CN109307764B - Application of a group of metabolic markers in preparation of glioma diagnostic kit - Google Patents
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Abstract
The invention discloses application of a group of metabolic markers in preparation of a glioma diagnostic kit. The invention discovers that methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine can be jointly used for diagnosing glioma, and the diagnosis accuracy is high. Those skilled in the art can develop a kit for early diagnosis of glioblastoma containing standards of four compounds, methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine, for detecting methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in a sample to be tested. The kit can also contain a standard product of 2-chloro-L-phenylalanine which is used as an internal detection standard.
Description
Technical Field
The invention belongs to the field of biochemistry, and relates to application of metabolic markers in disease diagnosis, in particular to application of a group of metabolic markers in preparation of a glioma diagnosis kit.
Background
Glioma (Glioma) is a primary tumor originating from brain or spinal glial cells, and is the most common among intracranial tumors, mostly malignant, accounting for 80% of all malignant brain tumors. Glioma is characterized by invasive growth, is fuzzy with surrounding tissue boundaries, has the characteristics of high morbidity, high recurrence rate, high mortality rate and low cure rate, and seriously threatens human health. Active surgery-assisted chemoradiotherapy remains the current main strategy for treating glioma, and the surgical prognosis is closely related to the grade or malignancy of glioma, so that early diagnosis and early intervention are the keys for improving the prognosis and reducing the fatality rate of patients.
Since most early stage gliomas have hidden diseases and no specificity in clinical symptoms, clinical diagnosis of gliomas currently depends mainly on imaging examinations such as cranial CT and MRI and on histopathological examination of living bodies. However, CT examination does not distinguish glioma well from other brain pathologies (e.g., inflammation, ischemia, etc.) and has some radiation hazard; the examination cost of MRI is expensive; the diagnosis accuracy of tissue biopsy is higher than that of imaging diagnosis, but certain misdiagnosis rate still exists under the influence of factors such as tumor heterogeneity, target region selection and the like, and the tissue biopsy is an invasive diagnosis method. In summary, various factors limit the application of these diagnostic techniques to early diagnosis of glioma and to population screening. In recent years, molecular biomarkers such as Isocitrate Dehydrogenase (IDH), tumor protein p53(TP53), phosphatase and tensin homolog (PTEN) have been highlighted in clinical diagnosis of glioma, but most of these markers are concentrated on the gene and protein level, and thus, sensitivity and specificity are insufficient.
Therefore, the development of new diagnostic markers for glioma, which are rapid, noninvasive and effective, is necessary.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the application of the metabolic marker in the preparation of a glioma diagnostic kit.
The purpose of the invention is realized by the following technical scheme:
a group of metabolic markers for diagnosing glioma consists of methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine.
The metabolic marker is applied to the preparation of a glioma diagnostic kit.
Further, the kit contains a standard of methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine.
Furthermore, the kit also contains a standard product of 2-chloro-L-phenylalanine and the like.
And testing the diagnosis accuracy of the four metabolic markers on the glioma by drawing an ROC curve. The results show that: in the training set, when methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine or lauroyl-L-carnitine are singly used for glioma diagnosis, the area under the ROC curve (AUC) is 0.797, 0.809, 0.804 and 0.776 respectively, and when four markers are jointly used for diagnosis, the AUC reaches 0.959 (the sensitivity is 88.0 percent, and the specificity is 93.5 percent); in the test set, when methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine or lauroyl-L-carnitine are singly used for glioma diagnosis, the area under the ROC curve (AUC) is respectively 0.748, 0.873, 0.788 and 0.712, and when four markers are jointly used for diagnosis, the AUC reaches 0.968 (the sensitivity is 95.6 percent, and the specificity is 86.0 percent). As known to those skilled in the art, the area AUC under the ROC curve in the ROC curve evaluation method is closer to 1 when the area AUC is greater than 0.5, which indicates that the diagnosis effect is better. AUC has lower accuracy when being 0.5-0.7, AUC has certain accuracy when being 0.7-0.9, and AUC has higher accuracy when being more than 0.9. Proved by verification, when the methylene-L-glutamic acid, the allyl cysteine, the decanoyl-L-carnitine and the lauroyl-L-carnitine are singly used for diagnosing the glioma, the AUC is about 0.8 and only has certain accuracy; when the four components are jointly applied, the AUC is as high as about 0.96, and the accuracy is high.
In the training set, when the four metabolic markers of methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine were jointly diagnosed, the optimal Cut-off value (Cut-off value) of 0.444 was obtained based on the optimal sensitivity and specificity. The critical value is used for sample prediction, and the result shows that: the prediction accuracy of the metabolic marker group consisting of the four metabolites to the training set is 90.6%, and the prediction accuracy to the test set is 90.5%. The accuracy is high.
Has the advantages that:
the invention discovers that methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine can be jointly used for diagnosing glioma, and the diagnosis accuracy is high. Those skilled in the art can develop a kit for early diagnosis of glioblastoma containing standards of four compounds, methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine, for detecting methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in a sample to be tested. The kit can also contain a standard product of 2-chloro-L-phenylalanine which is used as an internal detection standard.
Drawings
FIG. 1 is a graph of Principal Component Analysis (PCA) scores of vs healthy controls of glioma patients in the examples;
FIG. 2 is a graph of the orthogonal partial least squares discriminant analysis (OPLS-DA) scores of vs healthy controls of glioma patients in the examples;
FIG. 3 is a displacement test chart of OPLS-DA model established for vs healthy control group of glioma patients in example;
FIG. 4 is a ROC analysis of the working characteristic curves (ROC) of the subjects diagnosed with the training set glioma using the four metabolic markers methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in the examples, alone and in combination;
FIG. 5 is a ROC analysis of the working characteristic curves (ROC) of the subjects diagnosed with test glioma sets using the four metabolic markers methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in the examples, alone and in combination;
FIG. 6 is a graph showing the accuracy of diagnosis of glioma patients using the four metabolic markers methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in combination in a training set in the examples;
FIG. 7 is a graph showing the accuracy of diagnosis of glioma patients in the test set using the four metabolic markers methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in combination.
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and examples, but not intended to limit the scope of the invention.
First, experimental instrument and reagent
An Ultimate 3000 ultra-high performance liquid chromatography system (Dionex, usa) in series with a Q-active quadrupole-electrostatic field orbitrap high resolution mass spectrometer (Thermo Fisher Scientific, usa); the column was Waters ACQUITY UPLC BEH C18 (50X 2.1mm, 1.7 μm); heraeus Fresco 17 centrifuge (Thermo Fisher Scientific).
HPLC grade acetonitrile, methanol, formic acid were purchased from Thermo Fisher, USA; the experimental water is Wahaha purified water; the 2-chloro-L-phenylalanine standard was purchased from J & K Chemical (Beijing, China).
Second, Experimental methods
1. Experimental sample
After patient consent was obtained, 95 cases of glioma patients who were admitted to the hospital in the first subsidiary hospital of zheng zhou university from 2016 12 months to 2017 months were collected based on the results of imaging examination and post-operative pathological diagnosis, and all glioma patients had no other metabolic diseases. In addition, 96 healthy volunteers were collected from the physical examination department as a normal control group. All blood samples were collected in the fasting state in the early morning of the patient, 3mL of venous blood was collected from each subject and placed in EDTA anticoagulant blood collection tubes, after collection, centrifugation was carried out at 3000r/min for 10min at 4 ℃, supernatant (plasma) was aspirated, and after split charging, frozen and stored immediately in a refrigerator at-80 ℃. Selecting 96 samples (including 46 healthy human samples and 50 glioma patient samples) from the 191 samples at random to form a training set, and searching for differential metabolites of glioma patients and healthy people to construct a diagnosis model; the remaining 95 samples (including 50 healthy human samples and 45 glioma patient samples) constitute the test set for verifying the ability of differential metabolites as metabolic markers for diagnosing gliomas.
2. Sample preparation
Taking out the plasma sample, placing the plasma sample on ice for thawing, uniformly mixing the plasma sample in a vortex manner, sucking 50 mu L of the plasma sample into a 1.5mL centrifuge tube, adding 150 mu L of methanol solution containing the internal standard (containing 50ng/mL of 2-chloro-L-phenylalanine), uniformly mixing the plasma sample in a vortex manner for 30 seconds, centrifuging the mixture for 10min at 13000rpm (4 ℃), sucking the supernatant into a sample injection vial, and thus obtaining the plasma sample.
Quality Control (QC) sample: and (3) respectively absorbing 5 mu L of the plasma samples, uniformly mixing in a vortex mode, and carrying out QC sample pretreatment according to the same method to obtain the plasma sample. In order to ensure the reliability of data, the QC sample analysis is interspersed in the process of collecting all sample metabonomics data, 5 QC samples are continuously detected before the sample analysis, the sample analysis is started after the instrument is stabilized, and the QC solution is detected every 10 samples.
3. Method for detecting metabolites in a sample
Chromatographic conditions chromatographic separation was carried out by ultra high performance liquid chromatography (UPLC, Waters Ultimate 3000, USA) using an ACQUITY UPLC BEH C18 (50X 2.1mm, 1.7 μm) at a column temperature of 40 ℃. The mobile phase composition is that A phase is acetonitrile, and B phase is 0.1% formic acid water solution. Gradient elution conditions: 0-0.5 min, 5% A; 0.5-1.0 min, 5% -60% A; 0.5-1.0 min, 5% -60% A; 1.0-7.0 min, 60% -80% A; 7.0-9.0 min, 80-100% A; 9.0-11.0 min, 100% A; 11.0-13.0 min, 5% A; the flow rate is 0.2mL/min, and the effluent liquid after the column is not shunted and is directly connected to a mass spectrum for detection.
The mass spectrum conditional mass spectrometry adopts Q active four-stage rod-electrostatic field orbital trap high resolution mass spectrum, and the ion source is a heatable electrospray ion source (HESI). The temperature of the auxiliary gas is 300 ℃, the temperature of the ion source is 350 ℃, the temperature of the capillary tube is 320 ℃, the flow rate of the auxiliary gas is 10 mu L/min, the resolution of the mass spectrum is 17,500, and the scanning mode of mass spectrometry: full scan/ddms2, and scanning range m/z 80.00-1200.00. The collision energy gradients were 20, 30 and 40 eV. The positive ion mode is adopted for detection, the spraying voltage is 3.50kV, and the sheath gas flow rate is 40 mu L/min. All samples were injected randomly, and a needle blank was inserted for each 10 samples tested to avoid cross-contamination.
4. Data processing and analysis
Data pre-processing such as peak extraction, peak alignment, peak correction, normalization and the like is carried out on data acquired by Thermo Xcalibur 3.0 through Sieve software (version 2.2, Thermo Fisher Scientific), and a three-dimensional data matrix consisting of a sample name, spectral peak information (including retention time and mass-to-charge ratio) and a peak area is output. The data matrix was imported into SIMCA (version14.0, Umetrics) for multivariate statistical analysis. Obtaining a variable Importance ranking VIP (variable Importance in the project) value by establishing an orthogonal partial least squares discriminant analysis (OPLS-DA) model. Metabolites that contribute significantly (VIP >1.5) and have significant changes (p <0.05) to differentiate metabolic differences between glioma patients and normal populations are selected as differential metabolites that characterize gliomas.
The different endogenous metabolites are identified by methods such as spectrum library auxiliary retrieval of software CompundDiscoverTM 2.1, structure analysis software Mass Frontier, Human Metabolomics Database (HMDB) and the like, and the structure of the compound is finally confirmed by comparing with a standard product.
The method comprises the steps of screening out factors with obvious influence as independent variables by utilizing SPSS software (version 22.0, SPSS, Chicago, Illinois) through stepwise regression analysis, establishing an optimal regression equation, finding out potential biomarkers for distinguishing glioma patients and normal people, and evaluating the diagnosis accuracy, sensitivity and specificity of the potential biomarkers in training and testing sets by combining Logistic regression and Receiver Operator Characteristics (ROC) curve analysis. And (3) calculating the maximum value of the Yoden index through ROC curve analysis, wherein the coordinate value is a Cut-off value, predicting a test set sample, and evaluating the prediction capability of the metabolic marker group on glioma.
Third, experimental results
Through Principal Component Analysis (PCA) and construction of an OPLS-DA model (as shown in figures 1-3, A in figures 1-3 represents a training set, and B represents a validation set), it can be seen that in the training set and the testing set, Glioma groups (Glioma, G) and Normal control groups (Normal, H) can be obviously separated and respectively aggregated, and therefore, the metabolic profiles of Glioma patients and healthy people are obviously different. The aggregation of QC samples in the PCA image is good, which reflects that the instrument and the method adopted by the research have good stability and repeatability. Model evaluation parameters of the OPLS-DA show that the model has good interpretability and prediction capability, and a response sequencing test (RPT) method is adopted to carry out replacement verification through 200 modeling, so that the model is proved to be stable and reliable without an overfitting phenomenon.
Using VIP >1.5 and p <0.05 as standard to screen out different metabolites, further obtaining 4 metabolic markers by SPSS stepwise regression analysis and screening, wherein the metabolic markers are respectively: methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine, the results are shown in table 1. Compared with the normal group, the expression level of the 4 metabolic markers in the plasma of the glioma patients is remarkably reduced, the change multiple is 0.5-0.8, and the 4 metabolic markers can be used as potential diagnostic markers of the glioma.
TABLE 1 plasma metabolism markers for patients with gliomas
The diagnosis accuracy of the four metabolic markers on glioma is further tested by drawing an ROC curve. The results show that: in the training set, when methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine are singly used for glioma diagnosis, the area under the ROC curve (AUC) is 0.797, 0.809, 0.804 and 0.776 respectively, and when four markers are jointly used for diagnosis, the AUC reaches 0.959 (the sensitivity is 88.0 percent, and the specificity is 93.5 percent); in the test set, when methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine are singly used for glioma diagnosis, the area under the ROC curve (AUC) is 0.748, 0.873, 0.788 and 0.712 respectively, and when four markers are jointly used for diagnosis, the AUC reaches 0.968 (the sensitivity is 95.6 percent, and the specificity is 86.0 percent). The results are shown in FIGS. 4 to 5.
As known to those skilled in the art, the area AUC under the ROC curve in the ROC curve evaluation method is closer to 1 when the area AUC is greater than 0.5, which indicates that the diagnosis effect is better. AUC has lower accuracy when being 0.5-0.7, AUC has certain accuracy when being 0.7-0.9, and AUC has higher accuracy when being more than 0.9. Proved by verification, when the methylene-L-glutamic acid, the allyl cysteine, the decanoyl-L-carnitine and the lauroyl-L-carnitine are singly used for diagnosing the glioma, the AUC is about 0.8 and only has certain accuracy; when the four compounds are jointly applied, the AUC is as high as about 0.96, and the diagnosis accuracy is high.
In the training set, when the four metabolic markers of methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine were jointly diagnosed, the optimal Cut-off value (Cut-off value) of 0.444 was obtained based on the optimal sensitivity and specificity. The critical value is used for sample prediction, and the result shows that: the prediction accuracy of the metabolic marker group consisting of the four metabolites to the training set is 90.6%, and the prediction accuracy to the test set is 90.5%. The results are shown in FIGS. 6 to 7.
In conclusion, methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine can be jointly used for diagnosing glioma, and the diagnosis accuracy is high. Those skilled in the art can develop a kit for early diagnosis of glioblastoma containing standards of four compounds, methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine, for detecting methylene-L-glutamic acid, allylcysteine, decanoyl-L-carnitine and lauroyl-L-carnitine in a sample to be tested. The kit can also contain a standard product of 2-chloro-L-phenylalanine which is used as an internal detection standard.
The above-described embodiments are intended to be illustrative of the nature of the invention, but those skilled in the art will recognize that the scope of the invention is not limited to the specific embodiments.
Claims (4)
1. A set of metabolic markers for diagnosing glioma, characterized by: consisting of methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine.
2. Use of the metabolic marker of claim 1 for the preparation of a glioma diagnostic kit.
3. Use according to claim 2, characterized in that: the kit contains methylene-L-glutamic acid, allyl cysteine, decanoyl-L-carnitine and lauroyl-L-carnitine standard substances.
4. Use according to claim 3, characterized in that: the kit also contains a standard substance of 2-chloro-L-phenylalanine.
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| CN110646557A (en) * | 2019-10-12 | 2020-01-03 | 北京航空航天大学 | Urine metabolic marker of glioblastoma patient carrying IDH gene mutation and application thereof |
| CN112201356B (en) * | 2020-10-09 | 2022-02-01 | 郑州大学第一附属医院 | Construction method of oral squamous cell carcinoma diagnosis model, marker and application thereof |
| CN113984935B (en) * | 2021-11-17 | 2024-04-02 | 东莞理工学院 | Method for researching metabolic characteristics of acetoacidophile based on metabonomic analysis |
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