Download Methods RNA Extraction and RNA

Methods
RNA Extraction and RNA-seq
Tumors collected on necropsy were snap frozen and stored at -80’C. Douncers were used to homogenize
tumors in RLT buffer from Qiagen RNeasy Mini Prep Kit. The slurry was then passed through a 20-gauge
needle 5 times. RNA extract was then conducted as recommended by the Qiagen RNeasy Mini Prep Kit.
Quality control determined all samples had RIN values 8.8-10, demonstrating high quality RNA. Beckman
Coulter conducted sequencing with paired-end sequencing of 100bp fragment length. Sequencing depth was
25 million reads on average per sample.
Results
Five liver and nine tumor samples underwent RNA-sequencing (RNA-seq). These tumors had a range of
therapies and some increased in tumor burden over the 6 cycles of treatment while others shrank in size
(Table 1). Variation in tumor gene expression due to treatment should be negated across the tumor group.
Each sample had 20-39 million reads after filtering to remove adaptor sequences and low scoring sections.
One tumor was sequenced twice as an internal control (2c and 2d). These samples clustered together
throughout our analysis.
When tumors were compared to normal liver, 452 genes were differentially expressed (p<0.01). Pathway
analysis identified cell cycle, amino sugar and nucleotide sugar metabolism, drug metabolism, p53 signaling,
TGF-beta signaling, and others (Figure 1A). When tumors that increased in size were compared to normal
liver, 938 differentially expressed genes were identified (p<0.01), while regressing tumors vs. normal liver had
147 genes that were differentially expressed (p<0.01). These two lists had 103 genes in common. Similar
pathways were enriched for grow vs. liver as were identified with tumor vs. liver, including cell cycle, drug
metabolism, TGF-beta signaling, p53 signaling, amino sugar and nucleotide sugar metabolism and others
(Figure 1B). Regress vs. liver pathway analysis shows a smaller p value for pathways like bladder cancer and
melanoma while still enriching for the same pathways observed in the other two analyses (Figure 1C).
However, none of the 452 differentially expressed genes in tumor vs. liver were differentially expressed in grow
regress. When we directly compare grow vs. regressing tumors, nine genes were differentially expressed
(Table 2). Four of these genes have been previously identified to play a role in HCC while two others have
been associated with other types of cancer.
To assess the similarities between HCC that develops in C3HeB/FeJ mice and humans, we chose two human
HCC datasets from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus
(GEO) Database. GSE62232 has 81 HCC samples and 10 non-matched normal liver samples. According to
the GEO database, most of the HCC had hepatitis B or C viral infection or cirrhosis, most commonly due to
chronic alcohol use (1). When comparing HCC vs. normal liver, 6148 genes were differentially expressed.
GSE63898 has 228 HCC samples and 168 non-matched cirrhosis samples. This data set also had a mix of
etiology including hepatitis B or C infection and chronic alcohol use (2). When comparing HCC vs. cirrhosis,
5514 genes were differentially expressed. When the 452 differentially expressed genes from mouse HCC were
compared to the differentially expressed genes from GSE62232, there were 158 genes in common. When
compared to GSE63898, there were 198 genes in common. Between these two lists of genes differentially
expressed in a human dataset and in our mouse dataset, there were 105 genes in common. Pathway analysis
of these genes identified the recurrent cell cycle, p53 signaling, amino sugar and nucleotide sugar metabolism
and drug metabolism pathways as expected (Figure 2). Several pathways related to meiosis were enriched but
include the same genes as the cell cycle pathway.
To look at gene expression in a more comprehensive manner, we have generated the concept of eigengenes.
Eigengenes are calculated using a Bayesian network to identify modules of genes that are regulated in a
similar manner based on their gene expression patterns. Principal component analysis is used to give weight
to each gene within a module. The weights are multiplied by the gene expression and then added together to
give a single value to represent each module. We did not have sufficient number of samples to do a complete
eigengene analysis so we combined the 452 differentially expressed genes into one module and calculated the
eigengene values for each sample (Figure 3). With this method, tumor and liver samples are clustered with
each other. One tumor sample, the tumor which had the greatest reduction in tumor volume (22773-5c),
clustered with the liver samples.
The two human datasets had sufficient samples to run a complete pigengene analysis. GSE62232 had 29
modules (Figure 4A). A cutoff of -0.023 on the eigengene value for Module 5 was sufficient to identify tumor vs.
liver with no errors (Figure 4B). Module 5 contains 652 genes. When projected onto the mouse data, there is
some separation of tumor and liver samples, but there are a few that cluster with the wrong group (Figure 4C).
Therefore, this gene list is not as good at differentiating tumor vs. liver in mouse as in humans. Module 5 had
an unusually high number of genes on chromosome 1 (Figure 4D), which is commonly amplified in human
HCC and is homologous to mouse chromosome 1, where Hsc-7 is located (3). We projected each of the
modules onto the mouse dataset to determine if any were good at differentiating tumor vs. liver. Of note,
Module 14, with 154 genes, separated grow tumors from regress tumors and liver samples, with the exception
of one regress tumor that clustered with the grow tumors (Figure 4E). The pathways enriched by this gene set
include Gene Ontology (GO) terms related to angiogenesis and cell migration/cell motility.
GSE63898 had 24 modules (Figure 5A). Module 15 with a cutoff of -0.006 for the pigengene value was the
best single module for identifying tumor vs. cirrhosis, but makes 28 errors (7%) in classification (Figure 5B).
When module 18 is used to divide the group of samples with Module 15 <-0.006, the error rate is reduce to
2.1% (Figure 5C). Module 15 contains 80 genes, a subset of which are metallothionen family members and are
upregulated in cirrhosis vs. tumors, while the rest are upregulated in HCC. Module 15 is overrepresented on
chromosome 1. When projected on the mouse dataset, regress and liver samples have lower eigengene
values while grow tumors have higher eigengene values, with two exceptions (Figure 5D). Module 18 contains
68 genes, most of which have reduced expression in HCC samples compared to cirrhotic patients. These
genes enrich in KEGG pathways including normal liver functions such as gastric acid secretion, bile secretion,
and Wnt signaling. However, Module 18 does not do a good job differentiating between tumor and liver when
projected onto the mouse dataset (Figure 5E). When we projected each of the other modules onto the mouse
dataset to determine how they cluster the mouse dataset, Module 5 was of interest. Module 5 contains 428
genes with functions including amino acid metabolism, fatty acid metabolism, PPAR signaling, and
Cytochrome P450 metabolism of xenobiotics. When projected onto the mouse dataset, tumors have lower
pigengene values compared to liver samples, with the exception of two regress tumors with high pigengene
values that cluster with the liver samples (Figure 5F). One of the regress samples (5c) that clustered with liver
has the greatest reduction in tumor volume (-179.14%).
Also of interest, one liver and regress tumor are from the same mouse (1i3a1b). However, these two samples
did not cluster together in any of these analyses. This reinforces that there is a distinct difference in gene
expression between liver and HCC within the same mouse, despite these tumors arising spontaneously from
the liver within C3HeB/FeJ mice.
Next, we projected a module containing the 452 differentially expressed in mouse tumors onto the human
datasets (Figure 6). Of interest, there is variability in the pigengene values within the tumor samples of each
human dataset. This supports the hypothesis that C3HeB/FeJ HCC is similar to a subset of human HCC.
However, in both datasets, there is a subset of human HCC that would be classified as normal based on this
gene set.
Finally, we projected each human dataset back onto the other human dataset to determine how closely they
were related. Of the differentially expressed gene in each dataset, 2,302 genes were differentially expressed
in both. When GSE63898 eigengene modules were projected onto GSE62232, the normal liver samples
clustered with a subset of the HCC samples (Figure 7A). When GSE62232 eigengene modules were projected
onto GSE63898, most of the HCC samples clustered together with the exception of a few HCC samples that
mixed in with cirrhosis samples (Figure 7B). Generally, these results show that while the datasets are diverse,
they are not significantly differences. Some of the differences observed between the datasets may also be due
to the difference in control tissue, normal liver for GSE62232 and cirrhotic liver for GSE63898. Both of these
studies highlight the diversity in gene expression across HCC samples and support the need for a molecular
subtype classification system for HCC.
Discussion
C3HeB/FeJ HCC has similarities in aberrant gene expression with human HCC. The main pathway enrichment
that is observed in all comparisons between the mouse tumors and liver demonstrated significant changes in
cell cycle. There are not many genes differentially expressed in tumors that are increasing in size vs. those that
are regressing. This may be due to the fact that tumors were being treated with different agents and thus drug
treatment specific gene expression changes are not detected. If the drugs were each working in independent
mechanisms, we would not observe common gene expression changes across the group. However, it is
surprising that apoptosis pathways are not enriched since all of the therapies are presumably inducing cell
death one way or another. This could be because the cells were already dead at the timepoint when we
collected tumors.
The eigengene analysis gives us a unique way to look at gene expression in these datasets. When the
differentially expressed genes from tumor vs. liver in the mouse dataset are combined into a single module, we
can easily differentiate between mouse tumors and liver, with the exception of one tumor sample, which also
had the greatest regression in size (Figure 3). Using the human datasets, we were able to identify several
modules of genes that had some effectiveness in differentiating mouse tumor from liver. Interestingly, despite
finding only 9 genes differentially expressed between grow vs. regress tumors, some of the regress tumors
often clustered with the liver samples when eigengene modules were projected onto the mouse dataset. Of
note, GSE62232 Module 14 (Figure 4E) was able to differentiate between grow tumors vs. regress or normal
liver samples. When pathway analysis was conducted on the genes in this module, pathways related to
angiogenesis and cell motility were enriched, which are features we would expect of growing tumors.
When projecting the eigengene module generated from the differentially expressed genes in mouse tumors vs.
liver, a subset of HCC in each of the human datasets has similar pigengene values. This supports that these
genes are also differentially expressed and of importance in this subset of human HCC. Therefore it follows
that there are features in common between these tumors and C3HeB/FeJ mouse model can be used to model
this subset of human HCC.
Also of note, GSE62232 Module 5 had a higher than predicted number of genes on chromosome 1 (Figure
4D). Human chromosome 1 is homologous to mouse chromosome 1, this is the location of one of the Hcs loci,
Hcs-7, that predispose C3H mice with developing HCC (3, 4). Hsc loci were identified with quantitative trait loci
mapping of microsatellite markers (3, 4). Therefore, it has only been narrowed down to a distal region of
chromosome 1 but not on a gene specific level (3). Chromosome 1 is amplified in one half of human HCC (5).
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Table 1. Samples Used for RNA-Seq
Mouse ID
% Change Log2 TTV
1i1k1a
N/A – Liver
2b2c
N/A – Liver
2a5a
N/A – Liver
1i3a1b
N/A – Liver
1k3b
+333.21
3b
+147.04
2c
+128.73
1h4a
+109.74
1h1a
+104.11
5c
-179.14
1i3a1b
-145.25
1i3a2a
-135.06
1j3b
-44.47
Treatment
PCB
PCB
PCB
PCB
DMSO
SAHA+TMZ
SAHA+TMZ
DMSO
SAHA
TMZ (DMSO solv)
PCB
TMZ (PCB solv)
DMSO
Table 2. Differentially expressed genes in grow vs. regress tumors
Gene Symbol
Nedd9
Gene name
neural precursor cell
expressed,
developmentally
down-regulated gene 9
Function/Relationship with Cancer
References
(6)
High NEDD9 expression associated with poor overall
survival in HCC
Dpt
dermatopontin
Silencing by hypermethylation leads to metastasis in
HCC; downregulated in human HCC
(7)
Choline transporter; downregulated with choline
deficiency (Choline deficient diet causes HCC in
mouse models)
(8)
Slc44a1
solute carrier family
44, member 1
Ddx6
DEAD (Asp-Glu-AlaAsp) box polypeptide 6
Important in HCV; Overexpression of Dead box
helicase involved in progression to HCC; HCV
interacts with DEAD box proteins
Sipa1
Dcun1d4
Tbc1d24
signal-induced
proliferation
associated gene 1
DCN1, defective in
cullin neddylation 1,
domain containing 4 (S.
cerevisiae)
TBC1 domain family,
member 24
GTPase-activating protein; Polymorphisms
associated with risk for sporadic breast cancer and
metastasis
Upregulated in gastric cancer; Dcun1d1 involved in
E3 neddylation of cullin; Dcun1d3 involved in cell
cycle progression and proliferation
Part of BAF complex; Recessive genetic disorders
Add1
adducin 1 (alpha)
Also called SREBP1 when describing role in adipocyte
diff and cholesterol homeostasis; helix-loop-helix
transcription factor which binds E box and activates
PGC1α
Lyn
Yamaguchi sarcoma
viral (v-yes-1)
oncogene homolog
Important in erythroid differentiation; associated
with poor prognosis in renal cancer
(9)
(10, 11)
(12-14)
(15)
(16)
(17, 18)
Figure 1. Pathways analysis for Mouse Dataset
A. Tumor vs. liver
B. Grow vs. liver
C. Regress vs. liver
Figure 2. Pathway enrichment for Genes in Common Between Two Human HCC Datasets and Mouse Dataset
Figure 3. Eigengene analysis of C3HeB/FeJ Liver and Tumor Samples
Figure 4A. Eigengene Modules for GSE62232
Figure 4B. Decision Tree for GSE62232
Figure 4C. GSE62232 Module 5 Gene Set Projected onto Mouse Data
’
Figure 4D. Chromosomal Distribution of GSE62232 Module 5.
Figure 4E. GSE62232 Module 14 Projected onto Mouse Dataset
Figure 5A. GSE63898 Eigengene Modules
Figure 5B. Decision Tree for Differentiating GSE63898 Tumor vs. Cirrhosis Using Only One Module
5C. Best Fit Decision Tree for Differentiating GSE63898 Tumor vs. Cirrhosis
Figure 5D. GSE63898 Module 15 Projected onto Mouse Dataset
Figure 5E. GSE63898 Module 18 Projected onto Mouse Dataset
Figure 5F. GSE63898 Module 5 Projected on Mouse Dataset
Figure 6. Mouse Differentially Expressed Gene Module Projected onto Human Datasets
A. GSE63898
B. GSE62232
Figure 7. Human Dataset Eigengene Modules Projected Onto Other Human Dataset
A. GSE63898 Modules Projected Onto GSE62232
Figure 7B. GSE62232 Eigengene Modules Projected Onto GSE63898