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Introduction
Since the completion of the human genome
draft[1,2], there was a recurrent need for informaticians to integrate various
biology information with the human genome. The capability
of viewing different comparative data of a chromosome or
the entire genome becomes highly desirable for determining
the gene expression level from all of the individual
compartments[2]. For example, microarrays and serial analyses of
gene expression (SAGE) are the 2 main approaches for
high-throughput gene expression analysis, and even though these
techniques have been applied broadly, each approach has
its own advantages and disadvantages. They are both
effective in gene expression profiling research; however, at
individual gene expression level, the results of microarrays
are not solid enough and the results of SAGE are subject to
size and reliability of the SAGE libraries. Cross-examination
of different sources of gene expression relational data is not
only important to perform a comprehensive analysis of gene
expression profile, but also provides an approach to revise
each gene expression level.
Presently, the methods of analyses concentrate on
integrated analyses generated by a single type of platform. The
studies have attempted to integrate data of a similar nature,
especially transcriptome data. For example, Lamb
et al[3] integrated gene expression data from different sources to
develop a mechanistic understanding of the function of
cyclin D1 in tumorigenesis. Twenty one genes were found
to be induced by both wild-type and mutant cyclin D1.
Genome alterations, such as chromosomal gains and
losses, deletions, amplification, and methylation, can be
studied with technologies of loss of heterozygosity (LOH),
comparative genomic hybridization (CGH), and CGH
array[4]. It has been discovered that most genome alterations affect
mRNA expression[5]. Thus, it would be very useful to
integrate genome alterations and transcriptome
profiles[6], which, hampered by the fact that different studies use different
platforms, protocols, and analysis techniques, remains a
tough problem.
In this report, we use an approach of integrating a highly
diverse collection of liver cancer genome-wide datasets at
both the genome and transcriptome levels to discover the
robust gene signatures (RGS) in liver cancer.
Materials and methods
System flow The system flow includes 5 major steps: (i)
data preparation; (ii) initial analysis of each dataset; (iii)
unification of the log ratio value of each experiment with
t-statistics algorithm; (iv) integrative analysis with 1-class
Significance Analysis of Microarrays (SAM), coupled with
ranking score method; and (v) gene expression map generation.
Data preparation LOH data in liver cancer was obtained
by searching PubMed
(http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed) with key words (LOH or "loss of
heterozygosity") and "liver cancer". Microarray datasets
were downloaded from the Stanford Microarray Database
(SMD, http://genome-www5.stanford.edu),
http://www.genome.wi.mit.edu/MPR, and Gene Expression Omnibus
(GEO, http://www.ncbi.nlm.nih.gov/geo). Two pairs of liver
cancer SAGE libraries were downloaded from the Cancer
Genome Anatomy Project (CGAP, http://cgap.nci.nih.gov/).
The samples used in all the transcriptome datasets were
reviewed and assigned to one of the liver cancer sample
subtypes, including focal nodular hyperplasia (FNH), liver
adenocarcinoma (Adenoma), primary tumor (PrimTumor), cell
line (Cellline), and metastatic liver cancer (Metastatic). All
the transcriptome datasets were accordingly split into 1 or
more subtype datasets, which were named by the following
method: DatabaseFirstAuthorCancerSubType (eg
XINCHEN-LiverAdenoma) or DatabaseIdPlatformCancer-SubType (eg,
GSE3500GPL2831LiverCellline).
Initial analysis for each present
datasets We developed an algorithm of transforming LOH frequency to log ratio
value. If a gene is located within or overlaps with a LOH
region, then the possibility of under-representation of the
target gene, called PU, is defined as:
where W is a weight factor and F is LOH frequency. A
median frequency value of all LOH reports was used for the
PU calculation if a LOH description had no frequency value in
the corresponding report. The W value was set to 5 in default,
so PU would be between -5 and 0. We did this because that
most downregulated gene expression values (log ratio values)
were in that range, hence, PU and gene expression
M value had a similar range and distribution.
Fisher's exact test[7] was performed on SAGE or Expressed
Sequence Taq (EST) data between the normal and tumor
libraries. The false discovery rate (FDR) values were
calculated and applied as the threshold to screen the differentially
expressed genes (DEG, QDEFAULT=0.10). The log ratio
value, usually named the M value, was calculated between 2
SAGE or EST libraries for each unique tag or gene. The
algorithm was described earlier[7]. If a particular tag had
count nA in library A and count
nB in library B, and if the total
number of tags counted was tA for library A and
tB for library B, then the
M value was calculated as:
M=log2(nA+0.1)+log
2(tB-nB+0.1)-log
2(nB+0.1)-log2(
tA-nA+0.1)
This algorithm simulated M value computation in the
microarray data processing, and the resultant value had very
similar performance of the M value in the microarray.
The SAM method[8] was conducted to screen DEG in the
microarray datasets. The significance threshold was set to
Q<0.10.
Combination of log ratio values Penalized
t-statistics[9] was applied to combine the
M values in parallel experiments within a dataset. The combined values were used in further
integrative analyses.
Integrated analyses One-class SAM statistics, coupled
with an algorithm of computing ranking score, named the
R value, were applied in integrated analyses. The
R value was defined as follows: any DEG log ratio values in all present
dataset had a mean called X _. If >0, for each positive log ratio
value (Xn), Rn was assigned 1, and for each negative
Xn, Rn was set to -2; if
X _<0, for each positive
Xn, Rn was assigned
-2, and for each negative Xn,
Rn was set to 1. Consequently, the
R value was calculated as:
Dual significance thresholds, Q<0.01 in SAM and
R>9 in the ranking score, were used to screen the DEG among all the
datasets.
Unsupervised hierarchical cluster methodology was
conducted to the RGS genes in all the present datasets by using
software package R (http://www.r-project.org/) Gene
Ontology (GO, http://www.geneontology.org). Category
enrichment analysis was performed by using GoMiner software
(Genomics and Bioinformatics Group (GBG) of LMP, Georgia
Tech/Emory University, USA)[10].
System assessment A comprehensive review of the
published papers was conducted to validate the screened DEG.
Two measurements, namely accuracy (Ac) and correlation
coefficients (CC), were used to assess the results of our
approach[11]. The upregulation and downregulation of gene
expression were regarded as positive and negative,
respectively. The validated gene expression information
based on both literature and RT-PCR was used as a "golden
criterion" to evaluate the predicted results. The 4
measurements were each calculated in terms of true positive
(TP), false negative (FN), true negative
(TN), and false positive (FP), as described
earlier[11]. Each measurement was given
as follows:
Gene expression map generation The human genome
(hg18)-based gene expression map was implemented in
Practical Extraction and Report Language (PERL) for the
visualization and comparison of all included datasets. The UC
Santa Cruz (UCSC) databases
(http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database) were used to obtain the
information of each genome element, including chromosome
size, the location of the cytogenetic band, centromere, and
stalk.
Results and Discussion
Preliminary analysis Two microarray datasets, GSE3500
and SMD_XINCHEN, were downloaded from the GEO and SMD databases, respectively. After the assignment of the
samples to liver cancer subtypes, 16 microarray datasets,
namely GSE3500GPL2831LiverCellline,
GSE3500GPL2831-LiverPrimTumor, GSE3500GPL2948LiverPrimTumor,
GSE3500GPL3007LiverPrimTumor, GSE3500GPL3008Liver-PrimTumor, GSE3500GPL3009LiverFNH,
GSE3500GPL3009-LiverPrimTumor, GSE3500GPL3011LiverAdenoma,
GSE3500GPL3011LiverPrimTumo,
GSE3500rGPL2935-Liver-Tumor, GSE3536LiverCellLine,
SMD_XINCHENLiver-Adenoma, SMD_XINCHENLiverCellline,
SMD_XINCHEN-LiverFNH, SMD_XINCHENLiverMetastatic, and
SMD_XIN-CHENLiverPritumor, were collected, and from them, 1156,
3119, 3083, 2267, 614, 1016, 2998, 481, 2427, 1235, 2230, 2910,
1949, 3200, 2188, and 2904 DEG were screened, respectively,
with a significance threshold of Q<0.10. One liver normal
SAGE library, namely SAGE_Liver_normal_B_1, and 2 liver
tumor SAGE libraries, namely
SAGE_Liver_cholangiocar-cinoma_B_K1 and SAGE_Liver_cholangiocarcinoma_B_K2D
were downloaded from CGAP. From the 2 cancer library
(separately) versus the 1 normal library, in total, 1194 and
1020 DEG were obtained via Fisher's exact test, respectively.
A total number of 1 625 051 gene expression measurements
were analyzed.
Since LOH has a higher resolution than traditional CGH
and has been broadly applied in genome alteration studies
in many kinds of cancers, LOH data were adopted in this
work to represent genome alterations. By reviewing the
abstracts of 568 previous reports, 252 liver cancer-related LOH
results were obtained (see supplementary file Table S1). All
the LOH regions and markers were mapped to the human
genome physical map (hg18).
RGS of liver cancer The RGS in liver cancer was
identified. By using the significance threshold of
Q<0.01, 5 genes, namely, KIAA0101, TM2D1, MYB, JUN, and CCK,
were present in at least 14 of 19 liver cancer signatures
(R>13), 90 genes were present in at least 13 signatures
(R>12), and 202 genes in at least 12 signatures
(R>11; Table S2). Our discussion focuses on the 90 genes
(R>12), including 43 under-represented and 47 over-represented genes. The
hierarchical clustering result of the 90 liver cancer RGS genes
is depicted in Figure 1. Many of the RGS genes have
previously been associated with cancers, such as JUN, APOE,
HRG, CCNA2, and LCK. We searched the RGS genes against
the Online Mendelian Inheritance in Man (OMIM) database
in the National Center for Biotechnology Information (NCBI)
and found that 41 of the 90 genes had previously been
associated with cancer (see supplementary file Table S3), while
the remaining 59 genes were potential cancer genes and
worthy of further study.
The GO category enrichment analysis was performed
using GoMiner software. We screened the GO categories at
the condition of 1000 random permutation and the statistical
threshold of FDR<0.001. For the under-represented RGS
genes, 14 GO terms, namely "apoptosis", "chromosomal
part", "cell division", "mitosis", "cell cycle", "chromosome",
"programmed cell death", "M phase", "mitotic cell cycle",
"intracellular non-membrane-bound organelle",
"non-membrane-bound organelle",
"M phase of mitotic cell cycle", "cell
cycle phase", and "cell cycle process" were screened
nevertheless for the over-represented RGS genes, 6 GO terms,
namely "lipid transporter activity", "extracellular space",
"extracellular region part", "extracellular region",
"immunoglobulin-mediated immune response", and "regulation of
lipoprotein lipase activity" represented meaningful biological
differences between RGS genes and auto-generated control
genes (see supplementary file Table S4). The enrichment
analysis results suggested that the under-represented RGS
genes in liver cancer moved towards the process of cell death,
cell cycle, and apoptosis, while the over-represented RGS
genes were in relation to extracellular activity.
System assessment We separately assessed 9 analyses
of single datasets: 7 microarray signatures of liver primary
tumor tissues, 2 pairs of SAGE libraries, and the integrated
analyses of all signatures. Table 1 shows the system
assessment results. As expected, the integrative analyses
generally had better performances than single dataset analyses.
For the single dataset analyses, the Ac value ranged from
56% to 78% and the CC value from 0.42 to 0.75, whereas for
the integration analyses, the Ac value was 92% and the
CC value was 0.88.
Genome-wide gene expression map in liver
cancer The gene expression map of chromosome 3 in liver cancer is shown
in Figure 2. A full view of all human genome and a
higher-resolution version of this map is is available as
supplementary file Figure S1. It may help oncologists to compare
different studies in a single eyeshot and can also be used as a
gene expression "database", which is convenient for
looking for specific genes.
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The following supplementary material is available for this article:
TableS1.xls 252 liver cancer LOH results.
TableS2.xls RGS genes in liver cancer obtained with integrative
analysis.
TableS3.xls OMIM analysis results of 90 RGS genes in liver cancer.
TableS4.xls GO category enrichment analysis of RGS genes in liver
cancer.
FigureS1.pdf, genome wide gene expression map in liver cancer.
This material is available as part of the online article from:
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1745-7254.
2007.00665.x
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