Bioinformatics Identification Of Novel Key Genes And Biological Pathways Associated With Breast Cancer

The interaction between an environmental factor and a genetically susceptible host may cause breast cancer like other cancers.Breast cancer is the second leading cause of cancer-related death in women worldwide,accounting for approximately 26%of all cancers in women.Breast cancer is presented as a pelvis that feels isolated from the rest of the breast tissue.The risk factor associated with breast cancer are obesity,alcoholism,hormone replacement therapy during menopause,ionizing radiation,early age at first menstruation,having children late in life or not at all,older age,having a prior history of breast cancer,family history of breast cancer,a lack of physical exercise.Basically,it's the disease of women and depends on genetic and lifestyle also.Epidemiological reports published in various parts of the world over the last two decades show a significant increase in breast cancer mortality.A number of treatments may be used in breast cancer,including surgery,radiation therapy,chemotherapy,hormonal therapy,and targeted therapy.Better characterization of established biomarkers and the discovery of novel biomarkers and possible treatment targets are important to improve prognostication and tailored therapy.Human epidermal growth factor receptor 2(HER2)and estrogen receptor alpha(ERa)are the most potent established biomarkers for breast cancer in both determining prognosis and predicting response to hormone therapy.However,there is an obvious need to identify additional biomarkers because some subtypes of breast cancer do not express ERa and/or HR2 and,additionally.There is no accurate correlation between the response of this biomarker and the targeted treatment.Therefore,evaluate potential new biomarkers for breast cancer diagnosis and to improve therapeutic treatment is argent needed.The rapid development of biomedical and information technology and the emergence of active health,precision medicine,and big data paradigms have brought new opportunities for the prevention and treatment of cancer.My research interest is in heterogeneity and exterior syndrome of cancer,mining of deep phenotype of cancer,combined drug use algorithm for cancer treatment,information model of prevention and treatment of cancer,and management of chronic diseases,systems and platforms.With the viewpoint,the aim of this thesis is to evaluate potential novel genes as biomarkers and pathways associated with a breast cancer diagnosis and to improve therapeutic strategies breast cancer by bio informatics methods.The work presented in this thesis contributes to identify and suggest a group of proteins coding genes that are candidates as potential novel biomarkers and/or drug targets to improve therapeutic strategies in breast cancer as well as understanding their molecular mechanism.In this thesis,we have utilized publicly available gene expression profiles of GSE53566 microarray data to identify differentially expressed genes(DEGs)using the GE02R web tool.We perform the Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis using the DAVID online tool.Protein-protein interaction(PPI)network of the DEGs was performed by the STRING database.Finally novel key genes detected by the top-ranked nodes using five topological analysis methods of the Cytoscape plugin"cytoHubba" and PubMed citation.We have utilized the GSCALite database to identify the drug sensitivity of key genes to target the diagnostic and prognostic biomarkers.We have identified 236 genes as DEGs between 8 breast cancer samples and corresponding normal samples involved in the biological process of signaling and metabolic systems.A total of 236 DEGs including 167 up-regulated and 69 down-regulated genes were identified in a breast cancer sample.Four novel key genes(SLC02A1,OAS2,OAS3,and ATF6B)were identified with top-ranked nodes.The survival analysis,ROC analysis,and validation of the expression highlighted crucial roles of the novel key genes and can be utilized as diagnostic and prognostic signatures in breast cancer.Our analysis of breast cancer datasets with bioinformatics methods,we identified and confirmed above 2 novel significant genes(SLCO2A1,and ATF6B)not only contribute to elucidating the pathogenesis of breast cancer,but also provide prognostic markers and therapeutic targets for breast cancer.