Comprehensive analysis of microRNAs in breast cancer
- Hong-Tai Chang†1, 2,
- Sung-Chou Li†3,
- Meng-Ru Ho4,
- Hung-Wei Pan5,
- Luo-Ping Ger5,
- Ling-Yueh Hu6,
- Shou-Yu Yu5,
- Wen-Hsiung Li4, 7 and
- Kuo-Wang Tsai5, 8Email author
© Chang et al.; licensee BioMed Central Ltd. 2012
Published: 13 December 2012
MicroRNAs (miRNAs) are short noncoding RNAs (approximately 22 nucleotides in length) that play important roles in breast cancer progression by downregulating gene expression. The detailed mechanisms and biological functions of miRNA molecules in breast carcinogenesis have yet to be fully elucidated. This study used bioinformatics and experimental approaches to conduct detailed analysis of the dysregulated miRNAs, arm selection preferences, 3' end modifications, and position shifts in isoforms of miRNAs (isomiRs) in breast cancer.
Next-generation sequencing (NGS) data on breast cancer was obtained from the NCBI Sequence Read Archive (SRA). The miRNA expression profiles and isomiRs in normal breast and breast tumor tissues were determined by mapping the clean reads back to human miRNAs. Differences in miRNA expression and pre-miRNA 5p/3p arm usage between normal and breast tumor tissues were further investigated using stem-loop reverse transcription and real-time polymerase chain reaction.
The analysis identified and confirmed the aberrant expression of 22 miRNAs in breast cancer. Results from pathway enrichment analysis further indicated that the aberrantly expressed miRNAs play important roles in breast carcinogenesis by regulating the mitogen-activated protein kinase (MAPK) signaling pathway. Data also indicated that the position shifts in isomiRs and 3' end modifications were consistent in breast tumor and adjacent normal tissues, and that 5p/3p arm usage of some miRNAs displayed significant preferences in breast cancer.
Expression pattern and arm selection of miRNAs are significantly varied in breast cancers through analyzing NGS data and experimental approach. These miRNA candidates have high potential to play critical roles in the progression of breast cancer and could potentially provide as targets for future therapy.
Breast cancer is one of the major causes of cancer-related deaths worldwide and the most common cancer among women . Metastatis to distant organs and lymph nodes represents a major problem, usually leading to high mortality. The investigation of breast cancer-associated genes for early detection or therapeutic targeting could potentially improve the survival rates of breast cancer patients.
MicroRNAs (miRNAs) are small RNA molecules with important regulatory functions in several physiological activities . MicroRNAs are processed from primary transcripts (pri-miRNAs) in 2 maturation steps. First, the pri-miRNAs are processed by Drosha, forming the precursor miRNAs (pre-miRNAs), composed of a 5p arm, a 3p arm, and a terminal loop, approximately 70 nucleotides in length. Following the transport of pre-miRNAs to the cytoplasm by exportin 5, they are further processed by Dicer to release the terminal loop and the duplex (5p arm/3p arm), 22 nucleotides in length. The 5p arm/3p arm of the duplex is unwound at the end because of weaker hydrogen binding. The 5p or the 3p arm is selectively loaded into the RNA-induced silencing complex (RISC) and serves as mature miRNA [3–5]. Recent studies described a phenomenon in which RNA editing or nucleotide addition generated 3' end sequence variants of miRNAs [6–13]. Fernandez-Valverde et al.  reported that miR-282 and miR-312a are enriched for 3' adenosine additions during early embryonic development, which increases miRNA stability or enhances miRNA and mRNA interaction.
MicroRNAs exert their effects by repressing their target genes. They downregulate target gene expression by repressing translation or by degrading mRNAs. Previous studies reported that miRNAs play important roles in the oncogenesis pathway [14–18]. The tumor-associated miRNAs were either tumor repressors, preferentially expressed in normal tissue, or onco-miRNAs, preferentially expressed in tumor tissue. These are aberrantly expressed in human breast cancer, including miR-9, miR-21, miR-31, miR-34a, miR-155, miR-200, miR-205, miR-206, and miR-335 [19–22]. Although several studies have investigated the functions of miRNAs in breast tumors, these only included a small fraction of existing miRNAs [23–26]. Using miRNA profiling approach, numerous breast cancer-associated miRNAs were identified [19, 27–30]. Ryu et al.  identified 189 candidate novel microRNAs in human breast cancer cell lines by deep sequencing technology. Therefore, emerging NGS technologies can be used not only to identify novel miRNAs, but can also be applied in several miRNA-associated studies.
In the studies using NGS data for miRNA profiling, it is usually observed that miRNA sequence reads exist as isoforms, named isomiRs, with position and length shift compared with the reference miRNAs . Recently, more and more studies worked on the isomiR issues, such as isomiR pattern preferences in specific libraries, target gene selection difference between different isomiRs and so on [6, 32]. Therefore, NGS data provides a good resource for miRNA expression profiling and isomiR related studies. In 2011, Farazi et al.,  used NGS data to determine miRNA expression profiles in breast tissues with differing tumor malignancies. They focused on the relevance of specific miRNAs and the tumor malignancy type, without providing further experimental validation. The present study applied their NGS data to conduct analysis of miRNA-associated changes in breast cancer, including differential miRNA expression, position shifts in isomiRs, 3' end modifications, and arm selection preferences of pre-miRNAs.
Materials and methods
Collection and preprocessing of sequence reads
The small RNA transcriptome data of breast tumor (accession number: SRP006574) was downloaded from the NCBI Sequence Read Archive (SRA). These data included more than 200 samples and was classified into 2 libraries: normal and tumor (invasive ductal carcinoma). The initial sequence reads were subjected to 3' adaptor trimming to generate the clean reads, as described previously [10, 13]. For higher confidence, only the clean reads with read count ≥ 2 were included in further analysis.
Mapping clean reads to pre-miRNAs
MicroRNA expression profiles in different libraries were determined by mapping the clean reads back to human pre-miRNAs (miRBase 17). Several miRNA genes show high similarity (such as 68 mir-548 paralogous miRNAs in humans). This results in multiple ambiguous hits when mapping a read back to human miRNAs if variations are allowed. To eliminate the ambiguous mapping hits, no mismatch was allowed during the mapping procedure. Previous reports described observing nucleotide additions at the 3' ends of miRNAs [31, 34–36] that could cause mismatches at the terminal of the mapping alignment. In order to follow the no-mismatch policy and keep the 3' end variation, like the method in Fernandez-Valverde's study , we trimmed out the terminal 3' end mismatch one by one until the perfect match reads were at least 18 nucleotides in length. By doing so, we can keep not only an at least 18-nt perfect alignment but also the 3' end variations.
Classifying non-miRNA reads into different data sets
The non-miRNA sequence reads were further classified into 9 classes by mapping to different data sets with bowtie , allowing a single nucleotide variation. The sequences of mRNAs and other ncRNAs were derived from NCBI RefSeq 47 . The sequences of tRNAs was downloaded from the Genomic tRNA database ; sequences of rRNAs were downloaded from the SILVA database . The sequences of snoRNAs, scaRNAs, and snRNAs were all downloaded from NONCODE . The sequence reads not belonging to any of the described RNA classes were uploaded to RepeatMasker for identification of repeat elements and classified as unknown.
Samples and RNA extraction
Ten paired (tumor and adjacent normal) samples were collected from breast cancer patients receiving surgical operation at the Department of Surgery, Kaohsiung Veterans General Hospital. The total RNA of tissue was extracted using a TRIzol reagent (Invitrogen, USA), according to the instruction manual. Briefly, tissue samples were homogenized in 1 ml of TRIzol reagent and mixed with 0.2 ml chloroform to extract protein; RNA was precipitated using 0.5 ml isopropanol. The concentration, purity, and amount of total RNA were determined using a Nanodrop 1000 spectrophotometer (Nanodrop Technologies Inc., USA).
Stem-loop reverse transcription (RT) and real-time PCR
Reverse transcription primers were specifically designed for the examined miRNAs according to the methods reported by Chen et al., . One microgram of total RNA was reverse transcribed in a stem-loop RT reaction with RT primers and SuperScript III Reverse Transcriptase according to the user's manual (Invitrogen, Carlsbad, CA, USA). The reaction was performed under the following incubation conditions: 30 min at 16°C, followed by 50 cycles of 20°C for 30 s, 42°C for 30 s, and 50°C for 1 s. The enzyme was subsequently inactivated by incubating at 85°C for 5 min. Real-time PCR reactions were performed using an miRNA-specific forward primer and a universal reverse primer with incubation at 94°C for 10 min, followed by 40 cycles of 94°C for 15s and 60°C for 32 s. Gene expression levels were detected using SYBR Green I assay (Applied Biosystems, Foster City, CA, USA), and miRNA expression levels were normalized to that of U6. The primer sequences for the examined miRNAs are listed in Additional File 1.
Pathway enrichment analysis
Human miRNA target gene data was downloaded from TargetScan 6.0. The target genes of differentially expressed miRNAs were extracted, then mapped onto KEGG pathways based on the enzyme commission (EC) numbers using the R package SubPathwayMiner v.3.1 . The hypergeometric test was then performed to identify significantly enriched pathways and calculate the false positive discovery rate in FDR-corrected q-value.
Results and discussion
Analysis of miRNA sequence reads
Categories of sequence reads in the 2 breast libraries.
# clean reads
2 785 848
38 335 412
% miRNA reads
# detected pre-miRNAs
# detected miRNAs
# detected miRNAs at opposite arm
% miRNA reads with 3' end modification
Categories of clean sequence reads in the 2 breast libraries
Differentially expressed miRNAs
Validation of differentially expressed miRNAs using PCR.
Enrichment analysis of miRNA-involved pathway
Following the identification of the differentially expressed miRNAs, the subsequent stage was to identify their functions as defined by their target genes. Several computational methods can be used to identify the putative target genes of miRNAs [46–49]. However, these computational methods typically depend on the hydrodynamic stability of the miRNA/3'UTR duplex, and usually produce several false positive results. The most recently developed method is dependent on computational identification and also on the enrichment analysis of a target gene pathway . The present study applied the same strategy and did the analysis on the miRNAs with the following features: with high expression alteration and high expression level in at least one library. So, we selected the union of the target genes of hsa-miR-141 and hsa-miR-200b for tumor-preferring miRNAs, and the union of the target genes of hsa-miR-22, hsa-miR-125b, and hsa-miR-99a for normal-preferring miRNAs. The 2 unions of genes were individually subjected to pathway enrichment analysis. The pathway enrichment analysis result showed that the target genes of tumor-preferring miRNAs are significantly enriched in the mitogen-activated protein kinase (MAPK) pathway, with a p-value of 2.1E-6 (Additional file 3); while, the target genes of normal-preferring miRNAs are significantly enriched also in the mitogen-activated protein kinase (MAPK) pathway, with a p-value of 2.4E-6 (Additional file 4).
The MAPK pathways are highly conserved kinase modules involved in fundamental cellular processes such as growth, proliferation, migration, and apoptosis . Studies have identified that the miR-200 family (miR-200a, miR-200b, miR-200c, miR-141 and miR-429) is overexpressed in breast cancer, promoting breast cancer metastasis and drug resistance [25, 52]. Studies have also reported that overexpression of miR-200 in epithelial cell lines leads to an inhibition of transforming growth factor-β (TGF-β) and induction of epithelial mesenchymal transition (EMT) [53, 54]. These results indicated that the miR-200 family plays dual roles in modulating breast cancer metastasis through the regulation of the complex MAPK signaling pathway. However, miR-99a plays opposite roles in the regulation of cancer progression. Oneyama et al.  reported that overexpression of miR-99a led to the suppression c-Src-transformed cell growth, by controlling the mTOR/FGFR3 pathway in various human cancers. In epithelial NMUMG cells, however, miR-99a promoted proliferation and migration by regulating TGF-β-induced breast EMT . Imbalance in the MAPK signaling pathway can, therefore, lead to promotion or inhibition of cancer cell progression. This data indicates that aberrant miRNA expression can result in the dysregulation of breast cancer cell proliferation, apoptosis, cell cycle, and migration through regulation of the component genes of the complex MAPK signaling pathway.
Previous studies usually focus on the regulation relationship between one miRNA versus one pathway. Since many miRNAs are simultaneous up- or down-regulation in the same tissue, we are curious if they simultaneously act on the same pathway. So, we pick up the target gene unions of up- or down-regulated miRNAs, followed by pathway enrichment analysis on the target genes of the same union. Our result showed that the simultaneously up-regulated or down-regulated miRNAs execute their functions by acting on the same MAPK pathway. Actually, we also did the pathway enrichment analysis as usual, one miRNA versus one pathway. And, we got the same conclusion that the simultaneously up- or down-regulated miRNAs simultaneously act on the same pathway.
MicroRNA 3' end additional nontemplate nucleotides in breast cancer
Position shifts in isomiRs in breast cancer
It has been widely observed that miRNA exists as isoforms, or isomiRs, generated by a position shift during the maturation process . The present study detected all isomiRs by mapping the clean reads back to pre-miRNAs. For example, hsa-miR-511 has 5 isomiRs, each with differing length. The expression of an miRNA can be derived by summarizing the read counts of its isomiRs (Figure 2). Morin et al.  showed that the miRBase reference miRNA (the isomiR with position shift 0,0 in Figure 2) is not always the most abundant isomiR. Among the detected miRNAs in libraries, approximately 55% are the most abundant isomiRs; most of the remaining miRNAs are the second or third most abundant isomiRs.
Arm selection preferences in breast cancer
Arm selection preference of 5p arm and 3p arm miRNAs in normal breast and breast tumor tissues.
The present study performed a series of sequence analysis to evaluate miRNA-associated changes in breast cancer, including miRNA expression, arm selection, 3' end modifications, and position shifts in isomiRs. We identified 22 differentially expressed miRNAs in normal breast and breast tumor tissue that might be involved in breast cancer progression through regulation of MAPK signaling. MicroRNAs widely displayed 3' end modifications and position shifts in isomiRs in breast cancer. However, no significant differences emerged between normal breast and breast tumor tissue during carcinogenesis. Arm usage of some miRNAs displayed significant preferences in breast cancer, suggesting that hydrogen bonding theory does not sufficiently explain 5p or 3p arm selection during carcinogenesis. Further investigation of the possible effects of arm selection of miRNAs on breast carcinogenesis is needed. The present study's findings provide insights into breast cancer that might facilitate the development of future cancer therapy.
This work was supported by grants from Kaohsiung Veterans General Hospital (VGHKS 101-010 and VGHKS101-118) and National Sciences Council (NSC100-2314-B-075B-008)
This article has been published as part of BMC Genomics Volume 13 Supplement 7, 2012: Eleventh International Conference on Bioinformatics (InCoB2012): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/13/S7.
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