Screening method for anti-myocardial fibrosis drugs

Abstract

The invention discloses a method for screening anti-myocardial fibrosis drugs, specifically: obtaining known marker genes of myocardial fibrosis; using the known marker genes to catch biological relationship with the known marker genes in biological big data Other genes or proteins, and limited to the genes that can be expressed in cardiac tissue, construct a specific myocardial fibrosis pathological gene module; construct a drug data set with transcriptomics data, and obtain drug transcription profile data; use gene expression deduction method according to drug The transcriptional profile data of the corresponding drug predicts the anti-myocardial fibrosis ability of the corresponding drug, and screens the desired drug according to the anti-myocardial fibrosis ability of the drug. Based on the different interactions between myocardial fibrosis marker genes and other genes, the present invention constructs a tissue-specific myocardial fibrosis module, screens potential anti-myocardial fibrosis drugs by computer, screens in batches, improves accuracy, high efficiency, and low cost .

Description

technical field [0001] The invention belongs to the technical field of biomedicine and relates to a screening method for anti-myocardial fibrosis drugs. Background technique [0002] Myocardial fibrosis refers to the excessive accumulation of collagen fibers in the normal tissue structure of the myocardium, the significant increase of collagen concentration in cardiac tissue, or the change of collagen composition. These changes often seriously affect the function of the heart, leading to the occurrence of various cardiovascular diseases and eventually progressing to heart failure. Anti-myocardial fibrosis is one of the key therapeutic strategies to improve various cardiovascular diseases, but its drug development is mainly based on scattered research on related targets and lacks systemicity. [0003] So far, many marker genes of myocardial fibrosis have been discovered, such as periostin (POSTN), platelet-derived growth factor receptor α (PDGFRA), discoid domain receptor 2 ...

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Problems solved by technology

For another example, the difference in expression of many genes may not be very large, but the effect is obvious, and will be screened out by the pre-set difference multiple standard (such as difference multiple >2, etc.); it is difficult to screen in large quantities, with low efficiency and poor accuracy. Greatly increase the cost of later test verification

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