CN112305119B - Biomarker for atherosclerotic cerebral infarction and application thereof - Google Patents
Biomarker for atherosclerotic cerebral infarction and application thereof Download PDFInfo
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Abstract
The invention discloses a biomarker of atherosclerotic cerebral infarction and application thereof, and particularly discloses that the levels of biomarkers PC (P-18:0/18:1(11Z)) and PC (P-18:0/18:1(11Z)) are remarkably reduced in patients with atherosclerotic cerebral infarction, and early diagnosis and early treatment of the atherosclerotic cerebral infarction can be realized by detecting the level of PC (P-18:0/18:1 (11Z)).
Description
Technical Field
The invention belongs to the field of biomedicine, and relates to a biomarker of atherosclerotic cerebral infarction and application thereof.
Background
Cerebral apoplexy is a main disease which seriously harms the life and health of the national people and becomes the first cause of death of the national people. Cerebral infarction accounts for 50-70% of all strokes, with cerebral infarction pathologically based on atherosclerotic lesions being the most common type. Therefore, intensive research on atherosclerosis and atherosclerotic cerebral infarction is a major topic for preventing and treating cerebral infarction.
Metabolomics, also known as "clinical biochemistry", is an emerging subject and technology of the "post-genomics" era and is one of the most active fields of life science research in the world today. Since the concept of metabonomics is proposed, the method has attracted great interest of scientists in various countries, and is widely applied to important fields of clinical medicine, pharmaceutical research, nutriology, food safety, environmental science, toxicology, plant microbiology and the like.
In the invention, blood of an atherosclerotic cerebral infarction patient and age and sex matched atherosclerotic patients is collected, and qualitative and quantitative analysis is carried out on a serum metabolome by adopting a non-targeted combined targeted metabolome method. Potential biomarkers are screened out through OPLS-DA supervised clustering analysis, difference multiple analysis and T test analysis, and further data analysis shows that metabolites are well distinguished from two groups, so that the method has a good clinical application prospect.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention aims to provide a biomarker related to atherosclerotic cerebral infarction, and whether a patient has atherosclerotic cerebral infarction can be judged by detecting the level of the biomarker, so that a new means is provided for early diagnosis of atherosclerotic cerebral infarction.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, the invention provides an atherosclerotic cerebral infarction biomarker which is PC (P-18:0/18:1 (11Z)).
In a second aspect, the invention provides the use of a reagent for detecting a biomarker according to the first aspect of the invention in a sample for the manufacture of a product for diagnosing atherosclerotic cerebral infarction.
Further, the product comprises reagents for detecting the marker by chromatography, spectroscopy, mass spectrometry, chemical analysis.
Further, the product comprises a reagent for detecting the marker by mass spectrometry combined with chromatography.
Further, the product also includes reagents for processing the sample.
Further, the sample is selected from blood, plasma or serum.
In a third aspect, the invention provides a product for diagnosing atherosclerotic cerebral infarction, the product comprising reagents for detecting a biomarker according to the first aspect of the invention in a sample.
Further, the product comprises a kit and a chip.
A fourth aspect of the invention provides a method of establishing a computational model for assessing a subject's risk of developing an atherosclerotic cerebral infarction, the method comprising:
a step of identifying a differentially expressed substance between an atherosclerotic cerebral infarcted patient and an atherosclerotic sample, wherein the differentially expressed substance comprises a biomarker according to the first aspect of the invention.
In a fifth aspect, the invention provides the use of a biomarker according to the first aspect of the invention in the manufacture of a medicament for the treatment of atherosclerotic cerebral infarction.
The invention has the advantages and beneficial effects that:
the invention discovers the biomarker related to the atherosclerotic cerebral infarction for the first time, and can judge whether a subject has the atherosclerotic cerebral infarction and the risk of having the atherosclerotic cerebral infarction by detecting the level of the biomarker so as to realize the diagnosis of the early stage of the atherosclerotic cerebral infarction, thereby carrying out intervention treatment at the early stage of the cerebral infarction and improving the life quality of patients.
Drawings
FIG. 1 is a statistical analysis diagram of OPLS-DA, wherein diagram A is a statistical analysis diagram of reverse chromatographic positive ions; FIG. B is a diagram of a negative ion statistical analysis of the reverse chromatogram; panel C is a hydrophilic chromatographic positive ion statistical analysis.
FIG. 2 is a horizontal view of PC (P-18:0/18:1(11Z)) in different groups.
FIG. 3 is a graph of the diagnostic performance of PC (P-18:0/18:1(11Z)) as the test variable.
Detailed Description
Metabolomics is an emerging research area downstream of genomics, proteomics, and transcriptomics. There are 40,000 various metabolites in humans, the concentration of which can provide a snapshot of the current health status of an individual. The metabolome is a quantitative collection of low molecular weight compounds produced by metabolism, such as metabolic substrates and products, lipids, small peptides, vitamins and other protein cofactors. The metabolome is downstream of the transcriptome and proteome, so any changes from the normal state are amplified and are numerically easier to handle. Metabolomics can be an accurate, consistent, and quantitative tool for examining and describing cell growth, maintenance, and function.
In the present invention, the term "biomarker" means a compound, preferably a metabolite, which is differentially present (i.e. increased or decreased) in a biological sample from a subject or group of subjects having a first phenotype (e.g. having a disease) compared to a biological sample from a subject or group of subjects having a second phenotype (e.g. no disease). Biomarkers can be differentially present at any level, but are typically present at levels that are increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more; or generally at a level that is reduced by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent). The biomarkers are preferably present differentially at levels of statistical significance (i.e. p-value less than 0.05 and/or q-value less than 0.10, as determined using the Welch's T-Test or the Wilcoxon rank-sum Test).
By "level" of a biomarker is meant the absolute or relative amount or concentration of the biomarker in a sample.
The term "sample" or "biological sample" means a biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting a desired biomarker, and may comprise cellular material and/or non-cellular material from a subject. The sample may be isolated from any suitable biological fluid, such as, for example, blood, plasma, serum, urine, or cerebrospinal fluid (CSF). As a preferred embodiment, the sample is selected from blood, plasma, serum.
The term "subject" means any animal, but is preferably a mammal, such as, for example, a human, monkey, non-human primate, mouse, or rabbit.
The atherosclerotic cerebral infarction biomarker disclosed by the invention is discovered by using a metabonomic spectrum analysis technology. In general, the metabolic profile of biological samples from human subjects diagnosed with atherosclerotic cerebral infarction and from one or more other groups of human subjects (e.g., control subjects with atherosclerosis) is determined. The metabolic profile of a biological sample from a subject having an atherosclerotic cerebral infarction is compared to the metabolic profile of a biological sample from one or more other groups of subjects. Molecules that are differentially present in the metabolic profile of a sample from a subject having an atherosclerotic cerebral infarction, including molecules that are differentially present at a statistically significant level, as compared to another group (e.g., a control subject having atherosclerosis) are identified as biomarkers to identify that group.
Diagnosis of atherosclerotic cerebral infarction
Identifying biomarkers for atherosclerotic cerebral infarction allows diagnosis (or aids in diagnosis) of disease in subjects exhibiting one or more symptoms consistent with the presence of an atherosclerotic cerebral infarction, and includes both a preliminary diagnosis of subjects not previously identified as having an atherosclerotic cerebral infarction and a diagnosis of recurrence of disease in subjects previously treated for an atherosclerotic cerebral infarction. A method of diagnosing (or aiding in diagnosing) whether a subject has an atherosclerotic cerebral infarction comprising (1) analyzing a biological sample from the subject to determine the level of a biomarker of an atherosclerotic cerebral infarction in the sample; and (2) comparing the level of the biomarker in the sample with an atherosclerotic cerebral infarction positive and/or an atherosclerotic cerebral infarction negative reference level of the biomarker to diagnose (or aid in diagnosing) whether the subject has an atherosclerotic cerebral infarction. The biomarker used included PC (P-18:0/18:1 (11Z)). When such a method is used to aid in the diagnosis of atherosclerotic cerebral infarction, the results of the method may be used in conjunction with other methods (or results thereof) that may be used to clinically determine whether a subject has an atherosclerotic cerebral infarction.
In the present invention, in order to evaluate the correlation between the metabolites and the atherosclerotic cerebral infarction, metabolic markers suitable for diagnosis and treatment of the atherosclerotic cerebral infarction are found by collecting samples of an atherosclerotic patient and the atherosclerotic cerebral infarction, analyzing the metabonomics of the samples in combination, screening metabolites whose contents show significant differences in the two groups, and further analyzing the diagnostic efficacy of the different metabolites.
The invention discovers the metabolic marker PC (P-18:0/18:1(11Z)) related to the atherosclerotic cerebral infarction for the first time through extensive and intensive research. PC (P-18:0/18:1(11Z)) is significantly reduced in atherosclerotic cerebral infarcted patients compared to atherosclerotic patients.
In the present invention, any suitable method may be used to analyse a biological sample to determine the level of the biomarker in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody ligation, other immunochemical techniques, and combinations thereof. Furthermore, the level of the one or more biomarkers can be measured indirectly, for example by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker desired to be measured.
After determining the level of the biomarker in the sample, comparing the level to an atherosclerotic cerebral infarction positive and/or an atherosclerotic cerebral infarction negative reference level to diagnose or help diagnose whether the subject has an atherosclerotic cerebral infarction. A match of the level of the biomarker in the sample to an atherosclerotic cerebral infarction positive reference level (e.g., a level that is the same as, substantially the same as, above and/or below a minimum and/or maximum of, and/or within a range of a reference level) is indicative of a diagnosis that the subject has an atherosclerotic cerebral infarction. A match of the level of the biomarker in the sample to an atherosclerotic cerebral infarction negative reference level (e.g., a level that is the same as, substantially the same as, above and/or below a minimum and/or maximum of, and/or within a range of reference levels) indicates that the subject is not diagnosed with an atherosclerotic cerebral infarction. Furthermore, the level of a biomarker that is differentially present (particularly at a statistically significant level) in the sample compared to an atherosclerotic cerebral infarction negative reference level is indicative of a diagnosis that the subject has an atherosclerotic cerebral infarction. The level of a biomarker that is differentially present (particularly at a statistically significant level) in the sample compared to an atherosclerotic cerebral infarction positive reference level indicates that the subject is not diagnosed with an atherosclerotic cerebral infarction.
The level of the biomarker can be compared to an atherosclerotic cerebral infarction positive and/or an atherosclerotic cerebral infarction negative reference level using a variety of techniques including a simple comparison (e.g., a manual comparison) of the level of the biomarker in the biological sample to an atherosclerotic cerebral infarction positive and/or an atherosclerotic cerebral infarction negative reference level. The level of the biomarker in the biological sample may also be compared to an atherosclerotic cerebral infarction positive and/or atherosclerotic cerebral infarction negative reference level using one or more statistical analyses (e.g., T-test, welch's T-test, Wilcoxon rank-sum test, Random Forest (Random Forest), T-score, Z-score) or using mathematical models (e.g., algorithms, statistical models, mixed effect models).
Product for diagnosing atherosclerotic cerebral infarction
The product for diagnosing atherosclerotic cerebral infarction comprises a reagent for detecting the level of a metabolite PC (P-18:0/18:1(11Z)) in a sample. The product may be in any form including, but not limited to, a kit, a chip.
As an alternative embodiment, the components of the kit may be packaged in one or more containers, such as one or more vials. In addition to the metabolite standards, the kit preferably further comprises a preservative or buffer for storage. In addition, the kit may contain instructions for use.
As an alternative embodiment, the chip has a reagent capable of detecting and/or quantifying one or more metabolites immobilized at predetermined locations on the substrate. As an illustrative example, a chip may be provided with reagents immobilized at discrete predetermined locations for detecting and quantifying the amount or concentration of PC (P-18:0/18:1(11Z)) in a sample; as described above, an increased level of this metabolite was found in a sample of a subject suffering from an atherosclerotic cerebral infarction. The chip may be configured such that a detectable output (e.g. a colour change) is provided only when the amount or concentration of the metabolite exceeds a threshold value selected or differentiated between the concentration of the metabolite indicative of an atherosclerotic subject and the amount or concentration of the metabolite indicative of a suffering from or susceptible to an atherosclerotic cerebral infarction. Thus, the presence of a detectable output (such as a color change) immediately indicates that the sample contains a significantly increased level of the metabolite, indicating that the subject is suffering from or susceptible to atherosclerotic cerebral infarction.
The present invention will be described in further detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention. The experimental methods in the examples, in which specific conditions are not specified, are generally carried out under conventional conditions.
Example screening and potency determination of metabolites associated with atherosclerotic cerebral infarction
1. Sample collection
Blood samples were collected from 21 patients with atherosclerotic cerebral infarction and 21 patients with atherosclerosis.
Atherosclerotic cerebral infarction group inclusion criteria:
1) the subject has signed an informed consent
2) Meets the acute cerebral infarction diagnosis standard of Chinese acute ischemic stroke diagnosis and treatment guidelines (2014 edition).
3) The etiological classification is atherosclerosis cerebral infarction.
4) Age 18-65 years old.
5)BMI 18.5-23.9kg/m2。
6) Blood routine: red blood cell count, MCHC, hemoglobin, white blood cell count, lymphocyte count, neutrophil count, monocyte count are in the normal range.
7) TG, TC, HDL-C, LDL-C, blood glucose, and glycated hemoglobin were in the normal range.
Exclusion criteria:
1) the combination of other diseases: nervous system diseases (past cerebral infarction, cerebral hemorrhage, multiple sclerosis, etc.); various chronic digestive system diseases, acute digestive system diseases within 3 months; circulatory disorders (coronary heart disease, heart failure, atrial fibrillation); respiratory diseases (chronic obstructive pulmonary disease, chronic bronchitis, asthma); metabolic diseases (obesity, hyperlipidemia, diabetes, metabolic syndrome, osteoporosis); urinary system diseases (chronic kidney disease, renal failure, kidney stones); hematological disorders (anemia); others (gout, depression, psychiatric disorders, chronic fatigue syndrome, fibromyalgia, food allergies, tumors).
2) The history of blood transfusion, operation and trauma of digestive system diseases.
3) Patients with abnormal electrocardiograms.
4) The following drugs were taken within 3 months: antibiotics, laxatives, clonazepam, sex hormone drugs, oral contraceptives, mesalamine, TNF-alpha inhibitors, immunosuppressants, antidepressants, PPIs, rupatadine, opioids, calcium agents, vitamin D, metformin, folic acid, beta-sympathetic inhalants, traditional Chinese medicines.
5) The probiotic preparation is administered within 3 months.
6) Antiplatelet and statins are applied before the disease.
7) Patients undergoing intravenous thrombolysis and endovascular intervention.
8) Pregnant or lactating women.
9) During this study, the patient had enrolled or planned to enroll in another clinical drug or device/interventional study.
The atherosclerotic group inclusion criteria were:
1) the subject has signed an informed consent.
2) Cervical vascular ultrasound and/or cervical vascular imaging is manifested as intracranial and extracranial vascular atherosclerosis.
3) Age 18-65 years old.
4)BMI 18.5-23.9kg/m2。
5) Blood routine: red blood cell count, MCHC, hemoglobin, white blood cell count, lymphocyte count, neutrophil count, monocyte count are in the normal range.
6) TG, TC, HDL-C, LDL-C, blood glucose, and glycated hemoglobin were in the normal range.
Exclusion criteria:
1) there are other diseases: nervous system diseases (cerebral infarction, cerebral hemorrhage, multiple sclerosis, etc.); various chronic digestive system diseases, acute digestive system diseases within 3 months; circulatory disorders (coronary heart disease, heart failure, atrial fibrillation); respiratory diseases (chronic obstructive pulmonary disease, chronic bronchitis, asthma); metabolic diseases (obesity, hyperlipidemia, diabetes, metabolic syndrome, osteoporosis); urinary system diseases (chronic kidney disease, renal failure, kidney stones); hematological disorders (anemia); others (gout, depression, psychiatric disorders, chronic fatigue syndrome, fibromyalgia, food allergies, tumors).
2) The history of blood transfusion, operation and trauma of digestive system diseases.
3) The electrocardiogram is abnormal.
4) The following drugs were taken within 3 months: antibiotics, laxatives, clonazepam, sex hormones, oral contraceptives, mesalamine, TNF-alpha inhibitors, immunosuppressants, antidepressants, PPIs, rupatadine, opioids, calcium agents, vitamin D, metformin, folic acid, beta-sympathetic inhalants, traditional Chinese medicines, antiplatelet drugs, and statins.
5) The probiotic preparation is administered within 3 months.
6) Pregnant or lactating women.
7) During this study, the subject has enrolled or is scheduled to enroll in another clinical drug or device/interventional study.
2. Non-targeted metabolomics detection
2.1 serum sample preparation
2.1.1 reverse phase chromatography method for processing serum samples
1) The plasma/serum samples were thawed on ice at 4 ℃ for 30-60 min.
2) Mu.l serum was taken to a labeled 1.5ml centrifuge tube and 300. mu.l methanol and 1ml methyl tert-butyl ether were added.
3) The protein was precipitated by shaking thoroughly for 15 s. Centrifuging at 12000rpm and 4 deg.C for 10min, collecting upper layer solution 100 μ l, placing in 200 μ l liner tube, and testing.
2.1.2 hydrophilic chromatography serum sample treatment method:
1) the plasma/serum samples were thawed on ice at 4 ℃ for 30-60 min.
2) Mu.l serum was taken to a labeled 1.5ml centrifuge tube and 150. mu.l acetonitrile was added.
3) The protein was precipitated by shaking thoroughly for 15 s. Centrifuging at 12000rpm and 4 deg.C for 10min, collecting upper layer solution 100 μ l, placing in 200 μ l liner tube, and testing.
2.2 chromatographic conditions
Chromatographic separation serum samples were analyzed by reverse phase chromatography and hydrophilic chromatography using U3000 flash liquid chromatography from Thermo Scientific.
2.2.1 reverse phase chromatographic separation conditions
Chromatography column waters UPLC HSS T3(1.8 μm 2.1mm 100 mm);
mobile phases a (acetonitrile/water 4:6, 0.1% formic acid, 10mM ammonium acetate) and B (acetonitrile/isopropanol 9:1, 0.1% formic acid, 10mM ammonium acetate);
elution procedure: see table 1;
flow rate: 0.3 ml/min;
the sample injection amount is 1.0 mu L;
column temperature: at 50 ℃.
2.2.1 conditions for hydrophilic chromatographic separation
Chromatography column waters UPLC BEH Amide (1.7 μm 2.1mm 100 mm);
mobile phases a (acetonitrile, 0.1% formic acid, 10mM ammonium acetate) and B (water, 0.1% formic acid, 10mM ammonium acetate);
elution procedure: see table 2;
flow rate: 0.3 ml/min;
sample introduction amount: 1.0 μ L;
column temperature: at 40 ℃.
TABLE 2 HILIC determination of polar Small molecule elution procedure
2.3 Mass Spectrometry conditions
Mass spectrometry uses a quadrupole rod orbited ion trap mass spectrometer equipped with a thermoelectric spray ion source. The voltages of the positive and negative ion sources were 3.7kV and 3.5kV, respectively. The capillary heating temperature was 320 ℃. The warp air pressure was 30psi and the assist air pressure was 10 psi. The evaporation temperature was 300 ℃ with volume heating. The tilted gas and the auxiliary gas are both nitrogen. The collision gas is nitrogen and the pressure is 1.5 mTorr. The first-order full scan parameters are: resolution 70000, automatic gain control target of 1 × 106Maximum isolation time 50ms, mass to charge ratio scan range 50-1500. The liquid system is controlled by Xcaliibur 2.2SP1.48 software, and both data acquisition and targeted metabolite quantitative processing are operated by the software.
3. Targeted metabonomic detection
3.1 serum sample processing method
1) Plasma samples were thawed by standing at 4 ℃ for 30 min.
2) A50. mu.l plasma sample was taken into a 1.5ml centrifuge tube, 150. mu.l methanol (containing indoleacetic acid-D2500 ppb, indolepropionic acid-D250 ppb) was added, and vortexed for 30 min.
3) Centrifuging at 12000rpm for 5min, collecting supernatant 100 μ l, placing in 200 μ l liner tube, and testing.
3.2 chromatographic conditions
The chromatographic separation adopts a Waters ACQUITY UPLC I-CLASS ultrahigh pressure liquid chromatographic system, and the chromatographic separation conditions are as follows:
chromatography column Waters UPLC BEH C8(1.7 μm 2.1mm 100 mm);
mobile phase A (water, 0.5Mm NH)4F) And B (methanol);
elution gradient: see table 3;
flow rate: 0.3 ml/min;
sample introduction amount: 1.0 μ L;
column temperature: at 45 ℃.
TABLE 3 elution procedure
3.3 Mass Spectrometry conditions
The mass spectrometer is a Waters XEVO TQ-XS type tandem quadrupole mass spectrometer. The voltage of the positive ion source is 3kv, and the voltage of the taper hole is 20V. The desolvation temperature is 550 ℃, and the source temperature is 150 ℃. The desolventizing air flow rate is 1000L/Hr, and the taper hole air flow rate is 7L/h.
3.4 Targeted Metabolic group data treatment
The peak area calculation of the targeted metabolome data adopts masslynx quantitative software, and the retention time allows the error to be 15 s. And the concentration calculation adopts a single-point isotope internal standard method to obtain a quantitative result.
4. Data processing
4.1 data quality control
To evaluate the stability and reproducibility of the system during sample collection, quality control samples were used. The quality control sample is obtained by transferring all samples into a fixed volume and uniformly mixing. The pretreatment method of the finger-controlled sample is the same as that of other samples. To obtain a reliable and reproducible metabolite, three factors need to be considered: 1) retention time, 2) signal strength, 3) mass accuracy. In the experiment, 5 blank sample balance chromatographic columns are adopted firstly, and then 3 quality control sample balance chromatographic columns are adopted. Then every 6-8 samples insert 1 quality control sample for monitoring the whole liquid quality system stability and repeatability. And simultaneously calculating the coefficient of variation value of the metabolic features extracted from the quality control samples, and deleting the metabolic features of which the coefficient of variation exceeds 15%.
4.2 PCA analysis
All collected data, no matter what separation mode or positive and negative ion mode, are processed by Progenetics QI software, and the steps include importing original data, aligning peaks, extracting peaks, normalizing, and finally forming a table of retention time, mass-to-charge ratio and peak intensity. The time for extracting peaks by the reversed phase chromatography and the hydrophilic chromatography is 1 to 16 and 1 to 12min in sequence. Various additive ions such as hydrogen and sodium are deconvoluted into each ion signature. Metabolite identification primary molecular weight matching was performed using the human metabolome database and the lipid database.
4.3 OPLS-DA analysis
In order to obtain metabolite information which shows significant difference between the atherosclerotic cerebral infarction group (BL) and the atherosclerotic group (AS), statistical analysis was further performed on the two groups of samples by using a supervised multidimensional statistical method, namely partial least squares discriminant analysis (OPLS-DA).
Differentially expressed metabolites were searched for using the VIP (variable immunity in the project) value (threshold >1) of the OPLS-DA model in combination with the p-value of t-test (p < 0.05). The qualitative method of differential metabolites was: search the online database (HMDB) (compare mass to charge ratio m/z of mass spectra or exact molecular mass, error limit 0.01 Da).
4.4 ROC analysis
According to the levels of the metabolites, a receiver operating characteristic curve (ROC) is drawn, two accurate confidence spaces are calculated, and the diagnostic efficacy of the differential metabolites is analyzed.
5. Results
The quality control result shows that the quality control samples are relatively gathered together, the system has good repeatability, and the acquired data can be further researched.
The results of the reverse chromatography positive ion, the reverse chromatography negative ion, and the hydrophilic chromatography positive ion are shown in table 4 and fig. 1, respectively.
TABLE 4 OPLS-DA analytical model parameters
Bioinformatic analysis results showed that the level of PC (P-18:0/18:1(11Z)) was significantly reduced in the atherosclerotic cerebral infarct group compared to the atherosclerotic group (fig. 2).
The diagnosis efficiency is judged by taking the content of PC (P-18:0/18:1(11Z)) as a detection variable, and the result shows that the area under the curve is 0.878, the cutoff value is 17106706.220, the sensitivity is 0.857, the specificity is 0.810 (figure 3), and the method has higher sensitivity, specificity and accuracy.
The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.
Claims (6)
1. Use of a reagent for the detection of the biomarker PC (P-18:0/18:1(11Z)) in a sample selected from blood, plasma or serum, for the manufacture of a product for the diagnosis of atherosclerotic cerebral infarction.
2. Use according to claim 1, wherein the product comprises reagents for detecting markers by chromatography, spectroscopy, mass spectrometry.
3. Use according to claim 2, wherein the product comprises reagents for mass spectrometry coupled with chromatographic detection of markers.
4. Use according to any of claims 1-3, wherein the product further comprises reagents for processing the sample.
5. The use according to claim 1, wherein the product comprises a kit, a chip.
6. A method of establishing a computational model for assessing a subject's risk of developing an atherosclerotic cerebral infarction, the method comprising:
a step of identifying a differentially expressed substance between an atherosclerotic cerebral infarcted patient and an atherosclerotic sample, wherein the differentially expressed substance comprises the biomarker PC (P-18:0/18:1(11Z)), the sample being selected from blood, plasma or serum.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011191158.3A CN112305119B (en) | 2020-10-30 | 2020-10-30 | Biomarker for atherosclerotic cerebral infarction and application thereof |
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| CN202011191158.3A CN112305119B (en) | 2020-10-30 | 2020-10-30 | Biomarker for atherosclerotic cerebral infarction and application thereof |
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| Publication Number | Publication Date |
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| CN112305119A CN112305119A (en) | 2021-02-02 |
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