Author ORCID Identifier
Date of Graduation
Doctor of Philosophy (PhD)
David J. McConkey, Ph.D.
Joya Chandra, Ph.D.
Varsha Gandhi, Ph.D.
Chunru Lin, M.D., Ph.D.
Xiaoping Su, Ph.D.
NON-CODING RNAS IDENTIFY THE INTRINSIC MOLECULAR SUBTYPES OF MUSCLE-INVASIVE BLADDER CANCER
Andrea Elizabeth Ochoa, B.S.
Advisory Professors: David J. McConkey, Ph.D. and Joya Chandra, Ph.D.
There has been a recent explosion of genomics data in muscle-invasive bladder cancer (MIBC) to better understand the underlying biology of the disease that leads to the high amount of heterogeneity that is seen clinically. These studies have identified relatively stable intrinsic molecular subtypes of MIBC that show similarities to the basal and luminal subtypes of breast cancer. However, previous studies have primarily focused on protein-coding genes or DNA mutations/alterations.
There is emerging evidence implicating non-coding RNAs (ncRNAs), both short (miRNA) and long (lncRNA), in the regulation of various biological processes involved in cancer development and progression. The molecular mechanisms of miRNAs are relatively straightforward by inhibiting their mRNA targets, but the molecular mechanisms of lncRNAs are largely unknown. The identification of miRNAs and lncRNAs that contribute to the gene expression patterns of basal and luminal subtypes of MIBC will add another layer of subtype regulation.
In this work, we sought to study the differences in miRNA and lncRNA expression across the subtypes of MIBC. We started with TCGA’s cohort of 408 tumors as a discovery cohort to identify differentially expressed miRNAs and lncRNAs that were specific to the basal and luminal subtypes of MIBC. We developed our own miRNA-sequencing data set to perform validation studies, and we found that the mRNA targets of the differentially expressed miRNAs were highly reminiscent of the already known basal and luminal subtype biology. We also developed bioinformatic analyses to extract lncRNA expression data that was used for unsupervised consensus clustering. Surprisingly, unsupervised analyses of the lncRNA expression data revealed two distinct clusters that exhibited more than 90% concordance with the subtype classifications made using mRNA expression data.
Taken together, the results presented here suggest that miRNA expression profiles, or lncRNA expression profiles, could be used as an alternative strategy to identify MIBC subtype. These findings could have significant clinical implications in the development of diagnostic tools for MIBC since miRNAs and lncRNAs are both stably expressed in body fluids.
urothelial cancer, TCGA, consensus clustering, EMT, miRNA, lncRNA