Genomic "Dark Matter" in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers

Abstract

Prostate cancer is the most diagnosed cancer among men in the United States. While the majority of patients who undergo surgery (prostatectomy) will essentially be cured, about 30-40% men remain at risk for disease progression and recurrence. Currently, patients are deemed at risk by evaluation of clinical factors, but these do not resolve whether adjuvant therapy will significantly attenuate or delay disease progression for a patient at risk. Numerous efforts using mRNA-based biomarkers have been described for this purpose, but none have successfully reached widespread clinical practice in helping to make an adjuvant therapy decision. Here, we assess the utility of non-coding RNAs as biomarkers for prostate cancer recurrence based on high-resolution oligonucleotide microarray analysis of surgical tissue specimens from normal adjacent prostate, primary tumors, and metastases. We identify differentially expressed non-coding RNAs that distinguish between the different prostate tissue types and show that these non-coding RNAs can predict clinical outcomes in primary tumors. Together, these results suggest that non-coding RNAs are emerging from the "dark matter" of the genome as a new source of biomarkers for characterizing disease recurrence and progression. While this study shows that non-coding RNA biomarkers can be highly informative, future studies will be needed to further characterize the specific roles of these non-coding RNA biomarkers in the development of aggressive disease.

Keywords: clinical progression; microarrays; non-coding RNA; prognosis; prostate cancer.

Figure 1

Venn diagram of exonic (A) and non-exonic (B) features found differentially expressed in the following comparisons: normal vs. primary tumor tissue (N vs. P), primary tumor vs. metastatic tissue (P vs. M), and normal vs. metastatic tissue (N vs. M).

Figure 2

Distribution of non-exonic features (left) and overlapping annotated non-coding transcripts (right) found to be differentially expressed between normal and primary tumor (A,D), primary tumor and metastatic tissue (B,E), and normal vs. metastatic tissue (C,F). Features in the NC TRANSCRIPT slice of each pie chart (left) are assessed for their overlap with non-coding transcripts to generate the distribution of transcripts (shown at the right for each pairwise comparison). AS, antisense. UTR, untranslated region; lincRNA, long intergenic ncRNA.

Figure 3

Kaplan–Meier plots of the two groups of primary tumor samples classified by KNN (“normal-like” vs. “metastatic-like”) using the BCR end point for exonic (A) and non-exonic (B) features.

Figure A1

Multidimensional scaling plots of the distribution of primary tumor samples with (yellow) and without (blue) metastatic events compared to metastatic (red) and normal (green) tissues for exonic (A) and non-exonic (B) features. Metastatic and normal data points are included in the figure for illustrative purposes only.

Figure A2

Multidimensional scaling plots of the distribution of primary tumor samples with Gleason score of 6 (blue), 7 (purple), 8 and 9 (both in yellow) compared to metastatic (red) and normal (green) tissues for exonic (A) and non-exonic (B) features. Metastatic and normal data points are included in the figure for illustrative purposes only.