The up-regulation of Myb may help mediate EGCG inhibition effect on mouse lung adenocarcinoma
Zhou et al. Human Genomics
The up-regulation of Myb may help mediate EGCG inhibition effect on mouse lung adenocarcinoma
Hong Zhou 1
Joseph Manthey 1
Ekaterina Lioutikova 1
William Yang 0
Kenji Yoshigoe 0
Mary Qu Yang 0
Hong Wang 2
0 Joint Bioinformatics Ph.D. Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences , 2801 S. University Avenue, Little Rock, AR 72204 , USA
1 Department of Mathematical Science, School of Health and Natural Sciences, University of Saint Joseph , 1678 Asylum Avenue, West Hartford, CT 06117 , USA
2 Susan L. Cullman Laboratory for Cancer Research, Department of Chemical Biology and Centre for Cancer Prevention Research, Ernest Mario School of Pharmacy , Rutgers , The State University of New Jersey , 164 Frelinghuysen Road, Piscataway, NJ 08854 , USA
Background: Green tea polyphenol epigallocatechin-3-gallate (EGCG) has been demonstrated to inhibit cancer in experimental studies through its antioxidant activity and modulations on cellular functions by binding specific proteins. By means of computational analysis and functional genomic approaches, we previously identified a set of protein coding genes and microRNAs whose expressions were significantly modulated in response to the EGCG treatment in tobacco carcinogen-induced lung adenocarcinoma in A/J mice. However, to what degree these genes are involved in the cancer inhibition of EGCG remains unclear. Results: In this study, we further employed statistical methods and literature research to analyze these data in combination with The Cancer Genome Atlas (TCGA) lung adenocarcinoma datasets for additional data mining. Under the assumption that, if a gene mediates EGCG's cancer inhibition, its expression level change caused by EGCG should be opposite to what occurred in the carcinogenesis, we identified Myb and Peg3 as the primary putative genes involved in the cancer inhibitory activity. Further analysis suggested that the regulation of Myb could be mediated through an EGCG-upregulated microRNA, miR-449c-5p. Conclusions: Although the actions of EGCG involve multiple targets/pathways, further analysis by mining the existing genomic datasets revealed that the upregulations of Myb and Peg3 are likely the key anti-cancer events of EGCG in vivo.
Green tea polyphenol epigallocatechin-3-gallate (EGCG)
has been shown to have preventive effect for several
diseases including cancer [
]. A substantial number of
studies have been conducted to uncover the cancer
preventive mechanisms of EGCG at the cellular and molecular
levels including experimental studies using animal models.
The results from these studies support that the treatment
with EGCG or EGCG-rich tea extract leads to a wide range
of responses and that the cancer prevention activities are
most likely to be mediated through multiple mechanisms
resulted from direct scavenging of stress molecules such as
reactive oxygen species (ROS) and/or the physical
interactions of EGCG with specific proteins to modulate gene
expression and cellular signaling. However, most
experimental evidence supporting anti-cancer mechanisms of
EGCG are obtained from in vitro studies. Whether or not
these mechanisms play significant roles in the cancer
prevention/inhibition in vivo remains to be determined.
The tobacco carcinogen-induced lung carcinogenesis
in A/J mice is a well-characterized animal model. It has
been shown using this animal model that tumor
multiplicity and size are effectively inhibited when mice are
fed an experimental diet containing EGCG [
this animal model, our recent study has demonstrated
that cellular changes in lung cancer cells treated with
EGCG are associated with alterations in both messenger
RNA (mRNA) and microRNA (miRNA) expressions .
Computational analysis on microarray data revealed that
the EGCG-induced expression changes of some genes
also involved miRNA-mediated gene regulation [
It is well-recognized that EGCG treatment can cause a
wide range of responses at both the cellular and molecular
]. Using “EGCG” as the keyword to search through
NIH’s TOXNET databases, specifically the Comparative
Toxicogenomics Database (CTD) which provides scientific
data describing relationships between chemicals, genes,
and human diseases, we found that EGCG treatment or
co-treatment with other agents can impact the expression
of about 2000 genes in both humans and mice. Our recent
study also showed that, in the carcinogen-induced lung
tumor in A/J mice, EGCG treatment induces the
expression level change of 367 genes (at least onefold change)
]. Since the large group of genes up- or down-regulated
by EGCG includes genes which are not related to lung
], identifying the relevant genes would
advance our understanding of the cancer inhibition
mechanism of EGCG.
In this study, we employed statistical approaches and
literature research and combined the data from The Cancer
Genome Atlas (TCGA) [
] to further determine the
candidate genes, including miRNA, which participate in the
EGCG inhibition mechanism. We focused on exploring
the lung cancer-related genes and identifying the ones that
could potentially play predominant roles in mediating the
cancer inhibition by EGCG in the carcinogen-induced A/J
mouse lung cancer model.
Microarray data of mRNA expression and miRNA
expression were obtained from two sources. The first
was from our previous study that compared the mRNA
and miRNA expression profiles in A/J mice bearing
(NNK)induced lung tumor fed on the diet containing 0.4 %
EGCG or the control AIN93M diet [
]. In this dataset,
mRNA expression profiles include 3 microarray results
of lung tumors collected from 3 control mice and 3
EGCG-treated mice, and the miRNA expression
profiles include microarray results of lung tumors
collected from 8 control mice and 8 EGCG-treated mice.
The second source was the gene expression profile batch
37 of lung adenocarcinoma obtained from TCGA data
]. The filter settings used to obtain this
dataset include Data Level = 3, Availability = Available,
Center/Platform = All, Access Tier = Public, and Tumor/
Normal = Tumor-matched.
When performing statistical analysis on the microarray
datasets, we made the following assumptions.
(1) There are random variations in gene expression
among samples which are independent of the
treatments. These random variations in gene
expression represent the normal variations of
individuals in a group of experimental animals.
(2) When treated with EGCG, the expression changes of
responsive genes are expected to be statistically
significant and consistent among all treated samples
compared to the control.
To analyze the mRNA expression profiles, within the 3
controls and the 3 EGCG-treated samples, we first
removed those genes whose expression in the 3 EGCG
treatments were not consistently up or down compared to
the average of the 3 controls. This means that we only
selected those genes whose expressions were consistently
up-regulated or down-regulated in all the 3 EGCG-treated
samples compared to the average of the 3 controls.
Then, we computed the difference between the
averaged expression levels of the 3 controls and the 3
treatments for each gene. The standard deviation was
calculated for the difference distribution, and z-scores
were obtained for each gene. Only those genes whose
z-scores were ≥3.00 or ≤−3.00 were considered genes
that were impacted significantly by EGCG treatment.
To analyze the miRNA profiles, with the 8 controls
and 8 EGCG treatments, the difference between
averaged control and treatment was obtained for each
miRNA. The standard deviation was calculated for the
differences, and z-scores were obtained for each
miRNA. We first selected those miRNAs whose
zscores were ≥2.00 or ≤−2.00. Then, among those
selected miRNAs, we conducted an independent sample
t test between the 8 controls and the 8 EGCG
samples. Only those miRNAs with p values of the t test
less than 0.05 were considered as miRNAs that were
responsive to EGCG treatment.
From TCGA database, we obtained microarray gene
expression data for 21 human lung adenocarcinoma
samples (batch 37) publically released by Michael Topal
and Katherine Hoadley from the University of North
Carolina at Chapel Hill on February 10, 2009 [
21 datasets represent the normalized gene expression
differences between lung adenocarcinoma and a normal cell
line. Although there are another 11 sets of such data in
batch 52, some gene expressions are null in batch 52, and
batch 52 was not used in this study. After the standard
deviation was calculated based on the average of the 21
datasets, z-scores were computed for each gene. Only those
genes whose expression levels are all up or all down in the
21 sets and whose z-scores were ≥3.00 or ≤−3.00 were
Gene pathway analyses were conducted using
iPathwayGuide (https://apps.advaitabio.com/ipg/home). The only
adjustable setting of iPathwayGuide is its DE threshold.
The settings used were as follows: Fold Change (log) =
1.0 and Adjusted p value = 0.01. Gene interaction
analysis was conducted using the PCViz web tool
In the comparison of the microarray results of mRNA
expression profiles of the 3 controls and 3 EGCG samples,
mRNAs whose expression levels are not consistently up
or down between the controls and EGCG-treated samples
were removed. We selected only these consistent data
records because they tend to be more reliable. Fourteen
thousand seven hundred sixty-five records were selected.
Z-scores were computed for every mRNA (gene). Only
those genes whose z-scores were ≥3.00 or ≤3.00 were
considered genes that are impacted significantly by EGCG
treatment, i.e., the expression changes of these genes
between the control and the sample are likely a result of the
EGCG treatment. There were 225 unique genes selected
(see Additional file 1). The large number of genes
impacted by EGCG treatment is expected as EGCG
treatment induces a wide range of biological responses [
In the A/J mouse lung carcinogenesis model, the tumor
multiplicity and size are effectively inhibited when mice
are fed a diet containing EGCG [
]. Apoptosis was
induced and pro-proliferation signalings were reduced in
tumors after a long-term treatment with EGCG [
The gene expression profiles showed that cell cycle
regulation and inflammation were the most impacted pathways
by a long-term EGCG treatment (~20 weeks) [
]. Since a
long-term EGCG treatment may cause multiple waves of
responses which can disguise the targeted genes or
pathways of EGCG, we preferred a short-term
treatment experiment for elucidating the earlier responses,
as described in our recent study in which EGCG was
applied for only 1 week [
]. Although it is somewhat
surprising that none of the 17 signature genes identified
by Lu et al. [
] were found among the 225 genes, such
a discrepancy indicates that the short-term treatment
causes a wider range of early responses while the result
induced by the long-term treatment represents the
overall consequence post the early response. Thus, the
result of short treatment offers better chance to identify
By analyzing the 225 genes using iPathwayGuide, the
only pathway approaching statistical significance (p =
0.08) is the Kyoto Encyclopedia of Genes and Genomes
(KEGG) chemokine pathway [
]. This pathway is
associated with multiple cellular functions such as cellular
growth and differentiation, cell survival, migration,
apoptosis, chemotaxis, and ROS production [
this is consistent with what was reported previously, either
with a long-term or short-term EGCG treatment [
]. This result supports that the anti-cancer mechanism
of EGCG is mediated through the regulation of cellular
growth, apoptosis, differentiation, and migration.
By applying the same statistical selection process on the
21 lung adenocarcinoma datasets collected from the
TCGA database, 478 genes were selected from the TCGA
LUAD datasets since they displayed significantly
upregulated or down-regulated expression levels in the lung
adenocarcinoma tissues. Comparing with the 225 genes
selected from EGCG treatment, 16 genes were found to
be in both groups. We postulated that if EGCG is truly
inhibiting the lung carcinogenesis, then it should reverse
the expression levels of some genes that were
overexpressed or under-expressed in lung cancer tissues. Thus,
we were searching for those genes whose expression levels
were reversed in the EGCG-treated samples when
compared with the TCGA LUAD datasets. In the overlapped
genes between the 478 genes selected from the TCGA
LUAD datasets and the 225 genes selected from
EGCGtreated group, only Myb, Peg3, and Myl4 display a reverse
expression pattern (Table 1). While Myl4 was not found
to be related to carcinogenesis based on the existing
literature, both Myb and Peg3 were reported to be involved in
]. Although Myb has been
considered as an oncogene [
], its down-regulation in
human lung cancer suggests that it may have dual functions.
Nevertheless, this comparison suggested that EGCG’s
inhibition on mouse lung carcinogenesis reversed the
carcinogenesis-associated changes, and Myb and Peg3 are
the putative genes that mediate such inhibition effect.
Using the functional annotation tool of David
Bioinformatics Resources 6.7 (http://david.abcc.ncifcrf.gov/) to
analyze these overlapped genes also identified F13a1,
Fmo3, Mmp9, and Myh11 as related to cancer. Among
these genes, Mmp9 is specifically related to lung cancer
]. However, these genes including Mmp9 are less
likely to be related to the inhibition function of EGCG
since they do not exhibit a reversed expression pattern.
In order to assess the possible roles of Myb, Peg3, and
Myl4, we employed the PCViz network visualization tool
(www.pathwaycommons.org), a web tool to construct
gene interaction network based on information mined
from published literature and to analyze the possible
gene interactions for Myb, Peg3, and Myl4. We found
that there are no identified gene interactions for both
Peg3 and Myl4, while Myb has the potential to control
the expression of a large number of genes including
Myc, Nras, and Bcl2 that are well-characterized for their
role in tumorigenesis (Fig. 1). This suggests that Myb
might play a much more critical role than Peg3 in
mediating the anti-cancer activity of EGCG.
A further analysis on those genes that were identified by
the PCViz network to be affected by Myb shows that there
are 29 mRNA genes whose expressions were affected by
EGCG treatment. However, there are only 3 genes, Col1a2,
Ccnb1, and Copa, whose expression levels could be
considered to be significantly impacted by EGCG treatment.
Col1a2 was up-regulated by 1.8-fold, while Ccnb1 and
Copa were down-regulated by 2.4- and 3.4-fold,
respectively. Clearly, much more studies are required before any
conclusion can be drawn on how Myb mediates EGCG’s
In our previous study, we demonstrated that miRNAs
can mediate the gene regulation by EGCG [
]. Here, we
used statistical methods to further investigate the
possible roles of miRNA in EGCG treatments, especially if
miRNA can possibly mediate the regulation of Myb and/
or Peg3 by EGCG. We first used statistical methods to
identify those miRNAs whose expression levels were
significantly modified by EGCG treatment. The selection of
such miRNAs is a two-step process. In step 1, the
averaged miRNA expression levels were computed for both
the 8 controls and 8 samples. The averaged difference
was then calculated for each miRNA. By treating the
pool of the averaged differences as normally distributed,
z-scores were computed for each miRNA. Only those
miRNAs for which |z| ≥ 2.00 were selected. In step 2, an
independent sample t test (two tails) was conducted
between the 8 controls and 8 EGCG-treated samples for
each selected miRNA. Those miRNAs that did not
show a statistically significant difference (p ≤ 0.05)
between the controls and samples were removed. Using
this selection process, 27 miRNAs were selected as
shown in Table 2.
Among the 27 miRNAs, 20 were up-regulated. Since the
function of miRNA is to suppress the maturation of its
targeting mRNAs, up-regulated expression of miRNA usually
result in down-regulated mRNA expression and vice versa.
Using both Diana microT-CDS v3.0 and miRDB
microRNA target prediction tools, we collected a number of
potential microRNA target genes for the 27 miRNAs in
Table 2. Based on the up/down-regulated expression levels
of these 27 miRNAs, we only selected a protein coding
gene as a target of a microRNA if (1) its expression change
is opposite to the change of the miRNA expression and (2)
it exists in the 225 gene groups. The results are
summarized in Table 3.
Thirty genes were found to be candidates targeted by
the 27 miRNAs. It is interesting to notice that of the 30
genes, only Fbp1 has its expression level up-regulated;
and of the 27 miRNAs, only 6 were found to regulate
gene expressions significantly. In this candidate gene list,
Myb gene is shown as a target of mmu-miR-449c-5p,
indicating that the miRNA such as miR-449c-5p may play
an important role in the inhibitory activity of EGCG.
Since EGCG treatment causes a wide range of responses
at both cellular and molecular levels, it is difficult to
elucidate how EGCG unleashes its anti-cancer effects,
especially in an in vivo model like the carcinogen-induced
lung adenocarcinoma in A/J mice. One possible approach
is to apply the genomic studies with bioinformatics
analysis integrated with literature research. We demonstrated
that this is a feasible approach in this study.
A critical assumption in this study is that most genes
have small variations on their expressions as a random
phenomenon, while genes impacted by EGCG treatment
would exhibit larger variations, i.e., statistically
significant variations. Though the distribution of the mRNA
expression differences between the averaged control and
EGCG treatment is not a normal distribution, the large
size of the sample (14,765 gene records) allows us to
make this assumption. We made a similar assumption
on the miRNA expression alterations caused by EGCG
treatment. However, the sample size of miRNAs is only
656, much smaller than that of mRNAs. To ensure the
significance of the miRNAs selected from our statistical
approach, we used a two-step process. In the first step,
z-score of 2.0 was used as the cutoff. A z-score of 2.0
was used instead of a z-score of 3.0 due to the relatively
small sample size of miRNA genes. In addition, the
independent sample t test used in the second step further
ensures the significance of the selected miRNAs. After
identifying 225 mRNAs and 27 miRNAs whose altered
expression levels are statistically significant, we assumed that
some of these selected genes, either mRNA or miRNA,
may be related to EGCG’s anti-cancer mechanism.
While the pathway analysis showed that this group of
225 genes is likely to be involved in the chemokine
pathway which plays an important role in migration, cell
growth and differentiation, apoptosis, and chemotaxis
], it did not reveal the genes related to anti-cancer
activity of EGCG. The TCGA lung adenocarcinoma
(LUAD) datasets contain the normalized gene expression
microarray data which records the fold changes between
lung adenocarcinoma tissues and a normal cell line. These
datasets serve as a good supplemental control to our
experiment. We expect that if a gene mediates the
anticancer activity of EGCG, then its expression level change
in our EGCG treatment experiment should be
opposite to its expression level change in the TCGA LUAD
datasets. Three genes, Myb, Peg3, and Myl4, were
found to match this criterion, and their expression
level changes are statistically significant. While our
literature research has not found any role of Myl4 in
carcinogenesis, Peg3 has been reported to act like a
tumor suppressor gene [
As a transcription factor, Myb gene has been reported
to be involved in the progression of different types of
cancer including lung cancer and has recently been
considered as an oncogene because of its critical role in cell
proliferation and differentiation [
27, 28, 31, 32
However, its expression which is reduced in TCGA
LUAD datasets suggests that Myb expression decrement
is necessary for human lung carcinogenesis. Although
we do not understand the reason, one possibility is that
Myb may have dual functions or tissue specific function.
If so, our result here supports that Myb is necessary for
EGCG to exert its anti-cancer effect. Interestingly, our
result further showed that Myb could be regulated by
EGCG down-regulated miRNA, miR-449c-5p. Again, if
Myb does not act as oncogene but mediates the cancer
inhibitory activity of EGCG, this result suggests that
miRNA-mediated gene regulation is an important action
of EGCG although these miRNAs may not be directly
regulated by EGCG as we discussed previously .
EGCG treatment leads to the expression level changes of a
large number of genes, causing a wide range of biological
responses. However, it is unlikely that majority of these
regulated genes mediate the anti-cancer activity of EGCG. Our
analysis on the whole transcriptome integrated with TCGA
datasets on human lung cancer revealed that EGCG
reverses the lung carcinogenesis-associated changes of
Myb and Peg3, suggesting that Myb and Peg3 play
key roles in the anti-cancer activity of EGCG. In
addition, our analysis of the miRNA profiles suggests
that the up-regulation of Myb by EGCG could be achieved
by EGCG-induced down-regulation of miRNA
mmumiR-449c-5p, supporting a role of miRNA in the
anticancer activity of EGCG.
Additional file 1: The mRNAs significantly regulated by EGCG in the
NNK-induced A/J mouse lung tumor. (36kb xlsx)
The authors declare that they have no competing interests.
HZ and MQY conceived the project and all authors participated in the
analysis of the data, interpretation of the results, and review of the paper.
HZ, HW, JM, EL, WY, KY and MQY performed the data analysis, HW helped
conduct the experiments. HZ, HW and JM drafted the paper. All authors read
and approved the final version of the manuscript.
HZ received TLC faculty research fund and publication fund from the
University of Saint Joseph. MQY and KY were supported by NIH
1R15GM114739. Funding for open-access publication of this article came
from NIH 1R15GM114739.
This article has been published as part of Human Genomics Volume 10
Supplement 2, 2016: From genes to systems genomics: human
genomics. The full contents of the supplement are available online at
1. Khan N , Afaq F , Saleem M , Ahmad N , Mukhtar H . Targeting multiple signaling pathways by green tea polyphenol (- ) - epigallocatechin - 3 -gallate . Cancer Res . 2006 ; 66 : 2500 - 5 .
2. Yang CS , Wang X , Lu G , Picinich SC . Cancer prevention by tea: animal studies, molecular mechanisms and human relevance . Nat Rev Cancer . 2009 ; 9 : 429 - 39 .
3. Yang CS , Wang H , Li GX , Yang Z , Guan F , Jin H . Cancer prevention by tea: evidence from laboratory studies . Pharmacol Res . 2011 ; 64 : 113 - 22 .
4. Kitamura M , Nishino T , Obata Y , Furusu A , Hishikawa Y , Koji T , Kohno S. Epigallocatechin gallate suppresses peritoneal fibrosis in mice . Chem Biol Interact . 2012 ; 195 : 95 - 104 .
5. Ju J , Lu G , Lambert JD , Yang CS . Inhibition of carcinogenesis by tea constituents . Semin Cancer Biol . 2007 ; 17 : 395 - 402 .
6. Zhou H , Chen J , Yang CS , Yang M , Deng Y , Wang H . Gene regulation mediated by microRNAs in response to green tea polyphenol EGCG in mouse lung cancer . BMC Genomics . 2014 ; 15 suppl 11 : S3 .
7. Kim HS , Quon MJ , Kim J . New insights into the mechanisms of polyphenols beyond antioxidant properties; lessons from the green tea polyphenol, epigallocatechin-3-gallate . Redox Biol . 2014 ; 2 : 187 - 95 .
8. El-Telbany A , Ma PC . Cancer genes in lung cancer: racial disparities: are there any? Genes Cancer . 2012 ; 3 : 467 - 80 .
9. https://tcga-data.nci.nih.gov/tcga/dataAccessMatrix.htm?mode=ApplyFilter, accessed on 13 Nov 2014 .
10. Lu G , Liao J , Yang G , Reuhl KR , Hao X , Yang CS . Inhibition of adenoma progression to adenocarcinoma in a 4-(methylnitrosamino)-1-(3-pyridyl)-1- butanone-induced lung tumorigenesis model in A/J mice by tea polyphenols and caffeine . Cancer Res . 2006 ; 66 : 11494 - 501 .
11. Li GX , Chen YK , Hou Z , Xiao H , Jin H , Lu G , Lee MJ , Liu B , Guan F , Yang Z , et al. Pro-oxidative activities and dose-response relationship of (- ) - epigallocatechin - 3 -gallate in the inhibition of lung cancer cell growth: a comparative study in vivo and in vitro . Carcinogenesis . 2010 ; 31 : 902 - 10 .
12. Lu Y , Yao R , Yan Y , Wang Y , Hara Y , Lubet RA , You M. A gene expression signature that can predict green tea exposure and chemopreventive efficacy of lung cancer in mice . Cancer Res . 2006 ; 66 : 1956 - 63 .
13. http://www.genome.jp/kegg/pathway.html. Accessed on 24 Nov 2014 .
14. Mellado M , Rodriguez-Frade JM , Mañes S , Martinez-A C. Chemokine signaling and functional responses: the role of receptor dimerization and TK papthway activation . Annu Rev Immunol . 2001 ; 19 : 397 - 421 .
15. Wong MM , Fish EN . Chemokines: attractive mediators of the immune response . Semin Immunol . 2003 ; 15 : 5 - 14 .
16. Curnock AP , Logan MK , Ward SG . Chemokine signaling: pivoting around multiple phosphoinostitide 3-kinases . Immunology . 2002 ; 105 : 125 - 36 .
17. Busillo JM , Benovic JL . Regulation of CXCR4 signaling . Biochim Biophys Acta . 1768 ; 2007 : 952 - 63 .
18. Olson TS , Ley K. Chemokines and chemokine receptors in leukocyte trafficking . Am J Physiol Regul Integr Comp Physiol . 2002 ; 283 : R7 - 28 .
19. Knall C , Young S , Nick JA , Buhl AM , Worthen GS , Johnson GL . Interleukin-8 regulation of the Ras/Raf/mitogen-activated protein kinase pathway in human neutrophils . J Biol Chem . 1996 ; 271 : 2832 - 8 .
20. Hauck CR , Klingbeil CK , Schlaepfer DD . Focal adhesion kinase functions as a receptor-proximal signaling component required for directed cell migration . Immunol Res . 2000 ; 21 : 293 - 303 .
21. Chandrasekar B , Bysani S , Mummidi S. CXCL16 signals via Gi, phosphatidylinositol 3-kinase, Akt, I kappa B kinase, and nuclear factorkappa B and induces cell-cell adhesion and aortic smooth muscle cell proliferation . J Biol Chem . 2004 ; 279 : 3188 - 96 .
22. Schwartzberg PL , Finkelstein LD , Readinger JA . TEC-family kinases: regulators of T-helper-cell differentiation . Nat Rev Immunol . 2005 ; 5 : 284 - 95 .
23. Ticcioni M , Essafi M , Jeandel PY , Davi F , Cassuto JP , Deckert M , Bernard A . Hemeostatic chemokines increase survival of B-chronic lymphocytic leukemia cells through inactivation of transcription factor FOXO3a . Oncegene . 2007 ; 26 : 7081 - 91 .
24. Johnson Z , Power CA , Weiss C , Rintelen F , Ji H , Ruckle T , Camps M , Wells TN , Schwarz MK , Proudfoot AE , Rommel C . Chemokine inhibition-why, when, where, which and how? Biochem Soc Trans . 2004 ; 32 : 366 - 77 .
25. Bach TL , Chen QM , Kerr WT , Wang Y , Lian L , Choi JK , Wu D , Kazanietz MG , Koretzky GA , Zigmond S , Abrams CS . Phospholipase cbeta is critical for T cell chemotaxis . J Immunol . 2007 ; 179 : 2223 - 7 .
26. Hornstein I , Alcover A , Katzav S. Vav proteins, masters of the world of cytoskeleton organization . Cell Signal . 2004 ; 16 : 1 - 11 .
27. George OL , Ness SA . Situational awareness: regulation of the Myb transcription factor in differentiation, the cell cycle and oncogenesis . Cancers . 2014 ;6: 2049 - 71 .
28. Stenman G , Andersson MK , Andrén Y . New tricks from an old oncogene gene fusion and copy number alterations of MYB in human cancer . Cell Cycle . 2010 ; 9 : 2986 - 95 .
29. Kohda T , Asai A , Kuroiwa Y , Kobayashi S , Aisaka K , Nagashima G , Yoshida MC , Kondo Y , Kagiyama N , Kirino T , Kaneko-Ishino T , Ishino F . Tumor suppressor activity of human imprinted gene PEG3 in a glioma cell line . Genes Cells . 2001 ; 6 ( 3 ): 237 - 47 .
30. Feng W , Marquez RT , Lu Z , Liu J , Lu KH , Issa JP , Fishman DM , Yu Y , Bast RC . Jr. Imprinted tumor suppressor genes ARHI and PEG3 are the most frequently down-regulated in human ovarian cancers by loss of heterozygosity and promoter methylation . Cancer . 2008 ; 112 ( 7 ): 1489 - 502 .
31. Ramsay RG , Gonda TJ . MYB function in normal and cancer cells . Nat Rev Cancer . 2008 ; 8 ( 7 ): 523 - 34 .
32. Introna M , Golay J . How can oncogenic transcription factors cause cancer: a critical review of the myb story . Leukemia . 1999 ; 13 ( 9 ): 1301 - 6 .
33. Li L , Tan J , Zhang Y , Han N , Di X , Xiao T , Cheng S , Gao Y , Liu Y. DLK1 promotes lung cancer cell invasion through upregulation of MMP9 expression depending on notch signaling . PLoS ONE . 2014 ; 9 ( 3 ): e91509 .
34. Bayramoglu A , Gunes HV , Metintas M , Değirmenci I , Mutlu F , Alatas F. The association of MMP-9 enzyme activity , MMP-9 C1562T polymorphism, and MMP-2 and -9 and TIMP-1 ,- 2 ,- 3 , and -4 gene expression in lung cancer . Genet Test Mol Biomarkers . 2009 ; 13 ( 5 ): 671 - 8 .
35. Wang RJ , Wu P , Cai GX , Wang ZM , Xu Y , Peng JJ , Sheng WQ , Lu HF , Cai SJ . Down-regulated MYH11 expression correlates with poor prognosis in stage II and III colorectal cancer . Asian Pac J Cancer Prev . 2014 ; 15 ( 17 ): 7223 - 8 .
36. Morrissey C , True LD , Roudier MP , Coleman IM , Hawley S , Nelson PS , Coleman R , Wang YC , Corey E , Langge PH , Higano CS , Vessella RL . Differential expression of angiogenesis associated genes in prostate cancer bone, liver and lymph node metastases . Clin Exp Metastasis . 2008 ; 25 ( 4 ): 377 - 88 .