Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

 Table of Contents  
ORIGINAL ARTICLE
Year : 2015  |  Volume : 11  |  Issue : 4  |  Page : 887-892

Construction and analysis of the regulatory network disturbed by the silenced Sp1 transcription factor in HeLa cells


Department of Gynaecology, First Hospital of Shanxi Medical University, Yingze, Taiyuan, Shanxi Province, China

Date of Web Publication15-Feb-2016

Correspondence Address:
Lijun Yu
Department of Gynaecology, First Hospital of Shanxi Medical University, No. 85, Jiefang South Road, Yingze District, Taiyuan City, Shanxi Province, 030001
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.140811

Rights and Permissions
 > Abstract 

Background: The objective of our study was to explore the characteristics of the regulatory network after siRNA-Sp1 (Specificity Protein 1) treatment in HeLa cells through the regulation network construction with bioinformatics methods.
Materials and Methods: Using GSE37935 datasets downloaded from Gene Expression Omnibus data, the differentially expressed genes (DEGs) were screened out by the limma package in R software. Combining the DEGs with the data from the microRNA (miRNA) databases and transcription factor databases, an integrated regulatory network was established with Cytoscape. Then the motifs in the network were examined by FANMOD.
Results: A total of 708 DEGs were screened, and a regulatory network consisting of 585 nodes and 1070 edges was constructed. By analyzing the two modules extracted from the network, we found that the most significant biological processes were cell cycle and apoptosis, some significant DEGs among them were CDKN1A, CUL5, and EGFR. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis discovered that DEGs, including EGFR, CDKN1A, RRM2B, and GADD45B, were significantly enriched in glioma pathway and p53 signaling pathway.
Conclusion: While Sp1 was silenced by siRNA, the regulatory network in HeLa cells changed a lot. Genes related to cell cycle and apoptosis in the cell nucleus were dysregulated and the p53 signaling pathway was disturbed.

Keywords: Gene expression profiling analyses, HeLa cells, integrated regulatory network, module, Sp1


How to cite this article:
Li F, Yu L, Li L, Wei M. Construction and analysis of the regulatory network disturbed by the silenced Sp1 transcription factor in HeLa cells. J Can Res Ther 2015;11:887-92

How to cite this URL:
Li F, Yu L, Li L, Wei M. Construction and analysis of the regulatory network disturbed by the silenced Sp1 transcription factor in HeLa cells. J Can Res Ther [serial online] 2015 [cited 2020 Jul 3];11:887-92. Available from: http://www.cancerjournal.net/text.asp?2015/11/4/887/140811


 > Introduction Top


The ubiquitous Sp1 transcription factor (TF) is the first identified member of a family of nuclear proteins, called Sp/KLF (specificity protein/Krόppel-like factor). With a DNA binding domain containing three zinc fingers, it binds GC-rich elements that are common regulatory elements in promoters of numerous genes. [1] By conducting a unbiased mapping of Sp1 binding sites in vivo, scientists reported that the human genome may contains at least 12,000 Sp1 binding sites. [2] Therefore it is not surprising that Sp1 has been reported to participate in many biological processes, such as cell growth, differentiation, proliferation, metabolism, apoptosis, and cell cycle. In addition, there are also some researches that claimed that it correlates to the tumorigenesis and development of various cancers. [3],[4],[5],[6],[7]

HeLa cells is an immortal cell line first obtained from the cervical cancer cells of Henrietta Lacks, and now it is the most widely used cell line for scientific research around the world. At present, there are several researches concerning the changes of Sp1 in HeLa cells. For example, Seve et al. reported that the conformation of Sp1 was changed by the Tat protein and its ability to bind to its cognate DNA sequence and to retain its zinc was affected too. [8] In another research, the regulation of Sp1 was reported to inhibit the Topo II alpha expression and therefore affect the HeLa cell apoptosis induced by genistein. [9] Interacting with p53, Sp1 inhibited the activity of Ki-67, which was associated with cell proliferation. [10] In a word, as a cell line derived from the cancer cells, HeLa cells were also influenced by the dysregulation of Sp1.

While all the biological processes consist of many components including DNA, RNA, proteins, and some micromolecules and their interplay, researches may be limited if we study individual gene alone. MicroRNAs (miRNAs) are a class of small noncoding RNA molecules. They play important roles in several biological processes (e.g. cell signaling, cell development, and cell death). [11] Up to now, most efforts have been made to find miRNAs and their targets. Sharing a similar regulatory logic with miRNA, TFs also triggered many biological events, dependent on the binding sites present on the target gene. In cells, these two important regulators were usually found cooperated in the gene regulatory network. Hence studies combining miRNAs and TFs with genes may prompt a more clear regulatory mechanism in cells.

In this study, we used the gene expression profile of GSE37935 downloaded from Gene Expression Omnibus (GEO) to investigate the changes of the regulatory network in HeLa cells in which Sp1 was silenced. With the miRNA and TF databases, we tried to find the most remarkable variation in the regulatory mechanism.


 > Materials and methods Top


Affymetrix microarray data

The expression profile of GSE37935 [12] was downloaded from GEO database (http://www.ncbi.nlm.nih.gov/geo/), which consisted of three samples of normal HeLa cell lines and three samples of HeLa cells disturbed by Sp1. Platform information was GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array. The original files (.raw) and annotation files were also downloaded from the platform.

Identification of differentially expressed genes

The R software version 2.15.3 [13] was used to analyze the microarray data. The raw expression datasets from all conditions were normalized using the Robust Multichip Averaging (RMA) method. [14] Then the limma package [15] in R, a linear regression model, was applied to analyze the significance of differential expression, and the Bayesian method [16],[17] was used to proceed multiple testing. Finally, the differentially expressed genes (DEGs) only with the cutoff criterion |logFC| larger than 0.5 and P less than 0.05 were selected.

Construction of the integrated regulatory network

The integrated regulatory network was constructed based on systematic integration of various high-throughput datasets. The miRNAs associated with DEGs were selected from miRNA regulation database (the integration database of miRecords, TarBase, starbase, and miR2Disease), [18],[19],[20],[21] and TFs associated with DEGs were selected from Transcriptional Regulatory Element Database (TRED). [22] The integrated miRNA-DEG-TF regulatory network was constructed by using Cytoscape software (http://cytoscape.org/), [23] which was an open source software for visualizing complex networks and integrating these networks with any type of attribute data.

Integrated Regulatory Network Motif Analysis

Motif is a small set of recurring regulation patterns in network, which possesses most of the functional features of the original network. [24] The motif analysis of the original integrated regulatory network was performed by FANMOD (http://theinf1.informatik.uni-jena.de/~wernicke/motifs/), [25] a tool for fast network motif detection. The significant motifs were screened by setting the motif size M = 4, P < 0.05 and Z-score > 2.

Functional modules analysis of network

The integrated regulatory network modules were constructed based on the motif analyses of original integrated regulatory network. The top-two motifs with P less than 0.05 and Z-score lager than 2 were screened. After that, the DEGs meeting with motif analyses were obtained from the original network by using the Perl script program (http://www.perl.org/). The two new modules corresponding to the original integrated network were constructed by integrated the motifs and DEGs finally.

The Gene Ontology (GO) [26] functional annotation and the following Kyoto Encyclopedia of Genes and Genomes (KEGG) [27] pathway enrichment analysis of module-related genes were done by tools of the DAVID (the Database for Annotation, Visualization and Integrated Discovery) software. [28] In both analyses, P < 0.05 was considered statistically significant.


 > Results Top


Identification of DEGs and construction of the network

A total of 708 DEGs with |logFC|>0.5 and P < 0.05 were considered statistically significant. With the derived regulatory relationships of miRNA-DEG and TF-DEG, we obtained a network with 587 nodes and 1070 edges [Figure 1].
Figure 1: Integrated miRNA-DEG-TF regulatory network Yellow nodes represent DEGs; green nodes represent TFs related to DEGs; blue nodes represent miRNAs related to DEGs; the green edges represent the regulatory of TFs; the amaranth edges represent the regulatory of miRNAs

Click here to view


Analysis of motifs and the corresponding modules

The top-five motif analysis results of the integrated regulatory network were chosen by setting motif-size M = 4, P less than 0.05, and Z-score lager than 2 [Figure 2], [Table 1]. All of the five motif categories not only had different geometrical structures, but also had different signs of regulation and represented several significant kinds of regulatory interactions, such as TF-Gene (motif 1 in [Figure 2]), TF-Gene and miRNA-Gene (motif 2 in [Figure 2]), and miRNA-Gene (motif 5 in [Figure 2]).
Figure 2: Motif analysis of the regulatory network Yellow nodes represent DEGs; green nodes represent TFs related to DEGs; blue nodes represent miRNAs related to DEGs. The motifs are motif-1 to motif-5 from left to right, respectively

Click here to view
Table 1: Motif analysis of regulatory network, the green nodes represent micro-RNA; the blue nodes represent TFs; the yellow nodes represent DEGs


Click here to view


GO analysis of the regulatory network modules

The top-two modules were constructed based on the motif analyses and primary integrated regulatory network, as shown in [Figure 3] (A and B). The GO functional annotation results revealed that module 1 was associated with 19 biological processes, and the two most notable processes were cell cycle (P = 0.001662) and regulation of apoptosis (P = 0.002121). Some salient genes enriched in these terms were CDKN1A (cyclin-dependent kinase inhibitor 1A) and CUL5 (cullin 5). Meanwhile, module 2 was also found mostly correlated to cell cycle process (P = 6.21E-04) and regulation of apoptosis (P = 0.003406) [Table 2]. Epidermal growth factor receptor (EGFR) and CDKN1A were found enriched in both terms.
Figure 3: Corresponding modules of the regulation network motifs A: blue nodes represent miRNAs, yellow nodes represent DEGs; green nodes represent TFs. B: blue nodes represent miRNAs, yellow nodes represent DEGs, green nodes represent TFs

Click here to view
Table 2:

Click here to view


Pathway analysis of modules

With the online biological classification tool DAVID, we observed that the glioma pathway was significantly enriched by DEGs in module 2, with P = 0.504202. Two genes involved in it were EGFR and CDKN1A. Interestingly, p53 signaling pathway was discovered enriched by DEGs in both modules, including CDKN1A, RRM2B, and GADD45B, with P = 0.457317 and 0.504202, respectively [Table 3].
Table 3: Pathways in module 1 and 2


Click here to view



 > Discussion Top


Providing the microarray experiments data in GSE37935, Oleaga et al. compared the gene expression in the Sp1 silenced HeLa cells with the controls, and then screened several target genes of Sp1. [12] However, as an important TF, the inactivation of Sp1 must induce variation in the related genes and even the corresponding regulatory network. In this work, by analyzing the gene expression data and their regulatory relationships with TFs and miRNAs, we obtained two functional modules from an original integrated regulatory network and identified some functional genes and pathways, which may play significant roles in the regulatory network of HeLa cells.

In our study, we found that most of the DEGs in the two modules were components in nucleus, which were involved in cell cycle and cell apoptosis. CDKN1A encodes a cyclin-dependent kinase inhibitor. It had been demonstrated that CDKN1A is a potent and reversible inhibitor of cell cycle progression and upregulated CDKN1A induces irreversible G1 arrest and apoptosis. [29] While the downregulation of CDKN1A was found involved in the inhibition of proliferation induced by Sp1 in vascular smooth muscle cells, [30] Deniaud et al. reported that they did not find a significant modification of CDKN1A gene expression upon Sp1 induction in Baf-3 cells. [31] CUL5 is a component of E3 ubiquitin ligase complexes and is involved in the regulation of the stability of p53. [32],[33] It has been reported as a cell cycle and apoptosis regulator in various kind of cells. [34] EGFR is an important signaling mediator, it plays a critical role in cell cycle arrest, apoptosis, or dedifferentiation. [35] In former researches, Sp1 has been reported to regulate apoptosis in a DNA binding-dependent manner in some cells [36],[37] and to induce an inhibition of cell cycle progression that precedes apoptosis. [38] These results suggest that Sp1 may participate in cell apoptosis and cell cycle by interacting with CDKN1A, CUL5, and EGFR in HeLa cells.

By proceeding with pathway analysis, we found that both the DEGs in two modules were enriched in p53 signaling pathway and three genes, including CDKN1A, RRM2B, and GADD45B, were enriched in this pathway. P53 is a vital tumor suppressor gene, it is activated by a number of stress signals, including DNA damage, oxidative stress, and activated oncogenes. With various genes regulated by p53, the p53 signaling pathway mainly includes three biological processes: Cell cycle arrest, cellular senescence, and apoptosis. [39] In a previous study, it has been reported that p53 regulates the activity of CDC25B (Cell Division Cycle 25 B, a gene involved in the cell cycle) via Sp1, [40] and the DNA binding domain of Sp1 is required for its interaction with p53. [41] These results suggest that Sp1 may play a critical role in cellular functions correlated by interacting with p53 signaling pathway.

As a cell cycle regulator, the expression of CDKN1A has been reported associated to the growth arrest of HeLa cells upon human papillomavirus (HPV) oncoproteins E2 overexpression, [42] which is consistent with our results. CDKN1A is also a direct target of miR-22. Regulated by p53, miR-22 then target CDKN1A CIP-1 RNA to facilitate the p53-dependent apoptosis, forming a p53-miR-22-CDKN1A axis. [43] Considering that Sp1 interacts with CDKN1A in HeLa cells, we believe that the relationship between Sp1 and CDKN1A may affect the whole p53 pathway.

Ribonucleotide reductase M2B (RRM2B) gene encodes one of the small subunit of a p53-inducible ribonucleotide reductase, which is an important enzyme in DNA synthesis. By supplying deoxyribonucleotides for DNA repair in cells arrested at G1 or G2, it plays a pivotal role in cell survival by repairing damaged DNA in a p53/TP53-dependent manner. [44] RRM1 is the other subunit of the ribonucleotide reductase, which interacts with RRM2B in DNA repair. In a previous study, Parker et al. reported that Sp1 was involved in the transcription of RRM1. [45] Hence we suggest that Sp1 may participate in the p53 signaling pathway by interacting with RRM2B.

Growth arrest and DNA-damage-inducible, beta (GADD45B) is a DNA-methylation-silenced tumor suppressor gene. It regulates cell growth, differentiation, and cell death following cellular exposure to diverse stimuli. [46] In prostate cancer cells, it was found upregulated by CG-5 by transcriptional repression of DNA (cytosine-5)-methyltransferase 1 (DNMT1), which was associated with reduced expression of Sp1 and E2F1 (E2F transcription factor 1). [47] The evidence confirmed that Sp1 indeed has regulated the expression of GADD45B, an important gene in p53 signaling pathway.

To our surprise, we found that the glioma pathway was present in the analysis and EGFR was one of the DEGs. Glioma is an aggressive primary brain tumor, in which the EGFR gene is frequently amplified or mutated. [48] For example, EGFR activation was reported to enhance the cyclooxygenase-2 expression through activation of Sp1/Sp3 in glioma. [49] This result demonstrates that the same process may also happen in the HeLa cells.

In conclusion, by combining the GEO expression profile data with the miRNA and TF databases, we got an integrated regulatory network of HeLa cells in which Sp1 was silenced and found that the cell nucleus components were saliently influenced by the inactivation of Sp1. Considering the pathway analysis results, we suggest that the cell cycle and cell apoptosis may be affected by the dysfunction of Sp1 and the remarkable DEGs in our modules, such as CDKN1A, CUL5, EGFR, RRM2B, and GADD45B, may play important roles in further study. However, further experiments are still indispensable to confirm our results.

 
 > References Top

1.
Philipsen S, Suske G. A tale of three fingers: The family of mammalian Sp/XKLF transcription factors. Nucleic Acids Res 1999;27:2991-3000.  Back to cited text no. 1
    
2.
Cawley S, Bekiranov S, Ng HH, Kapranov P, Sekinger EA, Kampa D, et al. Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 2004;116:499-509.  Back to cited text no. 2
    
3.
Li L, Davie JR. The role of Sp1 and Sp3 in normal and cancer cell biology. Ann Anat 2010;192:275-83.  Back to cited text no. 3
    
4.
Chuang JY, Wu CH, Lai MD, Chang WC, Hung JJ. Overexpression of Sp1 leads to p53-dependent apoptosis in cancer cells. Int J Cancer 2009;125:2066-76.  Back to cited text no. 4
    
5.
Wang YT, Chuang JY, Shen MR, Yang WB, Chang WC, Hung JJ. Sumoylation of specificity protein 1 augments its degradation by changing the localization and increasing the specificity protein 1 proteolytic process. J Mol Biol 2008;380:869-85.  Back to cited text no. 5
    
6.
Mertens-Talcott SU, Chintharlapalli S, Li X, Safe S. The oncogenic microRNA-27a targets genes that regulate specificity protein transcription factors and the G2-M checkpoint in MDA-MB-231 breast cancer cells. Cancer Res 2007;67:11001-11.  Back to cited text no. 6
    
7.
Abdelrahim M, Smith R 3 rd , Burghardt R, Safe S. Role of Sp proteins in regulation of vascular endothelial growth factor expression and proliferation of pancreatic cancer cells. Cancer Res 2004;64:6740-9.  Back to cited text no. 7
    
8.
Seve M, Favier A, Osman M, Hernandez D, Vaitaitis G, Flores NC, et al. The human immunodeficiency virus-1 Tat protein increases cell proliferation, alters sensitivity to zinc chelator-induced apoptosis, and changes Sp1 DNA binding in HeLa cells. Arch Biochem Biophys 1999;361:165-72.  Back to cited text no. 8
    
9.
Zhou N, Yan Y, Li W, Wang Y, Zheng L, Han S, et al. Genistein inhibition of topoisomerase IIalpha expression participated by Sp1 and Sp3 in HeLa cell. Int J Mol Sci 2009;10:3255-68.  Back to cited text no. 9
    
10.
Wang MJ, Pei DS, Qian GW, Yin XX, Cheng Q, Li LT, et al. p53 regulates Ki-67 promoter activity through p53- and Sp1-dependent manner in HeLa cells. Tumour Biol 2011;32:905-12.  Back to cited text no. 10
    
11.
Wu L, Belasco JG. Let me count the ways: Mechanisms of gene regulation by miRNAs and siRNAs. Mol Cell 2008;29:1-7.  Back to cited text no. 11
    
12.
Oleaga C, Welten S, Belloc A, Sole A, Rodriguez L, Mencia N, et al. Identification of novel Sp1 targets involved in proliferation and cancer by functional genomics. Biochem Pharmacol 2012;84:1581-91.  Back to cited text no. 12
    
13.
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2008, ISBN 3-900051-07-0, URL http://www.R-project.org/.  Back to cited text no. 13
    
14.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4:249-64.  Back to cited text no. 14
    
15.
Diboun I, Wernisch L, Orengo CA, Koltzenburg M. Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 2006;7:252.  Back to cited text no. 15
    
16.
Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:Article3.  Back to cited text no. 16
    
17.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995;57:289-300.  Back to cited text no. 17
    
18.
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Res 2009;37:D105-10.  Back to cited text no. 18
    
19.
Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 2006;12:192-7.  Back to cited text no. 19
    
20.
Yang JH, Li JH, Shao P, Zhou H, Chen YQ, Qu LH. StarBase: A database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res 2011;39:D202-9.  Back to cited text no. 20
    
21.
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, et al. miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 2009;37:D98-104.  Back to cited text no. 21
    
22.
Jiang C, Xuan Z, Zhao F, Zhang MQ. TRED: A transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 2007;35:D137-40.  Back to cited text no. 22
    
23.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504.  Back to cited text no. 23
    
24.
Masoudi-Nejad A, Schreiber F, Kashani ZR. Building blocks of biological networks: A review on major network motif discovery algorithms. IET Syst Biol 2012;6:164-74.  Back to cited text no. 24
    
25.
Wernicke S, Rasche F. FANMOD: A tool for fast network motif detection. Bioinformatics 2006;22:1152-3.  Back to cited text no. 25
    
26.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. Nat Genet 2000;25:25-9.  Back to cited text no. 26
    
27.
Kanehisa M. The KEGG database. Novartis Found. Symp., 2002; 247: 91-103, 119-128, 244-252.  Back to cited text no. 27
    
28.
Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.  Back to cited text no. 28
    
29.
Harada K, Furubo S, Ozaki S, Hiramatsu K, Sudo Y, Nakanuma Y. Increased expression of WAF1 in intrahepatic bile ducts in primary biliary cirrhosis relates to apoptosis. J Hepatol 2001;34:500-6.  Back to cited text no. 29
    
30.
Kavurma MM, Khachigian LM. Sp1 inhibits proliferation and induces apoptosis in vascular smooth muscle cells by repressing p21WAF1/Cip1 transcription and cyclin D1-Cdk4-p21WAF1/Cip1 complex formation. J Biol Chem 2003;278:32537-43.  Back to cited text no. 30
    
31.
Deniaud E, Baguet J, Chalard R, Blanquier B, Brinza L, Meunier J, et al. Overexpression of transcription factor Sp1 leads to gene expression perturbations and cell cycle inhibition. PLoS One 2009;4:e7035.  Back to cited text no. 31
    
32.
Querido E, Blanchette P, Yan Q, Kamura T, Morrison M, Boivin D, et al. Degradation of p53 by adenovirus E4orf6 and E1B55K proteins occurs via a novel mechanism involving a Cullin-containing complex. Genes Dev 2001;15:3104-17.  Back to cited text no. 32
    
33.
Blaise R, Masdehors P, Lauge A, Stoppa-Lyonnet D, Alapetite C, Merle-Beral H, et al. Chromosomal DNA and p53 stability, ubiquitin system and apoptosis in B-CLL lymphocytes. Leuk Lymphoma 2001;42:1173-80.  Back to cited text no. 33
    
34.
Kalla C, Scheuermann MO, Kube I, Schlotter M, Mertens D, Döhner H, et al. Analysis of 11q22-q23 deletion target genes in B-cell chronic lymphocytic leukaemia: Evidence for a pathogenic role of NPAT, CUL5, and PPP2R1B. Eur J Cancer 2007;43:1328-35.  Back to cited text no. 34
    
35.
Lui VW, Grandis JR. EGFR-mediated cell cycle regulation. Anticancer Res 2002;22:1-11.  Back to cited text no. 35
    
36.
Deniaud E, Baguet J, Mathieu AL, Pages G, Marvel J, Leverrier Y. Overexpression of Sp1 transcription factor induces apoptosis. Oncogene 2006;25:7096-105.  Back to cited text no. 36
    
37.
Black AR, Black JD, Azizkhan-Clifford J. Sp1 and kruppel-like factor family of transcription factors in cell growth regulation and cancer. J Cell Physiol 2001;188:143-60.  Back to cited text no. 37
    
38.
Huong PT, Soung NK, Jang JH, Cha-Molstad HJ, Sakchaisri K, Kim SO, et al. Regulation of CEP131 gene expression by SP1. Gene 2013;513:75-81.  Back to cited text no. 38
    
39.
Vazquez A, Bond EE, Levine AJ, Bond GL. The genetics of the p53 pathway, apoptosis and cancer therapy. Nat Rev Drug Discov 2008;7:979-87.  Back to cited text no. 39
    
40.
Dalvai M, Mondesert O, Bourdon JC, Ducommun B, Dozier C. Cdc25B is negatively regulated by p53 through Sp1 and NF-Y transcription factors. Oncogene 2011;30:2282-8.  Back to cited text no. 40
    
41.
Koutsodontis G, Tentes I, Papakosta P, Moustakas A, Kardassis D. Sp1 plays a critical role in the transcriptional activation of the human cyclin-dependent kinase inhibitor p21(WAF1/Cip1) gene by the p53 tumor suppressor protein. J Biol Chem 2001;276:29116-25.  Back to cited text no. 41
    
42.
Wells SI, Francis DA, Karpova AY, Dowhanick JJ, Benson JD, Howley PM. Papillomavirus E2 induces senescence in HPV-positive cells via pRB- and p21 (CIP)-dependent pathways. EMBO J 2000;19:5762-71.  Back to cited text no. 42
    
43.
Tsuchiya N, Izumiya M, Ogata-Kawata H, Okamoto K, Fujiwara Y, Nakai M, et al. Tumor suppressor miR-22 determines p53-dependent cellular fate through post-transcriptional regulation of p21. Cancer Res 2011;71:4628-39.  Back to cited text no. 43
    
44.
Xue L, Zhou B, Liu X, Wang T, Shih J, Qi C, et al. Structurally dependent redox property of ribonucleotide reductase subunit p53R2. Cancer Res 2006;66:1900-5.  Back to cited text no. 44
    
45.
Parker NJ, Begley CG, Fox RM. Human gene for the large subunit of ribonucleotide reductase (RRM1): Functional analysis of the promoter. Genomics 1995;27:280-5.  Back to cited text no. 45
    
46.
Major MB, Jones DA. Identification of a gadd45beta 3 enhancer that mediates SMAD3- and SMAD4-dependent transcriptional induction by transforming growth factor beta. J Biol Chem 2004;279 (7):5278-87.  Back to cited text no. 46
    
47.
Lin HY, Kuo YC, Weng YI, Lai IL, Huang TH, Lin SP, et al. Activation of silenced tumor suppressor genes in prostate cancer cells by a novel energy restriction-mimetic agent. Prostate 2012;72:1767-78.  Back to cited text no. 47
    
48.
Huang PH, Xu AM, White FM. Oncogenic EGFR signaling networks in glioma. Sci Signal 2009;2:re6.  Back to cited text no. 48
    
49.
Xu K, Shu HK. EGFR activation results in enhanced cyclooxygenase-2 expression through pz38 mitogen-activated protein kinase-dependent activation of the Sp1/Sp3 transcription factors in human gliomas. Cancer Res 2007;67:6121-9.  Back to cited text no. 49
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

Top
 
 
  Search
 
Similar in PUBMED
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  >Abstract>Introduction>Materials and me...>Results>Discussion>Article Figures>Article Tables
  In this article
>References

 Article Access Statistics
    Viewed3280    
    Printed45    
    Emailed0    
    PDF Downloaded96    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]