Journal of Proteomics & Bioinformatics
Open Access

Abstract

Identification of Breast Cancer Pathways Based on Gene Expression Data

Elham Musa Abdeljalil*, Murtada K. Elbashir and Abdallah Osman Akode

Pathway enrichment analysis assists researchers in gaining mechanical insights into the list of genes generated by genome-scale experiments (omics). In this work, we used breast cancer gene expression data to recognize genetic pathways. The pathways were conducted based on KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO-ALL (Gene Ontology) using the PathfindR tool. The gene expression data were downloaded from the Pan-Cancer-Atlas using the R studio program. Preprocessing steps were performed on the downloaded gene expression data. These steps are as follows: First, the outlier samples were removed second; a normalization process was applied to the data. Third, a filtering process is applied to the data. DESeq2 package is used to find Differentially Expressed Genes (DEGs).Thereafter, we used the pathfindR software to conduct the enrichment analysis .also, and we construct a Protein-Protein Interaction network in order to detect active sub networks. The results indicated that there are 73, 63 top pathways that are associated with our Differentially Expressed Genes on GO and KEGG pathways respectively. Moreover, the top genes related to our BRCA include NUP214 NUP62, NUP93, SUMO3, EIF2B1, EIF4A3, RNPS1, and SRRM1.

Published Date: 2022-11-28; Received Date: 2022-10-26