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CN111540469A - Method for constructing mathematical model for in-vitro detection of gastric cancer and application thereof - Google Patents

Method for constructing mathematical model for in-vitro detection of gastric cancer and application thereof Download PDF

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CN111540469A
CN111540469A CN202010482537.1A CN202010482537A CN111540469A CN 111540469 A CN111540469 A CN 111540469A CN 202010482537 A CN202010482537 A CN 202010482537A CN 111540469 A CN111540469 A CN 111540469A
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高金波
高俊顺
高俊莉
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Hangzhou Guangke Ander Biotechnology Co ltd
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Abstract

The application provides a method for constructing a mathematical model for detecting gastric cancer in vitro, which comprises the steps of obtaining the concentrations of at least two gastric cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration obtained by measurement into a logistic regression model to obtain an analysis result, and carrying out comprehensive gastric cancer analysis by using the concentration of each marker and the logistic regression analysis result. The application also provides an application of the method.

Description

Method for constructing mathematical model for in-vitro detection of gastric cancer and application thereof
Technical Field
The application relates to the technical field of medical diagnosis, in particular to a method for constructing a mathematical model for in-vitro detection of gastric cancer.
Background
Gastric cancer (gastric cancer) is a malignant tumor originated from gastric mucosal epithelium, the incidence rate of the gastric cancer is the first in various malignant tumors in China, the incidence rate of the gastric cancer is obviously different regionally, and the incidence rate of the gastric cancer is obviously higher in northwest and east coastal areas of China than in south areas. The good hair age is more than 50 years old, and the ratio of the incidence rates of men and women is 2: 1. gastric cancer tends to be younger due to changes in dietary structure, increased working pressure, infection with helicobacter pylori, and the like. Gastric cancer can occur in any part of the stomach, more than half of which occur in antrum, and the greater curvature, lesser curvature, anterior and posterior walls of the stomach can be affected. Most of gastric cancers belong to adenocarcinoma, have no obvious symptoms in the early stage, or have nonspecific symptoms such as epigastric discomfort, eructation and the like, are often similar to the symptoms of chronic stomach diseases such as gastritis, gastric ulcer and the like, and are easy to ignore, so the early diagnosis rate of the gastric cancers in China is still low at present. The prognosis of gastric cancer is related to the pathological stage, location, tissue type, biological behavior, and therapeutic measures of gastric cancer.
Like other cancers, gastric cancer can be found early and the cure rate is quite high. The data show that the 5-year survival rate of early gastric cancer is as high as more than 90%, and the number is about 20% when the later gastric cancer is reached.
However, the discovery rate of the early gastric cancer in China is less than 10%.
Early gastric cancer is mostly free of obvious symptoms, and even with symptoms, the symptoms are similar to the symptoms of common gastric diseases such as common gastritis and gastric ulcer, such as nausea, vomiting, acid regurgitation and the like.
At the advanced stage, symptoms of upper gastrointestinal discomfort are often manifested, such as general discomfort of the stomach, fullness after eating, decreased appetite and the like, and in this stage, stomachache and weight loss are the main clinical symptoms.
As tumors grow larger, tumors growing in different locations also show several different manifestations: if the tumor grows on the cardia and the stomach fundus, symptoms such as poststernal pain, progressive dysphagia and the like can also appear; if the tumor grows in the pylorus, pyloric obstruction and other manifestations may appear.
When the stomach cancer reaches the middle and late stages, the patient can have symptoms of bleeding, black stool, anemia, emaciation and the like.
In the ideal situation, when one needs to screen for gastric cancer, the screening can be done immediately by an effective means. However, to date, none of the screening approaches is perfect, 100% accurate. The invention provides a multidimensional combined method for diagnosing gastric cancer in vitro, which jointly detects protein markers, metabolites, cell-free DNA, cell-free non-coding RNA, autoantibodies, inflammatory factors, growth factors, circulating gastric cancer cells, DNA methylation, exosomes and the like related to gastric cancer, and improves the sensitivity and specificity of gastric cancer detection.
Disclosure of Invention
The main objective of the present application is to provide a method for constructing a mathematical model for in vitro detection of gastric cancer and its application, so as to improve the sensitivity and specificity of clinical detection of gastric cancer, no marker in the present detection of gastric cancer can diagnose gastric cancer with very high sensitivity and specificity results, most of gastric cancers adopt a joint inspection form, but all adopt molecular diagnosis or immunodiagnosis to detect several markers of one type, and do not combine the detections of various dimensions, in order to enhance the accuracy of prediction, it is better to combine the detection of both internal and external aspects: it is an object of the present invention to combine metabolites, exosomes, molecular diagnostics, immunodiagnosis.
The application provides a method for constructing a mathematical model for detecting gastric cancer in vitro, which comprises the steps of obtaining the concentrations of at least two gastric cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration obtained by measurement into a logistic regression model to obtain an analysis result, and carrying out comprehensive gastric cancer analysis by using the concentration of each marker and the logistic regression analysis result.
Preferably, the gastric cancer markers include at least one of the following categories:
gastric cancer protein markers, gastric cancer metabolite markers, gastric cancer-associated cell-free DNA, gastric cancer-associated DNA methylation markers, gastric cancer cell-free non-coding RNA, gastric cancer autoantibodies, gastric cancer inflammatory factors and growth factors, circulating gastric cancer cells, and gastric cancer exosomes.
Preferably, the gastric cancer protein marker is selected from any one or more of PG I/II, CA724, CA242, CA199, CA50, G-17, HP, CEA, CCDC49, RNF19, BFAR, COPS2, CTSF, NT5E, TERF1, CD44v9, PARP1, IPO-38, CYP1A1, GSTM1, S100A9, GIF, AAT, ANGPTL 2;
the gastric cancer metabolite markers are selected from any one or more of acetyl spermine, diacetyl spermine, lactic acid, succinic acid, malic acid, citric acid, pyruvic acid, 3-hydroxypropionic acid, serine, proline, valine, isoleucine, serine, 3-indole sulfate, hippurate, citrate, sarcosine, alanine, proline, serine, inositol and glycerol;
the gastric cancer molecular diagnostic marker is selected from any one or more of p53, C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133, cyclinB1, EGFR, Id2, LRP16, NF-kappa B, VEGFR 2, Syn, CgA, CD56, TMEFF2, SHP-1, miR-29C, miR-30a-5p, miR-148a, miR-375, miR-638, miR-106b, miR-20a, miR-221, miR-421, Let-7g, miR-433, miR-214, miR-21, miR-148a, miR-152, miR-451, miR-199a-3p, miR-195, miR-106b, miR-129, miR125b, miR-199a, miR-433, miR-223 and miR-218;
the gastric cancer autoantibody is selected from any one or more of NY-ESO-1, CTAG2, DDX53, MAGEC1, MAGEA3, AEG-1 and GRP 78;
the gastric cancer related inflammatory factors and growth factors are selected from any one or more of ERBB, HER2, EGFR, HER-2, VEGF, TGF, c-MET, IL-6, IL-11, Bcl-2, Fas, survivin, IL-1, IL-10, IL1B, TNFA, LTA, IL6, IL12p40, IL4, IL1RN, IL10 and TGFB 1;
the gastric cancer related exosomes are selected from any one or more of miR-27a, miR-451, miR-21-5p, miR-21, miR-221, TGF-beta 1, HMGB1, CagA, GKN1, UBR2, TRIM3, miR-130a, miR-27a, miR-21-5p, ZFAS1 and ciRS-133;
the gastric cancer related DNA methylation marker is selected from any one or more of Sox17, WNT5A, MLH1, p16, CDH1, RUNX3, MINT25, RORA, GDNF, ADAM23, PRDM5, MLF1, p53, KRAS, PIK3CA, ARID1A, MLL3, MLL, C-MET, ERBB4, CD44, hMLH1, CDKN1C, IGFBP3, PRDM5, MINT25, DAPK and GSTP 1.
Preferably, the formula of the logistic regression is:
Figure BDA0002516726230000041
wherein Logit (P) is the logistic regression model result of the same or different gastric cancer markers, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis and is a natural number, the concentration i of the marker is the concentration of the marker in the same or different categories, and n is an integer greater than or equal to 2.
Preferably, the sample tested comprises: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
Preferably, the detection technique comprises one or more of a radiological method, an immunological method, a fluorescence method, a flow fluorescence, a latex turbidimetry, a biochemical method, an enzymatic method, a PCR method, a sequencing method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric conversion method, and a photoelectric conversion method.
Preferably, the gastric cancer markers are gastric cancer protein markers, gastric cancer molecular diagnosis markers and gastric cancer related DNA methylation markers, the gastric cancer protein markers are PG I/II, CA724, CA242, CA50, G-17, CCDC49, RNF19, BFAR, COPS2 and CTSF, the gastric cancer molecular diagnosis markers are p53, C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133 and EGFR, the gastric cancer related DNA methylation markers are Sox17, WNT5A, MLH1, p16, CDH1, RUNX3 and MINT25, concentration values of the markers in samples are obtained, natural logarithm conversion is carried out, and a logistic regression analysis is carried out to obtain a regression model after non-contributing markers are removed: logit (p) ═ 3.736+1.814 × Ln (PG I/II) +0.854 × Ln (CA724) +0.754 × Ln (CA242) +0.321 × Ln (G-17) +0.784 × Ln (bfar) +1.014 × Ln (COPS2) +0.741 × Ln (p53) +0.654 × Ln (nm23) +0.789 × Ln (HER-2) +0.654 × Ln (Ki-67) +0.714 × Ln (Sox17) +0.324 × Ln (MLH1) +0.874 × Ln (RUNX3), in which the log is natural log.
When the value of the result of the computational analysis obtained from the mathematical model is not less than 3.521, the subject of the sample is considered to be at risk for cancer.
The application has the following advantages: the detection of the gastric cancer with different dimensionalities and different types of combination in combination with transverse and longitudinal combination and internal and external consideration overcomes the defects of low detection sensitivity and specificity and the like of one marker or one dimensionality in the market, greatly improves the accuracy and the precision of the diagnosis of the gastric cancer, can replace the traditional invasive diagnosis such as CT or biopsy puncture and the like, can judge the subtype of the gastric cancer, can also provide early diagnosis, early screening, auxiliary diagnosis or prognosis observation at the same time, and brings good news to patients.
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 are clearly and completely described, and it is obvious that the described embodiments are only some embodiments of the present application, 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.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The detection methodology used in the examples may be a commercially available reagent test kit or a self-made kit.
Example 1
13 gastric cancer protein marker concentrations (PG I/II, CA724, CA199, G-17, CEA, CCDC49, RNF19, BFAR, COPS2, CTSF, NT5E, TERF1) in blood samples were tested using a commercially available chemiluminescence assay kit, 14 gastric cancer molecular marker concentrations (C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133, cyclinB1, LRP16, NF- κ B, CgGA, CD56, TMEFF2) in blood samples were tested using fluorescence in situ hybridization or sequencing, and 14 gastric cancer-associated DNA methylation marker concentrations (Sox17, Runx3, WNT5A, MLH1, CDH1, RUNX3, CD44, hMLH1, 686 1, PRKN 849, PRIGP 8653, MIDAPK).
Performing logistic regression analysis on the tested concentration of the related marker to obtain Logit (P) ═ constant + lambda 1. multidot. P1+ lambda 2. multidot. P2+ eta 3. multidot. P3+ eta 4. multidot. P4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into a regression model, and comprehensively diagnosing whether the patient suffers from the gastric cancer and the risk of the gastric cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Figure BDA0002516726230000061
Example 2
Testing 10 gastric cancer protein marker concentrations (PG I/II, CA724, G-17, CCDC49, RNF19, BFAR, COPS2, CTSF, NT5E, TERF1) in blood samples by purchased or homemade chemiluminescence method kit, testing 9 gastric cancer molecular markers (miR-199a-3p, miR-195, miR-106b, miR-129, miR125b, miR199a, miR433, miR-223, miR-218) in blood samples by fluorescence in situ hybridization method, testing 7 gastric cancer autoantibodies concentrations (NY-ESO-1, CTAG2, DDX53, MAGEC1, MAGEA3, AEG-1, GRP78) in blood samples by purchased immunofluorescence method, testing 8 gastric cancer phase exosomes (miR-1, HMTGF-beta 1, MAGGB 1, CagA, KN-37, TRIG-1, TRIP 78) in urine or blood by flow-type fluorescence method, testing 8 gastric cancer phase exosomes (miR-11, miR-7 mRNA-7, miR-7-A-7-, RUNX3, MINT25, RORA, GDNF, ADAM23, PRDM5, hMLH1, IGFBP3, PRDM5, DAPK, GSTP1)
Performing logistic regression analysis on the tested concentration of the related marker to obtain Logit (P) ═ constant + lambda 1. multidot. P1+ lambda 2. multidot. P2+ eta 3. multidot. P3+ eta 4. multidot. P4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into a regression model, and comprehensively diagnosing whether the patient suffers from the gastric cancer and the risk of the gastric cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Figure BDA0002516726230000071
Example 3
Testing gastric cancer protein markers to be PG I/II, CA724, CA242, CA50, G-17, CCDC49, RNF19, BFAR, COPS2 and CTSF by using a purchased or self-made chemiluminescence method kit, wherein the gastric cancer molecular diagnosis markers are p53, C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133 and EGFR, the gastric cancer related DNA methylation markers are Sox17, WNT5A, MLH1, p16, CDH1, RUNX3 and MINT25, obtaining concentration values of the markers in a sample, carrying out natural logarithm conversion, carrying out logistic regression analysis, and removing the non-contributing markers to obtain a regression model: logit (p) ═ 3.736+1.814 × Ln (PG I/II) +0.854 × Ln (CA724) +0.754 × Ln (CA242) +0.321 × Ln (G-17) +0.784 × Ln (bfar) +1.014 × Ln (COPS2) +0.741 × Ln (p53) +0.654 × Ln (nm23) +0.789 × Ln (HER-2) +0.654 × Ln (Ki-67) +0.714 × Ln (Sox17) +0.324 × Ln (MLH1) +0.874 × Ln (RUNX3), in which the log is natural log.
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the stomach cancer is suffered or not and the risk of the stomach cancer according to the judgment standard of the calculated logit (P) and the value of the regression model logit (P).
Figure BDA0002516726230000072
Figure BDA0002516726230000081
Through experimental research, the combination of multiple dimensions and the combined mode of gastric cancer detection have higher sensitivity and specificity compared with one or more types of single detection, the sensitivity can reach 99 percent, and the specificity is 100 percent and is far better than gastric cancer diagnosis markers on the market.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. A method for constructing a mathematical model for in vitro detection of gastric cancer, the method comprising obtaining concentrations of at least two gastric cancer markers from a sample, performing logistic regression on the concentration values of each marker measured, substituting the concentrations measured into the logistic regression model to obtain analysis results, and performing comprehensive gastric cancer analysis using the concentration of each marker and the logistic regression analysis results.
2. The method of constructing a mathematical model for the in vitro detection of gastric cancer according to claim 1, wherein the gastric cancer markers comprise at least one of the following categories:
gastric cancer protein markers, gastric cancer metabolites, gastric cancer-associated cell-free DNA, gastric cancer DNA methylation markers, gastric cancer-associated cell-free non-coding RNA, gastric cancer autoantibodies, gastric cancer inflammatory factors and growth factors, circulating gastric cancer cells, and gastric cancer exosomes.
3. The method for constructing a mathematical model for detecting gastric cancer in vitro according to claim 1, wherein the gastric cancer protein markers are selected from any one or more of PG I/II, CA724, CA242, CA199, CA50, G-17, HP, CEA, CCDC49, RNF19, BFAR, COPS2, CTSF, NT5E, TERF1, CD44v9, PARP1, IPO-38, CYP1a1, GSTM1, S100a9, GIF, AAT, ANGPTL 2;
the gastric cancer metabolite markers are selected from any one or more of acetyl spermine, diacetyl spermine, lactic acid, succinic acid, malic acid, citric acid, pyruvic acid, 3-hydroxypropionic acid, serine, proline, valine, isoleucine, serine, 3-indole sulfate, hippurate, citrate, sarcosine, alanine, proline, serine, inositol and glycerol;
the gastric cancer molecular diagnostic marker is selected from any one or more of p53, C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133, cyclinB1, EGFR, Id2, LRP16, NF-kappa B, VEGFR 2, Syn, CgA, CD56, TMEFF2, SHP-1, miR-29C, miR-30a-5p, miR-148a, miR-375, miR-638, miR-106b, miR-20a, miR-221, miR-421, Let-7g, miR-433, miR-214, miR-21, miR-148a, miR-152, miR-451, miR-199a-3p, miR-195, miR-106b, miR-129, miR125b, miR-199a, miR-433, miR-223 and miR-218;
the gastric cancer autoantibody is selected from any one or more of NY-ESO-1, CTAG2, DDX53, MAGEC1, MAGEA3, AEG-1 and GRP 78;
the gastric cancer related inflammatory factors and growth factors are selected from any one or more of ERBB, HER2, EGFR, HER-2, VEGF, TGF, c-MET, IL-6, IL-11, Bcl-2, Fas, survivin, IL-1, IL-10, IL1B, TNFA, LTA, IL6, IL12p40, IL4, IL1RN, IL10 and TGFB 1;
the gastric cancer related exosomes are selected from any one or more of miR-27a, miR-451, miR-21-5p, miR-21, miR-221, TGF-beta 1, HMGB1, CagA, GKN1, UBR2, TRIM3, miR-130a, miR-27a, miR-21-5p, ZFAS1 and ciRS-133;
the gastric cancer related DNA methylation marker is selected from any one or more of Sox17, WNT5A, MLH1, p16, CDH1, RUNX3, MINT25, RORA, GDNF, ADAM23, PRDM5, MLF1, p53, KRAS, PIK3CA, ARID1A, MLL3, MLL, C-MET, ERBB4, CD44, hMLH1, CDKN1C, IGFBP3, PRDM5, MINT25, DAPK and GSTP 1.
4. The method of claim 3, wherein the logistic regression is formulated as:
Figure FDA0002516726220000021
wherein Logit (P) is the logistic regression model result of the same or different gastric cancer markers, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis and is a natural number, the concentration i of the marker is the concentration of the marker in the same or different categories, and n is an integer greater than or equal to 2.
5. The method for constructing a mathematical model for in vitro detection of gastric cancer according to claim 1, wherein the samples to be tested comprise: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
6. The method for constructing mathematical model for in vitro gastric cancer detection according to claim 1, wherein the detection technique comprises one or more of radiation method, immunological method, fluorescence method, flow fluorescence, latex turbidimetry, biochemical method, enzymatic method, PCR method, sequencing method, hybridization method, gas chromatography, liquid chromatography, chemiluminescence method, magnetoelectric and photoelectric conversion method.
7. The method for constructing a mathematical model for in vitro detection of gastric cancer according to claim 1, wherein the gastric cancer markers are gastric cancer protein markers, gastric cancer molecular diagnostic markers and gastric cancer-associated DNA methylation marker combinations, wherein the gastric cancer protein markers are PG I/II, CA724, CA242, CA50, G-17, CCDC49, RNF19, BFAR, COPS2, CTSF, the gastric cancer molecular diagnostic markers are p53, C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133, EGFR, the gastric cancer-associated DNA methylation markers are Sox17, WNT5A, MLH1, p16, h1, rucdnx 3, MINT25, the concentration values of these markers in the sample are obtained, natural logarithm conversion is performed, logistic regression analysis is performed, and after non-contribution markers are removed, the obtained model is: logit (p) ═ 3.736+1.814 × Ln (PG I/II) +0.854 × Ln (CA724) +0.754 × Ln (CA242) +0.321 × Ln (G-17) +0.784 × Ln (bfar) +1.014 × Ln (COPS2) +0.741 × Ln (p53) +0.654 × Ln (nm23) +0.789 × Ln (HER-2) +0.654 × Ln (Ki-67) +0.714 × Ln (Sox17) +0.324 × Ln (MLH1) +0.874 × Ln (RUNX3), in which the log is natural log.
8. Use of the method of constructing a mathematical model for in vitro detection of gastric cancer according to any one of claims 1 to 7 to obtain a mathematical model for predicting the risk of cancer in a subject sample, wherein the subject sample is considered to have a risk of gastric cancer when the value of the computational analysis result obtained from the mathematical model is equal to or greater than 3.521.
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CN113721020A (en) * 2021-09-14 2021-11-30 大连医科大学附属第二医院 Application of CTSF (cytokine induced plasma) in non-small cell lung cancer diagnosis
WO2021238086A1 (en) * 2020-05-29 2021-12-02 杭州广科安德生物科技有限公司 Method for constructing mathematical model for detecting lung cancer in vitro and application
CN113917148A (en) * 2021-09-27 2022-01-11 杭州广科安德生物科技有限公司 Protein marker combination for gastric cancer diagnosis and application thereof
CN116386716A (en) * 2023-06-06 2023-07-04 浙江省肿瘤医院 Metabolomics and methods for gastric cancer diagnosis
CN118191322A (en) * 2024-05-14 2024-06-14 哈尔滨脉图精准技术有限公司 Urine metabolic markers for gastric cancer detection and their applications

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Application publication date: 20200814