Co-regulatory network of transcription factor and microrna: A key player of gene regulation

Akshara Pande

doi:10.4103/bbrj.bbrj_182_21

REVIEW ARTICLE

Year : 2021 | Volume : 5 | Issue : 4 | Page : 374-379

Co-regulatory network of transcription factor and microrna: A key player of gene regulation

Akshara Pande
School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand, India

Date of Submission	10-Aug-2021
Date of Acceptance	17-Oct-2021
Date of Web Publication	14-Dec-2021

Correspondence Address:
Akshara Pande
School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand
India

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/bbrj.bbrj_182_21

Abstract

Transcription factor (TF) and microRNA (miRNA) interaction plays a vital role in the regulation of biological networks. TFs and miRNAs control the gene expression: TF at transcriptional level by affecting the messenger RNA (mRNA) transcription and miRNA at posttranscriptional level by affecting the transcription and translation. Furthermore, sometimes, both miRNAs and TFs regulate one another's expressions; as a consequence, this may influence the expression of the target gene. In order to understand the main co-regulatory mechanisms underlying, it is important to identify biologically relevant network motifs involving TFs, miRNAs and their targets. The present study focuses on TF, miRNA and target gene interactions.

Keywords: Disease, genes, messenger RNA, microRNA, posttranscription level, transcription factor

How to cite this article:
Pande A. Co-regulatory network of transcription factor and microrna: A key player of gene regulation. Biomed Biotechnol Res J 2021;5:374-9

How to cite this URL:
Pande A. Co-regulatory network of transcription factor and microrna: A key player of gene regulation. Biomed Biotechnol Res J [serial online] 2021 [cited 2023 Mar 28];5:374-9. Available from: https://www.bmbtrj.org/text.asp?2021/5/4/374/332454

Introduction

Networks can be described as an assembly of interconnected objects which can exchange information through interaction. Biological processes are possible because of an orchestrated regulated interaction of two molecules in time and over space. It is not just fascinating but also immensely useful to discover the kind of relationships that exist among these molecules and map the exchange of information through interactions. Network motif discovery is one of the ways to visualize these interactions. Biological networks may consist of several kinds of patterns called subgraphs or motifs, which repeat themselves in various or specific networks and can give the solution of unanswered biological problems. Analysis of such network motifs can provide insights about spatiotemporal regulation and identify vulnerable points in the network for genetic or pharmacological perturbations. Gene regulatory network is one of the biological networks, which plays an important role in the regulation of the biological system with the help of regulators such as DNA, RNA, proteins, and small molecules. The interplay of TF and small regulatory RNA is the key factor in the regulation of gene expression.^[1],[2] MicroRNAs (miRNAs) are involved in very essential biological processes such as cell metabolism,^[3] cell signalling,^[4] cell division and death,^[5] and cell motility.^[6] Thus, an abnormal expression of miRNA is expected to affect each of these important processes, thus leading to several diseases and sometimes fatal consequences. Some diseases with which miRNA are associated are various cancers,^{[7],[8],[9],[10]} cardiovascular diseases,^{[11],[12],[13]} neurodegenerative,^[14],[15] inflammatory diseases,^[16],[17] and viral^{[10],[18],[19]} and bacterial infections.^[20] Tuberculosis is one of the bacterial infections caused by Mycobacterium tuberculosis. Furci et al. showed that miRNA hosts response during intracellular mycobacterium infection of macrophages.^[21] A bioinformatics analysis by Alipoor et al. highlighted how cell metabolism is affected by miRNAs. In addition, they also showed interactions of host-pathogen and the regulation of cell function after infection with Mtb.^[22]

Transcription factor (TF) and small regulatory RNA with their common target may form particular network motifs and participate in the regulation of gene expression.^{[23],[24],[25]} However, TFs and miRNA participate at different levels; TFs function at transcriptional level while miRNAs function at posttranscriptional level. TFs-miRNAs together regulate target gene expression by forming network motifs (feedforward or feedback) and may be involved in various biological processes such as cell proliferation, cell differentiation, and development.^[26] There are various recurrent motifs of miRNA – TF in gene regulatory networks. Motifs are patterns which occur more often, even sometimes in different organisms (reviewed in^[27]). miRNA -TF motifs can be present in various diseases such as myocardial infarction (MI),^[28] cancers,^[29] and cystic fibrosis^[30]. Tsang et al. proposed the abundance of motifs of miRNA and its targets in gene networks as their expressions are likely to be correlated.^[31] In the eukaryotic genome, noncoding RNAs are transcribed as independent units or are embedded within other protein coding genes.^[32]

There are different logical methods and mathematical methods to model and simulate biological networks.

Transcription Factors and Noncoding RNA: Are Key Players in Programmed Response of Biological Systems

Boolean logic is one of such methods, which takes binary input (0 or 1) and gives the output depending upon the logic used. The computational cost of the Boolean model is low. Finite state linear model (FSLM) is one of the programmed devices which unite the merits of Boolean logic and continuous model. There are three building blocks of FSLM^[33] [Figure 1]a:

Figure 1: Biological networks are like programmed devices (a) Finite state linear model^[33] (b) A schematic example of TF-miRNA network (c) equivalent FSLM of schematic network of TF-miRNA. TF: Transcription factor, FSLM: Finite state linear model

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Binding sites
Control function
Substance generator.

A schematic of the TF-miRNA network is shown in [Figure 1]b. Here TF acts as an activator for miRNA gene and as a repressor for some other gene. miRNA binds to 3'UTR of another mRNA gene and downregulates its production. This forms a coherent type 3 network motif. As evident in [Figure 1]c, TF output acts as an input for mRNA gene and the negative output of both TF and miRNA acts as an input for target gene.

Boolean values (0/1) are used in digital systems with the help of logic gates: AND, OR, NOT, NAND, NOR, XOR, and XNOR. [Figure 2] describes the combination of inputs and outputs required in building the logic gates.

Figure 2: Basic logic gates and their truth table

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Taking the TF, miRNA and target genes' current states together, a transition table can be made. State transition table consists of current states and next states. Here, three genes (current states) are considered, so total 2³ = 8 entries should be present in the table. A transition table has been shown in [Table 1]. It is evident from the table that if any of the TF gene or miRNA gene or both are in the active state (i.e. 1 in current state), in the next state Target gene will be OFF. Thus, activation of miRNA gene alone or TF gene alone or activation of both in combinations is able to switch off target genes.

Table 1: Transition state table with current state (t) and next state (t +1)

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Based on the state transition table, state diagrams can be drawn [Figure 3]. State diagram tells about the path followed to reach the final state starting from the initial state. For instance, if the initial state is (0 1 0) i.e. where only miRNA gene is in an active state currently, this may lead to next state where all of them are in OFF state i.e. (0 0 0).

Figure 3: Possible state transitions for transition Table 1. Arrow indicates the direction of the path

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Transcription Factors and Noncoding RNA: Mathematical Modeling using Ordinary Differential Equations

Mathematical modeling is helpful in understanding the mechanistic advantages of biological systems. Mathematical simulations can be done for biological networks by perturbing any of the entities present in the network and providing results which guide researchers in designing new experiments.^[34] There are the reports available focusing on dynamics of miRNA or TF– mediated regulations in the network.^{[35],[36],[37]}

[Figure 1]b shows the schematic diagram consisting of TF, miRNA, and target gene. Ordinary differential equations (ODEs) are written below for this network. Let mi (t), m1(t), m2(t), T1(t), T2(t), mim2(t) be the concentrations of miRNA, mRNA of TF, mRNA of target gene, TF, target protein, and miRNA-mRNA2 complex, respectively. Transcription rate unit for mRNA and miRNAs transcription is in molecules per second, translation rate units are in per second, degradation rate units are in per second, association rate units (miRNA–mRNA) are in per molecules per second and dissociation rate units (miRNA–mRNA complex) are in per second. Rate of production of miRNA: K1, Rate constant for degradation of miRNA: D1, Rate of production of mRNA: K2, Rate constant for degradation of mRNA1: D2, Rate constant for protein formation by mRNA: C1, Rate constant for degradation of protein: D3, Rate constant for association of miRNA and mRNA: B1, Rate constant for dissociation of miRNA-mRNA complex: B2, Rate constant for translation by miRNA-mRNA complex: C2, Rate constant for degradation of miRNA-mRNA complex: Dc, Rate constant for TF1-mediated transcription: A1.

Mathematical equations:

Network Motifs Formed by Transcription Factor-miRNA-Target Genes

In cellular systems, TF and miRNAs are two significant controllers. When they form network motifs along with a target gene, they may play a significant role. Advancement of TF-miRNA-target genes may aid to know the mechanism of several diseases at a more elevated level. To understand some regulatory networks, consider one example [Figure 4] as follows. Gene1 and gene2 transcribes mRNAs (mRNA1 and mRNA2, respectively) which code for proteins which are TFs (TF1 and TF2, respectively). Gene3 transcribes mRNA and miRNA both. TF1 is coded by mRNA1 (transcribed by gene1), has binding sites in the promoter regions of all three genes, and can promote or repress the rate at which transcripts are transcribed. While TF2 has binding sites in the promoter region of gene3, like TF1 it also activates or represses the rate at which transcripts of gene3 are transcribed. miRNA targets 3͛ UTR of mRNA2 and downregulates TF2 production. [Figure 4] contains various network motifs, all of them have been demonstrated in [Figure 5].

Figure 4: Schematic of gene regulatory network consisting of TFs and miRNAs. TF: Transcription factor

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Figure 5: Network motifs (a) positive autoregulation (b) negative autoregulation (c) incoherent type I feed-forward loop (d) incoherent type II FFL (e) coherent type III FFL (e) coherent type IV FFL (f) incoherent type I feed FFL (g) incoherent type II FFL (h) coherent type III FFL (i) coherent type IV FFL (j) negative feedback (k) double negative feedback loop. FFL: Feed-forward loop

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Autoregulatory Loop

In this kind of network motif, both TF and miRNA regulate their own transcription. There may be two kinds of autoregulation: Positive and negative autoregulation (NAR). In positive autoregulation (PAR), TF or miRNA increases their own expression directly or indirectly [Figure 5]a while in case of NAR they decrease their expressions [Figure 5]b. E2F1 TF plays an important role in the cell cycle; by activation of DNA synthesis at the G1/S border and participation of E2F1 in the regulation of cell growth.^[27] TF E2F1-3a are involved in making PAR by promoting its own gene transcription.^[40] E2F activators (i. e. E2F1, E2F2, and E2F3a) form an autoregulatory loop (positive) by binding to their own gene promoter and hence increase transcription.^[38] Zisoulis et al. reported the first example of a direct miRNA self-regulatory cycle with the help of Caenorhabditis elegans let-7 miRNA.^[39] NAR motifs are found in almost all organisms. LexA is a transcriptional repressor in Escherichia coli, which negatively autoregulates its own transcription and represses the transcription of some other gene (like RecA).^[40],[41] NAR speeds up response time while PAR does the opposite. Due to noise, NAR decreases variability while PAR enhances fluctuation.

Feed-Forward Loops

The feed-forward loop (FFL) contains TF, say X, which regulates the second TF (or miRNA), say Y. Both X and Y bind to the promoter region of the target gene, say Z and co-regulate its transcription. Hence, there are a total three interactions possible among X, Y, and Z. Each of them can be positive (activated) or negative (suppressed). Hence, eight possible activator-repressor interactions. Four out of these eight are coherent FFL (C-FFL) [Figure 5]e, [Figure 5]f, [Figure 5]i, [Figure 5]j and other four are incoherent FFL (I-FFL) [Figure 5]c, [Figure 5]d, [Figure 5]g, [Figure 5]h. In C-FFL direct regulatory path (from X to Z) sign is similar to an indirect regulatory path (from X via Y to Z) sign.^[42] The FFLs are mostly found in network motifs in E. coli and yeast. Coherent FFL (C-FFL) and incoherent FFL (I-FFL) are different to each other as in response to signal alteration C-FFL generates delays while I-FFL produces pulse.^[27]

Feedback Loops

The feedback loop contains TF, say X, which activates the second TF (or miRNA) Y, but Y downregulates the expression of X. Sometimes, X and Y both downregulate each other expression then it is called double negative feedback loop. In human cancer cells, miR-200 family suppresses ZEB1 production and ZEB1 TF represses miR-200 transcription by forming a double negative feedback loop.^{[43],[44],[45]}

More than two network motifs can also act together for instance hsa-miR-20 target E2F1 mRNA and E2F TF binds to the promoter of miR-17-92 cluster. We already discussed E2F1 TF is involved in autoregulation as well. Together, they form an autoregulatory feedback loop [Figure 6].^[38]

Figure 6: Schematic diagram for hsa-miR-17/20 miRNA and E2F1 TF mediated regulation. TF: Transcription factor

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In recent years, there have been significant advances in bioinformatics and experimental research on the co-regulation of TFs and miRNAs. To explore the major mechanisms of co-regulation, it has become useful to identify models of coregulation. Many attempts have been made by the scientific community to collect such TF-miRNAs co-regulations. For the benefit of researchers, TF-miRNA co-regulation details are accumulated in various databases and web servers. A list of such databases/web servers is summarized in [Table 2].

Table 2: A list of few transcription factor-microRNA regulation database/web server

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Conclusion

In recent times, a large amount of data are available, which creates opportunities and challenges for computational approaches to explore the co-regulation of miRNAs and TFs. The computational approaches are useful for designing wet laboratory experiments. This review describes several computational methods for co-regulation of TFs-miRNAs. These methods provide a variety of perspectives on the mechanisms of TF-miRNA interplay and how they relate to their target genes. Further TF-miRNA network motifs may be able to explain the cause of many obscure diseases and may propose potential drugs to cure them.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

[53]

References

1.	Martinez NJ, Walhout AJ. The interplay between transcription factors and microRNAs in genome-scale regulatory networks. Bioessays 2009;31:435-45.
2.	Nazarov PV, Reinsbach SE, Muller A, Nicot N, Philippidou D, Vallar L, et al. Interplay of microRNAs, transcription factors and target genes: Linking dynamic expression changes to function. Nucleic Acids Res 2013;41:2817-31.
3.	Rayner KJ, Esau CC, Hussain FN, McDaniel AL, Marshall SM, van Gils JM, et al. Inhibition of miR-33a/b in non-human primates raises plasma HDL and lowers VLDL triglycerides. Nature 2011;478:404-7.
4.	Zhang P, Bill K, Liu J, Young E, Peng T, Bolshakov S, et al. MiR-155 is a liposarcoma oncogene that targets casein kinase-1α and enhances β-catenin signaling. Cancer Res 2012;72:1751-62.
5.	Ng R, Song G, Roll GR, Frandsen NM, Willenbring H. A microRNA-21 surge facilitates rapid cyclin D1 translation and cell cycle progression in mouse liver regeneration. J Clin Invest 2012;122:1097-108.
6.	Png KJ, Halberg N, Yoshida M, Tavazoie SF. A microRNA regulon that mediates endothelial recruitment and metastasis by cancer cells. Nature 2011;481:190-4.
7.	Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005;65:7065-70.
8.	Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006;9:189-98.
9.	Valeri N, Gasparini P, Fabbri M, Braconi C, Veronese A, Lovat F, et al. Modulation of mismatch repair and genomic stability by miR-155. Proc Natl Acad Sci U S A 2010;107:6982-7.
10.	Fang Y, Xue JL, Shen Q, Chen J, Tian L. MicroRNA-7 inhibits tumor growth and metastasis by targeting the phosphoinositide 3-kinase/Akt pathway in hepatocellular carcinoma. Hepatology 2012;55:1852-62.
11.	Ikeda S, Kong SW, Lu J, Bisping E, Zhang H, Allen PD, et al. Altered microRNA expression in human heart disease. Physiol Genomics 2007;31:367-73.
12.	Carè A, Catalucci D, Felicetti F, Bonci D, Addario A, Gallo P, et al. MicroRNA-133 controls cardiac hypertrophy. Nat Med 2007;13:613-8.
13.	Yang B, Lin H, Xiao J, Lu Y, Luo X, Li B, et al. The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2. Nat Med 2007;13:486-91.
14.	Martins M, Rosa A, Guedes LC, Fonseca BV, Gotovac K, Violante S, et al. Convergence of miRNA expression profiling, α-synuclein interacton and GWAS in Parkinson's disease. PLoS One 2011;6:e25443.
15.	Nunez-Iglesias J, Liu CC, Morgan TE, Finch CE, Zhou XJ. Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer's disease cortex reveals altered miRNA regulation. PLoS One 2010;5:e8898.
16.	O'Connell RM, Taganov KD, Boldin MP, Cheng G, Baltimore D. MicroRNA-155 is induced during the macrophage inflammatory response. Proc Natl Acad Sci U S A 2007;104:1604-9.
17.	Tili E, Michaille JJ, Cimino A, Costinean S, Dumitru CD, Adair B, et al. Modulation of miR-155 and miR-125b levels following lipopolysaccharide/TNF-alpha stimulation and their possible roles in regulating the response to endotoxin shock. J Immunol 2007;179:5082-9.
18.	Randall G, Panis M, Cooper JD, Tellinghuisen TL, Sukhodolets KE, Pfeffer S, et al. Cellular cofactors affecting hepatitis C virus infection and replication. Proc Natl Acad Sci U S A 2007;104:12884-9.
19.	Huang J, Wang F, Argyris E, Chen K, Liang Z, Tian H, et al. Cellular microRNAs contribute to HIV-1 latency in resting primary CD4+ T lymphocytes. Nat Med 2007;13:1241-7.
20.	Staedel C, Darfeuille F. MicroRNAs and bacterial infection. Cell Microbiol 2013;15:1496-507.
21.	Furci L, Schena E, Miotto P, Cirillo DM. Alteration of human macrophages microRNA expression profile upon infection with Mycobacterium tuberculosis. Int J Mycobacteriol 2013;2:128-34. [Full text]
22.	Alipoor SD, Adcock IM, Folkerts G, Garssen J, Mortaz E. A bioinformatics analysis of exosomal microRNAs released following mycobacterial infection. Int J Mycobacteriol 2019;8:218-22. [PUBMED] [Full text]
23.	Delfino KR, Rodriguez-Zas SL. Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence. PLoS One 2013;8:e58608.
24.	Lin Y, Sibanda VL, Zhang HM, Hu H, Liu H, Guo AY. MiRNA and TF co-regulatory network analysis for the pathology and recurrence of myocardial infarction. Sci Rep 2015;5:9653.
25.	Wu Q, Qin H, Zhao Q, He X×. Emerging role of transcription factor-microRNA-target gene feed-forward loops in cancer. Biomed Rep 2015;3:611-6.
26.	Zhang HM, Kuang S, Xiong X, Gao T, Liu C, Guo AY. Transcription factor and microRNA co-regulatory loops: Important regulatory motifs in biological processes and diseases. Brief Bioinform 2015;16:45-58.
27.	Alon U. Network motifs: Theory and experimental approaches. Nat Rev Genet 2007;8:450-61.
28.	Zhang G, Shi H, Wang L, Zhou M, Wang Z, Liu X, et al. MicroRNA and transcription factor mediated regulatory network analysis reveals critical regulators and regulatory modules in myocardial infarction. PLoS One 2015;10:e0135339.
29.	Hsieh WT, Tzeng KR, Ciou JS, Tsai JJ, Kurubanjerdjit N, Huang CH, et al. Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks. BMC Syst Biol 2015;9 Suppl 1:S5.
30.	Liu Z, Borlak J, Tong W. Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis. Genome Med 2014;6:94.
31.	Tsang J, Zhu J, van Oudenaarden A. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell 2007;26:753-67.
32.	Gokhale S, Hariharan M, Brahmachari SK, Gadgil C. A simple method for incorporating dynamic effects of intronic miRNA mediated regulation. Mol Biosyst 2012;8:2145-52.
33.	Schlitt T, Brazma A. Current approaches to gene regulatory network modelling. BMC Bioinformatics 2007;8 Suppl 6:S9.
34.	Fischer HP. Mathematical modeling of complex biological systems: From parts lists to understanding systems behavior. Alcohol Res Health 2008;31:49-59.
35.	Lai X, Wolkenhauer O, Vera J. Understanding microRNA-mediated gene regulatory networks through mathematical modelling. Nucleic Acids Res 2016;44:6019-35.
36.	Tindall MJ, Clerk A. Modelling negative feedback networks for activating transcription factor 3 predicts a dominant role for miRNAs in immediate early gene regulation. PLoS Comput Biol 2014;10:e1003597.
37.	Xu F, Liu Z, Shen J, Wang R. Dynamics of microRNA-mediated motifs. IET Syst Biol 2009;3:496-504.
38.	Sylvestre Y, De Guire V, Querido E, Mukhopadhyay UK, Bourdeau V, Major F, et al. An E2F/miR-20a autoregulatory feedback loop. J Biol Chem 2007;282:2135-43.
39.	Zisoulis DG, Kai ZS, Chang RK, Pasquinelli AE. Autoregulation of microRNA biogenesis by let-7 and Argonaute. Nature 2012;486:541-4.
40.	Marciano DC, Lua RC, Herman C, Lichtarge O. Cooperativity of negative autoregulation confers increased mutational robustness. Phys Rev Lett 2016;116:258104.
41.	Shimoni Y, Altuvia S, Margalit H, Biham O. Stochastic analysis of the SOS response in Escherichia coli. PLoS One 2009;4:e5363.
42.	Mangan S, Alon U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci U S A 2003;100:11980-5.
43.	Bracken CP, Gregory PA, Kolesnikoff N, Bert AG, Wang J, Shannon MF, et al. A double-negative feedback loop between ZEB1-SIP1 and the microRNA-200 family regulates epithelial-mesenchymal transition. Cancer Res 2008;68:7846-54.
44.	Burk U, Schubert J, Wellner U, Schmalhofer O, Vincan E, Spaderna S, et al. A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells. EMBO Rep 2008;9:582-9.
45.	Korpal M, Lee ES, Hu G, Kang Y. The miR-200 family inhibits epithelial-mesenchymal transition and cancer cell migration by direct targeting of E-cadherin transcriptional repressors ZEB1 and ZEB2. J Biol Chem 2008;283:14910-4.
46.	Tong Z, Cui Q, Wang J, Zhou Y. TransmiR v2.0: An updated transcription factor-microRNA regulation database. Nucleic Acids Res 2019;47:D253-8.
47.	Gupta A, Sugadev R, Sharma YK, Kumar B, Khurana P. miRFFLDB – An integrated resource for human miRNA-transcription factor-target gene coregulatory networks and associated feed forward loops. bioRxiv2020.05.16.097865. [doi: 10.1101/2020.05.16.097865].
48.	Friard O, Re A, Taverna D, De Bortoli M, Corá D. CircuitsDB: A database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse. BMC Bioinformatics 2010;11:435.
49.	Xie GY, Xia M, Miao YR, Luo M, Zhang Q, Guo AY. FFLtool: A web server for transcription factor and miRNA feed forward loop analysis in human. Bioinformatics 2019;36:2605-7.
50.	Li R, Chen H, Jiang S, Li W, Li H, Zhang Z, et al. CMTCN: A web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks. Peer J 2018;6:e5951.
51.	Liu ZP, Wu C, Miao H, Wu H. RegNetwork: An integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford) 2015;2015:bav095.
52.	Guebila MB, Lopes-Ramos CM, Weighill D, Sonawane AR, Burkholz R, Shamsaei B, et al. GRAND: A database of gene regulatory network models across human conditions. bioRxiv 2021;gkab778, https://doi.org/10.1093/nar/gkab778.
53.	Hamed M, Spaniol C, Nazarieh M, Helms V. TFmiR: A web server for constructing and analyzing disease-specific transcription factor and miRNA co-regulatory networks. Nucleic Acids Res 2015;43:W283-8.