CN113820436A - Noninvasive and rapid screening method for discovering polypeptide spectrum biomarker of superficial gastritis based on mass spectrometry technology and application thereof - Google Patents
Noninvasive and rapid screening method for discovering polypeptide spectrum biomarker of superficial gastritis based on mass spectrometry technology and application thereof Download PDFInfo
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Abstract
The invention relates to a noninvasive and rapid screening method for discovering a polypeptide spectrum biomarker of superficial gastritis based on a mass spectrometry technology and application thereof, and the noninvasive and rapid screening method comprises the following steps: (1) taking clinical saliva samples of patients with superficial gastritis and healthy people, and extracting polypeptides; (2) performing polypeptide peak analysis on the extracted polypeptide sample; (3) performing statistical analysis on the obtained polypeptide spectrum peak analysis result to obtain a significant difference peptide peak; (4) further, the establishment and verification of a diagnostic group are carried out, and the protein attribution of the peptide peak with the significant difference is identified. The invention creatively screens a series of biomarkers which can be used for diagnosing superficial gastritis from saliva, and the superficial gastritis biomarkers have high sensitivity, specificity and accuracy. And the superficial gastritis biomarkers are from saliva, so that a novel non-invasive detection method is provided for diagnosis of superficial gastritis. The method has the characteristics of no wound, high flux, micro sample loading, rapidness, convenience and the like.
Description
Technical Field
The invention belongs to the technical field of molecular diagnosis, particularly relates to a superficial gastritis biomarker and a screening method and application thereof, and particularly relates to a noninvasive and rapid screening method for discovering a superficial gastritis polypeptide spectrum biomarker based on a mass spectrometry technology and application thereof.
Background
Superficial gastritis is a multi-etiology, multi-gene variation, multi-order disease, chronic inflammation originating from gastric mucosa, and is detected by combining endoscopy and pathology. The classification of superficial gastritis is complicated and there are no recognized classification criteria and diagnostic criteria worldwide. The main cause of superficial gastritis (CG) is infection with Helicobacter Pylori (HP), which is affected by diet, environment, mood, behavioral habits, autoimmune diseases, and the like, and has no clinical manifestations. Hp is a carcinogenic factor I, Hp infection is one of the most common chronic infections of human, spontaneous clearance is difficult after short-time infection, and severe intestinal epithelization of gastric mucosa is automatically cleared when long-time infection is carried out. Helicobacter pylori can only be planted on gastric epithelium, and is difficult to plant when the gastric mucosa has severe intestinal metaplasia. The Hp infection can cause the inflammatory reaction of gastric mucosa, after long-term infection, patients can suffer from gastric mucosa atrophy and intestinal metaplasia, and the helicobacter pylori infection can increase the risk of gastric cancer by 2 times. The tumor and inflammation have a close relationship, a large number of inflammatory factors and immune cells exist in a tumor microenvironment, and the inflammation can promote the generation and the development of the cancer by promoting the growth of blood vessels, the proliferation of cancer cells and the invasion of the tumor, negatively regulating the immune response and changing the efficacy of certain anti-tumor drugs. The early detection of superficial gastritis and the monitoring of the development process of chronic inflammation are helpful for preventing gastric cancer and reducing the incidence rate and death rate of gastric cancer.
Currently, the common detection methods for superficial gastritis are serological examination, gastroscopy and/or histological examination of helicobacter pylori. Serological examination can assess whether a patient is infected with helicobacter pylori, but such examination cannot be used as a method for assessing the efficacy. Gastroscopes are invasive and the histopathological examination of the gastric mucosa is highly correlated with the location of the biopsy material. Identification of biomarkers based on clinical information and comprehensive genomic analysis can improve diagnosis and prognosis. The mass spectrometer and the bioinformatics tool can carry out high-precision and high-specificity proteomics analysis on a sample to be detected, but reports of the prior art for screening the superficial gastritis biomarkers by using the strategy are few.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a superficial gastritis biomarker and a screening method and application thereof, and particularly provides a noninvasive and rapid screening method for finding a superficial gastritis polypeptide spectrum biomarker based on a mass spectrometry technology and application thereof
The 'rapidness' means that a saliva sample can be spotted on a computer for detection by using a 96-pore plate by means of a Clin-TOF-MS mass spectrum technology, and large-sample high-throughput detection is carried out, and the method has the characteristics of rapidness, convenience (only 30min) and the like.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a noninvasive and rapid screening method for discovering a polypeptide spectrum biomarker of superficial gastritis based on a mass spectrometry technology, and the screening method comprises the following steps:
(1) taking clinical saliva samples of patients with superficial gastritis and clinical saliva samples of healthy people, and carrying out high-throughput one-step polypeptide extraction on the clinical saliva samples;
(2) performing polypeptide peak analysis on the polypeptide sample extracted in the step (1);
(3) performing statistical analysis on the polypeptide spectrum peak analysis result obtained in the step (2) to obtain a significant difference peptide peak;
(4) and establishing and verifying a superficial gastritis diagnosis group, and screening out a diagnosis group with an AUC value larger than 0.5, namely the superficial gastritis biomarker.
The significantly different peptide peak expression data are expressed as mean, median and interquartile range, which are statistically obtained by the BioExplorer TM 1.0 software.
Preferably, the polypeptide extraction in the step (1) is performed by using a protein magnetic bead kit.
Illustratively, the protein magnetic bead kit comprises: 20. mu.L of magnetic beads (Invitrogen,10mg/mL, Cat. No.354.01), 150. mu.L of magnetic bead binding buffer (CB), 500. mu.L of magnetic bead wash buffer (CW), and 10. mu.L of magnetic bead eluate (1% TFA).
The polypeptide extraction kit is based on the weak cation exchange principle, adopts the principle that magnetic beads specifically adsorb protein polypeptide in a protein polypeptide sample in a high-salt low-pH solution and release protein polypeptide molecules in a low-salt solution, captures the protein polypeptide in a biological sample under the condition of a specific medium, and the extracted protein polypeptide can be directly used for analyzing matrix-assisted laser desorption ionization time-of-flight mass spectrometry.
The method for extracting polypeptide by using the protein magnetic bead kit can exemplarily comprise the following steps: and taking out the magnetic bead kit from a refrigerator at 4 ℃, and manually turning the magnetic bead kit upside down to uniformly mix the magnetic beads. And (3) placing 200 mu L of eight-row sample tubes on a pore plate, adding 20 mu L of magnetic beads, 150 mu L of magnetic bead binding buffer (CB) and 10 mu L of saliva samples, sucking up and down by using a discharge gun, uniformly mixing to avoid bubbles, and standing at room temperature for 5 min. And (3) standing the sample tube on a magnetic bead separator for 1min, wherein the magnetic beads are attached to the wall and separated from the suspended liquid, and the liquid should be clear. The suspended liquid is aspirated, and the tip should avoid aspirating the magnetic beads. Putting the saliva sample into a pore plate, adding 180 mu L of magnetic bead cleaning solution buffer (CW), sucking up and down by a discharging gun, uniformly mixing to avoid air bubbles, and standing for 2 min. The sample tube was then allowed to stand on the magnetic bead separator for 1min, and the suspended liquid was aspirated off. The previous step was repeated once to ensure that the suspension was completely sucked away. The sample is placed on a pore plate, 10 mu L of magnetic bead eluent (1% TFA) is added to repeatedly suck and beat for more than ten times, and the sample is placed for 5min, so that the magnetic beads and the eluent are uniformly suspended, and bubbles are strictly avoided in the sucking and beating process. And (3) placing the sample on a magnetic bead separator, standing for 1min to fully separate the magnetic beads from the suspension, and removing the supernatant to a marked 0.2mL sample tube. The eluate with bead stabilizing buffer can be used for direct mass spectrometry or frozen at-20 deg.C for mass spectrometry within 24 h.
Preferably, the polypeptide peak analysis in step (2) is performed by using a time-of-flight mass spectrometer such as Clin-TOF-MS, and the analysis parameters comprise: linear mode, acceleration voltage 20kV, pulse voltage 1.9kV, cation ion source 200 μm, initial laser value 110, laser frequency 40Hz, laser energy 60 μ J, collected effective peak number 400shots, atlas cumulative number 100, cumulative bombardment number of each atlas 5, deviation of average molecular weight <100 ppm. The specific operation method may exemplarily include: entering a MALDI Control operation page of a computer, clicking Acquisition to install a plate, indicating that the sample can be detected when the vacuum indicator lamp does not flicker, and correcting after setting an Experiment Queue parameter. And after the correction is finished, the sample can be detected, sample application holes are selected on the electronic target plate, and the electronic target plate is shot. And controlling to strike different positions in the striking process to obtain an original mass spectrogram.
Three samples are taken from the normal group and the superficial gastritis group respectively, three times of repeated experiments are carried out on the same 96-well plate to determine deviation, and the intra-group correlation coefficient (ICC) is detected, wherein the intra-group correlation coefficient (ICC) >0.75 represents that the reliability of experimental data is good. Through SPSS statistics, the normal Intragroup Correlation Coefficient (ICC) is 0.819, and the intragroup correlation coefficient of superficial gastritis is 0.773, which indicates that the experimental data has good repeatability.
Preferably, the statistical analysis of the polypeptide peak analysis result obtained in step (2) in step (3) is performed by using BioExplorer TM 1.0 software and/or SPSS 26.0 statistical software.
Preferably, step (3) detects the correlation coefficient between groups and between groups, and a coefficient greater than 0.75 indicates good reproducibility and reliability of the test data.
Preferably, the significant difference peptide peak in step (3) refers to the difference peptide peak with detection rate of more than 90% and P value of less than 0.05.
In the present invention, the step (3) further comprises the following steps after obtaining the significantly different peptide peak: ROC curves were used to analyze the significantly different peptide peaks and calculate their AUC values.
The invention adopts ROC curve to analyze the peptide peak with obvious difference and calculates the AUC value to be obtained by SPSS software statistics. Receiver operating characteristic curve (ROC) curve and Area Under Curve (AUC) were used to evaluate the diagnostic performance of individual difference peptide peaks and diagnostic panel models: AUC is less than or equal to 0.5, the diagnosis accuracy is lower when the AUC is more than 0.5-0.7, the diagnosis accuracy is better when the AUC is more than or equal to 0.7-0.9, and the AUC is more than 0.9, which shows that the diagnosis accuracy is high.
In the present invention, the method for establishing and verifying the superficial gastritis diagnosis group in step (4) comprises the following steps:
selecting an optimal peptide peak combination by adopting a CFS algorithm, selecting significantly different peptide peaks according to each peak interval to form a diagnostic group, then establishing a diagnostic group model by using a KNN algorithm, calculating the accuracy, specificity, sensitivity and AUC (AUC) value of each diagnostic group, screening out the diagnostic group with the AUC value greater than 0.5, preferably selecting the diagnostic group with the AUC value greater than 0.7, and further screening out the diagnostic group with the largest AUC value as a biomarker of superficial gastritis.
Preferably, the specific method for selecting the significantly different peptide peaks to form the diagnostic group according to the interval of each peak is as follows: according to the overall peak interval, one or at least two combinations of several peptide peaks with relatively close peaks are taken into a diagnostic group, other peptide peaks are excluded from the diagnostic group, and the peptide peaks with significant differences are gradually excluded according to the rule to form a plurality of diagnostic groups step by step.
Preferably, when the accuracy, specificity, sensitivity and AUC values of each diagnosis group are calculated by the KNN algorithm, all sample data are randomly divided into a training set and a verification set, and the share ratio of the training set to the verification set is 3: 1.
In the present invention, the screening of the diagnostic panel in step (4) further comprises: and identifying the amino acid sequences of the polypeptides corresponding to the peptide peaks with the obvious differences in the diagnostic group by adopting a mass spectrometer, and then identifying the protein to which the amino acid sequences belong by using a database to serve as the biomarker of the superficial gastritis.
Preferably, the mass spectrometer uses a nano liquid chromatography-electrospray ionization-tandem mass spectrometer, and the mass spectrometry parameters comprise: the electrospray ion source, the spray voltage is 2kV, the ion source temperature is 250 ℃, the high-energy collision dissociation source HCD and the MS/MS ion threshold value is 1 multiplied by 105。
Preferably, the database comprises a Mascot 2.4.1 database. The search results were limited to homo sapiens, with the peptide mass tolerance set to. + -. 20ppm and the fragment mass tolerance set to. + -. 0.2 Da.
In a second aspect, the present invention provides a superficial gastritis biomarker obtained by screening using the screening method as described above, the superficial gastritis biomarker includes any one peptide peak or at least two peptide peaks with mass-to-charge ratio of 3003.4m/z, 3013m/z, 3040.6m/z, 3069.5m/z, 3090.4m/z, 3103.8m/z, 3338.5m/z, 3413.8m/z, 3472m/z, 3555m/z, 3672.1m/z, 3692.3m/z, 3774.9m/z, 3800.5m/z, 3871.9m/z, 4172.9m/z, 4207.3m/z, 4257.3m/z, 4300.6m/z, 4351.5m/z, 4368.3m/z, 4445.1m/z, 4611.9m/z, 4632.8m/z, 4652.8m/z, 4791.9m/z, 4871.3m/z, 4933.6m/z or 4953 m/z.
Preferably, the biomarker for superficial gastritis comprises a combination of peptide peaks with mass to charge ratios of 3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6 m/z.
According to the contents of the above screening method, the biomarkers of superficial gastritis can be summarized as follows:
the (I) biomarker for superficial gastritis comprises any one peptide peak with mass-to-charge ratio of 3003.4m/z, 3013m/z, 3040.6m/z, 3069.5m/z, 3090.4m/z, 3103.8m/z, 3338.5m/z, 3413.8m/z, 3472m/z, 3555m/z, 3672.1m/z, 3692.3m/z, 3774.9m/z, 3800.5m/z, 3871.9m/z, 4172.9m/z, 4207.3m/z, 4257.3m/z, 4300.6m/z, 4351.5m/z, 4368.3m/z, 4445.1m/z, 4611.9m/z, 4632.8m/z, 4652.8m/z, 4791.9m/z, 4871.3m/z, 4933.6m/z or 4953 m/z.
(II) biomarkers for superficial gastritis comprising peptide peak combinations with mass to charge ratios of 3040.6m/z, 3672.1m/z, 4611.9m/z and 4933.6 m/z.
(III) biomarkers for superficial gastritis comprising peptide peak combinations with mass to charge ratios of 3040.6m/z, 3672.1m/z, 3800.5m/z, 4611.9m/z and 4933.6 m/z.
(IV) biomarkers for superficial gastritis comprising peptide peak combinations with mass to charge ratios of 3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6 m/z.
(V) biomarkers for superficial gastritis comprising peptide peak combinations with mass to charge ratios of 3040.6m/z, 3338.5m/z, 3672.1m/z, 3800.5m/z, 4172.9m/z, 4611.9m/z and 4933.6 m/z.
(VI) biomarkers for superficial gastritis comprising peptide peak combinations with mass to charge ratios of 3040.6m/z and 4611.9 m/z.
(VII) biomarkers for superficial gastritis comprising combinations of peptide peaks with mass to charge ratios of 3040.6m/z, 3103.8m/z, 3338.5m/z, 3472m/z, 3672.1m/z, 3774.9m/z, 3800.5m/z, 4172.9m/z, 4351.5m/z, 4611.9m/z, 4791.9m/z and 4933.6 m/z.
(VIII) biomarkers for superficial gastritis include peptide peak combinations with mass to charge ratios of 3003.4m/z, 3013m/z, 3040.6m/z, 3069.5m/z, 3090.4m/z, 3103.8m/z, 3338.5m/z, 3413.8m/z, 3472m/z, 3555m/z, 3672.1m/z, 3692.3m/z, 3774.9m/z, 3800.5m/z, 3871.9m/z, 4172.9m/z, 4207.3m/z, 4257.3m/z, 4300.6m/z, 4351.5m/z, 4368.3m/z, 4445.1m/z, 4611.9m/z, 4632.8m/z, 4652.8m/z, 4791.9m/z, 4871.3m/z, 4933.6m/z and 4953 m/z.
(ix) the biomarker for superficial gastritis comprises cytokeratin 6 and/or FCGBP protein.
In a third aspect, the present invention provides the use of the biomarkers for superficial gastritis as described above in the preparation of diagnostic reagents for superficial gastritis. The superficial gastritis biomarker provided by the invention is derived from saliva, so that a novel non-invasive detection method is provided for diagnosis of superficial gastritis. The sensitivity, specificity and accuracy of the superficial gastritis biomarker related by the invention can reach more than 65% at most.
In a fourth aspect, the present invention provides a kit for diagnosis of superficial gastritis, comprising reagents for specifically detecting the biomarkers of superficial gastritis as described above.
In a fifth aspect, the present invention provides a screening system for biomarkers of superficial gastritis, comprising the following screening units:
(1) the polypeptide extraction unit is used for extracting polypeptides from clinical saliva samples of patients with superficial gastritis and clinical saliva samples of healthy people;
(2) the mass spectrum analysis unit is used for performing polypeptide peak analysis on the extracted polypeptide sample;
(3) the statistical analysis unit is used for carrying out statistical analysis on the obtained polypeptide spectrum peak analysis result to obtain a significant difference peptide peak;
(4) and the establishment and verification unit screens out the diagnosis group with the AUC value larger than 0.5 as the biological marker of the superficial gastritis.
Preferably, the polypeptide extraction is performed by using a protein magnetic bead kit.
Preferably, the polypeptide peak analysis is performed by using a time-of-flight mass spectrometer Clin-TOF-MS, and analysis parameters comprise: linear mode, acceleration voltage 20kV, pulse voltage 1.9kV, cation ion source 200 μm, initial laser value 110, laser frequency 40Hz, laser energy 60 μ J, collected effective peak number 400shots, atlas cumulative number 100, cumulative bombardment number of each atlas 5, deviation of average molecular weight <100 ppm. Preferably, the statistical analysis employs BioExplorer TM 1.0 software and/or SPSS 26.0 statistical software.
Preferably, the significant difference peptide peak refers to a difference peptide peak with a detection rate of more than 90% and a P value of less than 0.05.
Preferably, the screening system further comprises a ROC evaluation unit to calculate AUC values for significantly different peptide peaks.
Preferably, the screening system further comprises a superficial gastritis diagnostic group establishing and verifying unit, the superficial gastritis diagnostic group establishing and verifying unit selects an optimal peptide peak combination by adopting a CFS algorithm, meanwhile, significant difference peptide peaks are selected according to peak intervals to form a diagnostic group, then, a diagnostic group model is established by using a KNN algorithm, the accuracy, specificity, sensitivity and AUC value of each diagnostic group are calculated, and the diagnostic group with the AUC value larger than 0.5, preferably the diagnostic group with the AUC value larger than 0.7, further preferably the diagnostic group with the largest AUC value, namely the optimal diagnostic group, is selected as the superficial gastritis biomarker.
Preferably, the specific method for selecting the significantly different peptide peaks to form the diagnostic group according to the interval of each peak is as follows: according to the overall peak interval, one or at least two combinations of several peptide peaks with relatively close peaks are taken into a diagnostic group, other peptide peaks are excluded from the diagnostic group, and the peptide peaks with significant differences are gradually excluded according to the rule to form a plurality of diagnostic groups step by step.
Preferably, when the accuracy, specificity, sensitivity and AUC values of each diagnosis group are calculated by the KNN algorithm, all sample data are randomly divided into a training set and a verification set, and the share ratio of the training set to the verification set is 3: 1.
Preferably, the screening unit further comprises a protein attribution identification unit, wherein the protein attribution identification unit identifies the amino acid sequences of the polypeptides corresponding to the respective significantly different peptide peaks in the diagnostic panel by using a mass spectrometer, and then identifies the protein to which the amino acid sequences belong by using the database.
Preferably, the mass spectrometer uses a nano liquid chromatography-electrospray ionization-tandem mass spectrometer, and the mass spectrometry parameters comprise: the electrospray ion source, the spray voltage is 2kV, the ion source temperature is 250 ℃, the high-energy collision dissociation source HCD and the MS/MS ion threshold value is 1 multiplied by 105。
Preferably, the database comprises a Mascot 2.4.1 database.
In a sixth aspect, the present invention provides a superficial gastritis biomarker screened by using the screening system for superficial gastritis biomarkers as described above, the superficial gastritis biomarker includes any one peptide peak or at least two peptide peaks with mass-to-charge ratio of 3003.4m/z, 3013m/z, 3040.6m/z, 3069.5m/z, 3090.4m/z, 3103.8m/z, 3338.5m/z, 3413.8m/z, 3472m/z, 3555m/z, 3672.1m/z, 3692.3m/z, 3774.9m/z, 3800.5m/z, 3871.9m/z, 4172.9m/z, 4207.3m/z, 4257.3m/z, 4300.6m/z, 4351.5m/z, 4368.3m/z, 4445.1m/z, 4611.9m/z, 4632.8m/z, 4652.8m/z, 4791.9m/z, 4871.3m/z, 4933.6m/z or 4953 m/z.
Preferably, the biomarker for superficial gastritis comprises a combination of peptide peaks with mass to charge ratios of 3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6 m/z.
Preferably, the present invention provides a superficial gastritis biomarker selected using the screening system for superficial gastritis biomarkers as described above, the superficial gastritis biomarker including cytokeratin 6 and/or FCGBP protein.
In a seventh aspect, the present invention provides a use of the above-mentioned superficial gastritis biomarker in the preparation of a superficial gastritis diagnostic reagent.
In an eighth aspect, the present invention provides a kit for diagnosis of superficial gastritis, comprising reagents for specifically detecting the biomarkers of superficial gastritis as described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention creatively screens a series of biomarkers which can be used for diagnosing superficial gastritis from saliva by adopting a novel screening method based on clinical information and proteomic analysis, and the biomarkers of superficial gastritis are derived from the saliva, thereby providing a novel non-invasive detection method for diagnosing the superficial gastritis. The sensitivity, specificity and accuracy of the superficial gastritis biomarker related by the invention can reach more than 65% at most. Meanwhile, the screening method provided by the invention has the advantages that by means of the Clin-TOF-MS mass spectrometry technology, saliva samples can be subjected to sample application on a machine for detection, large-sample high-flux detection is performed, and the method has the characteristics of rapidness, simplicity and convenience (only 30min), high flux, micro sample loading amount (1 mu L), large-sample detection and the like.
Drawings
FIG. 1 is a mass spectrum of a healthy human saliva polypeptide sample;
FIG. 2 is a mass spectrum of a saliva polypeptide sample from a patient with superficial gastritis;
FIG. 3 is a peak intensity contrast plot and ROC plot for the best panel member 3040.6m/z (a is the peak intensity contrast plot, b is the ROC plot);
FIG. 4 is a peak intensity contrast plot and ROC plot for the best panel member 3672.1m/z (a is the peak intensity contrast plot, b is the ROC plot);
FIG. 5 is a peak intensity contrast plot and ROC plot for the best panel member 3800.5m/z (a is the peak intensity contrast plot, b is the ROC plot);
FIG. 6 is a peak intensity contrast plot and ROC plot for the best panel member 3871.9m/z (a is the peak intensity contrast plot, b is the ROC plot);
FIG. 7 is a peak intensity contrast plot and ROC plot for the best panel member 4611.9m/z (a is the peak intensity contrast plot, b is the ROC plot);
FIG. 8 is a peak intensity contrast plot and ROC plot for the best panel member 4933.6m/z (a is the peak intensity contrast plot, b is the ROC plot);
FIG. 9 is a schematic flow chart of the operation of example 1.
Detailed Description
The technical solution of the present invention is further explained by the following embodiments. It should be understood by those skilled in the art that the examples are only for the understanding of the present invention and should not be construed as the specific limitations of the present invention.
In the following embodiments, the training set refers to a set used for constructing model samples, and the verification set refers to a set used for verifying model samples.
Example 1
The embodiment provides a method for screening a biomarker of superficial gastritis, which comprises the following steps:
taking clinical saliva samples 1-64 of patients with superficial gastritis and 1-32 of healthy people, wherein the clinical saliva samples of the patients with superficial gastritis are 64 pathologically confirmed cases treated in tumor hospitals in Hunan province, subsidiary hospitals in traditional Chinese medicine and research institutes in Hunan province and Hunan province. After informed consent, 15mL of saliva of the patient is selected; wherein the clinical saliva samples 1-32 of healthy people are from random samples of traditional Chinese medicine clinical laboratory in Hunan province.
(II) performing polypeptide extraction on the sample, wherein the specific process comprises the following steps: and taking out the magnetic bead kit from a refrigerator at 4 ℃, and manually turning the magnetic bead kit upside down to uniformly mix the magnetic beads. And (3) placing 200 mu L of eight-row sample tubes on a pore plate, adding 20 mu L of magnetic beads, 150 mu L of magnetic bead binding buffer (CB) and 10 mu L of saliva samples, sucking up and down by using a discharge gun, uniformly mixing to avoid bubbles, and standing at room temperature for 5 min. And (3) standing the sample tube on a magnetic bead separator for 1min, wherein the magnetic beads are attached to the wall and separated from the suspended liquid, and the liquid should be clear. The suspended liquid is aspirated, and the tip should avoid aspirating the magnetic beads. Putting the saliva sample into a pore plate, adding 180 mu L of magnetic bead cleaning solution buffer (CW), sucking up and down by a discharging gun, uniformly mixing to avoid air bubbles, and standing for 2 min. The sample tube was then allowed to stand on the magnetic bead separator for 1min, and the suspended liquid was aspirated off. The previous step was repeated once to ensure that the suspension was completely sucked away. The sample is placed on a pore plate, 10 mu L of magnetic bead eluent (1% TFA) is added to repeatedly suck and beat for more than ten times, and the sample is placed for 5min, so that the magnetic beads and the eluent are uniformly suspended, and bubbles are strictly avoided in the sucking and beating process. And (3) placing the sample on a magnetic bead separator, standing for 1min to fully separate the magnetic beads from the suspension, moving the supernatant to a marked 0.2mL sample tube, and adding the eluent of the magnetic bead stabilizing buffer solution to directly perform mass spectrometry.
And (III) carrying out mass spectrum analysis on the extracted polypeptide sample by using Clin-TOF, entering a computer MALDI Control operation page, clicking Acquisition to install a plate, indicating that the sample can be detected when a vacuum indicator lamp does not flicker, and setting an expert Queue parameter for correction. And after the correction is finished, the sample can be detected, sample application holes are selected on the electronic target plate, and the electronic target plate is shot. And controlling to strike different positions in the striking process to obtain an original mass spectrogram. The mass spectrometry parameters include: linear mode, acceleration voltage 20kV, pulse voltage 1.9kV, cation ion source 200 μm, initial laser value 110, laser frequency 40Hz, laser energy 60 μ J, collected effective peak number 400shots, atlas cumulative number 100, cumulative bombardment number of each atlas 5, deviation of average molecular weight <100 ppm.
And (IV) introducing the original mass spectrogram into BioExplorer TM 1.0 software, selecting a significant difference peptide peak with a mass-to-charge ratio of 3000-: of the 29 differential peptide peaks, 18 up-regulated differential peptide peaks (ratio >1.2) and 11 down-regulated differential peptide peaks (ratio ≦ 0.8) were present. The mass spectra of the healthy human sample 30 and the superficial gastritis patient sample 48 are shown in fig. 1 and fig. 2, respectively.
Three samples were taken from each of the normal and superficial gastritis groups, and three replicates were performed using the same Clin-TOF mass spectrometer to determine the deviation, and intra-group correlation coefficients were detected, with a correlation coefficient >0.75 indicating good confidence in the experimental data. Through SPSS statistics, the normal Intragroup Correlation Coefficient (ICC) is 0.819, and the intragroup correlation coefficient of superficial gastritis is 0.773, which indicates that the experimental data has good repeatability.
TABLE 1
And (v) analyzing the 29 significantly different peptide peaks by using a ROC curve (receiver operating characteristic curve) and calculating AUC values thereof, as shown in table 1.
And (VI) the CFS algorithm (feature selection algorithm based on association rules) in Weka 3.7 is adopted to select the optimal peptide peak combination (3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6m/z), the AUC value is 0.863, and the diagnosis performance is good. In order to further determine better peptide peak combinations, the peptide peaks with significant differences are selected according to the interval of each peak to form a diagnosis group, and the specific method comprises the following steps: according to the overall peak interval, one or at least two combinations of several peptide peaks with relatively close peaks are taken as a diagnosis group, and other peptide peaks are excluded from the diagnosis group, and the peptide peaks with significant differences are gradually excluded according to the rule, so that 7 diagnosis groups are formed:
(a)3040.6m/z and 4611.9 m/z;
(b)3040.6m/z, 3672.1m/z, 4611.9m/z and 4933.6 m/z;
(c)3040.6m/z, 3672.1m/z, 3800.5m/z, 4611.9m/z and 4933.6 m/z;
(d)3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6 m/z;
(e)3040.6m/z, 3338.5m/z, 3672.1m/z, 3800.5m/z, 4172.9m/z, 4611.9m/z and 4933.6 m/z;
(f)3040.6m/z, 3103.8m/z, 3338.5m/z, 3472m/z, 3672.1m/z, 3774.9m/z, 3800.5m/z, 4172.9m/z, 4351.5m/z, 4611.9m/z, 4791.9m/z and 4933.6 m/z;
(g)3003.4m/z, 3013m/z, 3040.6m/z, 3069.5m/z, 3090.4m/z, 3103.8m/z, 3338.5m/z, 3413.8m/z, 3472m/z, 3555m/z, 3672.1m/z, 3692.3m/z, 3774.9m/z, 3800.5m/z, 3871.9m/z, 4172.9m/z, 4207.3m/z, 4257.3m/z, 4300.6m/z, 4351.5m/z, 4368.3m/z, 4445.1m/z, 4611.9m/z, 4632.8m/z, 4652.8m/z, 4791.9m/z, 4871.3m/z, 4933.6m/z and 4953 m/z.
(seventhly) all sample data were randomly divided into a training set and a verification set according to a ratio of 3:1, 15 patients with superficial gastritis in the verification set, 9 patients with healthy control group, 49 patients with superficial gastritis in the training set and 23 patients with healthy control group, and accuracy, specificity, sensitivity and AUC values (P <0.05 is statistically significant) of each diagnosis group were calculated by KNN algorithm (k ═ 3), and the results are shown in table 2: wherein the panel with AUC values greater than 0.5 can be used as a biomarker for superficial gastritis; the diagnosis group with the AUC value larger than 0.7 has better diagnosis accuracy as the biomarker of the superficial gastritis; the combination of the diagnostic groups (d), 3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6m/z, was the best diagnostic group with a sensitivity of 66.67%, specificity of 86.70%, accuracy of 79.17%, and AUC of 0.863, and had good diagnostic accuracy.
The peak intensity contrast plots and ROC plots for the best diagnostic panelists 3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6m/z are shown in FIGS. 3-8, respectively (where a is the peak intensity contrast plot and b is the ROC plot).
TABLE 2
| (a) | (b) | (c) | (d) | (e) | (f) | (g) | |
| Sensitivity of the device | 55.60％ | 44.40％ | 55.60％ | 66.67％ | 66.67％ | 44.40％ | 55.60％ |
| Degree of specificity | 66.70％ | 80.00％ | 80.00％ | 86.70％ | 73.33％ | 80.00％ | 73.30％ |
| Accuracy of | 62.50％ | 66.67％ | 70.83％ | 79.17％ | 70.83％ | 66.67％ | 66.67％ |
| AUC | 0.663 | 0.663 | 0.722 | 0.863 | 0.744 | 0.644 | 0.681 |
(eighth) identifying the amino acid sequence of the polypeptide corresponding to each significantly different peptide peak in the best panel using nano liquid chromatography-electrospray ionization-tandem mass spectrometry (nano-LC/ESI-MS/MS) consisting of the Aquity (TM) UPLC system (Waters, Milford, MA, USA) and the LTQ Orbitrap XL mass spectrometer equipped with a nano ESI source (Thermo Fisher scientific, Pittsburgh, PA, USA), followed by a search using the Mascot 2.4.1(Matrix Science, London, UK) database with peptide mass tolerance set to ± 20ppm, fragment mass tolerance set to ± 0.2Da, identifying the protein to which the amino acid sequence belongs, successfully identifying the protein to which 2 different peptide peaks belongs: 3040.6m/z has amino acid sequence aLYDAELSQMQTHISDTSVVLSMDNMDNNR, the protein is cytokeratin 6, the amino acid sequence 3672.1m/z has amino acid sequence lEQYEGPGFcGCPLAPGTGGPFTTcHAHVPPESFFk, the protein is FCGBP protein, therefore, the cytokeratin 6 and/or the FCGBP protein can be used as a biomarker of superficial gastritis, and has good diagnosis accuracy.
The applicant states that the present invention is illustrated by the above examples to show the superficial gastritis biomarkers and the screening method and application thereof, but the present invention is not limited by the above examples, i.e. it does not mean that the present invention must be implemented by the above examples. It should be understood by those skilled in the art that any modification of the present invention, equivalent substitutions of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific modes, etc., are within the scope and disclosure of the present invention.
The preferred embodiments of the present invention have been described in detail, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
Claims (10)
1. A noninvasive and rapid screening method for discovering a polypeptide spectrum biomarker of superficial gastritis based on a mass spectrometry technology is characterized by comprising the following steps:
(1) taking clinical saliva samples of patients with superficial gastritis and clinical saliva samples of healthy people, and carrying out high-throughput one-step polypeptide extraction on the clinical saliva samples;
(2) performing polypeptide peak analysis on the polypeptide sample extracted in the step (1);
(3) performing statistical analysis on the polypeptide spectrum peak analysis result obtained in the step (2) to obtain a significant difference peptide peak;
(4) and establishing and verifying a superficial gastritis diagnosis group, and screening out a diagnosis group with an AUC value larger than 0.5, namely the superficial gastritis biomarker.
2. The method for noninvasive and rapid screening of polypeptide spectrum biomarkers for superficial gastritis based on mass spectrometry as claimed in claim 1, wherein the polypeptide extraction in step (1) is performed by using protein magnetic bead kit;
preferably, the polypeptide peak analysis in the step (2) is carried out by using a proteomic matrix-assisted laser desorption ionization time-of-flight mass spectrum Clin-TOF-MS.
3. A non-invasive rapid screening method for polypeptide spectrum biomarkers of superficial gastritis based on mass spectrometry as claimed in claim 1 or 2, wherein the step (3) further comprises detecting the correlation coefficient between groups and within groups, wherein a coefficient greater than 0.75 indicates good repeatability and reliability of test data;
preferably, the significant difference peptide peak in step (3) refers to the difference peptide peak with detection rate of more than 90% and P value of less than 0.05.
4. The method for noninvasive and rapid screening of polypeptide spectrum biomarkers for superficial gastritis based on mass spectrometry as claimed in claim 1 or 2, wherein the step (4) of establishing and verifying a superficial gastritis diagnosis group comprises the following steps:
selecting an optimal peptide peak combination by adopting a CFS algorithm, selecting significantly different peptide peaks according to each peak interval to form a diagnostic group, then establishing a diagnostic group model by using a KNN algorithm, calculating the accuracy, specificity, sensitivity and AUC (AUC) value of each diagnostic group, and screening out the diagnostic group with the AUC value greater than 0.5, preferably the diagnostic group with the AUC value greater than 0.7, further preferably the diagnostic group with the maximum AUC value, namely the biomarker for superficial gastritis.
5. The method for noninvasive and rapid screening of polypeptide spectrum biomarkers for superficial gastritis based on mass spectrometry as claimed in claim 1, wherein said accuracy, specificity, sensitivity and AUC values of each diagnostic panel are calculated by KNN algorithm by randomly dividing all sample data into training set and verification set, and the ratio of the training set to the verification set is 3: 1.
6. The method for noninvasive and rapid screening of polypeptide spectrum biomarkers for superficial gastritis based on mass spectrometry as claimed in claim 1, wherein said screening of diagnostic groups further comprises: identifying the amino acid sequences of the polypeptides corresponding to the significant difference peptide peaks in the diagnostic group by adopting a mass spectrometer, and then identifying the protein to which the amino acid sequences belong by using a database;
preferably, the mass spectrometer uses a nano liquid chromatography-electrospray ionization-tandem mass spectrometer;
preferably, the database comprises a Mascot 2.4.1 database.
7. The superficial gastritis biomarker obtained by the screening method according to any one of claims 1 to 6, characterized in that the biomarker for superficial gastritis comprises any one or a combination of at least two peptide peaks with a mass to charge ratio of 3003.4m/z, 3013m/z, 3040.6m/z, 3069.5m/z, 3090.4m/z, 3103.8m/z, 3338.5m/z, 3413.8m/z, 3472m/z, 3555m/z, 3672.1m/z, 3692.3m/z, 3774.9m/z, 3800.5m/z, 3871.9m/z, 4172.9m/z, 4207.3m/z, 4257.3m/z, 4300.6m/z, 4351.5m/z, 4368.3m/z, 4445.1m/z, 4611.9m/z, 4632.8m/z, 4652.8m/z, 4791.9m/z, 4871.3m/z, 4933.6m/z or 4953 m/z;
preferably, the biomarker for superficial gastritis comprises a combination of peptide peaks with mass to charge ratios of 3040.6m/z, 3672.1m/z, 3800.5m/z, 3871.9m/z, 4611.9m/z and 4933.6 m/z.
8. The biomarker for superficial gastritis screened by the screening method according to any one of claims 1 to 6, wherein the biomarker for superficial gastritis comprises cytokeratin 6 and/or FCGBP protein.
9. Use of the biomarker for superficial gastritis as defined in claim 7 or 8 for preparing a diagnostic reagent for superficial gastritis.
10. A kit for diagnosis of superficial gastritis comprising reagents for specifically detecting the biomarkers of superficial gastritis as set forth in claim 7 or 8.
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