The American University in Cairo
School of Sciences and Engineering
META-ANALYSIS OF MICROARRAY DATA TO ASSESS GENDER BIASED
DIFFERENTIAL GENE EXPRESSION IN HEPATIC TISSUE
A Thesis Submitted to
The Biotechnology Graduate Program
In partial fulfillment for the pre-requisite
requirement for full admission to
the Ph.D Program in Applied Sciences
By: Amira ElBakry
Under the Supervision of: Dr. Rania Siam
Chair of the Biology Department
May/2015
ACKNOWLEDGMENTS
My unreserved gratitude goes to Dr. Rania Siam, for her unconditional support, invaluable help
and constant encouragement. Thank you for making this thesis possible, and for pushing
everything forward for me and with me. Working with you is a pleasure and a privilege.
Additionally, Mustafa Adel has provided much-needed help in many aspects of this thesis.
Thank you for taking the time, putting the effort, and always replying to my emails and
answering my many questions.
Appreciation is directed to Dr. Ahmed Moustafa, for supporting my idea and his invaluable
coaching in bioinformatics. I learned an incredible amount in a very short time.
Special thanks to Dr. Sherif El-Khamisy for inspiring this thesis, it was his idea I built most of
the foundation for the work on.
Also, acknowledgment is due for Ali El Behery, for helping me multiple times with my hopeless
programming and resolving every single error. Also, my appreciation for Yasmeen El Howeedy,
for taking time to share her experience and providing valuable help.
Thank you for everyone in the biology department that made my day easier, happier or just
listened to my complaining.
Finally, my eternal gratitude goes for my family, for their endless support every step of the way.
ii
ABSTRACT
Hepatocellular carcinoma (HCC) is the second deadliest cancer globally, and with an estimated
782,000 new cases in 2012, it is the fifth most common cancer in men and ninth in women. HCC
is of particular concern in Egypt because of the high prevalence of Hepatitis C Virus (HCV).
Due to its poor prognosis, HCC is the leading cause of cancer-related deaths in Egypt. A gender
disparity is observed in liver cancer cases, with higher prevalence in men by three to five fold.
This sex bias is even more pronounced in mouse models of HCC, which was found to be sex
hormone-dependent. Some studies have attempted to elucidate the molecular mechanisms of this
disparity; but with inconclusive and sometimes contradicting outcomes, they remain largely
unresolved. Understanding the natural protective mechanisms in females would allow for the
development of preventative and therapeutic strategies for patients at risk for HCC or already
inflicted with the disease. In this study, we applied a meta-analysis approach on already available
microarray data from human normal liver tissues to identify differentially expressed genes
between males and females. Microarray datasets were downloaded from the Gene Expression
Omnibus database, Robust Multiarray Average pre-processed and analyzed for differential
expression. The combination of 2 distinct datasets and analysis using a p-value cut-off of 0.05
and fold change cut-off of 2 revealed male up-regulated genes including RPS4Y1, EIF1AY,
CYorf15B, UTY, DDX3Y and USP9Y. Female up-regulated genes included XIST, PNPLA4 and
PZP. Our results confirm gender-specific differential expression patterns found in other tissues
and call for further investigation using a larger sample size and more sensitive approaches such
as RNA-Sequencing and, targeted protein-level studies.
iii
TABLE OF CONTENTS
LIST OF TABLES .............................................................................................................................................. v
LIST OF FIGURES ........................................................................................................................................... vi
ABBREVIATIONS .......................................................................................................................................... vii
1.
Introduction .......................................................................................................................................... 1
1.1 Gender Bias in Hepatocellular Carcinoma .......................................................................................... 1
1.2 Molecular Mechanisms of Gender Bias in HCC................................................................................... 2
1.3 Studying Gene Expression using Microarrays ..................................................................................... 4
1.4 Microarray Data pre-processing ......................................................................................................... 6
1.5 Differential Expression Analysis .......................................................................................................... 8
1.6 Accounting for Batch Effects ............................................................................................................. 10
2.
Materials and Methods ....................................................................................................................... 12
2.1 Data Collection and Processing ......................................................................................................... 12
2.2 Data Exploration and Differential Expression Analysis ..................................................................... 12
2.3 Dataset Merging and Batch Effect Removal ..................................................................................... 13
2.4 Gene ID conversion and functional annotation ................................................................................ 14
3. Results ..................................................................................................................................................... 14
3.1 Dataset Collection and Processing .................................................................................................... 14
3.2 Individual Dataset Analysis Using the T-test Method ....................................................................... 15
3.3 Individual Dataset Analysis Using the Limma Package and Bayesian statistics ................................ 16
3.4 Analysis of Merged Datasets Using the Student’s T-test .................................................................. 16
3.5 Batch Effects Removal ...................................................................................................................... 17
3.6 Gene Signature Validation ................................................................................................................ 18
4.
Discussion............................................................................................................................................ 18
TABLES......................................................................................................................................................... 26
FIGURES....................................................................................................................................................... 35
REFERENCES ................................................................................................................................................ 43
iv
LIST OF TABLES
Table 1. Summary of the datasets of microarray studies using human normal liver tissue. ......... 26
Table 2. Sample information of three microarray datasets with gender information ................... 27
Table 3. Summary of differentially expressed probes found in all three data set and their
corresponding gene names ............................................................................................................ 28
Table 4.Summary of differentially expressed probes identified in GSE14323 using the Limma
Package ......................................................................................................................................... 29
Table 5. Differentially expressed probes identified in GSE 23343 using the Limma Package. ... 30
Table 6. Differentially expressed probes and their corresponding gene names, identified in
merged datasets using the t-test method. ...................................................................................... 31
Table 7. Differentially expressed probes and their corresponding genes in dataset GSE14343
after batch effect removal using fRMA.. ...................................................................................... 32
Table 8. Differentially expressed probes and their corresponding genes in dataset GSE23343
after batch effect removal using fRMA. ....................................................................................... 33
Table 9. Differentially expressed probes and their corresponding genes in merged datasets after
batch effect removal using fRMA or using ComBat .................................................................... 34
v
LIST OF FIGURES
Figure 1: Hierarchical Cluster Analysis of 3 microarray datasets…………………………….…35
Figure 2: Differentially Expressed probes in individual datasets (t-test)………………………..36
Figure 3: Differentially Expressed probes in individual datasets (Limma)……………………...37
Figure 4: Differentially Expressed probes in merged datasets…………………………………..38
Figure 5: Hierarchical Cluster Analysis of merged microarray datasets………………….……..39
Figure 6: Differentially Expressed probes in individual datasets after batch effect removal……40
Figure 7: Differentially Expressed probed in merged datasets after batch effect removal………41
Figure 8: Gene Signature Validation………………………………………………………….....42
vi
ABBREVIATIONS
ANOVA
ComBat
DAVID
DDX3Y
DEN
DWD
EIF1AY
FC
fRMA
GEO
HBV
HCC
HCV
IL-6
KDM5D
LARP4B
MM
PM
PNPLA4
PRL
PRLR
PZP
RMA
RNA-Seq
RPS4Y1
RVM
SAM
SVA
TLR
TNF-α
USP94
UTY
WT
XIST
Analysis of variance
Combining Batches of Gene Expression Microarray Data
Database for Annotation, Visualization and Integrated Discovery
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3Y
Diethylnitrosamine
Distance-weighted discrimination
Eukaryotic translation initiation factor 1A-Y
Fold Change
Frozen robust multichip average
Gene Expression Omnibus
Heptitis B Virus
Hepatocellular carcinoma
Hepatitis C Virus
Interleukin 6
(K)-specific demethylase 6A
La ribonucleoprotein domain family, member 4B
Mismatch
Perfect Match
Patatin-like phospholipase domain containing 4
Prolactin
Prolactin Receptor
Pregnancy Zone Protein
Robust Multichip Average
RNA Sequencing
Ribosomal protein S4
random variance model
significance analysis of microarrays
Surrogate variable analysis
Toll-like Receptor
Tumor Necrosis Factor-α
ubiquitin specific peptidase 9
ubiquitously transcribed tetratricopeptide repeat gene
Wild Type
X (inactive)-specific transcript
vii
1. Introduction
1.1 Gender Bias in Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is one of the most common cancers in the world, with an
estimated 782,000 new cases in 20121. HCC is the fifth most common cancer in men and ninth in
women and with about 746,000 deaths in 2012, it is the second deadliest cancer globally 1. Risk
factors for HCC include alcoholic liver disease, infection with Hepatitis C virus (HCV) or
Hepatitis B Virus (HBV) , and aflatoxin exposure2. HCC is of particular concern in Egypt
because of the high prevalence of HCV. A recent study showed that more than 91% of HCC
patients surveyed tested positive for HCV 3, which is in agreement with a previous report
showing that almost 89% of surveyed HCC patients were HCV positive4. Owing to its poor
prognosis, HCC is the leading cause of cancer-related death in Egypt, and second in incidence to
breast cancer 1.
One important aspect of HCC is a global gender disparity, with a higher prevalence in
men by two to four fold, despite equal exposure to risk factors such as HCV and HBV 5. This
female protection seems to be hormone-dependent, as indicated by the rise in liver cancer
incidence amongst women who undergo menopause 6.
Furthermore, hormone replacement
therapy in women is associated with lower HCC incidence 7. This sex bias is even more
pronounced in mouse models of HCC, which was also found to be sex hormone-dependent
8,9
.
HCC is chemically induced in mice using a post natal injection of diethylnitrosamine (DEN),
which causes DNA damage and hepatocyte death. This triggers inflammatory responses from
Kupffer cells, which further promotes compensatory hepatocyte proliferation and leads to tumor
formation
10,11
. In experiments with DEN- induced HCC , almost all male mice develop the
disease, while only 30% of female mice progress to HCC 9. This effect is significantly reduced
1
by estrogen administration or castration. On the other hand, ovariectomy and / or testosterone
supplement significantly reduced female protection9. This disparity has also been shown to be
androgen-receptor-dependent in DEN-induced HCC mouse models12. Mice lacking functional
androgen receptors showed resistance to DEN-induced carcinogenesis, and females treated with
testosterone showed a higher incidence of liver tumors than untreated females
12
. In another
study, transgenic mice expressing Hepatitis B Surface Antigen and/or p53 were exposed to
aflatoxin in different combinations 8. All male mice with these three risk factors developed
tumors, compared to only 62% of females 8. Furthermore, males in all other groups developed
HCC more frequently than their female counterparts. Some studies have addressed the molecular
mechanisms of this disparity in an attempt to elucidate the underlying causes to this
phenomenon, but the picture is far from complete.
1.2 Molecular Mechanisms of Gender Bias in HCC
Liver cancer is strongly associated with inflammation13, and thus studies investigating the
molecular mechanisms underlying the observed gender bias in HCC found links between sex
hormones and inflammatory responses in the liver.
In a key article published in 2007, Naugler et al. found that interleukin-6 (IL-6) plays an
important role in promoting DEN-induced liver carcinogenesis in mice through signaling via a
Toll-like receptor (TLR) adaptor protein
14
. Males had a higher level of serum IL-6 after DEN
administration and a higher level of liver injury than females. IL6
-/-
males had lower HCC
incidence than wild type (WT), while no difference was observed between WT and IL6-/-.
Estrogen administration to males and ovariectomized females reduced liver injury through
suppression of IL-6 production. While this study identified the estrogen receptor ERα as the
receptor responsible for this effect, this was later questioned by another group. Bigsby et al.
2
found that ERα status did not affect tumorigenesis in females, and its absence in male mice
resulted in less tumors 15. Interestingly, estradiol treatment reduced the number of tumors in WT
male mice, but not in ERα-/- mice, suggesting a protective effect of ERα only for exogenously
administered estradiol 15.
Another hormone, prolactin (PRL), has also been indicated as an important factor in HCC
resistance in women. Hartwell et. al. found that the pituitary hormone PRL (expressed more in
females) restricts innate immune responses in the liver by inhibiting c-Myc activation16. PRL
was found to signal through a hepatocyte-predominant short-form prolactin receptors (PRLR-S)
to inhibit IL-1β, TNF-α, and TLR-4 induced innate responses. PRL acts by ubiquitinating a
group of proteins in a “Trafasome” thereby inhibiting their downstream interactions with c-Myc
activating pathways. This is in contrast to previous studies which show no role for PRL in HCC
gender bias. Tumor incidence in PRLR knockout was comparable to WT, and in famles,
ovariectomy induced tumorigenesis regardless of PRLR status15. This difference has been
attributed mainly to the biological differences between PRLR and PRL knockout mice.
In an attempt to identify potential oncogenes and their role in sex-bias occurrence on a
larger scale, one group used the sleeping beauty transposon system to screen for potential
oncogenes in mice. They found that transposon insertions in the epidermal growth factor receptor
(EGFR) gene were more common in tumors from male mice than female mice
17
. Additionally,
gene expression analysis in human liver samples revealed differences in male and female liver
tissues predominantly pertaining to inflammation, carcinogenesis and reproduction16. Together,
these results indicate a role for sex hormones, pituitary hormones and the innate immune system
in the gender disparity observed in HCC, the details of which remain largely uncharacterized.
3
The paradoxical results obtained by different groups highlight important points of
consideration while studying this aspect of HCC. First, the use of DEN-induced HCC model
represents a point of variation as the treatment regimen varies in dose, age of mice at
administration, and time of sample collection post DEN administration. Another factor to be
considered is the mice genetic background, which as demonstrated by Bigsby et al., affects the
number of tumors obtained by a certain treatment and the sex-dependent response15. For
example, ovariectomy did not affect the tumorigenesis in C57Bl/6J females, but showed an
effect in the mixed background strain 129Ola-X-C57Bl/6J. Additionally, HCC in humans is
largely associated with a other external factors2 (such as alcohol, viral infection, chemical
exposure), which are not represented in animal models. Therefore, it is evident that while mice
provide an invaluable experimental tool, they do not accurately reflect the pathogenesis of HCC
in humans, nor the sex bias observed. A valuable source of information would be clinical
samples from HCC patients and human liver tissues to provide a more accurate and reliable
1.3 Studying Gene Expression using Microarrays
One way to extract valuable information from clinical data is to utilize already available genomewide microarray data from human liver tissues to identify genes with potential roles in the sex
bias observed in HCC. High density oligonucleotide arrays represent a very useful tool for
studying gene expression as they allow the quantitative detection of mRNA. Microarray chips,
such as the Affymetrix GeneChip, use tens of thousands of synthetic oligonucleotides (25 bases
long) that hybridize to specific sequences of target genes18,19. Commercially available platforms
allowed the widespread use of microarrays for high throughput transcriptome studies, and the
accumulation of large amounts of gene expression data. Microarray studies are often used to
study disease pathogenesis or treatment effectiveness by comparing gene expression in normal
4
and diseased tissues, treated and untreated samples or multiple stages of one disease, among
many other alternatives. These approaches allow the large-scale identification of genes that are
up-regulated and down-regulated in experiment versus control, allowing subsequent downstream
functional analyses. For example, microarray studies have been used to identify gene expression
signatures in cancers of the colon20, prostate21, breast22, liver23,24 and
lung25
to identify
diagnostic and prognostic biomarkers and therapeutic targets. Public databases, such as the NCBI
Gene Expression Omnibus (GEO)26,27 and ArrayExpress28 allow access to a huge number of
microarray data.
Although various gene expression studies addressed different aspect of HCC
pathogenesis, only one study attempted to dissect the molecular basis of the observed gender
bias. Hartwell et al. carried out gene expression analysis on 7 male and 7 female human liver
samples to identify a gene signature that could be protective of females as compared to males16.
Their findings distinguished about 500 genes that are differentially expressed between the 2
groups, many of which have functions in cell cycle, inflammation and cancer16.
Our aim in the current study is to build on these results by using a larger sample size to
define a more specific signature, which can be followed in patients that develop HCC. One
approach to achieve this is to use data from multiple studies that are already published. However,
lack of correlation between platforms and experimental variations among labs do not allow a
direct comparison between heterogeneous studies 29,30. One way to overcome this is to carry out
a meta-analysis of the data. Meta-analysis is a statistical approach, consisting of a set of
statistical techniques that allow the combination of data from independent, but relevant, studies
31
. Combining data from various studies using a meta-analysis approach increases the statistical
power to allow the detection of small, but consistent changes that are otherwise missed in single5
study analyses 32. Furthermore, statistical approaches directed to overcome heterogeneity among
the different datasets increases the sensitivity and reproducibility of the result, when compared
to individual studies33. Another advantage of meta-analysis is its relative low-cost, as the data are
already available and many analysis tools are open-source.
The success of this approach is evident by the increasing number of researchers
employing it to generate novel information from already existing data, such as common
transcriptional profiles or gene signature of certain cancers, and even new prognostic biomarkers
34–37
. Rhodes et al. used meta-analysis on 40 independent microarray cancer studies and
identified a “meta-signature” of cancer transformation and progression35. In a more clinicallydriven approach, Mehra et al. combined 417 samples and identified GATA3 as a prognostic
marker for breast cancer34. Also, Ewald et al. used patient-derived microarray data to identify
pathways involved in the stage-wise progression of bladder cancer from papillary to muscleinvasive tumors38. In addition, efforts to compare and unify meta-analysis approaches and
workflows have been emerging to enhance the applicability of such studies and enhance their
outcome
31,39,40
. Overall, meta-analysis is a powerful approach to use already-available data to
answer novel questions, which is what we aim to accomplish in this study. However, as
explained below, careful consideration must be given to using public microarray datasets for
differential expression analysis, owing to the heterogeneity of their experimental design, the
platforms used, and the data processing methods.
1.4 Microarray Data Pre-processing
The output of microarray experiments, i.e. raw data, has to be pre-processed in order to reach
meaningful, informative values. Affymetrix microarray chips contain around 11-20 probes for
6
each gene, arranged in pairs known as “probe pairs”. Probe pairs consist of perfect match (PM),
which
match the target gene completely, and mismatch (MM) probes, which are the same sequences as
PM probes, but with a mismatched base in the middle (13th base) to account for non-specific
binding18. Therefore, in order to have a single expression value for each probe, the data from all
probes in a probe set needs to be summarized in an expression value. In addition, normalization
of the data is crucial to account for obscuring variations that occur during sample preparations,
handling, production and processing of the arrays.
One of the most widely-used methods for pre-processing of Affymetrix microarray data
is the Robust Multichip Average (RMA) method. The RMA expression measure is generated by
background correction of probe-level PM values, followed by quantile normalization of the data
and then linear fitting using the median polish method, to generate log expression values 41. The
quantile normalization approach utilizes information from all arrays with the goal of making the
distribution of probe intensities the same for all arrays in a particular set analyzed 42. Therefore,
it is important to process all arrays from an experiment together as one batch when using RMA.
When compared to other normalization methods, such as non-linear and scaling methods (used in
the Affymetrix algorithms), quantile normalization was most successful at reducing variance and
produced the smallest distances between arrays in pairwise comparisons which remained
constant across intensities42. It also performed better in terms of bias and speed. This
improvement in performance could be attributed to using all arrays in the normalization process,
rather than a single baseline, which is less representative of the complete data42.
7
Additionally, algorithms such as the Affymetrix MAS 5.0 algorithm and dChip software
developed by Li and Wong rely on subtracting the MM signal to correct for non-specific binding
43
. However, RMA does not use the MM probes for subtraction, and relies only on PM values,
as studies have shown that MM probes detect signal in addition to non-specific binding, and that
methods utilizing PM- MM or PM/MM values add noise and result in a
respectively
41,44
biased signal,
. In a series of spike-in experiments, RMA was compared to other expression
measures, such as Li and Wong’s model-based expression indexes (MBEI) and the MAS 5.0
signal, in which it performed better in 3 criteria: (1) precision, as estimated by the gene-specific
standard deviations (SD) across replicates; (2) consistency of estimates for fold change; and (3)
the specificity and sensitivity when using fold change to detect differential expression41,45.
1.5 Differential Expression Analysis
The simplest way to identify differentially expressed gene is using the Fold Change (FC)
indicator, which evaluates the average log ratio between 2 conditions or groups and considers
gene with FC above a certain (arbitrary) threshold differentially expressed. For example, if the
FC threshold is set at 2, genes that are found to be more abundant under one condition or less
abundant in the other by more than 2 fold are considered to be differentially expressed. However,
FC is not a statistical test; it does not provide any level of confidence and does not account for
variance across samples. Therefore, it is important to employ a statistical method to account for
this variance and to standardize differential expression.
A simple and popular method to employ is a standard t-test, which has been widely used
to detect differentially expressed gene for microarray studies46. The t-test is conducted for each
gene and the error variance is estimated based on the log ratios. For each gene, a t-statistic is
computed and then converted to a p-value. Typically, genes with p-values falling below a
8
specific threshold are considered significant. Despite the popular application of the t-test to
microarray data analysis, this approach has its drawbacks, particularly with low-variance genes
and small sample size, where some bias is introduced and the statistical power is compromised,
respectively
47,48
. Consequent to these criticisms, numerous statistical approaches have been
developed to address different areas of concern, in attempts to improve variance estimates,
accuracy and statistical power. The abundance and variety of these methodologies make it
difficult to decide on which method would is most effective in analyzing gene expression data
and which method would be most appropriate to specific experimental settings.
Fortunately, studies were designed to specifically address this issue, and some have
utilized data in which the differentially expressed genes are already known (for example, using
spike-in experiments) to evaluate the performance of statistical tests
47–50
. For example, one
study has compared the performance of 8 statistical methods of variance modeling in gene
expression data analysis: Welch's t-test, analysis of variance (ANOVA), Wilcoxon's test,
significance analysis of microarrays (SAM), random variance model (RVM), Limma, VarMixt
and SMVar
47
. Using Limma resulted in the best overall performance compared to the other 7
methods, where the most improvement was achieved compared to the t-test, especially with
small sample size, and in terms of ease of use and speed of execution.
Limma is a popular method used for microarray data analysis, which has recently been
adapted for RNA-sequencing (RNA-Seq) data as well. The principal idea in Limma is the use of
gene-wise linear model fitting to analyze an entire experiment as a whole
51
. The linear models
are fitted to each row, and regression coefficients and standard error generated for the compared
group. The parallel nature of gene expression experiments motivates the use of empirical Bayes
statistics which allows sharing of information between genes to account for variances across
9
genes and samples to obtain Bayes posterior variance estimators51. The estimated sample
variances are squeezed towards a common variance, leading to more stable inferences from small
sample size52. In addition, the use of gene-wise linear modeling allows flexibility in handling
different experimental designs. In testing for differential expression, empirical Bayes statistics,
such as moderated t-statistic and p-values are generated for each coefficient of the linear model
in order to assess the significance of these changes. Limma is able to reduce the false positive
rate in genes with low variance and improve the power for detecting genes with large variances.
The use of empirical Bayes statistics in Limma has been found favorable to other significance
testing methods in gene expression analysis, and its performance favored especially with small
samples49,50.
1.6 Accounting for Batch Effects
Another important source of variation in microarray data that needs to be addressed is the
variation introduced by non-biological factor that causes differences between samples, known as
the “batch effect”. Batch effects are introduced when samples are run in different “batches”,
which could be different runs, different days, using different reagents or different technicians
55
53–
. Batch effects can affect the downstream processing of the data, resulting in lower power to
detect real changes, or more the serious consequences of false or misleading biological
conclusions. Although batch effects have been demonstrated in early microarray experiments 56,
surprisingly, they have been reported only in small percentage of studies. For example, one study
reported that out of 219 papers published using microarray data, less than 10% addressed batch
effects 53. Moreover, upon examining data from 9 high throughput studies, Leek et al. found all
of them had considerable batch effects, with substantial percentages (32.1-99.5%). While batch
effects can be minimized through careful experimental design, the only way to avoid them
10
completely is by running samples as one batch. Therefore, several methods for batch effect
removal have been developed, including Distance-weighted discrimination (DWD)
57
, mean-
centering (PAMR)58, Surrogate variable analysis (SVA)59, Geometric ratio-based method
(Ratio_G)60 and Combating Batch Effects When Combining Batches of Gene Expression
Microarray Data (ComBat)54. When all these 5 methods were compared and performance
assessed based on accuracy, precision and variation reduction (batch effect removal), using
ComBat resulted in the best overall performance. In addition, ComBat was robust for handling
small batches that other methods did not perform well with53. This is attributable to the empirical
Bayes framework employed in the ComBat approach, which estimates location and scale
adjustment parameters for each gene independently by borrowing information across genes to
“shrink” the batch effect parameter estimates towards an overall mean of the estimates54. These
estimates are then used to adjust the data for batch effects, providing robust adjustment for small
batches (10<)54.
Although normalization of microarray data does not account for batch effects, some
modifications of existing methods attempt to address these effects in batches of datasets or single
arrays that can be analyzed individually or after before combining them with others. The frozen
robust multichip average (fRMA) is used to achieve the advantages of multichip processing to
single-array analysis by using a large dataset of representative samples to create a reference
distribution for the subsequent quantile normalization. Pre-computed estimates of probe effects
are used in concert with data from the set being analyzed to generate the summary expression
values. fRMA was found to perform similarly to RMA when the data was preprocessed as one
batch, but outperformed RMA in terms of precision when analyzing multiple batches.
11
Using the above tools for analyzing gene expression data from human liver tissues, we
aim to identify differentially expressed genes between males and females that could be
responsible for the inherent HCC resistance in females, which would be a starting point for
subsequent in silico analysis and in vitro and in vivo experiments.
2. Materials and Methods
2.1 Data Collection and Processing
The NCBI GEO database ((http://www.ncbi.nlm.nih.gov/geo/) was searched for “human liver”,
with the search filters set as follows: “Organism” set to “Homo sapiens”, “Data type” set to
“expression profiling by array”, “Attribute name” set to “tissue to satisfy our inclusion criteria
for whole-transcriptome studies using human tissues from normal livers. In order to unify gene
IDs and normalization, we only selected data produced using Affymetrix chips. Studies using
cell lines, animals, non-coding RNA profiling, or different platforms were excluded. For the
selected datasets, raw (.cel) files were downloaded and pre-processed using the RMA method to
generate expression value output41,45, which is summary measure of background-corrected,
normalized log-transformed probe intensities. RMA was applied by implementing the “just
RMA()” function in the Bioconductor Affy Package 61. As with all other statistical analyses used
in this study, all methods were implemented in the R statistical computing environment
(http://www.r-project.org/), and all packages are available from the open source Bioconductor
project62,63 (http://www.bioconductor.org/about/).
2.2 Data Exploration and Differential Expression Analysis
As an initial exploratory step, hierarchical cluster analysis was carried out using the “hclust”
function in R, with the default method of complete linkage, which defines the distance between 2
clusters as the maximum distances between its components. Distance matrices were constructed
12
by using the Pearson distances between columns (1- Pearson’s correlation coefficient), and the
analysis displayed as a dendrogram.
To identify differentially expressed genes between male and female groups, we used 2
methods: a standard t-test between the 2 groups and the Limma Bioconductor package which
implements linear modeling and Bayesian statistics51. Using the t-test, p-values were calculated
for each probe, and a cut-off of 0.05 was set so that only probes with p-values below this limit
are selected. Absolute fold change cut-off was set as 2, so genes which are up- or downregulated by at least 2 fold are selected. Probes that met both of these criteria were filtered for
further downstream analysis.
For the Limma Package, the “lmFit” function was used for the estimation of fold changes
and standard errors through fitting a linear model, which is specific by the design matrix, for
each gene52. The fitted model was then processed using the “eBayes” function to apply empirical
Bayes statistical methods and generate statistics such as the moderated t-statsitic and its
associated p-value, adjusted p-values for multiple testing and average log expression values52.
The selection cut-off for differentially expressed genes was set to a p-adjusted value of 0.05
(using the Holm’s sequential Bonferoni multiple testing correction method)64. This ensures that
errors introduced due to testing multiple hypotheses are corrected for and so minimizes false
positives.
2.3 Dataset Merging and Batch Effect Removal
The datasets were combined together using the inSilicoMerging Bioconductor package65, which
allows the merging of different datasets and the use of different methods for batch effect removal
65
. Using this package, we used the ComBat approach, which utilizes empirical Bayes statistics
13
to correct for variations between arrays due to use of different methodology, or data generated in
different laboratories, such as the case in this study 54.
Another method for batch effect adjustment that was employed was the fRMA method,
which allows pre-processing of individual datasets by using pre-computed probe effects and
variances during the normalization process
66
. This way, individually fRMA-processed datasets
can be computed without the need for further batch effect removal. Datasets were fRMA preprocessed using the “fRMA” function in the fRMA Bioconductor package, then combined using
the inSiliocMerging package and then analyzed for differentially expressed probes using the
Limma package.
2.4 Gene ID conversion and functional annotation
To convert probe IDs to gene names and identify the location and function of these genes, The
Database for Annotation, Visualization and Integrated Discovery version 6.7 (DAVID,
http://david.abcc.ncifcrf.gov/) tool was used 67,68. Each list of probes was copied and pasted into
the Gene List Manager and analyzed either by the Gene ID Conversion tool to identify the gene
corresponding to the selected probes or the Gene Functional Classification tool to group genes
into functional groups.
3. Results
3.1 Dataset Collection and Processing
The GEO search yielded 739 studies on Januray 2015, of which 7 studies were selected
(summarized in Table 1) that provided expression data for normal human liver. All selected
datasets were generated using the Affymetrix Human Genome U133 Plus 2.0 Array, except
GSE14323, which was generated using the Affymetrix GeneChip Human Genome U133A 2.0
14
Array.
Out of those, only 2 (GSE2334369, GSE1495170) had gender information readily
available in the database. We contacted the principal author for each study, and only the author
for GSE143323 provided the missing gender data
71
. Consequently we had a total of 3 studies
qualifying for the subsequent analysis. Collectively, the datasets included 27 male and 19 female
normal liver samples, adding up to 46 samples in total (summarized in Table 2). As the data
provided in the expression matrices was processed differently by each group, raw .cel files were
downloaded and pre-processed using the same RMA method45.
3.2 Individual Dataset Analysis Using the T-test Method
For each dataset, hierarchical clustering analysis was performed, using the complete linkage
method72, and plotted as dendrograms (Figure 1). Hierarchical clustering for all three datasets
did not generate a distinct gender cluster (data not shown). Each dataset was then divided into 2
groups of male and female samples and compared using a t-test statistic and FC. A cut-off of
0.05 for p-value and 2 for FC was used to filter the probes that were differentially expressed
between the 2 groups. The data are represented in a form of a heat maps depicting up-regulated
and down-regulated probes in each dataset (Figure 2). For dataset GSE14323, a total of 6 probes
were deferentially expressed and in GSE23343 a total of 19 deferentially expressed probes were
identified. Finally, dataset GSE14951 showed only 2 differentially expressed probes, that did not
produce complete segregation of the 2 groups. The DAVID67,68 tool was used to map the probe
ID to genes names. Table 3 summarizes the differentially expressed probes in all datasets,
highlighting their overlapping probes and their corresponding genes. Since multiple probes are
used to monitor the same gene, the total number of genes is sometimes smaller than the number
of probes.
Therefore, for GSE 14323, 6 probes corresponded to 5 genes including genes
involved in spermatogenesis, X-inactivation and protein biosynthesis. For GSE23343, 19 probes
15
corresponded to only 11 genes, including genes involved in hexose metabolism, histone
demethylatoin and protein deubiquitination.
Only one gene was found to be differentially
expressed in all three datasets, which is the Ribosomal protein S4 (RPS4Y1), a Y-linked gene.
Most of the genes found were either Y-linked or X-linked, with only 3 out of 20 found on
autosomes. Additionally, none of the genes in all datasets clustered into functional groups when
using the DAVID gene functional classification tool. Finally, for the apparent heterogeneity of
the GSE14951 dataset sample pool, it was excluded from further analysis.
3.3 Individual Dataset Analysis Using the Limma Package and Bayesian statistics
The Limma Bioconductor package51 was used to further analyze the data as an alternative
method to determine differentially expressed genes between the 2 groups in 2 datasets:
GSE14323 and GSE23343 (figure 3). For GSE14323, 9 probes were found to be differentially
expressed which included 5 of the 6 probes found by the t-test method. The 4 “new” probes were
already identified for the other dataset (GSE23343) using the t-test. On the other hand, only 8
probes were indentified for GSE23343, and again, considerable overlap was observed, as 7 out
of the 8 probes were also differentially expressed among the 19 probes identified in the t-test
method (data summarized in tables 4 and 5). Using the Limma package added only one new
gene, which is La ribonucleoprotein domain family, member 4B (LARP4B), found on
chromosome 10.
3.4 Analysis of Merged Datasets Using the t-test
To test if combining these 2 datasets would provide additional information, or allow for the
detection of changes that were not previously detected, the 2 datasets were merged together and a
t-test was used to identify significantly up- or down- regulated probes (figure 4). Ten probes
were identified to be differentially expressed, corresponding to 7 genes. All of these probes have
16
been identified in individual dataset analysis, and the genes they correspond to are Y- or Xlinked genes, with the exception of just one (summarized in table 6). A hierarchical cluster
analysis of the merged datasets resulted in the clustering of the 2 datasets into separate groups
(figure 5), regardless of the gender of the sample
3.5 Batch Effects Removal
To correct for differences between data generated in different laboratories, or at distinct time
periods, various batch effect removal tools were used. Two methods were employed to correct
for batch effect correction: either the fRMA normalization66 prior to merging the datasets or
using the ComBat54 tool after merging the datasets. In both cases, the Limma package51 was
employed to identify differentially expressed genes. For GSE14323, 12 probes were identified,
corresponding to 9 genes (figure 6A). Three new genes for this dataset were identified after batch
effect removal: patatin-like phospholipase domain containing 4 (PNPLA4), ubiquitously
transcribed tetratricopeptide repeat gene (UTY), and ubiquitin specific peptidase 9 (USP94)
(table 7). PNPLA4 is X-linked while UTY and USP94 are Y-linked. Also, USP94 was identified
before for the other dataset using Limma. For GSE23433, 9 probes were found to be
differentially expressed (figure 6B). As shown in table 8, these probes correspond to only 4
genes, none of which are new compared to previously identified probes for this dataset.
After merging the 2 fRMA-normalized datasets, we obtained 10 differentially expressed
probes figure 7). These corresponded to 8 genes, 7 of which were sex-chromosome linked, and
all of which have been already identified in earlier individual dataset analyses. Using the
ComBat method for batch effect removal, 13 probes were identified as differentially expressedcorresponding to 10 genes, with considerable overlap with those identified using fRMA (table 9).
17
Out of these ten, only one was not previously identified for either dataset, which is lysine (K)specific demethylase 6A (KDM5D), an X-linked gene.
3.6 Gene Signature Validation
Recently published microarray data showed around 500 genes to be significantly differentially
expressed between normal male and female human liver tissue16. Since our analysis did not
reveal gender bias in human liver tissues gene expression, we tested if this gene set would
produce a similar signature using our two datasets. The gene set was successful in distinguishing
between their male and female samples, as they clustered into two distinct classes, but there was
no clear signature of up- and down-regulated genes that could be distinguished.
4. Discussion
We used data from microarray studies of human liver tissue to compare female and male gene
expression and identify differential expression patterns. We identified a subset of X- and Ylinked genes that are up-regulated in females and males, respectively.
As microarray data available from public data bases are found in a host of different
formats, it is usually recommended to start any analysis with raw files to be pre-processed in a
unified manner, to make the output comparable. We chose the RMA pre-processing approach
due to its effectiveness and precision, as it is becoming the method of choice for Affymetrix
microarray data processing.
The initial clustering of the individual datasets did not show any gender-based
segregation, but as the analysis was done for all probes for each dataset, subsets of differentially
expressed genes could not be determined or visualized using this exploratory step. Therefore, the
t-test statistical analysis was carried out to identify differentially expressed genes between males
and females. Although using the t-test for this application has been criticized, it was used in the
18
initial exploration of the data because the experimental design is simple (only 2 groups
compared, representing 2 conditions) and also for comparison to other methods. Furthermore, the
fold change was used as an additional filtering criterion in the selection for biologically
meaningful differences. Although the number of identified probes was small for each dataset (19
for GSE23343, 6 for GSE14323, and 2 for GSE14951), there was considerable overlap between
them, indicating consistency for those genes. The most notable is RPS4Y1, that is up-regulated
in males in all datasets, which is not surprising given it is a Y-linked gene (functional analysis
for relevant probes will be discussed in later sections).
To verify these results and refine our analysis by using improved statistical approaches
that are more appropriate for small sample size, we used the Limma package for differential
expression. The Limma package has been shown to be superior in performance and accuracy
than various other statistical and increased power compared to the t-test. When we used Limma
for individual dataset analysis, we identified more probes for GSE14323 and less for GSE23343,
and overall identified just one new gene: LARP4B, an autosomal gene found on chromosome 10.
It is worth noting that the cut-off used for Limma was a p-adjusted value that corrects for
multiple testing, which is a stricter cut-off than using standard p-values in a t-test. This indicates
that Limma is indeed more powerful than the t-test, and was able to detect changes that are more
significant than the t-test could account for, and therefore Limma was used for all subsequent
differential expression analysis. This would also explain the lower number of probes obtained for
GSE23343. Similar to the individual analysis using the t-test, considerable overlap was found
between the differentially expressed genes identified using Limma for the 2 datasets.
Merging the two datasets together in a “meta-analysis” approach is expected to increase
the statistical power and allow for the detection of other small changes in gene expression.
19
However, using a t-test and fold change to filter out differentially expressed genes did not add
any new probes compared to those identified by individual analysis. To further explore the data,
a hierarchical analysis revealed that the datasets clustered into 2 different groups, regardless of
the gender. This indicates that non-biological differences arising due to the different origin of the
samples (i.e. batch effects) are interfering with the analysis and could be obscuring genderspecific changes. Therefore, before carrying out any further analysis on the merged datasets, we
used batch effect removal tools to overcome this variation.
For this purpose, 2 methods were used: fRMA and ComBat. fRMA was used to
preprocess the raw data for both sets before merging. The advantage of this approach is that each
set is analyzed individually and more datasets can be added to the analysis without the need to
repeat pre-processing or batch effect removal steps that were already done. For GSE14323, three
new genes were identified using Limma, while none were newly found for GSE23343. This
could mean that the inter-sample non-biological variations are marginal, or that these batch
effects were not obscuring considerable biological changes.
Finally, for the purpose of the meta-analysis approach we are employing in this study, the
2 datasets were merged and processed using ComBat for batch effect removal and limma for the
identification of differentially expressed genes. This was compared to merging the fRMAprocessed datasets. Again, similar previous results, merging the fRMA datasets did not result in
detecting new or different genes, and using ComBat only added one new gene: KDM5D.
Throughout our analysis, the differences in output we observed were obtained when we
used different statistical methods, e.g. t-test versus Limma, or RMA versus fRMA or ComBat.
Merging the datasets using the same pre-processing and downstream analysis algorithms resulted
in almost the same outcome, which indicated that merging the 2 datasets together does not
20
provide additional power to the analysis and does not add information that would have been
otherwise undetected. However, we only used 2 (relatively small) datasets, with sample sizes
below 25 for each group, which may have reduced the power of the study.
Compared to the ~500 genes identified by Hatrwell et al. to be differentially expressed
between males and females in human normal liver tissues, our identified genes are very few.
Although using these genes in hierarchical analysis allowed us to cluster male and females in
each dataset, they did not exhibit any consistent pattern for up-and down-regulated genes.
However, we examine below the genes that were found in common between this study and ours,
and additionally, genes that were found to be consistently deferentially expressed in our analysis.
For the male-dominated genes, we identified 6 that are consistently found to be up-regulated in
males: RPS4Y1, EIF1AY, CYorf15B, UTY, DDX3Y and USP9Y, all of which are Y-linked.
The first three were also found to be up-regulated in males by Hartwell et al. The most
prominent gene that is identified by all approaches employed in this study is RPS4Y1, which is
part of the 40S ribosomal small subunit73. The sex-bias in RPS4Y1 expression was observed in
human brain74, heart75 and was found to be pronounced in human prostate cancer tissues and cell
lines76. RPS4Y1 has a homologue on the X chromosome, RPS4X, which is known to escape Xinactivation, but was not found to be differentially expressed in our sample pool73. Therefore, upregulation of RPS4Y1 seems to be tissue non-specific, and its role in any potential tumor
transformation is unknown.
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3Y (DDX3Y) is an RNA helicase that is
essential for spermatogensis. DDX3Y was found to be widely transcribed but the protein was
only found in the testis77. The X-linked homologue, DDX3X is also ubiquitously expressed and
both function as nucleo-cytoplasmic shuttles for RNA77,78. DDX3Y was also up-regulated in
21
human heart, while DBY (another member of the DEAD box protein family) was also upregulated in male brain tissue. Another protein that is important for spermatogenesis is USP9Y,
is ubiquitously expressed in embryonic and adult tissues and is found up-regulated also in brain,
heart and prostate tissues of males74–76. It is a ubiquitin-specific protease that functions in the deubiquitination of target proteins, and thus plays a role in protein turnover and regulation 79,80.
Eukaryotic translation initiation factor 1A-Y (EIF1AY) is a translation initiation complex
that interacts with the ribosome, and is required for achieving the maximum rate in protein
biosynthesis81. It also may function in stabilizing the binding of the initiator Met-tRNA to 40S
ribosomal subunits82. UTY is also found to be up-regulated in male prostate, heart and brain
tissues, and it encodes a protein with tetratricopeptide repeats that has been found to have
multiple splice variants, the functions of which are unknown83. It has been found to code for a
male-specific minor histocompatability antigen involved in stem cell graft rejection84. Finally,
CYrof15B has also been found to be up-regulated in liver and heart tissue, but so far is found to
be a pseuodogene of the human taxilin gamma (TXLNG).
For the genes found to be up-regulated in females, 2 are X-linked: X (inactive)-specific transcript
(XIST), PNPLA4 , and the third, PZP, is autosomal, found on chromosome 12. XIST is found on
the X inactivation center (XIC) 85and which functions in silencing of X-linked genes through Xchromosome inactivation. XIST was found to be non-protein coding and instead functions as
structural RNA in the nucleus86. XIST is found to be up-regulated in human brain (specifically
neurons), heart and liver16,74,83. PNPLA4 belongs to a family of lipid hyrolases and is a potent
retinylester hydrolase in keratinocytes, affecting their morphology87. It is expressed in a variety
of tissues, albeit in different transcript lengths, suggesting differential processing across tissues88.
Finally, the only autosomal gene to be up-regulated in liver of females in our study that has been
22
previously reported is PZP. PZP is a plasma protein that functions as a peptidase inhibitor89. It
has been found to associate with TGF-beta-1 and TGF-beta-2 and regulates their plasma
clearance90. Since TGF-beta signaling is highly implicated in hepatic carcinogenesis, PZP may
play a role in this pathway. This is further corroborated by data from Genome-wide associated
study where single nucleotide polymorphisms in PZP associated with high serum AST91.
It is not unexpected to find X- and Y-linked genes to be differentially expressed among
females and males, but to explain why only a few of the >1300 gene on the X-chromosomes
have been found to be up-regulated in females and only a few Y-linked genes have been upregulated in males, some considerations have to be taken. X-inactivation of the X-chromosome
in somatic cells of females results in similar expression patterns for X-linked genes in males and
females. Dosage compensation for the active X-chromosome results in hypertrasncription of the
X-linked genes to reach the level of autosomal genes (present in 2 copies). This could explain the
lack of differential expression of most X-linked genes observed in our study93. To add another
level of complication, some genes are found to escape this X-inactivation, which results in higher
expression of these genes in females compared to males
92
. However, this inactivation is found
not be consistent between individuals, or within cells and tissues of an individual, leading to
variations in expression that are yet to be further analyzed94. Additionally, due to the presence of
various X-Y homologues (homologues of the same gene serving the same function), crosshybridization of the Y-specific probes to X-homologues has been reported and
could
compromises the ability of the Y-chromosome probes to differentiate male and female samples,
thereby resulting in similar expression levels74,95. Additionally, a lack of correlation between
mRNA levels and protein levels has been observed for some genes, as the case for DDX3Y,
23
necessitating downstream functional analysis on the protein level before solid conclusions can be
made77.
Overall, while the differentially expressed genes identified in this study did not produce any
functional clusters using the gene functional classification tool in DAVID, they are consistent
with other studies comparing male and female human tissues. The expression of these genes is
not specific to the liver, as they are found in other tissues as well including brain, heart and
prostate. However, several studies have linked sex chromosomal aberrations and Y-linked gene
expression to cancer. For example, up-regulation of Y-linked gene such as RPS4Y1, UTY,
EIF1AY and USP9Y has been found in prostate cancer tissue and cell lines, compared to benign
prostatic hyperplasia and normal testis76,96. In contrast, Y-chromosomal deletions have been also
associated
with
prostate
cancer97–99,
male
breast
carcinomas100
and
pancreatic
adenocarcinomas101. Loss of Y-Chromosome in peripheral blood cells was found to be
associated with higher risk of cancer in men102.
Additionally, rearrangements of the X-
chromosomes, including deletions and gains, have been associated with breast103, ovarian104 and
uterine cervix cancer105. Loss of heterozygousity (LOH) has been found in endocrine carcinomas
of the gastroenteropancreatic tract, lung and colorectal cancer106–108. Therefore there is a link
between sex-chromosome genes and both gender-specific and non-specific cancer, which
remains to be fully elucidated.
Although this study provides a preliminary meta-analysis of the gender-bias in normal
liver tissue utilizing two microarray datasets, further studies should analyze more samples, with
gender information for the utilized datasets. A larger sample pool would allow the stratification
of patients into age groups. As cancer is largely a disease of ageing, analyzing data from more
uniform age groups could be more informative. In addition, more advanced techniques, such as
24
RNA-Seq could provide more information about mutations in X- and Y- chromosome genes, as
well as a more accurate and sensitive measurement of X-Y homologues that could have
interfered with the microarray analysis. Finally, specific studies of already identified genes in the
liver and their corresponding proteins would provide more information about their role in liver
biology and regulation during carcinogenesis.
25
TABLES
Table 1. Summary of the datasets of microarray studies using human normal liver tissue.
Datasets with available gender information are highlighted
Accession Sample size
Sample types
Gender
19
4
7
10
Normal Liver
Normal liver (prior to transplantation)
Normal liver
Normal liver( with diabetes)
Yes
yes
GSE13471
4
Normal liver
none
Affymetrix (GPL570)
GSE6222
2
Normal liver
none
GSE45436
39
Normal liver
None
Affymetrix (GPL570)
Affymetrix (GPL570)
GSE38941
10
Normal liver
None
Affymetrix (GPL570)
GSE14323
GSE14951
GSE23343
26
Platform
Affymetrix (GPL571)
Affymetrix (GPL570)
yes Affymetrix (GPL570)
Table 2. Sample information of three microarray datasets with gender information
Acession
GSE14323
GSE14951
GSE23343
Total
Male
12
5
10
27
Female
7
5
7
19
27
Table 3. Summary of differentially expressed probes found in all three data set and their
corresponding gene names. Common probes found between datasets are highlighted.
GSE14951 GSE143233
201909_at 201909_at
224590_at
GSE23343
201909_at
224590_at
Gene Name
Ribosomal protein S4, Y-linked 1
X (inactive)-specific transcript (non-protein
coding)
203649_s_at
Phospholipase A2, group IIA (platelets, synovial
fluid)
204409_s_at 204409_s_at Eukaryotic translation initiation factor 1A, Ylinked
205000_at
205000_at
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Ylinked
214218_s_at 214218_s_at X (inactive)-specific transcript (non-protein
coding)
221728_x_at 221728_x_at X (inactive)-specific transcript (non-protein
coding)
206700_s_at Lysine (K)-specific demethylase 5D
207063_at
Chromosome Y open reading frame 14
207330_at
Pregnancy-zone protein
214131_at
Chromosome Y open reading frame 15B
204410_at
Eukaryotic translation initiation factor 1A, Ylinked
223645_s_at Chromosome Y open reading frame 15B
223646_s_at Chromosome Y open reading frame 15B
224588_at
X (inactive)-specific transcript (non-protein
coding)
205001_s_at DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Ylinked
227614_at
Hexokinase domain containing 1
227671_at
X (inactive)-specific transcript (non-protein
coding)
228492_at
Ubiquitin specific peptidase 9, Y-linked
235942_at
Hypothetical LOC401629
28
Table 4.Summary of differentially expressed probes identified in GSE14323 using the Limma
Package compared to those found using the T-test method, common probes are highlighted.
Limma
T-test
201909_at
204409_s_at
201909_at
204409_s_at
203649_s_at
GSE14323
Gene Name
Ribosomal protein S4, Y-linked 1
Eukaryotic translation initiation factor 1A, Y-linked
Phospholipase A2, group IIA (platelets, synovial fluid)
204410_at
Eukaryotic translation initiation factor 1A, Y-linked
205000_at
205000_at
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
205001_s_at
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
206700_s_at
Lysine (K)-specific demethylase 5D
214131_at
Chromosome Y open reading frame 15B
214218_s_at 214218_s_at X (inactive)-specific transcript (non-protein coding)
221728_x_at 221728_x_at X (inactive)-specific transcript (non-protein coding)
29
Y or Xlinked
Y
Y
Chr. 1
Y
Y
Y
Y
Y
X
X
Table 5. Differentially expressed probes identified in GSE 23343 using the Limma Package
compared to those found using the T-test method, common probes are highlighted.
Limma
T-test
201909_at
204409_s_at
201909_at
204409_s_at
204410_at
205000_at
205000_at
205001_s_at
221728_x_at
224588_at
224590_at
227671_at
214216_s_at
206700_s_at
207063_at
207330_at
214131_at
214218_s_at
221728_x_at
223645_s_at
223646_s_at
224588_at
224590_at
227614_at
227671_at
228492_at
235942_at
GSE23343
Gene Name
Ribosomal protein S4, Y-linked 1
Eukaryotic translation initiation factor 1A, Y-linked
Eukaryotic translation initiation factor 1A, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Ylinked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Ylinked
Lysine (K)-specific demethylase 5D
Chromosome Y open reading frame 14
Pregnancy-zone protein
Chromosome Y open reading frame 15B
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
Chromosome Y open reading frame 15B
Chromosome Y open reading frame 15B
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
Hexokinase domain containing 1
X (inactive)-specific transcript (non-protein coding)
Ubiquitin specific peptidase 9, Y-linked
Hypothetical LOC401629
La ribonucleoprotein domain family, member 4B
30
Y or Xlinked
Y
Y
Y
Y
Y
Y
Y
Chr. 12
Y
X
X
Y
Y
X
X
Chr. 10
X
Y
Y
Chr. 10
Table 6. Differentially expressed probes and their corresponding gene names, identified in
merged datasets using the t-test method.
Probe ID
201909_at
204409_s_at
204410_at
205000_at
205001_s_at
206700_s_at
207330_at
214131_at
214218_s_at
221728_x_at
Merged Datasets (GSE14323 & GSE 23343)
Gene Name
Ribosomal protein S4, Y-linked 1
Eukaryotic translation initiation factor 1A, Y-linked
Eukaryotic translation initiation factor 1A, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
Lysine (K)-specific demethylase 5D
Pregnancy-zone protein
Chromosome Y open reading frame 15B
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
31
Table 7. Differentially expressed probes and their corresponding genes in dataset GSE14343
after batch effect removal using fRMA. Newly identified genes are highlighted.
Probe ID
214131_at
204410_at
205001_s_at
206700_s_at
204409_s_at
205000_at
201909_at
209739_s_at
221728_x_at
214218_s_at
206624_at
211149_at
GSE14323 fRMA
Gene Name
Chromosome Y open reading frame 15B
Eukaryotic translation initiation factor 1A, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
Lysine (K)-specific demethylase 5D
Eukaryotic translation initiation factor 1A, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
Ribosomal protein S4, Y-linked 1
patatin-like phospholipase domain containing 4
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
ubiquitin specific peptidase 9, Y-linked
ubiquitously transcribed tetratricopeptide repeat gene, Y-linked
32
Table 8. Differentially expressed probes and their corresponding genes in dataset GSE23343
after batch effect removal using fRMA.
Probe ID
221728_x_at
227671_at
214218_s_at
224588_at
224590_at
205001_s_at
204409_s_at
205000_at
201909_at
GSE23343
Gene Name
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
Eukaryotic translation initiation factor 1A, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
Ribosomal protein S4, Y-linked 1
33
Table 9. Differentially expressed probes and their corresponding genes in merged datasets after
batch effect removal using fRMA or using ComBat, common probes are highlighted.
Probe ID
Merged fRMA Merged ComBat
201909_at
201909_at
204409_s_at
204409_s_at
204410_at
204410_at
205000_at
205000_at
205001_s_at
205001_s_at
206700_s_at
206700_s_at
211149_at
211149_at
214131_at
214131_at
214216_s_at
221728_x_at
221728_x_at
214218_s_at
203992_s_at
206624_at
209739_s_at
Gene Name
Ribosomal protein S4, Y-linked 1
Eukaryotic translation initiation factor 1A, Y-linked
Eukaryotic translation initiation factor 1A, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked
Lysine (K)-specific demethylase 5D
ubiquitously transcribed tetratricopeptide repeat gene, Y-linked
Chromosome Y open reading frame 15B
La ribonucleoprotein domain family, member 4B
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
X (inactive)-specific transcript (non-protein coding)
lysine (K)-specific demethylase 6A
ubiquitin specific peptidase 9, Y-linked
34
FIGURES
A.
B.
C.
Figure 1: Hierarchical Cluster Analysis of 3 microarray datasets of human normal liver
tissues: A. GSE14951 B. GSE14323 C. GSE23343
35
A.
Female
Male
B.
Female
Male
C.
Figure 2: Differentially Expressed probes in individual datasets (t-test). Heat Maps of
differentially expressed probes (t-test, P<0.05, fold change >2) between male and female
samples of human normal liver: Datasets: A. GSE14951 B. GSE14323 C. GSE23343
36
A.
Male
Female
B.
Female
Male
Figure 3: Differentially Expressed probes in individual datasets (Limma). Heat maps of
differentially expressed probes between male and female samples of human normal liver
identified using the Limma Package (P<0.05). A. Dataset GSE14323 B. Dataset GSE23343.
37
Male
Female
Figure 4: Differentially Expressed probes in merged datasets. Heat map of differentially
expressed probes between male and female samples of human normal liver tissues, identified in
merged datasets (GSE14323, GSE23343) using the t-test method ((P<0.05, Fold Change>2).
38
Figure 5: Hierarchical Cluster Analysis of merged microarray datasets (GSE14323,
GSE23343) of human normal liver tissues.
39
Male
Female
B.
Male
Female
Figure 6. Differentially Expressed probes in individual datasets after batch effect removal.
Heat maps of differentially expressed probes (Limma, P<0.05 )between male and female human
normal liver samples in microarray datasets A. GSE14323 and B. GSE23343 after removal of
batch effects using the fRMA method.
40
Male
Female
A.
Female
Male
B.
Figure 7: Differentially Expressed probes in merged datasets after batch effect removal.
Heat maps of differentially expressed probes (Limma, P<0.05 ) between male and female human
normal liver samples in merged microarray data sets (GSE14323 and GSE23343) after removal
of batch effects using A. fRMA and B. ComBat methods.
41
A.
B.
Figure 8: Gene Signature Validation. Hierarchical cluster analysis for microarray datasets A.
GSE14323 and B. GSE23343, using a subset of 500 genes identified previously by Hartewell et
al16.
42
REFERENCES
1. Ferlay, J. et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC
CancerBase No. 11 [Internet]. Lyon Fr. Int. Agency Res. Cancer 2013 at
<http://globocan.iarc.fr>
2. El-Serag, H. B. Hepatocellular Carcinoma. N. Engl. J. Med. 365, 1118–1127 (2011).
3. Shaker, M. K., Abdella, H. M., Khalifa, M. O. & Dorry, A. K. E. Epidemiological
characteristics of hepatocellular carcinoma in Egypt: a retrospective analysis of 1313 Cases.
Liver Int. n/a–n/a (2013). doi:10.1111/liv.12209
4. el-Zayadi, A.-R. et al. Hepatocellular carcinoma in Egypt: a single center study over a decade.
World J. Gastroenterol. WJG 11, 5193–5198 (2005).
5. Bosch, F. X., Ribes, J., Díaz, M. & Cléries, R. Primary liver cancer: worldwide incidence and
trends. Gastroenterology 127, S5–S16 (2004).
6. Mucci, L. A. et al. Age at menarche and age at menopause in relation to hepatocellular
carcinoma in women. BJOG Int. J. Obstet. Gynaecol. 108, 291–294 (2001).
7. Yu, M.-W. et al. Role of reproductive factors in hepatocellular carcinoma: Impact on hepatitis
B- and C-related risk. Hepatol. Baltim. Md 38, 1393–1400 (2003).
8. Ghebranious, N. & Sell, S. Hepatitis B injury, male gender, aflatoxin, and p53 expression
each contribute to hepatocarcinogenesis in transgenic mice. Hepatol. Baltim. Md 27, 383–391
(1998).
9. Nakatani, T., Roy, G., Fujimoto, N., Asahara, T. & Ito, A. Sex hormone dependency of
diethylnitrosamine-induced liver tumors in mice and chemoprevention by leuprorelin. Jpn. J.
Cancer Res. Gann 92, 249–256 (2001).
10.
Maeda, S., Kamata, H., Luo, J.-L., Leffert, H. & Karin, M. IKKbeta couples hepatocyte
death to cytokine-driven compensatory proliferation that promotes chemical
hepatocarcinogenesis. Cell 121, 977–990 (2005).
11.
Verna, L., Whysner, J. & Williams, G. M. N-nitrosodiethylamine mechanistic data and
risk assessment: bioactivation, DNA-adduct formation, mutagenicity, and tumor initiation.
Pharmacol. Ther. 71, 57–81 (1996).
12.
Kemp, C. J., Leary, C. N. & Drinkwater, N. R. Promotion of murine
hepatocarcinogenesis by testosterone is androgen receptor-dependent but not cell autonomous.
Proc. Natl. Acad. Sci. U. S. A. 86, 7505–7509 (1989).
13.
Berasain, C. et al. Inflammation and Liver Cancer: New Molecular Links. Ann. N. Y.
Acad. Sci. 1155, 206–221 (2009).
14.
Naugler, W. E. et al. Gender Disparity in Liver Cancer Due to Sex Differences in
MyD88-Dependent IL-6 Production. Science 317, 121–124 (2007).
15.
Bigsby, R. M. & Caperell-Grant, A. The role for estrogen receptor-alpha and prolactin
receptor in sex-dependent DEN-induced liver tumorigenesis. Carcinogenesis 32, 1162–1166
(2011).
16.
Hartwell, H. J., Petrosky, K. Y., Fox, J. G., Horseman, N. D. & Rogers, A. B. Prolactin
prevents hepatocellular carcinoma by restricting innate immune activation of c-Myc in mice.
Proc. Natl. Acad. Sci. 111, 11455–11460 (2014).
17.
Keng, V. W. et al. Sex bias occurrence of hepatocellular carcinoma in Poly7 molecular
subclass is associated with EGFR. Hepatology 57, 120–130 (2013).
18.
Lipshutz, R. J., Fodor, S. P. A., Gingeras, T. R. & Lockhart, D. J. High density synthetic
oligonucleotide arrays. Nat. Genet. 21, 20–24 (1999).
43
19.
Lockhart, D. J. et al. Expression monitoring by hybridization to high-density
oligonucleotide arrays. Nat. Biotechnol. 14, 1675–1680 (1996).
20.
Alon, U. et al. Broad patterns of gene expression revealed by clustering analysis of tumor
and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96, 6745–
6750 (1999).
21.
Dhanasekaran, S. M. et al. Delineation of prognostic biomarkers in prostate cancer.
Nature 412, 822–826 (2001).
22.
Van ’t Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast
cancer. Nature 415, 530–536 (2002).
23.
Chen, X. et al. Gene expression patterns in human liver cancers. Mol. Biol. Cell 13,
1929–1939 (2002).
24.
Okabe, H. et al. Genome-wide analysis of gene expression in human hepatocellular
carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis
and tumor progression. Cancer Res. 61, 2129–2137 (2001).
25.
Garber, M. E. et al. Diversity of gene expression in adenocarcinoma of the lung. Proc.
Natl. Acad. Sci. 98, 13784–13789 (2001).
26.
Barrett, T. et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic
Acids Res. 41, D991–D995 (2013).
27.
Edgar, R. Gene Expression Omnibus: NCBI gene expression and hybridization array data
repository. Nucleic Acids Res. 30, 207–210 (2002).
28.
Kolesnikov, N. et al. ArrayExpress update--simplifying data submissions. Nucleic Acids
Res. 43, D1113–1116 (2015).
29.
Kuo, W. P., Jenssen, T.-K., Butte, A. J., Ohno-Machado, L. & Kohane, I. S. Analysis of
matched mRNA measurements from two different microarray technologies. Bioinforma. Oxf.
Engl. 18, 405–412 (2002).
30.
Irizarry, R. A. et al. Multiple-laboratory comparison of microarray platforms. Nat.
Methods 2, 345–350 (2005).
31.
Ramasamy, A., Mondry, A., Holmes, C. C. & Altman, D. G. Key Issues in Conducting a
Meta-Analysis of Gene Expression Microarray Datasets. PLoS Med. 5, e184 (2008).
32.
Choi, J. K., Yu, U., Kim, S. & Yoo, O. J. Combining multiple microarray studies and
modeling interstudy variation. Bioinformatics 19, i84–i90 (2003).
33.
Hong, F. et al. RankProd: a bioconductor package for detecting differentially expressed
genes in meta-analysis. Bioinformatics 22, 2825–2827 (2006).
34.
Mehra, R. Identification of GATA3 as a Breast Cancer Prognostic Marker by Global
Gene Expression Meta-analysis. Cancer Res. 65, 11259–11264 (2005).
35.
Rhodes, D. R. et al. Large-scale meta-analysis of cancer microarray data identifies
common transcriptional profiles of neoplastic transformation and progression. Proc. Natl.
Acad. Sci. 101, 9309–9314 (2004).
36.
Grützmann, R. et al. Meta-analysis of microarray data on pancreatic cancer defines a set
of commonly dysregulated genes. Oncogene 24, 5079–5088 (2005).
37.
Lee, H. K. Coexpression Analysis of Human Genes Across Many Microarray Data Sets.
Genome Res. 14, 1085–1094 (2004).
38.
Ewald, J. A., Downs, T. M., Cetnar, J. P. & Ricke, W. A. Expression Microarray MetaAnalysis Identifies Genes Associated with Ras/MAPK and Related Pathways in Progression
of Muscle-Invasive Bladder Transition Cell Carcinoma. PLoS ONE 8, e55414 (2013).
44
39.
Hong, F. & Breitling, R. A comparison of meta-analysis methods for detecting
differentially expressed genes in microarray experiments. Bioinformatics 24, 374–382 (2008).
40.
Tseng, G. C., Ghosh, D. & Feingold, E. Comprehensive literature review and statistical
considerations for microarray meta-analysis. Nucleic Acids Res. 40, 3785–3799 (2012).
41.
Irizarry, R. A. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res.
31, 15e–15 (2003).
42.
Bolstad, B. M., Irizarry, R. ., Astrand, M. & Speed, T. P. A comparison of normalization
methods for high density oligonucleotide array data based on variance and bias.
Bioinformatics 19, 185–193 (2003).
43.
Li, C. & Hung Wong, W. Model-based analysis of oligonucleotide arrays: model
validation, design issues and standard error application. Genome Biol. 2, RESEARCH0032
(2001).
44.
Naef, F., Hacker, C. R., Patil, N. & Magnasco, M. Empirical characterization of the
expression ratio noise structure in high-density oligonucleotide arrays. Genome Biol. 3,
RESEARCH0018 (2002).
45.
Irizarry, R. A. Exploration, normalization, and summaries of high density oligonucleotide
array probe level data. Biostatistics 4, 249–264 (2003).
46.
Cui, X. & Churchill, G. A. Statistical tests for differential expression in cDNA
microarray experiments. Genome Biol. 4, 210 (2003).
47.
Jeanmougin, M. et al. Should We Abandon the t-Test in the Analysis of Gene Expression
Microarray Data: A Comparison of Variance Modeling Strategies. PLoS ONE 5, e12336
(2010).
48.
Murie, C., Woody, O., Lee, A. Y. & Nadon, R. Comparison of small n statistical tests of
differential expression applied to microarrays. BMC Bioinformatics 10, 45 (2009).
49.
Jeffery, I. B., Higgins, D. G. & Culhane, A. C. Comparison and evaluation of methods
for generating differentially expressed gene lists from microarray data. BMC Bioinformatics 7,
359 (2006).
50.
Kooperberg, C., Aragaki, A., Strand, A. D. & Olson, J. M. Significance testing for small
microarray experiments. Stat. Med. 24, 2281–2298 (2005).
51.
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing
and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).
52.
Smyth, G. K. Linear models and empirical bayes methods for assessing differential
expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).
53.
Chen, C. et al. Removing Batch Effects in Analysis of Expression Microarray Data: An
Evaluation of Six Batch Adjustment Methods. PLoS ONE 6, e17238 (2011).
54.
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression
data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
55.
Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in highthroughput data. Nat. Rev. Genet. 11, 733–739 (2010).
56.
Lander, E. S. Array of hope. Nat. Genet. 21, 3–4 (1999).
57.
Benito, M. et al. Adjustment of systematic microarray data biases. Bioinforma. Oxf. Engl.
20, 105–114 (2004).
58.
Sims, A. H. et al. The removal of multiplicative, systematic bias allows integration of
breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis.
BMC Med. Genomics 1, 42 (2008).
45
59.
Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by
surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).
60.
Luo, J. et al. A comparison of batch effect removal methods for enhancement of
prediction performance using MAQC-II microarray gene expression data. Pharmacogenomics
J. 10, 278–291 (2010).
61.
Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy--analysis of Affymetrix
GeneChip data at the probe level. Bioinforma. Oxf. Engl. 20, 307–315 (2004).
62.
Gentleman, R. C. et al. Bioconductor: open software development for computational
biology and bioinformatics. Genome Biol. 5, R80 (2004).
63.
Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat.
Methods 12, 115–121 (2015).
64.
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65–70
(1979).
65.
Taminau, J. et al. Unlocking the potential of publicly available microarray data using
inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinformatics 13, 335
(2012).
66.
McCall, M. N., Bolstad, B. M. & Irizarry, R. A. Frozen robust multiarray analysis
(fRMA). Biostatistics 11, 242–253 (2010).
67.
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths
toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13
(2009).
68.
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of
large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2008).
69.
Misu, H. et al. A liver-derived secretory protein, selenoprotein P, causes insulin
resistance. Cell Metab. 12, 483–495 (2010).
70.
Conti, A. et al. Wide gene expression profiling of ischemia-reperfusion injury in human
liver transplantation. Liver Transplant. Off. Publ. Am. Assoc. Study Liver Dis. Int. Liver
Transplant. Soc. 13, 99–113 (2007).
71.
Mas, V. R. et al. Genes involved in viral carcinogenesis and tumor initiation in hepatitis
C virus-induced hepatocellular carcinoma. Mol. Med. Camb. Mass 15, 85–94 (2009).
72.
Everitt, B. S., Landau, S. & Leese, M. Cluster Analysis. (London: Arnold.).
73.
Fisher, E. M. et al. Homologous ribosomal protein genes on the human X and Y
chromosomes: escape from X inactivation and possible implications for Turner syndrome.
Cell 63, 1205–1218 (1990).
74.
Vawter, M. P. et al. Gender-Specific Gene Expression in Post-Mortem Human Brain:
Localization to Sex Chromosomes. Neuropsychopharmacology 29, 373–384 (2004).
75.
Isensee, J. et al. Sexually dimorphic gene expression in the heart of mice and men. J.
Mol. Med. 86, 61–74 (2008).
76.
Dasari, V. K. et al. Expression analysis of Y chromosome genes in human prostate
cancer. J. Urol. 165, 1335–1341 (2001).
77.
Ditton, H. J., Zimmer, J., Kamp, C., Rajpert-De Meyts, E. & Vogt, P. H. The AZFa gene
DBY (DDX3Y) is widely transcribed but the protein is limited to the male germ cells by
translation control. Hum. Mol. Genet. 13, 2333–2341 (2004).
78.
Yedavalli, V. S. R. K., Neuveut, C., Chi, Y.-H., Kleiman, L. & Jeang, K.-T. Requirement
of DDX3 DEAD box RNA helicase for HIV-1 Rev-RRE export function. Cell 119, 381–392
(2004).
46
79.
Lee, K. H. et al. Ubiquitin-specific protease activity of USP9Y, a male infertility gene on
the Y chromosome. Reprod. Fertil. Dev. 15, 129–133 (2003).
80.
Brown, G. M. et al. Characterisation of the coding sequence and fine mapping of the
human DFFRY gene and comparative expression analysis and mapping to the Sxrb interval of
the mouse Y chromosome of the Dffry gene. Hum. Mol. Genet. 7, 97–107 (1998).
81.
Marintchev, A., Kolupaeva, V. G., Pestova, T. V. & Wagner, G. Mapping the binding
interface between human eukaryotic initiation factors 1A and 5B: a new interaction between
old partners. Proc. Natl. Acad. Sci. U. S. A. 100, 1535–1540 (2003).
82.
Luna, R. E. et al. The Interaction between Eukaryotic Initiation Factor 1A and eIF5
Retains eIF1 within Scanning Preinitiation Complexes. Biochemistry (Mosc.) 52, 9510–9518
(2013).
83.
Laaser, I., Theis, F. J., de Angelis, M. H., Kolb, H.-J. & Adamski, J. Huge splicing
frequency in human Y chromosomal UTY gene. Omics J. Integr. Biol. 15, 141–154 (2011).
84.
Vogt, M. H. J. et al. UTY gene codes for an HLA-B60–restricted human male-specific
minor histocompatibility antigen involved in stem cell graft rejection: characterization of the
critical polymorphic amino acid residues for T-cell recognition. Blood 96, 3126–3132 (2000).
85.
Brown, C. J. et al. The human XIST gene: analysis of a 17 kb inactive X-specific RNA
that contains conserved repeats and is highly localized within the nucleus. Cell 71, 527–542
(1992).
86.
Brockdorff, N. et al. Conservation of position and exclusive expression of mouse Xist
from the inactive X chromosome. Nature 351, 329–331 (1991).
87.
Kienesberger, P. C., Oberer, M., Lass, A. & Zechner, R. Mammalian patatin domain
containing proteins: a family with diverse lipolytic activities involved in multiple biological
functions. J. Lipid Res. 50, S63–S68 (2008).
88.
Lee, W. C., Salido, E. & Yen, P. H. Isolation of a new gene GS2 (DXS1283E) from a
CpG island between STS and KAL1 on Xp22.3. Genomics 22, 372–376 (1994).
89.
Valnickova, Z. et al. Activated human plasma carboxypeptidase B is retained in the blood
by binding to alpha2-macroglobulin and pregnancy zone protein. J. Biol. Chem. 271, 12937–
12943 (1996).
90.
Philip, A., Bostedt, L., Stigbrand, T. & O’CONNOR-McCOURT, M. D. Binding of
transforming growth factor-beta (TGF-beta) to pregnancy zone protein (PZP). Comparison to
the TGF-beta-alpha2-macroglobulin interaction. Eur. J. Biochem. 221, 687–693 (1994).
91.
Chalasani, N. et al. Genome-Wide Association Study Identifies Variants Associated With
Histologic Features of Nonalcoholic Fatty Liver Disease. Gastroenterology 139, 1567–
1576.e6 (2010).
92.
Nguyen, D. K. & Disteche, C. M. Dosage compensation of the active X chromosome in
mammals. Nat. Genet. 38, 47–53 (2006).
93.
Sudbrak, R. et al. X chromosome-specific cDNA arrays: identification of genes that
escape from X-inactivation and other applications. Hum. Mol. Genet. 10, 77–83 (2001).
94.
Heard, E. Dosage compensation in mammals: fine-tuning the expression of the X
chromosome. Genes Dev. 20, 1848–1867 (2006).
95.
Skaletsky, H. et al. The male-specific region of the human Y chromosome is a mosaic of
discrete sequence classes. Nature 423, 825–837 (2003).
96.
Lau, Y. F. & Zhang, J. Expression analysis of thirty one Y chromosome genes in human
prostate cancer. Mol. Carcinog. 27, 308–321 (2000).
47
97.
Jordan, J. J., Hanlon, A. L., Al-Saleem, T. I., Greenberg, R. E. & Tricoli, J. V. Loss of the
short arm of the Y chromosome in human prostate carcinoma. Cancer Genet. Cytogenet. 124,
122–126 (2001).
98.
König, J. J. et al. Loss and gain of chromosomes 1, 18, and Y in prostate cancer. The
Prostate 25, 281–291 (1994).
99.
Lundgren, R. et al. Cytogenetic analysis of 57 primary prostatic adenocarcinomas. Genes.
Chromosomes Cancer 4, 16–24 (1992).
100. Teixeira, M. R. et al. Chromosome banding analysis of gynecomastias and breast
carcinomas in men. Genes. Chromosomes Cancer 23, 16–20 (1998).
101. Wallrapp, C. et al. Loss of the Y chromosome is a frequent chromosomal imbalance in
pancreatic cancer and allows differentiation to chronic pancreatitis. Int. J. Cancer 91, 340–344
(2001).
102. Forsberg, L. A. et al. Mosaic loss of chromosome Y in peripheral blood is associated
with shorter survival and higher risk of cancer. Nat. Genet. 46, 624–628 (2014).
103. Piao, Z. & Malkhosyan, S. R. Frequent loss Xq25 on the inactive X chromosome in
primary breast carcinomas is associated with tumor grade and axillary lymph node metastasis.
Genes. Chromosomes Cancer 33, 262–269 (2002).
104. Choi, C. et al. Loss of heterozygosity at chromosome segment Xq25-26.1 in advanced
human ovarian carcinomas. Genes. Chromosomes Cancer 20, 234–242 (1997).
105. Kersemaekers, A. M., van de Vijver, M. J., Kenter, G. G. & Fleuren, G. J. Genetic
alterations during the progression of squamous cell carcinomas of the uterine cervix. Genes.
Chromosomes Cancer 26, 346–354 (1999).
106. Azzoni, C. et al. Xq25 and Xq26 identify the common minimal deletion region in
malignant gastroenteropancreatic endocrine carcinomas. Virchows Arch. 448, 119–126
(2006).
107. Bottarelli, L. et al. Sex Chromosome Alterations Associate with Tumor Progression in
Sporadic Colorectal Carcinomas. Clin. Cancer Res. 13, 4365–4370 (2007).
108. D’Adda, T. et al. Malignancy-associated X chromosome allelic losses in foregut
endocrine neoplasms: further evidence from lung tumors. Mod. Pathol. 18, 795–805 (2005).
48