Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method
© Wang et al.; licensee BioMed Central Ltd. 2014
Received: 13 February 2014
Accepted: 21 July 2014
Published: 29 July 2014
MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.
We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.
Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.
MicroRNAs (miRNAs), a class of ~22 nt endogenous small regulatory RNAs, can induce the degradation or translational repression of mRNA transcripts through sequence-specific binding to their 3’-UTRs [1, 2]. To date, many miRNAs and their target genes have been found to play important roles in various biological processes. The dys-regulations or perturbations of miRNA regulatory networks are closely related to many cellular phenotype changes and diseases [3, 4]. Identifications of the perturbed miRNAs regulatory networks are important for understanding the molecular mechanisms of the studied biological processes.
To study miRNA functions, biologists usually overexpress or knockdown specific miRNAs in cells and observe their impacts on cellular states and functions [5, 6]. The miRNA regulatory networks are usually cell-type specific , which makes it impractical to test and verify all miRNAs in all cellular conditions due to the high experimental cost. Currently, most miRNA-target annotations come from sequence-based predictions without cell-type or condition specific information . Therefore, some computational methods are developed to infer the perturbed miRNAs regulatory networks associated with specific phenotype changes by integrating the sequence-based miRNA-target predictions [8–10] with the high throughput genome-wide gene expression data. One popular method is gene set enrichment analysis (GSEA), which determines whether a pre-defined set of genes show statistically significant, concordant differences between two biological states or phenotypes . The hypothesis is that if the expressions of the miRNA targets are significantly changed, the corresponding miRNA should be aberrant or perturbed in the studied process . In addition, miRNAs generally fine-tune the expression of target genes [13–15]. The methods (such as GSEA) which only consider the expression changes of the direct target genes frequently fail to identify the perturbed miRNA regulatory networks. The intracellular system can be regarded as a complex molecular network, some studies combine the network information and the expression data to improve prediction performances . For example, GeneRank algorithm takes gene expression importance into account and employs random walk on gene-gene interaction network to re-score all genes . The new score better reflects the systematic importance of genes in cells and it can also be used to analyze miRNA target set enrichments. However, the gene expression changes should be the responses of driver perturbations on the global gene regulatory networks: when a miRNA is perturbed, it will firstly impact its direct targets and subsequently affect the expression of the downstream genes through intracellular molecular regulatory networks, and finally change the global gene expression patterns in cells. Therefore, a network propagation based model should be more reasonable for interpreting the global transcriptional response to miRNA perturbations than the methods only considering the differential information of miRNA target genes.
In this study, we proposed a network propagation based method (NP-method) to identify the perturbed miRNA regulatory networks from the gene expression data. It used random walk with restart [18, 19] in gene regulatory networks to estimate the global network effect of miRNA perturbation on its direct target genes, and meanwhile use a forward searching strategy  to find the key target genes regulated by the perturbed miRNAs, which are most likely to generate the observed global gene expression changes. We tested it on several gene expression datasets generated from miRNA overexpression or knockdown experiments. Resuls show that it can better rediscover the perturbed miRNAs than several compared methods. Then it was used to infer the perturbed miRNA regulatory networks in colorectal cancer from a gene expression dataset of clinical patient samples. Several known oncogenic and tumor-suppressive miRNAs, including miR-17, miR-26 and miR-145 were identified by NP-method.
Gene expression profiles
Gene expression data analyzed in this work
4 case + 4ctrl
4 case + 3ctrl
2 case + 2ctrl
3 case + 3ctrl
3 case + 3ctrl
3 case + 3ctrl
Case-ctrl, time-course (4 h, 8 h, 16 h, 24 h, 32 h, 72 h, 120 h)
12 cancer + 10 normal
Prior molecular regulation information
It is well known that some miRNAs belong to the same families with the same seed sequence, which is typically defined as position 2–8 from the 5' end of a mature miRNA and is very important for deciding which targets the miRNA regulates . The miRNAs within the same families may regulate similar targets and are often thought to have interrelated or redundant functions [25, 26]. So we focused our study objects on the miRNA families, which could also reduce the number of candidates and thus be better for the multiple testing correction in statistics . Therefore, for the miRNA-target regulation information, We collected the conserved targets of 153 miRNA families from the widely-used miR-target prediction database TargetScan v6.2 .
For the gene regulatory network information, we employed and compared two networks. One is a high-quality human gene transcriptional regulatory network, which comes from an open-access database of experimentally verified human transcriptional regulation interactions – HTRIdb . This network contains 18,310 nodes and 51,871 directed edges. The other one is a protein-protein interaction (PPI) network, which comes from the PPIs scored higher than 0.9 in database STRING v9.0 . This network contains 9,598 nodes and 57,326 edges, and is often used as a highly-reliable PPI network in systems or network biology. However, it is known that prior network knowledge usually contains some noises. To discuss the influence of the noisy edges, we randomly added and deleted 10% edges in the TF-gene regulatory network.
Random walk with restart from miRNA targets for modeling the network effect of miRNA perturbations
In viewpoint of network biology, perturbation of a miRNA firstly impacts its direct targets, and then the effect will propagate through intracellular molecular networks and ultimately influence the expression of all genes in cells (Figure 1 and Additional file 1: Figure S1). The exact gene regulatory parameters are unavailable, so we utilized a method named random walk with restart (RWR) to make use of the network topology for estimating the network effect of miRNA perturbations .
Assume that a gene regulatory network G contains N genes, and an adjacent matrix A with N*N dimension represents the gene regulatory interactions. A ij = 0 means no interaction between gene i and gene j. For the transcriptional regulation network, A is an unsymmetrical matrix where A ij = 1 means gene j regulates gene i. To make it nonsingular and reversible, we set its diagonal elements as 1e-10. While for the PPI network, A is symmetrical and A ij = A ji = 1 means gene i and gene j interact with each other. Each column of A was firstly scaled to have sum 1, and this produced a normalized adjacent matrix A’.
Here DE can be any measure of the gene differential expressions between two biological situations, such as fold-change, t-statistic or z-score, and it is transformed into the absolute value. N is the size of P and DE. and are the mean values. The score NPES quantifies the degree of miRNA-induced gene perturbed probabilities matching gene differential expression levels. The larger the score is, the better the miRNA interprets the observed gene expression changes.
Forward search the leading-edge targets of miRNAs
Averagely, a miRNA have hundreds of predicted targets, but not all of them are regulated in a specific cellular condition, and the same miRNA may regulate different subsets of targets under different conditions. Therefore, uncovering the key miRNA targets with relation to specific conditions is very important for understanding the function and regulatory mechanism of a miRNA. In this study, we borrowed the concept of leading edge subset of genes introduced by GSEA, which is a small group of genes in a specified gene set that can generate a maximal enrichment score to evaluate the differential expression of the gene set , and defined these key targets of a miRNA to be its leading-edge (LE) targets, which can maximize the NPES score and best explain the observed gene expression changes for the specified miRNA.
Let each target be the RWR seed at each time and calculate the corresponding NPES, then get a score vector [NPES 1 , NPES 2 , …, NPES x ];
Sort this score vector in descending order and sort targets accordingly, then get a target rank [t (1) , t (2) , …, t (x) ];
Start from the first target in the rank and add the rest one by one to compose new RWR seed sets and calculate the corresponding NPESs, then get a new score vector [NPES 1 ', NPES 2 ', …, NPES x '];
Extract the maximum score and the corresponding seed set to get the final NPES and the LE targets of the miRNA (Figure 1 S2).
Gene set permutation analysis to normalize NPES and estimate p-value
To avoid producing bias towards the miRNAs with large target set, we performed a permutation-based statistical analysis to normalize the NPES and assess its statistical significance. The gene labels of miRNA targets were randomly assigned from whole network genes, and then a group of new scores were calculated using the randomized miRNA target sets through all the above steps. This process was repeated several times (e.g. 1,000) to generate null distribution of the NPES for each miRNA.
Here the mean and standard deviation were calculated from the null distribution. Then the scores of different miRNAs were comparable, larger score implied the miRNA took more responsibility for the observed gene expression changes and should be more important for the studied biological process. We finally ranked miRNAs according to the normalized scores.
Comparisons with other methods
We compared NP-method with two other methods on predicting the perturbed miRNAs. One is the popular gene set enrichment analysis (GSEA), which determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states or phenotypes . We used software GSEA v2.0.14 Java version to analyze the differential expression of each miRNA’s target set and estimate the activity of corresponding miRNA. GSEA only uses the gene expression information, while the other method, termed GR.GSEA, further integrates gene-gene network information. It firstly applies the GeneRank algorithm to re-score all genes by using both gene differential expression and gene network information , then uses the new gene scores to execute GSEA and estimate the miRNA activities.
During the analysis of gene expression data coming from miRNA overexpression or knockdown experiments, we sorted miRNAs in descending order according to the normalized scores (i.e. the NPES zscore in NP-method, the normalized enrichment score in GSEA and GR.GSEA generated by the GSEA software), and compared these methods using the putative rank of the experimentally perturbed miRNAs. If the desired miRNA is ranked at the top, it implies the corresponding method can predict well enough. While analyzing the gene expression data from CRC patient, we used the area under the receiver operating characteristic (ROC) curve, named AUC, to evaluate the prediction of cancer associated miRNAs. Larger AUC means better prediction . For this analysis, we extracted those miRNAs associated with CRC from a miRNA-disease relationship database called miR2Disease to be gold standard miRNAs .
Rediscovering the experimentally perturbed miRNAs from gene expression data
Results of inferring the experimentally perturbed miRNAs using different methods and different networks
Enrichment results of validated and also CRC related miR-145 targets in the LE target sets
ap-value = 0.014
ap-value = 0.194
ap-value = 0.204
Analyzing time-course gene expressions in HepG2 cells transfected with miR-124
Since NP-method can identify key target genes of miRNAs, exploring the similarities and differences among the key targets of the same miRNA under different situations can further help to understand roles of miRNAs in different context. Besides, it is said that the influence of miRNA perturbation on gene expression is time-dependent . To check this and further test our method, we applied it on a time-course gene expression dataset from a miRNA transfection experiment (GSE6207). In detail, pre-miR-124 and negative control miRNA duplex were transfected into HepG2 cell line using the Reverse Transfection protocol recommended by Ambion, then the paired gene expressions at 7 time points (4, 8, 16, 24, 32, 72, 120 h) were measured using Affymetrix HG-U133Plus2 microarray platform . To avoid noise signals, we firstly filtered the low-expressed genes using a rank-based strategy: the genes whose expression values ranked at the lowest 20% in more than 80% samples were removed. This process generated an expression profile containing 15,444 genes, whose fold changes at each time point were then calculated to be the differential expression inputs of the three methods.
Putative ranks of miR-124 at each time point after its transfection
At the same time, NP-method identified 188, 165, 172, 184, 168, 197 and 231 LE targets respectively at the seven time points (Figure 3C, see more details in Additional file 3). These LE targets mostly have very large fold change ratios among all the miR-124 targets and also they can generate the largest NPES score (Additional file 1: Figure S2), which means that these key targets are principally regulated by the miRNA and contribute a lot to the observed gene expression changes. There are 523 LE targets in total, including some known functional targets of miR-124. For example, the oncogenes ROCK2 and EZH2 that are direct targets of tumor-suppressive miR-124 in hepatocellular carcinoma , and the IQGAP1 who is directly repressed by miR-124 in HCC cell lines and plays important functions in the cell adhesion and motility . We analyzed the functional enrichment of all these 523 LE targets using the DAVID Functional Annotation Tool , and found they were significantly enriched in the protein localization, transport and signal transduction functions (Additional file 1: Table S2, adjusted p-value Benjamini ≤ 0.05). Besides, there were seven common genes shared by every time-point’s LE target set. These genes should be regulated by miR-124 all the time after its transfection. They are CDK4, CD164, AMMECR1, RPIA, FAM177A1, RRBP1 and MBOAT5. The fold change patterns of these genes look very similar (Figure 3D), and according to the miRTarBase  the first five genes have been validated as direct targets of miR-124, so we guess RRBP1 and MBOAT5 are also its true targets in the HepG2 cells, which deserve further experimental verification.
Uncovering the perturbed miRNA regulatory networks in colorectal cancer
From the results (More details can be found in Additional file 4) we selected 10 most significant miRNA families with p-value < 0.01 to be the perturbed key miRNAs, of which most had been reported playing important roles in the colorectal cancer progression. For example, the miR-27a , miR-17 , miR-155 , miR-9  and miR-23a  can promote CRC cell proliferation, invasion or motility, and the miR-26b , miR-145 , miR-93  and miR-23b  can inhibit CRC tumor growth, proliferation and induce apoptosis. Together with their LE targets we constructed a miRNA regulatory network in Figure 4B, where the 10 diamond nodes represent the miRNA families and 538 circular nodes are the LE target genes. The colors of genes characterize their expression fold change: red means significant up-expression (fold change ≥ 1), green means significant down-expression (fold change ≤ -1) and pink means not significant change. In the network, miR-9 has the largest out-degree and regulates 142 genes, which again highlights its importance in CRC development; while ACVR1C, also known as ALK7, has the largest in-degree of 7 and is down-expressed in the studied patient samples (log2 fold change −0.91), it is a type I receptor for the TGFB family of signaling molecules and has been found inducing apoptosis through activating SMADs and MAPKS in tumor cells . Then we also applied the DAVID tool to analyze the functional pathway enrichment of these 538 LE target genes, and found they were significantly enriched in 5 KEGG pathways (Benjamini ≤ 0.05, Figure 4C), which are all directly relevant to the cancer development and progression. All these results indicate that our method successfully finds out the key miRNA regulatory sub-network that is functionally perturbed or dys-regulated in colorectal cancer.
We hypothesize that the miRNA’s impact on target genes should propagate across the whole gene network and this impact could be better interpreted by integrating the differential expressions of all network genes not just the miRNA target genes. So we propose a novel network propagation based method (NP-method) to infer the perturbed miRNA regulatory networks using the differential expression information of global gene network. It executes random walk with restart (RWR) from the miRNA targets in the gene regulatory network to model the intracellular propagation effect of the miRNA perturbation, and meanwhile adopts a forward searching strategy to find the leading-edge targets that are principally regulated by the perturbed miRNAs and result in the observed global gene expression changes.
The analyses of the miRNA perturbed cell line data demonstrated that NP-method could detect perturbed miRNAs from gene differential expression profiles better than GSEA and GR.GSEA. Except for the prediction of pivotal miRNAs, another advantage is to extract the context-specific leading-edge targets for miRNAs at the same time. Even those low-key but functional targets, whose differential expressions are not much prominent but their down-stream gene expressions are significantly changed in response to the miRNA perturbation, can be discovered by our method. For example the miR-34a regulates CUX1 in HCT116 cells. Besides, the analysis of time-course gene expressions from the miR-124 transfected cells revealed that the influence of miRNA perturbation in cells might be time-dependent and our method was more suitable for analyzing the perturbation effect at early time than other methods. In brief, NP-method can help to uncover the perturbed key miRNA regulatory networks in cellular processes of interest.
When analyzing the gene expression data of CRC patients, NP-method predicted the disease associated miRNAs better than other methods, which again proved its efficiency. And based on the results we successfully built a key miRNA regulatory sub-network that should be perturbed and play important functions in colorectal cancer. However, it is known that cancers are usually caused by multiple factors not just a single molecular deregulation like a miRNA overexpression or inhibition, so exploring the synergetic effect of a miRNA group should be more reasonable and meaningful. In this work, the NP-method considered the miRNAs or miRNA families as independent determiners of global gene expression changes and prioritized them according to the estimated network perturbation effect score (NPES). The top-ranked miRNAs are more likely to cause the observed gene differential expressions and are considered more important for the studied cellular process. In the future, we will take the miRNA cooperative regulation into account and try to infer the combination of miRNAs for better deciphering the miRNA-mediated cancer pathologies.
NP-method is not only applicable for analyzing miRNAs, but other problems about multiple interventions on a network are also theoretically appropriate. For example, some small-molecule drugs targeting several genes, proteins or enzymes in molecular networks . So our approach can also be used to study the transcriptomic influence of the pharmacological interventions in cells. And with the increasing concerns on multi-target therapeutics [62, 63], we believe that our method can be further developed and help to design high-efficient combinatorial therapies for complex diseases.
Here we developed a network propagation based method, which took advantage of the differential expression information of global gene network, to infer the perturbed functional miRNAs as well as their leading-edge targets. We demonstrated its reliability and usefulness on several cell line datasets and a clinical cancer dataset. Taken together, our method is a useful approach for studying the miRNA-mediated molecular mechanisms of complex biological processes.
This work was supported by NBRPC [2012CB316503], NSFC [61005040, 61370035, 61105003], Outstanding Tutors for doctoral dissertations of S&T project in Beijing  and Tsinghua National Laboratory for Information Science and Technology Cross-discipline Foundation.
- Lee RC, Ambros V: An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001, 294 (5543): 862-864.View ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297.View ArticlePubMedGoogle Scholar
- Visone R, Croce CM: MiRNAs and cancer. Am J Pathol. 2009, 174 (4): 1131-1138.View ArticlePubMed CentralPubMedGoogle Scholar
- Krol J, Loedige I, Filipowicz W: The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010, 11 (9): 597-610.PubMedGoogle Scholar
- Korner C, Keklikoglou I, Bender C, Worner A, Munstermann E, Wiemann S: MicroRNA-31 sensitizes human breast cells to apoptosis by direct targeting of protein kinase C epsilon (PKCepsilon). J Biol Chem. 2013, 288 (12): 8750-8761.View ArticlePubMed CentralPubMedGoogle Scholar
- Xia H, Ooi LL, Hui KM: MicroRNA-216a/217-induced epithelial-mesenchymal transition targets PTEN and SMAD7 to promote drug resistance and recurrence of liver cancer. Hepatology. 2013, 58 (2): 629-641.View ArticlePubMedGoogle Scholar
- Le HS, Bar-Joseph Z: Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation. Bioinformatics. 2013, 29 (13): i89-i97.View ArticlePubMed CentralPubMedGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120 (1): 15-20.View ArticlePubMedGoogle Scholar
- Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N: Combinatorial microRNA target predictions. Nat Genet. 2005, 37 (5): 495-500.View ArticlePubMedGoogle Scholar
- Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS: MicroRNA targets in Drosophila. Genome Biol. 2003, 5 (1): R1-View ArticlePubMed CentralPubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005, 102 (43): 15545-15550.View ArticlePubMed CentralPubMedGoogle Scholar
- Cheng C, Li LM: Inferring microRNA activities by combining gene expression with microRNA target prediction. PLoS One. 2008, 3 (4): e1989-View ArticlePubMed CentralPubMedGoogle Scholar
- Sevignani C, Calin GA, Siracusa LD, Croce CM: Mammalian microRNAs: a small world for fine-tuning gene expression. Mamm Genome. 2006, 17 (3): 189-202.View ArticlePubMed CentralPubMedGoogle Scholar
- Schratt G: Fine-tuning neural gene expression with microRNAs. Curr Opin Neurobiol. 2009, 19 (2): 213-219.View ArticlePubMedGoogle Scholar
- Ebert MS, Sharp PA: Roles for microRNAs in conferring robustness to biological processes. Cell. 2012, 149 (3): 515-524.View ArticlePubMed CentralPubMedGoogle Scholar
- Roy J, Winter C, Isik Z, Schroeder M: Network information improves cancer outcome prediction. Brief Bioinform. 2014, 15 (4): 612-625.View ArticlePubMedGoogle Scholar
- Morrison JL, Breitling R, Higham DJ, Gilbert DR: GeneRank: using search engine technology for the analysis of microarray experiments. BMC Bioinformatics. 2005, 6: 233-View ArticlePubMed CentralPubMedGoogle Scholar
- Pan JY, Yanh HJ, Faloutsos C, Duygulu P: Proc 10th ACM SIGKDD Int Conf Knowl Discovery Data Mining. Automatic multimedia cross-modal correlation discovery. 2004, 653-658.Google Scholar
- Ham B, Min D, Sohn K: A generalized random walk with restart and its application in depth up-sampling and interactive segmentation. IEEE Trans Image Process. 2013, 22 (7): 2574-2588.View ArticlePubMedGoogle Scholar
- Lutz RR, Woodhouse RM: Requirements analysis using forward and backward search. Ann Softw Eng. 1997, 3 (1): 459-475.View ArticleGoogle Scholar
- Network propagation based method (NP-method). [http://bioinfo.au.tsinghua.edu.cn/software/np/]
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Edgar R: NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009, 37 (Database issue): D885-D890.View ArticlePubMed CentralPubMedGoogle Scholar
- Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193.View ArticlePubMedGoogle Scholar
- Maglott D, Ostell J, Pruitt KD, Tatusova T: Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 2011, 39 (Database issue): D52-D57.View ArticlePubMed CentralPubMedGoogle Scholar
- Obad S, dos Santos CO, Petri A, Heidenblad M, Broom O, Ruse C, Fu C, Lindow M, Stenvang J, Straarup EM, Hansen HF, Koch T, Pappin D, Hannon GJ, Kauppinen S: Silencing of microRNA families by seed-targeting tiny LNAs. Nat Genet. 2011, 43 (4): 371-378.View ArticlePubMed CentralPubMedGoogle Scholar
- Frost RJ, Olson EN: Control of glucose homeostasis and insulin sensitivity by the Let-7 family of microRNAs. Proc Natl Acad Sci U S A. 2011, 108 (52): 21075-21080.View ArticlePubMed CentralPubMedGoogle Scholar
- Noble WS: How does multiple testing correction work?. Nat Biotechnol. 2009, 27 (12): 1135-1137.View ArticlePubMed CentralPubMedGoogle Scholar
- Bovolenta LA, Acencio ML, Lemke N: HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genomics. 2012, 13: 405-View ArticlePubMed CentralPubMedGoogle Scholar
- Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, Mering CV: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39 (Database issue): D561-D568.View ArticlePubMed CentralPubMedGoogle Scholar
- Forbes DA: What is a p value and what does it mean?. Evid Based Nurs. 2012, 15 (2): 34-View ArticlePubMedGoogle Scholar
- Zou KH, O’Malley AJ, Mauri L: Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation. 2007, 115 (5): 654-657.View ArticlePubMedGoogle Scholar
- Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y: miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009, 37 (Database issue): D98-D104.View ArticlePubMed CentralPubMedGoogle Scholar
- Gregersen LH, Jacobsen AB, Frankel LB, Wen J, Krogh A, Lund AH: MicroRNA-145 targets YES and STAT1 in colon cancer cells. PLoS One. 2010, 5 (1): e8836-View ArticlePubMed CentralPubMedGoogle Scholar
- Ostenfeld MS, Bramsen JB, Lamy P, Villadsen SB, Fristrup N, Sorensen KD, Ulhoi B, Borre M, Kjems J, Dyrskjot L, Orntoft TF: miR-145 induces caspase-dependent and -independent cell death in urothelial cancer cell lines with targeting of an expression signature present in Ta bladder tumors. Oncogene. 2010, 29 (7): 1073-1084.View ArticlePubMedGoogle Scholar
- Kano M, Seki N, Kikkawa N, Fujimura L, Hoshino I, Akutsu Y, Chiyomaru T, Enokida H, Nakagawa M, Matsubara H: miR-145, miR-133a and miR-133b: tumor-suppressive miRNAs target FSCN1 in esophageal squamous cell carcinoma. Int J Cancer. 2010, 127 (12): 2804-2814.View ArticlePubMedGoogle Scholar
- Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu MC, Huang CY, Tsou AP, Huang HD: miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011, 39 (Database issue): D163-D169.View ArticlePubMed CentralPubMedGoogle Scholar
- Xiong B, Cheng Y, Ma L, Zhang C: MiR-21 regulates biological behavior through the PTEN/PI-3 K/Akt signaling pathway in human colorectal cancer cells. Int J Oncol. 2013, 42 (1): 219-228.PubMedGoogle Scholar
- Qin X, Yan L, Zhao X, Li C, Fu Y: microRNA-21 overexpression contributes to cell proliferation by targeting PTEN in endometrioid endometrial cancer. Oncol Lett. 2012, 4 (6): 1290-1296.PubMed CentralPubMedGoogle Scholar
- Michl P, Ramjaun AR, Pardo OE, Warne PH, Wagner M, Poulsom R, D’Arrigo C, Ryder K, Menke A, Gress T, Downward J: CUTL1 is a target of TGF(beta) signaling that enhances cancer cell motility and invasiveness. Cancer Cell. 2005, 7 (6): 521-532.View ArticlePubMedGoogle Scholar
- Ripka S, Neesse A, Riedel J, Bug E, Aigner A, Poulsom R, Fulda S, Neoptolemos J, Greenhalf W, Barth P, Gress TM, Michl P: CUX1: target of Akt signalling and mediator of resistance to apoptosis in pancreatic cancer. Gut. 2010, 59 (8): 1101-1110.View ArticlePubMedGoogle Scholar
- Takahashi S, Fusaki N, Ohta S, Iwahori Y, Iizuka Y, Inagawa K, Kawakami Y, Yoshida K, Toda M: Downregulation of KIF23 suppresses glioma proliferation. J Neurooncol. 2012, 106 (3): 519-529.View ArticlePubMedGoogle Scholar
- Fischer M, Grundke I, Sohr S, Quaas M, Hoffmann S, Knorck A, Gumhold C, Rother K: p53 and cell cycle dependent transcription of kinesin family member 23 (KIF23) is controlled via a CHR promoter element bound by DREAM and MMB complexes. PLoS One. 2013, 8 (5): e63187-View ArticlePubMed CentralPubMedGoogle Scholar
- Hausser J, Syed AP, Selevsek N, van Nimwegen E, Jaskiewicz L, Aebersold R, Zavolan M: Timescales and bottlenecks in miRNA-dependent gene regulation. Mol Syst Biol. 2013, 9: 711-View ArticlePubMed CentralPubMedGoogle Scholar
- Wang X, Wang X: Systematic identification of microRNA functions by combining target prediction and expression profiling. Nucleic Acids Res. 2006, 34 (5): 1646-1652.View ArticlePubMed CentralPubMedGoogle Scholar
- Bail S, Swerdel M, Liu H, Jiao X, Goff LA, Hart RP, Kiledjian M: Differential regulation of microRNA stability. RNA. 2010, 16 (5): 1032-1039.View ArticlePubMed CentralPubMedGoogle Scholar
- Jaenicke R: Protein stability and molecular adaptation to extreme conditions. Eur J Biochem. 1991, 202 (3): 715-728.View ArticlePubMedGoogle Scholar
- Zheng F, Liao YJ, Cai MY, Liu YH, Liu TH, Chen SP, Bian XW, Guan XY, Lin MC, Zeng YX, Kung HF, Xie D: The putative tumour suppressor microRNA-124 modulates hepatocellular carcinoma cell aggressiveness by repressing ROCK2 and EZH2. Gut. 2012, 61 (2): 278-289.View ArticlePubMedGoogle Scholar
- Furuta M, Kozaki KI, Tanaka S, Arii S, Imoto I, Inazawa J: miR-124 and miR-203 are epigenetically silenced tumor-suppressive microRNAs in hepatocellular carcinoma. Carcinogenesis. 2010, 31 (5): 766-776.View ArticlePubMedGoogle Scholar
- da Huang W, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA: The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007, 8 (9): R183-View ArticlePubMedGoogle Scholar
- Hong Y, Ho KS, Eu KW, Cheah PY: A susceptibility gene set for early onset colorectal cancer that integrates diverse signaling pathways: implication for tumorigenesis. Clin Cancer Res. 2007, 13 (4): 1107-1114.View ArticlePubMedGoogle Scholar
- Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M: pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011, 12: 77-View ArticlePubMed CentralPubMedGoogle Scholar
- Jahid S, Sun J, Edwards RA, Dizon D, Panarelli NC, Milsom JW, Sikandar SS, Gumus ZH, Lipkin SM: miR-23a promotes the transition from indolent to invasive colorectal cancer. Cancer Discov. 2012, 2 (6): 540-553.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang J, Xiao Z, Lai D, Sun J, He C, Chu Z, Ye H, Chen S, Wang J: miR-21, miR-17 and miR-19a induced by phosphatase of regenerating liver-3 promote the proliferation and metastasis of colon cancer. Br J Cancer. 2012, 107 (2): 352-359.View ArticlePubMed CentralPubMedGoogle Scholar
- Bakirtzi K, Hatziapostolou M, Karagiannides I, Polytarchou C, Jaeger S, Iliopoulos D, Pothoulakis C: Neurotensin signaling activates microRNAs-21 and −155 and Akt, promotes tumor growth in mice, and is increased in human colon tumors. Gastroenterology. 2011, 141 (5): 1749-1761. e1741View ArticlePubMed CentralPubMedGoogle Scholar
- Zhu L, Chen H, Zhou D, Li D, Bai R, Zheng S, Ge W: MicroRNA-9 up-regulation is involved in colorectal cancer metastasis via promoting cell motility. Med Oncol. 2012, 29 (2): 1037-1043.View ArticlePubMedGoogle Scholar
- Ma YL, Zhang P, Wang F, Moyer MP, Yang JJ, Liu ZH, Peng JY, Chen HQ, Zhou YK, Liu WJ, Qin HL: Human embryonic stem cells and metastatic colorectal cancer cells shared the common endogenous human microRNA-26b. J Cell Mol Med. 2011, 15 (9): 1941-1954.View ArticlePubMed CentralPubMedGoogle Scholar
- Yin Y, Yan ZP, Lu NN, Xu Q, He J, Qian X, Yu J, Guan X, Jiang BH, Liu LZ: Downregulation of miR-145 associated with cancer progression and VEGF transcriptional activation by targeting N-RAS and IRS1. Biochim Biophys Acta. 2013, 1829 (2): 239-247.View ArticlePubMedGoogle Scholar
- Yang IP, Tsai HL, Hou MF, Chen KC, Tsai PC, Huang SW, Chou WW, Wang JY, Juo SH: MicroRNA-93 inhibits tumor growth and early relapse of human colorectal cancer by affecting genes involved in the cell cycle. Carcinogenesis. 2012, 33 (8): 1522-1530.View ArticlePubMedGoogle Scholar
- Zhang H, Hao Y, Yang J, Zhou Y, Li J, Yin S, Sun C, Ma M, Huang Y, Xi JJ: Genome-wide functional screening of miR-23b as a pleiotropic modulator suppressing cancer metastasis. Nat Commun. 2011, 2: 554-View ArticlePubMedGoogle Scholar
- Kim BC, van Gelder H, Kim TA, Lee HJ, Baik KG, Chun HH, Lee DA, Choi KS, Kim SJ: Activin receptor-like kinase-7 induces apoptosis through activation of MAPKs in a Smad3-dependent mechanism in hepatoma cells. J Biol Chem. 2004, 279 (27): 28458-28465.View ArticlePubMedGoogle Scholar
- Imming P, Sinning C, Meyer A: Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov. 2006, 5 (10): 821-834.View ArticlePubMedGoogle Scholar
- Al-Lazikani B, Banerji U, Workman P: Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol. 2012, 30 (7): 679-692.View ArticlePubMedGoogle Scholar
- Sanoudou D, Mountzios G, Arvanitis DA, Pectasides D: Array-based pharmacogenomics of molecular-targeted therapies in oncology. Pharmacogenomics J. 2012, 12 (3): 185-196.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.