Genomic data mining reveals hub genes and lncRNAs as prognostic biomarkers in breast cancer

Breast cancer is a complex disease that is characterized by altered functions of many genes. In this study, we aim to establish long non-coding RNAs as prognostic biomarkers as they play vital roles in their prognosis and progression. For this purpose, we retrieved four datasets (GSE10810, GSE42568, GSE65194, and GSE45827) from NCBI Gene Expression Omnibus. Using R studio, we identified differentially expressed genes followed by functional enrichment analysis with the help of DAVID software. DEGs were used to construct a protein-protein interaction map which we later used to identify highly correlated genes with the help of sub-network identification and centrality analysis in cytoscape software. Next, lncRNAs-mRNA coexpression analysis was performed to determine the potential role of lncRNAs. Later on, we assessed their role in survival with the help of Kaplein Meir's plot. The results of these analyses establish long noncoding RNAs i.e. EPB41L4A-AS1, LINC00667, MAGI2-AS3, and MALAT1 as prognostic biomarkers. This study provides a blueprint for identifying mRNA-lncRNA networks in any cancer, facilitating the use of lncRNAs as prognostic biomarkers and aiding in the identification of therapeutic targets. Future research may focus on validating this research biologically to further substantiate the role of lncRNAs as prognostic biomarkers in breast cancer.