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Epithelial to mesenchymal transition (EMT) is the process by which stationary epithelial cells transdifferentiate to mesenchymal cells with increased motility. EMT is integral in early stages of development and wound healing. Studies have shown that EMT could be a critical early event in tumor metastasis that is involved in acquisition of migratory and invasive properties in multiple carcinomas.
In this study, we used 15 published gene expression microarray datasets from Gene Expression Omnibus (GEO) that represent 12 cell lines from 6 cancer types across 95 observations (45 unique samples and 50 replicates) with different modes of induction of EMT or the reverse transition, mesenchymal to epithelial transition (MET). We integrated multiple gene expression datasets while considering study differences, batch effects, and noise in gene expression measurements. A universal differential EMT gene list was obtained by normalizing and correcting the data using four approaches, computing differential expression from each, and identifying a consensus ranking. We confirmed our discovery of novel EMT genes at mRNA and protein levels in an in vitro EMT model of prostate cancer – PC3 epi, EMT and Taxol resistant cell lines. We validate our discovery of C1orf116 as a novel EMT regulator by siRNA knockdown of C1orf116 in PC3 epithelial cells.
Among differentially expressed genes, we found known epithelial and mesenchymal marker genes such as CDH1 and ZEB1. Additionally, we discovered genes known in a subset of carcinomas that were unknown in prostate cancer. This included epithelial specific LSR and S100A14 and mesenchymal specific DPYSL3. Furthermore, we also discovered novel EMT genes including a poorly-characterized gene C1orf116. We show that decreased expression of C1orf116 is associated with poor prognosis in lung and prostate cancer patients. We demonstrate that knockdown of C1orf116 expression induced expression of mesenchymal genes in epithelial prostate cancer cell line PC3-epi cells, suggesting it as a candidate driver of the epithelial phenotype.
This comprehensive approach of statistical analysis and functional validation identified global expression patterns in EMT and candidate regulatory genes, thereby both extending current knowledge and identifying novel drivers of EMT.