CN111289736A - Slow obstructive pulmonary early diagnosis marker based on metabonomics and application thereof - Google Patents
Slow obstructive pulmonary early diagnosis marker based on metabonomics and application thereof Download PDFInfo
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Abstract
The invention discloses a slow obstructive pulmonary diagnostic marker based on metabonomics and application thereof, wherein the diagnostic marker comprises the following 28 plasma metabolic markers: phosphatidyl choline PC16: 1-36:3, phosphatidyl choline PC 26:0-22:4, phosphatidyl choline PC 26:0-22:3, phosphatidyl choline PC 47:5e, phosphatidyl choline PC 44:11e, phosphatidyl choline PC16:0-24:5, phosphatidyl choline PC20:2-20:3, phosphatidyl choline PC 40:10, phosphatidyl choline PC 38:9e, phosphatidyl choline PC18:1-20:4, phosphatidyl choline PC16:0-20:3, phenylacetaldehyde, dihydropyrimidine, nicotinamide, 4-methoxycinnamic acid, ketovaline, threonine, DL-3-aminoisobutyric acid, pyruvic acid, stachyose, caffeic acid, N-dimethylaniline, maltotriose, D (-) -gulonic acid-gamma-lactone, L-asparagine. The marker of the invention has good classification on the metabolome data of patients with chronic obstructive pulmonary disease and healthy people, and can accurately distinguish the patients with chronic obstructive pulmonary disease from the healthy people.
Description
The present invention claims priority from chinese patent application CN202010078213.1, and the contents of the specification, drawings and claims of this priority document are incorporated in their entirety into the present specification and are included as part of the original description of the present specification. Applicants further claim that applicants have the right to amend the description and claims of this invention based on this priority document.
Technical Field
The invention belongs to the field of clinical examination and diagnosis, and particularly relates to a slow obstructive pulmonary disease diagnostic marker based on metabonomics and application thereof.
Background
Chronic Obstructive Pulmonary Disease (COPD), is an important Chronic respiratory Disease that seriously harms human health. Epidemiological examination in China shows that the prevalence rate of chronic obstructive pulmonary disease of people over 40 years old is 13.7%. The world health organization reports that chronic obstructive pulmonary disease is currently located at the 4 th position of the global cause of death, and by 2020, chronic obstructive pulmonary disease will be located at the 5 th position of the economic burden of the world disease and the 3 rd position of the global cause of death. Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) are an important component of the slow obstructive pulmonary disease process. Acute exacerbation of chronic obstructive pulmonary disease has serious negative effects on the life quality, the pulmonary function, the disease process and the social and economic burden of patients, is an important factor for hospitalization and death of patients with chronic obstructive pulmonary disease, and is a main reason for the high medical cost of patients with chronic obstructive pulmonary disease. Acute exacerbations are associated with a higher percentage of hospitalizations, and thus prevention, early diagnosis, and early treatment are clinically significant and difficult medical tasks.
Chronic obstructive pulmonary disease is a recognized heterogeneous disease, and patients with chronic obstructive pulmonary disease have significant differences in clinical manifestations, disease progression, treatment response, lung function decline, quality of life and the like, and the heterogeneity is also reflected in the acute attack stage of chronic obstructive pulmonary disease. The chronic obstructive pulmonary disease has heterogeneity in the etiology, pathogenesis, clinical features and severity of acute exacerbations. Due to this heterogeneity, patients respond differently to existing treatment regimens. At present, the definition and diagnosis of chronic obstructive pulmonary acute exacerbation are the description of clinical symptoms, and the diagnosis omission and misdiagnosis are easily caused due to lack of quantitative indexes.
Chinese expert consensus (revised 2014) for diagnosing and treating chronic obstructive pulmonary disease (AECOPD) also indicates that a single biomarker is not available for clinical diagnosis and evaluation of chronic obstructive pulmonary disease acute exacerbation, and one or a group of biomarkers are expected to be available for more accurate etiological diagnosis later. The biomarker is used as a quantitative index, and the effect of the biomarker on the auxiliary quantitative diagnosis, the severity evaluation and the judgment of the etiology and prognosis of the acute exacerbation of the chronic obstructive pulmonary disease is the hot field of the current research on the chronic obstructive pulmonary disease.
Currently, there are many studies on chronic obstructive pulmonary disease and chronic obstructive pulmonary disease acute exacerbation biomarkers: biomarkers from the respiratory tract are theoretically strongly associated with the chronic obstructive pulmonary disease, and common specimens comprise expired air condensate, volatile gas, induced sputum, bronchial biopsy, lavage and the like, but the specimen collection and processing method is relatively complicated or invasive, and is not suitable for all patients with chronic obstructive pulmonary disease and acute exacerbation of the chronic obstructive pulmonary disease. In recent years, the focus of biomarker development has shifted to blood samples due to the recognition that chronic obstructive pulmonary disease is a systemic disease. Serum or plasma samples are readily available and measurements are readily standardized. The most studied and well correlated molecules to date include CRP, IL-6 and fibrinogen. However, the aforementioned inflammatory factors belong to acute phase proteins, lack specificity in patients with chronic obstructive pulmonary acute exacerbation, and are not ideal markers for chronic obstructive pulmonary acute exacerbation. At present, reliable biomarkers reflecting the occurrence and development of chronic obstructive pulmonary disease and acute exacerbation of the chronic obstructive pulmonary disease are still lacking.
In view of the heterogeneity of the chronic obstructive pulmonary disease and the acute exacerbation of the chronic obstructive pulmonary disease, a single biomarker is difficult to accurately reflect, and the establishment and development of metabonomics provide an effective means for solving the problem. Metabonomics mainly obtains dynamic change information of metabolites in organisms along with time and pathophysiological processes, including sugar, lipid, amino acid, vitamin and the like, by detecting the change of small molecule metabolites (MWK < 1000). The metabolite is the final product of the cell physiological activity and can truly and sensitively reflect the functional state of the cell. Metabonomics changes the traditional idea of single marker detection, and has unique advantages of diagnosing diseases by using a group of metabolite populations as 'mode markers'. Although metabolomics has started late, it has shown strong advantages over traditional diagnostic methods and research approaches. The slow obstructive pulmonary disease and the acute exacerbation of the slow obstructive pulmonary disease inevitably cause characteristic changes of endogenous small molecular metabolites in the process of occurrence and development, and metabonomics by means of advanced separation, analysis and calculation means has the capability and advantage of integrally distinguishing characteristic metabolites under different pathophysiological conditions.
In recent years, researches on pathogenesis, diagnosis, disease severity assessment, drug action targets and the like of chronic obstructive pulmonary disease by adopting a metabonomics technology are gradually increased. Adamo et al applied nuclear magnetic resonance method to obtain urine metabolic spectra of patients with chronic obstructive pulmonary disease, stable-phase chronic obstructive pulmonary disease and asthma before and after acute exacerbation, and established Partial Least Squares Regression (PLSR) differentiation model and intensively verify the correctness of diagnosis. The result shows that the urine metabolic spectra of the chronic obstructive pulmonary disease and the asthma are obviously different no matter in the acute exacerbation stage or the recovery stage after the acute exacerbation, and the correctness of the established metabonomics model for diagnosing the chronic obstructive pulmonary disease can reach 90%. Wang, etc. researches the serum and urine metabolic spectrums of patients with chronic obstructive pulmonary disease and normal control according to a nuclear magnetic resonance method and a computer mode identification method, and finds that the serum and urine metabolic spectrums of the patients with chronic obstructive pulmonary disease are changed compared with the normal control, and the patients with chronic obstructive pulmonary disease show abnormal lipid and amino acid metabolism. The application of the liquid phase mass spectrometry metabonomics technology to the chronic obstructive pulmonary disease and the report of the acute exacerbation of the chronic obstructive pulmonary disease are rarely seen at present.
Disclosure of Invention
In order to overcome the defects of the prior art and aim at the current situation that the chronic obstructive pulmonary disease and the chronic obstructive pulmonary disease acute exacerbation lack of reliable biomarkers, the serum of a patient is subjected to metabonomic analysis by a high performance liquid chromatography-mass spectrometry technology, so that differential metabolites between normal people and people with stable-stage chronic obstructive pulmonary disease and chronic obstructive pulmonary disease acute exacerbation are found, and specific differential metabolites of the chronic obstructive pulmonary disease, particularly the chronic obstructive pulmonary disease and the chronic obstructive pulmonary disease acute exacerbation, namely diagnostic molecules of the chronic obstructive pulmonary disease and the chronic obstructive pulmonary disease. The invention provides a diagnostic marker suitable for diagnosing chronic obstructive pulmonary disease and acute exacerbation of the chronic obstructive pulmonary disease, and application of the diagnostic marker in the diagnosis of the chronic obstructive pulmonary disease and the acute exacerbation of the chronic obstructive pulmonary disease.
The invention analyzes plasma samples of 48 acute stage chronic obstructive pulmonary disease patients, 48 stable stage chronic obstructive pulmonary disease patients and 48 healthy volunteers, respectively obtains the fingerprint of the micromolecule metabolite under the positive and negative ion mode by using a high performance liquid chromatography-mass spectrometry (LC-MS), and obtains the diagnosis marker suitable for the diagnosis of the chronic obstructive pulmonary disease, particularly the acute stage chronic obstructive pulmonary disease by analyzing and characteristic screening the fingerprint of the micromolecule metabolite of the acute stage chronic obstructive pulmonary disease patients, the stable stage chronic obstructive pulmonary disease patients and healthy normal control based on a machine learning support vector machine, thereby having higher clinical use and popularization value.
The specific technical scheme for realizing the invention is as follows: the invention provides a diagnosis marker for acute exacerbation of chronic obstructive pulmonary disease, in particular chronic obstructive pulmonary disease, based on metabonomics, wherein the diagnosis marker comprises or consists of the following plasma metabolism markers: phosphatidyl choline PC16: 1-36:3, phosphatidyl choline PC 26:0-22:4, phosphatidyl choline PC 26:0-22:3, phosphatidyl choline PC 47:5e, phosphatidyl choline PC 44:11e, phosphatidyl choline PC16:0-24:5, phosphatidyl choline PC20:2-20:3, phosphatidyl choline PC 40:10, phosphatidyl choline PC 38:9e, phosphatidyl choline PC18:1-20:4, phosphatidyl choline PC16:0-20:3, phenylacetaldehyde (phenylacetaldehyde), dihydrouracil (dihydrouracil), nicotinamide (nicotinamide), 4-methoxycinnamic acid (4-methoxycinnamic acid), ketovaline (ketovaline), threonine (threonine), DL-3-aminoisobutyric acid (DL-3-aminoisobutyric acid), pyruvic acid (pyruvic acid), stachyose (stachyoside), stachyose (stachyostatin), and the like, Caffeic acid (caffeic acid), N-dimethylaniline (N, N-dimethyllaniline), maltotriose (maltotriose), D (-) -gulono-gamma-lactone (D (-) -gulono-gamma-lactone), and L-asparagine (L-asparagine).
Another aspect of the present invention relates to a kit for diagnosing chronic obstructive pulmonary disease, comprising: at least 15 of the above-mentioned diagnostic markers are included, more preferably 24 or more, and still more preferably all of the diagnostic markers are included.
In a preferred embodiment of the invention, the kit is used as a control sample of a plasma sample from a patient with chronic obstructive pulmonary disease. The invention can realize diagnosis only by blood sampling detection without additionally and invasively collecting body fluid or tissue samples of the lung, has simple and quick diagnosis, is beneficial to early diagnosis and early treatment of chronic obstructive pulmonary disease and acute attack of the chronic obstructive pulmonary disease, and has good clinical use and popularization values.
In a preferred embodiment of the present invention, the diagnostic marker in the kit is used as a standard for high performance liquid chromatography-mass spectrometry. According to the invention, the original metabolic fingerprint of the sample containing the information of the chromatogram and the mass spectrum is obtained by combining the high performance liquid chromatography and the mass spectrum, the fingerprint formed by the standard substance is used as the standard map, and the metabolic fingerprint of the sample to be detected can be compared with the standard map so as to quickly and accurately obtain the diagnosis result.
The invention also provides a screening method of the diagnosis markers suitable for diagnosing the chronic obstructive pulmonary disease, which comprises the following steps:
(1) collecting plasma samples of acute stage chronic obstructive pulmonary disease patients, stable stage chronic obstructive pulmonary disease patients and healthy volunteers as analysis samples;
(2) performing non-targeted metabonomics analysis on each analysis sample by adopting a liquid chromatography-mass spectrometry combined technology to obtain an original metabolic fingerprint of each plasma sample;
(3) carrying out data preprocessing and multivariate statistical analysis on the obtained plasma metabonomics fingerprint to screen differential metabolites, and searching potential metabolic markers for distinguishing a slow obstructive pulmonary disease group and a healthy control group by a machine learning Support Vector Machine (SVM) algorithm;
(4) and (3) according to the primary and secondary mass spectrum information of the potential metabolic markers, the molecular mass and molecular formula of the markers are presumed, and the molecular mass and molecular formula are compared with spectrogram information in a metabolite spectrogram database (Massbank), so that the metabolites are identified, and the plasma metabolic markers suitable for diagnosing the chronic obstructive pulmonary disease are obtained. The combination of different plasma metabolism markers can be used as diagnostic markers suitable for the diagnosis of the chronic obstructive pulmonary disease.
The invention also provides a chronic obstructive pulmonary disease diagnostic kit which comprises the diagnostic marker.
The invention has the advantages that the plasma metabonomics technology is adopted to analyze the patients with chronic obstructive pulmonary disease in the acute stage, the patients with chronic obstructive pulmonary disease in the stable stage and healthy normal control to obtain the diagnosis marker suitable for diagnosis of the chronic obstructive pulmonary disease and acute attack of the chronic obstructive pulmonary disease, the marker has good classification on the metabonomic data of the patients with chronic obstructive pulmonary disease and healthy people, and the patients with chronic obstructive pulmonary disease and the healthy people can be accurately distinguished.
Drawings
FIG. 1 Total Ion Chromatograms (TICs) of original metabolic fingerprints, wherein positive ion mode, negative ion mode are involved, retention time is plotted on the horizontal axis and metabolite relative concentration is plotted on the vertical axis.
FIG. 2 is a graph of a machine learning Support Vector Machine (SVM) classification model with hydrophilic metabolite model results on the left and lipid metabolite model results on the right.
Figure 3 ROC curves of selected features of the SVM model, with area under the curve (AUC) and 95% Confidence Interval (CI) for the hydrophilic metabolite model on the left and area under the curve (AUC) and 95% Confidence Interval (CI) for the lipid metabolite model on the right, with sensitivity on the ordinate and specificity on the abscissa.
Detailed Description
The present invention is further illustrated below by reference to specific examples, which are provided only for the purpose of illustration and are not meant to limit the scope of the present invention.
Example 1:
1. study object
The study contained a total of 48 plasma samples from patients with stable stage chronic obstructive pulmonary disease and 48 plasma samples from patients with acute stage chronic obstructive pulmonary disease, from third hospital, Beijing university, and 48 plasma samples from healthy volunteers with normal lung function and no chronic cardiac and pulmonary disease.
2. Plasma non-targeted metabonomics analysis using liquid chromatography-mass spectrometry technology
All plasma samples were centrifuged and stored in a-80 ℃ freezer. During research, a plasma sample is taken out, and after sample pretreatment, non-targeted metabonomics analysis is carried out by using a high performance liquid chromatography-mass spectrometer to obtain a sample original metabolic fingerprint containing chromatographic and mass spectrum information. The specific operation is as follows:
2.1 instruments and reagents
The experimental apparatus comprises: high performance liquid chromatography mass spectrometer (U3000/QEAxctive, Thermo Fisher), high speed low temperature centrifuge (Beckman), vibration vortex apparatus, centrifugal concentrator, 4 deg.C refrigerator, and water purifier (Millipore).
The experiment consumptive material includes: waters XSelect CSH C18 chromatography column (specification 100X 4.6mm, 3.5 μm), 2ml EP tube, 1.5ml sample bottle, 300 μ l inner cannula, pipette, 1000 μ l tip, 200 μ l tip, marker, latex glove, mask.
The experimental reagent comprises: methanol (Thermo Fisher, mass purity), acetonitrile (Thermo Fisher, mass purity), isopropanol (Thermo Fisher, mass purity), formic acid (Sigma), pure water (TOC <10 ppb).
2.2 plasma sample pretreatment
Before the pretreatment of the plasma samples, 15 quality control samples (QC) were prepared (10. mu.l each of the slow obstructive pulmonary plasma samples and the healthy plasma samples were mixed and aliquoted). Carrying out sample pretreatment on all the chronic obstructive pulmonary plasma samples and the healthy plasma samples together with a quality control sample, and specifically operating as follows:
(1) pipette 50. mu.l of the assay sample or quality control sample into a 2.0ml EP (eppendorf) tube;
(2) adding 150 μ l methanol for extraction, and shaking for 5 min to precipitate protein;
(3) then centrifuged at 12000rpm for 10 minutes at 4 ℃ in a high speed centrifuge;
(4) transferring the supernatant obtained in the step (3) into an LC-MS sample introduction bottle, and storing at-80 ℃ for LC-MS detection.
2.3 plasma non-targeted metabolomics detection
Taking all the processed slow obstructive pulmonary plasma samples and healthy plasma samples as analysis samples, disordering the sequence and then randomly sequencing the sample injection to eliminate the bias brought by the sample injection sequence. One quality control sample was added every 10 analytical samples. The liquid chromatography and mass spectrometry methods used were as follows:
mobile phase: a is 0.1 percent formic acid, 60 percent acetonitrile and 40 percent water solution, B is 0.1 percent formic acid, 10 percent acetonitrile and 90 percent isopropanol solution;
flow rate: 0.5 ml/min; column temperature: 30 ℃; sample introduction volume: 10 mu l of the mixture;
chromatographic gradient elution conditions: 40% B at 0-l min, 40% B-50% B gradually increasing from 1-4 min, 50% B to 100% B gradually increasing from 4-12 min, 100% B at 12-15 min, a rapid decrease to 40% B at 15-15.5 min, then 40% B for 2 min.
The mass spectrometry method comprises the following steps: adopting positive ion mode ESI + and negative ion mode ESI of an electrospray ion source, wherein the ion source temperature is 320 ℃, the back blowing gas is set to be 2, the desolvation temperature is 300 ℃, and the sheath gas and the auxiliary gas are respectively set to be 40 and 10; the capillary voltage is +3kV and-3 kV respectively in the positive ion mode and the negative ion mode, and the taper hole voltage is 0V; the mode of acquisition is a data dependent mode (DDA); the mass-to-charge ratio range of the primary mass spectrum data acquisition is 200-1200 m/z, the acquisition resolution is 35000, and the number of target ions is 1x106The maximum ion implantation time is 80 ms; the secondary mass spectrum acquisition resolution is 17500, and the number of target ions is 1x105Maximum ion implantation time of 50ms, cycle number of 5 times, isolation window of 4.0m/z, collisionThe collision energy is 10, 20 and 30.
3. Plasma metabolism marker screening
The samples were analyzed according to the above described chromatographic mass spectrometry conditions to obtain the original metabolic fingerprints of all samples, wherein typical total ion flux chromatograms (EICs) for each group of samples are shown in fig. 1. And then analyzing the original metabolism fingerprint data by adopting a machine learning algorithm to screen a diagnosis marker capable of distinguishing patients with chronic obstructive pulmonary disease from healthy people, wherein the specific operation is as follows:
3.1 map data preprocessing
After the original metabolic fingerprint of a plasma sample is respectively detected and obtained under positive ion ESI + and negative ion ESI-by using a high performance liquid chromatography-mass spectrometer, Reinforcs file converter software is used for converting the spectrum into an ABF format file, and then MS-Dial software is used for preprocessing including retention time correction, peak identification, peak matching, peak alignment, noise filtration, data standardization and the like. The parameters are set as follows: the mass deviation of the primary mass spectrum is set to be 0.01Da, the mass deviation of the secondary mass spectrum is set to be 0.025Da, and other parameters are default values. And obtaining metabolites of each row after processing, wherein each row is an analysis sample, and the median is a two-dimensional matrix of the corresponding metabolite concentration. Wherein each metabolite peak is characterized using retention time and mass-to-charge ratio, and is subject to metabolite peak identification including isotope peaks, adducts and fragment ions and peak area integration. 8068 small molecule metabolite peaks in a positive ion mode and 4782 small molecule metabolite peaks in a negative ion mode are obtained after map pretreatment, and can be used for further data analysis.
3.2LC-MS Experimental quality control
When LC-MS plasma non-targeted metabonomics analysis is carried out, the prepared QC samples are uniformly inserted into the analysis samples according to the sequence that one QC sample is arranged in every 10 analysis samples, the quality control condition of the analysis samples from sample injection pretreatment to the analysis detection process is monitored in real time, the obtained original metabolic fingerprint is pretreated by MS-Dial software, the variation coefficient (% RSD) of each metabolite in the QC samples is calculated, the variation coefficient of most metabolites is controlled to be below 30%, the quality control condition of the samples from sample injection pretreatment to the analysis detection process is good, and the obtained omics metabolic data are real and credible.
3.3SVM modeling
Using a machine learning Support Vector Machine (SVM) algorithm to learn two-dimensional matrix data obtained by map preprocessing, randomly using 2/3 of the plasma sample data of the slow obstructive pulmonary patients and the healthy volunteers as a training set, learning 1/3 as a test set, and randomly and circularly iterating for 2000 times, wherein the obtained model is shown in figure 2, a disease group and a contrast group can be well classified, and meanwhile, the area under the curve (AUC) of an ROC curve is more than 0.99, which is shown in figure 3.
3.4 plasma metabolism marker screening and identification
According to the obtained SVM model, through a feature screening sequence based on machine learning, with the help of feature importance scores of SVM modeling and continuous accumulation of important features to form a model to be tested, the classification accuracy of the model is evaluated to display the classification efficiency of different models, and finally the screening of relative optimal feature numbers and combination modes is shown, wherein the standard for screening the optimal feature numbers and the combination modes is as follows: the model accuracy does not increase any more with increasing feature numbers. Referring to fig. 3, it can be seen that the AUC area of the ROC model can reach above 0.999 by using about 15 features, and then the overall effect of the ROC model is not increased any more by increasing the number of features. Therefore, we can select 15 characteristic metabolites from each of the hydrophilic metabolites and the lipid metabolites, and 30 characteristic metabolites are used as potential biomarkers, so that reliable disease and normal person differentiation can be realized.
The molecular mass and molecular formula of the marker are then deduced from the primary and secondary mass spectral information of these potential metabolic markers, and compared with spectral information in a metabolite spectral database (Massbank) to identify the metabolite.
Based on the above identification methods, we successfully identified 28 plasma metabolism markers as diagnostic markers suitable for diagnosis of chronic obstructive pulmonary disease. As shown in Table 2, these markers are phosphatidylcholine PC16: 1-36:3, phosphatidylcholine PC 26:0-22:4, phosphatidylcholine PC 26:0-22:3, phosphatidylcholine PC 47:5e, phosphatidylcholine PC 44:11e, phosphatidylcholine PC16:0-24:5, phosphatidylcholine PC20:2-20:3, phosphatidylcholine PC 40:10, phosphatidylcholine PC 38:9e, phosphatidylcholine PC18:1-20:4, phosphatidylcholine PC16:0-20:3, phenylacetaldehyde (phenylacetaldehyde), dihydrouracil (dihydrouracil), nicotinamide (nicotinic acid amide), 4-methoxycinnamic acid (4-methoxycinnamamic acid), ketovaline (ketovaline), threonine (threonine), DL-3-aminoisobutyric acid (DL-3-aminoisobutyric acid), pyruvic acid (pyruvic acid), Stachyose (stachyyose), caffeic acid (caffeic acid), N-dimethylaniline (N, N-dimethyllaniline), maltotriose (maltotriose), D (-) -gulono-gamma-lactone (D (-) -gulono-gamma-lactone), and L-asparagine (L-asparagine). Through examination of published documents, the 28 plasma metabolism markers are found in early diagnosis of chronic obstructive pulmonary disease for the first time, and have very important significance for diagnosis and treatment of the chronic obstructive pulmonary disease.
TABLE 2.28 plasma metabolism markers
The diagnosis marker can effectively distinguish patients with chronic obstructive pulmonary disease from healthy normal controls, is favorable for clinically auxiliary diagnosis of the chronic obstructive pulmonary disease and acute exacerbation of the chronic obstructive pulmonary disease, is greatly helpful for improving clinical diagnosis and evaluation of the chronic obstructive pulmonary disease, and has good clinical use and popularization values.
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 other embodiments based on the inventive idea of the present invention will also fall within the protective scope of the claims of the present invention for a person with ordinary skill in the art without departing from the principle of the present invention.
Claims (8)
1. The slow obstructive lung diagnosis marker based on metabonomics is characterized in that: the diagnostic marker comprises the following 28 compounds: phosphatidyl choline PC16: 1-36:3, phosphatidyl choline PC 26:0-22:4, phosphatidyl choline PC 26:0-22:3, phosphatidyl choline PC 47:5e, phosphatidyl choline PC 44:11e, phosphatidyl choline PC16:0-24:5, phosphatidyl choline PC20:2-20:3, phosphatidyl choline PC 40:10, phosphatidyl choline PC 38:9e, phosphatidyl choline PC18:1-20:4, phosphatidyl choline PC16:0-20:3, phenylacetaldehyde (phenylacetaldehyde), dihydrouracil (dihydrouracil), nicotinamide (nicotinamide), 4-methoxycinnamic acid (4-methoxycinnamic acid), ketovaline (ketovaline), threonine (threoninine), DL-3-aminoisobutyric acid (DL-3-aminoisobutyric acid), pyruvic acid (pyruvic acid), Stachyose (stachyyose), caffeic acid (caffeic acid), N-dimethylaniline (N, N-dimethyllaniline), maltotriose (maltotriose), D (-) -gulono-gamma-lactone (D (-) -gulono-gamma-lactone), and L-asparagine (L-asparagine).
2. A kit for diagnosing chronic obstructive pulmonary disease, comprising: comprising at least 15, more preferably more than 24, and even more preferably all diagnostic markers of claim 1.
3. Use of the chronic obstructive pulmonary disease diagnostic marker of claim 1 in the preparation of a chronic obstructive pulmonary disease diagnostic kit.
4. Use of the kit of claim 3 for the preparation of a kit for diagnosis of chronic obstructive pulmonary disease.
5. The use according to claim 3 or 4, wherein the kit is used as a control sample of a plasma sample from a patient with chronic obstructive pulmonary disease.
6. The use according to claim 5, wherein the diagnostic marker of the kit is used as a standard substance for high performance liquid chromatography-mass spectrometry.
7. The method for screening a diagnostic marker for chronic obstructive pulmonary disease according to claim 1, wherein: comprises the following steps:
(1) collecting plasma samples of acute stage chronic obstructive pulmonary disease patients, stable stage chronic obstructive pulmonary disease patients and healthy volunteers as analysis samples;
(2) performing non-targeted metabonomics analysis on each analysis sample by adopting a liquid chromatography-mass spectrometry combined technology to obtain an original metabolic fingerprint of each plasma sample;
(3) carrying out data preprocessing and multivariate statistical analysis on the obtained plasma metabonomics fingerprint to screen differential metabolites, and searching potential metabolic markers for distinguishing a slow obstructive pulmonary disease group and a healthy control group by a machine learning Support Vector Machine (SVM) algorithm;
(4) presuming the molecular mass and molecular formula of the marker according to the information of the primary and secondary mass spectra of the potential metabolic marker, and comparing the molecular mass and molecular formula with spectrogram information in a metabolite spectrogram database (Massbank), thereby identifying the metabolite and obtaining the plasma metabolic marker suitable for diagnosing the chronic obstructive pulmonary disease; the combination of different plasma metabolism markers can be used as diagnostic markers suitable for the diagnosis of the chronic obstructive pulmonary disease.
8. The method of screening for a diagnostic marker of slow obstructive lung according to claim 5, which is of non-diagnostic interest.
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| CN112285338A (en) * | 2020-10-20 | 2021-01-29 | 四川大学华西医院 | COPD diagnostic kit |
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| CN114137226B (en) * | 2021-12-02 | 2023-04-28 | 首都师范大学 | Early diagnosis marker for cerebral infarction, screening method and application thereof, and construction method and application of early diagnosis model for cerebral infarction |
| CN114705775A (en) * | 2022-03-31 | 2022-07-05 | 广东省结核病控制中心 | Serum metabolic marker for pulmonary tuberculosis evaluation and application thereof |
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| CN115876991A (en) * | 2023-03-08 | 2023-03-31 | 中国医学科学院北京协和医院 | Sugar chain marker for pulmonary embolism diagnosis and application thereof |
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