Purpose: miRNAs play a prominent role in a variety of physiologic and pathologic biologic processes, including cancer. For rectal cancers, only limited data are available on miRNA expression profiles, whereas the underlying genomic and transcriptomic aberrations have been firmly established. We therefore, aimed to comprehensively map the miRNA expression patterns of this disease.
Experimental Design: Tumor biopsies and corresponding matched mucosa samples were prospectively collected from 57 patients with locally advanced rectal cancers. Total RNA was extracted, and tumor and mucosa miRNA expression profiles were subsequently established for all patients. The expression of selected miRNAs was validated using semi-quantitative real-time PCR.
Results: Forty-nine miRNAs were significantly differentially expressed (log2-fold difference >0.5 and P < 0.001) between rectal cancer and normal rectal mucosa. The predicted targets for these miRNAs were enriched for the following pathways: Wnt, TGF-beta, mTOR, insulin, mitogen-activated protein kinase, and ErbB signaling. Thirteen of these 49 miRNAs seem to be rectal cancer-specific, and have not been previously reported for colon cancers: miR-492, miR-542-5p, miR-584, miR-483-5p, miR-144, miR-2110, miR-652, miR-375, miR-147b, miR-148a, miR-190, miR-26a/b, and miR-338-3p. Of clinical impact, miR-135b expression correlated significantly with disease-free and cancer-specific survival in an independent multicenter cohort of 116 patients.
Conclusion: This comprehensive analysis of the rectal cancer miRNAome uncovered novel miRNAs and pathways associated with rectal cancer. This information contributes to a detailed view of this disease. Moreover, the identification and validation of miR-135b may help to identify novel molecular targets and pathways for therapeutic exploitation. Clin Cancer Res; 18(18); 4919–30. ©2012 AACR.
The response of locally advanced rectal cancers to preoperative radiochemotherapy varies tremendously. Although a good response to preoperative treatment is associated with favorable outcome, molecular markers that allow therapy stratification are still lacking. The identification of differentially regulated miRNAs could therefore contribute to a better understanding of the underlying mechanisms of rectal cancer and its response to preoperative treatment. Furthermore, the detection of miRNAs that correlate with prognosis before preoperative therapy would open up the possibility for treatment individualization in rectal cancer.
Cancer cells exhibit complex alterations of their genome, transcriptome, and epigenome. More recently, evidence has accumulated that a class of small noncoding RNAs, termed miRNAs, also contribute to tumor initiation and progression, in addition to their pivotal role in essential biologic processes such as development, cellular differentiation, proliferation, and apoptosis. The number of verified human miRNAs is constantly growing.
Colorectal cancer is the third most common cause of cancer and the second leading cause of cancer death in Europe and the United States (1). From a clinical point of view, cancers of the colon and the rectum are 2 distinct entities that require different treatment strategies. Accordingly, when analyzing the genetics and biology of these tumor entities, they should be treated separately. With respect to colon cancer, extensive catalogues of deregulated miRNAs have been identified in the past few years. For rectal cancer, however, only very limited data are available (2), particularly because previous contributions failed to separate these entities.
For this reason, we aimed to establish comprehensive miRNA expression patterns of this common tumor. We therefore prospectively collected tumor biopsies from 57 patients with locally advanced rectal cancers enrolled in phase-III clinical trials to ensure standardized acquisition of biomaterial and follow-up. To enable a direct comparison of rectal cancer to matched normal rectal mucosa, corresponding nontumor biopsies from each individual patient were ascertained as well. Accordingly, we present here the hitherto largest and most comprehensive comparative analysis of locally advanced rectal cancers, which serves as the basis for the rectal cancer miRNAome.
Materials and Methods
Patients and sample collection
For this study, we prospectively collected pretherapeutic biopsies from 57 patients with locally advanced rectal cancer who were treated at the Department of General and Visceral Surgery, University Medical Center, Göttingen, Germany. To further validate miRNA findings, a completely independent set of 116 patients was recruited from our and 4 additional surgical departments in Germany (Erlangen, Möenchengladbach, Oldenburg, and Kassel). All patients were enrolled or treated according to the CAO/ARO/AIO-94 (3) and CAO/ARO/AIO-04 trial of the German Rectal Cancer Study Group. Written informed consent was obtained from all patients according to the guidelines approved by the local ethic committee, and the clinical data are summarized in Supplementary Table S1.
All patients received a total radiation dose of 50.4 Gy (single dose of 1.8 Gy) accompanied by either a 120-hour continuous intravenous application of 5-FU (1,000 mg/m2/day on days 1 to 5 and days 29 to 33), or a combination of an intravenous infusion of oxaliplatin (50 mg/m2 on days 1, 8, 22, und 29 over 2-hour) and a continuous infusion of 5-FU (250 mg/m2/day on days 1 to 14 and 22 to 35). Six weeks after preoperative radiochemotherapy (RCT), surgery with quality controlled total mesorectal excision (TME) was done. Four to 6 weeks after surgery, multimodal therapy was completed by either 4 cycles of 5-FU (500 mg/m2/day) bolus infusion (days 1 to 5) or with 8 cycles of 5-FU (2,400 mg/m2) continuous infusion with folinic acid (400 mg/m2) and oxaliplatin (100 mg/m2) infusion.
Biopsies were acquired before any treatment was administered. Along with the tumor (T) adjacent nontumorous mucosa (N) was taken that had to be at least 2 cm distant from the tumor. Biopsies were then immediately transferred into RNAlater RNA Stabilization Reagent (Qiagen), and stored over night at 4°C to allow saturation of the entire biopsy. Long-term storage was done at −20°C.
RNA isolation and microRNA expression profiling
RNA was extracted using TRIZOL (Invitrogen) as previously described (4). Nucleic acid quantity, quality, and purity were determined using a spectrophotometer (Nanodrop) and a 2100 Bioanalyzer (Agilent Technologies). Subsequently, miRNA expression profiling was carried out on locked nucleic acid (LNA)-enhanced miRCURY microarrays (Exiqon). These arrays contain Tm-normalized capture probes for 2,090 miRNAs. All hybridizations were done against a common reference pool consisting of an equimolar mixture of total RNA from all samples. This enables both 1- and 2-channel data analysis, as described in detail by Søkilde and colleagues (5).
One microgram of total RNA from each sample was labeled using the miRCURY LNA miRNA Power Labeling Kit (Exiqon) following a 2-step protocol: First, calf intestinal alkaline phosphatase (CIAP) was applied to remove terminal 5′phosphates. Second, fluorescent labels were attached enzymatically to the 3′-end of the miRNAs: Sample-specific RNA was labeled with Hy3 (green channel) fluorophore, whereas the common reference RNA pool was labeled with Hy5 (red channel). The labeled RNA was hybridized to miRCURY arrays for 16 hours at 65°C in a Tecan HS4800 hybridization station. After washing and drying, the microarrays were scanned in an Agilent G2565BA Microarray Scanner System (Agilent Technologies) and the images were quantified using ImaGene v. 8.0 (BioDiscovery).
Low-level analysis was carried out in the R statistical computing environment, including importing and preprocessing of the data using the LIMMA package (available at http://bioconductor.org). After excluding flagged spots from the analysis, the “normexp” background correction method, plus “offset = 50” was applied. For single channel analysis, the intensities were log2 transformed and quantile normalized as implemented in LIMMA. Both log2 intensities (single channel analysis) and log2 ratios (dual channel analysis) of 4 intraslide replicates were averaged.
To identify miRNAs that were differentially expressed, we applied the empirical Bayes, moderated t-statistics implemented in LIMMA. Linear models were fitted to the data, and comparisons of interest were extracted as contrasts. Unsupervised hierarchical clustering with complete linkage, using both Euclidean and 1-Pearson correlation as distance metrics, was applied to cluster the samples according to their miRNA expression levels. Both group comparisons (tumor versus normal) as well as within-patient comparisons (individual T/N ratios) were applied to identify the most reliable subset of miRNAs specific for rectal cancer. Only those miRNAs that were significant in both analyses based on (|log2(T/N)|>0.5 tumor vs. mucosa and P < 0.001) were used for validation and are discussed in the results. All P-values were adjusted for multiple testing using the function “p.adjust” and the false discovery rate (FDR) method described by Benjamini and Hochberg (6). Data visualization, including dynamic principle component analysis (PCA; based on variance filtering, significance level and FDR), heat maps and clustering was done in Qlucore Omics Explorer v.2.2 (Qlucore AB).
For disease-free (DFS) and cancer-specific survival (CSS) analysis, Kaplan–Meier plots and the Cox proportional hazards model were applied using the R package “survival.” A step-forward ANOVA model was used for pair-wise comparison of biomarkers, the expression of which was log2 transformed and treated as continuous variables. DFS was defined as time from resection of the tumor (patients considered as tumor-free) until the development of distant or local recurrence, and CSS as time until tumor-related death.
Bioinformatic analysis and target prediction
Differentially expressed miRNAs (see criteria above) were analyzed by using the commercial software IPA v.8.8 (www.ingenuity.com, Ingenuity Systems), and by DIANA-miRPath, a free web-based tool that carries out enrichment analysis of miRNA target genes to all known KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways (7). mRNA expression values of genes indicated in the IPA network were overlaid accordingly and were retrieved from a previous publication (8) comparing 65 matched rectal cancer and mucosa samples (GSE20842). This was possible because the samples used within this study for miRNA expression profiling have been previously analyzed using whole genome expression arrays. Potential miRNA targets were assessed by several target prediction algorithms to identify overlapping target genes: MicroCosm Targets (http://www.ebi.ac.uk/enright-srv/microcosm/cgi-bin/targets/v5/search.pl), Diana Lab microT (http://diana.cslab.ece.ntua.gr/microT/), PicTar (http://pictar.mdc-berlin.de/), and TargetScan 5 (http://www.targetscan.org/).
As an additional approach, miRNA target predictions were retrieved from miRecords V.3 (http://mirecords.biolead.org/) and compared with the mRNA expression levels from our previous analysis (ref. 8; GSE20842). Target genes were matched to the array probes via ENSEMBL ID or Gene Symbol. In case of hits to multiple probes, only the probe with the most significant differential expression between tumor and mucosa was taken. A cutoff of an FDR < 0.05 for differential gene expression between tumor and mucosa was used to determine significantly differentially up- or down-regulated genes. Fisher's Exact Test was used to determine statistical overrepresentation in each target gene set, in the case of upregulated miRNAs a one sided test was used on the down-regulated genes, in case of downregulated miRNAs a one sided test on upregulated genes; FDR was used to adjust the P values for multiple testing (6).
Semi-quantitative real-time PCR
Semi-quantitative real-time PCR (semi-qRT-PCR) was carried out using Taqman miRNA assays (Applied Biosystems) and a BioMark 48.48 Dynamic Array System (Fluidigm) according to the manufacturer's instructions. After reverse transcription, pre-amplified cDNA of 24 rectal cancers and 24 matched mucosa samples was loaded into the Dynamic Array along with ×2 Master Mix (Applied Biosystems) and Loading Agent (Fluidigm). Based on the microarray data, 10 miRNAs differentially expressed between cancerous and normal tissues as well as 3 housekeeping miRNAs were selected for PCR validation. On the basis of their stable expression over all patients and representing different levels of expression intensity (high, medium, and low), 3 small nucleolar RNAs (snoRNA), i.e., U66, U44, and U48, were chosen as endogenous normalization controls. U44 and U48 have previously been used to normalize miRNA experiments (9, 10). A potential bias due to its localization within the growth arrest specific 5 (GAS5) transcript has been reported for U44 (11). These data, however, were retrieved from a mixed cohort of breast as well as head and neck cancers and did not hold true in our dataset.
All assays were done in triplicate. The target sequences for the PCR reactions are listed in Supplementary Table S2. Replicates with a Ct SD greater than 1 were omitted from further analysis. miRNA expression was quantified as ΔCt values, where Ct = threshold cycle, ΔCt = (Ct target miRNA − average Ct of RNU66, U44, and U48), and ΔΔCt values, ΔΔCt = (ΔCt target miRNA tumor tissue − ΔCt target miRNA matched normal tissue) were used to quantify miRNA expression of tumor compared with matched mucosa. ΔCt was calculated using Fluidigm Real-Time PCR Analysis software (v2.1).
In addition to this technical validation of the miRNA array data, semi-qRT-PCR was used to measure the expression levels of miR-135b in a completely independent set of 116 rectal cancer patients. These PCR experiments were done using the ABI 7900HT system (Applied Biosystem). Specific primers for miR-135b as well as for U66, U44, and U48 were obtained from Qiagen. According to the manufacturer's instructions, cDNA containing universal tag was generated from total RNA using the miScript Reverse Transcription Kit (Qiagen). Quantification was carried out using QuantiTect SBYR Green PCR Master Mix (Qiagen).
The rectal cancer miRNAome—differentially expressed microRNAs
In contrast to colon cancer, only very limited data on miRNA expression profiles are available for rectal cancer (2, 12, 13). Toward the establishment of comprehensive miRNA expression patterns, we profiled pretherapeutic tumor biopsies that were prospectively collected from 57 patients with locally advanced rectal cancers. All patients were enrolled in phase-III clinical trials to ensure standardized acquisition of biomaterial. To enable a direct comparison of rectal cancer to matched normal rectal mucosa, corresponding nontumor biopsies from each individual patient were hybridized as well. An updated version of a recently described microarray platform (14) containing LNA-enhanced capture probes for quantitation of 2,090 mature miRNAs, including 238 proprietary miRNAs identified by 454 sequencing (15), 10 snoRNA, and 55 viral miRNAs relevant for human was used for this analysis.
Following variance filtering (SD > 0.2) and inclusion of only signals that were higher than twice the background, 732 miRNAs were left for further analysis and data were uploaded to GEO Database (GSE38389). Of these, 49 differed with high significance (|log2(T/N)|>0.5 and P < 0.001) between normal and rectal cancer tissue. This miRNA signature was able to separate the tumor from the normal group in the 2-way hierarchical cluster shown in Fig. 1A. Twenty of these differentially expressed miRNAs were upregulated, and 29 were downregulated in tumor versus mucosa (Table 1), suggesting a general repression of miRNA expression in cancer. This would be in line with the original observations by Lu and colleagues (16) for a variety of human cancers. However, using a looser filter, e.g., |log2(T/N)|>0.2 and P < 0.01, we identified 266 miRNAs as differentially expressed, and of these, 107 are downregulated in rectal cancer compared with normal mucosa, whereas 159 are upregulated (Supplementary Table S3). Therefore, the general perception that miRNAs are downregulated in human cancer might not apply to all tumor entities.
We observed a larger variation in miRNA expression levels among rectal cancer samples (around 50% higher SD between all tumor samples than between all adjacent mucosa samples) than among the corresponding normal tissue, which is illustrated by the more scattered distribution of tumor samples (red) compared with normal samples (green) in the PCA plot, Fig. 1B. This suggests that the tumor population is more heterogeneous than the corresponding nontumor tissue.
Comparison of microRNA expression profiles from rectal and colon cancer
Cancers of the colon and the rectum represent 2 distinct clinical entities that require different treatment strategies. We therefore compared our rectal cancer specific miRNA profiles to previously published miRNA data on colon cancer. In Table 1, we have indicated that out of the 49 significantly differentially expressed miRNAs, 28 have already been identified in colon cancer, including miR-21, miR-31, miR-135b, miR-223 (all up-regulated), miR-195, miR-378, miR-192–194–215, miR-143–145, miR-1, and members of the miR-30 and let-7 family (all downregulated). Therefore, the remaining 21 miRNAs potentially define rectal cancer as a disease molecularly distinct from colon tumors.
It should be kept in mind, however, that most data on colon cancer rely on older microarrays that do not contain many of the “novel” miRNAs detected in this study: besides the 6 proprietary miRPlus sequences, the following 15 miRNA seem to be rectal cancer-specific: miR-492, miR-542-5p, miR-584, miR-483-5p, miR-144, miR-2110, miR-652*, and the C/D box snoRNA, SNORD12B was detected as up-regulated in rectal cancer. MiR-375, miR-147b, miR-148a, miR-190, miR-26a/b, miR-29c, and miR-338-3p were found to be downregulated. However, the comparison of colon and rectal cancer miRNA expression patterns should be done with caution as these results have been retrieved from several different studies.
Interestingly, miR-652* was identified to be differentially regulated. As, in compliance with the latest release 17 of miRBase (17), this novel miRNA has not been entered yet. For future reference, this miRNA (which has the sequence ACAACCCUAGGAGAGGGUGCCA) should be denoted miR-652-5p (Supplementary Fig. S1).
Technical validation of microRNA expression levels by qPCR
The expression of 10 miRNAs, which were selected based on a combination of fold-change and P-value (indicated in Table 1), was validated using semi-qRT-PCR in 48 samples (24 matched tumor-mucosa pairs). Except for miR-492 and miR-29c (Supplementary Fig. S2), the miRNA expression levels correlated very well between the 2 methods (Fig. 2), with a range of R2 from 0.293 to 0.744.
Predicted targets are enriched for cancer-related pathways
We applied both IPA and DIANA-miRpath (7) to visualize potential interaction networks based on target predictions for the 49 miRNAs that were differentially expressed between normal and rectal cancer tissue (Table 1); mRNA expression levels for the 57 patients were overlaid on the IPA network. One such interaction map is illustrated in Fig. 3, which represents a composite of merging the 3 top networks with higher significant scores according to the IPA. The genes included to generate each individual network were identified as predicted target genes of our differentially expressed set of miRNAs according to the Diana microT4 databases. Ultimately, this network aims at representing the set of predicted target genes whose gene expression might change as a consequence of differentially expressed miRNAs in the tumor compared with matched normal mucosa. The mRNA levels of these genes were retrieved from a previous analysis (8). Genes whose expression did not change in the tumor versus matched normal mucosa comparison were excluded from the network, as we believe that their biologic significance in this particular context was unclear and could be misleading. Within this figure, some of the notable miRNA-regulated targets are MYC, APC, ERBB2-ERBB3, ZEB1, and FOXD3. All of these genes have previously been implicated in tumorigenesis. On the basis of target enrichment for the top-regulated miRNAs, the predicted KEGG pathway analysis revealed Wnt, TGF-beta, mTOR, insulin, mitogen-activated protein kinase (MAPK), and ErbB signaling.
Comparison of different databases revealed highly discrepant results. For example, miR-135b was found to have 428 predicted targets by PicTar, 510 targets by TargetScan, and 663 targets by micro-T3, but only 7 targets were overlapping among the top-100 hits (Supplementary Fig. S3). So far, only one miR-135b target, namely APC, has been experimentally verified (18); this gene was ranked 117 by TargetScan, 181 by micro-T3, and was not identified by PicTar.
Difficulties in miRNA target prediction became obvious again when we used IPA to generate the interaction network shown in Fig. 3. The miR-135b-APC association was not detected, nor was there any reference to it in the IPA knowledge database, which is the basis for generating the pathways. Only after manual curation, the interactions for both LEMD1 and adenomatous polyposis coli (APC) with miR-135b could be included into this figure. Therefore, the number of possible pathways increases, and we present other probable interactions that are based on the most up- and downregulated miRNAs and their associated predicted mRNA targets in Supplementary Fig. S4. However, the biologic significance of our observations is strengthened through the identification of many miRNAs that seem to participate in regulatory networks involved in tumorigenesis, such as the Wnt/β-catenin, MYC, and EGFR/KRAS pathways (19).
As an additional approach to increase the number of reliable miRNA target genes, we used an mRNA expression dataset and compared these results to the predicted targets retrieved from a database for validated miRNA target prediction. This was possible because we recently published the mRNA expression signatures for the tumor and mucosa samples used in this analysis (8). Twenty-six miRNAs could be retrieved from the database predicting 203 mRNA targets. We based this analysis on the principle assumption that, in general, the upregulation of a miRNA should result in a downregulation of its mRNA, and vice versa. Although target enrichment analysis was only significant for miRNA-21 (P = 0.003), a total of 104 target genes were found to be regulated as expected, 74 were regulated in the opposite direction (Supplementary Table S4).
Expression of miR-135b correlates with prognosis
Finally, we aimed to test whether the expression of any of the differentially expressed miRNAs was associated with patients' DFS or CSS. Toward this goal, we performed a rank regression analysis using tumor, normal, and tumor/normal ratios for all miRNAs. Interestingly in tumors, expression of miR-135b significantly correlated with CCS. It seems that patients with above median expression levels of miR-135b have a more favorable prognosis (no deaths) than patients with below median miR-135b expression (P = 0.0015; data not shown).
To validate these findings, semi-qRT-PCR was done to measure the expression levels of miR-135b in a completely independent set of 116 rectal cancer patients that have been treated at 5 different hospitals in Germany (Göttingen, Erlangen, Moenchengladbach, Oldenburg, and Kassel). Based on the median expression, both DFS (P = 0.042) and CCS (P = 0.00049) were significantly better in patients with high expression levels of miR-135b (Fig. 4).
No significant correlation was found between miR-135b expression levels and age [cor = −0.0009; 95% CI, −0.16 to 0.16; P = 0.991], clinical Union Internationale Contre le Cancer (UICC) stage (cor = −0.018; 95% CI, 0.25 to 0.07; Pearson correlation, P = 0.26) or ypN stage [cor = 0.11 (−0.27 to 0.05); Pearson correlation, P = 0.16]. However, ypUICC stage (cor = 0.21; 95% CI, 0.36to −0.05; Pearson correlation, (P = 0.01) as well as tumor regression grading (cor = 0.2; 95% CI, 0.04 to 0.36; Pearson correlation, P = 0.01) turned out to be significantly correlated.
Several groups have published miRNA expression signatures for colorectal cancer based on real-time PCR (RT-PCR;20), microarray (21–23), bead array (24), and sequencing (25) approaches. However, colon and rectal cancer should be studied separately based on their molecular and clinical differences. We therefore focused our analysis on a well-defined group of locally advanced rectal cancer. To screen for differential miRNA expression, both unpaired (tumor versus normal group) as well as paired (individual tumor/normal ratios) analyses were done. Both analyses gave very similar results in terms of significance. Because the group-wise comparison is more conservative, we focused on these results.
microRNAs upregulated in cancer
Based on our filter criteria (|log2 tumor/mucosa| >0.5 and P < 0.001), 49 miRNAs were differentially expressed between cancer and mucosa. As expected, the upregulation of several miRNAs could be confirmed such as miR-21, miR-223, miR-31, and miR-675 (20, 23, 26). The latter was shown to downregulate the expression of the retinoblastoma tumor suppressor gene, RB1 (27). miR-18a (P < 5.8E-8) and miR-17 (P < 1.9–E4) were found to be upregulated in the tumor samples as well. Both miRNAs belong to the miR-17-92 cluster (oncomir-1) located on chromosome 13, which is frequently increased in copy number in colorectal cancer (28).
Of the remaining miRNAs that were identified to be upregulated in the tumors, none has been reported to occur in comparison of colon or colorectal cancer to normal tissue. Therefore, these miRNAs are likely to be rectal cancer specific. However, because previous studies are limited due to the lack of stratification into colon and rectal cancers, herein identified miRNAs were potentially not included in the prior analyses. To our knowledge, only one recent article by Slattery and colleagues (2) has examined the miRNA profiles of rectal and colon cancer in a systematic manner. However, 2 distinct differences are evident. First, formalin fixed paraffin embedded (FFPE) tissue was used by Slattery and colleagues. Second, only subgroups like CpG island methylator phenotype positive (CIMP), KRAS mutated or TP53 mutated colon or rectal cancer tumors were compared with normal tissue. Nevertheless, 8 of 23 potentially rectal cancer specific miRNAs have not been described by Slattery and colleagues (2).
One of the newly identified miRNAs was miR-492. In our dataset, miR-492 was the single most upregulated miRNA (up to 16-fold higher). Its pre-miRNA sequence overlaps a pseudo-gene of Keratin-19 (KRT19) that is highly expressed in tumors of the bottom gastrointestinal tract (29). Their co-expression has recently been shown for hepatoblastoma (30). However, our attempts to validate the miR-492 expression with qRT-PCR data failed. As a potential explanation we hypothesized that this could be due to cross-reaction on the array, for example between the complementary mRNA or the pre-miRNA and the probe for miR-492. An alternative explanation might be that the PCR assay did not detect posttranscriptionally modified miRNAs (31). Another hypothesis is that although we found miR-492 to be highly expressed in rectal cancer by array analysis, this miRNA has escaped detection in all deep sequencing studies conducted so far according to deepBase (http://deepbase.sysu.edu.cn/browseAllRNA.php) (32). Therefore, it is likely, that miR-492 is either modified, for example by posttranscriptional processing (33) or not a genuine miRNA after all. However, because the signal-to-noise ratio (SNR) for miR-492 detection is very high, we do not consider this finding as a technical artifact.
Among the remaining miRNAs, a cancer related expression was reported for miR-483-5p (34), miR-542-5p (35), miR-584 (36) as well as for miR-144 (37) and miR-2110 (38). Of interest was the identification of miR-652*. Although miR-652 has previously been identified to be differentially regulated in colorectal cancer (25), miR-652* has not been entered to miRBase (17) yet, and can therefore be considered as a newly identified miRNA and should be denoted miR-652-5p.
microRNAs downregulated in cancer
Just as for the upregulated miRNAs, many of the down-regulated miRNAs have previously been described in studies analyzing colorectal cancer (39), such as miR-378 (40), miR-195 (41) or the miR-143–145 cluster (39). In addition, let-7a, let-7f, and representative miRNAs from the miR 200 family confirmed previous findings. One miRNA that has not been associated with colon cancer was miR-375. miR-375 was the single most downregulated miRNA in rectal cancer (3-fold change). Along with miR-148a that targets BCL2 (42), we also identified miR-190, which plays a role in pancreatic cancer tissues and cell lines (43).
Another miRNA, miR-29c, has been very recently shown to be downregulated in the mesenchymal part of endometrial carcinosarcoma (44). For miR-29c, however, we only observed a poor correlation between microarray and qRT-PCR data. This discrepancy could be due to either cross-reaction on the array, for example between the highly similar miRNAs, miR-29a/miR-29b/miR-29c, or because the PCR assay may not detect posttranscriptionally modified miRNAs (31).
Interestingly, miR-26 that was described to be associated with inhibition of tumorigenesis (45, 46) was found to be downregulated as well. However, in previous studies, it has been found to be stably expressed in colorectal cancer. Therefore, together with let-7a, it was suggested as a reference gene in colorectal miRNA studies (10). Finding both miR-26 and let-7a to be downregulated in rectal cancer compared with adjacent mucosa, their application as “housekeeping” miRNAs for rectal cancer analyses should be considered with caution.
Pathway and target predictions—caveats
Depending on the target prediction tools applied, very different results were obtained, and there was only little overlap between the various algorithms. This may be due to both methodologic differences, including diverse criteria for defining miRNA binding regions and differences in the databases used for the target prediction (47, 48).
Therefore, in order to decrease the number of potential miRNA targets, we only applied validated targets, and compared this list of mRNA targets to the true mRNA expression values that we previously established for these samples (8). For miR-21, the predicted targets from the databank and the regulated mRNAs correlated significantly (P = 0.003). Furthermore, the identification of miR-135b and APC (18), miR-200b and ZEB1 (49) or miR-195 and CCND1 (50) validates the principle approach to screen for potential miRNA targets.
Difficulties in miRNA target prediction not only affect the target prediction itself but also the generation of interaction networks. In this particular analysis, manual curation was necessary for both LEMD1 and APC with miR-135b to identify regulatory networks involved in tumorigenesis, such as the Wnt/β-catenin, MYC, and EGFR/KRAS pathways (19).
Conclusively, any study on pathway analyses and/or the prediction of miRNA targets that does not follow-up on the in silico predictions with functional validation of the putative targets or at least applies mRNA data from corresponding samples should be regarded with great caution. In addition, this underlines the importance of an analytic, knowledge-based approach, rather than on relying solely on “streamlined” statistical visualization tools.
From a clinical point of view, the identification of miR-135b (P < 2.8E-11) was of particular interest. In the miRNA array analysis, miR-135b was the only miRNA that correlated with survival. We had therefore done a completely independent PCR-based validation on a large multicenter cohort of 116 patients. Based on the mean expression we were able to confirm that both DFS and CSS were significantly better for patients with high expression levels of miR-135b in the tumor. The clear separation between patients with a good and a bad prognosis based on miR-135b expression is promising for the development of miRNA-based biomarkers for rectal cancer. However, the application of this marker needs further validation in an even larger patient cohort to assess the feasibility of setting-up a cut-off as this cut-off might vary between different patients groups.
The finding that miR-135b is differentially regulated between tumor and mucosa as well as being associated with prognosis may be explained by its possible association with rectal tumors in terms of being a marker rather than having a “true” biologic function within the tumorigenic process itself. As such, it would be useful as a prognostic marker, but not as a therapeutic target. In addition, miRNAs are known to have a myriad of potential target genes, likely with different binding affinities. One could therefore imagine that whereas increased expression of miR-135b could lead to tumor formation (i.e., through inhibition of a tumor suppressor gene), expression at higher levels could in addition result in suppression of genes required for a more aggressive phenotype (i.e., invasion or metastasis) or resistance to chemotherapy, resulting in a better prognosis.
In summary, by applying global miRNA expression profiling to screen 57 locally advanced rectal carcinomas and 57 corresponding matched normal mucosa biopsies, we identified 49 differentially expressed miRNAs. The robustness of our results was shown through rigorous real-time PCR validation. A comprehensive analysis revealed miR-492, miR-542-5p, miR-584, miR-483-5p, miR-144, miR-2110, miR-652*, miR-375, miR-147b, miR-148a, miR-190, miR-26a/b, and miR-338-3p as rectal cancer-specific. Target prediction analyses suggested that these miRNAs regulate prominent KEGG pathways such as Wnt, TGF-beta, mTOR, insulin, MAPK, and ErbB signaling, which adds weight to the biologic significance of this study. Importantly, the expression levels of miR-135b correlated significantly with DFS and CCS.
Disclosure of Potential Conflicts of Interest
S. Møller has ownership interests (including patents) in Exiqon. S. Møller, R. Søkilde, B. Kaczkowski, and T. Litman are former employees of Exiqon. No potential conflicts of interest were disclosed by the other authors.
Conception and design: J. Gaedcke, M.Grade, J. Camps, M. J. Difilippantonio, S. Møller, T. Beissbarth, T. Ried, T. Litman
Development of methodology: J. Gaedcke, J. Camps
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Gaedcke, M.Grade, J. Camps, R. Søkilde, C.C. Harris, B. M. Ghadimi
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Gaedcke, J. Camps, R. Søkilde, B. Kaczkowski, A.J Schetter, C.C. Harris, B. M. Ghadimi, T. Beissbarth, T. Ried, T. Litman
Writing, review, and/or revision of the manuscript: J. Gaedcke, M.Grade, J. Camps, A.J Schetter, M. J. Difilippantonio, C.C. Harris, B. M. Ghadimi, T. Beissbarth, T. Ried, T. Litman
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J. Gaedcke, R. Søkilde, B. M. Ghadimi
Study supervision: J. Gaedcke, T. Ried, T. Litman
This work was supported by the Deutsche Forschungsgemeinschaft (KFO 179) and by the Intramural Research Program of the NIH, National Cancer Institute. This work was part of a CRADA between the NCI and Exiqon A/S (CRADA #2254).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
The authors thank Dara Wangsa and Chang-Rong Lai for their assistance with the PCR assays, and Yue Hu for help with the miRNA target correlation analysis. The authors are very grateful to their collaborators in the Departments of Surgery in Erlangen (Dr. Hohenberger, Dr. Matzel), Mönchengladbach (Dr. Kania, Dr. Grünewald), Oldenburg (Dr. Weyhe, Dr. Burkowski) and Kassel (Dr. Hesterberg, Dr. Schrader) who helped us to realize this multicenter validation study.
Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
- Received January 10, 2012.
- Revision received June 4, 2012.
- Accepted July 12, 2012.
- ©2012 American Association for Cancer Research.