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Method for constructing mathematical model for detecting gastric cancer in vitro and application thereof

A mathematical model and external detection technology, applied in the field of medical diagnosis, to achieve the effect of improving precision and accuracy

Pending Publication Date: 2020-08-14
HANGZHOU GUANGKEANDE BIOTECHNOLOGY CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, for now, no screening method is perfect and 100% accurate

Method used

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  • Method for constructing mathematical model for detecting gastric cancer in vitro and application thereof
  • Method for constructing mathematical model for detecting gastric cancer in vitro and application thereof
  • Method for constructing mathematical model for detecting gastric cancer in vitro and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] Use the purchased chemiluminescence detection kit to test the concentration of 13 gastric cancer protein markers (PG I / II, CA724, CA199, G-17, CEA, CCDC49, RNF19, BFAR, COPS2, CTSF, NT5E, TERF1) in blood samples , using fluorescent in situ hybridization or sequencing to test the concentrations of 14 gastric cancer molecular markers (C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133, cyclinB1, LRP16 , NF-κB, CgA, CD56, TMEFF2), the concentrations of 14 gastric cancer-related DNA methylation markers (Sox17, Runx3, WNT5A, MLH1, CDH1, RUNX3, CD44, hMLH1, CDKN1C , IGFBP3, PRDM5, MINT25, DAPK, GSTP1).

[0034] Perform logistic regression analysis on the test concentrations of the above-mentioned relevant markers to obtain Logit(P)=constant+λ1*P1+λ2*P2+η3*P3+η4*P4...

[0035] Then test the concentration of each marker in the unknown blood sample and substitute it into the regression model. According to the calculated Logit (P) and the judgment standard of the regression...

Embodiment 2

[0038] The concentration of 10 gastric cancer protein markers (PG I / II, CA724, G-17, CCDC49, RNF19, BFAR, COPS2, CTSF, NT5E, TERF1) in blood samples was tested with a purchased or self-made chemiluminescent method kit. Nine gastric cancer molecular markers (miR-199a-3p, miR-195, miR-106b, miR-129, miR125b, miR199a, miR433, miR-223, miR-218) in blood samples were tested by fluorescence in situ hybridization. The concentration of 7 kinds of gastric cancer autoantibodies (NY-ESO-1, CTAG2, DDX53, MAGEC1, MAGEA3, AEG-1, GRP78) in blood samples was detected by immunofluorescence method, and 8 kinds of gastric cancer were detected in urine or blood by flow cytometry fluorescence method Related exosomes (miR-221, TGF-β1, HMGB1, CagA, GKN1, UBR2, TRIM3, miR-130a), 12 gastric cancer-related DNA methylation markers in urine or blood were detected by flow cytometry ( WNT5A, RUNX3, MINT25, RORA, GDNF, ADAM23, PRDM5, hMLH1, IGFBP3, PRDM5, DAPK, GSTP1)

[0039] Perform logistic regression a...

Embodiment 3

[0043] Using purchased or self-made chemiluminescence kits, test gastric cancer protein markers as PG I / II, CA724, CA242, CA50, G-17, CCDC49, RNF19, BFAR, COPS2, CTSF, gastric cancer molecular diagnostic markers as p53 , C-erbB-2, ETFR, nm23, E-Cad, BCL6B, HER-2, Ki-67, CD133, EGFR, gastric cancer-related DNA methylation markers are Sox17, WNT5A, MLH1, p16, CDH1, RUNX3, MINT25, obtain the concentration values ​​of these markers in the sample, carry out natural logarithmic transformation, after logistic regression analysis, after removing the markers without contribution, the regression model obtained 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), where Ln is natural logarithm .

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Abstract

The invention provides a method for constructing a mathematical model for detecting gastric cancer in vitro. The method comprises the following steps: obtaining concentrations of at least two gastriccancer markers from a sample; carrying out logistic regression on the measured concentration value of each marker; substituting the measured concentration into a logistic regression model to obtain ananalysis result; and performing comprehensive gastric cancer analysis by using the concentration of each marker and a logistic regression analysis result. The invention further provides an application of the method.

Description

technical field [0001] The present application relates to the technical field of medical diagnosis, in particular to a method for constructing a mathematical model for detecting gastric cancer in vitro. Background technique [0002] Gastric cancer (gastric carcinoma) is a malignant tumor originating from the gastric mucosal epithelium. It ranks first in the incidence of various malignant tumors in my country. The incidence of gastric cancer has obvious regional differences. The incidence of gastric cancer in the northwest and eastern coastal areas of my country is higher than that in the south. area is significantly higher. The age of onset is more than 50 years old, and the ratio of male to female incidence is 2:1. Due to changes in diet structure, increased work pressure, and Helicobacter pylori infection, gastric cancer tends to be younger. Gastric cancer can occur in any part of the stomach, and more than half of them occur in the gastric antrum. The greater curvature, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H50/70G16B20/00
CPCG16H50/20G16H50/70G16B20/00
Inventor 高金波高俊顺高俊莉
Owner HANGZHOU GUANGKEANDE BIOTECHNOLOGY CO LTD
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