A multidimensional integration analysis reveals potential bridging targets in the process of colorectal cancer liver metastasis
A multidimensional integration analysis reveals potential bridging targets in the process of colorectal cancer liver metastasis
Bo Gao 0 1 2
Tian Yu 2
Dongbo Xue 1 2
Boshi Sun 2 5
Qin Shao 0 2
Hani Choudhry 2 4
Victoria Marcus 0 2
Jiannis Ragoussis 2
Yuguo Zhang 2 3
Weihui Zhang 1 2
Zu-hua Gao 0 2
0 Department of Pathology, The Research Institute of McGill University Health Center , Montreal, Qu eÂbec , Canada , 3 Section of Immunity , Infection and Inflammation , Division of Applied Medicine, School of Medicine and Dentistry, Institute of Medical Sciences, University of Aberdeen , Aberdeen, Scotland , United Kingdom
1 Department of General Surgery, the First Affiliated Hospital of Harbin Medical University , Harbin , China
2 Editor: Klaus Roemer, Universitat des Saarlandes , GERMANY
3 Department of Traditional and Western Medical Hepatology, The Third Hospital of Hebei Medical University , Shijiazhuang , China
4 Department of Biochemistry, Faculty of Science, Cancer and Mutagenesis Unit, King Fahd Center for Medical Research, Center of Innovation in Personalized Medicine, King Abdulaziz University , Jeddah , Saudi Arabia , 6 McGill University and Genome Quebec Innovation Centre , Montreal, Qu eÂbec , Canada
5 Department of General Surgery, the Second Affiliated Hospital of Harbin Medical University , Harbin , China
Approximately 9% of cancer-related deaths are caused by colorectal cancer. Liver metastasis is a major factor for the high colorectal cancer mortality rate. However, the molecular mechanism underlying colorectal cancer liver metastasis remains unclear. Using a global and multidimensional integration approach, we studied sequencing data, protein-protein interactions, and regulation of transcription factor and non-coding RNAs in primary tumor samples and liver metastasis samples to unveil the potential bridging molecules and the regulators that functionally link different stages of colorectal cancer liver metastasis. Primary tumor samples and liver metastasis samples had modules with significant overlap and crosstalk from which we identified several bridging genes (e.g. KNG1 and COX5B), transcription factors (e.g. E2F4 and CDX2), microRNAs (e.g. miR-590-3p and miR-203) and lncRNAs (e.g. lincIRX5 and lincFOXF1) that may play an important role in the process of colorectal cancer liver metastasis. This study enhances our understanding of the genetic alterations and transcriptional regulation that drive the metastatic process, but also provides the methodology to guide the studies on other metastatic cancers.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: Harbin Medical University Postgraduate
Innovative Research Projects, YJSCX2015-19HYD,
Dr Bo Gao. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Colorectal cancer (CRC) is the second most common cancer in men, the third most common
cancer in women, and accounts for approximately 9% of all cancer deaths. Liver metastasis
is a major factor that is responsible for the high mortality rate seen in patients with CRC.
Approximately 30±50% patients either already have liver metastasis at the time of the CRC
diagnosis, or will have liver metastasis after radical resection of the primary lesion.
Unfortunately, only 10±20% of liver metastatic lesions can be surgically removed[
]. The median
survival time of patients with untreated liver metastatic lesions is only 6.9 months, and the
5-year survival rate is close to 0%. Even when the metastatic lesions can be completely resected,
the average survival time is only 35 months . Therefore, it is imperative to elucidate the
underlying molecular mechanisms of CRC liver metastasis in order to better treat these
patients and improve their survival.
Tumor invasion and metastasis are dynamic processes involving multiple steps. These
processes primarily include: 1) massive proliferation of tumor cells in primary lesions, 2) the
acquirement of metastatic genes by a small population of tumor cells, 3) the detachment of
tumor cells from primary lesions, 4) the invasion of the basement membrane, 5) entry into and
exit from the circulatory system, and 6) the colonization and formation of secondary tumors at
a distant site . Based on the characteristics of tumor metastasis, our group collected samples
from CRC patients in different stages of liver metastasis for next generation sequencing. The
data were uploaded into the Gene Expression Omnibus (GEO) database (GSE72718).
Over decades of intensive research, several individual functional molecules have been
identified in the process of CRC liver metastasis, such as KRAS, BRAF, and EGFR[5, 6]. However,
the progression of tumor metastasis likely involves a coordinated effect on multiple biological
processes including the differential expression of genes and the abnormal regulation of
transcription and translation[7±9]. Therefore, compared with single-line mechanistic studies, a
multidimensional integration analysis from a global perspective can help us to
comprehensively and accurately understand the mechanisms that underlie CRC liver metastasis.
In this study, we used a global and multidimensional integration strategy to analyze the
sequencing data, protein-protein interaction (PPI) and the regulation data of transcription
factor (TF) and non-coding RNA from samples obtained from primary non-metastatic colorectal
tumor (PNMCT), primary metastatic colorectal tumor (PMCT), and their paired metastatic
CRC samples in the liver (LMCT). This approach enabled us to identify the potential bridging
molecules that play a role in the process of CRC liver metastasis including modules with
significant overlap and crosstalk. Furthermore, some potential molecular targets including TFs,
miRNAs, and lncRNAs were identified through the establishment of a regulatory network of
modules. Our results from this comprehensive analysis demonstrate a new methodology for
studying the molecular mechanisms of CRC liver metastasis.
Materials and methods
Sample collection, gene sequencing and identification of differentially expressed genes (DEGs)
This study was approved by GEN (Genetics/Population Research/Investigator Initiated
Studies) Search Ethics of McGill University Health Center. (Approval number:14-448GEN
(HRR#4226). The patient consent was waived by the ethics committee as our study only used
archived tissue and had no to patient care. With the approval of the institutional ethics review
board, we collected 10 PNMCT samples, 9 PMCT samples and 9 LMCT samples from 19
patients with CRC who received the diagnosis and treatment in McGill University Health
Center. This investigation was conducted according to the Declaration of Helsinki. Affymetrix
Human Transcriptome Array 2.0 was used to sequence the gene expression of these samples.
The sequencing data was uploaded to the GEO database (No. GSE72718).
Based on these sequencing data, we used the R limma package to identify the DEGs in
PMCT (PMCT vs PNMC) and LMCT (LMCT vs PNMCT). The threshold of DEGs is
|log2Fold Change|>|log21.2| and p value<0.05. Moreover, we established the heatmap of DEGs.
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Finally, we used the principal component analysis (PCA) method to confirm the correlation
between disease and DEGs.
PPI network and module analysis
The STRING database V10 (http://string-db.org/) and Cytoscape software were used to
establish the PPI networks of PMCT and LMCT. MCODE, a Cytoscape plugin that finds highly
interconnected regions in a network, was used to identify the DEG modules in PPI network
Exploring modules with significant overlap between PMCT and LMCT
For the PMCT- and LMCT-associated module pair, we computed the significance of their
overlapping DEGs using a hypergeometric test as follows: M and n represent the number of
genes in PMCT and LMCT modules, respectively. N is the number of genes in the STRING
database and m is the number of overlapping DEGs. The threshold of an overlapping
module pair was p<0.05.
Exploring modules with significant crosstalk between PMCT and LMCT
The significance of crosstalk between the sub-network modules of PMCT and LMCT is
primarily determined by their interaction times and by comparing results of random computation.
One pair of sub-network modules of PMCT and LMCT had m times of participation
interaction in actual conditions. The original PPI network was randomized 1000 times by maintaining
the degree of distribution of the unchanged nodes. The two sub-network modules with the
same size as the original network modules were randomly screened. We computed the
interaction times in random sub-network modules in the same pair of PMCT and LMCT
modules. The p value for the significance of interaction between a single pair of sub-network
modules was calculated as the randomized simulation computation of interaction times larger than
the actual participation interaction times divided by 1000 times. Interactive sub-network
modules with a p value lower than 0.05 were considered significant interactive sub-networks.
Establishing regulatory networks of modules with significant overlap and crosstalk between PMCT and LMCT
For the PMCT and LMCT module pair, we determined their regulators (TFs and miRNAs) as:
(i) at least two regulations between the regulator and each module of the pair and (ii)
significant enrichment of targets for each regulator per module with a p value cutoff of 0.05 . In
this study, we used the ChIPBase database to predict TFs, several databases including
miRecords, MiRWalk2.0, miRanda, MiRTarget2, PicTar, PITA and TargetScan to predict miRNAs,
and the database lncRNA2target to predict lncRNAs.
The cancer genome atlas (TCGA) database analysis
To verify the results of the overlap and crosstalk networks, we used TCGA database (https://
cancergenome.nih.gov) to analysis the expression of target genes and their effect on the
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survival rate of CRC patients. Cbioportal (http://www.cbioportal.org) was used to analysis 276
CRC samples in TCGA database. Enter the information to the web page of Cbioportal as
following: 1) Select Genomic Profiles: mRNA Expression data; 2) Select Patient/Case Set: Tumors
with sequencing and CAN data (212); 3) Enter Gene Set: the target genes; 4) Click on
ªOncoPrintº and ªSurvivalº. Logrank test was used to analysis the significance of survival rate.
Finally, we used Illustrator software to combine the survival rate files.
Identification of DEGs in PMCT and LMCT
Based on the dynamic process of tumor metastasis, we collected PNMCT samples from 10
patients with CRC who had no liver metastases within ten years of follow up, PMCT and
LMCT samples from 9 patients with CRC who had liver metastases. (Fig 1A) We sequenced
these samples and established a heatmap of DEGs. Analyzing the DEGs using the R limma
package, we identified 2060 DEGs (1034 up-regulated; 1026 down-regulated) and 2837
DEGs (1414 up-regulated; 1423 down-regulated) in PMCT and LMCT, respectively. A Venn
diagram shows that there are a total of 527 overlapping DEGs (243 up-regulated; 280
downregulated) in the two groups. (Fig 1B)
Validation of the correlation between the DEGs and the samples
To demonstrate the correlation between DEGs and the samples tested, we performed PCA on
28 samples using the expression matrix of all DEGs. The results show that 4370 DEGs divide
the 28 samples into 3 groups, as follows: the PNMCT, PMCT, and LMCT groups (Fig 1C).
Identification of PPI networks and functional modules in LMCT and
To clarify the regulatory relationship of the DEGs between the LMCT and PMCT groups, we
used the STRING database to mine the PPI pairs of DEGs in the two groups. We identified
1836 PPI pairs among 489 DEGs in the PMCT group and established a PPI network (Fig 2).
The size of the nodes in this network indicates the degree value of the gene. The number of
neighboring nodes directly connected to the node indicates the importance of the node in the
network. The degree values of the network were then analyzed using Cytoscape software. The
results show that gene nodes including EGFR, EP300, MAPK1, KNG1, PTEN, and BMP4 in
the PPI network have larger degree values. To understand the major functions of the PPI
network, we used the DAVID database (https://david.ncifcrf.gov) to perform a Gene Ontology
(GO) analysis on the gene nodes in the PPI network. The results show that GO terms with a p
value cutoff of 0.01 were primarily involved in the ªpositive regulation of cell proliferationº and
ªmitochondrial transportº (S1 Table). These findings are consistent with the massive
proliferation of tumors cells in the primary lesions, which is one of the steps in the process of metastasis
. Furthermore, mitochondria are the most important energy-providing organelles in
eukaryotic cells, and functional changes in mitochondria are closely associated with the progression
and metastasis of tumors. When we compared the PMCT group with the PNMCT group, we
found that the genes in the PMCT group are consistent with increased proliferation and more
active mitochondria. These results support the notion that cellular proliferation and increased
energy reserves of individual tumor cells are associated with the capacity to metastasize.
We identified 4729 PPI pairs among 1136 differentially expressed genes of LMCT and
established a PPI network (Fig 3). The gene nodes JUN, MAPK1, KDM6A, POTEJ, HSPA5,
and KNG1 in the PPI network have larger degree values. GO terms with a p value cutoff of
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Fig 1. Sequencing data analysis. (A) Illustration of the 3 groups of tumor samples (i.e., PNMCT, PMCT and LMCT) that have been sequenced. (B) Venn
diagram of DEGs. UP and DOWN represent up-regulated and down-regulated genes, respectivity. (C) Validation of the correlation between DEGs and
samples. LMCT, PMCT and PNMCT samples are represented as red, green and blue ellipses, respectivity.
0.01 for the nodes in the PPI network include the positive regulation of metabolism of a variety
of substances (e.g., ªthe positive regulation of macromolecules, nitrogen compounds, and
RNA/nucleic acidsº; S2 Table). Previous studies have shown that active carbohydrate and
nucleic acid metabolism are features of the distant metastasis of tumors[18, 19]. Glucose
metabolism can provide a large amount of energy to support tumor metastasis and can provide
the raw materials for nucleic acid synthesis. The increase in nucleic acid metabolism suggests
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that the processes of transcription and translation of genes and the transmission of genetic
information during tumor metastasis are more active. These results indicate that energy
metabolism and cellular activity are increased in the PMCT group.
Fig 2. PPI network of PMCT. The STRING database was used to mine the PPI pairs of DEGs in PMCT. We identified 1836 PPI pairs among 489
differentially expressed genes and established a PPI network. Red and green nodes represent up-regulated and down-regulated genes, respectivety. The
nodes represented by EGFR, EP300, MAPK1, KNG1, PTEN, and BMP4 in the PPI network had larger degree values.
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Fig 3. PPI network of LMCT. Using the STRING database, we identified 4729 PPI pairs among 1136 DEGs of LMCT and established a PPI network. Red
and green nodes represent up-regulated and down-regulated genes, respectivety. The nodes represented by JUN, MAPK1, KDM6A, POTEJ, HSPA5, and
KNG1 in the PPI network had larger degree values.
Using the PPI network of PMCT and LMCT, we identified the functional modules with the
Cytoscape plugin MCODE. We identified 16 and 38 modules with an MCODE score cutoff of
1.5 in the PPI networks of PMCT and LMCT, respectively (S3 and S4 Tables).
Modules with significant overlap between PMCT and LMCT
After performing the hypergeometric tests, three pairs of gene modules were found to have
significant overlap between PMCT and LMCT with a p value cutoff of 0.05 (S5 Table). These 3
pairs of modules have a total of 82 DEGs including 7 overlapping DEGs (Fig 4).
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PMCT-2 and LMCT-2 module pairs demonstrated overlaps in KNG1 and KISS1R. KNG1
is a potential prognostic marker of CRC, as the survival rate of CRC patients with positive
KNG1 expression is lower than that of CRC patients with negative KNG1 expression. In
addition, Ji et al have shown that silencing KISS1 and KISS1R promotes the growth and
metastasis of CRC cell lines in vitro suggesting that KISS1 and KISS1R are also potential therapeutic
targets. We found that the expression of KISS1 is decreased in PMCT and LMCT, which
might result in the occurrence of CRC metastasis. The results of the GO analysis show that
these 2 modules are both closely associated with G-protein coupled receptor (GPCR) signaling,
which is also the GO term with the most statistical significance (p = 3.46E-11) (S6 Table). Liu
et al have shown that GPCRs promote tumor metastasis in two ways: 1) they activate the Rho
GTPases and change the cytoskeleton of cancer cells; 2) they provide nutrients for
angiogenesis. Therefore, GPCRs may also be crucial regulators for CRC liver metastasis.
The PMCT-6 and LMCT-6 module pairs demonstrated overlaps in COX5B, UQCR10, and
NDUFS7. Wu et al have shown that COX5B is an important target of gastrin and that COX5B
could regulate ATP metabolism and cell growth of CRC cells. In addition, COX5B is also
associated with tumor proliferation. Gao et al have shown that breast cancer cells exhibit
proliferation inhibition and aging after COX5B knockout. UQCR10 and NDUFS7 are both
associated with mitochondrial function and cellular energy metabolism; however, their specific
function in tumors remains unclear. GO analysis reveals that the functions of this module pair
are all related to energy metabolism and ªoxidative reductionº. Tumor proliferation and
metastasis require a large amount of energy and tumor cells are provided with energy from a variety of
sources through a series of oxidative reduction reactions[
]. Our results suggest that this group
of overlapping modules might provide the energy for the development of CRC liver metastasis.
Fig 4. Modules with significant overlap between PMCT and LMCT. Each module was extracted after mapping DEGs to the human PPI
network using the Cytoscape MCODE plugin. The module pairs with significant overlap between PMCT and LMCT were determined by a
hypergeometric test with a cutoff of 0.05. Node size is shown according to its network degree. The module number is marked beside the module.
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The PMCT-6 and LMCT-12 module pairs demonstrated an overlap in the PTEN and
COPS5 genes. These 2 genes are both closely associated with CRC metastasis. Zhong et al have
shown that COPS5 silencing significantly inhibits the ability of CRC cells to invade and it
promotes cell cycle arrest[
]. Chowdhury et al have shown that the restoration of PTEN activity
reduces liver metastasis of in situ CRC[
]. GO analysis shows that the functions of genes
within this module are mainly associated with the formation of microtubule structure (i.e.,
ªmicrotubule-based processº). As an important cytoskeletal component, microtubules
maintain cell morphology, participate in cell movement and transport of intracellular materials,
and are closely associated with tumor metastasis[
]. Our results indicate that both of these
overlapping modules have functions related to the regulation of liver metastasis of CRC.
Modules with significant crosstalk between PMCT and LMCT
In addition to overlapping modules, the interaction among genes in different modules also
featured crosstalk. After random computation and comparison, we found that the 3 pairs of PPI
sub-networks between PMCT and LMCT had significant crosstalk with a p value cutoff of 0.05
(S7 Table). These 3 pairs of modules have a total of 95 significant DEGs including 2
overlapping DEGs, 56 DEGs in PMCT, 27 DEGs in LMCT, and 56 crosstalk pairs (Fig 5). GO analysis
shows that the functions of genes in PMCT-5 and LMCT-7 are similar and are mainly involved
in DNA metabolic processes such as DNA synthesis and assembly and cell cycle regulation.
This increase in DNA synthesis suggests that cellular processes such as cell division and cell
cycle regulation during tumor metastasis are even more active than in non-metastatic cells.
Some studies have indicated that cell cycle-related genes are closely associated with tumor
Fig 5. Modules with significant crosstalk between PMCT and LMCT. The module pairs with significant crosstalk were computed in comparison with
1000 random networks, with a p value cutoff of 0.05. Crosstalk interactions are shown in black. Node size is shown according to its network degree.
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metastasis. For example, cyclin E1 (CCNE1) in the LMCT-7 module has been shown to
promote metastasis of ovarian cancer and bladder cancer[
]. The gene nodes in PMCT-5
(e.g., H1F0) might influence the expression and function of important genes (e.g., CCNE1) in
the LMCT-7 module through crosstalk, which in turn regulates the cell cycle and metastasis of
CRC cells. Therefore, this pair of modules with crosstalk interactions might be a bridge that
connects PMCT and LMCT through the biological functions described above.
In the LMCT-10 and PMCT-9 module pairs, the functions of LMCT-10 are primarily
related to immune regulation while the functions of PMCT-9 involve the positive regulation of
cell proliferation. Tumor metastasis is closely associated with immune regulation in tumors.
The immune escape mechanism helps circulating tumor cells to avoid immune-mediated cell
death and facilitates the metastasis of tumor cells to distant organs[
]. It is worth noting that
the genes that exhibit crosstalk in this pair of modules are mainly interleukins and their
receptors (e.g., IL-3, IL-7R). Yu et al have shown that IL-3 promotes the growth and invasiveness of
prostate cancer cells[
]. IL-7R is highly expressed in lung cancer and CRC and it promotes
lung cancer vascular endothelial growth and metastasis[
]. Therefore, it is possible that these
metastasis-related interleukins and receptors might promote metastasis of CRC and could
serve as a bridge that connects the two disease modules.
Due to the combination of overlap and crosstalk within the energy metabolism-related
module PMCT-6, this module might be the most important module in the 2 sub-networks.
The PMCT-6 module not only overlapped with the 2 LMCT modules (LMCT-6 and
LMCT12) but also demonstrated crosstalk with LMCT-7 (S8 Table). Studies have shown that the 3
genes in LMCT-7 are all associated with tumor metastasis: PRKCI is over-expressed in lymph
node metastasis of esophageal cancer and can be used as a biomarker of metastasis[
can promote metastasis of breast cancer and lung cancer[
], and MPP3 can promote the
migration and invasiveness of liver cancer. This LMCT-7 module might have the same
function (i.e., the promotion of tumor metastasis) in CRC. Our results also show that EGFR is
an important gene node in PMCT-6. EGFR is one of the most important receptors of tumor
cell growth via a variety of signaling pathways. Some studies have shown that the expression of
the metastasis-promoting gene JAG1 in lung cancer cells depends on activation by EGFR[
Our results also reveal that the EGFR and JAG1 crosstalk pair are expressed during the process
of liver metastasis in cases of CRC. Therefore, EGFR in PMCT might promote CRC liver
metastasis through the alteration of JAG1 expression in LMCT.
Transcription regulation networks of modules with significant overlap and crosstalk between PMCT and LMCT
To clarify the underlying transcription and post-transcription regulation networks of the
modules with overlap and crosstalk, we explored the miRNAs, lncRNAs, and TFs that are
responsible for the regulation of genes in these 2 modules. A total of 4 upstream miRNAs, 9 upstream
lncRNAs, and 69 TFs that met the conditions were predicted among genes in the 82
overlapping network modules (Fig 6). DEGs in the 9 network modules with significant crosstalk were
used to predict miRNAs, lncRNAs, and TFs, and the results showed that 24 upstream miRNAs,
9 lncRNAs, and 84 TFs met the conditions (p<0.05) (Fig 7).
We found that some of the TFs that we identified have already been implicated in the
regulation of liver metastasis in the setting of CRC. For example, CDX2 is a TF with tumor
suppressor functions that has been shown to be minimally expressed in CRC cell lines; the
overexpression of CDX2 significantly inhibits the invasive ability of CRC cells[
]. Pancione et al
have reported that dysregulation of INI1 could promote the occurrence of CRC liver
]. Lwamoto et al have reported that the over-expression of E2F1 could promote
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Fig 6. Transcription regulation networks of modules with significant overlap between PMCT and LMCT. TFs and miRNAs were computed based on
the number of their interactions with the module pair and the enrichment significance of their regulating targets. lncRNAs were predicted using the
lncRNA2target database. Network nodes are colored as PMCT, LMCT DEGs and overlapping DEGs with size showing their network degree. TFs are
represented as white triangles, miRNAs are grey triangles, while lncRNAs are yellow diamonds.
metastases of CRC to the liver and lung[
]. These results, to some extent, validate the
accuracy of our predicted transcription regulation networks. In addition, we identified some TFs
that function in the regulation of tumor metastasis. For example, HNF4A, which can promote
the metastasis of liver cancer, was shown to be closely associated with the
epithelial-mesenchymal transition (EMT)[
]; E2F4 has been shown to be minimally expressed in breast cancer
and is associated with the occurrence of metastasis[
]; and ERG has been shown to promote
metastasis of prostate cancer[
]. The roles of these TFs in the regulation of metastasis of CRC
have not been studied; however, these TFs could be potential targets for the treatment of CRC
liver metastasis. Notably, among the top 10 TFs, 9 TFs including CDX2 and INI1 could
regulate both the overlapping and crosstalk networks, which suggests that they might have broader
functions than the TFs that only regulate a single network.
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Fig 7. Transcription regulation networks of modules with significant crosstalk between PMCT and LMCT. Network nodes are colored as PMCT and
LMCT DEGs with their size showing their network degree. TFs are represented as white triangles, pivot miRNAs are grey triangles, while lncRNAs are yellow
In addition to TFs, non-coding genes are also very important for the post-transcriptional
regulation of overlapping and crosstalk networks. In the transcription regulation networks of
overlapping modules, all of the miRNAs that we identified are closely associated with tumor
metastasis. For example, miR-216b and miR-486-5p inhibit invasion and metastasis of liver
], while miR-421 can promote metastasis of gastric cancer and
neuroblastoma. In the transcription regulation networks of crosstalk modules, miR-1[
], miR-1297 [
], and miR-429[
] all have inhibitory functions on the metastasis of CRC,
which further validates the accuracy of our prediction. Furthermore, some of other miRNAs
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we identified are also closely associated with metastasis of other tumor types. For example,
miR-203 can inhibit the proliferation and metastasis of liver cancer, miR-520d-3p can
inhibit the proliferation and metastasis of gastric cancer[
], and miR-520b can inhibit the
migration of breast cancer cells[
]. miR-590-3p had the largest degree value in the two
networks when the results were combined for these two networks. Pang et al have reported that
miR-590-3p can inhibit the migration, invasion, and EMT of glioblastoma[
]. Our miRNA
prediction results suggest that besides the 4 miRNAs that have been demonstrated to regulate
the metastatic capacity of CRC, other miRNAs also have the ability to regulate tumor
metastasis through the regulation of genes in modules that demonstrate overlap and crosstalk.
Therefore, these miRNAs represent potential novel targets for the treatment of liver metastasis in
patients with CRC.
In contrast to miRNAs that have been extensively investigated, studies on the functions
of lncRNAs are still at the initial stage, and the functions of many lncRNAs are still unclear.
However, recent studies have indicated that some of our screened lncRNAs possess regulatory
functions in the metastasis of CRC. For example, MALATA1 and BANCR can promote the
migration and invasion of CRC[
], while TUG1, which is associated with a poor
prognosis, can promote the development of EMT and the metastasis of CRC cells. Although some
lncRNAs have not been studied in CRC liver metastasis, lncRNAs have the ability to regulate
the metastasis of other types of tumors. For example, lincIRX5 can promote the invasion and
metastasis of gliomas. Moreover, lincFOXF1 can inhibit the metastasis of gastric cancer
]. The functions of other lncRNAs have not yet been elucidated. lncRNAs with a
demonstrated ability to regulate tumor metastasis (e.g., lincIRX5 and lincFOXF1) and lncRNAs
with unclear functions (e.g., lincMTX2 and lincTNS1) might have the potential to regulate
CRC liver metastasis in a manner similar to that of TUG1 and MALAT1.
TCGA database analysis
We used TCGA database to analysis the expression of target genes in overlap and crosstalk
network and their effect on the survival rate of CRC patients. The results showed that five target
genes in the overlap and crosstalk networks were differentially expressed in CRC samples
and could effect the survival rate of CRC patients. (i.e., JAG1, KNG1, MYO5C, MYCN and
ACTN2) Among 276 CRC samples in TCGA database, we found the CRC patients with
alteration in KNG1 (p = 0.0215) or MYO5C (p = 0.0312) had lower survival rates than the CRC
patients with normal KNG1 or MYO5C expression. Moreover, the CRC patients with
alteration in JAG1 (p = 0.0077) or ACTN2 (p = 0.0233) or MYCN (p = 0.0297) had lower survival
rates than the CRC patients with normal JAG1 or ACTN2 or MYCN expression. (Fig 8) In this
study, we used a multidimensional integration analysis to establish the overlap and crosstalk
network in the process of CRC liver metastasis. We identified KNG1 and MYO5C in the
overlap network and MYCN, ACTN2 and JAG1 in the crosstalk network. The results of TCGA
analysis validated the results of sequencing and screening in this study.
High-throughput next generation sequencing offers a powerful tool for studying the
underlying genetic alterations of a disease process. Using this technology, we studied the genetic
changes that underlie different stages (from PNMCT through PMCT to LMCT) of CRC liver
metastasis. To fully unveil the global perspective provided by high-throughput sequencing
data, we applied an integration methodology based on sequencing data, PPI networks,
transcription regulation, and some non-coding RNA prediction databases to discover bridging
targets of CRC from a non-metastatic state to a metastatic state.
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Fig 8. The effect of the target genes on the survival rate of CRC patients in TCGA database. Among 276 CRC samples in TCGA database, we found
the CRC patients with alteration in two overlap network genes KNG1 (p = 0.0215) or MYO5C (p = 0.0312) had lower survival rates than the CRC patients with
normal KNG1 or MYO5C expression. Moreover, the CRC patients with alteration in three crosstalk network genes JAG1 (p = 0.0077) or ACTN2 (p = 0.0233)
or MYCN (p = 0.0297) had lower survival rates than the CRC patients with normal JAG1 or ACTN2 or MYCN expression.
Our established overlapping networks revealed 7 overlapping genes between the related
modules of PMCT and LMCT. These genes might regulate a variety of biological processes
associated with tumor metastasis including the regulation of GPCR signaling, cell
proliferation, and energy metabolism. As demonstrated, some overlapping genes such as KISS1R,
COPS5, and PTEN have been shown to have important regulatory functions in the process of
CRC metastasis [
21, 26, 27
]. Interestingly, modules that overlap usually have similar biological
functions. For example, the PMCT-6 and LMCT-6 module pairs are both associated with
energy metabolism while the PMCT-6 and LMCT-12 module pairs are both associated with
ªmicrotubule-based processesº. These results reveal a possible mechanism of CRC liver
metastasis: metastasis-related gene modules in PMCT could use the bridging function of overlapping
genes to induce changes in metastasis-related gene modules in LMCT. Furthermore, these
modules usually have a similar metastasis-related function, which suggests that they might also
participate in the transmission and enhancement of metastasis-related functions in the process
of tumor development. In addition to their overlapping relationship, the PMCT and LMCT
modules display interactive functions through crosstalk. GO analysis shows that the functions
of these modules with crosstalk are all closely associated with tumor activities. For example,
the LMCT-7 module can regulate the cell cycle, the PMCT-9 module can regulate cell
proliferation, and the LMCT-10 module can regulate immune responses. Our findings suggest that
PMCT modules might influence LMCT modules through the occurrence of crosstalk. For
example, interleukin receptors (IL-17R and IL-21RB1) in PMCT-9 can influence the action of
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interleukin genes (IL-3, -15 and -21) in LMCT-10 through crosstalk. Studies have shown that
interleukins and their receptors are closely associated with tumor metastasis; for example, IL-3
and IL-7R in different modules can promote both the proliferation and metastasis of tumors
]. The combination of overlap and crosstalk among module networks (Figs 4 and 5)
suggests that PMCT-6 might be a key module associated with CRC metastasis. PMCT-6 not
only influences the LMCT module through overlap to regulate energy metabolism in CRC
liver metastasis, but it also influences LMCT-7 through crosstalk, which then promotes CRC
metastasis to the liver.
Based on a comprehensive strategy integrating many types of databases and statistical
algorithms, we constructed transcription regulation networks including TFs, miRNAs, and
lncRNAs between the PMCT and LMCT groups, each of which had modules with considerable
overlap and crosstalk. After these 2 networks were combined, we found that 9 of the top 10
TFs could regulate gene nodes in these 2 networks. Some of these genes, such as CDX2, INI1,
and E2F1, have been shown to influence the metastasis of CRC[39±41], which validates the
accuracy of our screening. We have also found other TFs that have not previously been shown
to be associated with CRC liver metastasis. Analysis of the screened miRNAs shows that
miRNAs in the overlapping network are all associated with tumor metastasis; however, none of
them have been studied in CRC liver metastasis. Some miRNAs are able to inhibit CRC liver
metastasis through the regulation of networks with significant crosstalk, such as miR-1,
miR128, miR-1297, and miR-429[48±51]. Although miR-520d-3p, miR-203 and miR-590-3p have
been reported to inhibit other types of tumor metastasis, no functional studies have been
conducted on their role in CRC liver metastasis. Based on these results, we propose that these
miRNAs could potentially serve as novel targets for the treatment of CRC liver metastasis.
Furthermore, we also screened lncRNAs that could regulate the two networks. Similar to
our results with the TFs, 7 of the top 9 lncRNAs could regulate the two networks. Of these,
MALTA1 and TUG1 have been shown to promote the occurrence of the EMT, invasion, and
metastasis of CRC cells[
]. Although no reports have been published on the role of
lincIRX5 and lincFOXF1 in CRC liver metastasis, these genes have been shown to regulate
metastasis of other types of tumors. Thus far, only a few relevant studies have been published on
lncRNAs and CRC liver metastasis. Our predicted lncRNAs might serve as a basis for new
studies on the mechanism of CRC liver metastasis.
In summary, using next generation sequencing data from tumor samples, we used an
integrated approach to explore the potential molecular targets that play a bridging role in the
process of CRC liver metastasis. Our results demonstrate the power of a global and
multidimensional systematic approach on the discovery of potential molecular mechanisms of tumor
metastasis. This strategy not only unveils the dynamic links between PNMCT, PMCT and
LMCT, but could also be applicable to the study of other tumor metastasis, such as breast
cancer with bone metastasis, hepatocellular carcinoma with lung metastasis and lung cancer with
brain metastasis. In addition to further study on validated molecules, more efforts should be
made to study the remaining potential molecular targets (e.g., KNG1, E2F4, miR-590-3p and
lincIRX5). These new molecular targets may also play an important role in CRC liver
metastasis and could be new tools for research on mechanisms in the future.
S1 Table. The function of node genes in PMCT PPI network.
S2 Table. The function of node genes in LMCT PPI network.
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S3 Table. Gene modules in PMCT.
S4 Table. Gene modules in LMCT.
S5 Table. Modules with significant overlap.
S6 Table. The function of modules with significant overlap.
S7 Table. Modules with significant crosstalk.
S8 Table. The function of modules with significant crosstalk.
Conceptualization: BG TY DX JR ZG.
Data curation: HC.
Formal analysis: BG TY DX BS.
Funding acquisition: BG.
Methodology: BG TY DX QS HC ZG.
Project administration: ZG WZ.
Software: BG TY BS.
Supervision: ZG JR.
Validation: QS YZ WZ.
Writing ± original draft: BG ZG.
Writing ± review & editing: VM.
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