Dataset Information

Circulating miRNAs as non-invasive biomarkers to predict aggressive prostate cancer after radical prostatectomy.

ABSTRACT:

Background

Prostate cancer is an extremely heterogeneous disease. Despite being clinically similar, some tumours are more likely to recur after surgery compared to others. Distinguishing those that need adjuvant or salvage radiotherapy will improve patient outcomes. The goal of this study was to identify circulating microRNA that could independently predict prostate cancer patient risk stratification after radical prostatectomy.

Methods

Seventy-eight prostate cancer patients were recruited at the Odette Cancer Centre in Sunnybrook Health Sciences Centre. All patients had previously undergone radical prostatectomy. Blood samples were collected simultaneously for PSA testing and miRNA analysis using NanoString nCounter technology. Of the 78 samples, 75 had acceptable miRNA quantity and quality. Patients were stratified into high- and low-risk categories based on Gleason score, pathological T stage, surgical margin status, and diagnostic PSA: patients with Gleason???8; pT3a and positive margin; pT3b and any margin; or diagnostic PSA?>?20 µg/mL were classified as high-risk (n?=?44) and all other patients were classified as low-risk (n?=?31).

Results

Using our patient dataset, we identified a four-miRNA signature (miR-17, miR-20a, miR-20b, miR-106a) that can distinguish high- and low-risk patients, in addition to their pathological tumour stage. High expression of these miRNAs is associated with shorter time to biochemical recurrence in the TCGA dataset. These miRNAs confer an aggressive phenotype upon overexpression in vitro.

Conclusions

This proof-of-principle report highlights the potential of circulating miRNAs to independently predict risk stratification of prostate cancer patients after radical prostatectomy.

SUBMITTER: Hoey C

PROVIDER: S-EPMC6533745 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Publications

Circulating miRNAs as non-invasive biomarkers to predict aggressive prostate cancer after radical prostatectomy.

Hoey C C Ahmed M M Fotouhi Ghiam A A Vesprini D D Huang X X Commisso K K Commisso A A Ray J J Fokas E E Loblaw D A DA He H H HH Liu S K SK

Journal of translational medicine 20190523 1

<h4>Background</h4>Prostate cancer is an extremely heterogeneous disease. Despite being clinically similar, some tumours are more likely to recur after surgery compared to others. Distinguishing those that need adjuvant or salvage radiotherapy will improve patient outcomes. The goal of this study was to identify circulating microRNA that could independently predict prostate cancer patient risk stratification after radical prostatectomy.<h4>Methods</h4>Seventy-eight prostate cancer patients were ...[more]

PMID: 31122242

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Project description:Background: Cervical cancer is the most common malignant tumor in the female reproductive system, while the efficacy of routine screening strategy is unsatisfied. New molecular tests need to be developed. miRNAs participate in many pathologic processes, and circulating miRNAs are promising non-invasive biomarkers in tumors. Objectives: This study aimed to identify the circulating miRNAs associated with both cervical cancer and cervical intraepithelial neoplasia (CIN), and establish a non-invasive classifier for cervical lesions using circulating miRNAs. Methods: This study consisted of 5 steps: miRNAs screening, miRNAs validation, classifier establishment, independent validation and in silico analyses. Three cohorts were included in our study: In screening stage, 24 samples including 14 cases and 10 controls were retrieved; In validation stage, 380 samples including 200 cases and 180 controls were recruited; In independent validation stage, 47 samples comprising 26 cases and 21 controls were included. miRNAs were quantified by RT-qPCR. A classifier was built with random forest algorithm using validation samples and selected miRNAs, which were then validated in an independent cohort. To explore the function of selected miRNAs, in silico analyses were performed. Target genes of selected miRNAs were predicted by the overlap of three online tools. Enrichment analyses were executed with predicted target genes. Differential analysis of target genes was carried out with open access expression assay datasets of cervical tissues. Results: 6 miRNAs (hsa-miR-26b-5p, hsa-miR-146b-5p, hsa-miR-191-5p, hsa-miR-484, hsa-miR-574-3p, hsa-miR-625-3p) were screened out from 754 miRNAs. They were associated with cervical lesions and were selected to establish a classifier. The accuracy of the classifier were 0.7218 (0.7117, 0.7319) in validation samples, which was 0.7021 in the independent cohort. 958 target genes were predicted and enriched in 23 pathways (MAPK, human papillomavirus infection and Wnt signaling pathway, etc.). 55 genes were identified as the most likely target genes by differential analysis. Conclusion: The 6 circulating miRNAs were related to cervical lesions and could serve as non-invasive biomarkers.

Dataset Information

Circulating miRNAs as non-invasive biomarkers to predict aggressive prostate cancer after radical prostatectomy.

Background

Methods

Results

Conclusions

Publications

Circulating miRNAs as non-invasive biomarkers to predict aggressive prostate cancer after radical prostatectomy.

Similar Datasets

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