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DISCOVERY OF MICRORNA-REGULATED PROTEIN PATHWAY
AS BIO-MARKER FOR LUNG CANCER
Nilubon Kurubanjerdjit1,*, Nattakarn Iam-On1 and Ka-Lok Ng2,3
1
School of Information Technology, Mea Fah Luang University, Chiang Rai 57100, Thailand
Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan
3
Department of Medical Research, China Medical University Hospital, China Medical
University, Taichung, Taiwan 40402
*e-mail [email protected]; [email protected]
2
Abstract
Diagnosis of lung cancer at its early stage is important for improving the effect of patient’s
treatment and survival rate. MicroRNAs play crucial roles in human cancers, it was know that
they play an important role in the regulation of cell growth, differentiation, and apoptosis.
However, their role in cancer is not yet fully discovered and understood. The identification of
microRNA mechanism in cancer may provide important information of genetic regulation in
tumorigenesis. In this work, our results suggested that microRNAs may act as potential
biomarkers for lung cancer, by investigating their downstream regulatory pathways. The
microRNA-regulated protein pathways were constructed by integrating two pieces of our
previous research information; which are (i) lung cancer-associated proteins and (ii)
microRNA-target gene interactions. The pathways may insight reveal crucial mechanisms of
microRNA on their downstream proteins in lung cancer, and ultimately become valuable
therapeutic information for drug discovery or being key information for alternative treatment
discovery for lung cancer patient.
Keywords: microRNA, protein-protein interaction, lung cancer, machine learning, Naïve
Bayes, SVM, MCODE, protein pathway, clique percolation clustering
Introduction
Lung cancer is the leading cause of cancer-related deaths all over the world with a very
high mortality rate. The 5-year overall survival rate highly correlates with the time of
diagnosis and varies between 60-80% in clinical stage I to only 1% in clinical stage IV.
Unfortunately, lung cancer is mostly diagnosed in late stages (Calin et al. 2002). There are
two categories of lung cancer which are small-cell lung cancer (SCLC) and non-small cell
lung cancer (NSCLC) (Takamizawa et al. 2004).
MicroRNAs are a class of naturally occurring; small non-coding RNA endogenous
molecules of ribonucleic acid found in eukaryotic cells (Bartel 2004), up or down-regulation
of microRNA affect their target gene expression. The translational inhibition by microRNA is
a major mechanism in animal systems. A number of microRNA is known for functioning in
diverse processes related to cancer formation such as cell proliferation, cell death,
differentiation, and immunity (He and Hannon 2004). MicroRNA cancer biology is a major
research over the past decades. Many works have demonstrated the crucial role of
microRNAs in cancer though controlling expression of their target genes to facilitate tumour
growth, invasion and immunity system (Ebert and Sharp 2012). Alterations of the microRNA
expression have been associated with changes of cancer cells making microRNAs currently is
one of the most significant molecules involved in cancer research (Leidinger et al. 2012). The
work of Calin et al. (Calin et al. 2002) indicates for the first time a correlation of microRNA
degradation and cancer. Subsequently, there are significant reports about microRNAs
expression changes and cancer from many research works. The first finding of microRNA
expression inducing lung cancer was revealed by Takamizawa et al. (Takamizawa et al.
2004). This work confirmed that reduced microRNA let-7 expression was correlated with a
shorter post-operative survival. The work of Xiong et al. (Xiong et al. 2011) also reports that
let-7 targets BCL-2, which is a proto-oncogene, involves in regulation of apoptosis and its
expression inhibits the growth of A549 cells. Furthermore, the work of squeal-Kerscher et al.
(squela-Kerscher et al. 2008) confirms that let-7 reduces lung cancer tumor cell growth in
immunodeficient mice. There are many reports on other microRNAs that are deregulated in
lung cancer cell lines such as, miR-21, its expression involves negative prognostic factor for
the overall survival rate in non-small cell lung cancer patients (Markou et al. 2008). Besides,
Volinia et al. (Volinia et al. 2012) found that the down-regulation of let-7d, miR-210 and
miR-221 in ductal carcinoma in situ while they were up-regulated in the invasive transition.
The work of Yanaihara et al. (Yanaihara et al. 2006) identified 43 microRNAs that were
differentially expressed in microarrays between normal lung and non-small-cell lung cancer
(NSCLC) pairs. Moreover, Yang et al. (Yang et al. 2010) reported that miR-30, miR-7i and
miR-126 significantly down-regulated in squamous cell lung cancer. The recent study (Izzotti
2010) report that microRNAs expression changes induced by cigarette smoke may be
prevented by N-acetylcysteine, oltipraz, indole-3-carbinol, 5,6-benzoflavone, phenethyl
isothiocyanate and budesonide. MicroRNA expression patterns are potential prognostic tool,
the knowledge on microRNA roles as tumor suppressor or activator component in cancer will
be a valuable discovery to develop microRNA-based therapeutic treatment. Nowadays,
computational algorithms to detect microRNA and their potential targets have been
developed such as miRanda (Enright et al. 2003; John et al. 2004), TargetScan (Bartel 2009),
and PITA (Kertesz et al. 2007). Multiple factors are introduced to identify microRNA target
genes such as complementarily of different regions on microRNAs, binding site conservation
and also target sites accessibility. Different predictive algorithms are based on different
factors; therefore, integrating diverse algorithms may improve target prediction.
MiRanda is an algorithm written in C for finding target genes for microRNAs. This
algorithm was developed at the Computational Biology Center of Memorial Sloan-Kettering
Cancer Center. MiRanda identifies microRNA-target genes based on sequence
complementarily and conservation of target sites; whereas, PITA predicts microRNA-target
gene by calculate the free energies of RNA-RNA duplexes, PITA gives matching scores to
multiple biding sites, therefore, optimal combination of different algorithms may improve the
prediction performance.
Clique percolation clustering (Palla et al. 2005) is a well-known approach for analysing the
overlapping community structure of networks by building up communities from k-cliques
which are fully connected sub-graphs of k nodes. Any two k-cliques are adjacent if they share
k-1 common nodes. A k-clique community is constructed by merging all possible adjacent kcliques. The advantage of this approach is that it allows overlaps between the communities
that can be applied to discover significant proteins that involve in more than one community.
In this study, microRNA-regulated protein interaction pathways were constructed in order
to observe the roles of microRNA in lung cancer by integrating two pieces of information
from previous works, which are i) lung cancer-associated proteins (Kurubanjerdjit et al.
2015) and ii) microRNA target genes of lung cancer (Kurubanjerdjit et al. 2016). Currently,
there is growing number of evidence reporting that microRNAs polymorphisms associate
with drug metabolism and resistance. Furthermore, differentially expression of microRNA
plays crucial roles in sensitivity level of chemotherapeutic agents in lung cancer (Gong et al.
2014). Therefore, the drug target is integrated into the microRNA-regulated protein
interaction pathways to reveal relation of microRNA and drug for lung cancer.
Initially, the novel lung cancer–associated proteins based on MCODE clustering algorithm
(Bader and Hogue 2003) were discovered, then enrichment analysis was performed to
identify biological process and pathways that related to lung cancer formation. Secondly, an
approach to identify microRNA target gene in lung cancer was introduced by adopting
prediction scores from miRanda and PITA algorithms, then classification models were
implemented to make the final prediction. It is expected that this current study may provide
insight for the regulation of microRNA and its downstream protein interactions observed in
lung cancer to reveal the role of microRNA on biological processes related lung cancer. To
construct microRNA-regulated protein pathways, clique percolation clustering is adopted to
discover densely connected protein-protein interaction is referred to as a community which is
a functional module. Furthermore, the target drug of microRNA-related proteins also
observed in this study to explore microRNA-drug target interaction.
Methodology
Data Sources
1. MCODE-Clustering on protein-protein interaction
A collection of experimentally confirmed lung cancer proteins was obtained from two
resources, i.e., Online Mendelian Inheritance in Man (OMIM) and Lung Cancer Database
(HlungDB) (Yanaihara 2006). Experimentally confirmed human protein-protein interactions
(PPI) were obtained from BioGrid (Database of protein and genetic interactions). The OncoProtein (OCP) and Tumor Suppressor Protein (TSP) data are derived from the following three
databases: (1) Tumor Associated Gene database of Taiwan national Cheng Kung University
(http://www.binfo.ncku.edu.tw/TAG/), (2) Memorial Sloan-Kettering Cancer Center
(https://www.mskcc.org/) and (3) National Yang Ming University. This research collected
656 OCP and 1,024 TSP.
2. MicroRNA-target genes Prediction
The predicted lung cancer genes list was obtained from MCODE-Clustering on PPI
(Kurubanjerdjit et al. 2015) and their FASTA sequence was obtained from Uniprot (Bairoch
2005). A whole set of human microRNAs sequence was downloaded from mirBase (Sam
2005), list of 39,111 experimentally confirmed microRNA-target pairs were obtained from
mirTarBase (Sheng 2014).
System Workflow
Figure.1 depicts the two steps of this integrating system; firstly, protein clusters were
obtaining by using MCODE to extract associated lung cancer protein list which was an input
data for the next step. Then, the process of microRNA-target prediction was executed;
microRNA-target algorithms (PITA, miRanda) were applied on the clusters found by
MCODE. The miRanda and PITA prediction scores were obtained .These scores were used
as feature vectors for machine learning classifications to identify the final microRNA-target
results.
Figure.2 shows the process of protein clustering by MCODE. Initially, a set of PPIs of lung
cancer proteins with their protein interacting partners was submitted into AllegroMCODE
analyzer in the Cytoscape software (Shannon 2003) to obtain highly interconnected clusters
with their score and ranked in a network. Then, the clusters with their complex score over 1.5
and clusters associated by greater than 50% of lung cancer proteins were filtered out to be
determined. Besides, enrichment analysis was performed using DAVID (Huang et al. 2009)
with the p-value of 0.005. Then, the top five biological processes or KEGG pathways related
to cancer formation with the highest rank of p-value were investigated, and finally, we
inferred lung cancer-related TSP and OCP based on the PPI assumption, that is, if a protein X
interacts with a cancer protein (TSP or OCP), it is very likely that X may function as cancer
protein (Kurubanjerdjit et al. 2015).
Figure 1. The system flowchart of current study.
Figure 2. Protein Clustering by MCODE.
Figure.3 is a system flowchart of microRNA-target prediction. Initially, a training set was
generated by inputting experimentally confirmed microRNA sequences and their target
FASTA sequences into PITA and miRanda to get the prediction scores. The microRNAtarget pairs that satisfied these two algorithms were filtered as a positive set. A negative set is
the pairs that satisfied those two algorithms with the positive set subtracted. Secondly, a test
set was prepared by the two predictors. A whole set of microRNAs sequences and a set of
cancer associated genes, where FASTA sequences were submitted into predictors. The
microRNA-target pairs with two prediction scores that satisfied the predictors were extracted
to be the test set. Thirdly, Naïve Bayes and Support Vector Machine (SVM) (Cortes and
Vapnik 1995) were selected to classify the final prediction results. The training set was
submitted into these two classifiers with optimum parameter setting in order to build up the
classification models. Next, the test set was submitted to the two classifiers models to give
the final prediction result. Three parameters are required for miRanda execution; threshold
score, MFE and scaling factor. In this study, they were set to be 80, 14 kcal/mol and 2.0,
respectively. The max-score was observed in this study. Note that, the higher the score is the
better binding between microRNAs and their target genes.
For PITA, sequence of microRNA and UTR were analysed by default parameter setting. In
case of single binding site, the score is given by ΔΔG value; whereas, for multiple binding
sites, the scores were determined by the minimum value of their binding.
The open-access software RapidMiner (Hofmann and Klinkenberg 2013) was adopted as a
tool for classification in this work. Two classification algorithms; Naïve Bayes and Support
Vector Machine (SVM) were selected to predict which microRNAs are likely to target which
associated lung cancer genes.
For Naïve Bayes parameter setting, estimation mode is set to greedy, value of minimum
band-width is set to 0.1 and number of kernels is set to 10. There following five parameters
setting were used for the SVM classifier; ‘kernel type’ is set to radial, ‘kernel gamma’,
‘kernel cache’, ‘c value’ and ‘convergence epsilon’ are set to 1.0, 200, 0.5 and 0.001,
respectively. Once the two classifiers were optimized, they were adopted to evaluate the test
set. MicroRNA-target pairs obtained from these two classifiers were integrated and only the
pairs that were predicted by the two classifiers were filtered as the final results.
The result sets obtained from these two steps were constructed as the putative microRNAregulated protein pathways, the biological processes of microRNAs and their lung cancer
protein targets were investigated to infer the roles of microRNAs in lung cancer.
Figure 3. MicroRNA-target prediction.
Clustering microRNA-regulated proteins-cFinder
The two sets of results obtained from previous works; microRNA-target genes and proteinprotein interactions were combined to construct microRNA-regulated protein pathways for
major biological processes related to lung cancer; apoptosis, programmed cell death, small
cell lung cancer (SCLC), and non-small cell lung cancer (NSCLC). Clique percolation
clustering was adopted to construct microRNA-regulated protein pathways. In protein-protein
interaction network, a densely connected area is referred to as a cluster that all protein
members are likely to function in similar biological process. A set of proteins involved in
apoptosis, programmed cell death, SCLC, and NSCLC was inputted into the software,
cFinder (Adamcsek et al. 2006) to analyse the network of PPI based on clique percolation
clustering approach.
MicroRNA-regulated protein pathways with target drugs
Exploring the significant drugs for the pathways was performed based on the assumption that
the expression of specific microRNA may break novel paths for overcoming lung cancer
resistance and the personalised therapy. The Genomics of Drug Sensitivity in Cancer was
adopted in this study (http://www.cancerrxgene.org). It is the free accessible online software
constructed by Sanger Institute, Cancer Center of Massachusetts general hospital.
Results
Table I shows the total of seven clusters was obtained from Clique Percolation Clustering.
Those clusters compose of microRNAs and their related proteins, two clusters at k equals to 3
in apoptosis, two clusters at k equals to 3 in programmed cell death, one cluster at k equals to
4 in programmed cell death, one cluster at k equals to 3 of SCLC and one cluster at k equals
to 4 of NSCLC.
Table 1 Target Drug for microRNA-related proteins
Cluster
ID
k
Biological Process
1
2
3
4
5
6
7
3
3
4
4
3
3
4
Apoptosis
Apoptosis
Programmed cell death
Programmed cell death
Programmed cell death
SCLC
NSCLC
#microRNA
#protein
2
3
4
4
1
2
5
3
6
5
10
3
3
4
microRNA-regulated protein pathways in Apoptosis
Figure 4 shows the microRNA-regulated protein pathways (community) in apoptosis for
clique community with k equals to 3. CFLAR gene is a regulator of apoptosis and clearly
found that its related pathways are signaling by GPCR and apoptotic pathway in synovial
fibroblasts. Jin et al. (Jin et al. 2014) reported the role of miR-146a in human chondrocyte
apoptosis in response to mechanical injury, and may contribute to the mechanical injury of
chondrocyte. Furthermore, it was reported that miR-146a,b involved in human dendritic cell
apoptosis (Park et al. 2015). The mismatch of oxidized miR-184 with Bcl-xL and Bcl-w is
involved in the initiation of apoptosis (Wang et al. 2015).
Figure 5 depicts miR-422a with their protein pathway, this microRNA is found by X that it
plays an important role in colorectal cancer (Faltejskova et al. 2012). MYC is widely known
as a crucial regulator of cell cycle progression, apoptosis and cellular transformation. MiR34c is reported by (Catuogno et al. 2013) that it markedly increased resistance to paclitaxelinduced apoptosis. Mir-346 is reported that it up-regulates AGO2 protein expression to
augment the activity of other microRNAs and contributes to cervical cancer cell
malignancy(Guo et al. 2015).
Figure 4. microRNA-regulated protein
pathway in apoptosis (k=3).
Figure 5. microRNA-regulated protein
pathway in apoptosis (k=3).
microRNA-regulated protein pathway in program cell death
Figure 6 and Figure 7 display miR-520c-3p and their regulated protein pathway, it was
found that overexpression of miR-520c-3p dramatically inhibited the proliferation, cell cycle
progression, invasion, and migration of gastric cancer, while down-regulation promoted these
properties (Li et al. 2014). Consistently, the report from Jie (Jie 2015) also indicates that this
microRNA inhibits proliferation, apoptosis in NSCLC.
Figure 8 shows the regulation of miR-326 in programmed cell death. The work of Wang
(Wang 2013) indicated miR-326 functions as a tumor suppressor, the down-regulation of
miR-326 may have potential value for predicting clinical outcomes in glioma patients with
high pathological grades. This suggests that miR-326 is an important candidate tumour
suppressor, and its down-regulated expression may contribute to glioma progression.
Figure 6. microRNA-regulated protein
pathway in cell death (k=4).
Figure 7. microRNA-regulated protein
pathway in cell death (k=4).
Fig 8. microRNA-regulated protein pathway in cell death (k=3).
microRNA-regulated protein pathway in SCLC and NSCLC
Figure 9 depicts miR-590-3p pathway. This microRNA promotes cell proliferation and
invasion in T-cell acute lymphoblastic leukaemia by inhibiting RB1 (Miao 2016). Figure 10
shows miR-217 pathway, it functions as tumour suppressor gene and correlates with cell
resistance to cisplatin in lung cancer (Guo et al. 2014).
Figure 9. microRNA-regulated protein
pathway in SCLC (k=3).
Figure 10. microRNA-regulated protein
pathway in NSCLC (k=4).
microRNA pathways with their target drugs
Proteins in each microRNA-regulated pathway were mapped with their drug interaction from
DrugBank database (David et al. 2008) Table II depicts drugs, drug targets, and interacting
microRNAs found in our study. It was recorded by National Cancer Institute (NIH:
http://www.cancer.gov/about-cancer/treatment/drugs/) that cetuximab is EGFR inhibitor used
for the treatment of metastatic colorectal cancer, NSCLC and head and neck cancer. Besides,
bupivacaine 0.25% encapsulated by multilamellar liposomes was administered epidurally to a
patient suffering pain associated with lung cancer and the effect compared with a plain
bupivacaine solution of the same concentration (Lafont et al. 1996). The work of Carmazzi et
al. (Iorio et al. 2012) revealed mechanisms of nadroparin-mediated inhibition of proliferation
of two human lung cancer cell lines. Furthermore, Inazu et al. (Inazu et al. 2013) identified
choline transporter-like protein 1 (CTL1) -mediated choline transport system provides a
potential new target for therapeutic intervention in lung cancer.
Table II Drug, drug targets and interacting microRNAs.
Drug
Drug target
Interacting microRNA
Cetuximab, Etanercept
Immune Globulin Human
CFLAR
miR-146-5p, miR-184
Bupivacaine
Carboprost Tromethamine
Alprostadil
Nadroparin
Pseudoephedrine
2,6-Dihydroanthra/1
9-Cd/Pyrazol-6-One
Insulin Human
Choline, Vitamin C
HIPK2, MYC, DAXX, TP73, JUN
miR-34c-5p, miR-346
EP300, HIPK2, DAXX, TP73, JUN
EP300, HIPK2, MYC, DAXX, TP73,
PRKDC, TSC22D3, TBP, UBC
miR-422a
miR-34c-5p, miR-346
UBC, MYC, JUN, PRKDC, EP300,
DAXX, HIPK2, TBP, TP73
miR-422a,
miR-34c-5p,
miR-345, miR-520c-3p
Discussion and Conclusion
MicroRNAs are emerging as key components in gene regulatory pathways in human
cancers. It is known that their expression patterns are able to use as potential prognostic,
diagnostic and treatment methods. One important area for microRNA research is to
understand their functions and the relevant signaling pathways in initiation, and then tumors
progression and drug-resistance to be able to design. Discovery the roles of microRNA in
cancer would be helpful in developing novel technique in microRNA-based therapeutic
treatment. In this study, microRNA-regulated protein interaction pathways were constructed
by integrating the lung cancer-associated proteins and microRNA target genes from previous
research works. Next, microRNA-regulated protein pathways were created based on clique
percolation clustering to discover densely PPI interaction regions in apoptosis, programmed
cell death, SCLC and NSCLC biological process. Lastly, the drug of microRNA-target
protein is observed to explore microRNA-drug target interaction. The approach introduced in
this work and also the results should be of value for future studies to reveal the role of
microRNAs in cancer study.
Future work will focus on the co-interaction and regulation of microRNAs and how they
contribute together to lung cancer. Once gained understanding, it will be a valuable
knowledge for guiding the creation of new effective drug therapies.
Acknowledgements
The work of Nilubon Kurubanjerdjit and the work of Natthakan Iam-On are supported by
Mea Fah Luang University, Chiang Rai, Thailand. The work of Ka-Lok Ng is supported by
the Ministry of Science and Technology of Taiwan (MOST) under grants MOST 102-2632E-468-001-MY3, MOST 105-2632-E-468-002 and MOST 104-2221-E-468-012, and also
supported by Asia University under the grants 103-asia-06 and 104-asia-03.
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