Colorectal cancer (CRC) is one of the deadliest diseases in the world and it is the second most common cause of cancer-related deaths in women, and the third in men. Although CRC mortality rates have declined in Western population, incidence rate of CRC has been increasing in Asia. Identified risk factors for CRC include: aging, gender, family history, lifestyle, pre-existing conditions such as, lynch syndrome, inflammatory bowel disease. CRC is most of the time asymptomatic until the late stages and can be only diagnosed when the cancer is already metastasized [1].

CRC was used to be considered as a disease caused by genetic mutations. However, it is currently viewed as a complex malignancy including epigenetic abnormalities in addition to genetic mutations. Three different pathogenic pathways have been identified which play role in CRC development: chromosomal instability, microsatellite instability and CpG island methylator phenotype. Chromosomal instability (CI) means deregulation of oncogenes such as, KRAS (functions as a transmitter of key extracellular signals) and TP53 (p53 functions as a key transcriptional regulator in cell cycle regulation) and also tumor suppressor genes. Microsatellite instability (MI) is caused by the inactivation of mismatch repair genes such as, hMLH1, hMSH2, hMSH6, and hPMS2. MI can result in Lynch syndrome and sporadic tumors. The CpG island methylator phenotype is caused by hypermethylation of CpG islands at tumor suppressors gene promotors [2,3].

The most common screening methods for CRC are colonoscopy, flexible sigmoidoscopy, guaiac-based fecal occult blood tests, and fecal immunochemical tests. Having the fact that these methods are still limited by unsatisfactory sensitivity and specificity; researchers have investigated the potential use of miRNAs as diagnostic and prognostic biomarkers for CRC screening [1].

During the past decade, it has been discovered that miRNAs play important roles in cancer initiation, progression and metastasis. Functionally, miRNAs can mediate cell proliferation, cell cycle progression, differentiation, metabolism, apoptosis, invasion to trigger tumor formation and angiogenesis [4].

MiRNA is a ≈ 20 nucleotides long non-protein-coding singlestranded RNA molecule. They are involved in almost all biological pathways, including gene expression, cell cycle regulation, cellular development, proliferation, differentiation and apoptosis [5-9].

MiRNAs are transcribed by RNA polymerase II in nucleus into long primary transcripts of miRNA as hairpins (pri-miRNAs, 1-3 kilobases). Nuclear RNase III enzyme (also called Drosha) cleaves primiRNA into a precursor (pre-miRNA, around ≈ 70 nucleotides in length) hairpin-shaped dsRNA. Pre-miRNAs are then exported from nucleus into cytoplasm by a nuclear export factor, Exportin-5 (XPO5). Then, protein complex of RNase III, Dicer, trims pre-miRNA in the cytoplasm to produce mature double-stranded (miRNA/miRNA complex) miRNA duplexes (≈ 20 nucleotides in length). Next, these two strands of the duplexes are unwounded by helicase, and then combined with RNA-induced silencing complex (RISC) containing an argonaut protein. One strand of the duplex is removed and degraded while the other one (single-stranded miRNA, ssmiRNA) is actively interacts with target mRNA to regulate the expression [6,8-13]. The seed region (6-8 nucleotides) at 5’ ends of miRNAs mainly bind to the 3’-untranslated region (3’-UTR) of their target mRNAs with imperfect sequence complementarity. However, recent studies have reported that miRNAs can also bind to 5’UTR, or open reading frame (ORF) of the target mRNA [8]. By binding to their target mRNA, miRNAs can inhibit expression of proteins from mRNA (suppression of translation) or causing mRNA degradation [12,14-18].

Since miRNAs can function as oncogenic (oncomir) as well as tumor suppressor depending on the function of their target genes, miRNAs are known to play an important role in tumorigenesis. A particular miRNA may be found up regulated in some cancer types as oncogenic function, but down regulated in other cancers, indicative of tumor suppressor function [19]. It is possible that the function of a single miRNA is cell or tissue specific. Recent studies indicate that miRNAs may have more oncogenic than tumor suppressive function in CRC [11,20,21]. 1.900 precursor and 2.600 mature miRNA have been identified up to now and these miRNAs are able to regulate the expression of almost 60% of all protein-coding genes in human genome [6,8,9,14,22-24]. Due to its small size, a single miRNA may act on expression of thousands of mRNAs. However, a single gene can be regulated only by multiple different miRNAs [11,13,23,25]. Therefore, miRNA expression profiles have been shown as potential signatures which are highly tissue specific for the classification, diagnosis and progression of cancer. Early detection of CRC by using miRNAs as diagnostic biomarkers provides the best chance for predictive diagnosis and successful treatment of diseases [1,21,26].

In 2003, it was first reported by Michael that differentially expressed upregulated and downregulated miRNAs were associated with CRC development and progression [27]. Since then more than 100 miRNAs have been shown to be associated with CRC since then. It was also shown that downregulation of let-7a-1 in CRC samples. MiR-34a has been shown to block cell proliferation and it is downregulated in colon cancer cells, whereas miR-31, miR-96, miR-31, miR-135b, and miR-183 were up regulated in CRC cells in the same study [28].

MiRNA associations with specific tumor markers also have been identified. Loss of miR-34 expression has been shown to deteriorate TP53-mediated cell death while overexpression of miR-34 results in apoptosis. MiRNAs also have been exhibited to be associated with regulation of oncogenes and with tumor suppressor genes. MiR-143 and let-7 expression levels have been associated with KRAS2 mutations [21]. Furthermore, miRNA-21 and miRNA-183 were found to be upregulated in CRC and miRNA-143, miRNA-145 and miRNA-497 are down-regulated when compared to healthy subjects as stated by different studies [3,6,7,14,17,21,29-32]. Compared with those in normal tissue, miRNAs expression levels are down-regulated in malignant tissue, only several miRNAs are up regulated; most of them play oncogenic roles [8,15,33]. On the other hand, tumor suppressor’s miRNA-497 and miRNA-378c are mainly downregulated in colorectal cancer [29,30].

The first miRNA mimic entered the clinic for cancer therapy is synthetic miRNA-34 (tumor suppressor) loaded in liposomal nanoparticles [34]. In general, there are two strategies to developing miRNA-based therapeutics either by direct inhibition of the interaction between oncogenic miRNA and mRNA or replacement of target tumor suppressor miRNA genes to restore a loss of function [15]. Oncogenic miRNAs could be therapeutically targeted by repression. A simple method to block oncogenic miRNAs is the use of anti-miRNA oligonucleotides complementary to the sequence of the targeted mature oncogenic miRNAs (antagomirs). These oligonucleotides disrupt miRISC complex and prevent degradation of an mRNA which can then be translated. Another approach to more specifically inhibit the miRNA function is to use of miRNA masks which are complementary to binding sites in 3’UTR of target mRNA. MiRNA replacement therapy aims to substitution of tumor suppressor miRNAs absent or expressed at lower levels by using oligonucleotide mimics containing the same sequence as the mature miRNA. Restoration of silenced tumor suppressor miRNAs might produce beneficial effects on cell migration, invasion and increasing sensitivity to therapeutic agents [6,15,21,35].

Ethics statement

This study was approved by the Medical Ethical Committee of the Harran University, Turkey and has been performed in accordance with the ethical principles of the 2008 revised Declaration of Helsinki. The samples were used with written informed consents from patients and the approval of the Turkish Academy of Medical Sciences. Tissue samples (~0.5 cm³) from CRC patients were collected at surgery after the pathologist had confirmed the histopathology at the Hospital of Gaziantep Medical University. Normal tissue adjacent to the cancer tissue was used as control for this study. Subjects were signed by participants in a written informed consent form for this study. None of this CRC patients received chemotherapy or radiotherapy before the surgical resection. Each sample was placed in a cryovial and covered with the RNALater (Applied Biosystems) that prevents fragmentation of the fragile mRNA. Due to small sizes of miRNA (20-22 nt.), miRNA levels are remarkably stable in tissue samples when compared to mRNA. Tissue samples were homogenized in liquid nitrogen by using precellys homogenizer (Bertin) and stored at -86°C for subsequent analysis.

Total RNA isolation and reverse transcription of mature miRNAs

Total RNA containing miRNA was purified from homogenate by using the microRNAsy mini isolation kit (Qiagen) according to manufacturer’s instructions. The purity and concentration of total RNA was assessed by using NanoDrop spectrophotometer p360 (Implen). Total RNA samples were aliquoted and stored at -86°C until used.

Microarray profiling of miRNAs

MiRNA expression was profiled by using GeneChip miRNA 3.0 Array (Affymetrix) for five controls and seven CRC with stage I-IV samples. About 1,000 ng RNA from each sample were labeled by biotin using the FlashTag Biotin HSR RNA labeling kit (Affymetrix). Then, the labeled samples were hybridized with the human probes on the microarray chips for 16 hours at 48°C with a rotation speed of 60 rpm. After hybridization, the probe arrays were washed and stained by using the Fluidics Station 450 according to AGCC Fluidics Control Software. Then the fluorescence on the array was scanned using the Affymetrix^® GeneChip^® Scanner 3000 with a high resolution 6 g patch. The probe cell intensity files (*CEL files) generated by Affymetrix GeneChip^® Command Console^® software were transferred into probe level summarization files (*CHP files) using a robust multi-array (RMA) detection algorithm workflow. The *CHP files were further analyzed using Transcriptome Analysis Console (TAC) software, version 3.0, to identify and visualize the differentially expressed genes.

We identified the level of expressed miRNA with statistical significance from a volcano plot filtering between the colorectal cancerous and normal miRNAs from the experiment. Expression data of all probe sets detected by microarray analysis have been deposited in ArrayExpress, E-MTAB-4573.

In microarray analysis, we identified 279 aberrantly expressed miRNA genes that either have increased (250 miRNAs), or reduced (29 miRNAs) expression levels as shown in Table 1. We found that MiR-1201, miR-181-a, miR-7, miR-188, miR-552 and miR-183 were highly expressed in colon carcinoma tissues compared with the normal tissues (Table 2a). Among the overexpressed miRNAs, miR-1201 is the highest expressed one in CRC tissues. MiR-181, miR-183, 34a, miR-92, miR-21, miR-431 and miR-487a were identified as on co mirRNA in various tumor tissues [36-39]. MiR-21 has been shown to be overexpressed in many cancer types [18,24,37,39,40]. On the other hand, five miRNAs (miR-451, miR-486, miR-550, miR-635 and miR-10b) had decreased expression significantly in CRC tissue samples compared with the normal samples (Table 2b). These down regulated miRNAs were found to be tumor suppressors in CRC tissues in previous studies [29,30,39,41,42]. MiR-486 was found to be the lowest expressed one CRC tissues compared with the normal tissues. We also found some new aberrant expression miRNAs in colorectal cancer tissues (Tables 1-2b). Finally, we could expect that miRNAs have important role as tumor suppressors or oncogenic factors in the network of carcinogenesis. The genes with similar expression patterns are grouped together and connected by a series of branches (clustering tree or dendrogram). The threshold we used to screen up-regulated or down-regulated miRNAs is fold change ≥2.0 and p value˂0.05. The results of hierarchical clustering show aberrantly expressed miRNAs among CRC tissues and normal CR tissues (Figure 1). Principal component analysis was also used to find out the similarities and differences between CRC samples and normal tissues (Figure 2). Total RNA was obtained from tissues of control and CRC and hybridized to Affymetrix GeneChip^® miRNA 3.0 Arrays. After normalization, differential miRNA expression data was analyzed by unsupervised hierarchical clustering.

Table 1: Microarray Analysis: Differentially expressed miRNAs in CRC samples compared with samples of controls in the training and validation sets. Statistical analysis was performed using TAC software one way analysis of variance (ANOVA) was used. The statistical significance level was set at p-value<0.05. Greater than 2-fold changes were analyzed for up/down regulated miRNAs. A total of 279 miRNAs were found to be differentially expressed in the above table.

Table 2a: The most important significant increase and decrease rates of expression in miRNA transcripts, some upregulated miRNA transcripts.

Table 2b: The most important significant increase and decrease rates of expression in miRNA transcripts, some down-regulated miRNA transcripts.

Figure 1: Hierarchical Clustering of Gene Expression.

Figure 2: Principle Component Analyses (PCA). Each red dot represents a tumor sample and each blue dot represents control sample. According to the graph tumor and control samples were clustered at different localities.

Each row represents individual miRNA. In hierarchical clustering, genes with similar expression patterns are grouped together and connected by a series of branches (clustering tree or dendrogram). The red colors on the dendrogram indicate expression levels higher than the median. While green colors show expression levels lower than the median.

Our results show that most of miRNAs differentially expressed are up regulated miRNAs in CRC tissues compared to control tissues. Evidences have been accumulating that some miRNAs are overexpressed in CRC cells and might function as inhibitors of different tumor suppressor genes. There are three strategies for miRNA-based therapies are to inhibit oncogenic miRNAs or restoring tumor suppressor miRNAs. The first strategy is to direct inhibition of oncogenic microRNAs and this can be achieved by using singlestranded antisense oligonucleotides (approximately 20-22 nucleotides in long) that act through complementar base-pairing with target miRNAs. The second strategy is tumor suppressor miRNA replacement. This involves reintroducing synthetic miRNA or expression vectors that will produce the miRNA of interest to restore a loss of function. The third strategy is to develop drugs that decrease the levels of oncogenic miRNAs or increase the levels of tumor-suppressor miRNA for cancer prevention [21,43].

The most important challenge is the identification of definitive miRNA signatures for CRC by large and comprehensive profiling studies. This will allow the identification of certain diagnostic and prognostic biomarkers that can help physicians with patient evaluation and also prospective markers for the early therapy [8,12,15,44]. In summary, miRNAs are extremely important regulators of oncogenes and tumor suppressor genes that are responsible for pathologic processes associated with malignant progression. Although the roles of many different miRNA have been identified in various tumors, more study is still needed to fully understand the role of each miRNA and assessing their roles in personalized miRNA targeted cancer therapy [24]. In future cancer therapy, miRNAs may be important in deciding which drugs are selected for a patient and in determination of whether the patient has responded to the drug [23]. In conclusion, further efforts to get more sensitive, fast and effective methods are needed to address the role of miRNAs biomarkers in clinical diagnostic, prognostic and therapy. Finally, miRNAs will emerge as a powerful resource to advance the diagnosis and management of cancer including colorectal cancer.

We thank C. Gover for data management.