MicroRNA Expression Profiles in Upper Tract Urothelial Carcinoma Differentiate Tumor Grade, Stage, and Survival: Implications for Clinical Decision-Making

Brendan M. Browne, Kristian D. Stensland, Chintan K. Patel, Travis Sullivan, Eric J. Burks, David Canes, Jay Raman, Joshua Warrick, Kimberly M. Reiger-Christ

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Objective: To evaluate microRNA (miRNA) biomarkers for upper tract urothelial carcinoma (UTUC) to improve risk stratification. Methods: miRNA was isolated from 157 radical nephroureterectomy specimens from 2 institutions. The relative expression of miRNA was examined for high grade vs low grade tumors as well as muscle invasive vs nonmuscle invasive tumors. Recurrence free survival (RFS) and overall survival (OS) were also stratified using relative expression of specific miRNA. Results: The optimized model to identify high grade UTUC included miR-29b-2-5p, miR-18a-5p, miR-223-3p, and miR-199a-5p, generating a sensitivity of 83%, specificity of 85%, and generated a receiver operating characteristic (ROC) curve with area-under-the-curve of 0.86. Similarly, the model classifier for predicting ≥pT2 disease incorporated miR-10b-5p, miR-26a-5p-5p, miR-31-5p, and miR-146b-5p, producing a sensitivity of 64%, specificity of 96%, and area-under-the-curve of 0.90. RFS was best reflected by a combination of miR-10a-5p, miR-30c-5p, and miR-10b-5p, while OS was best predicted by miR-10a-5p, miR-199a-5p, miR-30c-5p, and miR-10b-5p. Conclusion: High-grade vs low-grade as well as muscle invasive vs nonmuscle invasive UTUC can be reliable distinguished with unique miRNA signatures. Furthermore, differential expression of UTUC miRNA produces robust classifiers for predicting RFS and OS that may help identify patients who would most benefit from adjuvant therapies.

Original languageEnglish (US)
Pages (from-to)93-100
Number of pages8
JournalUrology
Volume123
DOIs
StatePublished - Jan 1 2019

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MicroRNAs
Carcinoma
Survival
Neoplasms
Recurrence
Area Under Curve
Sensitivity and Specificity
Muscles
ROC Curve
Clinical Decision-Making
Biomarkers

All Science Journal Classification (ASJC) codes

  • Urology

Cite this

Browne, Brendan M. ; Stensland, Kristian D. ; Patel, Chintan K. ; Sullivan, Travis ; Burks, Eric J. ; Canes, David ; Raman, Jay ; Warrick, Joshua ; Reiger-Christ, Kimberly M. / MicroRNA Expression Profiles in Upper Tract Urothelial Carcinoma Differentiate Tumor Grade, Stage, and Survival : Implications for Clinical Decision-Making. In: Urology. 2019 ; Vol. 123. pp. 93-100.
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title = "MicroRNA Expression Profiles in Upper Tract Urothelial Carcinoma Differentiate Tumor Grade, Stage, and Survival: Implications for Clinical Decision-Making",
abstract = "Objective: To evaluate microRNA (miRNA) biomarkers for upper tract urothelial carcinoma (UTUC) to improve risk stratification. Methods: miRNA was isolated from 157 radical nephroureterectomy specimens from 2 institutions. The relative expression of miRNA was examined for high grade vs low grade tumors as well as muscle invasive vs nonmuscle invasive tumors. Recurrence free survival (RFS) and overall survival (OS) were also stratified using relative expression of specific miRNA. Results: The optimized model to identify high grade UTUC included miR-29b-2-5p, miR-18a-5p, miR-223-3p, and miR-199a-5p, generating a sensitivity of 83{\%}, specificity of 85{\%}, and generated a receiver operating characteristic (ROC) curve with area-under-the-curve of 0.86. Similarly, the model classifier for predicting ≥pT2 disease incorporated miR-10b-5p, miR-26a-5p-5p, miR-31-5p, and miR-146b-5p, producing a sensitivity of 64{\%}, specificity of 96{\%}, and area-under-the-curve of 0.90. RFS was best reflected by a combination of miR-10a-5p, miR-30c-5p, and miR-10b-5p, while OS was best predicted by miR-10a-5p, miR-199a-5p, miR-30c-5p, and miR-10b-5p. Conclusion: High-grade vs low-grade as well as muscle invasive vs nonmuscle invasive UTUC can be reliable distinguished with unique miRNA signatures. Furthermore, differential expression of UTUC miRNA produces robust classifiers for predicting RFS and OS that may help identify patients who would most benefit from adjuvant therapies.",
author = "Browne, {Brendan M.} and Stensland, {Kristian D.} and Patel, {Chintan K.} and Travis Sullivan and Burks, {Eric J.} and David Canes and Jay Raman and Joshua Warrick and Reiger-Christ, {Kimberly M.}",
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MicroRNA Expression Profiles in Upper Tract Urothelial Carcinoma Differentiate Tumor Grade, Stage, and Survival : Implications for Clinical Decision-Making. / Browne, Brendan M.; Stensland, Kristian D.; Patel, Chintan K.; Sullivan, Travis; Burks, Eric J.; Canes, David; Raman, Jay; Warrick, Joshua; Reiger-Christ, Kimberly M.

In: Urology, Vol. 123, 01.01.2019, p. 93-100.

Research output: Contribution to journalArticle

TY - JOUR

T1 - MicroRNA Expression Profiles in Upper Tract Urothelial Carcinoma Differentiate Tumor Grade, Stage, and Survival

T2 - Implications for Clinical Decision-Making

AU - Browne, Brendan M.

AU - Stensland, Kristian D.

AU - Patel, Chintan K.

AU - Sullivan, Travis

AU - Burks, Eric J.

AU - Canes, David

AU - Raman, Jay

AU - Warrick, Joshua

AU - Reiger-Christ, Kimberly M.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Objective: To evaluate microRNA (miRNA) biomarkers for upper tract urothelial carcinoma (UTUC) to improve risk stratification. Methods: miRNA was isolated from 157 radical nephroureterectomy specimens from 2 institutions. The relative expression of miRNA was examined for high grade vs low grade tumors as well as muscle invasive vs nonmuscle invasive tumors. Recurrence free survival (RFS) and overall survival (OS) were also stratified using relative expression of specific miRNA. Results: The optimized model to identify high grade UTUC included miR-29b-2-5p, miR-18a-5p, miR-223-3p, and miR-199a-5p, generating a sensitivity of 83%, specificity of 85%, and generated a receiver operating characteristic (ROC) curve with area-under-the-curve of 0.86. Similarly, the model classifier for predicting ≥pT2 disease incorporated miR-10b-5p, miR-26a-5p-5p, miR-31-5p, and miR-146b-5p, producing a sensitivity of 64%, specificity of 96%, and area-under-the-curve of 0.90. RFS was best reflected by a combination of miR-10a-5p, miR-30c-5p, and miR-10b-5p, while OS was best predicted by miR-10a-5p, miR-199a-5p, miR-30c-5p, and miR-10b-5p. Conclusion: High-grade vs low-grade as well as muscle invasive vs nonmuscle invasive UTUC can be reliable distinguished with unique miRNA signatures. Furthermore, differential expression of UTUC miRNA produces robust classifiers for predicting RFS and OS that may help identify patients who would most benefit from adjuvant therapies.

AB - Objective: To evaluate microRNA (miRNA) biomarkers for upper tract urothelial carcinoma (UTUC) to improve risk stratification. Methods: miRNA was isolated from 157 radical nephroureterectomy specimens from 2 institutions. The relative expression of miRNA was examined for high grade vs low grade tumors as well as muscle invasive vs nonmuscle invasive tumors. Recurrence free survival (RFS) and overall survival (OS) were also stratified using relative expression of specific miRNA. Results: The optimized model to identify high grade UTUC included miR-29b-2-5p, miR-18a-5p, miR-223-3p, and miR-199a-5p, generating a sensitivity of 83%, specificity of 85%, and generated a receiver operating characteristic (ROC) curve with area-under-the-curve of 0.86. Similarly, the model classifier for predicting ≥pT2 disease incorporated miR-10b-5p, miR-26a-5p-5p, miR-31-5p, and miR-146b-5p, producing a sensitivity of 64%, specificity of 96%, and area-under-the-curve of 0.90. RFS was best reflected by a combination of miR-10a-5p, miR-30c-5p, and miR-10b-5p, while OS was best predicted by miR-10a-5p, miR-199a-5p, miR-30c-5p, and miR-10b-5p. Conclusion: High-grade vs low-grade as well as muscle invasive vs nonmuscle invasive UTUC can be reliable distinguished with unique miRNA signatures. Furthermore, differential expression of UTUC miRNA produces robust classifiers for predicting RFS and OS that may help identify patients who would most benefit from adjuvant therapies.

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