C/D-box snoRNA-derived RNA production is associated with malignant transformation and metastatic progression in prostate cancer

Oncotarget17430 www.impactjournals.com/oncotarget www.impactjournals.com/oncotarget/ Oncotarget, Vol. 6, No. 19 C/D-box snoRNA-derived RNA production is associated with malignant transformation and metastatic progression in prostate cancer Elena S. Martens-Uzunova1, Youri Hoogstrate1, Anton Kalsbeek1, Bas Pigmans1, Mirella Vredenbregt-van den Berg1, Natasja Dits1, Søren Jensby Nielsen2,3, Adam Baker2,4, Tapio Visakorpi5, Chris Bangma1 and Guido Jenster1 1 Department of Urology, Erasmus MC, Rotterdam, The Netherlands 2 Exiqon A/S, Vedbaek, Denmark 3 Nuevolution A/S, Copenhagen, Denmark 4 Chr. Hansen A/S, Hørsholm, Denmark 5 Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere and Tampere University Hospital, Tampere, Finland Correspondence to: Elena Martens-Uzunova, email: [email protected] Keywords: snoRNA, sdRNA, SNORD78, GAS5, prostate cancer Received: February 05, 2015 Accepted: May 02, 2015 Published: May 19, 2015 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Small nucleolar RNAs (snoRNAs) are dynamically regulated in different tissues and affected in disease. SnoRNAs are processed further to stable smaller RNAs. We sequenced the small RNA transcriptome of prostate cancer (PCa) at different PCa stages and generated a quantified catalogue of 3927 small non-coding RNAs (sncRNAs) detected in normal and malignant prostate tissue. From these, only 1524 are microRNAs. The remaining 2401 sncRNAs represent stable sncRNAs species that originate from snoRNA, tRNA and other sncRNAs. We show that snoRNA-derived RNAs (sdRNAs) display stronger differential expression than microRNAs and are massively upregulated in PCa. SdRNAs account for at least one third of all small RNAs with expression changes in tumor compared to normal adjacent tissue. Multiple sdRNAs can be produced from one snoRNA in a manner related to the conservation of structural snoRNA motifs. Q-PCR analysis in an independent patient cohort (n=106) confirmed the processing patterns of selected snoRNAs (SNORD44, SNORD78, SNORD74 and SNORD81) and the cancer-associated up-regulation of their sdRNAs observed in sequencing data. Importantly, expression of SNORD78 and its sdRNA is significantly higher in a subset of patients that developed metastatic disease demonstrating that snoRNA and sdRNAs may present as novel diagnostic and/or prognostic biomarkers for PCa. INTRODUCTION Malignant transformation and cancer progression cause changes in the expression and function of microRNAs (miRNAs) [1, 2]. However, the effects of these processes on other small non-coding RNAs (sncRNAs) are less understood. Recently, we demonstrated the abundance and differential expression of small nucleolar RNA-derived RNAs (sdRNAs) in the small transcriptome of prostate cancer (PCa) [3]. It is generally accepted that small nucleolar RNAs (snoRNAs) are housekeeping, protein-noncoding molecules that associate with specific sets of proteins to maintain proper ribosomal maturation in the nucleolus. Still, several reports show that snoRNAs have tissue-specific expression [4, 5], and may present as novel cancer biomarkers. For example, the H/ACAbox snoRNA SNORA42 is commonly overexpressed in non-small cell lung cancer (NSCLC) and its expression is significantly inversely correlated with survival [6, 7]. Oncotarget17431 www.impactjournals.com/oncotarget Similarly, the levels of C/D-box snoRNAs SNORD33, SNORD66 and SNORD76 are significantly elevated in plasma from NSCLC patients compared with cancer-free controls and can provide potential biomarkers for early detection [8]. In chronic lymphocytic leukemia (CLL), heterogeneous snoRNA expression patterns discriminate major CLL subgroups and can stratify patients in different prognostic groups [9], while in multiple myeloma snoRNA expression patterns are associated with distinct molecular subtypes of the disease [10]. Furthermore, resent research demonstrates that the molecular alterations of snoRNA are functionally linked to basic cellular processes associated with cancer proposing either tumor suppressor or oncogene role for different snoRNAs. In NSCLC, SNORA42 acts as a putative oncogene. Its overexpression enhances cell proliferation and growth in bronchial epithelium and cancer cells, while its knockdown in NSCLC cells inhibits colony forming [7]. In acute promyelocytic leukemia, the SNORD112–114 is specifically activated in a subset of patients and may influence cell growth through a negative regulation of the cell cycle and the Rb pathway [11]. On the contrary, in peripheral T-cell lymphoma, over-expression of the candidate prognostic marker SNORD71 (HBII-239) is associated with favorable outcome [12]. The C/D box snoRNA SNORD50, a translocation partner of BCL6 in B-cell lymphoma [13], is a candidate tumor suppressor significantly associated with clinically relevant prostate [14] and breast [15] cancer. In hepatocellular carcinoma (HCC), SNORD113-1 has been identified as a tumor suppressor [16]. Down-regulation of this snoRNA is associated with decreased survival of HCC patients, while reconstitution of its expression suppresses tumorigenesis in vitro and in vivo. In glioblastoma, decreased expression of the GAS5 encoded SNORD76 is associated with an aggressive phenotype [17]. Ectopic expression of this tumor suppressor snoRNA inhibits tumorigenicity by arresting cancer cells in S phase in vitro and inhibits orthotopic tumor growth in vivo. In breast cancer and head and neck squamous cell carcinoma the low expression of another GAS5 encoded snoRNA, SNORD44, correlates with markers of aggressive pathology and poor prognosis [18, 19]. At present, little is known about the pathways of snoRNA turnover. Apparently, snoRNAs are further processed to sdRNAs in a vast variety of organisms [20]. It is yet unclear whether sdRNAs are novel functional entities or footprint-products of snoRNA downstream processing shielded from degradation by snoRNAinteracting proteins. A miRNA-like activity has been proposed for sdRNAs derived from H/ACA-box snoRNAs (H/ACA-sdRNAs) based on their apparent size of 20-24 nt equivalent to miRNAs, the ability to promote repression of complementary targets in vitro, and the association with Dicer and AGO complexes [21-27]. In contrast, a bimodal size distribution of 17-20 nt and 27-30 nt has been reported for sdRNAs derived from C/D-box snoRNAs (C/DsdRNAs) [3, 22, 23]. C/D-sdRNAs are not efficiently incorporated in AGO2 suggesting a different function for this type of sdRNAs [28]. In addition, it has been reported that the highly abundant in brain ‘orphan’ snoRNAs, SNORD115 and SNORD116, are processed into larger sdRNAs (34-73 nt) that complex with spliceosomal proteins and may regulate the alternative splicing of target mRNAs [29, 30]. Association of C/D-box snoRNAs with novel RNPs and involvement in alternative splicing has been previously observed in mice for the brain specific MbII-52 [31]. Interestingly, both MbII-52 and its human ortholog SNORD115 produce larger sdRNAs (34-73 nt). Similar observation has also been made for sdRNA regions of SNORD88C, which can influence the alternative splicing of FGFR3 pre-mRNA [32]. At the same time, studies in Drosophila sp. and in human cells show that snoRNAs are strongly enriched in the nuclear fractions of chromatin-associated RNA and possibly involved in the maintenance of open chromatin structure [33]. Here, we report the deep sequencing of patientderived samples from normal prostate, and PCa in different disease stages, which reveals sdRNA production from the vast majority of known human snoRNAs. At least 78 of the detected sdRNAs demonstrate strong differential expression in cancer. Furthermore, the expression of some sdRNAs and their precursors is associated with clinical progression and metastatic occurrence. RESULTS Library preparation and sequencing We generated 10 sncRNA libraries from normal adjacent prostate (NAP), benign prostate hyperplasia (BPH), different stages of PCa, and metastatic lymph node (LN) prepared from fresh-frozen patient material (FF) (Supplementary Table 1). To estimate the influence of sample storage on sncRNA abundance and stability, we prepared a replicate library from formalin-fixed, paraffinembedded tissue (FFPE) from tumor samples used for one of the fresh-frozen libraries (group 3). All sequencing reactions yielded approximately 14 million raw reads each (13,468,284 to 15,393,670) with the FFPE library producing the highest raw read number (Figure 1a). Annotation of the sncRNA transcriptome The correct mapping of sncRNA reads is challenged by the fact that predominant isoforms of miRNAs and other sncRNAs such as snoRNAs may vary from the mature sequences annotated in public databases. Differences can be caused by alternative 3’-end modifications [34] or alternative 5’-/3’-end positions of Oncotarget17432 www.impactjournals.com/oncotarget the detected sncRNA. Additionally, the length of mature sncRNA transcripts can be ambiguously annotated in different public databases. To map as many sequence reads as possible, we constructed a custom small noncoding RNA database (sncRNAdb) that consists of 2271 unique small non-coding RNA species corresponding to 2356 unique genomic loci (Supplementary Figure 1 and Supplementary File 1). Mapping to sncRNAdb resulted in the detection of a total of 1637 unique sncRNAs expressed across any of the 11 libraries with an average of 1229 per library. 70% to 84% of the reads generated from fresh-frozen samples and only 52% of the reads generated from FFPE could be annotated by sncRNAdb (Figure 1a and Supplementary Table 2). The majority of annotated reads mapped to 873 pre-miRNAs (85.5 - 95.6%), 385 tRNAs (1.89 - 7.4%), 228 C/D-box snoRNAs (0.3 - 1.9%), and 91 H/ACA-box snoRNAs (0.0 - 0.1%) (Figure 1b, 1c and Supplementary Tables 2 and 3). Interestingly, in PCa samples we detected up to 27% more C/D-box and up to 52% more H/ACA-box snoRNAs compared to NAP or BPH. Furthermore, total snoRNA read-counts were increased at least two-fold, indicating possible activation of snoRNA-gene expression in response to malignant transformation. In contrast, the number of detected miRNAs remained relatively stable and the total miRNA read-counts changed by no more than 19% (min. 9,202,300, max. 11,367,682) (Figure 1c, Supplementary Figure 2 and Supplementary Tables 3 and 4). We also examined the read-length associated with different types of sncRNAs. As expected, miRNA reads had a narrow size distribution between 21 and 23 nt in all libraries. Similar size range was observed for snRNAand scaRNA-derived RNAs in fresh-frozen libraries. In concordance with our previous results [3], we detected a size peak at 23 nt and a plateau between 26-28 nt for reads mapping to C/D-box snoRNAs. Interestingly, reads mapping to H/ACA snoRNAs and tRNAs demonstrated a shift in size distribution between normal and malignant Figure 1: Summary of sncRNA sequencing data from PCa patient samples. (a) Number of retrieved raw, extracted, annotated, and unique reads generated for each one of the sequencing libraries. (b) Number of detected sncRNA-species per library. (c) Relative abundance of different sncRNA-types per library. Read-length distribution in normal (d) and cancer libraries (e) derived from fresh-frozen, (f) and FFPE material. Each sncRNA type is represented by different color: miRNA (red), SNORD (dark blue), SNORA (orange), tRNA (green), scaRNA (black), rRNA (gray), snRNA (yellow), scRNA (magenta), other miscellaneous RNAs (light blue). Oncotarget17433 www.impactjournals.com/oncotarget samples (Figure 1d, 1e and Supplementary Figure 3) suggesting cancer-associated alterations in sncRNA processing. Comparison of the sncRNA composition of the FFPE library with its fresh-frozen counterpart demonstrated that the relative miRNA read-content in FFPE decreased 3.9-fold from 92% to 24% of the total annotated reads. On the contrary, the number of reads mapping to other sncRNA species was strongly elevated i.e. sequence read-counts were increased 152fold for snRNAs, 12.7-fold for H/ACA-box snoRNAs, 5.6-fold for tRNAs, and 2.7-fold for C/D-box snoRNAs (χ2 test, p < 0.0001 for all tested groups) (Figure 1c, Supplementary Figure 2 and Supplementary Table 4). The size distribution of read-length in FFPE material was also strongly affected for all examined ncRNA groups except for miRNAs. (Figure 1e,f and Supplementary Figure 3). These observations can be explained with the higher level of RNA degradation in FFPE for transcripts longer than miRNA [35, 36]. Mapping, and annotation of sncRNA-derived RNAs (sncdRNAs) The majority of miRNA reads in small RNA sequencing data map to the specific location on their premiRNA corresponding to the mature miRNA. Similarly, reads mapping to other sncRNAs, originate from specific positions on their precursor rather than being randomly derived and can represent specific, biologically functional, smaller RNA species, e.g. sdRNAs or tRNA fragments (tRFs) [37]. Nevertheless, the assignment of RNA-seq sequence-reads to specific sdRNAs or tRFs for quantitation purposes is hampered by the lack of proper annotation. Furthermore, many sncRNAs produce multiple fragments [29, 30] that may overlap each other, which further complicates the exact determination of their origin loci and a subsequent quantitative analysis. To correctly determine the boundaries of sdRNAs, tRFs and other sncRNA-derived RNAs (sncdRNAs) in our dataset and annotate their specific location on the precursor sequence, we applied the computational algorithm Oncotarget17434 www.impactjournals.com/oncotarget Figure 2: FlaiMapper results. (a)Total number of detected sncRNA precursors per RNA type and sequencing library. (b) Total number of sncdRNAs per precursor type and sequencing library. (c, d, e, f) Different types of sncRNAs produce different number of fragments. (g) Relation between the number of fragments produced per precursor RNA and the expression levels of individual fragments. (h) Relation between the number of fragments produced per precursor RNA and the expression levels of the most abundant fragment per precursor. Oncotarget17435 www.impactjournals.com/oncotarget Fragment Location Annotation and Identification Mapper (FlaiMapper) and evaluated its performance in this data set as described [38]. Shortly, FlaiMapper predicted 5’- and 3’-miRNA ends were compared with the 5’- and 3’-end boundaries of corresponding mature miRNAs in MiRBase, v17 [39]. 82% of the detected miRNAs had a correctly determined 5’-end exactly matching miRBase annotation. An additional 11 % had an offset of 1 nt. In agreement with previous observations [39], 3’-ends of mature miRNAs had higher variability and matched miRBase annotations for 45%. From the investigated miRNAs additional 33% had 1 nt offset, and 14%, 2 nt offset (Supplementary Figure 4). Given the high confidence with which FlaiMapper identified 5’- and 3’-end boundaries of bona fide miRNAs, we performed annotation of all sncdRNAs in our freshfrozen libraries. We detected 3927 unique sncdRNAs derived from different precursor classes. From these, 1524 Figure 3: Global expression changes of sncdRNAs in normal and malignant prostate tissue. Upper and middle panels present scatterplots comparing the normalized expression values of individual sncdRNA (dots) in each prostate cancer library (PCa) during progressing disease to these in the library prepared from normal adjacent prostate tissue (NAP). The expression of sncdRNAs in the hormone-refractory, transurethral resection of the prostate (TURP HR) library is also compared to the benign prostate hyperplasia (BPH) library since the latest represents the normal counterpart of malignant transurethral resection of the prostate material. Differences in sncdRNA expression between biological replicates of Gleason 6 cancers (PCa 6) as well as comparison of a fresh-frozen library (FF) with its formalin-fixed, paraffin-embedded (FFPE) counterpart derived from the same patients are presented in the lower panels. Each sncdRNA type is presented by a different color. Diagonal lines across each scatterplot represent fold change difference in expression. Middle line, crossing the horizontal and vertical axes at 0, no expression change; lines crossing the vertical and horizontal axes at 2, twofold expression change; lines crossing the vertical and horizontal axes at 4, four-fold expression change. Cured, no disease relapse after radical prostatectomy; rec., recurrent disease, biochemical or metastatic relapse after surgery; LN, metastatic lymph node sample; TERG+, TMPRSS2-ERG fusion gene event; TERG-, no TMPRSS2-ERG fusion event; Numbers (6, 7 or 8) after PCa indicate the pathological Gleason score of the tumors in the respective group Oncotarget17436 www.impactjournals.com/oncotarget originated from miRNAs, 1175 - from tRNAs, 657 – from C/D-box snoRNAs, 244 – from H/ACA-box snoRNAs, and 327 – from other sncRNA species (Supplementary Table 5 and Supplementary File 2). The total number of detected unique sncdRNAs was higher than the number of detected unique precursor species showing that individual sncRNA precursors produce more than one sncdRNA (Figure 2a, 2b and Supplementary Figure 5). For example, the majority of pre-miRNAs produced one or two miRNAs corresponding to the guide and passenger strand. For C/D box snoRNAs we detected between 1 and 6 sdRNAs originating from the same precursor, with the exception of the unusually long SNORD3A, SNORD3B and SNORD3C, which give rise to 10 to 13 C/D-sdRNAs. Most H/ACAbox snoRNAs produced between 1 and 3 sdRNAs, while for tRNAs we detected between 1 and 6 tRFs per precursor (Figure 2d-2f). Other examined sncRNAs in our libraries produced a varying number of fragments ranging from 3 for the telomerase RNA component to 28 for the small nuclear 7SK RNA (Figure 2c; Supplementary Figure 6 and 7). We next argued that the expression level of the sncRNA-precursor might positively influence the number of sncdRNAs detected per sncRNA. We examined the distribution of expression values of individual sncdRNAs in relation to the number of sncdRNAs derived per sncRNA and could not observe a strong dependency between the median expression levels of sncdRNAs and the total number of sncdRNAs produced per sncRNA. We obtained similar results when the expression level of the most abundant sncdRNA per precursor was used as a surrogate measure of the expression of the precursor RNA (Figure 2g, 2h). Based on these results, we can conclude that multiple sncdRNAs originating from the same sncRNA can be detected independently of their (low) expression level or the expression level of their precursor. Vice versa, different precursor RNAs can produce only one sncdRNAs with very high abundance. Hence, it can be assumed that the number and quantity of different sncdRNAs do not directly reflect the abundance of their precursor but, like miRNAs, are probably also influenced by additional aspects of cellular metabolism, e.g. association with protein complexes and/or turnover rates. The size of unique sdRNAs ranged between 15 and 29 nt (Supplementary Figure 6). However, when the expression of individual sdRNAs of the same length were accounted, we observed a predominant size of 23 nt for the majority H/ACA-sdRNAs and a binominal size distribution for C/D-sdRNAs with two predominant sizes of 22-23 nt and 28 nt (Supplementary Figure 7), which is in agreement with our previous findings and other reports [3, 22, 23, 40]. C/D-sdRNAs demonstrated a broader size distribution, which however could be a reflection of the broader size range of their precursors. sdRNAs are differentially expressed in prostate cancer Previously, we observed differential expression of sdRNAs between PCa specimens [3]. To examine if such changes are a cancer-specific event we compared the expression of FlaiMapper defined sncdRNAs between normal (NAP and BPH) and malignant tissues of progressing disease (PCa, LN, TURP). We detected between 34 and 202 sncdRNAs with significant differential expression (Table 1, Figure 3, and Supplementary File 3). Approximately one third of the differentially expressed RNAs in each comparison comprised C/D-sdRNAs upregulated in cancer (Figure 3). In contrast, only one sdRNA was differentially expressed between nonmalignant samples (NAP and BPH) and only five, between biological replicate samples (PCa, Gleason 6, groups 3, 4, and 10). This suggests that the accumulation of C/DsdRNAs is primarily driven by malignant transformation. To examine the effect of sample storage on fragment abundance we compared the expression of sncdRNAs between the FFPE sample and its fresh-frozen (FF) counterpart. We limited comparison analyses to sncRNAs detected in any of the FF libraries. MiRNAs had decreased expression in FFPE compared with sdRNAs, tRFs and other sncdRNAs (Table 1, Figure 3 and Supplementary Figure 8). Nevertheless, the reduction of miRNA expression in FFPE appears to be the result of a global decrease in miRNA read-counts compared to readcounts of other sncdRNAs (Figure 1c) since the relative expression of miRNAs correlated strongly between both conditions (Pearson ρ = 0.9289) (Supplementary Figure 8). This was not observed for sdRNAs (Pearson ρ = 0.6557 for C/D-sdRNAs and 0.3895 for H/ACA-sdRNAs) or other sncdRNAs, which have longer precursors and may be more susceptible to degradation in FFPE material. SdRNAs demonstrate specific global processing patterns in prostate tissue Given the discrete size and specific expression of sdRNAs, we examined detected snoRNAs for the presence of a common processing pattern. To be able to compare with miRNAs, we aligned all snoRNA and premiRNA sequences and visualized the position and relative abundance of the corresponding sdRNAs and miRNAs (Figure 4, Supplementary file 4 and 5). The majority of sdRNAs originated from equivalent locations of their precursors. Often, one predominant sdRNA was observed per precursor. The position of these predominant sdRNAs was not dependent on the total number of smaller species detected per precursor sequence, showing a rather uniform fragmentation pattern consistent with the precursor-type. This is in agreement with previously suggested specific snoRNAs processing and accumulation of smaller RNAs Oncotarget17437 www.impactjournals.com/oncotarget observed in cell lines [28, 32]. In our patient samples, predominant H/ACAsdRNAs originate from either the 5’-arm of the first H/ ACA-snoRNA hairpin (38.5%) or the 3’-arm of the second hairpin including the region of the ACA-box (31%) (Figure 4b). C/D-box snoRNA produce twice as many predominant sdRNAs originating from the 5’-terminus that contain a C-box (60.1%) compared to 3’-terminal sdRNAs that contain a D-box (30.1%) (Figure 4a). Interestingly, individual C/D-sdRNAs with highly similar sequences demonstrate almost identical fragmentation pattern, which is also dependent on the conservation of snoRNA structural features. For example, snoRNAs from the highly conserved, multiple gene-copy SNORD116 family (HBII-85), which have a degenerated C’-box (UGAGUGA) produce four sdRNAs where the most abundant one maps to the 5’-region covering the C-box. SnoRNAs from the SNORD115 (HBII-52) family with conserved C’/D’-boxes produce three sdRNAs, with the most abundant ones mapping to the middle-region and covering the entire K-loop including the C’/D’-box. In contrast, the larger snoRNAs from the SNORD3 family, which lack a conserved C-box, produce between 10 and 13 overlapping sdRNAs with the most predominant mapping to the 3’-end. SNORD115 and SNORD116 sdRNAs differ in size and position from the previously reported highly abundant psnoRNAs processed from the orthologous MBII-52 and MBII-85 detected by RNase protection assays [29, 30, 41]. This discrepancy could be explained by the implicit methodology differences between sncRNA sequencing and RNase protection assays. However, these differences could be also caused by tissue-specific sdRNA accumulation as previously described for sdRNAs originating from SNORD88C (HBII-180C) [32] or by the dependence of processing mechanisms on the structural conservation of C/D-box snoRNAs. Of note, SNORD115, SNORD116, or SNORD88C-originating sdRNAs were detected at low abundance in our samples. Processing and expression of sdRNAs originating from GAS5 encoded C/D-box snoRNAs is related to the conservation of structural C’/D’-boxes We investigated whether the fragmentation pattern of other C/D-box snoRNA is also dependent on structural feature conservation. For this we analyzed the positional origin of a highly abundant sdRNA produced from the 3’- end of SNORD78 [3] and other sdRNAs from the same locus. SNORD78 is intronically encoded by the Growth Arrest Specific 5 gene (GAS5) together with 9 other C/Dbox snoRNAs [42]. All 10 SNORDs are presumably simultaneously transcribed as a GAS5 precursor-transcript, which undergoes intron removal and posttranscriptional processing. We could detect sdRNAs from all 10 GAS5encoded snoRNAs. However, only four (SNORD44, SNORD78, SNORD74 and SNORD81) snoRNAs produced abundant sdRNAs (Figure 5 and Supplementary Figure 9). Interestingly, SNORD74 and SNORD81 produced three abundant sdRNAs with similar, relatively low expression levels that mapped to the 5’-, 3’-, and middle region of the snoRNAs. The 3’- and middle sdRNAs overlapped each other and covered the K-loop and the conserved canonical C’/D’-box (Figure 5). In contrast, SNORD78 and SNORD44, which lack the canonical C’/ D’-box, produced predominantly one 28 nt long sdRNA each, mapping to the 3’-arm for SNORD78 (sd78-3’) or the 5’-arm of SNORD44 (sd44-5’). sd78-3’ and sd44-5’ were strongly upregulated in samples prepared from malignant tissue compared to normal or benign, while middleand opposite arm-derived sdRNAs were present only at very low read-counts in all libraries (Supplementary Figure 9a and 10). SNORD78 and sd78-3’ expression is associated with metastatic PCa To validate our sequencing data we tested the expression of SNORD44, SNORD78, SNORD74, SNORD81, and their derivate sdRNAs, in an independent patient cohort of 106 fresh-frozen clinical samples by quantitative real-time PCR (Q-PCR). To evaluate whether increased sdRNA expression is a result of a general activation of the GAS5 locus, we also measured the expression of the spliced GAS5 transcript (Figure 6 and Supplementary Figure 9b). All tested snoRNAs and sdRNAs were upregulated in organ-confined PCa compared to normal adjacent controls. This was not related to an elevation of the spliced GAS5 transcript, which did not demonstrate pronounced expression changes between NAP and PCa. Interestingly, overlapping sdRNAs originating from the same snoRNA as well as full-length snoRNAs were simultaneously detectable by Q-PCR suggesting the existence of multiple conformational states of these snoRNAs. Sd78-3’, SNORD78 and GAS5 expression was also detectable in different normal basal prostate epithelium cell lines (PNT2C2, RWPE) prostate cancer cell lines (PC346C, LAPC4, VCAP, LNCAP, 22RV1, PC3, and DU145N) as well as in hepatocellular carcinoma (HEP3B) and colon adenocarcinoma (COLO205) cells demonstrating that SNORD78 processing to sd78-3’ is not restricted to prostate tissue or cells. Similarly to patient data, the expression levels of sd78-3’ and SNORD78 were not correlated to the expression of the GAS5 host gene (Supplementary Figure 11). Consistent with our previous results [3] sd78-3’ was upregulated in the LN library generated in this study, suggesting association of this sdRNA with aggressive disease. Therefore, in the validation cohort we stratified patients with organ-confined disease at the time of radical Oncotarget17438 www.impactjournals.com/oncotarget Figure 4: Global processing patterns and relative abundance of sdRNAs and miRNAs expressed in prostate (cancer) tissues. (a) Full-length C/D-box snoRNAs are aligned relative to the middle nucleotide of each sequence. (b) H/ACA-box snoRNAs are aligned based on the position of the H-box. (c) Pre-miRNAs are aligned relative to the middle nucleotide of each sequence. A green line represents each full-length sncRNA. Sequences are extended 10 nt at each end to avoid mapping ambiguity caused by incorect annotation. Positions of detected conserved H/ACA-boxes or C/D-boxes are shown in blue and red. Light and dark grey lines indicate the positional origin of sdRNAs, miRNAs and miRNAs*. The color intensity corresponds to the relative abundance of sncdRNAs originating from the same precursor (read-count as a percentage of the total read-count per precursor), e.g. if only one sdRNA per snoRNA-precursor is detected it is assigned 100% abundance, if two or more sdRNAs originate from the same snoRNA the sdRNA with the highest read-count is given the darkest color and the sdRNA with the lowest read-count - the lightest. Thin dashed lines separate each panel into three subgroups where sncRNAs producing only one sncdRNA are on top, sncRNAs producing two sncdRNAs are in the middle and those producing there or more sncdRNAs are on the bottom. The highly sequentially conserved, multiple gene-copy C/D-box snoRNAs from the SNORD116 (HBII-85) and SNORD115 (HBII-52) families are grouped together below other C/D-box snoRNAs. The X-axis indicates the position of sncdRNAs relative to the center of their precursor sequence. The Y-axis depicts the number of full-length sncRNA precursors.