Abstract
Although numerous approaches have been proposed to discern driver from
passenger, identification of driver genes remains a critical challenge
in the cancer genomics field. Driver genes with low mutated frequency
tend to be filtered in cancer research. In addition, the accumulation
of different omics data necessitates the development of algorithmic
frameworks for nominating putative driver genes. In this study, we
presented a novel framework to identify driver genes through
integrating multi-omics data such as somatic mutation, gene expression,
and copy number alterations. We developed a computational approach to
detect potential driver genes by virtue of their effect on their
neighbors in network. Application to three datasets (head and neck
squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney
renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA),
by comparing the Precision, Recall and F1 score, our method
outperformed DriverNet and MUFFINN in all three datasets. In addition,
our method was less affected by protein length compared with DriverNet.
Lastly, our method not only identified the known cancer genes but also
detected the potential rare drivers (PTPN6 in THCA, SRC, GRB2 and PTPN6
in KIRC, MAPK1 and SMAD2 in HNSC).
Keywords: driver genes, protein interaction network, integrative data
INTRODUCTION
With the development of high throughput sequencing, amounts of cancer
omics data have allowed us to better understand cancer biology [[26]1].
The Cancer Genome Atlas (TCGA) project stores omics data of more than
20 cancer types, thus allows us to study cancer driver genes (driver
gene is a specific type of cancer gene). However, the key question is
how to distinguish the driver genes, which confer a selective advantage
to tumor growth, from passengers, which provide no fitness advantage to
the tumor [[27]2]. Besides, how to subsequently integrate omics data,
including exploit protein interaction networks to detect cancer driver
genes remains a challenge. Computational approaches and tools have been
developed to identify driver genes. These methods can be categorized
into gene level and module level approaches [[28]3]. Gene level
approaches for identifying drivers mainly rely on the hypothesis that
driver has a more chance to be mutated across a set of tumors [[29]4,
[30]5]. These approaches including the mutational significant in cancer
(MuSiC) [[31]6], OncodriverCLUST [[32]7], and MuSigCV [[33]8], which
can identify the genes that harbor significantly more mutations than
background mutation rate. Although the gene level approaches can be
used to distinguish driver genes from passengers, rare mutations played
functional roles in later stages of tumor progression are failed to be
detected [[34]9]. What's more, cells are made up of multiple molecular
structures that form dynamic networks [[35]10]. Under a network, a
genetic aberration may affect its connection within the network
[[36]10]. The module level approaches using the network or pathway
information can be effective in identifying drivers. An example is that
Hamed and his colleges used the protein interaction network for
identifying cancer drivers [[37]11]. DawnRank, which used PageRank
algorithm to detect driver genes [[38]12], is an affective module level
method. Besides, DriverNet identifies drivers by estimating their
effect on mRNA expression [[39]13]. MUFFINN prioritizes driver genes
based on genes and their neighbors in functional network [[40]14]. In
addition, some approaches were developed to identify driver modules or
pathways, such as MEMCover and MEMo [[41]15, [42]16]. Although these
approaches are effective in detecting drivers, they have limitations.
First, the network is built based on static network, rather being
condition specific. Second, network edges are often not considered in
the majority of the aforementioned approaches.
In view of the functional relationship between gene pairs in a network
may radically boost the detection of drivers [[43]17]. The network
consists of nodes (which may stand for genes or proteins) and edges
(which may present the functional links that connect them). Merid et
al. performed a network-based algorithm to identify driver genes by
considering the relationship between the mutation events and functional
gene sets (FGS), the result showed a complementary to frequency-based
driver analyses [[44]17]. However, their method did not consider gene
expression data. In a biological network, an interacting gene pair
tends to present positive or negative correlations which are reflected
by the gene expression data. Alteration of these gene correlations
indicates system's condition (such as normal or disease state)
[[45]18]. Therefore, differentially correlated gene pairs can
distinguish tumor and normal sample [[46]19]. Thus, candidate potential
cancer genes can be detected by studying dynamic regulation between
genes, which may improve the detection of driver genes [[47]17].
In this paper, we presented a network-based approach which integrated
gene expression, mutation data, and PPI (protein-protein interaction)
data to distinguish between driver and passenger genes. Our approach
built on a hypothesis that a driver gene can be determined by its
neighbors. We firstly built a relationship network among the DCGs
(differentially coexpressed genes), functional genes, and then
calculated the impact of DCGs by weighting the relationship between the
DCGs and its connective functional genes on a bipartite graph. Finally,
we combined the mutation information to improve the effect of screening
drivers. In order to evaluate the performance of our approach, we
applied it to three datasets (KIRC (kidney renal clear cell carcinoma),
THCA (thyroid carcinoma), and HNSC (head and neck squamous cell
carcinoma)) to identify driver genes. We detected some potential rare
drivers that previously could not be identified by DriverNet such as
SMAD2 in HNSC [[48]13]. Besides, we also detected some known cancer
driver genes in each cancer dataset. All in all, our computational
method is effective to detect potential cancer drivers to improve
cancer-specific therapeutic targets.
RESULTS
Performance comparison
In order to assess the performance of our method's ability to detect
known driver genes, DriverNet and DawnRank and MUFFINN, and
frequency-based methods were used to be compared with our method in CGC
(the Cancer Gene Cense database) and driver gene list defined by 20/20
rules [[49]20] as benchmark of known drivers. We performed the
comparisons as follows: we used the same datasets to perform the
DriverNet, DawnRank, MUFFINN, and frequency-based method and our
method, respectively. We input these datasets into DriverNet, DawnRank,
and MUFFINN. Then ran the program with the default settings, and we ran
our method with the settings mentioned above. We calculated the DCGs
Z-score and the mutated gene Z-score respectively. Combining the two
scores and using the total score as the driver gene score. In CGC
benchmark dataset, to evaluate the comparison, we used the three
measures (Precision, Recall, and F1 score) mentioned in the Method
Section. Based on these measures, our approach showed a better
performance than MUFFINN and DriverNet and frequency-based method. We
first evaluated the performance of our method. In Figure [50]1,
Precision, Recall and F1 score curves of our method are both higher
than those with DriverNet, MUFFIN and frequency-based method, but
slightly worse than those with DawnRank method in THCA and HNSC
datasets. Although DriverNet performed comparably in ranking the top 5
genes in THCA and KIRC, it has poorer performance in all driver genes.
A potential explanation of the difference may lie in the CGC is not
cancer-specific and the cancer gene listed in CGC is not complete. In
Table [51]1, we can observe LYN is not a CGC gene, while they have been
reported to have an association with THCA [[52]21].
Figure 1. A comparison of the precision, recall, and F1 score for the top
ranking genes in our method and DriverNet.
Figure 1
[53]Open in a new tab
The X-axis represents the number of top ranking genes involved in the
precision, recall, and F1 score calculation. The Y-axis represents the
score of the given metric.
Table 1. Top 5 cancer–associated driver genes in each three cancer type.
Ranking Gene Driver gene score Annotation The number of mutated gene
THCA 1 EGFR 11.58 Sanger cancer gene 3
2 EP300 11.20 Sanger cancer gene 3
3 NRAS 9.97 Sanger cancer gene 31
4 LYN 9.78 3
5 PTPN11 9.42 Sanger cancer gene 11
HNSC 1 TP53 39.63906 Sanger cancer gene 172
2 PIK3CA 19.19902 Sanger cancer gene 100
3 EGFR 16.93629 Sanger cancer gene 40
4 EP300 16.62027 Sanger cancer gene 22
5 FADD 15.40049 81
KIRC 1 PBRM1 41.84646 Sanger cancer gene 138
2 SETD2 14.98057 Sanger cancer gene 51
3 BAP1 11.83806 Sanger cancer gene 42
4 SRC 11.66549 2
5 EP300 11.39506 Sanger cancer gene 6
[54]Open in a new tab
In benchmarking 20/20 rule dataset, we used the top ranked driver genes
(top 89, 100, and 100 driver genes for THCA, HNSC, and KIRC,
respectively (see [55]Supplementary Table 1) to compare with other
methods, which is shown in Figure [56]2. It can be seen that our method
outperformed the other four methods on the top 100 genes in HNSC and
KIRC dataset. In THCA, our method has a remarkably better performance
than frequency-based method, MUFFIN, and DriverNet, but slightly worse
than DawnRank. However, in the top 22 gene list, our method presents
advantage than DawnRank.
Figure 2. Cumulative numbers of retrieved cancer genes annotated by 20/20
rule within top 25, 50, 75, and 100 of HNSC and KIRC, top 22, 44, 66, and 89
of THCA using four different methods.
Figure 2
[57]Open in a new tab
For THCA, our method identified 89 genes (driver gene score ≥2) which
includes 28 genes found in CGC. 11 genes found in CGC (NRAS, HRAS,
PTPN, PTEN, RB1, EP300, ATM, PIK3R1, TP53, HSP90AA1, PML) were also
among the driver genes nominated by DriverNet approach. TG
(thyroglobulin) is the 13th-ranked driver in our method, and it is
altered in 20/435 THCA samples. TG plays a role in the pathogenesis of
papillary thyroid carcinoma and its malignant evolution [[58]22].
However, it ranked 478 and was not detected by DriverNet as top ranking
drivers.
For KIRC, DriverNet identified 100 driver genes, of which 21 driver
genes were found in CGC. Our method identified 127genes (driver gene
score ≥2), 52 genes out of them were found in CGC. 12 genes found in
CGC (PTEN, TP53, EGFR, EP300, SMARCA4, ATM, CREBBP, APC, NCOR1, XPO1,
AKT1, HSP90AA1) were also among the driver genes nominated by
DriverNet. We detected PBRM1 as the first ranked gene (mutated in 138
cases) [[59]23], whereas it was not detected by DriverNet.
For HNSC, DriverNet identified 202 driver genes, of which 23 were found
in CGC. Our method identified 202 genes (driver gene score≥2), among
them, 49 genes were included in CGC. 17 genes (TP53, PIK3CA, EGFR,
EP300, CREBBP, NOTCH1, SMAD4, FGFR1, HRAS, CASP8, NFE2L2, MDM2, RAC1,
HSP90AA1,AKT1, PLCG1, and CDH1) found in CGC were also among the driver
genes nominated by DriverNet. TP53, PIK3CA, HRAS, and EGFR are known
oncogenic drivers and it indicates that the efficiency of integrating
multi-omics data to detect drivers. In addition, STAT3 mediates the
cell cycle, regulates apoptosis, which has been reported to be
constitutively active in HNSC [[60]24]. However, STAT3 which was ranked
27th was not found by DriverNet.
Our method is less affected by noise than DriverNet. An illustration is
that TTN was ranked 23th in KIRC and 18th in HNSC by DriverNet. TTN is
the longest gene in the human genome, which has a higher mutation rate
and a potential to be artifacts [[61]25]. However, TTN was not detected
as driver genes in any three cancers according to our method.
Infrequent (rare) driver mutations identified in three cancer types (THCA,
KIRC, and HNSC)
In this section, we validated the ability of detecting rare drivers by
our method. We adopted the three criteria to identify rare driver
genes. Firstly, only the top 30 of driver genes in various samples were
considered as drivers. Secondly, the alteration frequency should be
lower than 2% in tumor samples. And finally the gene should not be
reported in CGC [[62]12].
In THCA, we found some novel rare driver genes. Of them, PTPN6 is the
most promising. PTPN6 has been described as a tumor suppressor gene
[[63]26]. PTPN6 participates in several cancer related pathways,
including adherens junction, T cell receptor signaling pathway, B cell
receptor signaling pathway, and Jak-STAT signaling pathway (see
[64]Supplementary Table 2). PTPN6 is the 11th-ranked driver in our
method, and it is altered in 0.23% THCA samples.
In KIRC, there were three candidate novel drivers including SRC, GRB2
and PTPN6. SRC is 5th-ranked driver in our method, and it is altered in
0.48% KIRC samples. SRC is human proto-oncogene, which was reported as
a novel therapeutic target in renal cell carcinoma [[65]27]. GRb2 (The
adapter protein growth factor receptor-bound 2), a scaffolding adaptor
protein, has recently been involved in a critical crosstalk between RTK
signals and the intracellular signals [[66]28]. The enrichment analysis
shows that GRb2 participates in multiple cancer related pathway, such
as chemokine signaling pathway, ErbB signaling pathway, MAPK signaling
pathway, Jak-STAT signaling pathway (see [67]Supplementary Table 2).
There is a significant association between the protein tyrosine
phosphatase PTPN6 (SHP-1) and GRB2 expression, which may amplify
tyrosine kinase signaling in human breast cancer [[68]29].
Nevertheless, direct demonstration of the relationship between the
PTPN6 (SHP-1) and GRB2 in KIRC has been reported. In the cluster 1 of
the Figure [69]3, we can observe that PTPN6 and GRB2 connect with each
other. GRB2 is the 6th-ranked driver in our method, and it is altered
in 0.24% KIRC samples. PTPN6 is the 12th-ranked driver in our method,
and it is altered in 0.72% HNSC samples. These findings suggest
potential crosstalk between mutant PTPN6 and GRB2.
Figure 3. The gene modules identified using the top 50 genes and their
corresponding interaction partners in KIRC.
Figure 3
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Genes in green ellipse represent the detected driver gene, while genes
in blue ellipse represent the driver genes’ interaction partners.
In HNSC, two potential novel drivers we found are MAPK1 and SMAD2.
SMAD2 is 28th-ranked driver in our method, and it is altered in 0.59%
HNSC samples. The algorithm also identified a well-established tumor
suppressor gene SMAD2. According to the pathway enrichment analysis,
SMAD2 is involved in TGF-beta signaling pathway, Cell cycle, and Wnt
signaling pathway (see [71]Supplementary Table 2). In addition, SMAD2
mutations in human head and neck cancer have been reported [[72]30].
Additionally, we identified one MAP kinase MAPK1, which is 19th-ranked
driver in our method, and it is altered in 0.79% HNSC samples. We can
observe that MAPK1 is involved in multiple pathways (see
[73]Supplementary Table 2). In a recent study, MAPK1 (p38) mediates
epithelial-mesenchymal transition to drive HNSC metastasis [[74]31].
Confirmation of cancer genes
In total, we found 89, 127, and 202 drivers in THCA, KIRC, and HNSC
respectively (see [75]Supplementary Table 1). The identified driver
genes were overlapped with CGC and shown in Figure [76]4. In THCA, it
can be seen that 28 driver genes out of top 89 driver genes are known
driver genes in CGC (p-value < 2.2e-16). In HNSC, of these top 202
driver genes, 49 driver genes are in CGC (p-value < 2.2e-16). In KIRC,
of these top 127 driver genes, 52 are identified in CGC. Our result
indicates that the detected driver genes are enriched among known
cancer related genes and cannot be selected randomly.
Figure 4. Overlap of the known cancer genes identified for three cancers.
Figure 4
[77]Open in a new tab
Venn diagram represents the overlap between each cancer-specific driver
genes and CGC. 572 known cancer genes were obtained from the CGC
database, 49, 28, 52 of which appear in HNSC drivers, THCA drivers, and
KIRC drivers respectively.
We examined the top 5 ranked in THCA, HNSC, KIRC respectively (Table
[78]1). In addition, 12 genes have been functionally linked to cancer
in multiple reports.
In THCA, EGFR is especially intriguing. It is a member of the protein
kinase superfamily, and ranked first in the predicted THCA driver
genes. EGFR mediated downstream signal transduction and was
overexpressed in an aplastic thyroid cancer cell lines, rendering this
receptor a potential target for molecular therapy [[79]32]. The
third-ranked gene, NRAS, encodes membrane-associated proteins that play
a vital role in the transduction of signals [[80]33], which has been
reported in thyroid cancer [[81]34]. The EP300 gene encodes p300, which
is significant in the processes of cell proliferation and
differentiation [[82]35]. It has altered protein expression in thyroid
cancer [[83]36]. LYN has been previously reported in thyroid cancer
[[84]21]. PTPN11 encodes protein-tyrosine phosphatase SHP2, and their
domains are involved in cellular signaling. Besides, PTPN11 was
significantly increase expressed in human thyroid carcinoma [[85]37].
All told, all the top 5 predicted driver genes have evidenced in the
literature for their roles in THCA.
In HNSC, TP53 was the top ranked driver in our analysis. Three (TP53,
PIK3CA, and EGFR) of the top 5 genes were previously known HNSC genes
[[86]38]. Daniel Martin and his colleges suggested that EP300 genomic
alternations may promote HNSC initiation and progression [[87]39]. It
is noted that FADD was ranked 5th in our result. FADD can recruit other
proteins to active NFκB and MAPK pathways [[88]40]. It was
overexpressed and considered to be a driver gene in HNSC [[89]41].
In KIRC, PBRM1 was the top ranked driver in our analysis. PBRM1, SETD2,
and BAP1 were previously known HNSC genes and recently found to be
altered in clear cell renal cell carcinoma [[90]23, [91]42]. They are
highly mutated (Table [92]1). SRC was identified as a novel rare gene.
It is one of the markers for low-grade renal cell carcinoma [[93]43].
EP300 is ranked 5th (Table [94]1) and we observed that it is mutated in
6 KIRC samples. It has been reported that EP300 behaves as a classical
tumor-suppressor gene in human cancers [[95]35].
EP300 was identified as a top gene in three datasets simultaneously
(Table [96]1). It was 25th-ranked, 14th-ranked, and 9th-ranked in THCA,
HNSC, and KIRC respectively by DawnRank method. In DriverNet, it seems
the same situation. EP300 was 16th-ranked, 15th-ranked, 19th-ranked in
THCA, HNSC, and KIRC respectively by DriverNet method. These results
further suggest that the important roles of EP300 in cancers.
DISCUSSION
In recent years, various computational approaches and tools have been
developed to identify drivers, however, there are some limitations in
detecting driver genes, for example, DriverNet has a bias toward long
mutated genes. In two benchmark datasets, our method outperforms
DriverNet, MUFFINN and frequency-based method in all three cancer
types, although the performance of DawnRank is slightly higher than
that of our approach in one of three databases. Our method is simple
and parameter free. Meanwhile, the result indicates that our pipeline
shows its ability to detect driver genes, even rare driver genes. Two
potential reasons may contribute to the result. First, we consider the
dynamic of network. Second, we construct a bipartite graph, which
represents the relationship between the DCGs and the functional genes
impacted by them in network. If a DCG impacts the more functional
genes, the more likely it may be the driver. Our method has the
following advantages. Firstly, this approach detects common and known
drivers with a better performance than previous method. Secondly, it
can filter out long genes with a higher mutation rate, such as the TTN
gene which can't contribute to cancer. Thirdly, it can find some
potential co-expression gene pairs, for instance, PTPN6 and GRB2 in
KIRC. Our benchmarking analysis suggests that our algorithm is robust
to noise and works well in three TCGA cancer types, making it general
to different cancer types if the mutation data and gene expression data
are available. In essence, the approach demands information on the
context of the DCG (differentially coexpressed gene) of interest,
functional gene set that constitute known cancer related pathways, and
the connections between genes in the global network. There are also,
however, limitations. One of the limitations is that HPRD is not large
enough, DCGs or functional genes not in the PPI were filtered out and
it may ignore some candidates. This can be improved with the growth of
the database of HPRD in the future. Another limitation is that the
network used in our method is not patient-specific or cancer-specific.
Therefore, perturbations specific to patient may be obscured by this
pipeline. The last limitation is that our approach detects potential
drivers rely on the common effect of DCGs altering the functional gene.
However, this may not be the case for all drivers. As more and more
efforts are being devoted into understanding cancer genomes, we expect
that our method's ability to detect drivers would also improve.
Taken together, we developed a practical analysis pipeline to predict
potential driver genes in cancer. Although this study focuses on THCA,
KIRC and HNSC, the method is broadly applicable to any other cancer
types for which mutation and expression data are available. In
addition, our results demonstrate the efficiency of integrative
analysis across three cancer types, not only the known cancer genes
were identified, but also the potential rare drivers were detected. In
future, we will combine the gene expression, copy number variation, and
methylation data to construct a heterogeneous network. In addition, we
will apply machine learning method to improve the performance. We
expect this approach can generalize well to perform the future studies,
including determine the optimal treatment tactics for each patient
through integrating patient-specific omics data.
MATERIALS AND METHODS
Datasets and pre-processing
The RNASeqV2 data (level three), gene mutation data (level two) and CNV
data were downloaded from TCGA data portal. RNAseq expression levels,
available as RSEM (RNAseq by Expectation Maximization) were transformed
to log2 (RSEM+1). The GISTIC (version 2) was applied to the DNA copy
number data. The information of three cancer types used in our method
was provided in Table [97]2. For gene expression dataset, NA values
were replaced by mean value.
Table 2. Overview of the number of samples for three cancer types with gene
expression and mutation data.
Cancer type Tumor expression samples Normal expression samples Somatic
mutation samples
KIRC 534 72 417
HNSC 522 44 509
THCA 513 59 435
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Mutation matrix
Mutation matrix combined somatic mutation data and CNV data by
extracting genes from deleted and amplified fragments in CNV data. The
common samples between the mutation data and CNV data were retained.
Mutation matrix (i, j) is a binary matrix where M (i, j) =1 indicates
sample j have a gene i mutated and M (i, j) =0 indicates sample j don't
have a gene i mutated.
Differentially coexpressed genes
Differential co-expression analysis is designed to examine the
alternation in gene expression correlation between the tumor samples
and the normal samples, which is developed as a complementary approach
to traditional differential expression analysis [[99]44]. DCGs were
obtained by using Differential coexpression profile (DCp)function in
DCGL (differentially coexpressed genes and links) package [[100]44]. We
used the Pearson Correlation Coefficient (PCC) to measure the
relationships between the expression profiles of all gene pairs and
calculated the false discovery rate (FDR) by Benjamini–Hochberg method
to adjust the raw p values [[101]45]. Gene with threshold of FDR less
than 0.25 were selected as DCGs [[102]46].
Network construction and functional gene sets
The network is an undirected graph G (V, E) where V stands for the
genes and edges (i, j) E are weighted by PCC. Protein interaction
network was sourced from HPRD ([103]http://www.hprd.org) database and
protein self-interactions were removed, resulting in 39240 interactions
among 9616 proteins. Functional gene sets (FGS) were obtained from
[[104]47], which contain all of the KEGG pathways [[105]48] and 15 GO
terms [[106]49], which could be related to hallmarks of tumor
[[107]50]. Then a matrix was used to leverage to DCGs to their
consequent effect on functional gene. The associations between DCGs and
functional genes were built using a bipartite graph where left nodes
represent DCGs and right nodes stand for functional genes. We formulate
the network with DCGs = {g[1],g[2],…g[n]}, functional genes =
{g[1],g[2],…g[m]}. Nodes g[n] in the left partition and nodes g[m] in
the right interact have an edge, if g[g] is DCG, g[m] is a member of
functional gene set, and g[n] and g[m] interact according to known PPI
network.
Details of our algorithm
In this study, we developed a pipeline to identify drivers. Figure
[108]5 shows the schematic overview of approaches used in our study.
Firstly, using the DCp function in DCGL package, we picked out DCGs. To
improve statistical confidence, DCGs must have FDR<0.25. Secondly, we
computed the DCGs score (Z-score) as:
[MATH:
z=dAF−μAFσAF
, :MATH]
Figure 5. Identification of cancer related genes based on protein-protein
interactions (PPIs).
Figure 5
[109]Open in a new tab
where d[AF] is the total score of weighted network between genes in the
DCGs and the FGS, μ[AF] is the expected mean of d[AF], and σ[AF] is the
standard deviation of d[AF]. Thirdly, in order to improve the accuracy
of detecting driver genes, mutation information was combined.
Therefore, we calculated the number of each mutated gene in mutation
matrix. We normalized it and got the mutated gene Z-score. Finally, the
driver gene score was assigned by summing the corresponding DCG Z-score
and mutated gene Z-score. We only considered genes with driver gene
score ≥2 as potential driver genes.
Performance benchmarking analysis of our method
In order to metric the efficiency of our results, we took CGC
([110]http://cancer.sanger.ac.uk/cancergenome/projects/census/) as a
benchmark to evaluate the effect of our method. In practice, the gold
standard of known drivers is impractical in the absence of ground
truth. However, well-studied CGC provides an approximate benchmark of
known drivers [[111]12, [112]13]. Besides, we consider additional
dataset to assess the method. As defined by the 20/20 rule, only 138
driver genes have been discovered to date. Both of these datasets were
used to assess the accuracy of our method. We compared our method with
the DriverNet, DawnRank, frequency-based method and MUFFINN method.
In accordance with both approaches mentioned above, we used the same
datasets to perform the analysis and restricted the comparisons to
three tumor types only (HNSC, KIRC, and THCA). We adopted three
measures (Precision, Recall and F1 score) as follows:
[MATH:
Precision=(#Mutated genes in CGC)∩(#Genes found in our method)(#
Genes found in our method) :MATH]
[MATH:
Recall=(#Mutated genes in CGC)<
mo>∩(#Genes found in our meth
od)(#Mutated genes in CGC), :MATH]
[MATH:
F1 score=2×Precision×RecallPrecision+Recall. :MATH]
Significance estimation of the potential driver genes
In order to evaluate the significance of the identified driver genes,
we performed a hypergeometric test to calculate the probability of a
random overlap:
[MATH:
P(X≥<
mtext>x)=1−∑k=0x−1(Mk<
mo>)(N<
/mtext>−Mn−k)(Nn), :MATH]
where N is the total number of genes, M and n are the number of genes
in two sets, and k is the number of the overlapped genes of the two
sets.
Functional enrichment analysis of driver genes and recognition modules
In order to annotate driver genes detected in our result, we used the
online DAVID [[113]51] website and observed significant enrichment of
these genes in the term of KEGG pathway. Briefly, KEGG pathway terms
were annotated to statistical significance in the gene set. Enrichment
was calculated through the hyper-geometric test using a FDR less than
0.05. Molecular Complex Detection (MCODE) [[114]52] that detects
densely connected regions in large protein interaction networks were
used to recognize modules. MCODE weights all nodes depended on their
local network density by setting the highest k-core of the vertex
neighborhood. We set the highest k-core is 2.
SUPPLEMENTARY MATERIALS FIGURES AND TABLES
[115]oncotarget-08-58050-s001.pdf^ (1,006KB, pdf)
[116]oncotarget-08-58050-s002.xls^ (57.5KB, xls)
[117]oncotarget-08-58050-s003.xls^ (57.5KB, xls)
Acknowledgments