Khan M 1 ,3, Ahmed G 1, Hussain S 2 ,Singh R 3, Spinella M3 1 Department of Computer Science, National University Of Computing and Emerging technologies 2 University of Pennsylvania 3 Department of Comparative Biosciences ,University of Illinois Urbana Champaign
Poster # 79
Testicular germ cell tumors (TGCTs) represents the most common cancer affecting young men. TGCTs are hypersensitive to cisplatin-based therapies, possessing a high cure rate of approximately 80%. However, resistance can occur causing poor patient prognosis. The mechanisms of cisplatin resistant in these tumors remain largely undefined. Recently, our lab generated unique cisplatin resistant cell lines and using these novel models we uncovered repression of polycomb signaling as a potentially pervasive epigenetic mechanism for chemotherapy resistance in testicular cancer. To better understand the molecular mechanism of cisplatin resistance, the current study utilized transcriptomic analysis of parental and cisplatin refractory TGCT cell lines treated with vehicle or cisplatin. The analysis encompassed RNAseq analysis, followed by Gene Set Enrichment Analysis (GSEA),leading edge analysis and Gene Ontology (GO) analysis. To compare the behavior of cisplatin treated and untreated cells, differential gene expression data was modelled into co-expression networks to identifying differences in gene interactivity before and after cisplatin treatment in parental and resistant cells. In ongoing work, we are conducting an empirical investigation involving various deep learning models for the prediction of molecular subtypes of TGCTs leveraging gene expression data. The first phase of this study was conducted under the supervision of Dr. Michael Spinella, Department of Comparative Biosciences, College of Veterinary Medicine University of Illinois(UIUC). The deep learning module will be done under the guidance of Dr. Shahid Hussain, (University of Pennsylvania, USA) and Dr. Ghufran Ahmed(FAST NU ,Pakistan)
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