Transcriptome profiling research in urothelial cell carcinoma
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 high-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
Code (0)
등록된 구현이 없습니다.
Tasks
PrognosisSimilar Papers 제목 키워드 기반
End-to-end Multiple Instance Learning for Whole-Slide Cytopathology of Urothelial Carcinoma
As a non-invasive approach, cytopathology of urine sediment is a highly promising approach to diagnosing urothelial carcinoma. However, computational assessment of the cytopathological status of a sample raises the chall…
Multiple Instance Learningwhole slide imagesEnhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling
Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling. Recently, vibrational strong coupling between biomolecules and Mid-infrared (MI…
Computational challenges of cell cycle analysis using single cell transcriptomics
The cell cycle is one of the most fundamental biological processes important for understanding normal physiology and various pathologies such as cancer. Single cell RNA sequencing technologies give an opportunity to anal…
Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning
Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in…
Defining the relationship between cathepsin B and esophageal adenocarcinoma: conjoint analysis of Mendelian randomization, transcriptome-wide association studies, and single-cell RNA sequencing data
Background: Esophageal cancer poses a significant global health challenge, with the incidence of esophageal adenocarcinoma (EAC), a predominant subtype, increasing notably in Western countries. Cathepsins, a family of ly…