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

Classification of gastric cancer subtypes and application thereof

A technology of gastric cancer and classification, applied in the field of genetic engineering, to avoid inapplicable drugs, improve survival rate, reduce toxicity and side effects

Pending Publication Date: 2022-02-08
福州大彻精准医学科技有限公司
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the clinical utility of the above regimen requires more validation as there is little difference in prognosis between different histological subgroups

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
  • Classification of gastric cancer subtypes and application thereof
  • Classification of gastric cancer subtypes and application thereof
  • Classification of gastric cancer subtypes and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036]The classification of gastric cancer subtypes, by collecting gene expression data of gastric cancer patients, using ComBat to remove the batch effect and using unsupervised cluster analysis method to obtain four subtypes of gastric cancer patients, including mesenchymal type, immune type, and classic type and metabolites, in this example, the gene expression data (GSE15459 and GSE34942) of 248 gastric cancer patients in Singapore and 300 gene expression data (GSE62254) of gastric cancer patients in South Korea were collected, and ComBat removed the batch effect and unsupervised clustering The analysis method has been disclosed in the following non-patent literature (Lei, Z., et al., Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil. Gastroenterology, 2013.145(3): p.554-65), This method in the present invention is the prior art, and will not be repeated here.

[0037] Further, the gene fingerprint f...

Embodiment 2

[0052] The invention also discloses the application of gastric cancer subtype classification in the guidance of gastric cancer medication; when the gene fingerprint is identified as interstitial type, gastric cancer patients are suitable for anti-angiogenesis drugs; when the gene fingerprint is identified as immune type, gastric cancer patients are suitable for PD1 Immunopharmaceuticals; when the genetic fingerprint is identified as a proliferative type, gastric cancer patients are suitable for paclitaxel or docetaxel; when the genetic fingerprint is identified as a metabolic type, gastric cancer patients are suitable for 5-fluorouracil / platinum;

[0053] Among them, in this example, the RNA-seq data of the gene expression of 44 gastric cancer patients were downloaded from the European nucleic acid database (PRJEB25780), and the clinical data were downloaded from the non-patent literature (Kim, S.T., et al., Comprehensive molecular characterization of clinical responses to Down...

Embodiment 3

[0060] The present invention also discloses a method for classifying gastric cancer subtypes of gastric cancer patients, using a support vector machine (SVM) machine learning method and based on gene fingerprints to establish the above-mentioned prediction model for gastric cancer subtype classification, specifically: from The gene fingerprint data is extracted from the gene expression profile of gastric cancer patients, and all patient samples of the batch are normalized for each gene to obtain the z value, that is, for each gene, z=(gene expression value-mean value) / standard deviation ; Then use statistical software to perform 10 times of cross-validation to obtain multiple sets of optimized parameters gamma and cost; finally, use the optimized parameters to establish a prediction model to determine the subtype of the patient's tumor specimen;

[0061] In addition to this embodiment, other machine learning methods can also be used to establish a prediction model, such as a te...

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

No PUM Login to View More

Abstract

The invention discloses classification of gastric cancer subtypes and application thereof. On the basis of collecting gene expression data of patients with gastric cancer, ComBat is adopted to remove batch effect, and an unsupervised clustering analysis method is used to obtain four subtypes of the patients with gastric cancer, and the four subtypes comprise a mesenchymal type, an immune type, a classical type and a metabolic type. Gastric cancer patients of different types have different reactions to PD1 immune drugs and chemotherapeutic drugs, and interstitial gastric cancer patients are significantly benefited from anti-angiogenesis drugs. The reaction of a gastric cancer patient to a PD1 immune drug predicted by immune typing is superior to that of a traditional biomarker, including microsatellite instability, EBV virus, PD-L1 CPS expression and tumor mutation load. Typical gastric cancer patients are significantly benefited from cell cycle inhibitors. The metabolic patients are obviously benefited from 5-fluorouracil and platinum. The survival rate of gastric cancer patients can be greatly improved by adopting proper medicines according to the four types, and inapplicable medicines are avoided as much as possible, so that the toxicity and side effects of chemotherapy can be reduced.

Description

technical field [0001] The invention relates to the technical field of genetic engineering, in particular to the classification and application of gastric cancer subtypes. Background technique [0002] Gastric cancer is one of the most widely distributed cancers in the world. Among cancers, the incidence of gastric cancer in Asian countries is very high, which is the main cause of cancer death. High-throughput cancer genomics has confirmed that gastric cancer is not a single disease, but a disease composed of multiple subtypes. Therefore, it is necessary to distinguish the molecular subtypes of gastric cancer and formulate different gastric cancer treatment options according to the genomic shape. [0003] The Lauren classification is more famous in the pathological research of gastric cancer, which can be classified into intestinal type, diffuse type and mixed type by observing the morphology of cancer cells under a microscope. Lauren classification can roughly understand th...

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 Applications(China)
IPC IPC(8): G16B40/30G16B25/10G16B50/10G16H50/20G16H20/10A61K45/00A61K31/337A61K31/513A61K33/243A61P35/00
CPCG16B40/30G16B25/10G16B50/10G16H50/20G16H20/10A61K45/00A61K31/337A61K31/513A61K33/243A61P35/00
Inventor 雷政登
Owner 福州大彻精准医学科技有限公司
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