers: adenosine, adenine, and N-acetylneuraminic acid; the reagents also include a plasma exosome separation reagent.

2. A system for predicting whether an individual has pancreatic cancer, characterized in that, The system includes a data analysis module; the data analysis module is used to analyze the detection values ​​of biomarkers, the biomarkers being adenosine, adenine, and N-acetylneuraminic acid; the detection values ​​of the biomarkers are the detection values ​​of biomarkers for detecting exosomes in plasma.

3. The system as described in claim 2, characterized in that, The data analysis module uses random forest or logistic regression equations to build models for analysis.

4. The system as described in claim 3, characterized in that, The data analysis module calculates the predictive value for whether an individual has pancreatic cancer by substituting the detection value of the biomarker into the logistic regression equation, thereby assessing whether the individual has pancreatic cancer. The logistic regression equation is: Z = 45.4514*adenosine - 71.4211*adenine - 0.2959*N-acetylneuraminic acid - 3.1162.

5. The system as described in claim 4, characterized in that, Furthermore, when Z is greater than -0.697, the probability of an individual having pancreatic cancer is high; when Z is less than -0.697, the probability of an individual having pancreatic cancer is low.

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