MicroRNAs (miRNAs) have been proved to play an important role in various cellular processes and function as tumor suppressors or oncogenes in cancers including leukemia. The identification of a large number of novel miRNAs and other small regulatory RNAs will provide valuable insights into the roles they play in tumorgenesis.
To gain further understanding of the role of miRNAs relevant to acute lymphoblastic leukemia (ALL), we employed the sequencing-by-synthesis (SBS) strategy to sequence small RNA libraries prepared from ALL patients and normal donors. In total we identified 159 novel miRNAs and 116 novel miRNA*s from both libraries. Among the 159 novel miRNAs, 42 were identified with high stringency in our data set. Furthermore, we demonstrated the different expression patterns of 20 newly identified and several known miRNAs between ALL patients and normal donors, suggesting these miRNAs may be associated with ALL and could constitute an ALL-specific miRNA signature. Interestingly, GO “biological process” classifications revealed that a set of significantly abnormally expressed miRNAs are associated with disease relapse, which implies that these dysregulated miRNAs might promote the progression of ALL by regulating genes involved in the pathway of the disease development.
The study presents a comprehensive picture of the expression of small RNAs in human acute lymphoblastic leukemia and highlights novel and known miRNAs differentially expressed between ALL patients and normal donors. To our knowledge, this is the first study to look at genome-wide known and novel miRNA expression patterns in in human acute lymphoblastic leukemia. Our data revealed that these deregulated miRNAs may be associated with ALL or the onset of relapse.
Citation: Zhang H, Yang J-H, Zheng Y-S, Zhang P, Chen X, Wu J, et al. (2009) Genome-Wide Analysis of Small RNA and Novel MicroRNA Discovery in Human Acute Lymphoblastic Leukemia Based on Extensive Sequencing Approach. PLoS ONE 4(9): e6849. https://doi.org/10.1371/journal.pone.0006849
Editor: Baohong Zhang, East Carolina University, United States of America
Received: June 13, 2009; Accepted: August 3, 2009; Published: September 2, 2009
Copyright: © 2009 Zhang et al. 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.
Funding: The study was supported by the National Natural Science Foundation of China (No. 30672254 and 30872784 to Y.Q.C. and No. 30772498 to P.Z.), National High-Tech Program (863, No. 2008AA02Z106 to Y.Q.C) from the Ministry of Science and Technology, China, and funds from National Science and Technology Department and Guangdong Province (2005CB724600 and NSF-05200303 to L.H Q). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
MicroRNAs (miRNAs) are a class of endogenous noncoding RNAs, between 19 to 25 nucleotides in size, which can regulate gene expression at either the transcriptional or post-transcriptional level . Studies have shown that miRNAs play an important role in development and various cellular processes, such as differentiation, growth, and death . A connection between miRNAs and cancers has also been elucidated . It has been shown that about 50% of annotated human miRNAs are located at fragile sites and genomic regions involved in cancers . Other data indicates that miRNAs function as both tumor suppressors and oncogenes in tumorigenesis and cancer-specific miRNA expression signatures have been identified in many cancers –. Identification of a large number of novel miRNAs and other small regulatory RNAs is critical to provide valuable insights into the role these may play in tumorgenesis.
Recently, the sequencing-based method has been widely applied for identifying and profiling novel miRNAs candidates. Studies on novel miRNAs based on the sequencing method have been reported in chicken, human embryonic stem cells, solid cancers, etc. –. In leukemia, Marton et al.  used a cloning-based sequencing approach to study small RNAs from chronic lymphocytic leukemia (CLL) patients and five novel miRNA candidates were identified, which could be relevant in CLL pathogenesis. Another study examined miRNA expression profiles and isolated many novel miRNAs candidates from leukemia cells mainly present in acute myeloid leukemia (AML) by means of miRNA amplification and sequencing . Their results suggest that there are still many novel miRNAs existing in leukemia cells. Recently, the Illumina massively parallel sequencing platform was used to carry out an in-depth analysis of the miRNA transcriptome in a murine AML leukemia progression model and 55 novel miRNAs were identified, some of which could be relevant to the pathogenesis of AML , indicating that the high-throughput sequencing method can be used as a new and powerful tool to identify unannotated novel miRNA candidates which are lowly abundant or nonconserved but relevant to the diseased state.
Acute lymphoblastic leukemia (ALL) is a malignant disorder of lymphoid progenitor cells and characterized by chromosomal abnormalities, which occurs at all ages but with peak prevalence between the ages of 2 and 5 years . With modern treatment strategies using risk-adapted combination chemotherapy, the cure rates of childhood ALL are almost 80% . However, 20–30% of children still relapse and conventional intensive chemotherapy can only cure up to 30% of children who have relapsed –. As treatment of relapsed disease remains a challenging, the accurate assignment of individual patients to risk groups is a critical issue for optimal outcome, , . Although microRNA expression signatures associated with cytogenetics and the clinical outcome of ALL have been reported –, these efforts are limited as they are mainly restricted to the detection and profiling of previously identified miRNA sequences.
To gain a more complete and unbiased view of the small RNA transcriptome and further understand the role of miRNAs relevant to ALL, we employed a sequencing-by-synthesis (SBS) strategy to globally study small RNAs, especially miRNAs that have thus far been proven difficult to find using traditional cloning and in silico predictions. We present a comprehensive picture of the expression of small RNAs and have identified 159 novel miRNAs in total. Notably, the unique expression patterns in ALL patients and GO analysis suggest a set of miRNAs may be associated with disease relapse, which provides valuable insight into the pathogenesis of ALL relapse.
Annotation of small RNAs and identification of novel miRNA genes from patient group and normal donor
To discover additional miRNAs that have escaped detection in previous studies associated with ALL –, we employed SBS strategy to sequence two small RNA libraries of pooled bone marrows from a patient group (P) and a normal donor group (N). This yielded 4,418,887 (P) and 4,361,324 (N) reads, respectively. After computer filtering to remove ambiguous reads, 831,728 (P) and 1,993,962 (N) sequence reads were obtained corresponding to 60,460 (P) and 113,414 (N) unique reads (18 nt∼30 nt). Unique sequences were mapped to the human genome (Mar. 2006, version hg18, NCBI Build 36.1) using MegaBLAST  (version 2.2.18), and those with perfect matches across their entire length were retained. Consequently, a total of 15,537 and 23,859 unique sequences represented by 608,358 (P) and 1,327,742 (N) reads were perfectly matched to 3,905,630 and 4,119,366 genome loci, respectively. The schematic representation of small RNA library sequencing and subsequent bioinformatic analysis is shown in Fig. 1. These sequences were annotated based on their overlap with publicly available genome annotations (Table S1) and a total of 472 known miRNAs were identified with a greater total number of miRNAs expressed in the normal donor group than in the patient group (Fig. 2A). By analyzing the precursor sequence of miRNAs, we identified a total of 74 novel miRNA*(star)s (Table S2) in both groups. Mature miRNAs are processed from the stem of a hairpin precursor, and the miRNA*(star) sequence corresponds to the section of this hairpin that remains complementary to the mature form with approximately 2-nucleotide 3′ overhangs , . Among them, we found that some miRNA*s such as hsa-miR-766* were significantly up-regulated in the patient group, whilst some miRNA*s such as hsa-miR-1307* were significantly down-regulated in the patient group with fold changes >2.0 and a p-value of <0.001, suggesting miRNA*s could be involved in the progression of ALL.
(A) Known miRNAs unique in each group and shared by both. (B) MiRNA*s of known miRNAs unique in each group and shared by both.
To identify novel miRNAs among the unclassified sequences in our libraries, we removed all known miRNAs, other small RNAs and genomic repeats. A total of 12,798 unique sequences (corresponding to 55,128 reads) in the normal donor library and 7,356 (corresponding to 29,599 reads) in the ALL library were unannotated. We next evaluated reads that fell within potential miRNA-like hairpins considering the criteria: 1) z-score less than −3.0, this discriminate significantly miRNAs from other structured RNAs –. 2) A total number of reads from two libraries at least three reads in harpins or 3) the presence of reads corresponding to the predicted miRNA* species , . Novel candidate miRNAs had to satisfy criteria 1, and one of criteria 2 and 3. One hundred and fifty nine novel miRNAs(Fig. S1) and 116 novel miRNA*s were identified from the two libraries. As described by Ruby et al. , the observation of both a candidate miRNA and a corresponding candidate miRNA* in a set of reads provides particularly compelling evidence for Dicer-like processing from an RNA hairpin. Therefore, 42 out of 159 novel candidate miRNAs met the criteria. Among the 42 novel miRNAs meeting the highly stringenct criteria, 22 and 5 novel miRNAs are unique in normal donor and ALL patient groups, respectively. The other 15 novel miRNAs are shared by both groups (Table 1, Fig. 2B).
Differentially expressed patterns of known and novel miRNAs between control and patient group
Using this sequencing platform, we performed a systematic miRNA expression profiling analysis of patient samples. A total of 847 miRNAs including miRNA*s (miRBase 11.0) were evaluated and the relative expression of each miRNA between control and patient were examined, 472 of which were detected in control or patient samples. Only 77 miRNAs were up-regulated and 67 miRNAs were down-regulated in the patient group when compared to the control group with fold changes >2.0 and a P-value of <0.001 (Table S3). The top 40 miRNAs differentially expressed in patient samples with counts>200 and fold changes>2.0 and P-value of <0.001 are listed in Table S4. Of these, miR-9*, miR-9, miR-181a and miR-128 exhibited a significantly high abundance, whilst miR-582-5p, miR-223, miR-143, miR-126 etc. displayed the most significantly reduction in the patient group (Fig. 3). We further performed qRT-PCR to examine the expression levels of randomly selected miR-223. Results showed that expression of miR-223 is significantly reduced over 2 fold compared to control in 85% (17/20) of ALL patients (part of data is shown in Fig. 4A). Additionally, we have found that some data in this study are consistent with previous findings reported by Mi et al  using microarray analysis. For example, miR-128, miR-130b and miR-210 were up-regulated and miR-424, miR-223, miR-23a, miR-27a were down-regulated in the patient group, indicating that there is a concordance between the findings despite using completely different miRNA expression analysis platforms, which suggests that these novel techniques are robust.
Box plots (median and interquartile range) show the distribution of fold change of expression in ALL patients compared with normal donor samples.
To investigate the potential disease relevance of novel miRNAs, we compared different expression of novel miRNAs in the patient group with normal control. Only 20 out of 201 newly identified miRNAs/miRNA*s exhibited significantly differential expression between the two groups with fold changes >2.0 and a P-value of <0.001. Among them, 6 miRNAs exhibited increased expression and 14 other miRNAs showed reduced expression (Table 2). The six most highly expressed miRNAs are miR-1943, miR-1841, miR-1931, miR-1987, miR-1890, miR-1902 and the most lowly expressed miRNAs are miR-1893, miR-1971*, miR-1834, miR-1842*, miR-1842. We further used qRT-PCR to examine expression levels of three randomly selected highly down-regulated novel miRNA candidates: miR-1842, miR-1859* and miR-1852 in 20 ALL patients' and three normal donors' samples. Results showed that all these miRNAs can be detected in all clinical samples. Interestingly, we found that miR-1842 (16/20), miR-1852 (13/20) and miR-1859* (14/20) had significantly reduced expressions in most ALL patients compared to normal donors. This is consistent with our results derived using Solexa sequencing technology and suggests that these novel miRNAs may have functional relevance in the pathogenesis of ALL (part of data is shown in Fig. 4B–D).
Gene Ontology analysis reveals a set of abnormally expressed miRNAs involved in the pathway of nervous system development
The targets of the 171 known and novel miRNAs displaying significantly different expression between the normal donors and patient group (fold changes >2.0 and a p-value of <0.001, Table S5) – were predicted by miRNAs TargetScan to further elucidate their pathological relevance. By examining the significant GO “biological process” classifications that are over-represented among these likely targets genes of the differentially expressed miRNAs, we analyzed the functional annotation for predicted target sets expected to shed light on the specific function of miRNAs significant to ALL biology. Clustering of over-represented GO classes in predicted targets of up-regulated and down-regualted miRNAs in ALL showed that the most significant GO terms were genes involved in cell adhesion, hemophilic cell adhesion, transcription and regulation of transcription (Fig. 5). Interestingly, another cluster of highly significant GO terms was associated with nervous system development, i.e. neuronal cell differentiation, which might be associated with the pathogenesis of ALL with subsequent central nervous system (CNS) relapse, suggesting that a set of significantly dysregulated miRNAs might promote the progression of CNS relapse in ALL by regulating genes in the pathway of nervous system development.
To gain insight into the roles of miRNAs in the progression of ALL, we have employed SBS strategy to globally study small RNAs, especially miRNA expression profiles in ALL patients and their normal counterparts. Our results revealed that there are a large number of miRNAs deregulated in ALL patients, of which some known and novel miRNAs may be involved in the progression of ALL or relapse.
Although 678 human miRNA sequences have been cataloged (miRBase, release 11.0, 2008) and identified by either cloning or computational prediction, the identification of unannotated and cancer-specific novel miRNAs candidates which are of low abundance or nonconserved is important to comprehensively understand the pathogenesis of leukemia and explore new therapeutic strategies. In this study, we identified 275 novel miRNAs/miRNA*s. Amongst these were 20 newly identified miRNAs, including 6 up-regulated and 14 down-regulated miRNAs exhibiting significantly differential expression in the patient group with fold changes >2.0 and a p-value of <0.001. We further used qRT-PCR to validate the expression of miR-1842 and miR-1852 and the results revealed that 80% (16/20) and 65% (13/20) of ALL patients had significantly down-regulated miR-1842 and miR-1852, respectively. This indicates that these novel miRNAs may have functional relevance in the pathogenesis of ALL. Schotte et al.  identified 8 new miRNA genes from childhood ALL patients using a cloning strategy. However, these 8 new miRNA genes were filtered out in our study for they mostly overlapped with tRNA, rRNA, AluSx, HY3 RNA, HY5 RNA,or due to poor secondary structure.
MiRNA*, also called passenger strand, has been considered as an inactive strand and prone to be degradation . However, recent studies found that both pre-miRNA arms can functionally suppress the expression of their target genes and the miRNA* strand has inhibitory activity , –. By analyzing the precursor sequence of miRNAs, we identified a total of 116 novel miRNA*s, including 74 miRNA*s of known miRNAs and 42 miRNA*s of novel miRNAs and found that some miRNA*s were significantly dysregulated in ALL patient group with fold changes >2.0 and a p-value of <0.001. For example, we elucidated that miR-1859* was significantly down-regulated about 4 fold in 70% (14/20) patients, suggesting miRNA*s could be involved in the progression of ALL. Interestingly, BCL-2, an anti-apoptotic gene, was predicted to be one of the conserved targets of miR-1859* based on the targetscan prediction algorithm (www.targetscan.com) for the seed region (TTGTCCT). Overexpression of the BCL-2 protein has been frequently observed in many cancers including ALL –. Recently, it has been demonstrated that ALL cells are dependent on BCL-2, suggesting BCL-2 might be a more clinically significant parameter in ALL . Therefore it is likely that down-regulated miRNA-1859* contributes to the progression of ALL by targeting the anti-apoptotic gene BCL-2.
In order to highlight the genes that are likely to be the targets of the known and novel miRNAs and to analyse their relevance to the pathogenesis of ALL, we also examined the significant GO “biological process” classifications that are over-represented among these targets genes of the 171 known and novel differentially expressed miRNAs. This analysis revealed that the most significant GO terms were genes involved in cell adhesion, hemophilic cell adhesion, transcription and regulation of transcriptio. Interestingly, target prediction of the miRNA pattern revealed that some abnormally expressed miRNAs may be involved in the pathway of nervous system development, i.e. neuronal cell differentiation. Notably, we found that the nervous system development related gene Sox11 was the target of miR-1986. Sox11 is important for neurogenesis, neural cell survival and neurite outgrowth –, which implies that miRNAs might be associated with the pathogenesis of ALL with subsequent central nervous system (CNS) relapse. CNS relapse is the main source of extramedullary relapse in ALL, and has accounted for 30–40% of initial relapses in some clinical trials , . Further study of the differential expression patterns of known and novel miRNAs, together with the clinic follow up information, may reveal a miRNA signature as a biomarker for detection of early CNS relapse in pediatric ALL patients.
In conclusion, we present a comprehensive picture of the expression of small RNAs and in total have identified 159 novel miRNAs and 116 novel miRNA*s using an SBS strategy and computational analysis. To our knowledge, this is the first study to look at known and novel miRNA expression pattern in human ALL. Notably, we confirmed the expressions of some novel miRNAs and demonstrated that miRNAs are different between ALL patients and normal donors. Our data suggests these deregulated known and novel miRNAs may be associated with ALL or the onset of CNS relapses due to ALL.
Clinical samples and Ethics Statement
A total of 32 bone marrow samples including 27 ALL untreated pediatric patients and 5 normal donors were enrolled from the First and Second Affiliated Hospital of Sun Yat-sen University in this study. Among them, 3 ALL patients' and 2 normal donors' (control group) bone marrows were pooled respectively for small RNA library construction for Solexa sequencing. Another 24 ALL patients and 3 normal donors were used as a validation set to confirm the miRNA differential expression by qRT-PCR. Bone marrow samples were taken by bone marrow puncture at diagnosis. Written informed consent for biological studies was obtained from all patients analyzed. The study was approved by the Ethics Committee of the affiliated hospitals of Sun Yat-sen University.
Total RNA isolation, small RNA library preparation, and nucleotide sequencing
Total RNA was isolated with Trizol (Invitrogen, Carlsbad, CA) according to the instructions of the manufacturer. Small RNA library preparation and sequencing were performed with Solexa sequencing Technology (BGI, Shenzhen, China). Briefly, total RNA was size fractionated on a 15% tris-borate-EDTA (TBE) urea polyacrylamide gel and a 15–30 nt fraction was excised, using 10 bp ladder (Invitrogen) as marker. RNA was eluted from the polyacrylamide gel slice by Spin-X filter (Fisher) and precipitated by addition of 750 µL of 100% ethanol and 3 µL of glycogen and incubating at −80°C for 30 minutes. After washing with 75% ethanol, the RNA pellet was dissolved in DEPC-treated water. The RNA was dephosphorylated by alkaline phosphatase and recovered by ethanol precipitation. The small RNAs pools were then ligated sequentially to a 5′RNA adapter (5′-GUUCAGAGUUCUACAGUCCGACGAUC-3′) with T4 RNA ligase (Promega). The ligated RNA was size fractionated on a 15% TBE urea polyacrylamide gel, and a 40–60 nt fraction was excised. The RNA was eluted, precipitated and resuspended as described above. The 3′RNA adapter (5′-pUCGUAUGCCGUCUUCUGCUUGidT-3′; p, phosphate; idT, inverted deoxythymidine) was subsequently ligated to the precipitated RNA with T4 RNA ligase. Ligated RNA was size fractionated on a 10% TBE urea polyacrylamide gel, and the 70–90 nt fraction was excised. The RNA was eluted from the polyacrylamide gel and precipitated and resuspended in DEPC water. Then, reverse transcription reaction was preformed after ligation with adapters using Superscript II reverse transcriptase (Invitrogen) and Solexa's small RNA RT-Primer (5′-CAAGCAGAAGACGGCATACGA-3′) and followed by PCR amplification with Hotstart Phusion DNA Polymerase (NEB) in 15 cycles using Solexa's small RNA primer set (5′-CAAGCAGAAGACGGCATACGA-3′; 5′-AATGATACGGCGACCACCGACAGGTTCAGAGTTCTACAGTCCGA-3′). PCR products were purified on a 6% TBE polyacrylamide gel using a 25 bp DNA marker (Invitrogen). An approximately 92 bp fraction was excised, eluted and precipitated by ethanol. The pellet was dissolved in EB buffer and diluted for sequencing on the Solexa Genome Sequencer.
Initial processing of reads
Low quality reads were filtered and 3′ adaptor sequence trimmed. Adaptor contaminants were cleaned up and 18–30 nt reads collected. The remaining reads were discarded. We calculated sequencing frequency of each unique small RNA sequence, the number of reads for each sequence reflecting relative abundance. Each unique sequence was aligned to the human reference genome (Version hg18, Mar. 2006, NCBI Build 36.1, obtained from the UCSC Genome Browser download page) using MegaBLAST  with the following options: -W 15 –F F. Only perfect matches over their entire length were set aside. All mapped sequences were also searched against the human miRNA, tRNA, rRNA, snoRNA, snRNA, scRNA or refGene (exonic) (miRBase 11.0, UCSC annotation). These annotated RNAs were removed. To avoid repeat associated sequences, reads with more than five total matching positions in the genome were discarded. We aligned known mature miRNA sequence to the human genome and 97% known mature sequences are less than five positions (Fig. S2). The genomic loci corresponding to sequences remaining after these filtering steps were then analyzed for the hairpin secondary structure that is characteristic of miRNA precursors. Each miRNA tally from each library was normalized to the total number of miRNA hairpin-matching reads for that library, and those normalized tallies were used for relative expression analysis.
Known miRNAs expression analysis
Relative expression analysis was sought to determine the expression preferences of individual miRNAs between these two libraries. The number of reads matching a particular mature miRNA was calculated, but only sequence matches that overlapped at least 10 nt with the dominantly abundant mature miRNA sequence contributed to the miRNA tally. The Audic and Claverie test was used to establish the statistical significance of the difference in read frequencies for predicting differential miRNA expression based on the comparison of tag counts generated from digital expression analyses . The program winflat (http://www.igs.cnrs-mrs.fr/SpipInternet/IMG/tgz/winflat.tgz) was used to compute the probability of differential miRNA. A p-value of ≤0.001 was chosen as a threshold for determining significant differential miRNA expression.
Novel miRNAs prediction
Sequences from 100 nucleotide (nt) upstream to 100 nt downstream of the remaining aligned reads were extracted from the human genome. Potential miRNA stem loops were located by sliding a 100 nt window advancing by 10-nt increments along the strand of read sequences and folding the window with the secondary structure prediction program RNAfold to identify stem-loop structures with a folding free energy of at most −18 kcal/mole (Mfe≤−18 kcal/mole). For overlapped hairpins, we only took the one that had the greatest number of paired arm bases. Forked hairpins were permitted provided that the longest forked segment contained no more than eight base pairs. For forked hairpins, we folded the sequence again with the program RNAshapes  and discarded the forked hairpins without stem-loop optional structure. Only structures that: 1) folded into hairpins, 2) contained a read in one of the hairpin arm, 3) loop length of hairpin is less than 20 and minimum pairs of arm is 15, 4) more than 2 reads located within hairpin, 5) the read sequences does not span the loop and 6) potential precursors are consistent with miRNA biogenesis , , were used in further analysis. We also used a recently discovered property of miRNAs to have lower folding free energies than random sequences with the same nucleotide content –. We thus use z-scores as described by Washietl et al  to filter the candidates. Only hairpins with the z-score less than −3.0 were novel miRNA genes. Furthermore, the observation of both a candidate miRNA and a corresponding candidate miRNA* was defined as high stringency. The sequence with dominantly abundant reads was named as mature miRNA and its corresponding complementary sequence as miRNA*.
Target prediction and Gene Ontology (GO) analysis
The conserved targets of known miRNAs and novel miRNAs were predicted by TargetScan (http://www.targetscan.org/vert_42/vert_42_data_download/targetscan_41.zip) –. Only miRNAs with significant over-expression in either C group or P group were included in target analyses. GO  terms and gene information were downloaded from the NCBI website (ftp://ftp.ncbi.nih.gov/gene/DATA) on August 2008. The functional categories scrutinized are biological processes and molecular functions as defined in the Gene Ontology Consortium database (http://www.geneontology.org). Significant overrepresentation of particular GO terms in the dataset was determined using the software GeneMerge with corrections for multiple tests . Bonferroni-corrected p-values are reported and a cutoff of 0.1 on the Bonferroni-corrected P value was applied. As described by Lall et al , Bonferroni-corrected P values for overrepresented GO terms for the targets of each miRNA are plotted on a negative log2 scale (e.g. p-values 20 = 1, 2−6 = 0.015625). We performed 2-way hierarchical clustering with the program MeV , with the Pearson correlation coefficient and average linkage clustering.
Quantitative real-time reverse transcription PCR assays
Quantitative real-time RT-PCR (qRT-PCR) was performed as described and employed a Hairpin-itTM miRNAs Real-Time PCR Quantitation Kit containing stem-loop like RT primer, miRNA specific PCR primers and Molecular Beacon probe (GenePharma, Shanghai, China). Briefly, RNA was reverse-transcribed to cDNA with miRNA specific stem-loop like RT primer and the expression of each miRNA relative to normal control was determined using the 2-ΔΔCT method. Comparative real-time PCR was performed in triplicate. The no-template real-time PCR was used as negative control in each PCR reaction.
Alignment of 159 novel miRNAs in C group and P group(q means the read comes from C group; t means the read comes from P group)
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We thank the following investigators and hospitals that provided samples for the analysis: Dr. Hai-Xia Guo, the Second Affiliated Hospital of Sun Yat-sen University; Li-Bing Huang, the First Affiliated Hospital of Sun Yat-sen University and Li-Ming Tu Guangdong Provincial People's Hospital. We also thank Ms. Xiao-Hong Chen, Qiao-Juan Huang and Yi-Ling Chen for technical support and Dr. Farid Ghadessy for editing the manuscript.
Conceived and designed the experiments: HZ JHY YSZ YQC. Performed the experiments: HZ YSZ XC JW XQL ZYK HZ. Analyzed the data: HZ JHY YSZ PZ XC JW LX XQL ZYK HZ LHQ YQC. Contributed reagents/materials/analysis tools: PZ LX XQL LHQ YQC. Wrote the paper: HZ JHY YSZ PZ YQC.
- 1. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297.
- 2. Cheng AM, Byrom MW, Shelton J, Ford LP (2005) Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res 33: 1290–1297.
- 3. Calin GA, Croce CM (2006) MicroRNA-cancer connection: the beginning of a new tale. Cancer Res 66: 7390–7394.
- 4. Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, et al. (2004) Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 101: 2999–3004.
- 5. Zhang B, Pan X, Cobb GP, Anderson TA (2007) microRNAs as oncogenes and tumor suppressors. Dev Biol 302: 1–12.
- 6. Chen CZ (2005) MicroRNAs as oncogenes and tumor suppressors. N Engl J Med 353: 1768–1771.
- 7. Esquela-Kerscher A, Slack FJ (2006) Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 6: 259–269.
- 8. Babiarz JE, Ruby JG, Wang Y, Bartel DP, Blelloch R (2008) Mouse ES cells express endogenous shRNAs, siRNAs, and other Microprocessor-independent, Dicer-dependent small RNAs. Genes Dev 22: 2773–2785.
- 9. Berezikov E, van Tetering G, Verheul M, van de Belt J, van Laake L, et al. (2006) Many novel mammalian microRNA candidates identified by extensive cloning and RAKE analysis. Genome Res 16: 1289–1298.
- 10. Glazov EA, Cottee PA, Barris WC, Moore RJ, Dalrymple BP, et al. (2008) A microRNA catalog of the developing chicken embryo identified by a deep sequencing approach. Genome Res 18: 957–964.
- 11. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, et al. (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129: 1401–1414.
- 12. Lui WO, Pourmand N, Patterson BK, Fire A (2007) Patterns of known and novel small RNAs in human cervical cancer. Cancer Res 67: 6031–6043.
- 13. Ruby JG, Jan C, Player C, Axtell MJ, Lee W, et al. (2006) Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell 127: 1193–1207.
- 14. Morin RD, O'Connor MD, Griffith M, Kuchenbauer F, Delaney A, et al. (2008) Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 18: 610–621.
- 15. Marton S, Garcia MR, Robello C, Persson H, Trajtenberg F, et al. (2008) Small RNAs analysis in CLL reveals a deregulation of miRNA expression and novel miRNA candidates of putative relevance in CLL pathogenesis. Leukemia 22: 330–338.
- 16. Takada S, Yamashita Y, Berezikov E, Hatanaka H, Fujiwara SI, et al. (2008) MicroRNA expression profiles of human leukemias. Leukemia 22: 1274–1278.
- 17. Kuchenbauer F, Morin RD, Argiropoulos B, Petriv OI, Griffith M, et al. (2008) In-depth characterization of the microRNA transcriptome in a leukemia progression model. Genome Res 18: 1787–1797.
- 18. Pui CH, Robison LL, Look AT (2008) Acute lymphoblastic leukaemia. Lancet 371: 1030–1043.
- 19. Faderl S, Jeha S, Kantarjian HM (2003) The biology and therapy of adult acute lymphoblastic leukemia. Cancer 98: 1337–1354.
- 20. Lange BJ, Bostrom BC, Cherlow JM, Sensel MG, La MK, et al. (2002) Double-delayed intensification improves event-free survival for children with intermediate-risk acute lymphoblastic leukemia: a report from the Children's Cancer Group. Blood 99: 825–833.
- 21. Hutchinson RJ, Gaynon PS, Sather H, Bertolone SJ, Cooper HA, et al. (2003) Intensification of therapy for children with lower-risk acute lymphoblastic leukemia: long-term follow-up of patients treated on Children's Cancer Group Trial 1881. J Clin Oncol 21: 1790–1797.
- 22. Bostrom BC, Sensel MR, Sather HN, Gaynon PS, La MK, et al. (2003) Dexamethasone versus prednisone and daily oral versus weekly intravenous mercaptopurine for patients with standard-risk acute lymphoblastic leukemia: a report from the Children's Cancer Group. Blood 101: 3809–3817.
- 23. Pui CH, Howard SC (2008) Current management and challenges of malignant disease in the CNS in paediatric leukaemia. Lancet Oncol 9: 257–268.
- 24. Schotte D, Chau JC, Sylvester G, Liu G, Chen C, et al. (2009) Identification of new microRNA genes and aberrant microRNA profiles in childhood acute lymphoblastic leukemia. Leukemia 23: 313–322.
- 25. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, et al. (2005) MicroRNA expression profiles classify human cancers. Nature 435: 834–838.
- 26. Mi S, Lu J, Sun M, Li Z, Zhang H, et al. (2007) MicroRNA expression signatures accurately discriminate acute lymphoblastic leukemia from acute myeloid leukemia. Proc Natl Acad Sci U S A 104: 19971–19976.
- 27. Zanette DL, Rivadavia F, Molfetta GA, Barbuzano FG, Proto-Siqueira R, et al. (2007) miRNA expression profiles in chronic lymphocytic and acute lymphocytic leukemia. Braz J Med Biol Res 40: 1435–1440.
- 28. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402.
- 29. Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, et al. (2003) The microRNAs of Caenorhabditis elegans. Genes Dev 17: 991–1008.
- 30. Okamura K, Phillips MD, Tyler DM, Duan H, Chou YT, et al. (2008) The regulatory activity of microRNA* species has substantial influence on microRNA and 3′ UTR evolution. Nat Struct Mol Biol 15: 354–363.
- 31. Bonnet E, Wuyts J, Rouze P, Van de Peer Y (2004) Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20: 2911–2917.
- 32. Clote P, Ferre F, Kranakis E, Krizanc D (2005) Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. Rna 11: 578–591.
- 33. Washietl S, Hofacker IL, Stadler PF (2005) Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci U S A 102: 2454–2459.
- 34. Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, et al. (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26: 407–415.
- 35. Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian microRNA targets. Cell 115: 787–798.
- 36. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120: 15–20.
- 37. O'Toole AS, Miller S, Haines N, Zink MC, Serra MJ (2006) Comprehensive thermodynamic analysis of 3′ double-nucleotide overhangs neighboring Watson-Crick terminal base pairs. Nucleic Acids Res 34: 3338–3344.
- 38. Ro S, Park C, Young D, Sanders KM, Yan W (2007) Tissue-dependent paired expression of miRNAs. Nucleic Acids Res 35: 5944–5953.
- 39. Seitz H, Ghildiyal M, Zamore PD (2008) Argonaute loading improves the 5′ precision of both MicroRNAs and their miRNA strands in flies. Curr Biol 18: 147–151.
- 40. Narayan S, Chandra J, Sharma M, Naithani R, Sharma S (2007) Expression of apoptosis regulators Bcl-2 and Bax in childhood acute lymphoblastic leukemia. Hematology 12: 39–43.
- 41. Wojcik I, Szybka M, Golanska E, Rieske P, Blonski JZ, et al. (2005) Abnormalities of the P53, MDM2, BCL2 and BAX genes in acute leukemias. Neoplasma 52: 318–324.
- 42. Del Gaizo Moore V, Schlis KD, Sallan SE, Armstrong SA, Letai A (2008) BCL-2 dependence and ABT-737 sensitivity in acute lymphoblastic leukemia. Blood 111: 2300–2309.
- 43. Wang X, Asplund AC, Porwit A, Flygare J, Smith CI, et al. (2008) The subcellular Sox11 distribution pattern identifies subsets of mantle cell lymphoma: correlation to overall survival. Br J Haematol 143: 248–252.
- 44. Bergsland M, Werme M, Malewicz M, Perlmann T, Muhr J (2006) The establishment of neuronal properties is controlled by Sox4 and Sox11. Genes Dev 20: 3475–3486.
- 45. Pui CH (2003) Toward optimal central nervous system-directed treatment in childhood acute lymphoblastic leukemia. J Clin Oncol 21: 179–181.
- 46. Audic S, Claverie JM (1997) The significance of digital gene expression profiles. Genome Res 7: 986–995.
- 47. Hofacker IL FW, Stadler PF, Bonhoeffer LS, Tacker M, Schuster P (1994) Fast folding and comparison of RNA secondary structures. Monatsh Chem 125: 167–188.
- 48. Steffen P, Voss B, Rehmsmeier M, Reeder J, Giegerich R (2006) RNAshapes: an integrated RNA analysis package based on abstract shapes. Bioinformatics 22: 500–503.
- 49. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29.
- 50. Castillo-Davis CI, Hartl DL (2003) GeneMerge—post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19: 891–892.
- 51. Lall S, Grun D, Krek A, Chen K, Wang YL, et al. (2006) A genome-wide map of conserved microRNA targets in C. elegans. Curr Biol 16: 460–471.
- 52. Saeed AI, Sharov V, White J, Li J, Liang W, et al. (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34: 374–378.
- 53. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, et al. (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33: e179.
- 54. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25: 402–408.