CN115598348A - Method for screening early gastric cancer marker and manufacturing protein chip for detection - Google Patents
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Abstract
The invention discloses a protein marker for early screening of gastric cancer and application of a protein chip thereof. The invention screens and further identifies 11 specific proteins (Q8 NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207 and Q9H 939) related to the early stage of gastric cancer and corresponding protein chips. The invention compares plasma proteomes of patients and healthy volunteers by using a liquid chromatogram-tandem mass spectrum (LC-MS/MS) and a tandem mass label (TMT) label, applies a Logistic regression model and an orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA) model, and can accurately predict the probability and the stage of gastric cancer of an individual according to proteomic analysis results among groups, be applied to early screening of the gastric cancer, realize the early warning accuracy of the gastric cancer of more than 66 percent and achieve the late state disease of about 86 percent. The kit can realize rapid detection on high-risk people, and can effectively improve the early discovery rate and survival rate of the gastric cancer.
Description
Technical Field
The invention relates to the technical field of biology, in particular to a method for screening a protein chip for predicting, early warning and detecting early gastric tumor patients at high risk and application of the protein chip.
Background
There are many types of gastric disorders, including: chronic enteritis, colitis, chronic gastritis (superficial, erosive, atrophic, reflux), antral gastritis, gastric ulcer, gastrorrhagia, gastric perforation, duodenal ulcer, gastric cancer, etc. The general tests include 13C breath test (gold standard for detecting helicobacter pylori HP), serum anti-HP antibody measurement, X-ray examination, digestive endoscopy, capsule endoscopy, gastric juice analysis, electrogastrogram, and gastric function. For screening for gastric cancer, the most effective method at present is gastroenteroscopy.
Early gastric cancer generally has no obvious symptoms, and certain symptoms can not appear to a certain extent until the advanced stage, such as: epigastric pain, abdominal distension, inappetence, emaciation, nausea, vomiting, dysphagia, etc., but the symptoms are not typical, are not specific to stomach cancer, and are also found in benign lesions such as chronic gastritis, gastric ulcer, functional dyspepsia, gastroesophageal reflux disease, etc. When canceration occurs on the basis of the original stomach illness, the symptoms of the stomach illness exist for a long time, so that the stomach illness is easier to ignore and a patient does not see a doctor in time.
The marker detection is a hotspot of current research due to the advantages of small invasiveness, easy detection and the like, the sensitivity and the specificity of the markers CEA, CA199 and CA724 used clinically at present are poor, the CA724 positive rate is only 47.7 percent, the CEA is 25 percent, the CA19-9 is 25 percent, and an effective method for screening early gastric cancer is not available at present.
In recent years, the proteomics research has made a great progress in technology, more and more new technologies for proteomics research appear, new research strategies are emerging continuously, and each proteomics research means has advantages for different research samples and different research purposes; the proteomics research strategy can be used for dynamically and contrastively analyzing the changes of protein expression profiles under different health and disease states at high flux, and can be effectively applied to the research on the aspects of screening and identification of tumor markers, tumor classification, evaluation of tumor treatment effect, tumor occurrence mechanism and the like, so that the diagnosis, classification and curative effect evaluation of tumors are comprehensively judged by the changes of the protein profiles or gene expression profiles which are judged and developed by using single tumor markers in the past. The search for tumor markers with high sensitivity and specificity from serum, which can be used for early diagnosis of gastric cancer, is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a protein chip for early screening of gastric cancer in order to solve the problem of lack of a prediction early-warning detection marker of a patient with high risk of early gastric tumor at present.
The invention further provides a manufacturing method of the protein chip.
The invention further provides an application of the protein chip.
In order to achieve the purpose, the invention adopts the following technical scheme:
a protein chip for early screening of gastric cancer comprises a solid phase carrier and a protein chip distributed on the solid phase carrier in an array manner
The differential protein antibody of (1), comprising: q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207, Q9H939.
The further differential protein screening process is as follows: establishing 4 control groups of proteomics of a plurality of persons in the development processes of normal, precancerous lesion, early stage and progressive stage, comparing plasma proteomics of patients and healthy volunteers by using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) and a tandem mass label (TMT) label, searching generated MS/MS mass spectra by using a Uniprot Human database, and performing data analysis by using R software (www.r-project. Org). Differentially expressed proteins were identified between the group of atypical hyperplasias or low and high grade intraepithelial neoplasia, early gastric carcinoma, advanced gastric carcinoma and healthy controls by learning t-test with an initial p-value cutoff of 0.05.
The further differential protein verification process is as follows: a logistic regression model containing differentially expressed proteins was constructed to distinguish between cases and controls. The OPLS-DA model was constructed using the R package MetabioAnalystR and ropls. The R2Y and Q2Y indices are used to evaluate the performance of the model. Variability in the projected importance (VIP) score reflects both the load weight of each component and the variability of the response it interprets, and is therefore useful for feature selection. To further select potential biomarkers for EGC, a supervised multivariate technique OPLS-DA was used. Each group was clearly separated from the control group, indicating the potential efficiency of plasma proteomic findings. We then used statistical data called VIP scores to select biomarkers. A cutoff value of VIP >2 was used and 43 proteins were selected. KEGG pathway analysis of these biomarkers revealed rich terminology including porphyrin and chlorophyll metabolism as well as nitrogen metabolism. Using LC-MS/MS in combination with TMT markers, we identified 90 direct markers of gastric cancer tumor-associated (figure 1) and 11 differentially expressed maximal proteins from each group of patients and healthy control population: q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207, Q9H939.
Further, the protein chip also comprises a positive control and a negative control.
Further, the solid phase carrier is a glass slide, a titration plate or a filter membrane.
Furthermore, the protein chip is prepared by respectively spotting the differential protein antibody, the positive control and the negative control on the solid phase carrier by using a spot gene microarray commercial spotting instrument or a membrane spraying method.
Further, the differential protein antibody, positive control and negative control are spotted for 3-5 replicates.
A manufacturing method of a protein chip for early screening of gastric cancer comprises the following steps:
1) Solid phase carrier and treatment thereof: processing a solid phase carrier by using an agarose membrane, wherein the solid phase carrier is provided with a negative control hole and a positive control hole;
2) Protein pretreatment: solubilizing the differential protein antibody;
3) Spotting a microarray: spotting the differential protein antibody, the positive control and the negative control obtained in the step 2) on a solid phase carrier by using a commercial spotting instrument of a spotted gene microarray or a membrane spraying method, wherein 3-5 spotting of each differential protein antibody, the positive control and the negative control are repeated;
4) Fixing the microarray: using streptavidin on the solid phase carrier obtained in the step 3);
5) Closing the microarray: and after the differential protein antibody microarray is fixed, using BSA or glycerol to seal the chip, and standing to obtain the protein chip.
Further, the differential protein antibody comprises: q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207, Q9H939.
After a sample is obtained, the sample is labeled by biotin and co-incubated with the protein chip containing the differential proteins Q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207 and Q9H939, and after chemical coupling, a biotin labeled antibody of the target protein is added; after the reaction is finished, detecting HRP-streptavidin or fluorescein-streptavidin chip signals by combining fluorescence and colorimetric scanning, quantizing and standardizing the data by combining system software, establishing a certain calculation model, and performing data analysis and modeling; statistical analysis is carried out by adopting SPSS 22.0 statistical software, the measured data are expressed by mean +/-standard deviation, statistical analysis is carried out by adopting independent sample t test, and the counting data are tested by chi-square test. Differences with p <0.05 are statistically significant, and the difference criteria: AUC >0.7.
The invention has the beneficial effects that: the liquid chromatography-tandem mass spectrometry (LC-MS/MS) of the present invention was combined with Tandem Mass Tag (TMT) tags to compare plasma proteomes of patients and healthy volunteers. Applying a Logistic regression model and an orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA) model, finally further establishing a gastric cancer diagnosis model according to proteomic analysis results among groups, finding out differential proteins, and manufacturing a protein chip; the chip can accurately predict the probability and the stage of the gastric cancer of an individual, is applied to early screening of the gastric cancer, can realize the early warning accuracy rate of the gastric cancer of more than 66 percent, and can reach about 86 percent of the late illness state. The kit can realize rapid detection on high-risk people, and can effectively improve the early discovery rate and survival rate of the gastric cancer.
The invention 1) requires very small sample volumes (10-100 microliters); 2) High signal-to-noise ratio, high accuracy and high sensitivity (monoclonal antibody); 3) Rapid, miniaturized and automated (within 3 hours); 4) Quantitative detection can be carried out through a standard curve; therefore, the product developed by the invention can greatly improve the accuracy and efficiency of clinical diagnosis of patients with early gastric cancer, and has strong application innovativeness and very important clinical research significance.
Drawings
FIG. 1: selected direct correlation marker of gastric cancer tumor
Detailed Description
The technical solution of the present invention will be described clearly and completely with reference to the experimental data, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, any methods and materials similar or equivalent to those described herein can be used in the practice of the present invention. The preferred embodiments and materials described herein are exemplary only.
Differential protein screening
(1) Blood sample collection
175 healthy patients (atypical hyperplasia or low-grade or high-grade intraepithelial neoplasia 30 cases, early gastric cancer 30 cases; advanced gastric cancer 30 cases, and healthy subjects) were selected at XX three hospital according to the gastroscopy and biopsy pathological gold standard, and the study was approved by the ethical committee of XX hospital, and after all participants signed informed consent, blood specimens were collected according to the laboratory standard.
(2) Quantitative detection
Quantitative assays were performed in the laboratory using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in combination with Tandem Mass Tag (TMT) tags.
TMT-labeled peptides were isolated by gradient elution at a flow rate of 0.250. Mu.l/min for 120 minutes using an EASY-nLC 1000 system (Thermo Fisher Scientific, waltham, mass., USA) directly with a Q-Exacitve HF-X spectrometer (Thermo Fisher Scientific, waltham, USA). The analytical column was a fused silica capillary column (75 μm inner diameter, 150 mm length; packed with C-18 resin, likexindun, mass.). Mobile phase a consisted of 0.1% formic acid and mobile phase B consisted of 100% acetonitrile and 0.1% formic acid. For quantitative proteomic analysis, the Q-Exacitve HF-X spectrometer was operated in a data-dependent acquisition mode using Xcalibur 3.0.63 software (Thermo Fisher Scientific, waltham, ma, usa), and maximum spectra were obtained in Orbitrap (350-1550 m/z,120,000 resolution) with only one complete scan quality Automatic Gain Control (AGC) target value of 2e 6. A data-dependent acquisition method was performed, the generated MS/MS mass spectra were collected at a resolution of 17500, AGC target 1e6, and the maximum Injection Time (IT) of the first 40 ions observed in each mass spectrum was 50 MS. The isolation window was set to 1.2 Da width, the dynamic exclusion time was 20 s, and the collision energy was set to 38%.
The resulting MS/MS mass spectra were searched against the Uniprot Human database (https:// www.uniprot.org; august 10, 2016; 89,105 sequences) using the SEQUEST search engine (PD, thermo Fisher Scientific, waltham, mass., USA) in the Proteome Discoverer 2.1 software. The search criteria were as follows: full trypsin specificity is required; allowing for one-time offset cutting; carbamoylmethylation (C) and TMT hexamers (K and N-terminal) are set as fixed modifications; the oxidation (M) is set as a variable modification. For all MSs collected in the Orbitrap mass analyser, the precursor ion mass tolerance was set to 10 ppm; the fragment ion mass tolerance was set to 20 mmu for all MS2 mass spectra obtained. The false discovery rate of the peptides was calculated using Percolator supplied by PD. When the q value is less than 1%, the peptide profile match is considered correct. When searched against the reverse bait database, false findings were determined based on peptide profile matching. Only peptides assigned to a given proteome are considered unique. The false discovery rate of protein discovery was also set to 0.01. Relative protein quantification was performed according to the manufacturer's instructions using PD2.1 for six reporter ionic strengths per peptide. Only proteins with two or more unique peptide stretch matches were quantified. The protein ratio was calculated as the median of all peptide hits belonging to the protein. The quantitative accuracy is expressed as protein ratio variability.
(3) Analysis of results
1) Identification of differentially expressed proteins
Data analysis was performed using R software (www.r-project. Proteins with deletion values above 50% of each group were deleted from the analysis. The remaining missing values are estimated using k-nearest neighbor (KNN). The data matrix is then log2 transformed to approximate a gaussian distribution. Proteins differentially expressed between the dysplasia or low and high grade intraepithelial neoplasia group, the early gastric cancer group, the advanced gastric cancer group and the healthy control group were identified by learning t-test with an original p-value cutoff of 0.05.
2) Logistic model and cross validation
A logistic regression model containing differentially expressed proteins was constructed to distinguish between cases and controls. Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic performance of the model. The area under the ROC curve (AUC), optimal cut-off, sensitivity, specificity and accuracy were calculated using R package pROC.
To evaluate the predicted performance of the model, leave-one-out cross-validation (LOOCV) was performed. In more detail, the logical model was estimated using 29 samples, the remaining samples being left as the test set. The entire cross-validation process was repeated 30 times to cover all samples and the average accuracy was calculated.
3) Orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA)
The OPLS-DA model was constructed using the R package MetabioAnalystR and ropls. The R2Y and Q2Y indices are used to evaluate the performance of the model. Variability in the projected importance (VIP) score reflects both the load weight of each component and the variability of the response it interprets, and is therefore useful for feature selection.
(4) Diagnostic model
The sensitivity and specificity of the combined model reached 100% and 100%, respectively, indicating that its performance was significantly better than each protein alone. However, this result may result from an overfitting of the model. To evaluate the predictive performance of the model, we used the LOOCV method to divide the entire data set into a training set and a test set. Thus, the sensitivity and specificity of LOOCV were 66.7% and 86.7%, respectively.
To further select potential biomarkers for EGC, a supervised multivariate technique OPLS-DA was used. Each group was clearly separated from the control group, indicating the potential efficiency of plasma proteomic findings. We then used statistical data called VIP scores to select biomarkers. A cutoff value of VIP >2 was used and 43 proteins were selected. KEGG pathway analysis of these biomarkers revealed rich terminology, including porphyrin and chlorophyll metabolism as well as nitrogen metabolism.
Using LC-MS/MS in combination with TMT markers, we identified 90 direct markers of gastric cancer tumor associated and 11 differentially expressed maximal proteins from each group of patients and healthy control population.
Protein chip preparation:
solid support and treatment thereof
The carrier must be selected in accordance with 1) the surface has active groups which can undergo chemical reactions; 2) The protein molecules bound on the unit carrier reach the optimal capacity; has enough stability; has good biocompatibility. Typical carriers include titer plates, filters, gels, and slides, with slides being commonly used. The solid phase support is first treated with an agarose membrane.
Protein pretreatment
Candidate antibodies to the difference protein with high purity and intact biological activity were selected for solubilization.
Spotting microarray
And (3) spotting the pretreated candidate differential protein antibody on a solid phase carrier according to a set sequence by using a spotting gene microarray commercialized spotting instrument or a membrane spraying method. Each protein antibody of interest was spotted in 3-5 replicates. And negative and positive control holes are required. The dot spacing was 4.5mm, and the diameter of the dots was 250 μm for the negative control (Normal IgG) and the positive control (Anti-RNAPolymerase II).
Differential protein antibodies include: q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207, Q9H939.
Immobilization of microarrays
Solid phase carriers such as glass slides and the like need to generate aldehyde group-fixed protein based on chemical modification, and are commonly used as follows: streptavidin.
Closed microarrays
After the differential protein antibody microarray is fixed, the chip is sealed by BSA or glycerol, and the chip is kept stand for later use.
Application verification of the protein chip:
randomly obtaining serum of normal and early gastric cancer patients, incubating with a protein chip after biotin labeling, adding a biotin labeled antibody of target protein, and finally detecting a chip signal based on HRP-streptavidin or fluorescein-streptavidin for judging the expression level of candidate markers
Sample acquisition, processing and grouping
Obtaining 15mL of peripheral blood of a patient with normal stomach cancer and early stage, reserving 10mL of blood, separating human PBMC (best separation is carried out in 2 h, and the longest separation is not more than 6 h) by using a Ficol lymphocyte separation solution after blood injection, and extracting PBMC protein. Leaving and taking 5mL of the serum, incubating at 37 ℃ for 30 min, centrifuging at a high speed (1500 rpm/min,15 min), collecting supernatant, combining with an albumin removal kit (Bio-Rad), and specifically removing high-abundance albumin in the serum based on affinity resin for subsequent proteomics detection.
Study grouping:
1) Normal group serum
2) Normal group peripheral blood mononuclear cells
3) Serum for disease group
4) Peripheral blood mononuclear cells were used in the disease group.
Protein chip incubation
Obtaining serum and monocyte samples of a cohort (200 normal/200 patients), labeling the samples with biotin, and incubating the samples with a protein chip; after chemical coupling, adding a biotin-labeled antibody of the target protein, and strictly controlling the reaction temperature and time; finally, stopping the reaction and cleaning the protein chip;
fluorescence detection
Setting parameters, and detecting signals of an HRP-streptavidin or fluorescein-streptavidin chip by combining fluorescence and colorimetric scanning. And combining system software to quantize and standardize the data, establish a certain calculation model, and perform data analysis and modeling.
Statistical analysis
The data is statistically analyzed by SPSS 22.0 statistical software, the measurement data is expressed by mean + -standard deviation, and is statistically analyzed by independent sample t test, and the counting data is checked by chi-square test. Differences with p <0.05 were statistically significant. The standard of variation: AUC >0.7.
The embodiments of the present invention have been described in detail, but the embodiments are merely examples, and the present invention is not limited to the embodiments described above. Any equivalent modifications and substitutions to those skilled in the art are also within the scope of the present invention. Accordingly, equivalent alterations and modifications are intended to be included within the scope of the present invention, without departing from the spirit and scope of the invention.
Claims (5)
1. A marker and its protein chip for early detection of gastric cancer, wherein the marker comprises one or more of Q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0A0G2JMC9, P08493, P16157, A0A087WTY6, P14207 and Q9H939.
2. The differential protein of claim 1 has a sensitivity and specificity of 66.7% and 86.7%, respectively.
3. The protein chip for early detection of gastric cancer according to claim 1, characterized by comprising one or more of the markers Q8NBP7, P00441, Q86UD1, A0A2R8Y7X9, P62979, A0G2JMC9, P08493, P16157, A0a087WTY6, P14207, Q9H939.
4. The protein chip for early detection of gastric cancer according to claim 3, wherein the protein chip further comprises a positive control and a negative control.
5. The protein chip for early detection of gastric cancer according to claims 3-4, wherein the differential protein antibody, the positive control and the negative control are spotted for 3-5 repeats.
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| CN117169504A (en) * | 2023-08-29 | 2023-12-05 | 杭州广科安德生物科技有限公司 | Biomarker for gastric cancer related parameter detection and related prediction system and application |
| CN117169504B (en) * | 2023-08-29 | 2024-06-07 | 杭州广科安德生物科技有限公司 | Biomarker for gastric cancer related parameter detection and related prediction system and application |
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| US20220291217A1 (en) * | 2019-09-05 | 2022-09-15 | Japanese Foundation For Cancer Research | Gastric cancer marker and examination method using same |
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| CN117169504A (en) * | 2023-08-29 | 2023-12-05 | 杭州广科安德生物科技有限公司 | Biomarker for gastric cancer related parameter detection and related prediction system and application |
| CN117169504B (en) * | 2023-08-29 | 2024-06-07 | 杭州广科安德生物科技有限公司 | Biomarker for gastric cancer related parameter detection and related prediction system and application |
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