WO2013016966A1 - Modèle de spectrométrie de masse pour la détection de protéines du cancer du poumon et son procédé de construction - Google Patents

Modèle de spectrométrie de masse pour la détection de protéines du cancer du poumon et son procédé de construction Download PDF

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WO2013016966A1
WO2013016966A1 PCT/CN2012/074917 CN2012074917W WO2013016966A1 WO 2013016966 A1 WO2013016966 A1 WO 2013016966A1 CN 2012074917 W CN2012074917 W CN 2012074917W WO 2013016966 A1 WO2013016966 A1 WO 2013016966A1
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lung cancer
mass
model
serum
peaks
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PCT/CN2012/074917
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Chinese (zh)
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马庆伟
赵艳梅
胡晓慧
任晶
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毅新兴业（北京）科技有限公司
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Publication of WO2013016966A1 publication Critical patent/WO2013016966A1/fr

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    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6884—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids from lung
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7023—(Hyper)proliferation
    • G01N2800/7028—Cancer

Definitions

  • the invention relates to the field of malignant tumor detection, and is a novel non-invasive detection method for early detection and detection of lung cancer in vitro. Background technique
  • lung cancer has gradually replaced liver cancer as the leading cause of death in malignant tumors in China, accounting for 22.7% of all malignant tumor deaths, and the incidence and mortality of lung cancer continue to rise.
  • the number of patients with lung cancer in China has increased by 120,000 since 2000-2005.
  • the number of male lung cancer patients increased from 260,000 in 2000 to 330,000 in 2005
  • the number of female lung cancer patients increased from 120,000 to 170,000.
  • the incidence of lung cancer in China is increasing by 26.9% per year. If effective control measures are not taken in time, it is estimated that by 2025, the number of lung cancer patients in China will reach 1 million, making it the "world's first" lung cancer country.
  • the early diagnosis of lung cancer is helpful to help patients with early treatment.
  • the early treatment of lung cancer has a good prognosis and is not easy to relapse.
  • the symptoms of lung cancer include cough, hemoptysis, chest pain, dyspnea, and various symptoms of obstruction, compression, and metastasis caused by tumors. The symptoms are light and heavy, and early warnings are not easy.
  • Clinicians are more vigilant about middle-aged coughs or blood stasis and lung X-ray examinations of unidentified block shadows or inflammatory cases, highly suspected lung cancer, and timely and thorough examination.
  • Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is a new type of soft ionization biomass developed in recent years.
  • the principle is to irradiate a sample with a matrix to form a co-crystal film, which is absorbed by the laser.
  • the principle of TOF is that ions accelerate through the flight pipeline under the action of electric field, and are detected according to the flight time of the detector.
  • the mass-to-charge ratio (M/Z) of the measured ions is proportional to the flight time of the ions, and the ions are detected.
  • the accuracy of MALDI-TOF is as high as 0.1% ⁇ 0.01%, which is much higher than the conventional SDS electrophoresis and high-efficiency gel chromosomal technology, there are still some defects in the application of tumor markers, especially lung cancer markers. Therefore, MALDI-TOF-MS technology has not been used in China to obtain the detection of lung cancer markers or lung cancer serum characteristic proteins. Summary of the invention
  • the object of the present invention is to overcome the deficiencies of existing lung cancer markers or lung cancer serum characteristic protein detection techniques, and to propose a grammar model for detecting serum characteristic proteins of lung cancer and a preparation method thereof.
  • a first object of the present invention is to provide a mass spectrometric marker for detecting a characteristic protein of lung cancer, which has a mass-to-charge ratio of 4054 m/z, 2273 m/z, 4091 m/z, 3955 m/z, 3192 m/z, 3884 m. /z , 5337m/z peak.
  • a second object of the present invention is to provide a linguistic model for detecting a characteristic protein of lung cancer, which has a mass-to-charge ratio of 4054 m/z, 2273 m/z, 4091 m/z, 3955 m/z, and 3192 m/z. , 3884m/z and 5337m/z peaks, the proteins characterized by 4054m/z, 2273m/z, 4091m/z, 3955m/z, 3192m/z, 3884m/z and 5337m/z are the serum characteristic proteins of lung cancer.
  • the expression of the characteristic proteins of 4054m/z, 2273m/z, 4091m/z, 3955m/z and 3192m/z was down-regulated, and when the expressions of 3884m/z and 5337m/z were up-regulated, it was predicted to be a lung cancer patient or a potential patient.
  • the critical values of the down-regulated expression of the characteristic proteins of 4054m/z, 2273m/z, 4091m/z, 3955m/z and 3192m/z were 141.88 ⁇ 57.55, 37 ⁇ 13 ⁇ 8 ⁇ 15, 189.43 ⁇ 61.91, 130.06 ⁇ 110.44 and 78.65 ⁇ 23.6.
  • amino acid sequences of the polypeptides having a mass-to-charge ratio of 4054 m/z, 2273 m/z, 4091 m/z, 3955 m/z, 3192 m/z, 3884 m/z and 5337 m/z are respectively SEQ ID No. 1 , SEQ ID No. 2, SEQ ID No. 3, SEQ ID No. 4, SEQ ID No. 5, SEQ ID No. 6 and SEQ ID No. 7.
  • the method for constructing the grammar model includes:
  • the peptides were determined by differential polypeptides 4054m/z, 2273m/z, 4091m/z, 3955m/z, 3192m/z, 3884m/z and 5337m/z, and lung cancer was detected based on these seven mass-to-charge ratio peaks.
  • step 2) comprises purifying and stabilizing serum proteins or polypeptides in the sample using magnetic beads.
  • the step 3) uses the WCX2 chip to adsorb the two groups of serum proteins, and reads the two groups of serum proteins combined with the weak cation WCX2 chip to obtain the fingerprint map of the two groups of serum polypeptides.
  • the invention combines the bioinformatics method to screen out corresponding lung cancer markers and establish a detection model for analysis and detection.
  • the bioinformatics method includes standardizing the fingerprint and obtaining the result.
  • the data is subjected to experimental quality control processing, screening of desired serum signature proteins and establishing a linguistic model, and optionally including the use of genetic algorithms in conjunction with nearest neighbor algorithms to establish and validate grammatical models, and the like.
  • the experimental quality control treatment preserves the mass spectrogram data with a peak number greater than 50, and uses the intra-group variation coefficient of Sigma serum to ensure the consistency of the experiment, thereby performing the consistency range according to the coefficient of variation. filter.
  • the coefficient of variation is preferably 16.2%.
  • the present invention detects a mass spectrometric marker of a characteristic protein of lung cancer, can be used to establish a serum characteristic protein language model and is applied to early detection and screening of lung cancer, and the qualitative language model of the present invention can be used for early detection and screening of lung cancer.
  • the present invention Compared with other methods for detecting lung cancer, the present invention has the following advantages:
  • the present invention adopts a combination of multiple characteristic proteins of lung cancer patients and normal humans to detect lung cancer serum, and adopts a combination of traditional statistics and modern bioinformatics methods for data processing, thereby obtaining lung cancer patients.
  • healthy human serum protein fingerprinting detection model, and the discovery of a series of protein mass-to-charge ratio peaks provide the basis and resources for finding new and more ideal tumor markers.
  • the design method of the model of the present invention is reasonable and feasible, and provides a new screening method for providing clinical cure rate of lung cancer, and also provides a new idea for exploring the mechanism of tumor development.
  • Figure 1 is a peptide map of serum of some healthy human serum and lung patients, wherein AC is serum of lung cancer patients and DF is serum of healthy humans.
  • Figure 2 Five Sigma serum mass spectrograms of repeated samples.
  • Figure 3-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a charge-to-mass ratio of 4054 m/z for the model;
  • Figure 3-B shows the protein based on the protein peak. Simulate a gel electropherogram, in which the arrow points to the characteristic protein band of the model of 4054 m/z.
  • Figure 4-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a mass-to-mass ratio of 2273 m/z for the model;
  • Figure 4-B shows the protein based on the protein peak. Simulate a gel electropherogram, in which the arrow points to the 2273 m/z characteristic protein band of the model.
  • Figure 5-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a charge-to-mass ratio of 4091 m/z.
  • Figure 5-B shows the protein based on the protein peak.
  • a simulated gel electropherogram is shown, with the arrow pointing to the characteristic protein band of the model of 4091 m/z.
  • Figure 6-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a charge-to-mass ratio of 3955 m/z for the model;
  • Figure 6-B shows the protein based on the protein peak.
  • a simulated gel electrophoresis pattern in which the arrow points to the characteristic protein band of the model of 3955 m/z.
  • Figure 7-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a charge-to-mass ratio of 3192 m/z for the model;
  • Figure 7-B shows the protein based on the protein peak.
  • a simulated gel electropherogram is shown, with the arrow pointing to the 3192 m/z characteristic protein band of the model.
  • Figure 8-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a charge-to-mass ratio of 3884 m/z for the model;
  • Figure 8-B shows the protein based on the protein peak.
  • a simulated gel electropherogram is shown, with the arrow pointing to the 3884 m/z characteristic protein band of the model.
  • Figure 9-A shows the expression level of the protein peak in all model samples.
  • the arrow points to the characteristic protein peak of lung cancer with a charge-to-mass ratio of 5337 m/z for the model;
  • Figure 9-B shows the protein based on the protein peak. Simulate a gel electropherogram, with the arrow pointing to the 5337 m/z characteristic protein band of the model.
  • the invention discloses a grammatical model for detecting lung cancer protein and a construction method thereof, and those skilled in the art You can learn from the contents of this article and improve the process parameters. It is to be understood that all such alternatives and modifications are obvious to those skilled in the art and are considered to be included in the present invention.
  • the method and the application of the present invention have been described by the preferred embodiments, and it is obvious that the method and application described herein may be modified or appropriately modified and combined without departing from the scope of the present invention. The technique of the present invention is applied.
  • Example 1 Establishment of a lung cancer grammar model
  • Clinprotools performs data preprocessing, and the processed data is processed by the genetic algorithm package genalg of statistical analysis software R2.6.2.
  • Collection of serum Collect venous blood in BD tubes to avoid hemolysis. Slowly shake the tube up and down five times to mix the clots in the blood. Blood was condensed at room temperature (25 ° C) for 1 hour and placed vertically. The blood must be precisely condensed for one hour, otherwise different peptide spectra will result from different clotting times of the sample.
  • the SST tube vacuum blood collection tube, BD
  • the SST tube was centrifuged at 1.400-2.000 g for 10 minutes at room temperature using a clinical centrifuge. Pipette the serum (supernatant) into the corresponding labeled tube. Label a clean 0.5 ml centrifuge tube, 50 ⁇ l of the same serum sample, and dispense multiple tubes.
  • Serum samples were immediately frozen at -80 °C. Repeated freeze-thaw serum samples are likely to cause precipitation of the polypeptide, which causes the peptide language to lose part of the polypeptide, and repeated freezing and thawing should be avoided. Frozen Serum is stored in permanent storage and to be dispensed. Serum can be stored at -80 °C for many years after dispensing. Magnetic Bead Treatment of Serum Samples: Prior to the ClinProt experiment, one tube of each of the dispensed serum samples was taken from a low temperature freezer and placed on wet ice. Freeze for 60 - 90 minutes.
  • ⁇ magnetic bead binding buffer BS
  • ⁇ mixed magnetic bead suspension 5 ⁇ 1 serum sample into the sample tube
  • mix After standing at room temperature for 5 min, the sample tube was placed in a magnetic bead separator. The magnetic beads are attached for 1 minute, the magnetic beads are separated from the suspended liquid, the suspended liquid is sucked off, and ⁇ magnetic bead cleaning buffer (WS) is added to the sample tube, and the two holes are repeated between the two holes before and after the magnetic bead separator. Move the sample tube 10 times. The sample tube is allowed to stand on the magnetic bead separator for the last time, and the magnetic beads are separated from the suspended liquid to absorb the suspended liquid.
  • BS ⁇ magnetic bead binding buffer
  • WS ⁇ magnetic bead cleaning buffer
  • the relative importance of each of the mass-to-charge ratio protein peaks for each type of sample is different.
  • the T test P value and the subject acceptance curve (ROC) method are used together to evaluate the relative importance of each protein peak.
  • Genetic algorithm is a very effective global randomization search algorithm, which draws on the natural selection of the biological world. And the mechanism of natural inheritance, its main feature is that the group search strategy and the information exchange between individuals in the group do not depend on the gradient information.
  • the genetic algorithm operates on a group of individuals. Through genetic operators, information between individuals can be exchanged. Individuals in such groups can be optimized from generation to generation and gradually approach the optimal solution. It is especially suitable for dealing with complex and nonlinear problems that are difficult to solve by traditional search methods, and can be widely used in the field of combinatorial optimization involving high dimensional space.
  • the genetic algorithm of the method of the present invention searches for sub-optimal feature subsets from the feature space formed by statistically differential protein subsets.
  • the classification function uses the nearest neighbor algorithm (KN).
  • the cross-validation process was introduced in the process of training the genetic algorithm set nearest neighbor algorithm classification, in which 80% of the randomly selected samples were used to establish the model, and the remaining 20% was used as verification. It can supervise the training process and avoid the “over-learning” phenomenon that the established model performs well on the modeled samples and performs poorly on the predicted samples.
  • the verification samples are used to test the classification ability of the established model.
  • Mobile phase A 5% acetonitrile, 0.1% aqueous solution of citric acid
  • Mobile phase B 95% acetonitrile, 0.1% aqueous solution of citric acid, all solutions were HPLC grade.
  • the capture flow rate is 15 ⁇ 1/ ⁇ , and the capture time is 3min.
  • Analytical flow rate 300ml/min; analysis time 60min, column temperature 35 °C; Partial Loop mode injection, injection volume 18 ⁇ 1.
  • the SequestTM search retrieves the database as IPI Human (version 3.45, entry 71983) and attaches its anti-library to the end of the database to reduce false positives.
  • the mother ion error was set to 50 ppm
  • the fragment ion error was set to IDa
  • the enzyme digestion method was non-enzymatically cut.
  • the peptides and protein results displayed under this parameter are highly accurate and are set according to the literature and international proteome regulations.
  • Figures 3 to 9 are the maps of healthy and lung cancer samples with protein-to-mass ratio peaks of 4054 m/z, 2273 m/z, 4091 m/z, 3955 m/z, 3192 m/z, 3884 m/z, and 5337 m/z.
  • the peak expression level of the protein in the training sample red indicates the normal group, green indicates the disease group, and the following is the simulated gel map corresponding to the protein peak, where the arrow points to the characteristic protein band of lung cancer. .
  • a total of 148 serum samples were selected, of which 94 were used for model training, 60 of 94 were from lung cancer, and 34 were from healthy people; the remaining 54 were used for model testing, and 44 of 54 samples were from For lung cancer patients, another 10 patients were from healthy people; lung cancer patients were confirmed by postoperative pathology report. All serum samples were taken on an empty stomach in the morning, separated and stored in a -80 low temperature freezer.
  • the trait model of lung cancer protein was established by using the characteristic protein peak of lung cancer screened in Example 1.
  • the model is defined as 7 input variables: 4054m/z, 2273m/z, 4091m/z, 3955m/z, 3192m/z, 3884m/z, 5337m/z.
  • Example 1 After establishing the model, 94 samples were used for training, as described in Example 1. Among them, 60 were in the lung cancer group and 34 in the normal group.
  • the model training recognition rate is 98.53%. Cross-validation was performed using a random selection method with a verification result of 91.83%. The model has good predictive power.
  • the number of sample cases predicts the prediction rate of normal group predicted by lung cancer group% Lung cancer group 44 42 2 95.5 normal group 10 1 9 90

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Abstract

L'invention concerne un procédé de construction d'un modèle de spectrométrie de masse pour la détection de protéines caractéristiques du cancer du poumon. Le modèle de spectrométrie de masse comprend des pics ayant un rapport masse/charge de 4054m/z, 2273m/z, 4091m/z, 3955m/z, 3192m/z, 3884m/z et 5337m/z, les protéines caractérisées en tant que rapports masse/charge de 4054m/z, 2273m/z, 4091m/z, 3955m/z, 53192m/z, 3884m/z et 5337m/z dans le modèle étant des protéines sériques caractéristiques du cancer du poumon. Lorsque les expressions des pics de 4054m/z, 2273m/z, 4091m/z, 3955m/z et 3192m/z sont régulées à la baisse et les expressions des pics de 3884m/z et 337m/z sont régulées à la hausse, il est indiqué que le sujet est un patient atteint ou potentiellement atteint d'un cancer du poumon. Le modèle de spectrométrie de masse de la présente invention peut être utilisé pour la détection précoce et le dépistage du cancer du poumon. Le procédé présente l'avantage d'un fonctionnement simple et facile, et d'une grande précision, permettant ainsi la détection précoce et le dépistage du cancer du poumon d'une nouvelle manière. L'invention concerne un procédé de construction d'un modèle de spectrométrie de masse pour la détection de protéines caractéristiques du cancer du poumon. Le modèle de spectrométrie de masse comprend des pics ayant un rapport masse/charge de 4054m/z, 2273m/z, 4091m/z, 3955m/z, 3192m/z, 3884m/z et 5337m/z, les protéines caractérisées en tant que rapports masse/charge de 4054m/z, 2273m/z, 4091m/z, 3955m/z, 3192m/z, 3884m/z et 5337m/z dans le modèle étant des protéines sériques caractéristiques du cancer du poumon. Lorsque les expressions des pics de 4054m/z, 2273m/z, 4091m/z, 3955m/z et 3192m/z sont régulées à la baisse et les expressions des pics de 33884m/z et 5337m/z sont régulées à la hausse, il est indiqué que le sujet est un patient atteint ou potentiellement atteint d'un cancer du poumon. Le modèle de spectrométrie de masse de la présente invention peut être utilisé pour la détection précoce et le dépistage du cancer du poumon. Le procédé présente l'avantage d'un fonctionnement simple et facile, et d'une grande précision, permettant ainsi la détection précoce et le dépistage du cancer du poumon d'une nouvelle manière.
PCT/CN2012/074917 2011-07-29 2012-04-28 Modèle de spectrométrie de masse pour la détection de protéines du cancer du poumon et son procédé de construction WO2013016966A1 (fr)

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CN103224922B (zh) * 2013-04-11 2014-12-10 西安交通大学 一种新的胃癌标志物及其检测方法和应用
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CN110658252A (zh) * 2019-10-01 2020-01-07 长沙湘华质谱医学科技有限公司 用于质谱诊断地中海贫血症的特征蛋白质谱模型及其用途
CN110632326A (zh) * 2019-10-01 2019-12-31 北京毅新博创生物科技有限公司 用于质谱诊断地中海贫血症的特征蛋白标记组合物及其诊断产品
CN110658251A (zh) * 2019-10-01 2020-01-07 长沙湘华质谱医学科技有限公司 表征地中海贫血症的特征蛋白组合物或质谱模型的用途
CN111933211B (zh) * 2020-06-28 2023-10-31 北京谷海天目生物医学科技有限公司 癌症精准化疗分型标志物筛选方法、化疗敏感性的分子分型方法和应用
CN112946053B (zh) * 2020-10-16 2023-06-27 北京毅新博创生物科技有限公司 用于制备诊断新冠病毒感染检测产品的特征多肽组合物
CN116010663B (zh) * 2023-03-21 2023-06-30 上海美吉生物医药科技有限公司 一种tmt项目图谱解析和数据分析的方法及系统

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