CN114414819B - Biomarker for diagnosing pneumoconiosis and application thereof - Google Patents

Biomarker for diagnosing pneumoconiosis and application thereof Download PDF

Info

Publication number
CN114414819B
CN114414819B CN202210314434.3A CN202210314434A CN114414819B CN 114414819 B CN114414819 B CN 114414819B CN 202210314434 A CN202210314434 A CN 202210314434A CN 114414819 B CN114414819 B CN 114414819B
Authority
CN
China
Prior art keywords
pneumoconiosis
equal
less
decision tree
control group
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210314434.3A
Other languages
Chinese (zh)
Other versions
CN114414819A (en
Inventor
陈显扬
薛腾
丁春光
彭方达
常婷婷
宋王婷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhongyuan Birui Biotechnology Zhuhai Hengqin Co ltd
Original Assignee
Zhongyuan Birui Biotechnology Zhuhai Hengqin Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhongyuan Birui Biotechnology Zhuhai Hengqin Co ltd filed Critical Zhongyuan Birui Biotechnology Zhuhai Hengqin Co ltd
Priority to CN202210314434.3A priority Critical patent/CN114414819B/en
Publication of CN114414819A publication Critical patent/CN114414819A/en
Application granted granted Critical
Publication of CN114414819B publication Critical patent/CN114414819B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/04—Preparation or injection of sample to be analysed
    • G01N30/06—Preparation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
    • G01N30/8679—Target compound analysis, i.e. whereby a limited number of peaks is analysed
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/04—Preparation or injection of sample to be analysed
    • G01N30/06—Preparation
    • G01N2030/062—Preparation extracting sample from raw material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2405/00—Assays, e.g. immunoassays or enzyme assays, involving lipids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/12—Pulmonary diseases
    • G01N2800/122—Chronic or obstructive airway disorders, e.g. asthma COPD
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease

Landscapes

  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Hematology (AREA)
  • Biotechnology (AREA)
  • Public Health (AREA)
  • Urology & Nephrology (AREA)
  • General Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • Databases & Information Systems (AREA)
  • Biophysics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Cell Biology (AREA)
  • Software Systems (AREA)
  • Bioethics (AREA)
  • Microbiology (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Library & Information Science (AREA)
  • Probability & Statistics with Applications (AREA)
  • Primary Health Care (AREA)

Abstract

The invention provides a biomarker for diagnosing pneumoconiosis and application thereof, wherein the biomarker is application of 2-chloropalmitic acid in preparing a detection reagent for diagnosing the pneumoconiosis, and the biomarker 2-chloropalmitic acid is combined with diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A to judge the risk of the pneumoconiosis and prevent the pneumoconiosis in advance.

Description

Biomarker for diagnosing pneumoconiosis and application thereof
Technical Field
The invention belongs to the technical field of biological detection, and particularly relates to a biomarker for diagnosing pneumoconiosis and application thereof.
Background
Pneumoconiosis, known by the school name Pneumoconiosis (Pneumoconiosis), is a typical and serious occupational disease. For occupational reasons, workers inhale productive dust (dust) for a long time, which easily causes retention of dust in the lungs, causing diffuse fibrosis of lung tissues, and the higher the content of silica in the dust, the shorter the onset time of pneumoconiosis, the more serious the lesion. Currently, pneumoconiosis diagnosis can only identify patients by means of lung CT or tissue biopsy. The onset time of pneumoconiosis is influenced by individual constitution and protective measures, and the pneumoconiosis may be suffered from diseases for several months, and the duration is from several years to ten years. However, generally speaking, if respiratory protection is not provided, the environment is not good, and the pneumoconiosis can be suffered in a short time. Based on the specificity of the pathology of pneumoconiosis and the uncertainty of the pathogenesis, the detection mode which is high in price and poor in timeliness is continuously used, and is not suitable for detecting and diagnosing the pneumoconiosis, and when many patients are diagnosed with the pneumoconiosis, the middle and late stages are extremely unfavorable for subsequent treatment, so that the patients are suffered from long-term pain and affliction.
Metabolomics is an emerging omics technology that plays an increasingly important role in biological research because it can reveal unique chemical fingerprints of the cellular metabolism of the body. Metabolomics, an unbiased research method of small molecule metabolites, can reflect the state of an organism by analyzing the change of endogenous metabolites, and further identify specific biomarkers or marker groups. How to find a biomarker easy to detect and a method for predicting and diagnosing pneumoconiosis is an urgent technical problem to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention provides application of the biomarker 2-chloropalmitic acid in preparing a detection reagent for diagnosing pneumoconiosis.
In order to achieve the purpose, the invention adopts the following technical scheme that:
application of 2-chloropalmitic acid as a biomarker in preparing a detection reagent for diagnosing pneumoconiosis.
Biomarkers also include diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)), 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme a.
Application of 2-chloropalmitic acid serving as a biomarker, diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A in preparation of detection reagents for diagnosing pneumoconiosis.
The use as described above, preferably, 2-chloropalmitic acid is combined with diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)), 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A to determine whether or not there is a risk of pneumoconiosis.
When the content of 2-chloropalmitic acid is R51 and the content of diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) is R9 and the content of 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A is R5, the patient is not determined as a pneumoconiosis if any of the following conditions is satisfied, and the patient is determined as a pneumoconiosis if all of the conditions except the four conditions are satisfied;
（1）R51≥0.69，R9＜0.21；
(2) r51 is more than or equal to 0.69, R9 is more than or equal to 0.21, R51 is more than or equal to 2.1, R9 is more than or equal to 0.54, and R5 is less than 0.81;
（3）0.69≤R51＜1，0.21≤R9＜1.4，0.26≤R5＜0.42；
(4) r51 is more than or equal to 0.69 and less than 2.5, R9 is more than or equal to 0.21 and less than 0.55, R51 is less than 1, and R5 is more than or equal to 0.26 and less than 0.42.
The application as described above, preferably, the pneumoconiosis is predicted according to the TC value in the machine learning conditional probability decision tree model composed of the three markers by detecting the content of 2-chloropalmitic acid in serum, and combining the content of diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A: if TC is more than or equal to 0.515, the patient is judged to be pneumoconiosis; if TC <0.515, then the disease is a non-dust lung disease.
In the above application, preferably, the machine learning conditional probability decision tree model is constructed by using rpart package of R language to build the decision tree model, setting the variable with dustless lung disease as the factorial variable, and setting the "method" parameter in the modeling as "class", which indicates to build the classification decision tree model; the 'model' parameter is set to 'False', which indicates that no model frame copy is reserved in the resampling result; the "parms" parameter is set to 1, indicating that the coefficient of variation of the prior distribution has a coefficient of influence on the cleavage rate set to 1.
The invention has the beneficial effects that:
the invention provides a novel biomarker 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A2-hydroxy-6-oxocyclohexane-1-carbonyl-CoA and a model for distinguishing pneumoconiosis, can be used for early discovery, diagnosis and prediction of pneumoconiosis, and is applied to preparation of a detection kit for detecting pneumoconiosis.
The biomarker for diagnosing the pneumoconiosis cerebri provided by the invention comprises 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A, combines the contents of diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-chloropalmitic acid, predicts the pneumoconiosis according to the TC value in a machine learning conditional probability decision tree model consisting of the three markers, is helpful for diagnosing whether the pneumoconiosis is prone, and can be used for early prevention.
Drawings
FIG. 1 is a graph of S-plot of a control group in normal environment in positive ion mode compared to a control group in environment susceptible to pneumoconiosis;
FIG. 2 is a S-plot of a control group in normal environment in negative ion mode compared to a control group in environment susceptible to pneumoconiosis;
FIG. 3 is S-plot of an environmental control group susceptible to pneumoconiosis in positive ion mode compared with an experimental group suffering from pneumoconiosis;
FIG. 4 is a graph of S-plot comparing an environmental control group susceptible to pneumoconiosis with an experimental group suffering from pneumoconiosis in negative ion mode;
FIG. 5 is a graph of a compound of VIP >1 in positive ion mode in a normal environmental control group compared to an environmental control group susceptible to pneumoconiosis;
FIG. 6 is a graph of a compound of VIP >1 in negative ion mode in a normal environmental control group compared to an environmental control group susceptible to pneumoconiosis;
FIG. 7 is a graph of the score for (O) PLS-DA in positive ion mode in comparison to a normal environmental control group and an environmental control group susceptible to pneumoconiosis;
FIG. 8 is a graph of the score for (O) PLS-DA in negative ion mode in comparison to a normal environmental control group and an environmental control group susceptible to pneumoconiosis;
FIG. 9 shows a comparison of VIP >1 in a positive ion mode in an environmental control group susceptible to pneumoconiosis and an experimental group suffering from pneumoconiosis;
FIG. 10 shows a compound with VIP >1 in negative ion mode in comparison to an experimental group with pneumoconiosis in an environmental control group susceptible to pneumoconiosis;
FIG. 11 is a graph of the score for (O) PLS-DA in positive ion mode compared to an experimental group with pneumoconiosis in an environmental control group susceptible to pneumoconiosis;
FIG. 12 is a graph of the score for (O) PLS-DA in comparison of an environmental control group susceptible to pneumoconiosis in negative ion mode with an experimental group suffering from pneumoconiosis;
FIG. 13 is a graph of initial marker up-regulation and down-regulation of Wien in a normal environment control group and a pneumoconiosis susceptible environment control group, and in a pneumoconiosis susceptible environment control group and an experimental group;
FIG. 14 is a probability model diagram of decision trees established for final markers compared between a control group of pneumoconiosis susceptible environments and an experimental group of pneumoconiosis (variables R5+ R9+ R51);
FIG. 15 is a decision tree probability model evaluation graph (variables R5+ R9+ R51) of the final markers obtained by comparing the control group of the environment susceptible to pneumoconiosis with the experimental group suffering from pneumoconiosis.
Detailed Description
The following examples are intended to further illustrate the invention but should not be construed as limiting it. Modifications and alterations of this invention are within the scope of this invention without departing from the spirit and nature of this invention.
Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. Reagents used in the invention: the manufacturers of isopropanol, formic acid, acetonitrile, ammonium formate and LC-MS grades are Fisher.
Example 1
Collecting samples: the serum of people who live or work in normal environment without pneumoconiosis, people who live or work in environment susceptible to pneumoconiosis without pneumoconiosis and pneumoconiosis patients is selected for test.
196 samples were modeled, ranging in age from 30 years old or older, where:
the lung function index of the control population (CK) 50 people living or working in a normal environment without pneumoconiosis and the lung function index of the control population (CFD) 85 people living or working in an environment susceptible to pneumoconiosis without pneumoconiosis are normal, and the chest X-ray imaging detection shows that no abnormality exists.
Pneumoconiosis patient population (CFB), 61: the lung function index is abnormal, and chest X-ray imaging detection shows that the abnormality exists.
Thawing collected serum samples of the above population on ice, extracting 200 μ L of serum with 600 μ L of pre-cooled isopropanol, vortexing for 1min, incubating at room temperature for 10min, storing the extraction mixture at-20 ℃ overnight, centrifuging at 12000rpm for 20min in a cryogenic centrifuge (E3116R, ESSENSCIEN, usa), adding 260 μ L of supernatant to a new centrifuge tube, adding 130 μ L of isopropanol, 130 μ L of acetonitrile and 65 μ L of ultrapure water, i.e. adjusting the ratio of sample isopropanol/acetonitrile/water by volume to 2.5: 1: 1. samples were stored at-80 ℃ prior to LC-MS analysis. In addition, 10. mu.L of each extraction mixture was combined together to prepare a mixed QC sample.
Ultra-high performance liquid chromatography-mass spectrometry combined method for lipidomics
Samples were analyzed by ultra high performance liquid chromatography (UPLC; model: ACQUITY UPLC I-Class system; manufacturer: Waters, Manchester, UK) coupled to a Xevo-G2XS high resolution time of flight (QTOF) mass spectrometer (Waters) with ESI. Using a CQUITY UPLC BEH C18 column (2.1X 100 mm, 1.7 μm, Waters), mobile phase A: the mobile phase is 10mM ammonium formate-0.1% formic acid-acetonitrile-ultrapure water; the mobile phase B is as follows: 10mM ammonium formate-0.1% formic acid-isopropanol-acetonitrile. Prior to large scale studies, pilot experiments including 10, 15 and 20 minute elution periods were performed to assess the potential impact of mobile phase composition and flow rate on lipid retention time. In PIM, abundant lipid precursor ions and fragments are separated in the same order, with similar peak shapes and ionic strengths. In addition, the mixed QC samples with 10 minute elution periods also showed similar basal peak intensities of precursors and debris as the test samples. The flow rate of the mobile phase was 0.4 mL/min. The column was initially eluted with 40% B, then a linear gradient to 43% B in 2 minutes, then increasing the percentage of B to 50% in 0.1 min. In the next 3.9 minutes, the gradient further increased to 54% B, then the amount of B increased to 70% in 0.1 minutes. In the final part of the gradient, the amount of B increased to 99% in 1.9 min. Finally, solution B returned to 40% in 0.1min and the column was equilibrated for 1.9 min before the next injection. The sample injection amount is 5 mu L each time, and a Xevo-G2XS QTOF mass spectrometer is used for detecting the lipid under positive and negative modes, wherein the collection range is m/z 50-1200 years, and the collection time is 0.2 s/time. The ion source temperature is 120 ℃, the desolventizing temperature is 600 ℃, the gas flow is 1000L/h, and nitrogen is used as flowing gas. The capillary voltage was 2.0kV (+)/cone voltage was 1.5kV (-), and the cone voltage was 30V. Standard mass measurements were performed with leucine enkephalin, calibrated with sodium formate solution. Samples were randomly ordered. One QC sample was injected every 10 samples and analyzed to investigate the reproducibility of the data.
The preparation method of the mobile phase A comprises the steps of weighing 0.63 g of ammonium formate and 10 g of formic acid, dissolving the ammonium formate and the formic acid by using an acetonitrile-water solution (the volume ratio of acetonitrile to water is 60: 40), and fixing the volume to 1000 mL.
The preparation method of the mobile phase B comprises the steps of weighing 0.63 g of ammonium formate and 10 g of formic acid, dissolving the ammonium formate and the formic acid by using an isopropanol-acetonitrile solution (the volume ratio of isopropanol to acetonitrile is 90: 10, and v/v), and fixing the volume to 1000 mL.
Data acquisition was performed using data acquisition software (MassLynx4.1; manufacturer: Waters), results analysis:
1. finding serum differential substances using classical statistics
Mass spectral data were converted into statistical data form using Progenesis QI and orthogonal partial least squares discriminant analysis (OPLS-DA) combined with Orthogonal Signal Correction (OSC) and PLS-DA (partial minimum discriminant analysis) methods to screen for differential variables by removing irrelevant differences. As shown in FIG. 1, FIG. 1 is a S-plot diagram comparing a normal environmental control group with an environmental control group susceptible to pneumoconiosis in the positive ion mode, FIG. 2 is a S-plot diagram comparing a normal environmental control group with an environmental control group susceptible to pneumoconiosis in the negative ion mode, FIG. 3 is a S-plot diagram comparing a control group susceptible to pneumoconiosis in the positive ion mode with an experimental group suffering pneumoconiosis in the negative ion mode, FIG. 4 is a S-plot diagram comparing a control group susceptible to pneumoconiosis in the negative ion mode with an experimental group suffering pneumoconiosis in the negative ion mode, wherein the abscissa represents the co-correlation coefficient of the principal component and the metabolite, and the ordinate represents the correlation coefficient of the principal component and the metabolite. Under the condition that p is less than 0.05, compared with an environmental control group which is easy to be infected with pneumoconiosis, the normal environmental control group has 2099 difference foreign bodies in the positive ion mode and 1383 difference foreign bodies in the negative ion mode; under the condition that p is less than 0.05, 2109 difference foreign matters exist in the positive ion mode and 1383 difference foreign matters exist in the negative ion mode of the normal environment control group compared with the environment control group which is easy to be infected with the pneumoconiosis.
2. Finding significantly varying differential substances in serum using multivariate statistics
Orthogonal partial least squares discriminant analysis (OPLS-DA) combines Orthogonal Signal Correction (OSC) and PLS-DA (partial minimum discriminant analysis) methods to screen for differential variables by removing irrelevant differences. As shown in fig. 5 and 6: the VIP value is a variable importance projection of a first main component of PLSDA when a normal environment control group and an environmental control group susceptible to pneumoconiosis are compared under a positive and negative ion mode, and VIP >1 is usually taken as a common judgment standard of metabonomics and is taken as one of the standards for screening differential metabolites; fig. 7 and 8 are graphs of scores obtained by dimension reduction of the first principal component and the second principal component in the two groups of the normal environmental control group (CK) and the pneumoconiosis susceptibility environmental control group (CFD) under the positive and negative ion mode, wherein the abscissa represents the difference between groups, the ordinate represents the difference within groups, and the two groups of results are better separated, which illustrates that the method can be used. The same modeling principle is applied to the control group (CFD) of the pneumoconiosis-susceptible environment and the experimental group (CFB) (VIP graph is shown in FIGS. 9 and 10, and principal component graph is shown in FIGS. 11 and 12). Under the condition that p <0.05 and VIP >1 are simultaneously satisfied: in comparison of the normal environmental control group (CK) and the pneumoconiosis susceptible environmental control group (CFD), there were 92 different impurities in the positive ion mode and 261 different impurities in the negative ion mode; in comparison of the pneumoconiosis-susceptible environmental control group (CFD) and the pneumoconiosis-susceptible experimental group (CFB), there were 45 different impurities in the positive ion mode and 27 different impurities in the negative ion mode, for a total of 72. Fig. 5 and 6, and fig. 9 and 10 illustrate that there are many compounds with significant enrichment (VIP value greater than 1) in both CK versus CFD and CFD versus CFB groups, and interference factors may be present in these three groups when compared in pairs. Fig. 7 and 8, and fig. 11 and 12 illustrate that when comparing two by two between the three groups, the CK group can have a good modeling effect when comparing with the CFD group, and the CFD group can have a good modeling effect when comparing with the CFB group, and the sample information between the two groups can be effectively extracted.
3. Confounding factor rejection of differential substances
Further comparisons were made for the case of a bad foreign body up-regulation or down-regulation. Since pneumoconiosis was not detected in non-pneumoconiosis-susceptible environments, confounding elimination was performed on the total of compounds that differed between the CFD and CFB groups. In fig. 13, the differences obtained in the CFD group and CFB group were removed from the differences overlapping with the differences obtained in the CK group and CFD group, and finally 26 CFB group up-regulating foreign substances with high specificity and 27 CFB group down-regulating foreign substances with high specificity were obtained.
4. Filter analysis of feature importance
To further narrow the range, 53 compounds were subjected to a number of assays to screen the following compounds, specifically as shown in table 1 below.
TABLE 1 pneumoconiosis-associated lipid screening results Table
Figure 568890DEST_PATH_IMAGE001
5. Ten-fold cross validation result of internal population
In order to improve the biological diagnosis effect of the variable-quantity compound, a suitable model needs to be found according to the biomarkers for further analysis. Because of the existence of non-linearity in the real world, a semi-parameter decision tree probability model is selected in the embodiment, a variable with a dust-free lung disease is set as a factor variable, and a "method" parameter in modeling is set as "class", which indicates that a classification decision tree model is established; the parameter of 'model' is set to 'False', which indicates that no model frame copy is reserved in the resampling result; the "parms" parameter is set to 1, indicating that the coefficient of variation of the prior distribution has a coefficient of influence on the cleavage rate set to 1; the model consists of nodes and directed edges. There are two types of nodes: internal nodes and leaf nodes, wherein an internal node represents a feature or attribute and a leaf node represents a class. Generally, a decision tree includes a root node, a plurality of internal nodes, and a plurality of leaf nodes. The leaf nodes correspond to the decision results, and each of the other nodes corresponds to an attribute test. And the sample set contained in each node is divided into the sub-nodes according to the attribute test result, the root node contains the sample complete set, and a path from the root node to each leaf node corresponds to a judgment test sequence.
Randomly dividing the whole population into 10 parts, selecting 1 part as a verification set and the others as training sets, repeating the steps for ten times, and investigating the optimal variable combination. And (3) observing the internal stability, including AUC, sensitivity and specificity, of the five compound combinations in the construction process of the decision tree model, averaging, and performing statistical significance calculation, wherein the result is shown in the following table 2, the sequence numbers in the table correspond to the serial numbers of the verification in the internal verification, the modeling independent variables are the four compounds, and the dependent variable is the outcome variable TC.
TABLE 2
Figure DEST_PATH_IMAGE002
The decision tree model formed by combining the three compounds has excellent internal stability, and the average AUC value is stabilized to about 0.80.
Based on the above analysis, a decision tree model is established: establishing a decision tree model by using an rpart packet of an R language, setting a variable with a dustless lung disease as a factor variable, and setting a "method" parameter in the modeling as "class", which indicates that a classification decision tree model is established; the 'model' parameter is set to 'False', which indicates that no model frame copy is reserved in the resampling result; the "parms" parameter is set to 1, indicating that the coefficient of variation of the prior distribution has a coefficient of influence on the split rate set to 1, and the model map is shown in fig. 14. In combination, when the R51 value is greater than or equal to 0.69, the R9 value is less than 0.21, and the patient is not determined as a pneumoconiosis patient; when the value of R51 is 0.69 or more, the case where the value of R9 is 0.21 or more, the value of R51 is 2.5 or more, the value of R9 is 0.54 or more, and the value of R5 is less than 0.81 is not judged as a pneumoconiosis patient; when the value of R51 is 0.69 or more, the case where the value of R9 is 0.21 or more and the value of R51 is less than 2.5 and the value of R51 is less than 1 and the value of R9 is less than 1.4 and the value of R5 is 0.26 or more and the value of R5 is less than 0.42 is not judged as a pneumoconiosis patient; when the value of R51 is 0.69 or more, the case where the value of R9 is 0.21 or more and the value of R51 is less than 2.5 and the value of R51 is less than 1 and the value of R9 is less than 1.4 and the value of R5 is 0.26 or more and the value of R5 is 0.42 or more and the value of R9 is less than 0.55 is not judged as a pneumoconiosis patient. In all cases except the four cases, the examiner is determined to be a pneumoconiosis patient.
That is, the content of each substance in the serum was measured, and the content of 2-chloropalmitic acid was designated as R51, and the content of diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) was designated as R9, and the content of 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme a was designated as R5, and if any of the following conditions is satisfied, the patient was not determined to be a pneumoconiosis patient, and the patient was determined to be a pneumoconiosis patient in all of the cases other than these four cases.
（1）R51≥0.69，R9＜0.21；
(2) R51 is more than or equal to 0.69, R9 is more than or equal to 0.21, R51 is more than or equal to 2.1, R9 is more than or equal to 0.54, and R5 is less than 0.81;
（3）0.69≤R51＜1，0.21≤R9＜1.4，0.26≤R5＜0.42；
(4) r51 is more than or equal to 0.69 and less than 2.5, R9 is more than or equal to 0.21 and less than 0.55, R51 is less than 1, and R5 is more than or equal to 0.26 and less than 0.42.
When in application, the measured values of the three indexes can be brought into a model to obtain a TC value through computer operation; if TC is larger than or equal to 0.515, the sample is judged to be pneumoconiosis; if TC <0.515, then there is no lung disease.
6. External data set, decision tree model validation
The accuracy of the results is verified through the data set of the external crowd, and corresponding ROC curve graphs are drawn, and the results are as follows:
and (3) verifying the population: 204 persons (outside population), the sampling criteria were the same as the sample population described above. A control population 101 of people who are not suffering from a pneumoconiosis disease and a population 103 of patients suffering from a pneumoconiosis disease who live or work in an environment susceptible to pneumoconiosis. And carrying out decision tree model verification.
The variables in the model are the 3 metabolites R5+ R9+ R51, a decision tree model is established according to the compound content values, and the evaluation graph of the model is shown in figure 15.
Sensitivity =0.903
Specificity =0.754
Accuracy (Accuracy) =0.848
Threshold (= 0.515)
Predicting pneumoconiosis according to TC values of three marker detection data of the sample, which are substituted into the formed machine learning conditional probability decision tree model, and if TC is more than or equal to 0.515, judging that the sample is pneumoconiosis; if TC <0.515, then there is no lung disease.
And (3) displaying data: the 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A, combined with diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-chloropalmitic acid show very high diagnostic capability, and can be applied to clinical kits in the future.
Through comparative analysis on sample information, the following results are obtained: compared with the environmental control group susceptible to pneumoconiosis, the 2-chloropalmitic acid of the above 3 biomarkers is decreased in the pneumoconiosis patient group, and the 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A and diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) are opposite.

Claims (1)

1. Use of biomarkers 2-chloropalmitic acid in combination with diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A in the preparation of a detection reagent for diagnosing pneumoconiosis;
detecting the content of 2-chloropalmitic acid in serum by an ultra-performance liquid chromatography-mass spectrometry combined method, wherein the combined content of diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) and 2-hydroxy-6-oxocyclohexane-1-carbonyl-coenzyme A is recorded as R9 and the content of 2-chloropalmitic acid R51 and diglyceride (18:1(9Z)/18:3(9Z,12Z,15Z)) is recorded as R5, if any one of the following conditions is met, the lung disease is not judged, and if the other conditions except the four conditions are all judged to be lung disease dust;
（1）R51≥0.69，R9＜0.21；
(2) r51 is more than or equal to 0.69, R9 is more than or equal to 0.21, R51 is more than or equal to 2.1, R9 is more than or equal to 0.54, and R5 is less than 0.81;
（3）0.69≤R51＜1，0.21≤R9＜1.4，0.26≤R5＜0.42；
(4) r51 is more than or equal to 0.69 and less than 2.5, R9 is more than or equal to 0.21 and less than 0.55, R51 is less than 1, R5 is more than or equal to 0.26 and less than 0.42;
or predicting the pneumoconiosis according to the TC value in the machine learning conditional probability decision tree model formed by the three markers: if TC is more than or equal to 0.515, the patient is judged to be pneumoconiosis; if TC <0.515, then the disease is a non-dust lung disease;
the machine learning conditional probability decision tree model is constructed by using an rpart packet of an R language to establish a decision tree model, setting a variable with a dust-free lung disease as a factor variable, and setting a 'method' parameter in the modeling as 'class', which indicates that a classification decision tree model is established; the 'model' parameter is set to 'False', which indicates that no model frame copy is reserved in the resampling result; the "parms" parameter is set to 1, indicating that the coefficient of variation of the prior distribution has a coefficient of influence on the cleavage rate set to 1.
CN202210314434.3A 2022-03-28 2022-03-28 Biomarker for diagnosing pneumoconiosis and application thereof Active CN114414819B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210314434.3A CN114414819B (en) 2022-03-28 2022-03-28 Biomarker for diagnosing pneumoconiosis and application thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210314434.3A CN114414819B (en) 2022-03-28 2022-03-28 Biomarker for diagnosing pneumoconiosis and application thereof

Publications (2)

Publication Number Publication Date
CN114414819A CN114414819A (en) 2022-04-29
CN114414819B true CN114414819B (en) 2022-06-21

Family

ID=81262812

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210314434.3A Active CN114414819B (en) 2022-03-28 2022-03-28 Biomarker for diagnosing pneumoconiosis and application thereof

Country Status (1)

Country Link
CN (1) CN114414819B (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110412194A (en) * 2018-04-26 2019-11-05 缪荣明 A kind of method of liquid chromatographic detection apolipoprotein concentration

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110412194A (en) * 2018-04-26 2019-11-05 缪荣明 A kind of method of liquid chromatographic detection apolipoprotein concentration

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
代金娥.尘肺病患者下呼吸道菌群及代谢物结构特征研究.《中国优秀硕士学位论文全文数据库》.2022, *
汪光荣等.矽肺和煤工尘肺中的细胞因子及细胞因子网络.《江西医药》.2008,(第06期), *
郭维新.矽肺与肺磷脂.《中国职业医学》.(第01期), *

Also Published As

Publication number Publication date
CN114414819A (en) 2022-04-29

Similar Documents

Publication Publication Date Title
CN109884302B (en) Lung cancer early diagnosis marker based on metabonomics and artificial intelligence technology and application thereof
CN111289736A (en) Slow obstructive pulmonary early diagnosis marker based on metabonomics and application thereof
CN112798771B (en) Biomarker for diagnosing leukoencephalopathy and application thereof
US20150126403A1 (en) Method of identifying proteins in human serum indicative of pathologies of human lung tissues
CN115932277A (en) Breast cancer diagnosis marker, screening method and quantification method thereof, and diagnostic model construction method and application
CN112151121A (en) Diagnostic marker, kit and screening method for esophageal cancer diagnosis and construction method of esophageal cancer diagnosis model
CN111279193B (en) Behcet&#39;s disease diagnosis kit and method for detecting metabolite difference in urine
CN113447599B (en) Biomarker for diagnosing cerebral infarction of patient with leukoencephalopathy and application of biomarker
CN113433254B (en) Biomarker for diagnosing cerebral infarction of patient with leukoencephalopathy and application of biomarker
CN114414819B (en) Biomarker for diagnosing pneumoconiosis and application thereof
CN114019061B (en) Biomarker for Parkinson disease detection and application thereof
CN114414809B (en) Use of biomarkers for diagnosing pneumoconiosis
CN114354952B (en) Parkinson disease metabonomics biomarker and application thereof
CN113447600B (en) Biomarker for diagnosing cerebral infarction of patient with leukoencephalopathy and application of biomarker
CN114414818B (en) Application of biomarker for detecting pneumoconiosis
CN114280202B (en) Biomarker for diagnosing cadmium poisoning and application thereof
CN115714013A (en) Construction method of clinical prediction model for pneumonia diagnosis
CN114544982A (en) Biomarker for pneumoconiosis diagnosis and application thereof
CN113552228A (en) Combined markers for diagnosing childhood bronchiolitis and application and detection kit thereof
CN114019079B (en) Biomarker for diagnosing Parkinson&#39;s disease and application thereof
CN114019078B (en) Application of biomarker for Parkinson&#39;s disease diagnosis
CN114047281B (en) Combination of biomarkers for parkinson&#39;s disease and uses thereof
CN115825308A (en) Application of nasopharyngeal carcinoma related urine marker in preparation of product for diagnosing/prognosing nasopharyngeal carcinoma
CN118033023A (en) Lung cancer biomarker and application thereof
CN114264756A (en) Biomarker R1 for diagnosing Parkinson&#39;s disease and application thereof

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant