Abstract
Urokinase plasminogen activator receptor (PLAUR) has been implicated in a variety of physiological and pathological conditions. The multi-functionality of PLAUR is due to its capacity to interact with many co-receptors to regulate extracellular proteolysis and intracellular signaling. Recent reports are identifying novel functions of PLAUR which were not evident in the past; however, the molecular mechanisms of PLAUR signaling are not completely understood. Here, we have compared the transcriptomes of silencing control (sicon) and PLAUR silenced (PLAURsi) MDA-MB-231 breast cancer cells on treatment with radiation. We isolated RNA from the cells, synthesized cDNA and measured the gene expression changes by microarray. We identified 24 downregulated and 53 upregulated genes, which were significantly (P-value < 0.005) affected by PLAUR silencing. Our analysis revealed 415 canonical pathways and 743 causal disease networks affected on silencing PLAUR. Transcriptomic changes and predicted pathways supported and consolidated some of the earlier understanding in the context of PLAUR signaling; including our recent observations in DNA damage and repair process. In addition, we have identified several novel pathways where PLAUR is implicated.
Highlights
High-throughput technologies such as microarray, genome sequencing, mass-spectrometry, and genome-wide association studies have been very helpful in deciphering the differences between normal and cancer cells [1]
The first method involved filtering the genes using Qlucore Omics explorer with a P-value cutoff of 0.05; this resulted in around 370 upregulated and 347 downregulated genes, these genes were further processed by ingenuity pathway analysis (IPA) software to reveal gene interacting networks and functional pathways
We had two parameters to assess, to see the effect of PLAUR silencing alone or effects of PLAUR silencing on induction of DNA damage; the 100 most up- and down-regulated genes in these cases are shown in Supplementary Tables 1-4
Summary
High-throughput technologies such as microarray, genome sequencing, mass-spectrometry, and genome-wide association studies have been very helpful in deciphering the differences between normal and cancer cells [1]. Protein profiling has helped us to understand the regulation of a huge number of proteins and in the prediction of regulatory pathway networks. We try here to merge the high throughput power of microarray analysis and pathway prediction programs for better visualization and understanding of gene interaction networks. Even in a normal cell, DNA is constantly at risk of damage due to various endogenous and exogenous factors; it has been predicted that oxidative stress inside a cell can damage the DNA 10,000 times per day [3]. To battle DNA damage, cells have evolved numerous sophisticated repair mechanisms for specific kinds of damage; these mechanisms interact and overlap to maintain genome integrity
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