A biological network-based screening method for cancer driver genes

A technology of driving genes and screening methods, applied in the field of cancer medicine, can solve problems such as inability to make correct judgments, and inability to truly reflect the impact of mutant gene cell functions

Inactive Publication Date: 2017-11-28
XI AN JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the method that only relies on the mutation frequency cannot truly reflect the impact of the mutated gene on cell function, and it is also impossible to correctly judge whether the genetic structure change of this gene really contributes to the survival advantage of cancer

Method used

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  • A biological network-based screening method for cancer driver genes
  • A biological network-based screening method for cancer driver genes
  • A biological network-based screening method for cancer driver genes

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Experimental program
Comparison scheme
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Embodiment 1

[0032] 1. Experimental materials

[0033] Hepatocellular carcinoma chip expression profile data: downloaded from GEO database (http: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE50579);

[0034] R: open source computing platform, downloaded from R official website;

[0035] Limma analysis package: open source analysis package, downloaded from the Bioconductor website;

[0036] Protein interaction data: download from BioGRID and HPRD databases;

[0037] 2. Experimental method

[0038] (1) 70 cases of hepatocellular carcinoma expression profile chips and 10 cases of normal liver tissue expression profile chips from the GEO database numbered GSE50579 were used for quality control on the R platform using the Limma software package, and qualified chip data were selected for the next step Comparative analysis of expression profiles;

[0039] (2) Using the Limma software package, the expression profiles of 70 cases of hepatocellular carcinoma were compared with those of 10 case...

Embodiment 2

[0048]1. Experimental materials

[0049] Non-small cell lung chip expression profile data: downloaded from the GEO database (http: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE33532);

[0050] R: open source computing platform, downloaded from R official website;

[0051] Limma analysis package: open source analysis package, downloaded from the Bioconductor website;

[0052] Protein interaction data: download from BioGRID and HPRD databases;

[0053] 2. Experimental method:

[0054] (1) 80 non-small cell lung cancer expression profile chips and 20 normal lung tissue expression profile chips from the GEO database numbered GSE33532 were used for quality control on the R platform using the Limma software package, and qualified chip data were selected for the next step. One-step expression profile comparison analysis;

[0055] (2) Using the Limma software package, the expression profiles of 80 cases of non-small cell lung cancer were compared with those of 10 cases of normal...

Embodiment 3

[0064] 1. Experimental materials

[0065] Gastric cancer chip expression profile data: downloaded from the GEO database (http: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi?acc=GSE30727);

[0066] R: open source computing platform, downloaded from R official website;

[0067] Limma analysis package: open source analysis package, downloaded from the Bioconductor website;

[0068] Protein interaction data: download from BioGRID and HPRD databases;

[0069] 2. Experimental method:

[0070] (1) 30 cases of gastric cancer expression profile chip and 30 cases of normal gastric tissue expression profile chip from the GEO database numbered GSE30727 were used for quality control on the R platform using the Limma software package, and the chip data with qualified quality were selected for the next step of expression profile comparative analysis;

[0071] (2) Using the Limma software package, the expression profiles of 30 cases of gastric cancer were compared with those of 30 cases of norm...

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Abstract

A method for screening cancer driver genes based on biological networks, comprising the following steps: (1) screening of cancer differentially expressed genes; (2) construction of cancer biomolecular networks; (3) calculation of gene cancer driving forces based on cancer biomolecular networks; (4) Determine the cancer driver gene according to the ranking of the driving force calculation results; the method of the present invention is not only closely combined with mature and reliable modern high-throughput technology, but also conforms to the theory of systems biology and network pharmacology, and the combination with the biological network makes the cancer driver gene The screening is more in line with the biological process in the organism, laying an important foundation for further research and development of gene-targeted drugs.

Description

technical field [0001] The invention relates to the field of cancer medicine, in particular to a biological network-based screening method for cancer driver genes. Background technique [0002] The pathogenesis of cancer is a process of continuous accumulation of cancer cell survival advantages dominated by mutated driver genes. During the whole process of cancer pathogenesis, cancer cells accumulate survival advantages, expand continuously, infiltrate the surrounding tissue environment, and the process of spreading requires a series of changes in the genetic structure of genes. Genes that can bring survival advantages to cancer cells after changes in these genetic structures are called driver genes, and they are key factors leading to the pathogenesis of cancer. [0003] Not all genes detected as changes in tumor cells relative to normal cells are driver genes. Studies based on cancers that occur in tissues where daily cell division occurs frequently, such as the blood an...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/18C12Q1/68
Inventor 张虎勤何冰刘芳娥杜建强赵静林松刘治镇
Owner XI AN JIAOTONG UNIV
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