CN113917148A - Protein marker combination for gastric cancer diagnosis and application thereof - Google Patents
Protein marker combination for gastric cancer diagnosis and application thereof Download PDFInfo
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Abstract
The invention discloses a protein marker combination for gastric cancer diagnosis and application thereof, wherein the protein marker combination is selected from at least one of COL10A1, GKN1, GKN2 and LIPF, and is selected from at least one of PGI/II and G-17. The invention has the beneficial effects that: the protein marker has high correlation with gastric cancer, and the accuracy of gastric cancer diagnosis can be greatly improved by jointly detecting the levels of the novel protein marker and the conventional marker for representing gastric atrophy in a sample. Compared with the traditional invasive diagnosis methods such as digestive tract endoscopy or biopsy puncture, the noninvasive examination method has better universality; compared with the detection of clinical serum conventional tumor markers (CEA, CA19-9), the kit provided by the invention adopts the combined detection of a plurality of protein markers and combines with a calculation model, has higher diagnosis sensitivity and specificity on gastric cancer, can be used for early screening and auxiliary diagnosis of gastric cancer, and effectively promotes accurate diagnosis and treatment of gastric cancer.
Description
Technical Field
The invention belongs to the technical field of medical diagnosis, and particularly relates to a protein marker combination for gastric cancer diagnosis and application thereof.
Background
Gastric cancer is the fifth most common malignant cancer worldwide, and the death rate is the fourth of the tumors of the whole body, thus seriously threatening the health of human beings. According to the latest data of the world cancer report of 2020 world of the World Health Organization (WHO), 47.9 thousands of new gastric cancer cases (44 percent of the global gastric cancer incidence) and 37.4 thousands of deaths (49 percent of the global gastric cancer mortality) in China in 2020 are shown, and the incidence and the mortality are third. Due to special dietary habits, the number of people with high gastric cancer risk in China is huge, and with the trend of aging of the population, the disease burden of gastric cancer in China is continuously increased. However, because the symptoms of early gastric cancer are atypical, the early diagnosis rate of gastric cancer in China is less than 20%, 70% -80% of patients have been diagnosed in middle and late stages, and the prognosis is poor (5-year survival rate of gastric cancer patients in IV stage is less than 2%).
Currently, the screening mode of gastric cancer is mainly upper gastrointestinal endoscopy. However, the upper gastrointestinal endoscopy requires advanced instruments and equipment and special operators, has high technical requirements, high cost, invasiveness, needs to be fasted in advance, has poor compliance of subjects, and is not suitable for repeated examination and population screening. In addition, there are other screening methods such as H.pylori (Hp) antibody assay, serum Pepsinogen (PGI/II) assay, gastrin (gastrin-17, G-17) assay, etc. PGI/II, G-17 are essentially markers for atrophy, and help to identify people at high risk for atrophic gastritis, but are not suitable for diagnosis of gastric cancer. When PGI/II or G-17 levels are abnormal, further diagnosis of the occurrence of cancer must be made by gastroscopy and biopsy. Helicobacter pylori infection is closely related to gastric cancer, about two thirds of gastric cancer is related to helicobacter pylori infection, so that detection of helicobacter pylori is clinically used as one of gastric cancer screening methods, but the method has high false positive rate, and a positive result is not enough to be used as a basis for gastric cancer diagnosis, so that the method is not recommended to be used for wide population screening. Serum conventional tumor markers, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9(CA19-9), are also widely applied in clinical practice, but their diagnostic sensitivity is low (the sensitivity of early tumor is less than 20%, the sensitivity of late tumor is 20-50%), and they are not suitable for early diagnosis of gastric cancer.
Disclosure of Invention
The main purpose of the application is to find out a novel biomarker related to gastric cancer diagnosis, and improve the sensitivity and specificity of clinical gastric cancer diagnosis by constructing an in vitro diagnosis combined detection calculation model, thereby providing a new means for early screening and auxiliary diagnosis of gastric cancer.
In order to achieve the above purpose, the invention provides the following technical scheme:
a protein marker combination for detecting gastric cancer, the protein marker combination being at least one selected from the group consisting of COL10A1, GKN1, GKN2, LIPF, and at least one selected from the group consisting of PGI/II, G-17.
No marker for gastric cancer detection can diagnose gastric cancer with very high sensitivity and specificity at the same time, and non-tumor areas of gastric cancer patients are often accompanied with atrophic gastritis, so that the detection is carried out by combining the found novel protein marker and conventional markers (PGI/II, G-17) for representing gastric antral atrophy, and the detection accuracy of gastric cancer can be effectively improved.
The above-mentioned combination of protein markers for gastric cancer detection is, in a preferred embodiment, a radiation method, an immunological method, a fluorescence method, a flow fluorescence method, a latex turbidimetric method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method.
The above protein marker combination for detecting gastric cancer is a preferred embodiment, wherein the test sample is selected from tissues, blood, urine, saliva or feces of human or animal body.
In a second aspect of the present invention, there is provided a kit for detecting gastric cancer, comprising a reagent for specific binding of the above-mentioned combination of protein markers, a reagent for preparing a biopsy sample, a reagent for homogenizing tissue, a reagent for preparing a peptide fragment sample, or a reagent for desalting a peptide fragment.
Preferably, the reagents required for preparing the biopsy sample are 4% paraformaldehyde, 20% sucrose (with 0.05% NaN)3) Etc.; the reagents required for tissue homogenization are PBS (0.01M, pH 7.4), SDT lysis buffer (4% SDS, 0.1M Tris-HCl pH7.6, 0.1M DTT), etc.; a reagent for preparing a peptide fragment sample (such as Trypsin/Lys-C and the like which are enzymolysis reagents required for FASP enzymolysis of proteins of cell lysate); reagents required for desalting the peptide fragment (0.1% TFA in water, 75:25 acetonitrile/water without any TFA, 5:95 acetonitrile/water with 0.2% formic acid and 0.01% TFA, etc.).
In the kit for detecting gastric cancer, as a preferred embodiment, the reagent specifically binding to the protein marker combination is a peptide, a peptide mimetic, a nucleic acid aptamer, an antibody or an antigen-binding fragment thereof.
In a third aspect of the present invention, a method for constructing a gastric cancer in vitro diagnosis calculation model based on protein marker combined detection is provided, the method comprises detecting the concentrations of at least two protein markers according to claim 1 from a sample, and performing logistic regression on the determined concentrations of the protein markers to obtain a regression equation, and establishing a calculation model;
the logistic regression equation is:
wherein Logit (P) is the result of a logistic regression model of the gastric cancer protein markers, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis, the concentration i of the marker is the concentration of each protein marker, and n is an integer greater than or equal to 2.
The method for constructing the gastric cancer in-vitro diagnosis calculation model based on the combined detection of the protein markers comprises the following steps of selecting a sample from tissues, blood, urine, saliva or feces of a human body or an animal body;
the detection method comprises the following steps: a radiation method, an immunological method, a fluorescence method, a flow fluorescence method, a latex turbidimetry, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.
The invention has the beneficial effects that: the protein marker combination for detecting gastric cancer comprises the steps of detecting the concentrations of at least two protein markers from a sample, carrying out logistic regression on the concentrations of the detected protein markers to obtain a regression equation, and establishing a calculation model. Substituting the concentration of each detected protein marker into a regression equation to obtain the risk probability of the gastric cancer; further, determining a probability cutoff value (cut-off) through a point with the maximum johnson index, and finally determining whether each sample has gastric cancer and the risk of gastric cancer; effectively improves the accuracy of the detection of the gastric cancer.
The protein marker has high correlation with gastric cancer, and the accuracy of gastric cancer diagnosis can be greatly improved by jointly detecting the levels of the novel protein marker and the conventional marker for representing gastric atrophy in a sample. Compared with the traditional invasive diagnosis methods such as digestive tract endoscopy or biopsy puncture, the noninvasive examination method has better universality; compared with the detection of clinical serum conventional tumor markers (CEA, CA19-9), the kit provided by the invention adopts the combined detection of a plurality of protein markers and combines with a calculation model, has higher diagnosis sensitivity and specificity on gastric cancer, can be used for early screening and auxiliary diagnosis of gastric cancer, and effectively promotes accurate diagnosis and treatment of gastric cancer.
Drawings
FIG. 1 shows the receiver operating characteristic curve (ROC) of the combined detection of protein markers (COL10A1+ GKN1+ GKN2+ LIPF + PGI/II + G-17) based on tissue samples (80 cases of gastric cancer, 80 cases of normal);
FIG. 2 shows the receiver operating characteristic curve (ROC) of protein marker combination assay (COL10A1+ GKN1+ GKN2+ LIPF + PGI/II + G-17) based on clinical serum samples (40 controls, 60 gastric cancers);
FIG. 3 shows the receiver operating characteristic curve (ROC) of protein marker combination assay (COL10A1+ GKN1+ GKN2+ LIPF + PGI/II + G-17) based on clinical serum samples (60 cases of gastric cancer, 60 cases of chronic gastritis).
Detailed Description
In order to make the technical solutions in the embodiments of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to examples, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The invention discloses a protein marker combination for detecting gastric cancer, which is selected from at least one of COL10A1, GKN1, GKN2 and LIPF, and at least one of PGI/II and G-17.
Preferably, the detection method is a radiological method, an immunological method, a fluorescent method, a flow fluorescent method, a latex turbidimetric method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.
Preferably, the test sample is selected from the group consisting of tissue, blood, urine, saliva or feces of a human or animal body.
The invention also provides a kit for detecting gastric cancer, which comprises a reagent specifically combined with the protein marker combination, a reagent required for preparing a biopsy sample, a reagent required for tissue homogenate, a reagent for preparing a peptide fragment sample or a reagent required for desalting the peptide fragment.
Preferably, the agent that specifically binds to the combination of protein markers is a peptide, a peptide mimetic, a nucleic acid aptamer, an antibody, or an antigen-binding fragment thereof.
The invention also provides a method for constructing a gastric cancer in-vitro diagnosis calculation model based on protein marker combined detection, which is characterized by comprising the steps of detecting the concentrations of at least two protein markers in claim 1 from a sample, performing logistic regression on the determined concentrations of the protein markers to obtain a regression equation, and establishing a calculation model;
the logistic regression equation is:
wherein Logit (P) is the result of a logistic regression model of the gastric cancer protein markers, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis, the concentration i of the marker is the concentration of each protein marker, and n is an integer greater than or equal to 2.
Preferably, the sample is selected from the group consisting of tissue, blood, urine, saliva or feces of a human or animal body;
the detection method comprises the following steps: a radiation method, an immunological method, a fluorescence method, a flow fluorescence method, a latex turbidimetry, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.
Example 1
1. Gastric cancer (stomach) proteomics data and clinical information were downloaded from the CPTAC database, and the sample size was 80:80 for gastric cancer tissue and normal tissue.
1.1 the concentrations of COL10A1, GKN1, GKN2, LIPF, PGI, PGII, and G-17, which are protein markers according to the present invention, were extracted in the stomach cancer tissues and the tissues adjacent to the cancer. Further, the concentration of the related protein marker is subjected to natural logarithm conversion, and a regression equation is obtained through logistic regression analysis: logit (p) ═ C + α 1 × Ln (COL10a1) + α 2 × Ln (GKN1) + α 3 × Ln (GKN2) + α 4 × Ln (lipf) + α 5 × Ln (PGI/II) + α 6 × Ln (G17). Substituting the detected protein marker concentration of each sample into a regression equation, calculating the gastric cancer suffering probability of each sample, determining a probability cutoff value (cut-off) through a point with the maximum York index, and finally determining whether each sample suffers from gastric cancer and the gastric cancer risk (see Table 1).
TABLE 1 interpretation of results of logistic regression analysis on partial samples
Remarking: when the logit (p) value is greater than the cutoff value, there is a risk of gastric cancer; samples 1 to 5 were 5 samples randomly extracted from 80 gastric cancer tissues and 80 normal tissues.
The cutoff value was determined by substituting the concentration of the protein marker detected in each sample into the logistic regression equation to calculate the probability of gastric cancer in each sample, and determining the point with the highest john's index.
Taking the data in Table 1 as an example, the concentrations of the protein markers COL10A1, GKN1, GKN2, LIPF, PGI, PGII, G-17 in 80 gastric cancer and 80 normal tissue samples were substituted into the regression equation: logit (p) ═ C + α 1 × Ln (COL10a1) + α 2 × Ln (GKN1) + α 3 × Ln (GKN2) + α 4 × Ln (lipf) + α 5 × Ln (PGI/II) + α 6 × Ln (G17), logit (p), i.e., the probability, of each sample can be calculated, and then the probability of the point at which the jotan index is the maximum, i.e., the cutoff value (cut-off), is 0.537 by software.
In the table of table 1:
samples 1, 3, 5 are 3 samples randomly drawn from 80 gastric cancer tissues;
samples 2 and 4 are 2 samples taken randomly from 80 normal tissues.
1.2 diagnostic Performance of protein markers in gastric cancer tissues
Subject operating curves (ROCs) were plotted using the R package "pROC" (version 1.15.0), and AUC values, sensitivity and specificity were analyzed to judge marker diagnostic performance.
Remarking: AUC is the area under the operating curve of the recipient and is an index for characterizing diagnostic performance or accuracy, and the closer the AUC is to 1, the better the diagnostic performance is.
The diagnostic performance of protein markers in gastric cancer tissues is shown in table 2. The single markers of COL10A1, GKN1, GKN2, LIPF, PGI/II and G-17 independently show higher diagnostic performance in gastric cancer tissues, and the AUC is more than 0.8. As shown in fig. 1: the AUC of the joint diagnosis performance of COL10A1+ GKN1+ GKN2+ LIPF + PGI/II + G-17 can reach 0.99, and the sensitivity and the specificity are both more than 90%.
TABLE 2 diagnostic Performance of protein markers in gastric cancer tissue
| Gene | AUC | Sensitivity of the probe | Specificity of |
| COL10A1 | 0.983 | 91.1％ | 99.4％ |
| GKN1 | 0.842 | 84.3％ | 69.0％ |
| GKN2 | 0.801 | 81.4％ | 68.4％ |
| LIPF | 0.884 | 97.6％ | 70.7％ |
| PGI/II | 0.817 | 96.9％ | 71.3％ |
| G17 | 0.713 | 73.2％ | 89.1％ |
| COL10A1+PGI/II | 0.989 | 89.5％ | 89.2％ |
| COL10A1+GKN2+PGI/II | 0.990 | 94.1％ | 99.6％ |
| COL10A1+GKN1+G-17 | 0.983 | 89.5％ | 99.2％ |
| GKN1+GKN2+LIPF+PGI/II | 0.937 | 95.9％ | 85.3％ |
| COL10A1+GKN1+GKN2+LIPF+PGI/II+G-17 | 0.991 | 95.1％ | 100％ |
Example 2
2. Gastric cancer blood sample based diagnostic performance analysis
2.1 the concentrations of protein markers (COL10A1, GKN1, GKN2, LIPF, PGI, PGII, G-17) in clinical serum samples (40 controls, 60 gastric cancers, 60 chronic gastritis) were tested separately with a purchased enzyme-linked immunoassay kit, and clinical pathology information of the cases was also registered.
Further, the concentrations of the protein markers of the 3 groups of cases were subjected to natural logarithm transformation, and after removing non-contributing markers by logistic regression analysis, a regression equation was obtained: logit (p) ═ C + α 1 × Ln (COL10a1) + α 2 × Ln (GKN1) + α 3 × Ln (GKN2) + α 4 × Ln (lipf) + α 5 × Ln (PGI/II) + α 6 × Ln (G17). Substituting the detected protein marker concentration of each sample into a regression equation to calculate the gastric cancer probability of each sample, determining a probability cutoff value (cut-off) through a point with the maximum johnson index, and finally determining whether each sample has gastric cancer and the gastric cancer risk, wherein the results are shown in tables 3 and 4.
TABLE 3 interpretation of logistic regression analysis results (gastric cancer vs. health)
Remarking: when the logit (p) value is greater than the cutoff value, there is a risk of gastric cancer; samples 1-5 were 5 samples randomly drawn from 40 control, 60 gastric cancer serum samples.
Samples 1, 3, 5 in table 3 are 3 samples randomly drawn from 60 gastric cancer serum samples;
samples 2 and 4 in Table 3 are 2 samples randomly drawn from 40 control serum samples.
TABLE 4 interpretation of logistic regression analysis results (gastric cancer vs. chronic gastritis)
Remarking: when the logit (p) value is greater than the cutoff value, there is a risk of gastric cancer; samples 1-5 were 5 samples randomly drawn from 60 gastric cancer, 60 chronic gastritis serum samples.
Samples 2 and 3 in table 4 are 2 samples randomly drawn from 60 serum samples of gastric cancer;
samples 1, 4, and 5 in Table 4 are 3 samples randomly drawn from 60 serum samples of chronic gastritis.
2.2 Using the R package "pROC" (version 1.15.0), Receiver Operating Curves (ROC) for gastric cancer to differentiate healthy from chronic gastritis are drawn, AUC values, sensitivity and specificity are analyzed, and marker diagnostic performance is judged.
Remarking: AUC is the area under the operating curve of the recipient and is an index for characterizing diagnostic performance or accuracy, and the closer the AUC is to 1, the better the diagnostic performance is.
The diagnostic performance of the protein markers for gastric cancer and healthy controls is shown in table 5, and when the single marker is used independently, the diagnostic performance of COL10a1 is the best, and AUC is 0.86. The performance of protein marker combination diagnosis is better than the performance of single marker used independently, as shown in fig. 2: the AUC of the joint diagnosis performance of COL10A1+ GKN1+ GKN2+ LIPF + PGI/II + G-17 can reach 0.92, and the sensitivity and the specificity are both more than 80%.
TABLE 5 diagnostic Properties of protein markers differentiating gastric cancer from health
| Gene | AUC | Sensitivity of the probe | Specificity of |
| COL10A1 | 0.857 | 89.3％ | 68.7％ |
| GKN1 | 0.772 | 96.4％ | 56.2％ |
| GKN2 | 0.850 | 89.3％ | 75.0％ |
| LIPF | 0.819 | 89.3％ | 66.7％ |
| PGI/II | 0.565 | 42.4％ | 68.5％ |
| G17 | 0.532 | 43.2％ | 59.1％ |
| COL10A1+PGI/II | 0.866 | 81.3％ | 79.2％ |
| COL10A1+GKN2+PGI/II | 0.886 | 82.1％ | 87.5％ |
| COL10A1+GKN1+G-17 | 0.883 | 89.5％ | 75.2％ |
| GKN1+GKN2+LIPF+PGI/II | 0.857 | 89.5％ | 85.3％ |
| COL10A1+GKN1+GKN2+LIPF+PGI/II+G-17 | 0.917 | 82.1％ | 91.7％ |
Further, the protein markers of the present invention also have certain diagnostic properties against gastric cancer and chronic gastritis (see table 6).
When the single marker is used independently, the diagnostic performance of GKN2 is best, and AUC is 0.80. The performance of the protein marker combination diagnosis is better than the performance of the individual markers used independently, as shown in fig. 3: the AUC of the joint diagnosis performance of COL10A1+ GKN1+ GKN2+ LIPF + PGI/II + G-17 can reach 0.84, and the sensitivity and the specificity are both more than 75%.
TABLE 6 diagnostic Properties of protein markers differentiating gastric cancer and chronic gastritis
| Gene | AUC | Sensitivity of the probe | Specificity of |
| COL10A1 | 0.731 | 81.7％ | 60.0％ |
| GKN1 | 0.741 | 66.7％ | 76.7％ |
| GKN2 | 0.795 | 68.3％ | 76.7％ |
| LIPF | 0.779 | 63.3％ | 83.3％ |
| PGI/II | 0.685 | 61.7％ | 73.3％ |
| G17 | 0.507 | 62.2％ | 60.1％ |
| COL10A1+PGI/II | 0.731 | 81.3％ | 73.2％ |
| COL10A1+GKN2+PGI/II | 0.829 | 76.7％ | 83.3％ |
| COL10A1+GKN1+G-17 | 0.785 | 79.4％ | 75.2％ |
| GKN1+GKN2+LIPF+PGI/II | 0.797 | 74.5％ | 68.3％ |
| COL10A1+GKN1+GKN2+LIPF+PGI/II+G-17 | 0.844 | 86.7％ | 76.0％ |
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and additions can be made without departing from the method of the present invention, and these modifications and additions should also be regarded as the protection scope of the present invention.
Claims (7)
1. A protein marker combination for detecting gastric cancer, wherein the protein marker combination is at least one selected from the group consisting of COL10A1, GKN1, GKN2 and LIPF, and at least one selected from the group consisting of PGI/II and G-17.
2. The protein marker combination for detecting gastric cancer according to claim 1, wherein the detection method is a radiological method, an immunological method, a fluorescent method, a flow-type fluorescence method, a latex turbidimetric method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.
3. The protein marker combination for detecting gastric cancer according to claim 2, wherein the test sample is selected from the group consisting of tissue, blood, urine, saliva and stool of a human or animal body.
4. A kit for detecting gastric cancer, comprising a reagent that specifically binds to the combination of protein markers of claim 1, a reagent for preparing a biopsy, a reagent for homogenizing tissue, a reagent for preparing a peptide sample, or a reagent for desalting a peptide.
5. The kit for detecting gastric cancer according to claim 4, wherein the reagent that specifically binds to the combination of protein markers is a peptide, a peptide mimetic, a nucleic acid aptamer, an antibody, or an antigen-binding fragment thereof.
6. A method for constructing a gastric cancer in-vitro diagnosis calculation model based on protein marker combined detection, which is characterized by comprising the steps of detecting the concentrations of at least two protein markers in claim 1 from a sample, performing logistic regression on the determined concentrations of the protein markers to obtain a regression equation, and establishing the calculation model;
the logistic regression equation is:
wherein Logit (P) is the result of a logistic regression model of the gastric cancer protein markers, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis, the concentration i of the marker is the concentration of each protein marker, and n is an integer greater than or equal to 2.
7. The method of claim 6, wherein the sample is selected from the group consisting of tissue, blood, urine, saliva, or stool of a human or animal body;
the detection method comprises the following steps: a radiation method, an immunological method, a fluorescence method, a flow fluorescence method, a latex turbidimetry, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric method, or a photoelectric conversion method.
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