Pathway analysis of Disease-Gene associated network in the human breast cancer
MetadataShow full item record
The research of human genes and diseases is very interrelated and can lead to an improvement in healthcare, disease diagnostics, and drug discovery. In this work, a system was established to construct similarity measures of gene pair mutation for human breast cancer and then performed network analysis to identify disease-related genes. The overlapping position of the interacting genes was used to calculate their similarity coefficient. Using this similarity coefficient the co-occur genes were analyzed and built up a network of the gene cluster. Finally, a significant pathway was detected which was followed by the genes in a cluster. In this study, the process of constructing the gene regulatory networkin breast cancer was refined. A network topography for measuring gene-pair mutation similarity had been taken using their position where they overlap to induce a significantly mutated network. We aim to evaluate whether the identified network can be used as a biomarkerfor predicting breast cancer patient endurance. Common genes were estimated for different cancer types (i.e. lung cancer, prostate cancer, and breast cancer) from Gene bank. On a breast cancer case study, the system predicted an average 80% breastrelated genes. These common genes were matched with reference breast cancer genes from clinical data in cBioPortal. Using the position of the gene pair in the genome similarity coefficient was measured. After that gene clusters were detected using similarity score. Finally, we identified the JAK-STAT signaling pathway in which clustered genes were enriched. It was found that 3 out of 4 datasets contained the MTOR NEDD9 EPOR gene cluster. This gene cluster followed the JAK-STAT signaling KEGG pathway. The JAKSTAT pathway played a vital role in cytokine-mediated immune responses, mainly cytokine receptors and they were able to polarize T-helper cells. Other gene clusters were SMAD4 PDGFRA KIT EGFR KDR ERBB3 and ERCC2 COL18A1 ERCC1. They followed the MAPK signaling pathway and the nucleotide excision repair pathway. Dysregulations in both pathways played a vital role in various cancer development. Our research showed that this study has the potential to identify disease-gene associated networks as a biological marker that may be useful to breast cancer patients for selecting the finest treatment. These common genes can be found in different cancers so that we can compare our work in case of other (lung cancer and prostate cancer) cancer types.