Pathway Analysis Method Based On The Change Of Regulatory Relationships Between Genes

With the change of ecological environment,the incidence rate of cancer has been rising all over the world,especially in China.With the development of next-generation sequencing(NGS)technology,researchers are getting to know more and more about various cancer omics data.A large number of studies have shown that the occurrence of cancer is related to the functional dysfunction of the pathway.Identifying cancer-related pathways could help researchers to better understand and overcome diseases of cancer.This research mainly studies cancer and related pathways from two aspects to screen out more closely related cancer characteristic pathways.First of all,in the recognition methods of cancer-related pathways,the existing signal pathway analysis methods mainly consider the change of gene expression level,but do not consider the change of the strength of relationship between genes.To solve this problem,we focused on the gene pair relationships with regulatory relationships in the pathway,in which Pearson correlation coefficients were used to determine the relationship strength between gene pairs.By analyzing the changes in the regulatory strength between disease and normal conditions,the pathways that are significantly associated with the disease are identified.Based on this idea,we developed a pathway analysis method Pathway Analysis of Intergene Regulation(PAIGR)and compared the results of this method with those of existing pathway analysis methods.The results showed that PAIGR can more accurately find disease-related pathways.Secondly,in the field of pathway analysis,few studies consider the specific relationship between pathways and diseases.In order to study whether there is a specific relationship between pathways and diseases,we used the pathway as a node to establish the interaction network between pathways and carry out analysis.In the inter-pathway interaction network,the degree of the pathway nodes is distributed in power law,and the inter-pathway interaction network is scale-free network,that is,a few pathways participate in most life activities,while most pathways only participate in part of life activities,which confirms to the general biological metabolism law.Therefore,we conducted disease specificity analysis based on nodal centrality analysis and found that there was specificity between the pathway with small nodal degree and the disease.So,we used node degree analysis method to analyze the colon cancer related pathways found by PAIGR and found that most of the pathways found by PAIGR were also disease specific.Based on our studies,we can conclude that the pathway labeled by PAIGR has not only a high correlation with the disease,but also a good specificity.