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Early gastric cancer prognosis differential gene and recurrence prediction model

A technology for early gastric cancer and gastric cancer, applied in the field of biological diagnosis, can solve the problems of less research at the gene level and lack of tumor recurrence prediction models

Active Publication Date: 2022-03-01
PEKING UNION MEDICAL COLLEGE HOSPITAL CHINESE ACAD OF MEDICAL SCI
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  • 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

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  • Early gastric cancer prognosis differential gene and recurrence prediction model
  • Early gastric cancer prognosis differential gene and recurrence prediction model
  • Early gastric cancer prognosis differential gene and recurrence prediction model

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 ...

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Abstract

The invention relates to establishment of an early gastric cancer recurrence prediction model, 25 potential genes related to early gastric cancer recurrence are screened out by using two batches of gene chip transcriptome data of GSE130823 and GSE55696, and the recurrence prediction model based on eight genes of AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 is established. The model has excellent sensitivity, that is, all patients which are predicted to be not relapsed do not relapse, and the clinical prompting significance is that the reexamination follow-up diagnosis frequency of the patients can be adjusted according to the reexamination follow-up diagnosis frequency.

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

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Application Information

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Patent Type & Authority Applications(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
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