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Thomas Flaig, MD


Using RNA-Sequencing Data to Establish a Prognostic Model for Bladder Cancer

By: Vanessa A. Carter, BS
Posted: Monday, May 15, 2023

A recent study conducted by Jiansong Wang, PhD, of the Second Affiliated Hospital of Kunming Medical University, Yunnan Institute of Urology, Wuhua District, China, and colleagues combined bulk RNA-sequencing and single-cell RNA-sequencing data to construct a prognostic model for bladder cancer. Their novel prognostic model appeared to predict the overall survival of patients with bladder cancer, and the results of this trial were published in the Journal of Translational Medicine.

“The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics,” mentioned the investigators. “Overall, this study could be used as a reliable predictor of bladder cancer efficacy, opening up new avenues for targeted treatment of bladder cancer in the future.”

The Gene Expression Omnibus and The Cancer Genome Atlas databases were queried for single-cell and bulk RNA-sequencing data on 165 patients with bladder cancer, respectively. Marker genes, genes of bladder cancer key modules, and differentially expressed genes affecting overall survival were used to construct a prognostic model by Least Absolute Shrinkage and Selection Operator and univariate Cox analyses.

A total of 19 cell subpopulations and 7 core cell types were identified from single-cell RNA-sequencing data. Single-sample gene set enrichment analysis demonstrated downregulation of all seven core cell types in tumor samples of bladder cancer. Furthermore, 1,556 differentially expressed genes and 474 marker genes were identified from the bulk and single-cell RNA-sequencing data sets, respectively; weighted gene correlation network analysis found 2,334 genes associated with a key module.

The expression levels of three signature genes—MAP1B, PCOLCE2, and ELN—served as the basis for the prognostic model. Of note, two external validation sets and one internal training set validated the feasibility of this model. Ultimately, patients who displayed high-risk scores seemed to be predisposed to have a greater tumor mutational burden, a higher infiltration of immune cells, poor overall survival, and a decreased probability of responding to immunotherapy.

Disclosure: The study authors reported no conflicts of interest.

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