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International Journal of Genomics
Volume 2017, Article ID 3538568, 11 pages
https://doi.org/10.1155/2017/3538568
Research Article

A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis

College of Chemistry, Sichuan University, Chengdu 610064, China

Correspondence should be addressed to Zhining Wen; moc.361@gninihz_w and Menglong Li; nc.ude.ucs@lmil

Received 29 April 2017; Accepted 10 July 2017; Published 2 August 2017

Academic Editor: Brian Wigdahl

Copyright © 2017 Jiwei Xue et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

MicroRNA (miRNA) plays an important role in the degradation and inhibition of mRNAs and is a kind of essential drug targets for cancer therapy. To facilitate the clinical cancer research, we proposed a network-based strategy to identify the cancer-related miRNAs and to predict their targeted genes based on the gene expression profiles. The strategy was validated by using the data sets of acute myeloid leukemia (AML), breast invasive carcinoma (BRCA), and kidney renal clear cell carcinoma (KIRC). The results showed that in the top 20 miRNAs ranked by their degrees, 90.0% (18/20), 70.0% (14/20), and 70.0% (14/20) miRNAs were found to be associated with the cancers for AML, BRCA, and KIRC, respectively. The KEGG pathways and GO terms enriched with the genes that were predicted as the targets of the cancer-related miRNAs were significantly associated with the biological processes of cancers. In addition, several genes, which were predicted to be regulated by more than three miRNAs, were identified to be the potential drug targets annotated by using the human protein atlas database. Our results demonstrated that the proposed strategy can be helpful for predicting the miRNA-mRNA interactions in tumorigenesis and identifying the cancer-related miRNAs as the potential drug targets.

1. Introduction

MicroRNAs (miRNAs) are a class of endogenous small noncoding RNA molecule with a length of ~22 nucleotides, which regulate gene expression posttranscriptionally [1]. miRNAs can combine with mRNAs to form the RNA-induced silencing complex (RISC) and degrade the mRNAs or inhibit the translation of the target genes [2]. The “seed sequence” with a length of 2 ~ 8 nt at the 5′ end of the miRNA plays an important role in target recognition by binding to the complementary sequences in the untranslated regions (3′-UTRs) of mRNAs [3]. A single miRNA may have the capability to target multiple mRNAs [4, 5] and participates in multiple signaling pathways and biological processes in mammals. It has been reported that miRNAs are involved in numerous cancer-relevant processes such as cell growth, proliferation, apoptosis, migration, and metabolism [6, 7]. The aberrant expression of miRNAs is related to different types of diseases and cancers, such as coronary artery disease [8], gastric cancer [9], lung cancer [10], and breast cancer [11].

Based on the increasing number of studies, miRNAs are being explored as the diagnostic and prognostic biomarkers and as the therapeutic targets for cancer treatment [12]. Previous studies revealed that miRNAs mainly acted as the oncogenic targets or tumor suppressors in the gene regulatory networks [13]. Therefore, two miRNA-based therapeutic strategies were proposed to restore or inhibit miRNA function through miRNA mimics and inhibitors (anti-miRs) [14]. As reported, numerous tumor-suppressive miRNAs and oncogenic miRNAs are promising drug candidates for the treatment of cancers and other diseases [15]. Although most of the miRNA-targeted drugs are still in the preclinical trials, antimiR-122, which is a LNA- (locked nucleic acid-) modified antisense inhibitor, has reached phase II trials for treating hepatitis [16] and the mimics of miR-34, which were encapsulated in lipid nanoparticles, have reached phase I clinical trials for the cancer treatment [17, 18]. Therefore, it is essential to identify the key miRNA candidates for the development of miRNA-based therapeutics of the cancers. In recent years, numerous databases, such as miRBase [19], miRanda [20], DIANA-TarBase [21], and HMDD v2.0 [22], have been developed to investigate the key role of miRNAs in the biological processes and reveal the miRNA-mRNA interaction mechanisms. However, considering the fact that a single miRNA will simultaneously target multiple genes, the miRNA-based therapeutics, which were designed to modulate miRNA expression levels, will affect hundreds of genes. It would be harmful for the patient to randomly regulate the hundreds of transcripts [23]. Thus, it is important to provide an exhaustive analysis of the key miRNAs and the miRNA-mRNA interactions before applying the miRNA-based therapeutics to the clinical trials.

In our study, we proposed a strategy by using the graphical lasso algorithm [24] to discover the key miRNAs and the miRNA-mRNA interaction in tumorigenesis based on the expression levels of miRNAs and mRNAs. A bipartite network with the miRNAs as hubs was constructed to explore the interactions between the miRNAs and mRNAs, and the top 20 miRNAs ranked by their degrees in the network were verified by using three miRNA disease association databases, namely, miRCancer [25], miR2Disease [26], and HMDD v2.0 [22]. Moreover, the gene set enrichment analysis was conducted for the genes that were predicted as the targets in the network by using Database for the Annotation, Visualization, and Integrated Discovery (DAVID) v6.7 [27]. The proposed strategy was validated by using three cancer data sets. Our results showed that for both three data sets, most of the top 20 miRNAs as well as their targeted genes in the network were highly associated with cancers. In addition, the genes, which were predicted to be regulated by more than three cancer-related miRNAs in our study, had been reported as the potential drug targets in previous studies, indicating the satisfactory performance of our proposed strategy on predicting the cancer-related miRNAs and the interactions between miRNAs and their targeted genes.

2. Materials and Methods

2.1. Datasets

The miRNA expression data, the mRNA expression data, and the clinical data of three types of cancers, namely, acute myeloid leukemia (AML) [28], breast invasive carcinoma (BRCA) [29], and kidney renal clear cell carcinoma (KIRC) [30], were downloaded from the Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/) data portal. The miRNA-seq data in three data sets were generated by an Illumina Genome Analyzer in the Baylor College Human Genome Sequencing Center (BCGSC). The mRNA-seq data of AML (downloaded on November 7, 2016) were generated by an Illumina Genome Analyzer in the Baylor College Human Genome Sequencing Center (BCGSC). The mRNA-seq data of the BRCA (downloaded on December 15, 2014) and KIRC (downloaded on November 6, 2016) were produced by an Illumina HiSeq 2000 sequencer of the University of North Carolina (UNC). For the three data sets, the read counts for each miRNA and mRNA (data in level 3) were considered the expression level of the miRNA and the mRNA, respectively. In total, we collected 149, 829, and 253 samples for the data sets of AML, BRCA, and KIRC, respectively.

2.2. Study Design

In our study, the graphical lasso algorithm was proposed to construct the miRNA-mRNA interaction network. Figure 1 showed the overview of our study design. Three cancer data sets, namely, AML, BRCA, and KIRC, were downloaded from the TCGA database, and the differentially expressed miRNA and mRNAs were separately identified for each of the data sets by using the fold change ranking combined with a nonstringent value cutoff. Based on the expression profiles of the differentially expressed miRNAs and mRNAs, the interaction network was constructed by the graphical lasso algorithm, including the connections among the miRNAs and the mRNAs, as well as the connections between miRNAs and mRNAs. The miRNAs and their connected mRNAs in the network were extracted and regrouped into subnetworks, representing the interactions between miRNAs and mRNAs.

Figure 1: The overview of the study design.

To validate whether the cancer-related miRNAs and their key targeted genes can be well characterized by our miRNA-mRNA interaction network or not, we annotated the top 20 miRNAs, which were ranked by their degrees (the number of connections), by using three disease-related miRNA databases, namely, miRCancer [25], miR2Disease [26], and HMDD v2.0 [22], for each of the data sets. Meanwhile, the gene set enrichment analysis was conducted with the targeted genes of the cancer-specific miRNAs by using DAVID v6.7. We checked whether or not the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms were associated with cancers. In addition, we mainly discussed the functions of those genes that were predicted as the targets of more than three miRNAs.

2.3. Identification of Differentially Expressed mRNAs and miRNAs

To identify the differentially expressed mRNAs and miRNAs, we firstly divided the samples into two groups for each of the cancer types according to the clinical endpoints. For AML data set, the patients were subdivided into high-risk and low-risk groups according to their survival time. The patients with the survival days longer than one year were assigned to the low-risk group, and the patients with the survival days less than or equal to one year were assigned to the high-risk group. For BRCA data set, the patients were divided into the estrogen receptor- (ER-) positive group and the ER-negative group according to their estrogen receptor status [29]. As to the KIRC data set, the patients in the pathological stages I and II were assigned into the low-risk group and the patients in stages III and IV were assigned into the high-risk group. Then, for all the data sets, Student’s t-test value was calculated for each of the miRNAs and mRNAs by comparing the expression profiles of the miRNAs and mRNAs between the patient groups. We kept the miRNAs and mRNAs with and calculated the fold changes of them between the compared patient groups, respectively. Finally, the miRNAs and the mRNAs with fold change greater than 1.5 (FC > 1.5) or less than 0.667 (FC < 0.667) were considered the differentially expressed miRNAs and mRNAs, respectively.

2.4. Construction of the miRNA-mRNA Interaction Network

As reported, Gaussian graphical models (GGMs) have been widely used to identify the dependent relationship among the variables and to be applied on the biological network inference [31, 32]. In GGMs, the conditional dependence of the two nodes was estimated by an inverse covariance matrix. A nonzero number in the inverse covariance matrix indicates a connection between two nodes [33]. The network inference actually is the estimation of the inverse covariance matrix, and numerous algorithms have been proposed to solve this problem [34]. Notably, based on the GGMs, a more reasonable approach named graphical lasso was proposed to directly estimate a sparse inverse covariance matrix by using the L1 (lasso) penalty [24, 35].

We assume a designed n × m matrix where n indicates the number of samples and m is the number of genes or miRNAs. Let θ = Σ−1 and let S be the empirical covariance matrix; the problem of estimating θ is converted to maximize the penalized log-likelihood: where tr indicates the trace. ||θ||1 is the L1 norm of the matrix, which is the maximum value of the sum of the absolute values of the elements in each of the columns in θ, and ρ is a nonnegative tuning parameter, which controls the sparseness of the network.

In fact, the graphical lasso gets a θm×m matrix to construct the network by using an n × m matrix as an input. We have two matrices Xn×j (j miRNA expression profiles of n samples) and Yn×k (k mRNA expression profiles of n samples). Therefore, we integrated these two matrices into the matrix Zn×(j+k), which were used to construct an interaction network including the connections among the miRNAs and the mRNAs, as well as the connections between the miRNAs and mRNAs. In our study, only the differentially expressed miRNAs and the mRNAs were used to construct the interaction network and the penalty parameter ρ was set to 2.0 for all the data sets. We mainly concentrated on the interactions between the miRNAs and the mRNA in the network.

3. Results

3.1. Most of the Top 20 miRNAs Were Highly Associated with Cancers

For AML data set, 34 differentially expressed miRNAs and 798 differentially expressed mRNAs were identified from 706 miRNAs and 20,319 mRNAs, respectively. Considering the miRNAs as the hubs of the miRNA-mRNA interaction network, we selected the top 20 miRNAs ranked by their degrees and listed them in Table 1. It can be seen from the table that 90% (18/20) miRNAs were associated with the cancers after being annotated by the three disease-related miRNA databases. Among the cancer-related miRNAs, five miRNAs, namely, hsa-mir-217, hsa-mir-188, hsa-mir-125b-1, hsa-mir-100, and hsa-mir-181d, were reported to be associated with the acute myeloid leukemia. Figure 2 showed the subnetworks including these five miRNAs as hubs and their targeted mRNAs.

Table 1: The annotation of the top 20 miRNAs in AML.
Figure 2: The miRNA-mRNA interaction subnetwork in AML. The five miRNAs in the network were reported to be associated with AML. In the figure, 14 mRNAs (cyan dots) and 3 mRNAs (red dots) were predicted to be connected with three and four miRNAs, respectively. The genes correlated with cancers were marked with their gene symbols.

For the data sets of BRCA and KIRC, we identified 266 and 54 differentially expressed miRNAs from 1043 and 1046 miRNAs, respectively, and identified 6021 and 1647 differentially expressed mRNAs from 20,502 and 20,503 mRNAs, respectively. The top 20 miRNAs ranked by their degrees in the miRNA-mRNA interaction network of the BRCA data set were listed in Table 2. It can be seen that 70% (14/20) miRNAs were annotated to be associated with cancers and four out of them, namely, hsa-mir-9-3, hsa-mir-449a, hsa-mir-135a-1, and hsa-mir-137, were breast cancer-specific miRNAs. Table 3 showed the top 20 miRNAs that were obtained from the interaction network of KIRC. 14 out of 20 (70%) miRNAs were reported to be associated with cancers, and four out of them, namely, hsa-mir-1291, hsa-mir-200b, hsa-mir-134, and hsa-mir-218-2 were directly associated with the renal cell carcinoma. The subnetworks of the specific cancer-related miRNAs and their targeted mRNAs for the data sets of BRCA and KIRC were shown in Figures 3 and 4, respectively.

Table 2: The annotation of the top 20 miRNAs in BRCA.
Table 3: The annotation of the top 20 miRNAs in KIRC.
Figure 3: The miRNA-mRNA interaction subnetwork in BRCA. The four miRNAs in the network were reported to be associated with AML. In the figure, 13 mRNAs (cyan dots) and 2 mRNAs (red dots) were predicted to be connected with three and four miRNAs, respectively. The genes correlated with cancers were marked with their gene symbols.
Figure 4: The miRNA-mRNA interaction subnetwork in KIRC. The four miRNAs in the network were reported to be associated with AML. In the figure, 49 mRNAs (cyan dots) and 7 mRNAs (red dots) were predicted to be connected with three and four miRNAs, respectively. The genes correlated with cancers were marked with their gene symbols.
3.2. The mRNAs Targeted by the Cancer-Specific miRNAs Were Significantly Associated with the Biological Process of Cancers

The gene set enrichment analysis was conducted to investigate the gene functions by using the mRNAs, which were predicted as the targets of the cancer-specific miRNAs. For the data sets of AML, BRCA, and KIRC, 255, 853, and 670 targeted mRNAs were used for the gene set enrichment analysis, respectively. The top 5 significantly enriched KEGG pathways were listed in Table 4. For the data sets of AML, BRCA, and KIRC, there were one, five, and two signaling pathways, respectively, which were reported to be associated with cancers. Likewise, the top 5 significantly enriched GO terms related to the biological process and the molecular functions were listed in Table 5. There were four, nine, and eight GO terms for the data sets of AML, BRCA, and KIRC, respectively, which were associated with the tumorigenesis of the cancers.

Table 4: The top 5 KEGG pathways enriched with the genes connected with the cancer-specific miRNAs.
Table 5: The top 5 GO terms enriched with the genes connected with the cancer-specific miRNAs.

When focusing on the mRNAs that were predicted to be the targets of multiple miRNAs, we found 14, 13, and 49 mRNAs targeted by three miRNAs in the miRNA-mRNA interaction networks of AML, BRCA, and KIRC, respectively. Moreover, three, two, and seven mRNAs were predicted to be targeted by four miRNAs in the networks of AML, BRCA, and KIRC, respectively. Figures 2, 3, and 4 showed the mRNAs targeted by three miRNAs (cyan dots) and four miRNAs (red dots). We also annotated these genes by using the GeneCards database v4.4.2 (http://www.genecards.org/) and found four, three, and nine genes from the networks of AML, BRCA, and KIRC, respectively, which were reported to be associated with cancers. The HUGO gene symbols of the cancer-related genes were marked in Figures 2, 3, and 4.

4. Discussion

In this study, we proposed a new strategy to construct the miRNA-mRNA interaction network based on the expression profiles of miRNAs and mRNAs. The connections between miRNAs and mRNAs were created by the graphical lasso algorithm. We applied the strategy to the three cancer data sets and successfully identified a number of cancer-related miRNAs and their targeted mRNAs.

For the AML data set, 90% miRNAs in the top 20 miRNAs were found to be associated with cancers (Table 1). Among these miRNAs, hsa-mir-100 was considered a potential tumor-related miRNA, which has been reported to regulate cell differentiation by targeting RBSP3 in acute myeloid leukemia [36]. The pediatric AML patients with the upregulation of miR-100 may have poor relapse-free and overall survival [37]. Moreover, the downregulation of miR-181 family members including miR-181a, miR-181b, miR-181c, and miR-181d was associated with poor prognosis in cytogenetically normal acute myeloid leukemia [38]. For the BRCA data set, 70% miRNAs in Table 2 were associated with cancers and four of them were specifically associated with the breast cancer. has-miR-9 acted as a tumor suppressor, which can inhibit the proliferation of breast cancer cells [39]. miR-137 is a potential tumor suppressor miRNA, which negatively regulates the gene ERRα (estrogen-related receptor alpha) by targeting the two functional sites in the 3′-UTR of ERRα [40]. As to the KIRC data set, 70% miRNAs in Table 3 were associated with cancers and four of them had been reported to be associated with the development of the renal cell carcinoma. hsa-mir-134 had been reported as a tumor suppressor and can obstruct the tumor growth and metastasis by inhibiting epithelial-mesenchymal transition (EMT) in renal cell carcinoma cells [41]. miR-218 can mediate the focal adhesion pathway and inhibit the cell migration and invasion in renal cell carcinoma [42].

We also inspected the gene functions of the mRNAs, which were predicted to be the targets of the cancer-related miRNAs. The results of gene set enrichment analysis showed that the majority of the KEGG pathways (Table 4) and GO terms (Table 5) were significantly associated with the cancers. In the interaction subnetworks (Figures 2, 3, and 4), several mRNAs targeted by multiple cancer-specific miRNAs were found to have key roles in cancers. For example, the gene SOX17, which was predicted to be regulated by four miRNAs in the subnetwork of KIRC, was considered an important tumor suppressor with aberrant methylation for the cancers [43, 44]. In addition, the genes targeted by more than three miRNAs in the subnetworks were mapped to the Human Protein Atlas database v16.1 (http://www.proteinatlas.org), and 8 genes were annotated as the potential drug targets (Table 6).

Table 6: The annotation of the key genes connected with more than three cancer-specific miRNAs in the miRNA-mRNA interaction networks.

Note that compared to the conventional drug therapies, the miRNA-targeting drugs have been regarded as a high-value therapy because miRNA may modulate multiple biological processes and pathways. However, there are a lot of challenges for utilizing miRNAs as potential therapeutic targets [45]. The miRNAs regulate tens of thousands of genes which could contribute to both efficacy and unexpected side effects. Therefore, the downstream analysis of genes and pathways regulated by miRNAs should be further elucidated and explored. Due to the complex regulatory mechanisms of the miRNAs, it is still challenging to successfully translate the miRNA-based therapy to the clinics [46]. It is a crucial step in miRNA drug discovery [47] to identify the specific miRNAs as drug targets and clarify the mechanisms of the actions for the key miRNAs. The network-based approach proposed in our study can identify the key miRNAs as well as their targeted mRNAs, which were also significantly associated with the biological process of cancers. It would be helpful for providing the complementary support to the miRNA-targeting drug discovery. In addition, the potential mRNA target could be enriched by integrating the protein structure information and medicinal chemistry. Furthermore, the accumulative information about the side effect and off target relationship from available public resources, such as PharmGKB [48] and the Comparative Toxicogenomics Database (CTD) [49], could be utilized to prioritize the genes regulated by miRNAs for therapeutic target discovery.

5. Conclusions

The network-based strategy proposed in our study can efficiently construct the miRNA-mRNA interaction network in tumorigenesis, which included the important cancer-related miRNAs and their targeted genes. The miRNAs and the targeted genes predicted by using the interaction networks may be considered the potential candidates of drug targets in the cancer research.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the grants from the National Natural Science Foundation of China (no. 21575094 and no. 21375090).

References

  1. Y. Huang, X. J. Shen, Q. Zou, and Q. L. Zhao, “Biological functions of MicroRNAs,” Journal of Physiology and Biochemistry, vol. 67, no. 1, pp. 129–139, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. S. H. Chan and L. H. Wang, “Regulation of cancer metastasis by microRNAs,” Journal of Biomedical Science, vol. 22, no. 1, p. 9, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. A. E. Pasquinelli, “MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship,” Nature Reviews Genetics, vol. 13, no. 4, pp. 271–282, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. D. M. Pereira, P. M. Rodrigues, P. M. Borralho, and C. M. Rodrigues, “Delivering the promise of miRNA cancer therapeutics,” Drug Discovery Today, vol. 18, no. 5-6, pp. 282–289, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. K. B. Reddy, “MicroRNA (miRNA) in cancer,” Cancer Cell International, vol. 15, no. 1, pp. 1–6, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Garzon, M. Fabbri, A. Cimmino, G. A. Calin, and C. M. Croce, “MicroRNA expression and function in cancer,” Trends in Molecular Medicine, vol. 12, no. 12, p. 580, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. M. D. Jansson and A. H. Lund, “MicroRNA and cancer,” Molecular Oncology, vol. 6, no. 6, p. 590, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Jin, C. J. Yang, X. Xu, J. N. Cao, Q. T. Feng, and J. Yang, “MiR-214 regulates the pathogenesis of patients with coronary artery disease by targeting VEGF,” Molecular and Cellular Biochemistry, vol. 402, no. 1, p. 111, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Mi, D. Zhang, W. Jiang et al., “miR-181a-5p promotes the progression of gastric cancer via RASSF6-mediated MAPK signalling activation,” Cancer Letters, vol. 389, p. 11, 2016. View at Publisher · View at Google Scholar
  10. C. G. Li, M. F. Pu, C. Z. Li et al., “MicroRNA-1304 suppresses human non-small cell lung cancer cell growth in vitro by targeting heme oxygenase-1,” Acta Pharmacologica Sinica, vol. 38, no. 1, pp. 110–119, 2017. View at Publisher · View at Google Scholar
  11. M. V. Iorio, M. Ferracin, C. G. Liu et al., “MicroRNA gene expression deregulation in human breast cancer,” Cancer Research, vol. 65, no. 16, pp. 7065–7070, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. M. V. Iorio and C. M. Croce, “MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review,” EMBO Molecular Medicine, vol. 4, no. 3, pp. 143–159, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. G. A. Calin and C. M. Croce, “MicroRNA signatures in human cancers,” Nature Reviews Cancer, vol. 6, no. 11, pp. 857–866, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. N. Tyagi, S. Arora, S. K. Deshmukh, S. Singh, S. Marimuthu, and A. P. Singh, “Exploiting nanotechnology for the development of microRNA-based cancer therapeutics,” Journal of Biomedical Nanotechnology, vol. 12, no. 1, pp. 28–42, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Rupaimoole and F. J. Slack, “MicroRNA therapeutics: towards a new era for the management of cancer and other diseases,” Nature Reviews Drug Discovery, vol. 16, no. 3, pp. 203–222, 2017. View at Publisher · View at Google Scholar
  16. H. L. Janssen, H. W. Reesink, E. J. Lawitz et al., “Treatment of HCV infection by targeting microRNA,” New England Journal of Medicine, vol. 368, no. 18, pp. 1685–1694, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. A. G. Bader, “miR-34–a microRNA replacement therapy is headed to the clinic,” Frontiers in Genetics, vol. 3, p. 120, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. M. S. Beg, A. J. Brenner, J. Sachdev et al., “Phase I study of MRX34, a liposomal miR-34a mimic, administered twice weekly in patients with advanced solid tumors,” Investigational New Drugs, vol. 35, no. 2, pp. 1–9, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Kozomara and S. Griffiths-Jones, “miRBase: annotating high confidence microRNAs using deep sequencing data,” Nucleic Acids Research, vol. 42, Database issue, pp. D68–D73, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. B. John, A. J. Enright, A. Aravin, T. Tuschl, C. Sander, and D. S. Marks, “Human microRNA targets,” PLoS Biology, vol. 2, no. 11, article e363, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. I. S. Vlachos, M. D. Paraskevopoulou, D. Karagkouni et al., “DIANA-TarBase v7. 0: indexing more than half a million experimentally supported miRNA: mRNA interactions,” Nucleic Acids Research, vol. 43, Data issue, pp. D153–D159, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Li, C. Qiu, J. Tu et al., “HMDD v2. 0: a database for experimentally supported human microRNA and disease associations,” Nucleic Acids Research, vol. 42, Database issue, pp. D1070–D1074, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. P. Hydbring and G. Badalian-Very, “Clinical applications of microRNAs,” F1000 Research, vol. 2, no. 2, p. 136, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Friedman, T. Hastie, and R. Tibshirani, “Sparse inverse covariance estimation with the graphical lasso,” Biostatistics, vol. 9, no. 3, pp. 432–441, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Xie, Q. Ding, H. Han, and D. Wu, “miRCancer: a microRNA–cancer association database constructed by text mining on literature,” Bioinformatics, vol. 29, no. 5, pp. 638–644, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. Jiang, Y. Wang, Y. Hao et al., “miR2Disease: a manually curated database for microRNA deregulation in human disease,” Nucleic Acids Research, vol. 37, Supplement 1, pp. D98–D104, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. G. Dennis Jr, B. T. Sherman, D. A. Hosack et al., “DAVID: database for annotation, visualization, and integrated discovery,” Genome Biology, vol. 4, no. 9, article R60, 2003. View at Publisher · View at Google Scholar
  28. Network TCGA, Cancer Genome Atlas Research Network, T. J. Ley et al., “Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia,” New England Journal of Medicine, vol. 368, no. 22, pp. 2059–2074, 2013. View at Publisher · View at Google Scholar
  29. Cancer Genome Atlas Network, D. C. Koboldt, R. S. Fulton et al., “Comprehensive molecular portraits of human breast tumors,” Nature, vol. 490, no. 7418, pp. 61–70, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. The Cancer Genome Atlas Research Network, “Comprehensive molecular characterization of clear cell renal cell carcinoma,” Nature, vol. 499, no. 7456, pp. 43–49, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Dobra, C. Hans, B. Jones, J. R. Nevins, G. Yao, and M. West, “Sparse graphical models for exploring gene expression data,” Journal of Multivariate Analysis, vol. 90, no. 1, pp. 196–212, 2004. View at Google Scholar
  32. G. Song, L. Han, and K. Xie, “Overlapping decomposition for Gaussian graphical modeling,” IEEE Transactions on Knowledge & Data Engineering, vol. 27, no. 8, pp. 2217–2230, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Zuo, Y. Cui, G. Yu, R. Li, and H. W. Ressom, “Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO,” BMC Bioinformatics, vol. 18, no. 1, p. 99, 2017. View at Publisher · View at Google Scholar
  34. X. F. Zhang, L. Ou-Yang, X. M. Zhao, and H. Yan, “Differential network analysis from cross-platform gene expression data,” Scientific Reports, vol. 6, article 34112, 2016. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Xu, R. Jing, Y. Liu, Y. Dong, Z. Wen, and M. Li, “A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network,” Scientific Reports, vol. 6, article 28720, 2016. View at Publisher · View at Google Scholar · View at Scopus
  36. Y. S. Zheng, H. Zhang, X. J. Zhang et al., “MiR-100 regulates cell differentiation and survival by targeting RBSP3, a phosphatase-like tumor suppressor in acute myeloid leukemia,” Oncogene, vol. 31, no. 1, p. 80, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Bai, A. Guo, Z. Hong, and W. Kuai, “Upregulation of microRNA-100 predicts poor prognosis in patients with pediatric acute myeloid leukemia,” Onco Targets Therapy, vol. 5, pp. 213–219, 2012, default. View at Publisher · View at Google Scholar · View at Scopus
  38. Z. Li, H. Huang, Y. Li et al., “Up-regulation of a HOXA-PBX3 homeobox-gene signature following down-regulation of miR-181 is associated with adverse prognosis in patients with cytogenetically abnormal AML,” Blood, vol. 119, no. 10, p. 2314, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. S. D. Selcuklu, M. T. Donoghue, K. Rehmet et al., “MicroRNA-9 inhibition of cell proliferation and identification of novel miR-9 targets by transcriptome profiling in breast cancer cells,” Journal of Biological Chemistry, vol. 287, no. 35, pp. 29516–29528, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Zhao, Y. Li, G. Lou et al., “MiR-137 targets estrogen-related receptor alpha and impairs the proliferative and migratory capacity of breast cancer cells,” PloS One, vol. 7, no. 6, article e39102, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. Y. Liu, M. Zhang, J. Qian et al., “miR-134 functions as a tumor suppressor in cell proliferation and epithelial-to-mesenchymal transition by targeting KRAS in renal cell carcinoma cells,” Dna & Cell Biology, vol. 34, no. 6, pp. 429–436, 2015. View at Publisher · View at Google Scholar · View at Scopus
  42. T. Yamasaki, N. Seki, H. Yoshino et al., “MicroRNA-218 inhibits cell migration and invasion in renal cell carcinoma through targeting caveolin-2 involved in focal adhesion pathway,” Journal of Urology, vol. 190, no. 3, pp. 1059–1068, 2013. View at Publisher · View at Google Scholar · View at Scopus
  43. I. Balgkouranidou, A. Karayiannakis, D. Matthaios et al., “Assessment of SOX17 DNA methylation in cell free DNA from patients with operable gastric cancer. Association with prognostic variables and survival,” Clinical Chemistry & Laboratory Medicine, vol. 51, no. 7, pp. 1505–1510, 2013. View at Publisher · View at Google Scholar · View at Scopus
  44. D. Fu, C. Ren, H. Tan et al., “Sox17 promoter methylation in plasma DNA is associated with poor survival and can be used as a prognostic factor in breast cancer,” Medicine, vol. 4, no. 3, pp. 143–159, 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. M. F. Schmidt, “Drug target miRNAs: chances and challenges,” Trends in Biotechnology, vol. 32, no. 11, pp. 578–585, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. J. K. Nagpal, R. Rani, B. Trink, and K. S. Saini, “Targeting miRNAs for drug discovery: a new paradigm,” Current Molecular Medicine, vol. 10, no. 5, pp. 503–510, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. S. P. Nana-Sinkam and C. M. Croce, “Clinical applications for microRNAs in cancer,” Clinical Pharmacology & Therapeutics, vol. 93, no. 1, p. 98, 2013. View at Publisher · View at Google Scholar · View at Scopus
  48. C. F. Thorn, T. E. Klein, and R. B. Altman, “PharmGKB: the pharmacogenomics knowledge base,” Methods in Molecular Biology, vol. 1015, pp. 311–320, 2013. View at Publisher · View at Google Scholar · View at Scopus
  49. C. J. Mattingly, G. T. Colby, J. N. Forrest, and J. L. Boyer, “The Comparative Toxicogenomics Database (CTD),” Environmental Health Perspectives, vol. 111, no. 6, pp. 793–795, 2003. View at Google Scholar