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. 2009;4(4):e5311.
doi: 10.1371/journal.pone.0005311. Epub 2009 Apr 23.

Repertoire of microRNAs in epithelial ovarian cancer as determined by next generation sequencing of small RNA cDNA libraries

Stacia K Wyman  1 , Rachael K Parkin, Patrick S Mitchell, Brian R Fritz, Kathy O'Briant, Andrew K Godwin, Nicole Urban, Charles W Drescher, Beatrice S Knudsen, Muneesh Tewari
Affiliations

Repertoire of microRNAs in epithelial ovarian cancer as determined by next generation sequencing of small RNA cDNA libraries

Stacia K Wyman et al. PLoS One. 2009.

Abstract

Background: MicroRNAs (miRNAs) are small regulatory RNAs that are implicated in cancer pathogenesis and have recently shown promise as blood-based biomarkers for cancer detection. Epithelial ovarian cancer is a deadly disease for which improved outcomes could be achieved by successful early detection and enhanced understanding of molecular pathogenesis that leads to improved therapies. A critical step toward these goals is to establish a comprehensive view of miRNAs expressed in epithelial ovarian cancer tissues as well as in normal ovarian surface epithelial cells.

Methodology: We used massively parallel pyrosequencing (i.e., "454 sequencing") to discover and characterize novel and known miRNAs expressed in primary cultures of normal human ovarian surface epithelium (HOSE) and in tissue from three of the most common histotypes of ovarian cancer. Deep sequencing of small RNA cDNA libraries derived from normal HOSE and ovarian cancer samples yielded a total of 738,710 high-quality sequence reads, generating comprehensive digital profiles of miRNA expression. Expression profiles for 498 previously annotated miRNAs were delineated and we discovered six novel miRNAs and 39 candidate miRNAs. A set of 124 miRNAs was differentially expressed in normal versus cancer samples and 38 miRNAs were differentially expressed across histologic subtypes of ovarian cancer. Taqman qRT-PCR performed on a subset of miRNAs confirmed results of the sequencing-based study.

Conclusions: This report expands the body of miRNAs known to be expressed in epithelial ovarian cancer and provides a useful resource for future studies of the role of miRNAs in the pathogenesis and early detection of ovarian cancer.

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Conflict of interest statement

Competing Interests: M. Tewari is a paid member of the Scientific Advisory Board of Combimatrix, Inc.

Figures

Figure 1
Figure 1. Small RNA cDNA library generation and data analysis pipeline.
A. Small RNAs were isolated from normal primary HOSE cultures and from serous (OSC), clear cell (OCC) and endometrioid (OEC) ovarian cancer tissues. Following 5′ and 3′ linker ligation, RT-PCR was performed to generate four independent cDNA libraries of small RNAs that were then used as templates for massively parallel pyrosequencing (454 sequencing). B. Description of the steps in the data analysis pipeline. The initial step of the data analysis was removal of sequences corresponding to 18 nt and 24 nt RNA markers that had been spiked into the total RNA prior to gel electrophoresis. Percentage of total reads from HOSE, OSC, OCC and OEC datasets classified into the designated categories and filtered out at each step are listed in the table on the right. At the bottom of the table, the number of “unique sequences” represents the non-redundant sequences derived after collapsing multiple reads of identical sequence. Some canonical sequences map to more than one locus in the genome. At completion of the analysis, 199 sequences totaling 215 reads and mapping to 568 loci met our criteria for canonical hairpin-derived sequences from the combined datasets.
Figure 2
Figure 2. Comparison of abundance of known miRNAs across datasets.
Datasets are compared pairwise, plotting the log of the fraction of total reads for a given miRNA in a given dataset against its corresponding value in the second dataset. For each plot, only miRNAs that were sequenced in both datasets are plotted; miRNAs that were sequenced only in one of the two datasets are not shown. The top row (A), compares ovarian cancer datasets (OSC, OCC and OEC) to normal primary HOSE cultures; the bottom row (B), compares ovarian tumor histologic subtypes to each other. Red dots signify miRNAs that had a fold change ≥4.
Figure 3
Figure 3. Differential expression of miRNAs in ovarian tumor histologic subtypes relative to normal primary HOSE.
Venn diagrams depict numbers of miRNAs differentially expressed in ovarian cancer histologic subtypes relative to normal primary human ovarian surface epithelium (HOSE) cultures. Criteria for a miRNA as differentially expressed was defined by a fold-change ≥4 with a Fisher's exact test P-value<5.0×10−6 for miRNAs that had detectable expression in both HOSE and ovarian cancer samples, or by a Fisher's exact test P-value<5.0×10−6 alone in cases where a fold-change value was indefinable due to zero reads for a given miRNA in either the HOSE or ovarian cancer comparison sample. A total of 74 over-expressed miRNAs (Panel A) and 49 under-expressed miRNAs (Panel B) in ovarian cancer with respect to normal HOSE were identified. MicroRNAs that were found to be consistently over-expressed or consistently under-expressed in all three ovarian cancer histologic subtypes are listed by name.
Figure 4
Figure 4. Validation of 454 sequencing-derived miRNA expression results by qRT-PCR: miRNAs differentially expressed in ovarian cancer relative to normal HOSE.
The graphs display miRNA Taqman qRT-PCR results for a sampling of miRNAs identified by 454 sequencing to be over-expressed (Panel A) or under-expressed (Panel B) in ovarian cancer relative to normal HOSE. Relative expression values derived from 454 sequencing are displayed on the left segment of each graph for comparison. Relative expression values of miRNAs in endometrioid (OEC, black bars), serous (OSC, white bars) and clear cell (OCC, grey bars) cancers are depicted. MicroRNAs that were not detected in normal HOSE by 454 sequencing but were detected in ovarian cancer are presented in Panel C, where miRNAs are ordered by descending expression (which corresponds to ascending cycle threshold, Ct) in normal HOSE. 454 sequencing data is given in terms of percentage of total reads within each sample. UD, undetectable by miRNA TaqMan qRT-PCR.
Figure 5
Figure 5. Validation of 454 sequencing-derived miRNA expression results by qRT-PCR: miRNAs differentially expressed between ovarian cancer histologic subtypes.
The graphs display Taqman qRT-PCR results for miRNAs differentially expressed between ovarian cancer subtypes, with 454 sequencing-derived data presented for comparison in the left segment of each graph. Relative expression values of miRNAs specifically over-expressed in endometrioid (Panel A, OEC, black bars), serous (Panel B, OSC, white bars) and clear cell (Panel C, OCC, grey bars) cancers are depicted. UD, undetectable by miRNA TaqMan qRT-PCR.
Figure 6
Figure 6. Novel miRNAs discovered by 454 sequencing.
Putative secondary structures for the six novel miRNAs discovered in this study are shown. Novel miRNA sequences are shown in red and star forms of novel miRNAs are shown in blue (if identified in our sequencing data). Sequences are novel with respect to miRBase release 12.0 , . Identifiers in parentheses refer to the miRNA star forms where these were identified in the 454 sequencing data. Corresponding details of the novel miRNAs are given in the table below the hairpin structures.
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