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Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis
GENE-41046; No. of pages: 8; 4C: Gene xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
Research paper
Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis☆,☆☆ Yuan Yin a,1, Mingxu Song a,1, Bing Gu b,c, Xiaowei Qi d, Yaling Hu a, Yuyang Feng a, Heyong Liu a, Leyuan Zhou a, Zehua Bian a, Jiwei Zhang a, Xianbo Zuo e, Zhaohui Huang a,⁎ a
Wuxi Oncology Institute, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, China Department of Laboratory Medicine, Affiliated Hospital of Xuzhou Medical College, Xuzhou, Jiangsu 221002, China Medical Technology Institute of Xuzhou Medical College, Xuzhou, Jiangsu 221002, China d Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, China e Institute of Dermatology and Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China b c
a r t i c l e
i n f o
Article history: Received 20 August 2015 Received in revised form 10 November 2015 Accepted 8 December 2015 Available online xxxx Keywords: Colorectal cancer MicroRNA Bioinformatic analysis KEGG pathway analysis Regulatory networks
a b s t r a c t The development of colorectal cancers (CRC) is accompanied with the acquisition and maintenance of specific genomic alterations. These alterations can emerge in premalignant adenomas and faithfully maintained in highly advanced tumors. miRNAs are a class of small non-coding RNAs that are frequently deregulated in human cancers and negatively regulate a wide variety of protein coding genes. To identify the sequential alterations of miRNAs and its regulatory networks during CRC development and progression, we detected the miRNA expression profiles of tissue samples from normal colon, colorectal adenoma and CRC using miRNA microarray. qRT-PCR assay was used to validate and select the miRNAs with differential expression among the three groups, and the computer-aided algorithms of TargetScan, miRanda, miRwalk, RNAhybrid and PicTar were used to search for the possible targets of the selected 8 miRNAs (miR-18a, miR-18b, miR-31, miR-142-5p, miR-145, miR-212, miR-451, and miR-638) with continuous alterated expression. These potential target genes were enriched in several key signal transduction pathways (KEGG pathway analysis), which have been proved to be closely related to colorectal tumorigenesis. To confirm the reliability of the analyses, we identified that the metastasis-related gene ZO-1 is a certain target of miR-212 in CRC and keeps declining during CRC progression. By following these analyses, we might gain an in-depth understanding of the molecular regulatory networks of colorectal tumorigenesis and provide new potential targets for the diagnostic and therapeutic interventions of this disease. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Colorectal cancer (CRC) is one of the most frequent cancers and the fifth leading cause of cancer death in China (Siegel et al., 2015). Most of CRCs develop from colonic mucosal lesions and colorectal adenomas (Habermann et al., 2007). Studies have shown that the formation of CRC may last more than ten years, and it will take about five years for endoscopic recognizable adenomas developing into invasive cancers
Abbreviations: CRC, colorectal cancer; miRNA, microRNA; qRT-PCR, quantificational real-time polymerase chain reaction; TJP1, tight junction protein 1, also known as ZO-1. ☆ Grant support: This study was partially supported by grants from the National Natural Science Foundation of China (nos. 81301784, 81000867, 81272299 and 81301920) and the Natural Science Foundation of Jiangsu Province (BK20151108 and BK20150004). ☆☆ Disclosure of potential conflicts of interest: No potential conflicts of interest were disclosed. ⁎ Corresponding author at: Wuxi Oncology Institute, Affiliated Hospital of Jiangnan University, 200 Huihe Road, Wuxi, Jiangsu 214062, China. E-mail address: [email protected] (Z. Huang). 1 These authors contributed equally to this work.
(Balaguer, 2013). Thus, it's of important significance to study the dynamic change process of CRC. microRNAs (miRNAs) are a type of small non-coding RNA molecules that play critical roles in most physiological and pathological processes, including cell proliferation, differentiation, apoptosis, and immune response (Bartel, 2004). By base pairing to the complementary 3′untranslated region (3′ UTR) of its target mRNAs, miRNA negatively regulates gene expression at the posttranscriptional level, resulting in the block of translation or the degradation of the target mRNAs. Through this translational repression and gene silencing function, miRNA regulates up to one third of the total human protein-coding genes (He and Hannon, 2004). The development of CRC accompanied with the acquisition and maintenance of specific genomic alterations (Rao and Yamada, 2013). miRNAs have also been considered to be implicated in the progression of CRC (Pizzini et al., 2013). Previous studies found that many miRNAs, such as miR-143 (Chen et al., 2009), miR-21 (Xiong et al., 2013), and miR-145 (Liu et al., 2015; Qin et al., 2015), miR-95 (Huang et al., 2011), miR-204 (Yin et al., 2014), together with their target genes, are involved in the development and progression of CRC. However, the
http://dx.doi.org/10.1016/j.gene.2015.12.015 0378-1119/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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dynamic molecular changes through CRC development at the wholegenome level are poorly understood. Moreover, the regulatory networks of miRNAs and their targets are extremely complicated, as each individual miRNA may target hundreds of genes, and one mRNA can also be the target of multiple miRNAs (Di Leva et al., 2014). This prompted us to fully identify and study the key miRNAs and their targets in CRC tumorigenesis at the global genomic level. In the present study, we performed miRNA profiling analyses on normal colon mucosa, colorectal adenomas and CRC tissues to identify the key miRNAs involved in CRC tumorigenesis. Bioinformatics tools were then used to search for the miRNA-targeted signaling pathways and to construct their regulatory networks in CRC. Our results provide insights into the molecular regulatory networks of colorectal tumorigenesis and may suggest new targets for the diagnostic and therapeutic interventions of CRC. 2. Materials and methods
2.5. Luciferase reporter assay A luciferase reporter assay was performed as previously described (Chen et al., 2009), to test the binding of miR-212 to its target ZO-1. The ZO-1 3′ UTR segment, containing a presumed miR-212 complementary site (seed sequence, GACTGTT), was amplified from human genomic DNA by PCR (Sense 1: 5′GCAAAGC ATTACGGATAGC3′; Antisense 1: AGACGACGAGGTGGTAGGT; Sense 2: TGGACTAGTCTCTTGAAATATA GGAACTTAAATAATGTGAAACTGG; Antisense 2: GATACGCGTGAAACTA GA AAACAGTGGGCAAACAGACCAAGC). The PCR products were inserted into the pMIR-report plasmid (Applied Biosystems), and efficient insertion was confirmed by sequencing. Caco2 cells were cultured in 6-well plates and cotransfected with 1 μg of firefly luciferase reporter plasmid, 0.5 μg of β-galactosidase expression vector (Applied Biosystems), and 100 pmol miR-212 mimic, miR-212 inhibitor, or scrambled control RNA using Lipofectamine 2000 (Invitrogen). The β-galactosidase vector was used as transfection control. 24 h after transfection, cells were assayed using luciferase assay kit (Promega).
2.1. Tissue collection Tissue samples were obtained from CRC and colorectal adenoma patients who underwent surgical resection from the Affiliated Hospital of Jiangnan University. The normal colorectal tissues (NAT) were 5 cm away from the incisal edge of CRC tissues. All patients were ranging from 42 to 73 years old (57.4 ± 13.22) and had not received radiotherapy, chemotherapy, or other medication therapy. All of the samples were collected according to the Institutional Review Board-approved protocol and the written informed consent from each patient. 2.2. RNA isolation Tissue fragments were immediately frozen in liquid nitrogen at the time of surgery and stored at −80 °C until use. Total RNA was extracted using TRIzol reagent (Invitrogen). RNA concentrations were determined using a NanoDrop 2000 spectrophotometer (Thermo). 2.3. Microarray and qRT-PCR Total RNA was purified from tissues samples using mirVANA miRNA isolation kit (Ambion) to enrich small RNA fraction. RNA labeling and hybridization on miRNA microarray chips (CapitalBio Corp) were performed as previously described (Thomson et al., 2004). Hybridization signals were detected and scanner images were quantified. Data were assessed using unsupervised hierarchical clustering to generate both gene and sample trees. cDNA was synthesized with the PrimeScript RT reagent Kit (TaKaRa). Stem-loop qRT-PCR assays using TaqMan miRNA probes (Applied Biosystems) to quantify mature miRNA levels were performed using Applied Biosystems 7300 Sequence Detection System. The relative levels of miRNAs were normalized to U6, a ubiquitously expressed small nuclear RNA. 2.4. Bioinformatic analysis Selected miRNAs were loaded into the computer-aided algorithms of TargetScan, miRanda, miRwalk, RNAhybrid, and PicTar for target prediction (Krek et al., 2005). The presumed targets predicted by at all of the algorithms were compiled together and applied to bioinformatic analysis. Subsequent bioinformatic analysis of these target genes was performed by Kyoto Encyclopedia of Genes and Genomes Pathway analysis (KEGG Pathway analysis) (http://www.genome.jp/kegg/) and GO analysis (Wrzodek et al., 2013). Pathways were selected with a P value b 0.05 and gene count N 2. The target genes enriched in KEGG pathways and related miRNAs were visualized by Cytoscape software for network construction (Shannon et al., 2003).
2.6. Western blotting ZO-1 protein level was quantified by Western blotting with a rabbit polyclonal anti-ZO-1 antibody (1:1000, Santa Cruz). The normalization was performed by blotting the same samples with a mouse antiGAPDH antibody (1:1000, Santa Cruz).
2.7. Statistical analysis Data shown were expressed as means ± SEM (standard error) of three or more independent experiments, and the differences were considered statistically significant at b 0.05 by using the Student's t-test.
3. Results 3.1. Identification of miRNAs differentially expressed during colorectal tumorigenesis MiRNA expression profiles in 8 NATs, 7 colorectal adenomas and 15 CRC tissues were analyzed using a microarray to screen differentially expressed miRNAs during colorectal tumorigenesis. The result indicated a clear discrimination among adenomatous, cancerous and normal tissues. Unsupervised hierarchical clustering significantly distinguished the CRCs from adenomas and NATs (Fig. 1A). Then, we validated these differentially expressed miRNAs in these 30 tissues using TaqMan probe-based real-time qRT-PCR assay. Consistent with the microarray data, 67 miRNAs were dysregulated in CRC tumorigenesis (Supplementary Table 1). These miRNAs were then divided into 4 groups, miRNAs upregulated continuously (Fig. 1B), miRNAs downregulated continuously (Fig. 1C), miRNAs upregulated in adenoma but downregulated in CRC (Supplementary Table 1), and miRNAs downregulated in adenoma but upregulated in CRC (Supplementary Table 1). We speculated that miRNAs changed continuously were more commonly involved in the process of benign adenomas developing into malignancy than those with fluctuated expression during colorectal tumorigenesis, and 28 continually changing miRNAs were concerned (Fig. 1D). Among these miRNAs, eight miRNAs (miR-18a, miR-18b, miR-31, miR-142-5p, miR-145, miR-212, miR-451, and miR-638) were selected for further study for their relatively higher or lower expression in colorectal tissues (Table 1). They are significantly altered both between NATs and adenomas, as well as between adenomas and CRCs (fold change N1.5, P b 0.01) (Fig. 1, B–D). These data suggest that deregulation of the eight miRNAs may play essential roles in CRC tumorigenesis.
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Fig. 1. Differentially expressed miRNAs during colorectal tumorigenesis. A, unsupervised hierarchical clustering of miRNA expression profiles among adenomatous, cancerous and normal tissues (7 adenomatous, 15 tumors and 8 normal tissues) measured by microarray analysis. B, upregulated miRNAs validated by quantitative real-time RT-PCR (P b 0.01). C, downregulated miRNAs validated by quantitative real-time RT-PCR (P b 0.01). D, selection of miRNAs significantly altered in colorectal tumorigenesis for further study. (upregulate or downregulated continuously, fold change N1.5, P b 0.01).
3.2. Prediction and validation of target genes of the eight miRNAs Using five computer-aided algorithms, TargetScan, miRanda, miRwalk, RNAhybrid and PicTar, we obtained a list of predicted target genes that are potentially regulated by miR-18a, miR-18b, miR-31, miR-142-5p, miR-145, miR-212, miR-451, and miR-638. Target genes predicted by all the five algorithms were selected for experimental verification (Table 2). To test the reliability of the algorithms, tight junction protein 1 (TJP1, also known as ZO-1), a predicted target of miR-212, was selected. We cloned the 3′UTR of ZO-1into a luciferase reporter vector (Fig. 2A). The results of luciferase reporter assay revealed that miR-212 could inhibit the reporter gene expression in the recombinant plasmids of ZO-1 3′UTR, while silencing miR-212 resulted in increasing
Table 1 Identification of miRNAs significantly differentially expressed in colorectal tumorigenesis. miRNA
NAT
Adenoma
CRC
P value
miRNAs upregulated continuously hsa-miR-18a 1.00 hsa-miR-18b 1.00 hsa-miR-31 1.00 hsa-miR-142-5p 1.00 hsa-miR-212 1.00
2.08 1.86 1.72 1.79 2.41
3.09 3.00 5.04 2.86 5.28
0.0098 0.0035 0.0030 0.0042 0.0047
miRNAs downregulated continuously hsa-miR-145 1.00 hsa-miR-451 1.00 hsa-miR-638 1.00
0.63 0.69 0.65
0.41 0.23 0.34
0.0070 0.0028 0.0030
expression of the reporter gene (Fig. 2B). To determine whether the overexpression or knockdown of miR-212 also had an impact on the ZO-1 protein levels, we determined the expression of ZO-1 protein by Western blotting. In concordance with these results, the endogenous ZO-1 protein levels were also downregulated in miR212-overexpressed cells and could be restored in miR-212-depleted CRC cells (Fig. 2C). In addition, we chose KRAS for further validation for its central situation in the constructed network and its comprehensive functions in human cancers. To do that, we overexpressed miR-18a, miR-31 and miR-212 in colon cancer cell line LoVo, and then detected the expression change of KRAS using Western blotting. As expected, KRAS expression was indeed underregulated in cells with overexpressed miR-18a, miR-31 or miR-212 compared with the control
Table 2 Number of predicted target genes of deregulated miRNAs.
miRNA
Target gene number
hsa-miR-18a hsa-miR-18b hsa-miR-31 hsa-miR-142-5p hsa-miR-145 hsa-miR-212 hsa-miR-451 hsa-miR-638
63 9 143 51 250 51 110 11
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Fig. 2. Validation of one target gene of the miR-212 according to bioinformatic analysis. A, Schematic description of conserved binding site of ZO-1 for miR-212. B, analysis of the luciferase activity of the luciferase reporter plasmid containing ZO-1 3′UTR. C, suppressed expression of endogenous ZO-1 by miR-212.
cells (Supplementary Fig. 1). Taken together, these results demonstrate that the prediction results based on five algorithms were reliable and valuable. 3.3. The roles of target genes in CRC tumorigenesis according to KEGG and GO analyses To further evaluate the role of deregulated miRNAs and their target genes in CRC tumorigenesis, hundreds of predicted target genes were subjected to enrichment analysis of gene function by GO analysis and enrichment analysis of cell signaling pathways using KEGG
pathway database. The functions of these target genes were enriched in “membrane-enclosed lumen (GO: 0031974)”, “tight junction (GO: 0005923)” and “transcription factor complex (GO: 0005667)” (P b 0.001) (Table 3). We found that the most significantly enriched pathways were related to cancer, MAPK signaling or tight junction pathways (P b 0.001) (Table 4). The rate of target genes enriched in the “Pathways in cancer (hsa05200)” was up to 15.8% (67/424) (Table 4), while 34, 26 and 25 target genes were enriched in the “CRC pathway (hsa05210)” (Table 4 and Supplementary Fig. 2), “Tight junction (hsa04530)” (Table 4 and Fig. 3), and “MAPK signaling pathway (hsa04010)” (Table 4 and Supplementary Fig. 3), respectively.
Table 3 The enriched functions of deregulated miRNA Targets analyzed by GO. GO ID
GO-CC analysis
Gene number percentage
P value
0031974 0005923 0005667 0044459 0030054 0043233 0005856 0005911 0005694 0005654 0070013 0016323 0005912 0070161 0043296 0016327 0070160 0043228 0005913 0005625
Membrane-enclosed lumen Tight junction Transcription factor complex Plasma membrane part Cell junction Organelle lumen Cytoskeleton Cell–cell junction Chromosome Nucleoplasm Intracellular organelle lumen Basolateral plasma membrane Adherens junction Anchoring junction Apical junction complex Apicolateral plasma membrane Occluding junction Non-membrane-bounded organelle Cell–cell adherens junction Soluble fraction
25.7%(109/424) 25.2%(107/424) 17.7%(75/424) 24.1%(102/424) 24.1%(102/424) 22.9%(97/424) 20.0%(85/424) 24.1%(102/424) 17.2%(73/424) 17.0%(72/424) 13.0%(55/424) 12.3%(52/424) 11.8%(50/424) 11.3%(48/424) 11.3%(48/424) 10.4%(44/424) 10.4%(44/424) 8.7%(37/424) 8.7%(37/424) 8.3%(35/424)
3.48E − 16 6.99E − 16 2.11E − 03 2.54E − 05 2.54E − 05 4.82E − 18 3.17E − 04 2.59E − 14 3.53E − 18 7.87E − 09 1.28E − 07 4.73E − 05 2.43E − 02 1.73E − 12 2.30E − 09 2.45E − 02 3.49E − 02 1.15E − 04 1.19E − 04 1.01E − 02
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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3.4. Establishment of miRNA regulated gene networks associated with colorectal tumorigenesis
Table 4 The enriched KEGG pathways of deregulated miRNA targets. KEGG pathway
Gene number percentage
P value
Pathways in cancer(hsa05200) MAPK signaling pathway(hsa04010) Tight junction(hsa04530) Colorectal cancer pathway(hsa05210) Cytokine–cytokine receptor interaction(hsa04060) Focal adhesion(hsa04510) Pancreatic cancer(hsa05212) Chemokine signaling pathway(hsa04062) Neurotrophin signaling pathway(hsa04722) Toll-like receptor signaling pathway(hsa04620) Regulation of actin cytoskeleton(hsa04810) Adherens junction(hsa04520) Cell cycle(hsa04110) ErbB signaling pathway(hsa04012) T cell receptor signaling pathway(hsa04660) Non-small cell lung cancer(hsa05223) Wnt signaling pathway(hsa04310) Prostate cancer(hsa05215) TGF-beta signaling pathway(hsa04350) Apoptosis(hsa04210)
15.8% (67/424) 8% (34/424) 6.1% (26/424) 5.9% (25/424) 5.9% (25/424) 5.7% (24/424) 5.4% (23/424) 5.4% (23/424) 5.4% (23/424) 5.2% (22/424) 5.2% (22/424) 5.0% (21/424) 4.7% (20/424) 4.7% (20/424) 4.5% (19/424) 4.5% (19/424) 4.2% (18/424) 4.2% (18/424) 4.0% (17/424) 4.0% (17/424)
1.40E − 26 1.60E − 07 1.40E − 09 1.90E − 13 1.80E − 04 9.80E − 11 1.80E − 18 1.50E − 05 2.50E − 10 6.80E − 10 3.70E − 04 1.80E − 11 2.10E − 07 1.50E − 09 2.60E − 09 1.10E − 12 2.00E − 04 9.90E − 16 3.50E − 07 8.90E − 06
We next investigated the regulatory networks associated with colorectal tumorigenesis by combining “CRC pathway”, “MAPK signaling pathway” and “Tight junction”. The eight miRNAs and their predicted target genes enriched in the three pathways (Table 5) were subjected to Cytoscape software for network analysis. The results were illustrated by the networks depicted in Fig. 4A, which represent the predicted relationships among the genes co-regulated by the eight miRNAs during colorectal tumorigenesis. For instance, we found that the apoptosis regulator BCL2, the tumor suppressor TP53, the cell–cell junction transducer ZO-1 and other tumor-related genes were the most significant enriched regulators in the sets of genes affected by the eight miRNAs. To confirm the validity of the regulatory networks constructed, we further detected the miR-212 expression and the ZO-1 protein levels in an additional group of NAT, adenoma and CRC tissues. As shown in Fig. 4B and 4C, the miR-212 expression levels were inverse correlated with the ZO-1 protein levels in these tissues. The results suggest that these miRNA regulated genes are active components associated with colorectal tumorigenesis.
Fig. 3. Tight junction pathway in KEGG pathways. Red star marked genes represent genes targeted by the selected miRNAs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Table 5 miRNA-regulated genes in three selected KEGG pathways. Tight junction
MAPK signaling pathway
Colorectal cancer pathway
Genes
miRNAs
Genes
miRNAs
Genes
miRNAs
F11r INADL ACTG1 CTNNB1 Cdc42 CLDN1 csdA Cdk4 HCLS1 MYH11 NRAS Ocln pten ppp2r1b Ppp2r2a Rhoa mllt4 SPTAN1 ZAK ZO-1 ZO-2 ZO-3 Kras akt1 Src YES1
miR-145 miR-31 miR-145 miR-145 miR-145 miR-18b miR-212 miR-145 miR-145 miR-145 miR-145 miR-18b miR-18a,18b,31,145,451 miR-145 miR-31 miR-31 miR-212 miR-145 miR-145 miR-18a,31,212 miR-18b miR-18b miR-18a,18b,31,145,212 miR-18a,145,451 miR-145 miR-145
Rasa1 BDNF Cdc42 Egf egfr FGF2 Fgfr1 fgfr3 MAPK3 IL1B Jun Mapk14 Mapk7 Mapk9 MAP2K1 NRAS Pdgfb Pak1 ppp3ca RPS6KA5 Stk3 Srf STMN1 ZAK TGFB1 Tgfbr2 TNF tp53 Kras akt1 FOS MYC BRAF RAF1
miR-31,451 miR-18a,212 miR-145 miR-212 miR-18b,145,212 miR-31 miR-451 miR-31 miR-145,212 miR-31,142 miR-145 miR-145 miR-145 miR-451 miR-451 miR-145 miR-145 miR-145 miR-145 miR-212 miR-145 miR-145 miR-31 miR-145 miR-18a miR-145 miR-31,142,451 miR-18a,31,145 miR-18a,18b,31,145,212 miR-18a,145,451,638 miR-31 miR-18a,18b,31,145,212 miR-31 miR-212
BCL2 BAX SMAD2 SMAD3 SMAD4 CTNNB1 CCND1 egfr MAPK3 IGF1R Jun Lef1 MET Mapk9 MAP2K1 PIK3CA TGFB1 Tgfbr2 tp53 Kras akt1 FOS MYC BRAF RAF1
miR-18a,145,451 miR-31 miR-18a,145 miR-18a,31,145,451 miR-145 miR-145 miR-31,142,145 miR-18b,145,212 miR-145,212 miR-31,145 miR-145 miR-145 miR-31 miR-451 miR-451 miR-145,451,638 miR-18a miR-145 miR-18a,31,145 miR-18a,18b,31,145,212 miR-18a,145,451,638 miR-31 miR-18a,18b,31,145,212 miR-31 miR-212
4. Discussion Colorectal tumorigenesis is a complicated and multistep process involving a series of genetic and epigenetic alterations (Tortolina et al., 2012). Knowledge about the dynamic molecular variations underlying colorectal carcinogenesis are crucial for the better explore the molecular mechanism CRC (Michor et al., 2005). Because CRC progresses through a normal-adenoma-carcinoma course, we investigated the carcinogenesis in tissue samples histologically confirmed to be NATs, adenomas and CRC, respectively. In this study, eight miRNAs (including 5 upregulated and 3 downregulated in CRC) were identified to be significantly altered both between NATs and adenomas, as well as between adenomas and CRCs. Of these eight miRNAs, miR-18a (Humphreys et al., 2014), miR-31 (Sun et al., 2013), miR-145 (Liu et al., 2015; Qin et al., 2015), miR-212 (Meng et al., 2013), miR-451 (Wang et al., 2010) and miR638 (Zhang et al., 2014) have been reported playing key regulatory roles in CRC. In agreement with these researches, our results suggested that these miRNAs may have important roles on colorectal tumorigenesis. In the present study, we demonstrated that the eight miRNAs were in constant processes of change. Moreover, we searched the target genes of these miRNAs and studied their roles in biological processes of CRC, and constructed regulatory networks based on the wholegenomic miRNA expression profile. Using miRNA microarray and subsequent qRT-PCR validation, eight differentially expressed miRNAs were identified. Then, five computeraided algorithms, TargetScan, miRanda, miRwalk, RNAhybrid and PicTar were used to search for the targets of the eight dysregulated miRNAs. Any one of the algorithms may not be reliable enough to identify targets of miRNA, so presumed targets from five methods were intersected to obtain overlapped gene set. Luciferase and Western blot analysis of ZO-1, one predicted new target of miR-212, confirmed that this
target-searching operation was efficient and accurate. Based on the prediction of targets, KEGG enrichment analysis was applied to demonstrate their functions in colorectal tumorigenesis, and showed that many of the target genes were involved in human cancers and related signaling pathways. We chose three pathways which among the most related processes to CRC (Colorectal cancer pathway, MAPK signaling pathway and Tight junction), and constructed miRNA regulatory networks based on overlapping parts of these three pathways. The results showed that some key oncogenes, such as MYC, EGFR, KRAS and IGF1R (Chang et al., 2014; Kantor et al., 2014), or tumor-suppressor genes such as PTEN and TP53 (Kara et al., 2012), were centrally located in the regulatory networks and might play key roles in colorectal tumorigenesis. To validate the networks, miR-212, which had been reported to be downregulated in CRC and inhibit CRC metastasis (Meng et al., 2013), and one of its predicted target ZO-1 were selected for further analysis. Interestingly, ZO-1, which was known as a migration-related gene, was also considered to be important in the networks. ZO-1 has a vital role in maintaining cell to cell integrity and the loss of cohesion of the structure can lead to invasion and thus metastasis of cancer cells (Martin and Jiang, 2009; Zhou et al., 2014). In contrast to the upregulation of miR-212 in CRC, ZO-1 expression was downregulated in CRC tissues, confirming the regulatory effect of miR-212 on ZO-1 at the level of clinical sample. Our results showed that ZO-1 is indeed a direct target of miR-212 in CRC and revealed a new miR-212/ZO-1 signaling in CRC. Expanding insights into the miRNA regulated networks involved in colorectal tumorigenesis will yield important clues to improve our understanding of the complex and multistep molecular pathogenesis of CRC. Bioinformatics tools would help us to get wider perspective of the disease and shed light on new strategies to search candidate targets for its diagnosis, prognosis and therapy. We hope that bioinformatics-
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Fig. 4. Establishment and validation of miRNA regulated gene networks associated with colorectal tumorigenesis. A, construction of regulatory networks from miRNAs and related protein coding genes in the three selected KEGG pathways by Cytoscape software. B and C, the inverse correlation between miR-212 and ZO-1 protein in an additional group of tissues were validated by quantitative real-time RT-PCR (B) (P b 0.01) and Western blot, respectively.
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Research paper
Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis☆,☆☆ Yuan Yin a,1, Mingxu Song a,1, Bing Gu b,c, Xiaowei Qi d, Yaling Hu a, Yuyang Feng a, Heyong Liu a, Leyuan Zhou a, Zehua Bian a, Jiwei Zhang a, Xianbo Zuo e, Zhaohui Huang a,⁎ a
Wuxi Oncology Institute, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, China Department of Laboratory Medicine, Affiliated Hospital of Xuzhou Medical College, Xuzhou, Jiangsu 221002, China Medical Technology Institute of Xuzhou Medical College, Xuzhou, Jiangsu 221002, China d Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, China e Institute of Dermatology and Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China b c
a r t i c l e
i n f o
Article history: Received 20 August 2015 Received in revised form 10 November 2015 Accepted 8 December 2015 Available online xxxx Keywords: Colorectal cancer MicroRNA Bioinformatic analysis KEGG pathway analysis Regulatory networks
a b s t r a c t The development of colorectal cancers (CRC) is accompanied with the acquisition and maintenance of specific genomic alterations. These alterations can emerge in premalignant adenomas and faithfully maintained in highly advanced tumors. miRNAs are a class of small non-coding RNAs that are frequently deregulated in human cancers and negatively regulate a wide variety of protein coding genes. To identify the sequential alterations of miRNAs and its regulatory networks during CRC development and progression, we detected the miRNA expression profiles of tissue samples from normal colon, colorectal adenoma and CRC using miRNA microarray. qRT-PCR assay was used to validate and select the miRNAs with differential expression among the three groups, and the computer-aided algorithms of TargetScan, miRanda, miRwalk, RNAhybrid and PicTar were used to search for the possible targets of the selected 8 miRNAs (miR-18a, miR-18b, miR-31, miR-142-5p, miR-145, miR-212, miR-451, and miR-638) with continuous alterated expression. These potential target genes were enriched in several key signal transduction pathways (KEGG pathway analysis), which have been proved to be closely related to colorectal tumorigenesis. To confirm the reliability of the analyses, we identified that the metastasis-related gene ZO-1 is a certain target of miR-212 in CRC and keeps declining during CRC progression. By following these analyses, we might gain an in-depth understanding of the molecular regulatory networks of colorectal tumorigenesis and provide new potential targets for the diagnostic and therapeutic interventions of this disease. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Colorectal cancer (CRC) is one of the most frequent cancers and the fifth leading cause of cancer death in China (Siegel et al., 2015). Most of CRCs develop from colonic mucosal lesions and colorectal adenomas (Habermann et al., 2007). Studies have shown that the formation of CRC may last more than ten years, and it will take about five years for endoscopic recognizable adenomas developing into invasive cancers
Abbreviations: CRC, colorectal cancer; miRNA, microRNA; qRT-PCR, quantificational real-time polymerase chain reaction; TJP1, tight junction protein 1, also known as ZO-1. ☆ Grant support: This study was partially supported by grants from the National Natural Science Foundation of China (nos. 81301784, 81000867, 81272299 and 81301920) and the Natural Science Foundation of Jiangsu Province (BK20151108 and BK20150004). ☆☆ Disclosure of potential conflicts of interest: No potential conflicts of interest were disclosed. ⁎ Corresponding author at: Wuxi Oncology Institute, Affiliated Hospital of Jiangnan University, 200 Huihe Road, Wuxi, Jiangsu 214062, China. E-mail address: [email protected] (Z. Huang). 1 These authors contributed equally to this work.
(Balaguer, 2013). Thus, it's of important significance to study the dynamic change process of CRC. microRNAs (miRNAs) are a type of small non-coding RNA molecules that play critical roles in most physiological and pathological processes, including cell proliferation, differentiation, apoptosis, and immune response (Bartel, 2004). By base pairing to the complementary 3′untranslated region (3′ UTR) of its target mRNAs, miRNA negatively regulates gene expression at the posttranscriptional level, resulting in the block of translation or the degradation of the target mRNAs. Through this translational repression and gene silencing function, miRNA regulates up to one third of the total human protein-coding genes (He and Hannon, 2004). The development of CRC accompanied with the acquisition and maintenance of specific genomic alterations (Rao and Yamada, 2013). miRNAs have also been considered to be implicated in the progression of CRC (Pizzini et al., 2013). Previous studies found that many miRNAs, such as miR-143 (Chen et al., 2009), miR-21 (Xiong et al., 2013), and miR-145 (Liu et al., 2015; Qin et al., 2015), miR-95 (Huang et al., 2011), miR-204 (Yin et al., 2014), together with their target genes, are involved in the development and progression of CRC. However, the
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Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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dynamic molecular changes through CRC development at the wholegenome level are poorly understood. Moreover, the regulatory networks of miRNAs and their targets are extremely complicated, as each individual miRNA may target hundreds of genes, and one mRNA can also be the target of multiple miRNAs (Di Leva et al., 2014). This prompted us to fully identify and study the key miRNAs and their targets in CRC tumorigenesis at the global genomic level. In the present study, we performed miRNA profiling analyses on normal colon mucosa, colorectal adenomas and CRC tissues to identify the key miRNAs involved in CRC tumorigenesis. Bioinformatics tools were then used to search for the miRNA-targeted signaling pathways and to construct their regulatory networks in CRC. Our results provide insights into the molecular regulatory networks of colorectal tumorigenesis and may suggest new targets for the diagnostic and therapeutic interventions of CRC. 2. Materials and methods
2.5. Luciferase reporter assay A luciferase reporter assay was performed as previously described (Chen et al., 2009), to test the binding of miR-212 to its target ZO-1. The ZO-1 3′ UTR segment, containing a presumed miR-212 complementary site (seed sequence, GACTGTT), was amplified from human genomic DNA by PCR (Sense 1: 5′GCAAAGC ATTACGGATAGC3′; Antisense 1: AGACGACGAGGTGGTAGGT; Sense 2: TGGACTAGTCTCTTGAAATATA GGAACTTAAATAATGTGAAACTGG; Antisense 2: GATACGCGTGAAACTA GA AAACAGTGGGCAAACAGACCAAGC). The PCR products were inserted into the pMIR-report plasmid (Applied Biosystems), and efficient insertion was confirmed by sequencing. Caco2 cells were cultured in 6-well plates and cotransfected with 1 μg of firefly luciferase reporter plasmid, 0.5 μg of β-galactosidase expression vector (Applied Biosystems), and 100 pmol miR-212 mimic, miR-212 inhibitor, or scrambled control RNA using Lipofectamine 2000 (Invitrogen). The β-galactosidase vector was used as transfection control. 24 h after transfection, cells were assayed using luciferase assay kit (Promega).
2.1. Tissue collection Tissue samples were obtained from CRC and colorectal adenoma patients who underwent surgical resection from the Affiliated Hospital of Jiangnan University. The normal colorectal tissues (NAT) were 5 cm away from the incisal edge of CRC tissues. All patients were ranging from 42 to 73 years old (57.4 ± 13.22) and had not received radiotherapy, chemotherapy, or other medication therapy. All of the samples were collected according to the Institutional Review Board-approved protocol and the written informed consent from each patient. 2.2. RNA isolation Tissue fragments were immediately frozen in liquid nitrogen at the time of surgery and stored at −80 °C until use. Total RNA was extracted using TRIzol reagent (Invitrogen). RNA concentrations were determined using a NanoDrop 2000 spectrophotometer (Thermo). 2.3. Microarray and qRT-PCR Total RNA was purified from tissues samples using mirVANA miRNA isolation kit (Ambion) to enrich small RNA fraction. RNA labeling and hybridization on miRNA microarray chips (CapitalBio Corp) were performed as previously described (Thomson et al., 2004). Hybridization signals were detected and scanner images were quantified. Data were assessed using unsupervised hierarchical clustering to generate both gene and sample trees. cDNA was synthesized with the PrimeScript RT reagent Kit (TaKaRa). Stem-loop qRT-PCR assays using TaqMan miRNA probes (Applied Biosystems) to quantify mature miRNA levels were performed using Applied Biosystems 7300 Sequence Detection System. The relative levels of miRNAs were normalized to U6, a ubiquitously expressed small nuclear RNA. 2.4. Bioinformatic analysis Selected miRNAs were loaded into the computer-aided algorithms of TargetScan, miRanda, miRwalk, RNAhybrid, and PicTar for target prediction (Krek et al., 2005). The presumed targets predicted by at all of the algorithms were compiled together and applied to bioinformatic analysis. Subsequent bioinformatic analysis of these target genes was performed by Kyoto Encyclopedia of Genes and Genomes Pathway analysis (KEGG Pathway analysis) (http://www.genome.jp/kegg/) and GO analysis (Wrzodek et al., 2013). Pathways were selected with a P value b 0.05 and gene count N 2. The target genes enriched in KEGG pathways and related miRNAs were visualized by Cytoscape software for network construction (Shannon et al., 2003).
2.6. Western blotting ZO-1 protein level was quantified by Western blotting with a rabbit polyclonal anti-ZO-1 antibody (1:1000, Santa Cruz). The normalization was performed by blotting the same samples with a mouse antiGAPDH antibody (1:1000, Santa Cruz).
2.7. Statistical analysis Data shown were expressed as means ± SEM (standard error) of three or more independent experiments, and the differences were considered statistically significant at b 0.05 by using the Student's t-test.
3. Results 3.1. Identification of miRNAs differentially expressed during colorectal tumorigenesis MiRNA expression profiles in 8 NATs, 7 colorectal adenomas and 15 CRC tissues were analyzed using a microarray to screen differentially expressed miRNAs during colorectal tumorigenesis. The result indicated a clear discrimination among adenomatous, cancerous and normal tissues. Unsupervised hierarchical clustering significantly distinguished the CRCs from adenomas and NATs (Fig. 1A). Then, we validated these differentially expressed miRNAs in these 30 tissues using TaqMan probe-based real-time qRT-PCR assay. Consistent with the microarray data, 67 miRNAs were dysregulated in CRC tumorigenesis (Supplementary Table 1). These miRNAs were then divided into 4 groups, miRNAs upregulated continuously (Fig. 1B), miRNAs downregulated continuously (Fig. 1C), miRNAs upregulated in adenoma but downregulated in CRC (Supplementary Table 1), and miRNAs downregulated in adenoma but upregulated in CRC (Supplementary Table 1). We speculated that miRNAs changed continuously were more commonly involved in the process of benign adenomas developing into malignancy than those with fluctuated expression during colorectal tumorigenesis, and 28 continually changing miRNAs were concerned (Fig. 1D). Among these miRNAs, eight miRNAs (miR-18a, miR-18b, miR-31, miR-142-5p, miR-145, miR-212, miR-451, and miR-638) were selected for further study for their relatively higher or lower expression in colorectal tissues (Table 1). They are significantly altered both between NATs and adenomas, as well as between adenomas and CRCs (fold change N1.5, P b 0.01) (Fig. 1, B–D). These data suggest that deregulation of the eight miRNAs may play essential roles in CRC tumorigenesis.
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Fig. 1. Differentially expressed miRNAs during colorectal tumorigenesis. A, unsupervised hierarchical clustering of miRNA expression profiles among adenomatous, cancerous and normal tissues (7 adenomatous, 15 tumors and 8 normal tissues) measured by microarray analysis. B, upregulated miRNAs validated by quantitative real-time RT-PCR (P b 0.01). C, downregulated miRNAs validated by quantitative real-time RT-PCR (P b 0.01). D, selection of miRNAs significantly altered in colorectal tumorigenesis for further study. (upregulate or downregulated continuously, fold change N1.5, P b 0.01).
3.2. Prediction and validation of target genes of the eight miRNAs Using five computer-aided algorithms, TargetScan, miRanda, miRwalk, RNAhybrid and PicTar, we obtained a list of predicted target genes that are potentially regulated by miR-18a, miR-18b, miR-31, miR-142-5p, miR-145, miR-212, miR-451, and miR-638. Target genes predicted by all the five algorithms were selected for experimental verification (Table 2). To test the reliability of the algorithms, tight junction protein 1 (TJP1, also known as ZO-1), a predicted target of miR-212, was selected. We cloned the 3′UTR of ZO-1into a luciferase reporter vector (Fig. 2A). The results of luciferase reporter assay revealed that miR-212 could inhibit the reporter gene expression in the recombinant plasmids of ZO-1 3′UTR, while silencing miR-212 resulted in increasing
Table 1 Identification of miRNAs significantly differentially expressed in colorectal tumorigenesis. miRNA
NAT
Adenoma
CRC
P value
miRNAs upregulated continuously hsa-miR-18a 1.00 hsa-miR-18b 1.00 hsa-miR-31 1.00 hsa-miR-142-5p 1.00 hsa-miR-212 1.00
2.08 1.86 1.72 1.79 2.41
3.09 3.00 5.04 2.86 5.28
0.0098 0.0035 0.0030 0.0042 0.0047
miRNAs downregulated continuously hsa-miR-145 1.00 hsa-miR-451 1.00 hsa-miR-638 1.00
0.63 0.69 0.65
0.41 0.23 0.34
0.0070 0.0028 0.0030
expression of the reporter gene (Fig. 2B). To determine whether the overexpression or knockdown of miR-212 also had an impact on the ZO-1 protein levels, we determined the expression of ZO-1 protein by Western blotting. In concordance with these results, the endogenous ZO-1 protein levels were also downregulated in miR212-overexpressed cells and could be restored in miR-212-depleted CRC cells (Fig. 2C). In addition, we chose KRAS for further validation for its central situation in the constructed network and its comprehensive functions in human cancers. To do that, we overexpressed miR-18a, miR-31 and miR-212 in colon cancer cell line LoVo, and then detected the expression change of KRAS using Western blotting. As expected, KRAS expression was indeed underregulated in cells with overexpressed miR-18a, miR-31 or miR-212 compared with the control
Table 2 Number of predicted target genes of deregulated miRNAs.
miRNA
Target gene number
hsa-miR-18a hsa-miR-18b hsa-miR-31 hsa-miR-142-5p hsa-miR-145 hsa-miR-212 hsa-miR-451 hsa-miR-638
63 9 143 51 250 51 110 11
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Fig. 2. Validation of one target gene of the miR-212 according to bioinformatic analysis. A, Schematic description of conserved binding site of ZO-1 for miR-212. B, analysis of the luciferase activity of the luciferase reporter plasmid containing ZO-1 3′UTR. C, suppressed expression of endogenous ZO-1 by miR-212.
cells (Supplementary Fig. 1). Taken together, these results demonstrate that the prediction results based on five algorithms were reliable and valuable. 3.3. The roles of target genes in CRC tumorigenesis according to KEGG and GO analyses To further evaluate the role of deregulated miRNAs and their target genes in CRC tumorigenesis, hundreds of predicted target genes were subjected to enrichment analysis of gene function by GO analysis and enrichment analysis of cell signaling pathways using KEGG
pathway database. The functions of these target genes were enriched in “membrane-enclosed lumen (GO: 0031974)”, “tight junction (GO: 0005923)” and “transcription factor complex (GO: 0005667)” (P b 0.001) (Table 3). We found that the most significantly enriched pathways were related to cancer, MAPK signaling or tight junction pathways (P b 0.001) (Table 4). The rate of target genes enriched in the “Pathways in cancer (hsa05200)” was up to 15.8% (67/424) (Table 4), while 34, 26 and 25 target genes were enriched in the “CRC pathway (hsa05210)” (Table 4 and Supplementary Fig. 2), “Tight junction (hsa04530)” (Table 4 and Fig. 3), and “MAPK signaling pathway (hsa04010)” (Table 4 and Supplementary Fig. 3), respectively.
Table 3 The enriched functions of deregulated miRNA Targets analyzed by GO. GO ID
GO-CC analysis
Gene number percentage
P value
0031974 0005923 0005667 0044459 0030054 0043233 0005856 0005911 0005694 0005654 0070013 0016323 0005912 0070161 0043296 0016327 0070160 0043228 0005913 0005625
Membrane-enclosed lumen Tight junction Transcription factor complex Plasma membrane part Cell junction Organelle lumen Cytoskeleton Cell–cell junction Chromosome Nucleoplasm Intracellular organelle lumen Basolateral plasma membrane Adherens junction Anchoring junction Apical junction complex Apicolateral plasma membrane Occluding junction Non-membrane-bounded organelle Cell–cell adherens junction Soluble fraction
25.7%(109/424) 25.2%(107/424) 17.7%(75/424) 24.1%(102/424) 24.1%(102/424) 22.9%(97/424) 20.0%(85/424) 24.1%(102/424) 17.2%(73/424) 17.0%(72/424) 13.0%(55/424) 12.3%(52/424) 11.8%(50/424) 11.3%(48/424) 11.3%(48/424) 10.4%(44/424) 10.4%(44/424) 8.7%(37/424) 8.7%(37/424) 8.3%(35/424)
3.48E − 16 6.99E − 16 2.11E − 03 2.54E − 05 2.54E − 05 4.82E − 18 3.17E − 04 2.59E − 14 3.53E − 18 7.87E − 09 1.28E − 07 4.73E − 05 2.43E − 02 1.73E − 12 2.30E − 09 2.45E − 02 3.49E − 02 1.15E − 04 1.19E − 04 1.01E − 02
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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3.4. Establishment of miRNA regulated gene networks associated with colorectal tumorigenesis
Table 4 The enriched KEGG pathways of deregulated miRNA targets. KEGG pathway
Gene number percentage
P value
Pathways in cancer(hsa05200) MAPK signaling pathway(hsa04010) Tight junction(hsa04530) Colorectal cancer pathway(hsa05210) Cytokine–cytokine receptor interaction(hsa04060) Focal adhesion(hsa04510) Pancreatic cancer(hsa05212) Chemokine signaling pathway(hsa04062) Neurotrophin signaling pathway(hsa04722) Toll-like receptor signaling pathway(hsa04620) Regulation of actin cytoskeleton(hsa04810) Adherens junction(hsa04520) Cell cycle(hsa04110) ErbB signaling pathway(hsa04012) T cell receptor signaling pathway(hsa04660) Non-small cell lung cancer(hsa05223) Wnt signaling pathway(hsa04310) Prostate cancer(hsa05215) TGF-beta signaling pathway(hsa04350) Apoptosis(hsa04210)
15.8% (67/424) 8% (34/424) 6.1% (26/424) 5.9% (25/424) 5.9% (25/424) 5.7% (24/424) 5.4% (23/424) 5.4% (23/424) 5.4% (23/424) 5.2% (22/424) 5.2% (22/424) 5.0% (21/424) 4.7% (20/424) 4.7% (20/424) 4.5% (19/424) 4.5% (19/424) 4.2% (18/424) 4.2% (18/424) 4.0% (17/424) 4.0% (17/424)
1.40E − 26 1.60E − 07 1.40E − 09 1.90E − 13 1.80E − 04 9.80E − 11 1.80E − 18 1.50E − 05 2.50E − 10 6.80E − 10 3.70E − 04 1.80E − 11 2.10E − 07 1.50E − 09 2.60E − 09 1.10E − 12 2.00E − 04 9.90E − 16 3.50E − 07 8.90E − 06
We next investigated the regulatory networks associated with colorectal tumorigenesis by combining “CRC pathway”, “MAPK signaling pathway” and “Tight junction”. The eight miRNAs and their predicted target genes enriched in the three pathways (Table 5) were subjected to Cytoscape software for network analysis. The results were illustrated by the networks depicted in Fig. 4A, which represent the predicted relationships among the genes co-regulated by the eight miRNAs during colorectal tumorigenesis. For instance, we found that the apoptosis regulator BCL2, the tumor suppressor TP53, the cell–cell junction transducer ZO-1 and other tumor-related genes were the most significant enriched regulators in the sets of genes affected by the eight miRNAs. To confirm the validity of the regulatory networks constructed, we further detected the miR-212 expression and the ZO-1 protein levels in an additional group of NAT, adenoma and CRC tissues. As shown in Fig. 4B and 4C, the miR-212 expression levels were inverse correlated with the ZO-1 protein levels in these tissues. The results suggest that these miRNA regulated genes are active components associated with colorectal tumorigenesis.
Fig. 3. Tight junction pathway in KEGG pathways. Red star marked genes represent genes targeted by the selected miRNAs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Table 5 miRNA-regulated genes in three selected KEGG pathways. Tight junction
MAPK signaling pathway
Colorectal cancer pathway
Genes
miRNAs
Genes
miRNAs
Genes
miRNAs
F11r INADL ACTG1 CTNNB1 Cdc42 CLDN1 csdA Cdk4 HCLS1 MYH11 NRAS Ocln pten ppp2r1b Ppp2r2a Rhoa mllt4 SPTAN1 ZAK ZO-1 ZO-2 ZO-3 Kras akt1 Src YES1
miR-145 miR-31 miR-145 miR-145 miR-145 miR-18b miR-212 miR-145 miR-145 miR-145 miR-145 miR-18b miR-18a,18b,31,145,451 miR-145 miR-31 miR-31 miR-212 miR-145 miR-145 miR-18a,31,212 miR-18b miR-18b miR-18a,18b,31,145,212 miR-18a,145,451 miR-145 miR-145
Rasa1 BDNF Cdc42 Egf egfr FGF2 Fgfr1 fgfr3 MAPK3 IL1B Jun Mapk14 Mapk7 Mapk9 MAP2K1 NRAS Pdgfb Pak1 ppp3ca RPS6KA5 Stk3 Srf STMN1 ZAK TGFB1 Tgfbr2 TNF tp53 Kras akt1 FOS MYC BRAF RAF1
miR-31,451 miR-18a,212 miR-145 miR-212 miR-18b,145,212 miR-31 miR-451 miR-31 miR-145,212 miR-31,142 miR-145 miR-145 miR-145 miR-451 miR-451 miR-145 miR-145 miR-145 miR-145 miR-212 miR-145 miR-145 miR-31 miR-145 miR-18a miR-145 miR-31,142,451 miR-18a,31,145 miR-18a,18b,31,145,212 miR-18a,145,451,638 miR-31 miR-18a,18b,31,145,212 miR-31 miR-212
BCL2 BAX SMAD2 SMAD3 SMAD4 CTNNB1 CCND1 egfr MAPK3 IGF1R Jun Lef1 MET Mapk9 MAP2K1 PIK3CA TGFB1 Tgfbr2 tp53 Kras akt1 FOS MYC BRAF RAF1
miR-18a,145,451 miR-31 miR-18a,145 miR-18a,31,145,451 miR-145 miR-145 miR-31,142,145 miR-18b,145,212 miR-145,212 miR-31,145 miR-145 miR-145 miR-31 miR-451 miR-451 miR-145,451,638 miR-18a miR-145 miR-18a,31,145 miR-18a,18b,31,145,212 miR-18a,145,451,638 miR-31 miR-18a,18b,31,145,212 miR-31 miR-212
4. Discussion Colorectal tumorigenesis is a complicated and multistep process involving a series of genetic and epigenetic alterations (Tortolina et al., 2012). Knowledge about the dynamic molecular variations underlying colorectal carcinogenesis are crucial for the better explore the molecular mechanism CRC (Michor et al., 2005). Because CRC progresses through a normal-adenoma-carcinoma course, we investigated the carcinogenesis in tissue samples histologically confirmed to be NATs, adenomas and CRC, respectively. In this study, eight miRNAs (including 5 upregulated and 3 downregulated in CRC) were identified to be significantly altered both between NATs and adenomas, as well as between adenomas and CRCs. Of these eight miRNAs, miR-18a (Humphreys et al., 2014), miR-31 (Sun et al., 2013), miR-145 (Liu et al., 2015; Qin et al., 2015), miR-212 (Meng et al., 2013), miR-451 (Wang et al., 2010) and miR638 (Zhang et al., 2014) have been reported playing key regulatory roles in CRC. In agreement with these researches, our results suggested that these miRNAs may have important roles on colorectal tumorigenesis. In the present study, we demonstrated that the eight miRNAs were in constant processes of change. Moreover, we searched the target genes of these miRNAs and studied their roles in biological processes of CRC, and constructed regulatory networks based on the wholegenomic miRNA expression profile. Using miRNA microarray and subsequent qRT-PCR validation, eight differentially expressed miRNAs were identified. Then, five computeraided algorithms, TargetScan, miRanda, miRwalk, RNAhybrid and PicTar were used to search for the targets of the eight dysregulated miRNAs. Any one of the algorithms may not be reliable enough to identify targets of miRNA, so presumed targets from five methods were intersected to obtain overlapped gene set. Luciferase and Western blot analysis of ZO-1, one predicted new target of miR-212, confirmed that this
target-searching operation was efficient and accurate. Based on the prediction of targets, KEGG enrichment analysis was applied to demonstrate their functions in colorectal tumorigenesis, and showed that many of the target genes were involved in human cancers and related signaling pathways. We chose three pathways which among the most related processes to CRC (Colorectal cancer pathway, MAPK signaling pathway and Tight junction), and constructed miRNA regulatory networks based on overlapping parts of these three pathways. The results showed that some key oncogenes, such as MYC, EGFR, KRAS and IGF1R (Chang et al., 2014; Kantor et al., 2014), or tumor-suppressor genes such as PTEN and TP53 (Kara et al., 2012), were centrally located in the regulatory networks and might play key roles in colorectal tumorigenesis. To validate the networks, miR-212, which had been reported to be downregulated in CRC and inhibit CRC metastasis (Meng et al., 2013), and one of its predicted target ZO-1 were selected for further analysis. Interestingly, ZO-1, which was known as a migration-related gene, was also considered to be important in the networks. ZO-1 has a vital role in maintaining cell to cell integrity and the loss of cohesion of the structure can lead to invasion and thus metastasis of cancer cells (Martin and Jiang, 2009; Zhou et al., 2014). In contrast to the upregulation of miR-212 in CRC, ZO-1 expression was downregulated in CRC tissues, confirming the regulatory effect of miR-212 on ZO-1 at the level of clinical sample. Our results showed that ZO-1 is indeed a direct target of miR-212 in CRC and revealed a new miR-212/ZO-1 signaling in CRC. Expanding insights into the miRNA regulated networks involved in colorectal tumorigenesis will yield important clues to improve our understanding of the complex and multistep molecular pathogenesis of CRC. Bioinformatics tools would help us to get wider perspective of the disease and shed light on new strategies to search candidate targets for its diagnosis, prognosis and therapy. We hope that bioinformatics-
Please cite this article as: Yin, Y., et al., Systematic analysis of key miRNAs and related signaling pathways in colorectal tumorigenesis, Gene (2015), http://dx.doi.org/10.1016/j.gene.2015.12.015
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Fig. 4. Establishment and validation of miRNA regulated gene networks associated with colorectal tumorigenesis. A, construction of regulatory networks from miRNAs and related protein coding genes in the three selected KEGG pathways by Cytoscape software. B and C, the inverse correlation between miR-212 and ZO-1 protein in an additional group of tissues were validated by quantitative real-time RT-PCR (B) (P b 0.01) and Western blot, respectively.
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