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Gastric mucosa lesion protein molecular typing, lesion progression and gastric cancer related protein marker and method for predicting lesion progression risk

A technology of molecular typing and gastric mucosa, applied in the field of clinical tumor medicine, can solve the problems of lack of multiple comparison correction verification of differential proteins, proteomic changes, etc.

Pending Publication Date: 2020-12-11
北京谷海天目生物医学科技有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

Literature search only found a few small-sample proteomics studies involving gastric mucosal lesions, the sample size ranged from 12 to 229, and most studies only had dozens of cases, and only mild gastric mucosal lesions were used as the control group to explore the proteomics of gastric cancer. However, there is no in-depth discussion on the proteomic changes in mild gastric mucosal lesions and the evolution of gastric mucosal lesions
In addition, the screening of differential proteins generally lacks correction for multiple comparisons and validation based on large independent samples
At the same time, some studies choose proteome detection based on specific chips. Compared with modern mass spectrometry technology, there are certain limitations in the depth of protein detection.

Method used

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  • Gastric mucosa lesion protein molecular typing, lesion progression and gastric cancer related protein marker and method for predicting lesion progression risk
  • Gastric mucosa lesion protein molecular typing, lesion progression and gastric cancer related protein marker and method for predicting lesion progression risk
  • Gastric mucosa lesion protein molecular typing, lesion progression and gastric cancer related protein marker and method for predicting lesion progression risk

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0106] Example 1. Obtaining protein expression profile data of gastric mucosal tissue samples obtained from clinical gastroscopy biopsy

[0107] The experimental samples were 169 gastroscopic biopsies of gastric mucosa tissue samples from the high-incidence site of gastric cancer in Linqu, Shandong Province and the Fifth Medical Center of the PLA General Hospital.

[0108] Protein extraction and analysis were performed on 169 gastric mucosal tissue samples from clinical gastroscopy biopsy. Through this step, the proteome data set corresponding to each sample was obtained, including the type, quantity and quantitative value of each protein.

[0109] 1. Lysis solution formula:

[0110] 1% (w / v) DOC (Deoxycholic acid), 10mM TCEP,

[0111] 40mM 2-chloroacetamide (CAA), 100mM Tris, pH 8.5.

[0112] 2. Operation steps

[0113] 1. Material collection: gastroscope samples, which are stored in clean EP tubes after collection;

[0114] 2. Cleavage sample: add 500uL lysate and homoge...

Embodiment 2

[0135] Part I: Proteomic Molecular Typing

[0136] Proteomic molecular typing of gastric mucosal lesions based on the data in Example 1, the specific steps are as follows:

[0137] 1) Protein expression profile preprocessing and experimental filtering

[0138] a) High-confidence protein screening: Quantitative proteins are required to contain at least one unique peptide with a Mascot ion score greater than or equal to 20, and at least two peptides with an ion score greater than or equal to 20, or three ions Peptides with a score greater than or equal to 20;

[0139] b) Quantitative data standardization based on the sum: using the non-labeled quantitative iBAQ method based on the peak area, the iBAQ value of a protein is the sum of the peak areas of all corresponding peptides of the protein / theoretical peptide number, each identified by calculation The ratio of the protein iBAQ value to the sum of all identified protein iBAQ values ​​was used to normalize the data to obtain a...

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Abstract

The invention relates to a method for analyzing molecular typing based on gastric mucosal lesion proteomics, different gastric mucosal lesion proteomics molecular subtype characteristics and correlation between the molecular typing and the subtype characteristics and the gastric mucosal lesion progress. A protein marker database related to gastric cancer and gastric mucosal lesion progress is established by calculating the relationship between protein expression and gastric mucosal tissue pathological state, proteome molecular subtype and gastric mucosal lesion progress, and a gastric mucosallesion sample disease progression risk scoring system is established. According to the invention, a molecular epidemiological research means is combined with bioinformatics analysis and machine learning, microscopic and macroscopic gastric cancer cause risk factors are integrated, a gastric mucosal lesion molecular typing framework and a progress risk prediction model are established, and a foundation is laid for finally constructing a comprehensive and systematic gastric cancer prevention strategy.

Description

technical field [0001] The invention relates to the field of tumor clinical medicine, in particular to a method for molecular typing of gastric mucosal lesion proteins, lesion progression, gastric cancer-related protein markers, and risk of lesion progression. Background technique [0002] Gastric cancer (GC) ranks fifth in the global tumor incidence spectrum and third in the death spectrum. China is one of the countries with the highest incidence and mortality rates of gastric cancer in the world. Nearly half of the world's gastric cancer incidence and death occur in China. The prevention of gastric cancer Prevention and control remain major public health challenges. Previous evidence has shown that gastric cancer, especially intestinal type gastric cancer, has undergone multi-stage complex dynamic evolution, including superficial gastritis (SG), chronic atrophic gastritis (CAG), intestinal metaplasia (IM) and dysplasia ( DYS), and eventually develop into gastric cancer. ...

Claims

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Application Information

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IPC IPC(8): G16B20/00G16B20/20G16B40/00G16H50/20G16H50/30G16H50/70G01N30/72
CPCG16B20/00G16B20/20G16B40/00G16H50/20G16H50/30G16H50/70G01N30/72G01N2030/8831G01N2030/8818
Inventor 秦钧李雪郑乃仁汪宜吴红星
Owner 北京谷海天目生物医学科技有限公司
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