MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer
© Chang et al; licensee BioMed Central Ltd. 2010
Received: 16 December 2009
Accepted: 29 April 2010
Published: 29 April 2010
Advances in high-throughput technologies and bioinformatics have transformed gene expression profiling methodologies. The results of microarray experiments are often validated using reverse transcription quantitative PCR (RT-qPCR), which is the most sensitive and reproducible method to quantify gene expression. Appropriate normalisation of RT-qPCR data using stably expressed reference genes is critical to ensure accurate and reliable results. Mi(cro)RNA expression profiles have been shown to be more accurate in disease classification than mRNA expression profiles. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in miRNA RT-qPCR studies.
We adopt and report a systematic approach to identify the most stable reference genes for miRNA expression studies by RT-qPCR in colorectal cancer (CRC). High-throughput miRNA profiling was performed on ten pairs of CRC and normal tissues. By using the mean expression value of all expressed miRNAs, we identified the most stable candidate reference genes for subsequent validation. As such the stability of a panel of miRNAs was examined on 35 tumour and 39 normal tissues. The effects of normalisers on the relative quantity of established oncogenic (miR-21 and miR-31) and tumour suppressor (miR-143 and miR-145) target miRNAs were assessed.
In the array experiment, miR-26a, miR-345, miR-425 and miR-454 were identified as having expression profiles closest to the global mean. From a panel of six miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) and two small nucleolar RNA genes (RNU48 and Z30), miR-16 and miR-345 were identified as the most stably expressed reference genes. The combined use of miR-16 and miR-345 to normalise expression data enabled detection of a significant dysregulation of all four target miRNAs between tumour and normal colorectal tissue.
Our study demonstrates that the top six most stably expressed miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) described herein should be validated as suitable reference genes in both high-throughput and lower throughput RT-qPCR colorectal miRNA studies.
Mi(cro)RNAs are short RNA molecules that bind (generally) to 3' UTR sequences of target messenger RNAs (mRNAs), thereby modulating their expression patterns. This modulated gene expression is manifest either as translational repression , or mRNA degradation whereby the RNA interference pathway is initiated to remove targeted sequences . MiRNAs play major roles in governing diverse biological processes such as differentiation, proliferation, and apoptosis [3, 4]. Individual miRNAs have been ascribed oncogenic and tumour suppressor functions , and aberrant miRNA expression has been implicated in many malignancies, including colorectal cancer (CRC) [6, 7]. Previous study demonstrated that miRNA profiles may be more accurate in disease classification than mRNA profiles . Moreover, miRNAs have been associated with CRC pathogenesis [9, 10], microsatellite stability status [11, 12], therapeutic outcome and prognosis [12–15].
High-throughput technology such as microarrays enables simultaneous quantification of hundreds of miRNAs in a single RNA sample. Meaningful interpretation of such large datasets has been made possible by recent advances in bioinformatics. It is critical that the findings of microarray screening methodologies are validated to produce scientifically robust results, using the most sensitive and reproducible method of gene expression quantitation, reverse transcription quantitative PCR (RT-qPCR) . In order to achieve accurate, reproducible and biologically relevant miRNA RT-qPCR data, non-biological sample-to-sample variation that could be introduced by protocol-dependent inconsistencies has to be corrected for by using reference genes. Use of unreliable reference genes for normalisation may lead to inaccurate quantitation of miRNAs of interest [17, 18]. Previous studies have demonstrated that a single universal reference gene for all tissue types is unlikely to exist [19–23], and the use of a single reference gene for normalisation leads to large errors and is therefore inappropriate [22, 24].
Despite increasing miRNA expression studies in CRC, no previous report detailed a robust identification and validation strategy for suitable reference genes for normalization. The aim of this study was to identify the most stable reference genes using a high-throughput approach, in ten pairs of stage II colorectal tumour and normal tissues. Following TaqMan array card analysis and the established approach of finding miRNAs whose expression pattern is similar to the global mean expression , miR-26a, miR-345, miR-425 and miR-454 were identified as the most stably expressed miRNAs. The stability of these miRNAs was further assessed by RT-qPCR in 74 colorectal tissues with an expanded panel of candidate reference miRNAs (let-7a, miR-16) and two small nucleolar RNAs (snoRNAs, RNU48 and Z30). Well established oncogenic miRNAs in CRC: miR-21 [7, 13, 26] and miR-31 , and tumour suppressor miRNAs: miR-143 [6, 27, 28] and miR-145 [6, 7, 12, 27] were used as target miRNAs to determine the effect of reference gene choice on relative quantitation.
Colorectal tissue samples
Clinicopathological data for 40 patients with colorectal cancer (tissues: colorectal tumour n = 35 and normal n = 39)
Characteristics (n = 40)
Age (mean ± standard deviation)
66.7 ± 13.1
Location of tumors
Pathologic T classification
Pathologic N classification
To isolate small RNA (<200 nucleotides), approximately 100 mg of tissue was homogenised using a bench-top homogeniser (Polytron PT1600E, Kinematica AG, Lucerne, Switzerland) in 1-2 mL of Qiazol (Qiagen, UK). Subsequent miRNA extraction was performed using the RNeasy Mini Kit and the RNeasy MinElute Cleanup Kit (Qiagen) according to the manufacturer's instructions. Concentration and purity of miRNA was assessed using the Nanodrop 1000 spectrophotometer (Nanodrop Technologies Inc., USA). Qualitative analysis of miRNA was performed using the Agilent 2100 Bioanalyzer and the Small RNA Assay (Agilent Technologies, USA) to determine the percentage of miRNA in the small RNA fraction.
TaqMan array cards
A TaqMan Human MicroRNA array card is a high throughput PCR-based miRNA array that enables analysis of 384 miRNA assays on a microfluidic card. Each card contains a mammalian U6 assay repeated 4 times, and an assay unrelated to any mammalian species ath-miR-159a to provide a process control. Simultaneous synthesis of cDNA for mature miRNAs was performed using Megaplex Reverse Transcription Human Pool A (Applied Biosystems), which is a set of pre-defined pools of 380 stem-looped reverse transcription primers. RT-qPCR was performed using the Applied Biosystems 7900HT Fast Real-Time PCR System, and default thermal-cycling conditions.
First strand cDNA was synthesised using gene-specific stem-loop primers. The primer sequences of let-7a and miR-16 have been previously described . Primers were obtained from MWG Biotech (Ebersberg, Germany) if sequences were available. Otherwise, assays containing stem-looped primer were purchased from Applied Biosystems. All reagents were included in the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). The reaction was performed using a GeneAmp PCR system 9700 thermal cycler (Applied Biosystems) with samples incubated at 16°C for 30 minutes, 42°C for 30 minutes and 85°C for 5 minutes. An RT-negative control was included in each batch of reactions.
Details of candidate reference genes and their amplification efficiencies
Negatively regulates RAS oncogene 
Negatively regulates B-cell lymphoma mRNA in chronic lymphocytic leukaemia 
Overexpressed in malignant mesothelioma 
No functionally verified targets
No functionally verified targets
Guides the 2'O-ribose methylation of 28S rRNA 
Guides the methylation of the Am47 residue in U6 snoRNA 
High throughput data generated from TaqMan array card RT-qPCR was analysed using qbasePLUS software (Biogazelle, Belgium) . Average values of triplicate Cq values were converted to relative quantities for NormFinder and geNorm analysis [21, 32]. The relative expression of target miRNAs (miR-21, miR-31, miR-143 and miR-145) normalised to one or more reference candidates was also determined using qbasePLUS software employing a generalised and universally applicable quantification model based on efficiency correction, error propagation and multiple reference gene normalisation .
Statistical analysis was performed using SPSS 14.0 (Chicago, IL, USA) and Minitab® 15 softwares (Minitab Ltd, Coventry, UK). Distribution of continuous data was determined using the Kolmogorov-Smirnov Z test. Two-sample t test was used to compare log 10 Cq values of candidate reference genes, and log 10 relative quantities of target miRNAs between tumour and normal tissues. The equivalence test was used to determine if reference genes were equivalently expressed between tumour and normal tissues . Difference in variance between genes was assessed using Bartlett's test. P values of less than 0.05 were considered statistically significant for all tests.
Identification of candidate reference genes by using the global mean expression value
We profiled a panel of 380 miRNAs and controls in 10 pairs of stage II colorectal tumour and normal tissues. To identify the most stably expressed miRNAs, a robust global mean expression normalisation strategy was applied . For each individual sample, the mean Cq values of all miRNAs that were expressed, and those that were expressed below cycle 35 were calculated. Expression stability of the mean global values, the geometric means of snoRNAs (U6, RNU44 and RNU48) and miRNAs (let-7a, miR-16, miR-17 and miR-103) were assessed using the GeNorm algorithm. Both the geometric mean of let-7a and the mean global expression values for all miRNAs were found to be the most stably expressed. U6 RNA was the least stably expressed reference gene. Four miRNAs that showed an expression profile closest to the mean were miR-26a, miR-345, miR-425 and miR-454. Two snoRNA genes (RNU48 and Z30) and one other miRNA (miR-16)  were chosen for further validation in a larger cohort. Known functions of the candidates are listed in Table 2.
Reference gene quantitation by RT-qPCR
Quantification cycle (Cq) values of candidate reference genes and target miRNAs (in triplicate) in colorectal tissues
Mean Cq ± s.d.
28.31 ± 1.60
23.61 ± 1.00
23.61 ± 1.49
27.43 ± 1.35
27.53 ± 1.36
30.14 ± 1.75
26.34 ± 2.09
31.17 ± 2.83
24.82 ± 4.07
29.36 ± 3.75
23.01 ± 2.84
24.84 ± 2.79
Analysis of reference genes' expression stability
Ranking and best combination of candidate reference genes based on expression stability values calculated by NormFinder and geNorm programs
Effect of reference genes on relative expression of target miRNAs
The discovery of miRNAs as crucial regulators of gene expression has resulted in the rapid expansion of understanding of gene regulation in normal development and disease. Previously, it was demonstrated that miRNA expression profiles may be more accurate in disease classification than mRNA expression profiles . However, accurate and reliable interpretation of RT-qPCR results depends heavily on the use of suitable reference genes for normalisation to eliminate or minimise non-biological variation between test samples. While reference genes for mRNA RT-qPCR studies have been well-established, few miRNA RT-qPCR studies have detailed the validation of reference genes for normalisation to date. Rigorous normalisation of miRNA data may be more critical than that of other RNA functional classes . Indeed, their capability to regulate multiple gene targets within the same pathway may amplify their biological effects , hence small changes in miRNA expression may be biologically and clinically significant.
Davoren et al. reported the first systematic assessments of candidate reference genes for miRNA RT-qPCR analysis in breast cancer . To our knowledge, such assessment and validation of reference genes for CRC studies has not been reported. The two most commonly used normalisers U6 and 5S RNAs were shown to be the two least stable RNA species . The use of rRNAs as reference genes has been debated as they can be expressed at much greater levels than target RNAs resulting in difficulty quantitating a lowly expressed target RNA [20, 22]. Furthermore, rRNAs have been shown to be involved in apoptosis  and cancer . Lastly, it has been argued before that it's best to normalise genes with reference genes belonging to the same RNA class . Let-7a was used as a normaliser in CRC miRNA RT-qPCR studies [7, 10]. However, its tumour-suppressor role in CRC has been reported . In a previous study, miR-191 and miR-25 were identified as the most stable pair of normalisers across 13 distinct human tissue types including 5 pairs of colon tumour and adjacent normal tissues. However, when analysis was performed on an extended cohort of lung cancer and normal tissues, miR-17-5p and miR-24 were the best normalisers . This demonstrates the importance of validating suitable reference genes in a tissue-specific context. Suitable reference genes for colorectal tissue-specific studies needs to be further assessed as previous reports have demonstrated that a single universal reference gene for all tissue types is unlikely to exist [19–23].
This is the first report detailing identification and validation of suitable reference genes for normalisation of miRNA RT-qPCR in human colorectal tissues. We profiled the expression of 380 miRNAs (including U6 rRNA) on 20 colorectal tissues. A robust method using the mean expression value was used to identify the most stably expressed miRNAs: let-7a, miR-26a, miR-345, miR-425 and miR-454. Mean normalisation was previously shown to outperform other methods of normalisation in terms of better reduction of technical variation and more accurate appreciation of biological changes . Validation by RT-qPCR was subsequently carried out in a larger cohort of 74 tissues with assessment of three more candidate reference genes (miR-16, RNU48 and Z30) . Our initial validation step confirmed no difference in reference gene quantities between tumour and normal tissues, allowing subsequent use of NormFinder and geNorm as these models assume that reference genes are not differentially expressed between experimental groups. Equivalent expression of reference genes between tumour and normal tissues was then confirmed using a fold change cut-off of 3 . Both NormFinder and geNorm identified miR-16 and miR-345 as the most stable normalisers. The five most stably expressed miRNAs in the TaqMan array card dataset of stage II tumours remained stably expressed when a larger cohort of variable disease stages was evaluated. This suggests that true reference genes are non-functional in the disease process, and should remain stably expressed throughout all stages, grades and subtypes.
As evident from our results, inappropriate use of reference genes can significantly alter the results of target miRNAs quantitation. With the use of the best combination of reference genes (miR-16 and miR-345), significant dysregulation of all four target miRNAs (miR-21, miR-31, miR-143 and miR-145) was detected. These target miRNAs have repeatedly been shown to be dysregulated in CRC in previous studies. However, despite a relatively large sample size, when inappropriate reference genes were used for normalisation, a true biological difference in expression between tumour and normal was not detected. Even though miR-345 and miR-454 detected significant difference between tumour and normal tissues when used alone as a reference gene, geNorm analysis identified them as only the third and the fifth most stably expressed genes. The p values of the differential expression of the four target miRNAs between tumour and normal tissues were slightly lower when using the miR-16/miR-345 combination in most instances, which could prove significant in a small scale study. Furthermore, previous studies have reported that the use of more than one reference genes increases the accuracy of quantitation compared to the use of a single reference gene [22, 32].
The results of our study have important implications for CRC translational research. The clinical and pathologically diverse nature of the tissues used in this study should be of interest to a broad spectrum of the CRC research community. While it may not be feasible due to cost and sample availability, the stability of the top six most stably expressed miRNAs in colorectal tissues (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR454) should be assessed to determine the most appropriate normalisers within each study as patient and tumour characteristics may vary between different study cohorts. Furthermore, with evidence to suggest that miRNA expression in formalin-fixed paraffin-embedded (FFPE) tissue samples remains relatively stable and consistent with that in fresh-frozen samples , and that reference miRNA stabilities are extremely consistent between the two tissue sources procured and processed independently of one another , the reference genes identified in this study may be useful for miRNA RT-qPCR study in FFPE colorectal tissues. This study also demonstrated that the use of the mean expression value is a useful means of identifying stable reference genes in high-throughput miRNA profiling studies, and the findings were confirmed to be robust after external validation.
The authors gratefully acknowledge Ms Emer Hennessy and Ms Catherine Curran for continued technical assistance and for curation of the Department of Surgery BioBank, NUI Galway.
- Lai EC: MicroRNAs are complementary to 3' UTR sequence motifs that mediate negative post-transcriptional regulation. Nat Genet. 2002, 30: 363-364. 10.1038/ng865.View ArticlePubMedGoogle Scholar
- Engels BM, Hutvagner G: Principles and effects of microRNA-mediated post-transcriptional gene regulation. Oncogene. 2006, 25: 6163-6169. 10.1038/sj.onc.1209909.View ArticlePubMedGoogle Scholar
- Chen CZ, Li L, Lodish HF, Bartel DP: MicroRNAs modulate hematopoietic lineage differentiation. Science. 2004, 303: 83-86. 10.1126/science.1091903.View ArticlePubMedGoogle Scholar
- Croce CM, Calin GA: miRNAs, cancer, and stem cell division. Cell. 2005, 122: 6-7. 10.1016/j.cell.2005.06.036.View ArticlePubMedGoogle Scholar
- Esquela-Kerscher A, Slack FJ: Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer. 2006, 6: 259-269. 10.1038/nrc1840.View ArticlePubMedGoogle Scholar
- Michael ZM, O'Connor SM, van Holst Pellekaan NG, Young GP, James RJ: Reduced accumulation of specific microRNAs in colorectal neoplasia. Mol Cancer Res. 2003, 1: 882-891.PubMedGoogle Scholar
- Bandrés E, Cubedo E, Agirre X, Malumbres R, Zárate R, Ramirez N, Abajo A, Navarro A, Moreno I, Monzó M, et al: Identification by real-time PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues. Mol Cancer. 2006, 5: 29-10.1186/1476-4598-5-29.View ArticlePubMedPubMed CentralGoogle Scholar
- Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, et al: MicroRNA expression profiles classify human cancers. Nature. 2005, 5: R13-Google Scholar
- Nagel R, le Sage C, Diosdado B, Waal van der M, Oude Vrielink JAF, Bolijn A, Meijer GA, Agami R: Regulation of the Adenomatous Polyposis Coli gene by the miR-135 family in colorectal cancer. Cancer Res. 2008, 68: 5795-5802. 10.1158/0008-5472.CAN-08-0951.View ArticlePubMedGoogle Scholar
- Monzo M, Navarro A, Bandres E, Artells R, Moreno I, Gel B, Ibeas R, Moreno J, Martinez F, Diaz T, et al: Overlapping expression of microRNAs in human embryonic colon and colorectal cancer. Cell Res. 2008, 18: 823-833. 10.1038/cr.2008.81.View ArticlePubMedGoogle Scholar
- Lanza G, Ferracin M, Gafà R, Veronese A, Spizzo R, Pichiorri F, Liu CG, Calin GA, Croce CM, Negrini M: mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer. Mol Cancer. 2007, 6: 54-10.1186/1476-4598-6-54.View ArticlePubMedPubMed CentralGoogle Scholar
- Schepeler T, Reinert JT, Ostenfeld MS, Christensen LL, Silahtaroglu AN, Dyrskjøt L, Wiuf C, Sørensen FJ, Kruhøffer M, Laurberg S, et al: Diagnostic and prognostic microRNAs in stage II colon cancer. Cancer Res. 2008, 68: 3416-3424. 10.1158/0008-5472.CAN-07-6110.View ArticleGoogle Scholar
- Schetter AJ, Leung SY, Sohn JJ, Zanetti KA, Bowman ED, Yanaihara N, Yuen ST, Chan TL, Kwong DL, Au GK, et al: MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA. 2008, 299: 425-436. 10.1001/jama.299.4.425.PubMedPubMed CentralGoogle Scholar
- Svoboda M, Izakovicova Holla L, Sefr R, Vrtkova I, Kocakova I, Tichy B, Dvorak J: MicroRNAs miR125b and miR137 are frequently upregulated in response to capecitabine chemoradiotherapy of rectal cancer. Int J Oncol. 2008, 34: 1069-1075.Google Scholar
- Motoyama K, Inoue H, Takatsuno Y, Tanaka F, Mimori K, Uetake H, Sugihara K, Mori M: Over- and under-expressed microRNAs in human colorectal cancer. Int J Oncol. 2009, 34: 1069-1075.PubMedGoogle Scholar
- Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative PCR. Genome Res. 1996, 6: 986-994. 10.1101/gr.6.10.986.View ArticlePubMedGoogle Scholar
- Davoren PA, McNeill RE, Lowery AJ, Kerin MJ, Miller N: Identification of suitable endogenous control genes for microRNA gene expression analysis in human breast cancer. BMC Molecular Biology. 2008, 9: 76-10.1186/1471-2199-9-76.View ArticlePubMedPubMed CentralGoogle Scholar
- Peltier HJ, Latham GJ: Normalization of microRNA expression levels in quantitative RT-PCR assays: Identification of suitable reference RNA targets in normal and cancerous human solid tissues. RNA. 2008, 14: 844-852. 10.1261/rna.939908.View ArticlePubMedPubMed CentralGoogle Scholar
- Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E: Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999, 75: 291-295. 10.1016/S0168-1656(99)00163-7.View ArticlePubMedGoogle Scholar
- Bustin SA: Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000, 25: 169-193. 10.1677/jme.0.0250169.View ArticlePubMedGoogle Scholar
- Bustin SA: Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002, 29: 23-39. 10.1677/jme.0.0290023.View ArticlePubMedGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3: RESEARCH0034-10.1186/gb-2002-3-7-research0034.View ArticlePubMedPubMed CentralGoogle Scholar
- Haller F, Kulle B, Schwager S, Gunawan B, von Heydebreck A, Sultmann H, Fuzesi L: Equivalence test in quantitative reverse transcription polymerase chain reaction: confirmation of reference genes suitable for normalization. Anal Biochem. 2004, 335: 1-9. 10.1016/j.ab.2004.08.024.View ArticlePubMedGoogle Scholar
- Tricarico C, Pinzani P, Bianchi S, Paglierani M, Distante V, Pazzagli M, Bustin SA, Orlando C: Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem. 2002, 309: 293-300. 10.1016/S0003-2697(02)00311-1.View ArticlePubMedGoogle Scholar
- Mestdagh P, van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J: A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009, 10: R64-10.1186/gb-2009-10-6-r64.View ArticlePubMedPubMed CentralGoogle Scholar
- Asangani IA, Rasheed SA, Nikolova DA, Leupold JH, Colburn NH, Post S, Allgayer H: MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor-suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene. 2007, 27: 2128-2136. 10.1038/sj.onc.1210856.View ArticlePubMedGoogle Scholar
- Akao Y, Nakagawa Y, Naoe T: MicroRNAs 143 and 145 are possible common onco-microRNAs in human cancers. Oncol Rep. 2006, 16: 845-850.PubMedGoogle Scholar
- Chen X, Guo X, Zhang H, Xiang Y, Chen J, Yin Y, Cai X, Wang K, Wang G, Ba Y, et al: Role of miR-143 targeting KRAS in colorectal tumorigenesis. Oncogene. 2009, 28: 1385-1392. 10.1038/onc.2008.474.View ArticlePubMedGoogle Scholar
- American Joint Committee on Cancer Cancer Staging Manual. 2002, 6Google Scholar
- Chen C, Ridzon DA, Broomer AJ, Zhou J, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, et al: Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005, 33: e179-10.1093/nar/gni178.View ArticlePubMedPubMed CentralGoogle Scholar
- Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J: qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007, 8: R19-10.1186/gb-2007-8-2-r19.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersen CL, Jensen JL, Orntoft TF: Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64: 5245-5250. 10.1158/0008-5472.CAN-04-0496.View ArticlePubMedGoogle Scholar
- Calin GA, Croce CM: MicroRNA-cancer connection: The beginning of a new tale. Cancer Res. 2006, 15: 7390-7394. 10.1158/0008-5472.CAN-06-0800.View ArticleGoogle Scholar
- Nadano D, Sato TA: Caspase-3-dependent and -independent degradation of 28 S ribosomal RNA may be involved in the inhibition of protein synthesis during apoptosis initiated by death receptor engagement. J Biol Chem. 2000, 275: 13967-13973. 10.1074/jbc.275.18.13967.View ArticlePubMedGoogle Scholar
- Chan MW, Wei SH, Wen P, Wang Z, Matei DE, Liu CJ, Liyanarachchi S, Brown R, Nephew KP, Yan PS, et al: Hypermethylation of 18S and 28S ribosomal DNAs predicts progression-free survival in patients with ovarian cancer. Clin Cancer Res. 2005, 11: 7376-7383. 10.1158/1078-0432.CCR-05-1100.View ArticlePubMedGoogle Scholar
- Xi Y, Nakajima G, Gavin E, Morris CG, Kudo K, Hayashi K, Ju J: Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalin-fixed paraffin-embedded samples. RNA. 2007, 13: 1668-1674. 10.1261/rna.642907.View ArticlePubMedPubMed CentralGoogle Scholar
- Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, Cheng A, Labourier E, Reinert KL, Brown D, Slack FJ: RAS is regulated by the let-7 microRNA family. Cell. 2005, 120: 635-647. 10.1016/j.cell.2005.01.014.View ArticlePubMedGoogle Scholar
- Cimmino A, Calin GA, Fabbri M, Iorio MV, Ferracin M, Shimizu M, Wojcik SE, Aqeilan RI, Zupo S, Dono M, et al: miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci USA. 2005, 102: 13944-13949. 10.1073/pnas.0506654102.View ArticlePubMedPubMed CentralGoogle Scholar
- Luzi E, Marini F, Sala SC, Tognarini I, Galli G, Brandi ML: Osteogenic differentiation of human adipose tissue-derived stem cells is modulated by the miR-26a targeting of the SMAD1 transcription factor. J Bone Miner Res. 2008, 23: 287-295. 10.1359/jbmr.071011.View ArticlePubMedGoogle Scholar
- Wong CF, Tellam RL: MicroRNA-26a targets the histone methyltransferase Enhancer of Zeste homolog 2 during myogenesis. J Biol Chem. 2008, 283: 9836-43. 10.1074/jbc.M709614200.View ArticlePubMedGoogle Scholar
- Guled M, Lahti L, Lindholm PM, Salmenkivi K, Baqwan I, Nicholson AG, Knuutila S: CDKN2A, NF2, and JUN are dysregulated among other genes by miRNAs in malignant mesothelioma - A miRNA microarray analysis. Genes Chromosomes Cancer. 2009, 48: 615-623. 10.1002/gcc.20669.View ArticlePubMedGoogle Scholar
- Galardi S, Fatica A, Bachi A, Scaloni A, Presutti C, Bozzoni I: Purified box C/D snoRNPs are able to reproduce site-specific 2'O-methylation of target RNA in vitro. Mol Cell Biol. 2002, 22: 6663-6668. 10.1128/MCB.22.19.6663-6668.2002.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhou H, Chen YQ, Du YP, Qu LH: The Schizosaccharomyces pombe mgU6-47 gene is required for 2'O-methylation of U6 snRNA at A41. Nucleic Acids Res. 2002, 30: 894-902. 10.1093/nar/30.4.894.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/10/173/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.