A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/ftp/arxiv/papers/2011/2011.10372.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
Urothelial cell carcinoma (UCC) is the ninth most common cancer that accounts for 4.7% of all the new cancer cases globally. UCC development and progression are due to complex and stochastic genetic programmes. To study the cascades of molecular events underlying the poor prognosis that may lead to limited treatment options for advanced disease and resistance to conventional therapies in UCC, transcriptomics technology (RNA-Seq), a method of analysing the RNA content of a sample using modern<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.10372v4">arXiv:2011.10372v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mavw6o43wbcl3jfdmqrpuzg57y">fatcat:mavw6o43wbcl3jfdmqrpuzg57y</a> </span>
more »... h-throughput sequencing platforms has been employed. Here we review the principles of RNA-Seq technology and summarize recent studies on human bladder cancer that employed this technique to unravel the pathogenesis of the disease, identify biomarkers, discover pathways and classify the disease state. We list the commonly used computational platforms and software that are publicly available for RNA-Seq analysis. Moreover, we discussed the future perspectives for RNA-Seq studies on bladder cancer and recommend the application of new technology called single cell sequencing (scRNA-Seq) to further understand the disease. Keywords: Transcriptome profiling, RNA-sequencing, genomics, bioinformatics, bladder cancer
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