Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Prognostic difference genes and recurrence prediction model in early gastric cancer

An early gastric cancer and gene technology, applied in the field of biodiagnosis, can solve the problems of lack of tumor recurrence prediction model and less research at the gene level.

Active Publication Date: 2022-04-19
PEKING UNION MEDICAL COLLEGE HOSPITAL CHINESE ACAD OF MEDICAL SCI
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, advanced age and Helicobacter pylori (Hp) infection are independent risk factors for metachronous recurrence in patients with EGC
Most of the studies on the recurrence of early gastric cancer are studies on clinicopathological factors, and there are few studies on the gene level, and there is a lack of accurate tumor recurrence prediction models

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Prognostic difference genes and recurrence prediction model in early gastric cancer
  • Prognostic difference genes and recurrence prediction model in early gastric cancer
  • Prognostic difference genes and recurrence prediction model in early gastric cancer

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0023] Example 1: The flow of this study

[0024] In this study, firstly, from the two sets of gene chip transcriptome data (GSE130823 and GSE55696), we screened out gastritis / control tissue → low-grade intraepithelial neoplasia (LGIN) → high-grade intraepithelial neoplasia (High-grade intraepithelial neoplasia (LGIN) grade intraepithelial neoplasia (HGIN)→Monotonically changing differentially expressed genes (mcDEGs) in EGC, and mcDEGs potentially associated with tumor recurrence were screened out by T test and single factor COX regression analysis. Then, the stage I / II patients in the external data set GSE62254 containing prognosis data were used as the training set, and the mcDEGs obtained from the screening were used as the training variables. The predicted outcome was tumor recurrence, and a recurrence prediction model based on the decision tree algorithm was constructed. Furthermore, 16 HGIN or EGC patients were prospectively collected as a validation set (4 relapsed, 12...

Embodiment 2

[0025] Example 2 The basic clinical information of the included research patients

[0026]A total of 94 samples were included in the first batch of gene chip samples. The subjects of the study were patients diagnosed with LGIN, HGIN or EGC in the Gastroenterology Department of Peking Union Medical College Hospital from 2011 to 2015. The test results were stored in the Gene Expression Comprehensive Database with the registration number GSE130823. A total of 77 samples were included in the second batch of gene chip samples. The research subjects were patients diagnosed with LGIN, HGIN, EGC and gastritis in the Department of Gastroenterology of Peking Union Medical College Hospital from March 2010 to May 2013, and the registration number was GSE55696. A total of 16 patients and 32 samples were included in the third batch of patients in the validation set. The patients were patients who visited the Department of Gastroenterology of Peking Union Medical College Hospital from Janu...

Embodiment 3

[0027] Example 3 Training and Verification of Risk Score Prediction Model Built Based on Multi-factor COX Regression Analysis

[0028] When screening mcDEGs in the early stage, the data of stage I / II patients in the external data set GSE62254 were used by single factor COX analysis, with recurrence as the outcome, and mcDEGs that were significantly related to the outcome were screened out. Corrplot package (0.88) in R language was used to further test the correlation of each mcDEGs, and the mcDEGs related to recurrence were included in LASSO regression analysis using glmnet package (4.1.1) to eliminate unnecessary or multicollinear genes. Then, multivariate COX regression analysis was performed on the remaining genes to determine whether these genes had a significant impact on recurrence, and a formula was constructed to calculate the patient's risk score. The risk score calculation formula used in the study is as follows: risk score=∑(X J*coef J), where X J is the normalized ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

PropertyMeasurementUnit
Sensitivityaaaaaaaaaa
Login to View More

Abstract

This application involves the establishment of a recurrence prediction model for early gastric cancer. By using two batches of gene chip transcriptome data, GSE130823 and GSE55696, a total of 25 genes potentially related to early gastric cancer recurrence were screened out, and AREG, LOC100507520, MMD, CH3L1, A recurrence prediction model based on eight genes: FOS, CCL20, CXCR2, and BATF3. This model has excellent sensitivity, that is, all patients predicted to have no recurrence have no recurrence, and the clinical significance is that the follow-up frequency of these patients can be adjusted accordingly.

Description

technical field [0001] This application relates to the field of biological diagnosis, specifically related to the differential genes of early gastric cancer (Early gastric cancer, EGC) prognosis, and the establishment of a recurrence prediction model. Background technique [0002] Gastric cancer is one of the common tumors that have a significant impact on human health. Many studies have shown that the progression of gastric cancer follows a clear multi-stage step-by-step evolution process: from initial inflammation and atrophy, to precancerous lesions (including LGIN and HGIN), to early gastric cancer, and further to advanced gastric cancer (Advanced gastric cancer, AGC). Early gastric cancer refers to gastric cancer with or without lymph node metastasis and lesions limited to the gastric mucosa or submucosa. Due to the long overall survival of patients with early gastric cancer, another prognostic indicator of clinical concern is tumor recurrence. The 5-year recurrence ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): C12Q1/6886C12N15/11
CPCC12Q1/6886C12Q2600/158C12Q2600/118
Inventor 王强吴晰张晟瑜张健辉徐平周雅轩杨爱明
Owner PEKING UNION MEDICAL COLLEGE HOSPITAL CHINESE ACAD OF MEDICAL SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com