Application Of The Differential Network Method Integrating Multi-omics Data In Breast Cancer Prognosis

Complex diseases such as cancer are usually the result of the interaction of many genes.The research on single gene level is not enough to meet the needs of biological research.As an important bioinformatics technique,differential network analysis can reveal the pathogenesis and biological mechanism of diseases by analyzing the differences between biomolecular networks in different states.Because the differential interaction network method JDINAC based on joint density estimation does not depend on the assumption of parameter distribution,it can well capture the nonlinear relationship between molecular interactions,and can realize the classification of samples in case group and control group while constructing the differential interaction network,so as to better identify the screening effect and classification performance of differential genes.Therefore,the JDINAC method was applied to the integration of multiple omics data related to breast cancer.In this paper,we downloaded the breast cancer related gene data from the public gene data platforms TCGA database and METABRIC database.We used the JDINAC method to integrate and analyze the three omics data of gene expression,copy number variation and gene mutation,and constructed differential gene interaction networks to identify the differential genes related to the prognosis of breast cancer.In this paper,breast cancer patients were classified according to four survival periods of 1 year,3 years,5 years and 10 years,and the samples were divided into case group(long-term survival)and control group(short-term survival).Considering the pair interaction between genes,the differential gene pairs in two groups of samples were identified,and then the differential gene interaction network was constructed according to the identified differential gene pairs,and the classification results were predicted.In this paper,the classification results of JDINAC method are compared with traditional machine learning methods such as logistic regression,support vector machine and random forest,and the prediction performance of JDINAC model under different omics data and different survival time classification criteria is compared respectively.We then identified differential genes by analyzing and comparing the interaction networks of differential genes under the four classifications.Finally,GO functional enrichment analysis was used to identify the biological pathways of the selected differential genes and analyze their involvement in biological processes and molecular functions.The experimental results show that the JDINAC model has good classification performance in both TCGA and METABRIC datasets.Meanwhile,we found that gene pairs(IL33,JAK2)interacted with each other in the differential interaction network constructed based on the TCGA dataset and in the GO functional enrichment results,gene pairs(CHMP6,CHMP7)interacted with each other in the differential gene interaction network constructed based on METABRIC data set and in the GO functional enrichment analysis.In addition,SMARCA2 was screened as differential genes in both TCGA and METABRIC databases.The findings of these differential genes and their interactions are consistent with the existing literature.In this paper,multiple omics data were integrated by the JDINAC method to construct differential gene interaction networks under different survival classification criteria,and differential genes related to breast cancer prognosis were found.The identified hub genes and differential interaction patterns were consistent with the experimental results of existing relevant studies and GO functional enrichment analysis.This study promotes the understanding of the molecular mechanism of breast cancer and can provide new ideas for clinical research and treatment.