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A marker for predicting the prognosis and suitability of immunotherapy in gastric cancer and its application

An immunotherapy and marker technology, applied in the field of biomedicine, can solve problems such as different prognosis

Active Publication Date: 2022-07-08
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the high heterogeneity of gastric cancer, patients with the same tumor-node-metastasis (TNM) stage may have different prognosis

Method used

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  • A marker for predicting the prognosis and suitability of immunotherapy in gastric cancer and its application
  • A marker for predicting the prognosis and suitability of immunotherapy in gastric cancer and its application
  • A marker for predicting the prognosis and suitability of immunotherapy in gastric cancer and its application

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] Example 1 Downloading of gastric cancer related gene data

[0063] from the Gene Expression Comprehensive Database ( http: / / www.ncbi.nlm.nih.gov / geo / ) Download the raw data and clinical data of gastric cancer transcriptome chip GSE26901 (n=109), GSE15460 (n=248), GSE62254 (n=300), GSE15459 (n=192), GSE84437 (n=433). Download gastric cancer transcriptome FPKM sequencing data and clinical data from The Cancer Genome Atlas (TCGA).

[0064] Transcriptome FPKM sequencing data were processed using the affy software package and the input R software package, and differential gene expression analysis was performed using the limma software package. Assessment of stromal and immune cell infiltration in the tumor immune microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tissue Using Expression Data) algorithm. The GSE62254 and GSE15460 datasets were combined using the sva R package to remove batch effects and a total of 548 samples were obta...

Embodiment 2

[0065] Example 2 Gastric cancer related gene data processing

[0066] 2.1 Construction of weighted gene co-expression network (WGCNA)

[0067] The gene expression data matrix of GSE26901 was constructed using the WGCNA R software package, and the genes with the variance in the top 25% were selected as the input data set for the subsequent WGCNA. Then use hierarchical clustering to remove abnormal samples, then build a scale-free network, and calculate the degree of dissimilarity of genes. According to the degree of dissimilarity, genes with similar expression profiles are modularized, and the correlation between the modules and the clinical phenotype of gastric cancer is calculated.

[0068] 2.2 Identify core modules

[0069] Input the genes in the module into the STRING (Search Tool for Retrieving Interacting Genes / Proteins) website for PPI (Protein-Protein Interaction) analysis, get a PPI score, then input the PPI score into Cytoscape for analysis with the MCODE plugin, and...

Embodiment 3

[0076] Example 3 Construction of prognostic risk model

[0077] 3.1WGCNA identified modules related to tumor progression and further screened core genes.

[0078] To identify genes associated with gastric cancer progression, a co-expression network was constructed in GSE26901 using WGCNA. After removing 5 outlier gastric cancer patient samples, the remaining 104 gastric cancer patient samples were used to construct an adjacency matrix graph ( figure 1 ). β = 9 is chosen as the soft threshold to construct the scale-free network graph ( figure 2 ), and finally constructed 10 gene co-expression modules ( image 3 ). By calculating the correlation between the gene co-expression module and clinical characteristics, it was found that the midnight-blue module had the strongest correlation with AJCC-stage ( Figure 4 ). In order to obtain the core genes, we continued to analyze the PPI network in the midnight-blue module on the STRING website, imported the results into Cytosca...

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Abstract

The present invention relates to a marker for predicting the prognosis of gastric cancer and the suitability of immunotherapy and its application. The markers are mRNA markers consisting of APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX. The invention also discloses a kit for predicting the prognosis of gastric cancer and the suitability of immunotherapy, and a method for predicting the prognosis and suitability of immunotherapy of gastric cancer. The invention finds the markers APOD, APOE, CCDC80, CTHRC1, FERMT2, GXYLT2 and SMPX related to the progression of gastric cancer through the technology of bioinformatics, and then obtains the risk score according to the risk model constructed by these 7 genes, which can be predicted by the risk score. The prognosis of gastric cancer and the applicability of predicting immunotherapy provide a reliable method for analyzing the prognosis and immunotherapy of gastric cancer patients.

Description

technical field [0001] The invention relates to a marker for predicting the prognosis of gastric cancer and the applicability of immunotherapy and its application, and belongs to the field of biomedicine. Background technique [0002] Gastric cancer is the fifth most common cancer worldwide and the third leading cause of cancer-related death. Many patients are diagnosed at an advanced stage, and another 25%-50% will develop metastases during the course of the disease. Despite continuous improvement in treatments, the 5-year survival rate for metastatic gastric cancer is only 5%-20%. Immunotherapy has broad application prospects in gastric cancer, and immune checkpoint blockade is currently recognized as a method for the treatment of chemotherapy-refractory gastric cancer. Immunotherapy alone or in combination has a positive impact on the treatment of gastric cancer, but due to the high heterogeneity of gastric cancer, the response rate of patients during immunotherapy is n...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B30/00G16B40/30G16H50/20
CPCG16B30/00G16B40/30G16H50/20
Inventor 贾继辉刘奋杨宗澄付悦
Owner SHANDONG UNIV
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