- Research article
- Open Access
New global analysis of the microRNA transcriptome of primary tumors and lymph node metastases of papillary thyroid cancer
- Manuel Saiselet1Email author,
- David Gacquer1,
- Alex Spinette2,
- Ligia Craciun2,
- Myriam Decaussin-Petrucci3,
- Guy Andry4,
- Vincent Detours†1 and
- Carine Maenhaut†1, 5
© Saiselet et al. 2015
- Received: 29 June 2015
- Accepted: 14 October 2015
- Published: 21 October 2015
Papillary Thyroid Cancer (PTC) is the most prevalent type of endocrine cancer. Its incidence has rapidly increased in recent decades but little is known regarding its complete microRNA transcriptome (miRNome). In addition, there is a need for molecular biomarkers allowing improved PTC diagnosis.
We performed small RNA deep-sequencing of 3 PTC, their matching normal tissues and lymph node metastases (LNM). We designed a new bioinformatics framework to handle each aspect of the miRNome: whole expression profiles, isomiRs distribution, non-templated additions distributions, RNA-editing or mutation. Results were validated experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the dataset from The Cancer Genome Atlas (small RNA deepsequencing of 59 normal samples, 495 PTC, and 8 LNM).
We performed small RNA deep-sequencing of 3 PTC, their matching normal tissues and lymph node metastases (LNM). We designed a new bioinformatics framework to handle each aspect of the miRNome: whole expression profiles, isomiRs distribution, non-templated additions distributions, RNA-editing or mutation. Results were validated experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the dataset from The Cancer Genome Atlas (small RNA deep-sequencing of 59 normal samples, 495 PTC, and 8 LNM). We confirmed already described up-regulations of microRNAs in PTC, such as miR-146b-5p or miR-222-3p, but we also identified down-regulated microRNAs, such as miR-7-5p or miR-30c-2-3p. We showed that these down-regulations are linked to the tumorigenesis process of thyrocytes. We selected the 14 most down-regulated microRNAs in PTC and we showed that they are potential biomarkers of PTC samples. Nevertheless, they can distinguish histological classical variants and follicular variants of PTC in the TCGA dataset. In addition, 12 of the 14 down-regulated microRNAs are significantly less expressed in aggressive PTC compared to non-aggressive PTC. We showed that the associated aggressive expression profile is mainly due to the presence of the BRAF V600E mutation. In general, primary tumors and LNM presented similar microRNA expression profiles but specific variations like the down-regulation of miR-7-2-3p and miR-30c-2-3p in LNM were observed. Investigations of the 5p-to-3p arm expression ratios, non-templated additions or isomiRs distributions revealed no major implication in PTC tumorigenesis process or LNM appearance.
Our results showed that down-regulated microRNAs can be used as new potential common biomarkers of PTC and to distinguish main subtypes of PTC. MicroRNA expressions can be linked to the development of LNM of PTC. The bioinformatics framework that we have developed can be used as a starting point for the global analysis of any microRNA deep-sequencing data in an unbiased way.
Thyroid cancer is the most prevalent type of endocrine cancer and papillary thyroid cancer (PTC) is, by far, the most frequent form 40 to 60 % patients present lymph node metastases (LNM). These metastases increase the risk of recurrence. The rate of mortality is low but the presence of LNM decreases long term survival, mainly for older patients (>45 years) [1, 2]. The diagnostic process is mainly based on the cytological profile of the tissue performed on a biopsy of the thyroid nodule. However, up to 30 % of the preoperative biopsies are inconclusive [3, 4]. The lobectomy or total thyroidectomy is then frequently proposed even if the probability of a malignant nodule is low and the surgery risky [5, 6]. There is thus a need for molecular biomarkers to avoid unnecessary surgery and to personalize treatment. microRNAs are among the most studied potential biomarkers of thyroid cancers [7, 8]. In addition, the implication of microRNAs in the aggressiveness of PTC and LNM formation is poorly known.
MicroRNAs are a class of small non-coding RNAs, 19 to 25 nucleotides long, which act on the stability and the translation efficiency of their target mRNAs . It has been described that each microRNA may control the expression of up to several hundreds of genes, involved in almost all programs of cell biology . The intermediate precursor (pre-miR) form is composed by a double-stranded duplex consisting of two arms (formerly known as miR/miR* but now referred as 5p-arm/3p-arm) and a terminal stem loop. In some cases, it has been shown that both arms can produce a functional mature microRNA . An expression ratio of the amount of the 5p-arm to the 3p-arm can then be calculated.
MicroRNAs may exist under different isoforms in the cells. These forms may result from alternative cleavages of the precursor during the maturation process. This leads to the coexistence, in individual cell types, of a diversity of sequence lengths, called isomiRs, with a variation of one or several bases at the 5′ end or the 3′ end of the microRNA . Furthermore, some sequence variations may also result from one or several nucleotide additions occurring after cleavage of the precursor. These supplemental nucleotides (mainly A or U bases) are added by nucleotidyl transferases and are generally found at the 3′ end of the mature microRNA. These additions create very particular isomiRs which are called non-templated additions (NTA). NTA generally induce a divergence between the microRNA sequence and its corresponding genomic sequence. SNPs also produce variations in microRNA sequences but their frequency in microRNAs is very low compared to the frequency of SNPs observed across the whole human genome . A-to-I RNA-editing involves the hydrolytic deamination of adenosine to inosine in double-stranded RNA. Since pre-miR form double-stranded duplexes, the editing machinery can modify microRNA sequences as well .
These different mechanisms of isoform diversification may co-exist for the same microRNA and the isoform profile of each microRNA may change between cell types. Changes in the 3′ part of the microRNAs are widely the most found, most of the time they do not lead to a modification of the function of the microRNAs [12, 14]. However, changes in the 5′ may drastically modify the selection of the mRNA targets .
It has been shown that microRNA expressions can discriminate human tumor samples from normal samples in different types of cancers . Many studies have shown that microRNAs are involved in the tumorigenesis and the metastatic process of many types of human cancers [17–19]. In particular, for thyroid cancers, numerous modulated microRNAs between normal and tumor samples have been identified . Many of these studies were performed with quantitative RT-PCR or microarrays. These methodologies are broadly used but are based on primer or probe specificity and can miss some expressions due to a lowest affinity for isoforms of the same mature microRNA. Small RNA deep-sequencing offers a more efficient way to study every aspect of the microRNA biogenesis [21, 22]. Some deep-sequencing studies suggest that NTA, isomiRs, 5p-to-3p arm expression ratio or A-to-I editing could be involved in the tumorigenesis of different cancer types [23–28] but only one study analyzed the isomiR distribution in PTC . Small RNA deep-sequencing analysis is often restricted by the necessity of bioinformatics analyses flexible enough to explore every aspect of the miRNome and to decrease technical biases [22, 29]. So far, most of the small RNA deep-sequencing studies used a “homemade” analysis method and there is no “gold standard” procedure.
In our study, we searched for modulated microRNAs in PTC and in LNM samples. We performed small RNA deep-sequencing of a first set of samples composed by 3 PTC, their matching normal tissues and LNM. The samples presented a weak (or lack of) contamination by normal cells, lymphocytes and fibroblasts. We collected and applied all the specific guidelines to design a dedicated bioinformatics framework for the analysis of each aspect of the miRNome from small RNA deep-sequencing data. The results were validated on about 600 independent samples: experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the data from The Cancer Genome Atlas (small RNA deep-sequencing data already processed of 495 PTC, 59 normal samples and 8 LNM) . We analyzed all miRNome variations of the samples in order to identify new strong and common biomarkers that might be related to tumorigenesis of the thyrocytes and LNM formation.
Cellular composition of the deep-sequenced samples
% of tumor cells
% of fibrosis
% of lymphatic cells
RNA and DNA extraction
Total RNA was extracted from tissues using Qiazol, followed by purification on miRNeasy columns (Qiagen) according to the manufacturer’s recommendations. Genomic DNA was extracted from the remaining mix of phenol-chloroform using homemade protocol described in Additional file 2.
Laser capture microdissection, qRT-PCR and other experimental procedures are described in Additional file 2.
Human small RNA deep-sequencing: mapping strategy and downstream analyses
Small RNA deep-sequencing of the 3 PTC and their matched samples were performed by the Beijing Genomics Institute (BGI) using the TruSeq Small RNA Sample Prep Kit (Illumina). 24 to 31 million of clean reads per sample were obtained. Illumina reads collapsed by sequence in fasta format are available from NCBI’s Gene Expression Omnibus  and are accessible through GEO series accession number GSE57780. In the following sections and in Additional file 2 we describe the bioinformatics pipeline that we used to analyze our small RNA deep-sequencing data and the already processed validation data from the TCGA.
microRNA reads mapping: from now on, we will refer to a microRNA tag as a subset of individual Illumina reads having identical sequences and that were collapsed together during data preprocessing. A tag is then defined by its DNA sequence and a count value corresponding to the number of Illumina reads sharing this sequence. To lower computational efforts, tags instead of individual reads were mapped to the specified reference. This increased alignment speed by several orders of magnitude. For the rest of this section, read mapping and tag mapping are equivalent expressions.
additions of non-templated nucleotides (NTA) will naturally introduce bases that differ from the reference mainly at the 3′ end of mature microRNA. Short read mappers do not distinguish NTA from “inner” mismatches that are due to SNPs, sequence variation or sequencing errors and this further leads to ambiguous alignments. This problem, referred as cross-mapping , occurs mainly for microRNAs appearing in families that differ only by one or two nucleotides;
the presence of multi-loci microRNAs (such as let-7a-1, let-7a-2, let-7a-3) complicates the definition of unique match. Such tags cannot be excluded on the simple basis that they map equally to multiple places, since the mature microRNA itself can be transcribed from multiple genomic loci;
cross-mapping issues may also appear when tags are aligned directly to microRNA precursors instead of the whole reference genome . Because small RNA libraries also contain other short RNAs (snRNAs, tRNAs…), these could be mistaken with mature microRNAs having a similar sequence.
We first applied this strategy using known microRNAs precursors (miRBase v19) as reference. Repeats were kept but tags that were mapped on the opposite strand of known pre-microRNA were considered incorrect and removed. We then used the full reference genome (hg19) as reference with three additional restrictions: (1) only a maximum of one mismatch was allowed; (2) a maximum of 2 bases were trimmed for each tag to avoid excessive length reduction; and (3) repeats were kept only if all matches corresponded to multiple loci of the same microRNA (e.g. multiple matches on let-7a-1, let-7a-2, let-7a-3 were all kept, matches on miR-548a, miR-548b, miR-548e were all discarded). With this strategy, up to 93 % of individual reads could be mapped during the first step when using the human genome hg19 as reference.
microRNA differential expression profiles: after aligning reads to the reference genome, we counted the number of mapped reads for both annotated and novel mature microRNAs. Expression analysis was performed using edgeR  distributed as an R  package available at Bioconductor . As advised in the documentation, we first extracted all microRNAs with a normalized expression (in CPM: Count Per Million mapped reads) of 1 CPM in at least 3 samples, leading to a total of 398 microRNAs that were used as input for differential expression analysis. We used a generalized linear model and designed our analysis so that 3 replicates were available per condition while retaining the paired information for tissue types collected from the same patient. This was done to remove baseline differences between patients when comparing microRNA expressions between conditions (e.g tumor versus normal samples, LNM versus normal samples and LNM versus tumor). By default, edgeR estimates the original size of each library by summing read counts across all genes for a given sample. Because let-7 microRNAs are highly expressed and appear as multi-loci microRNAs (e.g. hsa-let-7a-1, hsa-let-7a-2 and hsa-let-7a-3), several millions of reads were counted multiple times and library sizes were highly over-estimated. Therefore, we set library sizes to the total number of individual sequences present in fasta files for each sample prior computing normalization factors using edgeR calcNormFactors function. Multidimensional scaling analysis and hierarchical clustering were performed in R, Spearman correlations were performed in GaphPad Prism.
Down-regulated microRNAs are potential biomarkers of main subtypes of papillary thyroid cancers and associated lymph node metastases
▪ microRNA deep-sequencing data
▪ Experimental validation
We decided to focus our validations on the newly discovered down-regulated microRNAs in PTC and on microRNAs modulated between tumors and LNM. We used quantitative RT-PCR assays for 20 microRNAs selected from the most down-regulated microRNAs in tumors compared to normal samples (14 assays): miR-1179, miR-7-2-3p, miR-7-5p, miR-876-5p, miR-204-5p, miR-138-3p, miR-138-5p, miR-30c-2-3p, miR-139-3p, miR-139-5p, miR-451a, miR-504, miR-152, miR-873-5p (Fig. 3a) and from the most up- and down-regulated microRNAs in LNM compared to tumors (7 assays): the down-regulated miR-873-5p, miR-876-5p, miR-199b-5p and the up-regulated miR-375, miR-196a-5p, miR-509-3p, miR-509-5p (Fig. 3b). miR-873-5p and miR-876-5p were selected by both criteria. The well-known up-regulated microRNA in PTC miR-146b-5p was used as a control.
To confirm the specific thyroid cell expression of the modulated microRNAs, we performed Laser Captured Microdissection (LCM) on frozen sections from the deep-sequenced samples, and we analyzed the expression profiles of the 20 microRNAs by qRT-PCR in the normal and tumor thyroid cells. Expression variations identified by deep-sequencing, especially for the down-regulated microRNAs in tumors (miR-1179, miR-7-5p, miR-7-2-3p, miR-204-5p, miR-873-5p and miR-876-5p) and the up-regulation of miR-196a-5p in LNM, were detected in the microdissected samples as well (Additional file 1: Figure S1). In addition, we found a significant correlation between fold changes (tumor/normal or metastasis/tumor) obtained by qRT-PCR on deep-sequenced samples and on the microdissections of these samples (Spearman r = 0.63, P = 0.0045).
▪ In silico validation on the TCGA small RNA deep-sequencing dataset
In order to further validate our results on the largest set of independent PTC samples, we collected the TCGA public small RNA deep-sequencing data processed by the Broad Institute with an independent bioinformatics approach (see Additional file 2 for a complete description of collected datasets). The microRNA expression profiles of 59 normal samples, 495 primary tumors of all histological subtypes and 8 LNM were available. We used Table S2 from the recent TCGA publication on PTC  to obtain the clinical and pathological characteristics of samples. We analyzed the differential expression of the 20 microRNAs previously investigated by qRT-PCR.
Down-regulated microRNAs are biomarkers of PTC tumorigenesis and LNM formation
Down-regulated microRNAs can distinguish the two principal histological variants of PTC
Down-regulated microRNAs can distinguish aggressive from non-aggressive phenotype of PTC
We obtained similar results when we compared the normal samples with the BRAF V600E positive PTC samples (n = 233) and the BRAF negative PTC samples (n = 168) in the TCGA dataset (Fig. 6b). Again, non-annotated samples were not taken into account. The same 11 microRNAs plus miR-139-5p are down-regulated in both tumor types but, except for miR-451a and miR-504, they are significantly less expressed in BRAF V600E positive samples. miR-7-5p and miR-204-5p are only significantly down-regulated in BRAF V600E positive tumors. These results were not unexpected taking into account that in the 191 BRAF V600E positive PTC samples annotated for variant type, 179 are classical and only 12 are follicular. In addition, in the 151 BRAF V600E negative samples annotated for variants, only 80 are classical and 71 are follicular. The difference of the histological variants repartition in the BRAF V600E negative and positive tumor populations was assessed by a two-side Fisher’s exact test which was very significant: pval < 0.0001.
Globally, similar results were obtained when comparing LNM positive N1 (n = 206) vs LNM negative N0 (n = 214) PTC samples and high risk (n = 24) vs low risk (n = 171) PTC samples (Additional file 1: figure S2). As expected, the proportion of BRAF mutated tumors in the aggressive population is significantly higher than in the non-aggressive population (Fisher’s exact test pval < 0.01 in both comparisons).
In conclusion, we have validated the modulation of expression of our microRNAs in independent samples. These down-regulated microRNAs are systematically less expressed in more aggressive PTC samples, in correlation with the presence of the BRAF V600E mutation.
Deep investigation of the miRNome revealed no major implication of isoforms variation or arm shift in PTC tumorigenesis and lymph node metastatic progression
▪ 5p-to-3p arm ratio
▪ NTA distribution
▪ isomiRs distribution
We first compared the number of distinct isomiRs per mature microRNA between each of our deep-sequenced sample. A specific isomiR was considered present in a given sample if it accounted for at least 1 % of the expression of the mature microRNA. We found no difference between sample types. The vast majority of mature microRNAs, covered by a minimum of 100 reads presented between 2 and 8 isomiRs (Fig. 8c). We repeated this analysis with the TCGA data with or without filtering out samples with high tumor cells content but we found no difference between sample types (Fig. 8d,e). We then grouped isomiRs by classes, according to the modification of their start and end coordinates compared to their canonical counterpart. In order to analyze the distribution of these classes between sample types, we calculated the contribution of each class for every microRNA in the 9 samples that were deep-sequenced. We did not find any variation of these contributions between sample types (Additional file 1: Figure S6A). As expected, the canonical isoform defined in miRBase v19 was not always the most expressed. Indeed, we averaged class contribution across all 9 samples and the canonical isoform was the most expressed for only 48 % (93/194) of the selected microRNAs. The same values were observed for the end-site (3′) isomiR class. On the opposite, the start-site (5′) and both sites classes (5′and 3′) were dominant for only 1 % (2/194) and 3 % (6/194) of selected microRNAs respectively. We then redefined the canonical microRNA as the one having the highest relative contribution to total microRNA expression in the three normal samples. The classes were reassigned accordingly. In these conditions, the end-site (3′) class had still the most variable contribution across all selected microRNAs (ranging from 0 to 73 %) compared to classes involving 5’ coordinate shifts (ranging from 0 to 36 % and 0 to 30 % for start-site and both sites classes respectively) (Additional file 1: Figure S6B). Furthermore, there was still no variation of these contributions between sample types. We also searched for specific isomiRs whose contribution to total expression of the corresponding microRNA would be altered during the course of tumorigenesis and metastatic formation in the 9 samples. We found 5 microRNAs (let-7i-5p, miR-19b-3p, miR-27a-3p, miR-27b-3p, miR-148b-3p) for which at least one isomiR showed a variation of at least 20 % when comparing its mean contribution over normal tissues to primary tumors or LNM. However, all these isomiRs were 3′ variants of the canonical form, leaving the seed region unmodified. Additionally, we failed to confirm these observations using TCGA data. This suggests that isomiRs do not seem to be strongly related to tumor initiation or progression of thyrocytes.
MicroRNA A-to-I editing is present in weakly expressed mature microRNA in thyrocytes
We designed a custom variant calling approach to deal with the extreme coverage of some highly expressed microRNAs. Read alignments of the 9 deep-sequenced samples confirmed the A-to-I edition of the miR-376 family, which was already reported in glioblastoma cells . However, this microRNA is weakly expressed in each sample type. We identified new A-to-I edited positions. We identified 4 single nucleotide substitutions matching our filtering criteria but in weakly expressed microRNAs (Additional file 1: Table S3). In addition, editing frequencies obtained from read alignments did not show any significant difference between sample types. We further validated these editions by cDNA Sanger sequencing with primers designed to target the pri-miR sequence (Additional file 1: Table S4), and confirmed the absence of substitution in the DNA of these samples by genomic DNA sequencing. A-to-I RNA-editing (recognized as a G by the sequencer) was found for miR-605-3p in each sample type (Additional file 1: Figure S7). We also screened read alignments for other mutations located in mature microRNAs and potential variation of the mutational status between sample types but we did not find any candidate mutation.
In our study, we searched for new microRNA biomarkers of PTC tumorigenesis and lymph node metastasis formation. We used a custom bioinformatics framework for the analysis of the microRNA expression profiles of 3 PTC, matched normal samples and lymph node metastases. The choice of a first limited but well characterized set of samples is completely assumed. Indeed, this could lead to a loss of non-common variations in each sample but in this study we looked for strong and common new biomarkers of PTC. These samples were carefully selected on the basis of their normal or tumor cells composition to avoid cell contamination which would bias the results [41–43]. These samples were used as a small “training” set to find modulations in the whole miRNome, which were then validated in a very large amount of samples by two different approaches. First, by qRT-PCR on independent samples from 14 other patients. These samples were also carefully selected based on their normal or tumor cell composition. Secondly, we validated the results by an in silico analysis of the TCGA database which is the largest available dataset of PTC to our knowledge . qRT-PCR and in silico data were not always concordant. This can be due to different reasons. First, the number of samples analyzed is quite different between the 2 datasets. This variation changes the power of the associated statistics. Secondly, the methodologies are conceptually different . Finally, unlike qRT-PCR dataset, the TCGA dataset does not allow to adjust for interindividual variations by patient matched analyses without a great loss of information regarding LNM samples and not all the samples have been selected on the basis of their cell composition.
Our study revealed the strong ability of down-regulated microRNAs in PTC to distinguish tumors and LNM from normal thyroid samples. Their specific expression in thyrocytes was confirmed experimentally by LCM analyses, and then validated experimentally and in silico by selecting tumors with a low content of contaminating cells. Unlike commonly reported up-regulated microRNAs in PTC, very few down-regulated microRNAs were identified in microarrays studies [8, 20]. Only recent small RNA deep-sequencing studies showed a common list of down-regulated microRNAs in PTC [27, 30, 44]. However, their functions and abilities to distinguish sample types and tumor subtypes were little explored. This is the reason why we decided to focus our study on these microRNAs. In 2013, Swierniak et al. reported a list of 44 up- and down-regulated microRNAs in 14 PTC samples compared to matched normal thyroid samples . They used small RNA deep-sequencing analysis and validated their results by microarray on 9 additional sample pairs. Recently, Mancikova et al. described a smaller list composed by 10 up-regulated and 5 down-regulated microRNAs in PTC compared to normal samples , by performing small RNA deep-sequencing analysis on a training set of 35 PTC and 8 normal samples and a validation set of 43 PTC and 9 normal samples. Several microRNAs were common between both studies, but there were also differences. The number of samples considered, the RNA extraction method used and the bioinformatics pipeline used for the analysis of the deep-sequencing data may explain the discrepancies between the different studies. In our study, we identified by small RNA deep-sequencing analysis 31 up-regulated and 30 down-regulated microRNAs in PTC (classical variants) compared to matched normal samples. We confirmed that, as already described for up-regulation, consistent down-regulation of microRNAs occurs as well during PTC tumorigenesis. The list of the 14 most down-regulated microRNAs that we validated by qRT-PCR and in silico analyses, on subtypes of PTC, extends the observations of Swierniak et al. on the largest available set of PTC samples. In addition, we have extended these observations to LNM and have shown that these selected down-regulated microRNAs are differentially expressed between the 2 major histological subtypes of PTC (classical and follicular). Among the described down-regulated microRNAs, miR-7-5p has already been studied as a diagnostic marker for PTC but with limited efficacy (sensibility 82 %, specificity 73 %) [8, 45]. Accordingly in our work, miR-7-5p was not significantly modulated in follicular variants of PTC compared to normal samples. However, since it has been shown that a diagnostic signature based on multiple microRNA expressions can outperform previous single microRNA signatures , we propose to combine the down-regulated microRNA described in this study to the panel of microRNAs already explored to define a very efficient diagnostic signature of PTC in thyroid biopsies. However, more studies are required to assert the efficiency of any microRNA signature on rare subtypes of PTC, like diffuse sclerosing variants.
Many publications report that PTC samples with different degrees of aggressiveness present different microRNA expression profiles [46–48], on the basis of microarrays and qRT-PCR analyses. These studies revealed the common up-regulation of the well-characterized miR-146b-5p, miR-221-3p and miR-222-3p in aggressive PTC compared to non-aggressive samples but no commonly down-regulated microRNAs. Since the BRAF V600E mutation has been associated with PTC aggressiveness [49–51], Swierniak et al. searched for a BRAF V600E associated expression profile in their small RNA deep-sequencing data but without success . However, the recent global analysis of the TCGA data revealed that the BRAF mutational status deeply modulate the wide transcriptome of PTC . In particular miR-7-5p, miR-204-5p and other microRNAs expressions in PTC are associated with the presence of the BRAF V600E mutation. In our study, we found that 12 of the 14 strongest down-regulated microRNAs in PTC were significantly less expressed in BRAF V600E positive PTC samples compared to negative PTC samples. In addition, miR-7-5p and miR-204-5p were not significantly down-regulated in BRAF V600E negative PTC samples compared to normal samples. In accordance with our observations, Mancikova et al. recently showed that miR-7-5p and miR-204-5p expressions are deeply associated with the BRAF V600E mutational status . We found similar results when we compared tumors with a “high” clinical risk to tumors with a “low” clinical risk and tumors with LNM (N1) to tumors without LNM (N0), suggesting the general implication of the down-regulated microRNAs in the aggressiveness of the tumor. The “aggressiveness” of a tumor is a complex notion which involves clinical and pathological aspects like BRAF V600E mutational status, histological subtype, clinical tumor stage… We showed that the variations of microRNA expressions between PTC sample subtypes seemed mainly related to the BRAF V600E mutational status. This suggests that the constitutive activation of the RAS/RAF/MAPK pathway by the BRAF V600E mutation results in a higher deregulation of microRNA expressions. However, it is not clear if these modulations are a cause or a consequence of the aggressive phenotype.
The majority of the down-regulated microRNAs have been reported to be “tumor suppressor” microRNAs in other cancers. This is the case for miR-7-5p [52, 53] and miR-204-5p. Interestingly, one of the validated targets of miR-7-5p is EGFR (epidermal growth factor receptor) mRNA [54, 55] which is a major inducer of thyrocyte proliferation. miR-204-5p has been reported as a repressor of both MMP3 and MMP9 (matrix metalloproteinase) mRNA and also as a direct repressor of HMGA2 (High-mobility group AT-hook 2) mRNA. The proteins encoded by these mRNAs play an active role in the mobility and the invasiveness of cancer cells [56, 57]. So, in addition to their role as potential strong biomarkers candidates these microRNAs probably play a functional role in tumor progression.
The implication of microRNAs in lymph node metastasis formation is only poorly characterized. It has been shown in different cancer types that microRNA expressions in LNM reflect the microRNA expressions in the associated primary tumors [58–60]. Some studies suggest that LNM formation does not require major microRNA modulations  whereas another reports differentially expressed microRNAs between primary tumors and associated LNM , suggesting an implication of microRNAs in LNM formation. Concerning PTC metastasis development, very little is known. Our small RNA deep-sequencing results revealed similar microRNA expression profiles between primary tumors and associated LNM. In addition, we failed to validate by qRT-PCR 6 of the 7 selected microRNA modulations between tumors and LNM and only 2 (miR-873-5p, miR-876-5p) were validated in the TCGA dataset. However, among the validated down-regulated microRNAs in PTC, some of them are nevertheless significantly less expressed in the LNM than in the primary tumors, such as miR-7-2-3p, miR-30c-2-3p or miR-873-5p. The number of microRNA varied between our first deep-sequencing analysis, qRT-PCR and in silico validations but increased with the number of samples considered. We also showed that miR-196a-5p is up-regulated in LNM containing at least 70 % of tumor cells. In other cancers, miR-196a-5p and miR-30c-2-3p have also been respectively identified as up- [61–63] and down- [64, 65] regulated during migration and invasion. So, although the microRNA expression profiles of primary tumors of PTC and LNM are very similar, some microRNAs are modulated and could participate to metastasis formation. However, the amount of LNM used in our study is limited and further studies are therefore required to confirm these preliminary data as well as to characterize the function of these microARNs in LNM formation.
Our results showed that there is no major change in the 5p-to-3p expression ratios, the isomiR distribution and the NTA distribution which could be involved in PTC tumor development and constitute new biomarkers. However, our analyses are limited by the small number of samples in our “training” set. Furthermore since the TCGA small RNA deep-sequencing public data only include per-sample isoform read counts and normalized expression, it was not possible to use them to validate the results of the NTA analysis. In addition, unlike the TCGA mapping strategy, our read mapping strategy was designed to specifically distinguish 3′ NTA from isomiRs. It is thus possible that a given isoform “A” reported in the TCGA data actually corresponds to isoform “B” with one or several non-templated additions in our own dataset. This limits the accuracy of comparisons of isomiR distribution. Nonetheless, we showed that the large majority of microRNAs had a level of adenylation or uridylation below 30 % of all mapped reads and it is rare to find microRNA with a high level of adenylation and uridylation at the same time. This is in accordance with previous results [37, 66]. This particular issue only concerned isomiRs characterization and had no effect on expression profiles, obtained using all isoforms identified for a given mature microRNA. Additionally, qRT-PCR is inefficient for the validation of the results of the NTA and the isomiR analyses.
However, to our knowledge, this is the first work which explores 5p-to-3p expression ratios and NTA distribution in PTC. IsomiR distribution has already been studied by Swierniak et al. who showed that this distribution changes during PTC tumorigenesis . In accordance with them, we showed that the vast majority of mature microRNAs, covered by a minimum of 100 reads, presented between 2 and 8 isomiRs. However as opposed to what they report, we did not find modulation of the distribution between sample types, even when considering all the samples from the TCGA dataset. This discordance could be explained by the number of samples considered and by technical differences. Indeed, Swierniak et al used normal samples, normal tissues adjacent to tumor samples and tumors from 14 patients. In addition, they required perfect matches without any 3’ end NTA trimming when aligning reads. The selection of isomiR is also slightly different. We therefore concluded that the distribution of well-expressed isomiRs is similar in normal thyroid samples and PTC.
This absence of major modulation contradicts several studies on different cancer types which have mentioned that 5p-to-3p expression ratios, NTA and isomiR distributions may be altered in tumors [23–25, 27, 28]. Two non-exclusive hypotheses may explain these discrepancies: (1) there is no such modulation in the thyrocytes, neither during tumorigenesis nor during LNM development; (2) these modulations are more related to cellular contamination and read mapping strategy than to tumorigenesis. Studies reporting such modulations involve tumor samples without well-defined cellular content, and the mapping strategy differs between studies [23–25, 27, 28]. Indeed, contaminating cells like lymphocytes or fibroblasts and even normal cells could alter expression profiles and decrease the specific signal of the tumor cells [41–43]. However, even when using the full set of unfiltered TCGA samples, no modulation of 5p-to-3p expression ratios or isomiR distribution was found between the sample types.
microRNA editing was observed in thyrocytes, and we were able to validate a A-to-I editing event occurring in miR-605-3p. As the edited base was located in the seed sequence, this specific editing event may change the mRNA targets of this microRNA. However, no modulation was observed between samples types. We concluded that editing may be indeed present in mature low-expressed microRNAs in thyrocytes but does not seem to contribute to PTC tumorigenesis.
In our study, we developed a new flexible bioinformatics framework to explore every aspect of the miRNome. Small RNA deep-sequencing outperforms traditional quantitative technologies like qRT-PCR or microarrays . However, the absence of “gold standard” read mapping guidelines decreases the comparability of the studies and increases the risk of technical biases in the results. In 2010, De Hoon et al.  showed that, with an inappropriate analysis pipeline, a microRNA read may be mapped on a wrong location in the reference genome. This effect, known as cross-mapping effect, may dramatically change the conclusion of the analysis by creating wrong sequence variations and microRNA quantifications. Two years later, Alon et al.  showed that an alignment of the microRNA reads against the whole genome with up to one mismatch tolerated and a 3′ end NTA trimming procedure drastically decreases the level of cross-mapping. Recently, Muller at al.  showed that an analysis pipeline which does not consider isomiRs and NTA variations may result in a loss of 31 % of the microRNA expression signal. Indeed without isoform detection the number of individual microRNAs presenting more than 100 reads in at least one sample of the study decreased from 318 to 219 microRNAs. In our study, we designed our bioinformatics analysis pipeline in order to meet guidelines established by the previously mentioned authors: (1) the microRNA reads were aligned directly to the whole genome, because small RNA libraries also contain other short RNAs (snRNAs, tRNAs…) which could be confounded with mature microRNAs having a similar sequence; (2) the isomiR variations were captured by considering all reads mapped within a 5 base pairs window of every canonical microRNA; (3) the 3′ end NTA were iteratively trimmed until a match could be found and the information regarding the trimmed nucleotides were kept; (4) the number of allowed mismatches was gradually increased by a multistep alignment procedure so that perfect matches were always preferred. With this strategy, up to 93 % of individual reads could be mapped during the first step when using the human genome hg19 as reference (Fig. 1 and Methods). Considering that no “gold standard” exists in the literature and that all the authors do not explain in details their bioinformatics framework in such a way that non experts can reproduce the analyses, we addressed alignment issues previously reported in the literature and proposed unified guidelines to study every aspect of microRNA biogenesis from each small RNA deep-sequencing data in an unbiased way.
Down-regulation of microRNAs is a common feature occurring during PTC tumorigenesis. This observation was expanded to lymph node metastases and we showed that these modulations occur specifically in the thyroid tumor cells. These modulations are associated with the aggressiveness of the primary tumor and are further amplified in BRAF V600E positive cells. Our results suggest that the observed down-regulated microRNAs in PTC are not only potential strong biomarker candidates but may have a functional role at different levels during thyroid tumorigenesis and metastases formation. This should be explored by functional studies.
The first complete miRNome analysis of PTC that we performed showed that there is no major change in the 5p-to-3p expression ratios, the isomiRs and the NTA distributions, or in the A-to-I editing sites of mature microRNAs, suggesting that they do not contribute to PTC tumor development.
Considering that no “gold standard” exists in the literature, we propose unified guidelines to analyze each aspect of microRNA deep-sequencing data in an unbiased way.
Availability of supporting data
The data sets supporting the results of this article are available in the NCBI’s Gene Expression Omnibus repository, [http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE57780] and from Broad Institute’s Genome Data Analysis Center, [https://confluence.broadinstitute.org/display/GDAC/Download].
Authors want to thank Professor J. E Dumont for his conceptual support, Bernadette Bournonville and Chantal Degraef for her excellent technical support.
This work was supported by the “Fonds de la Recherche Scientifique Médicale” (FRSM) ; Fonds de la Recherche Scientifique-FNRS (grant number 19556102) ; WELBIO ; Plan Cancer Belgique ; Télévie ; Fonds pour la formation à la Recherche dans l’Industrie et dans l’Agriculture ; Fondation Rose et Jean Hoguet and les Amis de l’Institut Bordet.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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