| Research Article |
Open Access |
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| In Silico Gene Expression Based Analysis on Claudin Family Members
Association with Human Thyroid Cancer |
| Shaukat Iqbal Malik1*, Zoya Khalid2 and Sheema Sameen2 |
| 1Department of Bioinformatics, Mohammad Ali Jinnah University, Islamabad, Pakistan |
| 2Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad, Pakistan |
| *Corresponding author: |
Shaukat Iqbal Malik
Department of Bioinformatics
Mohammad Ali Jinnah University, Islamabad, Pakistan
E-mail: drsimalik@jinnah.edu.pk |
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| Received November 07, 2011; Accepted December 05, 2011; Published December 12, 2011 |
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| Citation: Malik SI, Khalid Z, Sameen S (2011) In Silico Gene Expression Based
Analysis on Claudin Family Members Association with Human Thyroid Cancer. J
Proteomics Bioinform 4: 278-283. doi:10.4172/jpb.1000201 |
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| Copyright: © 2011 Malik SI, et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. |
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| Abstract |
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| Thyroid cancer is one of the major types of cancers worldwide. A large amount of effort has been put in order
to find the genetic basis of this cancer. Many genes have been reported before out of which claudin family is
one of the example. The claudin protein family consists of more than 20 members which are made up of key
structural rudiments inside the tight junction. The association of claudin-1 with the thyroid cancer has already been
predicted experimentally. In order to investigate the genetic reason of human thyroid cancer computationally, the
systematic analysis of claudin gene family has been carried out. To fulfill this task the bioinformatics methodologies
are combined for the assessment of claudin gene family expressions. Results obtained showed and verified the
association of CLDN1 member with the thyroid cancer. |
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| Abbreviations |
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| PTC: Papillary Thyroid Carcinoma; SAGE: Serial
Analysis of Gene Expression; TPM: Tags per Million; B-RAF: v-Raf
murine sarcoma viral oncogene homolog B1; RAS Rat Sarcoma |
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| Introduction |
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| Thyroid cancer is the most common tumor of endocrine system.
It has three major subtypes out of which papillary thyroid carcinoma
accounts as the major type. The molecular genetics analysis of thyroid
cancer has revealed the presence of B-RAF and RAS point mutations
[1] |
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| Papillary thyroid carcinoma is well differentiated thyroid cancer.
Experimentally it has been shown that constitutive activation of MAP
(Mitogen Activating Protein) kinase effectors play a major role in
causing papillary thyroid carcinoma. In addition to that the activating
mutations of the tyrosine receptor kinases RET and NTRK and point
mutations of the intracellular signaling effectors RAS and BRAF are
found to be associated with this. The chromosomal recombination
is the cause of RET activation that ultimately fall out with a fusion
protein made up of the intracellular tyrosine kinase domain of RET
coupled to the N-terminal fragment of a heterologous gene, giving rise
to the RET/PTC oncoproteins [2]. In comparison with the childhood
and adult onest papillary thyroid carcinoma it has been reported that
in childhood papillary thyroid cancer, despite of history of radiation
exposure, RET/PTC rearrangements are a key event, while in adultonset
papillary thyroid cancer, the most common molecular event is
BRAF point mutation, instead of the RET/PTC rearrangements [3]. |
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| The previous analysis on BRAF gene shows that BRAF mutation
may be a key genetic factor for the metastatic progression of papillary
thyroid carcinoma. In addition to that it has also been reported that
this gene mutation is a major risk factor for loco regional lymph node
metastasis and has potential utility as a surrogate marker [4]. As 80% of
all the thyroid cancers diagnosed are of papillary carcinoma type ,this
study will mainly focus on this sub type of human thyroid cancer. |
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| The claudin protein family consists of more than 20 members
which are made up of key structural rudiments inside the tight
junction [5]. Tight Junctions have the capability to maintain the cell
polarity which may regulate cell proliferation and differentiation [6].
The claudin (CLDN) genes encode a family of proteins which play a
major role in the configuration of tight junction as well as associated with the proper functioning of these junctions. In the past decades, it
has become evident that CLDN gene expression is commonly mutated
in several human cancers [7,8]. |
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| There is a likelihood that along with the activation of MAP
kinase effectors, there are other gene family members as well that
might be involved in the pathogenesis of papillary thyroid cancer.
For that purpose a high throughput in silico analysis has been made
to systematically evaluate the gene expression of claudin gene family
members. It has been evident that mutations in claudin are associated
with various human cancers. For this the whole claudin family is
systematically analyzed to check its association with human thyroid
cancer. The wet lab analysis has previously been done which shows
the involvement of claudin-1 as a genetic change in causing papillary
thyroid cancer [9-11]. |
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| Methods |
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| The methodology includes the following bioinformatics tools: |
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| Gene finder |
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| With the help of this gene finder tool a particular gene or more than
one gene can be found. This tool is available at CGAP
http://cgap.nci.nih.gov/Genes/GeneFinder. By selecting the papillary thyroid cancer
and claudin gene family as search criteria the tool will provide the list
of claudin gene family members which are thought to be involved in
papillary thyroid cancer. |
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| SAGE and Virtual Northern Blot Analysis |
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| SAGE is a web based tool that is freely available at NCBI and on CGAP database. SAGE provides the quantitative and concurrent
analysis of a large number of transcripts and also makes available
applicable means for the evaluation of expressed genes in different
tissue states. SAGE measures the number of tags that contains the
adequate information to distinctively identify a transcript. There
are two SAGE libraries available, one for normal tissue and one for
papillary thyroid tissue. The chosen tumor tissue was follicular variant
as its library is available at SAGE genie [11]. SAGE Anatomic Viewer
which is available at http://cgap.nci.nih.gov/SAGE/AnatomicViewer
[12] was used for obtaining the tags. The reliable tags obtained from
SAGE will then mapped to the unigene clusters.
http://www.ncbi.nlm.nih.gov/unigene/. The authentic unigene clusters which were matched
to SAGE tags were then picked. These tags were then used for the
analysis of gene expression in normal and malignant tissue. |
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| Microarray analysis |
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| The two microarray datasets GDS1732 and GDS1665 available at
GEO website is taken http://www.ncbi.nlm.nih.gov/geo/ [13-16].The
first data set contains the expression profiling of 7 classical papillary
thyroid carcinoma (PTC) samples and 7 paired normal thyroid tissue
samples (normal group) while the second data set contains classical
papillary thyroid carcinoma (PTC) tumors from 9 patients and 9
control samples. This analysis includes the statistical parameter t-test,
and the clustering parameters to confirm the clauidin1 association with
papillary thyroid cancer. |
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| T-Test: It separates the significant and non-significant genes on the
basis of P-value .The P-value less than 0.005 (p<0.005) are considered
to be noteworthy. Further volcano plot marked the position of the
susceptible gene as either significant or non-significant. |
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| Clustering parameters: The genes which are found to be significant
from the T-test analysis are further employed to clustering parameters.
These parameters include the three algorithms which are Hierarchical
clustering algorithm, K-Means clustering and Self Organizing Tree
Algorithm. |
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| Results |
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| Gene finder |
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| The gene finder tool categorized those family members which are
more recurrently found to be linked with the papillary thyroid cancer
on the basis of evaluation of gene expression between normal and
malignant thyroid tissue. The gene members found by the gene finder
tool are then chosen for further analysis. It is summarized in Figure 1. |
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Figure 1: Short SAGE Map. |
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| SAGE and virtual northern blot analysis |
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| The reliable tags of 7 claudin genes were extracted as recognized
by the gene finder tool. The Tags per million counts (TPM) and the
fold change was computed for each gene respectively. The results are
summarized in Table 1. |
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Table 1: SAGE and Virtual Northern Blot Analysis. |
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| Microarray analysis of claudin gene family |
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| For the Micro array analysis the comparison is carried out between
the two phenotypes in both the data sets. The two data sets GDS1732
and GDS1665 available at GEO website is taken. The Table 2 listed
down the results obtained from microarray analysis which clearly
shows CLDN1 as the most significant gene according to the two fold change difference. Further the results obtained from t-test and
clustering parameters also confirmed the above mentioned outcomes
(Table 3, Figure 2, 3, 4 and 5 respectively). |
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Table 2: Microarray Analysis in normal and papillary thyroid tissue cells. |
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Table 3: Significant P-values in both the datasets. |
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Figure 2: Log p-value versus mean difference between the two phenotypes: Red color shows the more significant genes. The black color is indicating the non
significant gene. (A) GDS1732 (B) GDS1665 |
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Figure 3: Hierarchical Clustering diagrams of the two datasets (a) GDS1732 (b) GDS1665. Red color indicates the highly expressed genes, Green color shows down
regulating genes and Gray color indicates the missing values in the datasets. |
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Figure 4: K-Means Clustering diagram of the two datasets (a) GDS1732 (b) GDS1665. |
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Figure 5: SOTA performed. Heat Map Analysis: Clusters are showing up regulation and down regulation in case of PTC. Red color spectrum shows up regulation
while green color spectrum shows down regulation and Grey portion shows missing values in the datasets. (a) GDS1732 (b) GDS1665. |
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| Discussion |
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| In the past few years a large amount of effort has been done in finding the genetic cause of thyroid cancer. All the practice done was
based on experimental analysis. These experimental technologies had
revealed some gene family members which are deemed to be associated
with the human thyroid cancer. |
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| In this study instead of experimental analysis the in silico analysis
approach has been adopted for the assessment of gene expression
analysis in human thyroid carcinoma. To implement this approach
practically the claudin gene family members has been acquired. Up
to our information for the very first time, the systematic investigation
of claudin family members has been carried out for gene expression
analysis in papillary thyroid cancer a major type of thyroid carcinoma.
The methodology adopted will help in scanning the gene expression
patterns in less time and money. The results obtained from SAGE
and virtual Northern Blot analysis showed clearly that out of these 7
genes only CLDN1 is found to be over expressed by taking the fold
change value > 2 fold as significant. The other genes CLDND1, CLDN3, CLDN4, are found to be expressed in normal thyroid tissue. While
there is no significant difference of expression level found in CLDND2,
CLDN4, CLDN15 genes. |
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| In addition to this the microarray analysis also determined the
over expression of CLDN1 member in papillary thyroid carcinoma.
The claudin gene whose expression was high in both the data sets
is considered to be as the valid results and this could be judged by
comparing the fold change value. As mentioned above fold change
value which is greater than two fold is considered to be noteworthy.
This fold change difference is the minimum threshold value to compute
the difference in expression level. In accordance that the t-test also
shows CLDN1 as positive significant gene, which in turn means that
it is up regulating in papillary thyroid carcinoma. Furthermore all
the three means of clustering analysis predicted that CLDN1 is over
expressed in case of papillary thyroid cancer as its expression status is
higher in cancer datasets as compared to normal one. |
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| As stated above the association of CLDN1 with papillary
thyroid carcinoma has been predicted experimentally before. Our
computational approach has verified that CLDN1 act as a genetic
change in causing papillary thyroid cancer. |
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| CLDND1, CLDN3, and CLDN4 these three genes are found to be
down regulated by the SAGE analysis but in microarray analysis no
clear significance is found in both of the datasets. |
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| The in silico bioinformatics approaches are thought to be reliable
and accurate as it involved the data for analysis from reliable sources
and these are based upon the DNA sequencing. Combined serial
analysis can act as good starting point in disease gene discovery. The
use of in silico gene mining approaches provides an excellent scaffold
for the initial identification of key genes and gene clusters whose expression is altered in disease tissue which provides a road map to
assist the biologists. |
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| Conclusion |
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| This study verified the association of claudin-1 as a genetic cause
in causing papillary thyroid cancer. By this it can be inferred that
bioinformatics methodologies are convincing approaches for the
evaluation of gene expression. The results will further be verified by
experimental approaches but this study provides an initial point for the
biologists to find the new cancer insights. |
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