CN117330760A - Plasma exosome marker for early diagnosis of pancreatic cancer and application - Google Patents
Plasma exosome marker for early diagnosis of pancreatic cancer and application Download PDFInfo
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Abstract
The invention provides a plasma exosome marker for early diagnosis of pancreatic cancer and application thereof, wherein a series of biomarkers capable of early predicting pancreatic cancer occurrence risk are screened out by analyzing metabolites and proteins with significant differences in plasma separated exosomes of pancreatic cancer patients, pancreatitis patients and normal people by using methods of metabonomics and proteomics, and a diagnosis model of pancreatic cancer is constructed by further screening out a group of biomarkers.
Description
Technical Field
The invention relates to the field of medicine, in particular to a prediction system for predicting pancreatic cancer occurrence risk by detecting exosome metabolite abundance in a plasma sample and application thereof, wherein the prediction system is used for screening pancreatic cancer biomarkers by using metabonomics and diagnosing pancreatic cancer.
Background
Metabonomics (Metabolomics) is a discipline for the qualitative and quantitative analysis of small molecule metabolites with a relative molecular weight of less than 1000 in the body. The physiological and pathological conditions of the organism can be reflected by metabonomics analysis, and the differences among different individuals can be distinguished. With the development of mass spectrometry technology, liquid chromatography and mass spectrometry combined technology (LC-MS) have become the most important research tools in metabonomics research. Currently, metabonomics has been widely used in the field of clinical diagnostics, mainly to find metabolic markers associated with disease diagnosis and treatment.
At present, the clinical diagnosis means of pancreatic cancer mainly comprises imaging diagnosis, histopathological diagnosis and blood immune biochemical diagnosis, but because the imaging examination specificity is low and the histopathological diagnosis is difficult to implement the biopsy of lesion sites, the expression level of tumor markers in blood becomes a main detection index. Since the discovery in 1979, the carbohydrate antigen CA19-9 has remained the most common tumor marker in clinic to date, and is the only FDA approved biomarker for pancreatic cancer diagnosis. However, about 25% of pancreatic cancer patients have no abnormalities in CA19-9 levels. The development of new pancreatic cancer early diagnosis tumor markers with high sensitivity and high specificity is urgent.
The exosome is one kind of extracellular vesicle with the size of 50-150 nm and contains DNA, microRNA, protein or other signal molecule matter and has important function in cell-to-cell information communication. The analysis of abnormal plasma exosomes and their encapsulated molecules has profound implications for tumorigenesis and progression, exosomes are receiving increasing attention as a source of monitoring disease progression and finding biomarkers. For example, the serum of the patient with pancreatic cancer contains phosphatidylinositol proteoglycan-1 (GPC-1) which has higher abundance than that of the normal population in the serum of the patient with early pancreatic cancer, and can accurately and sensitively diagnose the early pancreatic cancer; the combined model comprising 5 exosome-based protein markers (EGRF, EPCAM, MUC1, GPC1 and WNT 2) showed higher sensitivity and specificity than the existing serum marker CA 19-9. These studies demonstrate the superiority and likelihood of serum-derived exosomes as early diagnostic markers for pancreatic cancer, and also demonstrate the necessity of potential markers to be validated for further in-depth studies.
It is therefore highly desirable to find a biomarker that can be conveniently and rapidly sampled and that can be used to predict early whether an individual is at risk for pancreatic cancer, thereby enabling a more efficient assessment of pancreatic cancer risk.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a biomarker for pancreatic cancer detection, which is used for conveniently and efficiently predicting whether an individual suffers from pancreatic cancer or not by analyzing metabolites with significant differences in exosomes in blood of pancreatic cancer patients and normal people and screening out a series of biomarkers capable of early predicting pancreatic cancer occurrence risks and further screening out a group of biomarkers from the biomarkers to construct a pancreatic cancer diagnosis model.
In one aspect, the invention provides the use of a biomarker for the preparation of a reagent for predicting whether an individual is pancreatic cancer, the biomarker being selected from one or more of the following combinations: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylethanolamine, N-acetylneuraminic acid.
According to the invention, through non-targeted metabonomics research, the plasma exosome samples of three groups of healthy groups, chronic pancreatitis patients and pancreatic cancer patients are analyzed by using a UPLC-MS/MS high performance liquid chromatography-tandem mass spectrometry combined method, and metabolites with obvious differences between pancreatic cancer samples and non-pancreatic cancer control samples (healthy) are screened by using four statistical methods of random forest, sPLS-DA, difference detection and SVM, and then quantitative and verification are carried out through targeted metabonomics, so that 10 plasma exosome metabolites are finally obtained, and can be used as biomarkers for efficiently predicting whether individuals are pancreatic cancer.
In some embodiments, the biomarker for predicting whether an individual is pancreatic cancer agent can be used as a detection target to prepare detection reagent, such as sample pretreatment reagent, antigen or antibody, and other biological reagents and kits suitable for detecting the biomarker; standardized reagents or kits suitable for LC-UV or LC-MS detection of the biomarkers, etc. can also be developed.
In some embodiments, the biomarkers of the invention are obtained by plasma sample screening, and are particularly suitable for development into plasma detection reagents or kits for pancreatic cancer prediction, and the like.
Further, the detecting the biomarker in the plasma sample is detecting the abundance or concentration of the biomarker of the exosome in the plasma sample of the individual.
Further, the biomarker is selected from one or more of the following: 3-amino-2-piperidone, trans-urocanic 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 phosphoethanolamine, N-acetylneuraminic acid.
By examining the concentration difference of the biomarker in the plasma exosomes of pancreatic cancer patients and normal persons, 3 biomarkers which are stably changed between pancreatic cancer patients and pancreatitis patients or normal controls are further selected from 10 biomarkers according to the verification result of targeted metabolomics, and can be used for more effectively distinguishing or predicting the risk of pancreatic cancer or constructing a diagnosis model of pancreatic cancer.
Further, the reagent is used for detecting a biomarker in plasma exosomes.
The invention screens biomarkers of pancreatic cancer from plasma exosomes, wherein the biomarkers have significant differences in the plasma exosomes of pancreatic cancer patients and non-pancreatic cancer patients (healthy and pancreatitis patients), and by collecting plasma exosomes samples, whether the individual has pancreatic cancer or is possibly suffering from pancreatic cancer can be predicted or assisted by detecting the biomarkers in the individual plasma exosomes, or the biomarkers in a certain population of plasma exosomes can be detected, so that the population is divided into pancreatic cancer groups or non-pancreatic cancer groups.
In another aspect, the invention provides a kit or chip for predicting whether an individual is pancreatic cancer, the kit or chip comprising a detection reagent for a biomarker as described above.
Further, the reagent is used for detecting a biomarker in plasma exosomes.
In yet another aspect, the invention provides a biomarker panel for predicting whether an individual is pancreatic cancer, the biomarker panel comprising the biomarker panel of: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylethanolamine, N-acetylneuraminic acid.
Further, the biomarker combination comprises the following biomarker combinations: adenosine, adenine, N-acetylneuraminic acid.
In yet another aspect, the present invention provides a system for predicting whether an individual is pancreatic cancer, the system comprising a data analysis module; the data analysis module is used for analyzing detection values of biomarkers, wherein the biomarkers are one or more selected from the following: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylethanolamine, N-acetylneuraminic acid.
Further, the biomarker is selected from one or more of the following: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, N-acetylneuraminic acid.
Further, the biomarker is selected from one or more of the following: adenosine, adenine, N-acetylneuraminic acid.
Further, the detection value of the biomarker is a detection value for detecting the biomarker in the plasma exosome.
Further, the detection value of the biomarker is the abundance or concentration of the biomarker in the plasma exosome sample of the test individual.
Further, the data analysis module adopts random forest or logistic regression equation to construct a model for analysis.
Further, the data analysis module calculates a predicted value for predicting whether the individual is pancreatic cancer by substituting the detected value of the biomarker into a logistic regression equation, thereby evaluating whether the individual is pancreatic cancer.
Further, the logistic regression equation is:
z= 45.4514 adenosine-71.4211 adenine-0.2959 n-acetylneuraminic acid-3.1162;
wherein the biomarker designation represents the concentration (ng/mL) of the corresponding biomarker in the plasma exosome sample.
Further, when Z is greater than-0.697, the likelihood that the individual is predicted to be pancreatic cancer is high; when Z is less than-0.697, the likelihood that the individual is predicted to be pancreatic cancer is low.
In yet another aspect, the invention provides the use of a system as described above for constructing a detection model for predicting whether an individual is a probability value for pancreatic cancer.
The beneficial effects of the invention are as follows:
1. 10 totally new plasma exosome biomarkers which can be used for early prediction of Pancreatic Cancer (PC) risk are selected;
2. screening out random forest diagnosis models of pancreatic cancer constructed by 1, 3, 6 and 10 biomarkers, and finding out that the model of pancreatic cancer constructed by 3 biomarkers is optimal;
3. the generalized linear regression model constructed by 3 biomarkers is compared with a random forest model, and the generalized linear regression model is found to further improve the detection accuracy and can be used for more efficiently predicting whether an individual suffers from pancreatic cancer or not, and the AUC value reaches 0.968;
4. the method is convenient and fast only by collecting the sample through the blood plasma, and has great advantages and prospects in clinic.
Drawings
FIG. 1 is a flow chart of a metabonomic screening for biomarkers in plasma exosomes in example 1;
FIG. 2 is the structural formula of 3-amino-2-piperidone in example 1;
FIG. 3 is a structural formula of trans-urocanic acid in example 1;
FIG. 4 is a structural formula of 4-cholesten-3-one in example 1;
FIG. 5 is a structural formula of adenosine in example 1;
FIG. 6 is a structural formula of adenine in example 1;
FIG. 7 is a structural formula of N-acetylneuraminic acid in example 1;
FIG. 8 is a structural formula of 1-stearoyl-2-arachidonylglycerol phosphatidylserine in example 1
FIG. 9 is a structural formula of 1-stearoyl-2-arachidonyl glycerophosphate ethanolamine in example 1
FIG. 10 is the 10 most influential metabolites in the predictions constructed in example 2;
FIG. 11 is a ROC curve of the prediction of whether pancreatic cancer model constructed with adenosine in example 2;
FIG. 12 is a ROC curve of the adenine construction prediction of pancreatic cancer model in example 2;
FIG. 13 is a ROC curve of N-acetylneuraminic acid prediction for pancreatic cancer models in example 2;
FIG. 14 is a ROC curve of the model of pancreatic cancer predicted by adenosine, adenine and N-acetylneuraminic acid together in example 2.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate an understanding of the invention and are not intended to limit the invention in any way. The reagents used in this example are all known products and are obtained by purchasing commercially available products.
Example 1 screening of pancreatic cancer biomarkers in plasma exosomes Using metabonomics
In the embodiment, three groups of plasma exosome samples of a healthy group, pancreatitis patients and pancreatic cancer patients are analyzed by a UPLC-MS/MS ultra-high performance liquid chromatography-tandem mass spectrometry combined method through non-targeted metabonomics study. Secondly, a model is constructed by a random forest or logistic regression equation to respectively screen metabolites with significant differences between pancreatic cancer samples and control samples, targeted metabonomics quantification and verification are carried out, the screened significant different metabolites are selected, and finally 3 plasma exosome metabolites are obtained and used as biomarkers, and the role of the biomarkers in pancreatic cancer diagnosis or differentiation is verified (the flow chart is shown in figure 1).
The method comprises the following specific steps:
1. experimental method
(1) Sample collection
Pancreatic cancer patients, chronic pancreatitis patients, and normal control healthy populations were recruited, and the control group contained age-matched normal individuals or individuals with no pancreatic disease (e.g., inguinal indirect hernia patients). Blood samples 8-12mL were collected from these three groups of patients, centrifuged at 4℃for 1600 Xg for 15 minutes, then 3000 Xg for 15 minutes to obtain 5-7mL of plasma samples, which were stored at-80℃for treatment.
(2) Sample processing
Exosome separation of plasma samples employs classical ultracentrifugation methods. Cells and debris from the plasma sample of Xiamen Lishi Co., ltd were centrifuged at 2000 Xg for 30 minutes and 10000 Xg for 45 minutes at 4 ℃. The supernatant was filtered through a 0.45 μm filter. The exosomes in the plasma were then ultracentrifuged at 4 ℃ for 70 minutes using a TI70 rotor at 100,000xg. After discarding the supernatant, the exosomes were resuspended in pre-chilled 1-fold PBS and re-ultracentrifuged at 100,000×g at 4 ℃ for 70min. Exosomes were resuspended in an appropriate amount of PBS, sent to determine protein concentration and characterize the exosomes. The remaining exosomes were stored at-80 ℃.
(3) LC-MS/MS detection and data processing
Extracting m/z ions from original mass spectrum data obtained by LC-MS/MS detection, searching a database to search and identify metabolites, checking the integral of chromatographic peaks of the metabolites to obtain peak areas, carrying out data normalization and missing value filling, and carrying out subsequent belief analysis on the obtained data matrix, wherein the statistical methods comprise sPLS-DA (sparse partial least squares discriminant analysis), volcano (volcanic chart), generalized linear regression model, random forest and the like, and respectively screening a differential metabolite ranking list which is most effective for grouping samples between pancreatic cancer samples and control samples.
2. Experimental results
10 metabolites, i.e. 10 biomarkers, with the largest differences between groups were screened by sPLS-DA, difference test and volcanic plot, as shown in Table 1.
Table 1, 10 pancreatic cancer biomarkers
Example 2: pancreatic cancer prediction model
This example uses the single biomarker or combination of biomarkers screened in example 1 to build a predictive or diagnostic model of pancreatic cancer. These models are used to distinguish pancreatic cancer from non-pancreatic cancer, or to screen out pancreatic cancer patients from a population, or to predict whether an individual is a pancreatic cancer patient or the likelihood that an individual will have colorectal cancer, and are specifically described below.
1. Single biomarker
The data is processed using R language software. According to the grouping of pancreatic cancer patients and non-pancreatic cancer people, the concentration change of metabolites in plasma exosome samples of the pancreatic cancer patients and the non-pancreatic cancer people is judged, 10 metabolites which have the greatest influence on the grouping are screened out from sPLS-DA (figure 8), and the regression model efficacy of 6 metabolites is further evaluated by adopting a calibration curve and ROC curve method in combination with clinical practical application.
The analysis results demonstrate that 6 biomarkers have a clear correlation with whether pancreatic cancer is present or not, and the analysis results are shown in tables 2 and 3.
TABLE 2 Single biomarker ROC analysis results
The level of correlation between the concentration change of 6 biomarkers and the presence or absence of pancreatic cancer can be distinguished by the AUC values in table 2, etc. The higher the AUC value, the more accurate the biomarker is to distinguish pancreatic cancer from non-pancreatic cancer.
As can be seen from table 2, the concentration change of any one of the 6 biomarkers is used alone to distinguish pancreatic cancer groups from non-pancreatic cancer groups, and the AUC value of the biomarker can reach more than 0.78, and the accuracy is high, wherein the highest AUC value is adenosine, 3-amino-2-piperidone and trans-urocanic acid, and the AUC value reaches 0.911.
The 6 biomarkers provided in table 2 were further quantified and validated in targeted metabonomics, where the correlation of adenosine, adenine, N-acetylneuraminic acid with pancreatic cancer patients was determined to be clear and stable, and the AUC value of adenosine was further increased to 0.952 after optimization of the regression model (fig. 9).
2. Combination of multiple biomarkers
Although it is also possible to distinguish between pancreatic and non-pancreatic cancer plasma exosomes samples or to predict pancreatic cancer using a single biomarker, the accuracy of the differentiation or prediction and stability between individuals may be low. Thus, multi-factor regression analysis was performed on the final 3 metabolites adenosine, adenine, N-acetylneuraminic acid, creating a generalized linear regression assessment model that predicts whether an individual will have pancreatic cancer:
z= 45.4514 adenosine-71.4211 adenine-0.2959 n-acetylneuraminic acid-3.1162;
wherein the biomarker designation represents the concentration (ng/mL) of the corresponding biomarker in the plasma exosome sample.
Further, when Z is greater than-0.697, the likelihood that the individual is predicted to be pancreatic cancer is high; when Z is less than-0.697, the likelihood that the individual is predicted to be pancreatic cancer is low.
The ROC curve of the logistic regression model for predicting whether an individual has pancreatic cancer provided in this example is shown in fig. 12, and the AUC value reaches 0.957, which is significantly improved compared with the independent regression model of 3 biomarkers.
The generalized linear regression model for predicting whether an individual has pancreatic cancer is adopted, 20 clinically known pancreatic cancer patients and 31 non-pancreatic cancer patients (containing 14 cases of chronic pancreatitis and 17 healthy controls) are taken as a total data set to be analyzed, and the analysis results are shown in table 3,
TABLE 3 analysis results of pancreatic cancer model for predicting whether individuals are
As can be seen from Table 3, the generalized linear regression evaluation model for predicting whether the individual pancreatic cancer is constructed by adopting 3 biomarkers alone and in combination is used for analysis, 19 out of 20 pancreatic cancer patients are detected, and the sensitivity reaches 95%; of 31 non-pancreatic cancer patients, 1 pancreatitis patient is classified into pancreatic cancer patient area, and the specificity reaches more than 96%; compared with the pancreatic cancer marker CA19-9 (accuracy 70%) which is most widely used in clinic at present, the predictive efficiency is effectively improved.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Claims (11)
1. Use of a biomarker for the preparation of a reagent for diagnosing whether an individual is pancreatic cancer, said biomarker being selected from one or a combination of: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylethanolamine, N-acetylneuraminic acid.
2. The use according to claim 1, wherein the biomarker is selected from one or more of the following: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, N-acetylneuraminic acid.
3. The use according to claim 1, wherein the biomarker is selected from one or more of the following: adenosine, adenine, N-acetylneuraminic acid.
4. The use of claim 3, wherein the combination of markers comprises the combination of: adenosine, adenine, N-acetylneuraminic acid.
5. A system for predicting whether an individual is pancreatic cancer, the system comprising a data analysis module; the data analysis module is used for analyzing detection values of biomarkers, wherein the biomarkers are one or more selected from the following: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, serine, N-acetylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylserine, 1-stearoyl-2-arachidonyl glycerophosphatidylethanolamine, N-acetylneuraminic acid.
6. The system of claim 5, wherein the biomarker is selected from one or more of the following: 3-amino-2-piperidone, trans-urocanic acid, 4-cholesten-3-one, adenosine, adenine, N-acetylneuraminic acid.
7. The system of claim 6, wherein the biomarker is selected from one or more of the following: adenosine, adenine, N-acetylneuraminic acid.
8. The system of claim 7, wherein the biomarker detection value is a biomarker detection value for detecting exosomes in plasma.
9. The system of claim 5, wherein the data analysis module uses a random forest or logistic regression equation to build a model for analysis.
10. The system of claim 69, wherein the data analysis module evaluates whether the individual is pancreatic cancer by substituting the detected value of the biomarker into a logistic regression equation to calculate a predicted value that predicts whether the individual is pancreatic cancer. The logistic regression equation is: z= 45.4514 adenosine-71.4211 adenine-0.2959 n-acetylneuraminic acid-3.1162.
11. The system of claim 10, further wherein when Z is greater than-0.697, the likelihood that the individual is predicted to be pancreatic cancer is high; when Z is less than-0.697, the likelihood that the individual is predicted to be pancreatic cancer is low.
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