Functional Analysis Of Differential Molecular Networks Based On Gene Ontology

The development of Gene Ontology(GO)has laid the foundation for functional analysis in the post-genomic era.Gene functional enrichment analysis is of great significance for understanding the operation mode and biological process of differential genes and revealing the pathogenic mechanism of complex diseases.It has become a conventional analysis method of bioinformatics.At present,gene functional enrichment analysis is mostly based on the gene sets,but the development of network biology has made humans realize that when the genes remain unchanged,the interaction changes will also change its phenotype.Therefore,enriching the functions of different interaction networks composed of the same genes is beneficial to reveal the pathogenic mechanism of the disease,which will guide the subsequent screening of biomarkers and the research of related targeted drugs and immune drugs.This thesis takes endometrial cancer as an example.The differential networks formed of endometrial cancer pathogenic genes and gene expression data are enriched by the semantic similarity-based network ontology analysis method(iNOA).The results of enrichment are evaluated by cancer functional assessment method(GO4Cancer)which is constructed by multiple cancers.In addition,10 biological processes closely related to cancer are used to verify the effectiveness of the cancer functional evaluation method,and the consistent and specific functions of cancers are given,which provide a reference standard for future research on cancer function.The main research contents of this thesis are divided into the following three parts:(1)Differential molecular networks in normal and disease are constructed.In this thesis,22 genes intersected by pathway genes and disease genes of endometrial cancer are combined with gene expression data to construct the network.The number and type of genes in the normal and disease networks are the same,but the interaction between them is changed.This thesis mainly studies the functional changes brought about by the rewiring of the network.(2)Develop a network ontology analysis method based on semantic similarity.Although the original network ontology analysis method can reveal the functional changes after network rewiring,it doesn't consider the semantic similarity between GO terms,which will loss the potential biological information.On the basis of original method,semantic similarity is added in the definition of edge annotation.It is verified that the method presented in this thesis is more effective and can capture deeper biological processes.The results show that there are 22 GO terms in the disease network and 9 terms in the normal network.(3)A cancer functional evaluation system is established.In order to evaluate the relationship between the functions of the differential network and the disease phenotype,this thesis extracts 17 cancer-related genes from public databases,and enrich their initial functions by hypergeometric test.Then the consistent and specific functions of cancers are obtained by de-redundancy and function integration.The similarity calculation between the consistent functions and the prior cancer functions verifies the effectiveness of the cancer functional evaluation system.Based on the established function evaluation method,it is found that the enrichment functions of the disease molecular network detected by iNOA are closer to the phenotype of endometrial cancer,which further verifies the effectiveness of the iNOA method.