CN115598348A - Method for screening early gastric cancer marker and manufacturing protein chip for detection - Google Patents
Method for screening early gastric cancer marker and manufacturing protein chip for detection Download PDFInfo
- Publication number
- CN115598348A CN115598348A CN202110722508.2A CN202110722508A CN115598348A CN 115598348 A CN115598348 A CN 115598348A CN 202110722508 A CN202110722508 A CN 202110722508A CN 115598348 A CN115598348 A CN 115598348A
- Authority
- CN
- China
- Prior art keywords
- gastric cancer
- protein
- early
- protein chip
- chip
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 108090000623 proteins and genes Proteins 0.000 title claims abstract description 79
- 102000004169 proteins and genes Human genes 0.000 title claims abstract description 76
- 238000001514 detection method Methods 0.000 title claims abstract description 14
- 238000012216 screening Methods 0.000 title abstract description 14
- 238000000034 method Methods 0.000 title description 14
- 201000011591 microinvasive gastric cancer Diseases 0.000 title description 10
- 238000004519 manufacturing process Methods 0.000 title description 5
- 239000000439 tumor marker Substances 0.000 title description 2
- 206010017758 gastric cancer Diseases 0.000 claims abstract description 30
- 208000005718 Stomach Neoplasms Diseases 0.000 claims abstract description 28
- 201000011549 stomach cancer Diseases 0.000 claims abstract description 28
- 239000013642 negative control Substances 0.000 claims description 10
- 239000013641 positive control Substances 0.000 claims description 10
- 230000035945 sensitivity Effects 0.000 claims description 7
- 239000003550 marker Substances 0.000 claims description 5
- 238000001294 liquid chromatography-tandem mass spectrometry Methods 0.000 abstract description 9
- 238000001819 mass spectrum Methods 0.000 abstract description 6
- 108010026552 Proteome Proteins 0.000 abstract description 4
- 201000010099 disease Diseases 0.000 abstract description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 4
- 238000007477 logistic regression Methods 0.000 abstract description 4
- 238000010239 partial least squares discriminant analysis Methods 0.000 abstract description 3
- 238000000575 proteomic method Methods 0.000 abstract description 3
- 230000004083 survival effect Effects 0.000 abstract description 2
- 239000007788 liquid Substances 0.000 abstract 1
- 239000012474 protein marker Substances 0.000 abstract 1
- YBJHBAHKTGYVGT-ZKWXMUAHSA-N (+)-Biotin Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)O)SC[C@@H]21 YBJHBAHKTGYVGT-ZKWXMUAHSA-N 0.000 description 12
- 239000007790 solid phase Substances 0.000 description 12
- 238000002493 microarray Methods 0.000 description 11
- 206010028980 Neoplasm Diseases 0.000 description 10
- 238000011160 research Methods 0.000 description 10
- 108090000765 processed proteins & peptides Proteins 0.000 description 8
- 210000002966 serum Anatomy 0.000 description 8
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 6
- 239000000090 biomarker Substances 0.000 description 6
- 229960002685 biotin Drugs 0.000 description 6
- 235000020958 biotin Nutrition 0.000 description 6
- 239000011616 biotin Substances 0.000 description 6
- 239000012528 membrane Substances 0.000 description 6
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- 210000004369 blood Anatomy 0.000 description 4
- 239000008280 blood Substances 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 4
- 230000004060 metabolic process Effects 0.000 description 4
- BDAGIHXWWSANSR-UHFFFAOYSA-N methanoic acid Natural products OC=O BDAGIHXWWSANSR-UHFFFAOYSA-N 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 210000003819 peripheral blood mononuclear cell Anatomy 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 208000024891 symptom Diseases 0.000 description 4
- 238000004885 tandem mass spectrometry Methods 0.000 description 4
- WEVYAHXRMPXWCK-UHFFFAOYSA-N Acetonitrile Chemical compound CC#N WEVYAHXRMPXWCK-UHFFFAOYSA-N 0.000 description 3
- 208000007882 Gastritis Diseases 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 238000002790 cross-validation Methods 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 208000020082 intraepithelial neoplasia Diseases 0.000 description 3
- 150000002500 ions Chemical class 0.000 description 3
- 102000004196 processed proteins & peptides Human genes 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 238000005507 spraying Methods 0.000 description 3
- 238000007619 statistical method Methods 0.000 description 3
- 210000002784 stomach Anatomy 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- OSWFIVFLDKOXQC-UHFFFAOYSA-N 4-(3-methoxyphenyl)aniline Chemical compound COC1=CC=CC(C=2C=CC(N)=CC=2)=C1 OSWFIVFLDKOXQC-UHFFFAOYSA-N 0.000 description 2
- 229920000936 Agarose Polymers 0.000 description 2
- 108010088751 Albumins Proteins 0.000 description 2
- 102000009027 Albumins Human genes 0.000 description 2
- 206010061968 Gastric neoplasm Diseases 0.000 description 2
- 208000007107 Stomach Ulcer Diseases 0.000 description 2
- 108010090804 Streptavidin Proteins 0.000 description 2
- 238000000692 Student's t-test Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000000969 carrier Substances 0.000 description 2
- 238000012412 chemical coupling Methods 0.000 description 2
- 238000000546 chi-square test Methods 0.000 description 2
- 229930002875 chlorophyll Natural products 0.000 description 2
- 235000019804 chlorophyll Nutrition 0.000 description 2
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 description 2
- 208000023652 chronic gastritis Diseases 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000001839 endoscopy Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 235000019253 formic acid Nutrition 0.000 description 2
- 208000010749 gastric carcinoma Diseases 0.000 description 2
- 201000005917 gastric ulcer Diseases 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 2
- 206010020718 hyperplasia Diseases 0.000 description 2
- 238000010832 independent-sample T-test Methods 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 230000003902 lesion Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- 238000003068 pathway analysis Methods 0.000 description 2
- 239000012071 phase Substances 0.000 description 2
- 150000004032 porphyrins Chemical class 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000011347 resin Substances 0.000 description 2
- 229920005989 resin Polymers 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 201000000498 stomach carcinoma Diseases 0.000 description 2
- 238000012353 t test Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 206010000060 Abdominal distension Diseases 0.000 description 1
- 206010000087 Abdominal pain upper Diseases 0.000 description 1
- 206010006895 Cachexia Diseases 0.000 description 1
- 208000019505 Deglutition disease Diseases 0.000 description 1
- 206010058314 Dysplasia Diseases 0.000 description 1
- 208000012895 Gastric disease Diseases 0.000 description 1
- 206010017815 Gastric perforation Diseases 0.000 description 1
- 241000590002 Helicobacter pylori Species 0.000 description 1
- 206010028813 Nausea Diseases 0.000 description 1
- 238000010847 SEQUEST Methods 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 102000004142 Trypsin Human genes 0.000 description 1
- 108090000631 Trypsin Proteins 0.000 description 1
- 206010047700 Vomiting Diseases 0.000 description 1
- 150000001299 aldehydes Chemical group 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004071 biological effect Effects 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 239000002775 capsule Substances 0.000 description 1
- 238000007385 chemical modification Methods 0.000 description 1
- 208000021735 chronic enteritis Diseases 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 206010009887 colitis Diseases 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 206010061428 decreased appetite Diseases 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000001079 digestive effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 208000000718 duodenal ulcer Diseases 0.000 description 1
- 201000006549 dyspepsia Diseases 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 238000010828 elution Methods 0.000 description 1
- 208000026500 emaciation Diseases 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000001917 fluorescence detection Methods 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 239000005350 fused silica glass Substances 0.000 description 1
- 210000004051 gastric juice Anatomy 0.000 description 1
- 208000021302 gastroesophageal reflux disease Diseases 0.000 description 1
- 238000002575 gastroscopy Methods 0.000 description 1
- 239000000499 gel Substances 0.000 description 1
- 238000012812 general test Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 229940037467 helicobacter pylori Drugs 0.000 description 1
- 238000011534 incubation Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 210000004698 lymphocyte Anatomy 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 210000001616 monocyte Anatomy 0.000 description 1
- 230000008693 nausea Effects 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 210000005259 peripheral blood Anatomy 0.000 description 1
- 239000011886 peripheral blood Substances 0.000 description 1
- 239000002243 precursor Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000012207 quantitative assay Methods 0.000 description 1
- 238000010992 reflux Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000007928 solubilization Effects 0.000 description 1
- 238000005063 solubilization Methods 0.000 description 1
- 230000003381 solubilizing effect Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000004206 stomach function Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000006228 supernatant Substances 0.000 description 1
- 238000004448 titration Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 239000012588 trypsin Substances 0.000 description 1
- 230000008673 vomiting Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—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
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- 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
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
- G01N33/57446—Specifically defined cancers of stomach or intestine
-
- 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
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Immunology (AREA)
- Molecular Biology (AREA)
- Chemical & Material Sciences (AREA)
- Biomedical Technology (AREA)
- Physics & Mathematics (AREA)
- Urology & Nephrology (AREA)
- Hematology (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Cell Biology (AREA)
- Pathology (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Microbiology (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Biotechnology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Hospice & Palliative Care (AREA)
- Oncology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Biophysics (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Investigating Or Analysing Biological Materials (AREA)
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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110722508.2A CN115598348A (en) | 2021-06-28 | 2021-06-28 | Method for screening early gastric cancer marker and manufacturing protein chip for detection |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110722508.2A CN115598348A (en) | 2021-06-28 | 2021-06-28 | Method for screening early gastric cancer marker and manufacturing protein chip for detection |
Publications (1)
Publication Number | Publication Date |
---|---|
CN115598348A true CN115598348A (en) | 2023-01-13 |
Family
ID=84840891
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110722508.2A Pending CN115598348A (en) | 2021-06-28 | 2021-06-28 | Method for screening early gastric cancer marker and manufacturing protein chip for detection |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115598348A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112904005A (en) * | 2021-01-26 | 2021-06-04 | 李欣 | Plasma exosome protein marker for nasopharyngeal carcinoma early screening and application thereof |
US20220291217A1 (en) * | 2019-09-05 | 2022-09-15 | Japanese Foundation For Cancer Research | Gastric cancer marker and examination method using same |
-
2021
- 2021-06-28 CN CN202110722508.2A patent/CN115598348A/en active Pending
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20220291217A1 (en) * | 2019-09-05 | 2022-09-15 | Japanese Foundation For Cancer Research | Gastric cancer marker and examination method using same |
CN112904005A (en) * | 2021-01-26 | 2021-06-04 | 李欣 | Plasma exosome protein marker for nasopharyngeal carcinoma early screening and application thereof |
Non-Patent Citations (1)
Title |
---|
BIN ZHOU等: "Plasma proteomics-based identification of novel biomarkers in early gastric cancer", CLINICAL BIOCHEMISTRY, vol. 76, 3 February 2020 (2020-02-03), pages 5 - 10, XP086011594, DOI: 10.1016/j.clinbiochem.2019.11.001 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
KR102014890B1 (en) | Method, array and use for determining the presence of pancreatic cancer | |
Zhang et al. | Tree analysis of mass spectral urine profiles discriminates transitional cell carcinoma of the bladder from noncancer patient | |
US20090204334A1 (en) | Lung cancer biomarkers | |
SG173310A1 (en) | Apolipoprotein fingerprinting technique | |
KR102289278B1 (en) | Biomarker panel for diagnosis of pancreatic cancer and its use | |
Zhao et al. | Plasma metabolic profiling and novel metabolite biomarkers for diagnosing prostate cancer | |
US20180100858A1 (en) | Protein biomarker panels for detecting colorectal cancer and advanced adenoma | |
Kohn et al. | Proteomics as a tool for biomarker discovery | |
US20050100967A1 (en) | Detection of endometrial pathology | |
US20070087392A1 (en) | Method for diagnosing head and neck squamous cell carcinoma | |
US20140236166A1 (en) | Biomarkers for distinguishing benign, pre-malignant, and malignant pancreatic cysts | |
KR101390590B1 (en) | Markers for pancreatic cancer recurrence prognosis prediction and its use | |
Massion et al. | Proteomic strategies for the characterization and the early detection of lung cancer | |
CN116879558B (en) | Ovarian cancer diagnosis marker, detection reagent and detection kit | |
Song et al. | MALDI‐TOF‐MS analysis in low molecular weight serum peptidome biomarkers for NSCLC | |
US20050202485A1 (en) | Method and compositions for detection of liver cancer | |
US20050158745A1 (en) | Methods and compositions for detection of nasopharyngeal carcinoma | |
CN115598348A (en) | Method for screening early gastric cancer marker and manufacturing protein chip for detection | |
KR101390543B1 (en) | Markers for diagnosing pancreatic cancer and its use | |
EP4359792A2 (en) | Protein biomarker indicators of neurological injury and/or disease and methods of use thereof | |
CN115372616A (en) | Gastric cancer related biomarker and application thereof | |
Bezdekova et al. | Prostate cancer diagnosed and staged using UV-irradiated urine samples and a paper-based analytical device | |
Gretzer et al. | Modern tumor marker discovery in urology: surface enhanced laser desorption and ionization (SELDI) | |
Kas | On the technicalities of discovering and applying protein biomarkers for cancer prevention | |
CN111748623A (en) | Predictive marker and kit for recurrence of liver cancer patient |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |