Cancer genome and non-specific gene tag-based large-scale drug relocation method

Abstract

The invention relates to a cancer genome and non-specific gene tag-based large-scale drug relocation method, and discloses a method for expression profile core labels of single human coding gene mutations without organization resource backgrounds through integrating and analyzing large-scale transcriptome data in different cancer types for the first time. On the basis of core labels, the inventionprovides a drug relocation method which aims at the in-vivo environment, is not based on model animals or cells and is capable of comprehensively covering more than 8000 human genome potential targetgenes for the first time, and designs a quantitative index for measuring interaction specificity between drugs and target genes for the first time, so that a drug relocation analysis method for comprehensively analyzing human drug target genes in a large scale is realized and a new way is provided or drug target point design and human disease treatment.

Description

technical field [0001] The invention belongs to the field of bioinformatics, and more specifically, the invention relates to a large-scale drug repositioning method based on cancer genome and non-specific gene tags. Background technique [0002] The current pharmaceutical industry faces three major challenges. First, the high cost input during drug development is out of proportion to the output of drugs that can be effectively used in clinical practice. Second, the drastic changes in the environment and the trend of population aging make the market demand for drug development is increasing day by day. Third, the current drug development is mostly based on in vitro cell screening and model animal testing, but in the end most of them are ineffective on humans or have strong side effects and cannot be used for clinical treatment. In order to speed up the drug development process and reduce risks, people are gradually turning their attention to drug repositioning research. No...

AI Technical Summary

Problems solved by technology

Third, the current drug development is mostly based on cell in vitro screening and model animal testing, but in the end most of them are ineffective on humans or have strong side effects and cannot be used for clinical treatment

1. To mine the expression profile tags of genome-wide genes from the existing database will face problems such as inconsistency in data platforms and differences in data batches, making it impossible to perform unified quantitative analysis

Second, a small number of existing open data for unified analysis and processing do not cover all gene targets. For example, The Encyclopedia of DNA Elements (ENCODE) database only contains 430 transcription factors, which is too narrow compared to the approximately 25,000 genes in the human genome

Third, most of the existing data used to analyze gene expression profiles come from genetic experiment data. However, these experiments can only be carried out on cell lines or model animals, and cannot be implemented in humans. Therefore, the representative gene functions of the human body are truly derived The expression profile label for has not been obtained

Fourth, all existing data carry a tissue-source-specific background, and the expression profile tags analyzed by traditional conventional methods will also be interfered by this

However, the tissue-derived context present in these gene expression profiling signatures prevents it from being integrated with data from other tissue sources

When the gene expression profile signatures and drug treatment data from two different tissue sources are analyzed together, it is impossible to determine whether the results predicted by the algorithm are caused by differences in their tissue sources, or are derived from the real drug-gene targeting relationship

Alternatively, one could limit the analysis to only gene expression profiling signatures and drug treatment data derived from the same cell or tissue, but this would greatly limit the size of the drugs and target genes that can be studied

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