Relevance of genes with higher information gain
BLCA related genes
CD8A achieved higher information gain considering DNA methylation. The CD8 antigen is a cell surface
glycoprotein found on most cytotoxic T lymphocyte mediating efficient cell-cell interaction within the immune
system. The potential for tumor control of the immune response through activated CD8+ T cells has received
attention (Feldmeyer et al. 2013).
MIR663A was associated with higher information gain considering DNA methylation. It was proposed that
MIR663A may play an important role in the malignant progression of chordoma (Long et al. 2013). Also
MIR663 may be a putative tumor suppressor gene in pediatric acute myeloid leukemia (Tao et al. 2013).
ZSCAN18 achieved higher information gain in the domain of DNA methylation. The high sensitivity and
specificity of ZSCAN18 as a biomarker for cholangiocarcinoma were identified (Andresen et al. 2012). A
putative tumor suppressor function of ZSCAN18 in renal cell carcinoma was also reported (Morris et al. 2011).
CLEC3B (previously TNA) was highly informative considering RNASeqV2. The CLEC3B gene encodes
tetranectin and low levels of tetranectin are associated with increased risk of second-line chemoresistance in
patients with ovarian cancer (Gronlund et al. 2006). In addition, a significant association between survival
duration and CLEC3B level in colorectal cancer was observed (Hogdall et al. 2002).
PYGM achieved higher information gain considering RNASeqV2. The loss of heterozygosity markers
mapped to chromosome band 11q13 (PYGM locus) was subsequently demonstrated in sporadic islet-cell tumors,
pituitary tumors, and parathyroid tumors (Friedman et al. 1992; Thakker et al. 1989).
ADH1B was highly informative considering RNASeqV2. This gene encodes a member of the alcohol
dehydrogenase family. It was reported that the earlier diagnosis age of esophageal squamous cell carcinoma
(ESCC) is associated with alcohol intake and ADH1B polymorphisms (Lee et al. 2009).
XPNPEP2 was associated with higher information gain in the domain of RNASeqV2. XPNPEP2 encodes
aminopeptidase P (APP) which is a membrane protein expressed on the surface of vascular endothelial and
lymphoid cells of various tissues. It was indicated that aminopeptidase P acts as a peptide receptor for a breasthoming peptide (Essler and Ruoslahti 2002).
KIAA0100 was selected considering somatic mutations. This gene is overexpressed in breast carcinoma
(Song et al. 2006).
ARID1A was highly informative in the domain of somatic mutations. The observations by Wiegand and
colleagues (Wiegand et al. 2010) implied the role of ARID1A as a tumor suppressor because the activity of this
gene is frequently disrupted in ovarian clear-cell and endometrioid carcinomas.
MUC16 (also known as CA125) was associated with higher information gain considering somatic mutations.
Besides the established role as a clinically reliable diagnostic marker for ovarian cancer (Felder et al. 2014),
there have been reports that MUC16 may be associated with pathological and survival outcomes in patients with
bladder cancer (Manvar et al. 2010).
ELF3 achieved higher information gain in the domain of somatic mutations. ELF3 is expressed in various
carcinomas and has been shown to promote the transcription of many genes implicated in cancer. Abnormal
expression of ELF3 has been implicated in the lung and breast cancer (Hou et al. 2004).
CMTM2 was selected when considering the combination of CNV and DNA methylation. It was known that
CMTM2 is significantly associated with colorectal cancer (Fang et al. 2012).
BOLL was highly informative in the combination of CNV and DNA methylation. Primary lung
adenocarcinomas from never smokers showed significantly higher prevalence for methylation of BOLL than
smokers (current and former) (Tessema et al. 2009).
DRD4 achieved higher information gain considering the combination of CNV and DNA methylation. In a
study on gastric and colorectal cancers for polymorphism, DRD4 was reported as significantly associated with
gastric cancer (Ikeda et al. 2008).
PCP4 was selected considering the combination of CNV and RNASeqV2. An anti-apoptosis function of
PCP4 in human breast cancer cells was reported. The anti-proliferative effects of PCP4 knock-down mediated
through the decreased Akt phosphorylation was demonstrated (Hamada et al. 2014).
MYRIP achieved higher information gain in the domain of CNV and RNASeqV2. It was reported that
methylation of CpG sites of the potential tumor suppressor, MYRIP, is associated with hepatocellular carcinoma
recurrence (Yang et al. 2011).
FAM107A was highly informative considering the combination of CNV and RNASeqV2. FAM107A is a
candidate tumor suppressor gene located on chromosome 3p21.1. Down-regulation of FAM107A has been
observed in various cancers such as non-small-cell lung, renal cell and prostate cancers (Nakajima and Koizumi
2014).
RAB11FIP1 was associates with higher information gain in the combination of CNV and somatic mutations.
The RAB-coupling protein (RAB11FIP1) is a driving force for the 8p11-12 amplicon in human breast cancer
and mouse xenograft models of mammary carcinogenesis (Subramani and Alahari 2010).
MMP23B was highly informative in the domain of DNA methylation and RNASeqV2. The cellular
processes related with matrix metalloproteases (MMPs) include tissue remodeling, cell proliferation, cell
migration, differentiation, apoptosis, and immune response. Furthermore, MMPs contribute to tissue
degradation, tumor progression, and invasion (Galea et al. 2014).
CDO1 achieved higher information gain considering the combination of DNA methylation and RNASeqV2.
Because of its relation with crucial mechanisms, CDO1 is believed as a critical tumor suppressor gene. The
sensitive methylation trait of CDO1 is observed in various cancers such as breast, colorectal, esophageal, lung,
bladder, and gastric cancer (Yamashita et al. 2014).
ZIC5 was associated with higher information gain in the case of the combination of DNA methylation and
RNASeqV2. It is known that ZIC proteins are essential for proliferation of meningeal cell progenitors and it was
observed that ZIC5 transcript level in meningiomas was higher than those in whole brain or normal dura mater
(Aruga et al. 2010).
PCDHA6 was selected considering the combination of DNA methylation and somatic mutations. In breast
cancer, this gene is located in a genomic region of agglomerative epigenetic aberrations (Novak et al. 2008).
CMTM2 was highly informative in the combination of DNA methylation and somatic mutations. CMTM2
was one of genes exhibiting hyper-methylation in their promoter regions associated with colorectal cancer (Fang
et al. 2012).
PRDM14 achieved higher information gain considering the combination of DNA methylation and somatic
mutations. The aberrant expression pattern of PRDM14 was observed in various cancers including non-small
cell lung cancer and breast cancer (Nishikawa et al. 2007; Zhang et al. 2013).
FOXG1 was selected as a higher information gene considering the combination of DNA methylation and
somatic mutations. One of substantial mechanisms in cancer development is loss of responsiveness to the
growth inhibitory effect of TGF-. It was shown that the overexpressed FOXG1 could suppress the TGF/Smad pathway-induced p21WAF1/CIP1 expression in ovarian cancer cells (Chan et al. 2009).
KRT24 was selected in the combination of RNASeqV2 and Somatic mutations. KRT24 plays key roles in
apoptosis, adhesive migratory, and inflammatory signaling. Furthermore, it was observed that KRT24 was
consistently up-regulated in the mucosa of colorectal cancer (CRC) patients compared with healthy controls
(Hong et al. 2007).
ITIH5 was associated with higher information gain in the combination of RNASeqV2 and somatic mutations.
ITIH5 was strongly expressed in epithelial cells of normal breast and it was lost or strongly reduced in patients
of invasive breast cancer. It was indicated that ITIH5 is a candidate tumor suppressor gene (Veeck et al. 2008).
ZNF695 was highly informative considering the combination of RNASeqV2 and somatic mutations. It was
suggested that ZNF695 protein regulates the expression of genes involved in a mechanism concerning DNA
repair and there is a possibility that cancer cells acquire proliferation ability by loss of ZNF695 function
(Takahashi et al. 2015).
KIRP related genes
SDK1 was associated with higher information gain considering CNV. The sphingosine (Sph) induced
apoptotic process associated with activation of caspase 3 and release of SDK1 may promote the proapoptotic
effect (Suzuki et al. 2004).
IFITM10 was highly informative considering DNA methylation and the combination of CNV and DNA
methylation. The observation that the siRNA knockdown of CTSD-IFITM10 was associated with a decrease in
live cells implies that this fusion plays a role in breast cancer cell proliferation (Varley et al. 2014).
KLHDC7B was associated with high information gain considering DNA methylation. In breast cancer
samples, the promoter region of KLHDC7B gene was hyper-methylated (Guenin et al. 2012).
BNC1 was prominent when considering DNA methylation and the combination of CNV and DNA
methylation. The p53-family member, p63 is a transcription factor that influences cellular adhesion, motility,
proliferation, survival and apoptosis, and has a major role in regulating epithelial stem cells. It was shown that
p63 induces the expression of the basal epithelial transcription factor, BNC1 (Boldrup et al. 2012). Boldrup and
colleagues (Boldrup et al. 2012) showed that BNC1 is a direct transcriptional target of p63 and up-regulation of
BNC1 is a common event in squamous cell carcinomas of the head and neck. Their work identified a new
transcriptional program mediated by p63 regulation of the BNC1 transcription factor in squamous cell
carcinomas.
COL9A2 was highly informative considering DNA methylation and the combination of CNV and DNA
methylation. It was one of aberrantly methylated genes in gastric cancer cases considering association with
Epstein-Barr virus (Matsusaka et al. 2011).
CALB1 was selected when considering RNASeqV2. CALB1 was one of commonly dysregulated genes
shared by urothelial and squamous carcinoma (Hansel et al. 2013). CALB1 is expressed primarily in the kidney
and is involved in the regulation of the reabsorption of calcium in the distal tubule. It was suggested that CALB1
genotypes may be a risk factor for incident renal cell carcinoma among smokers (Southard et al. 2012).
TYRP1 achieved higher information gain in the case of RNASeqV2. TYRP1 mRNA expression level was
indicated as a prognostic marker for melanoma (Journe et al. 2011). Tyrosinase-related proteins influence the
biology of melanocytes and melanoma (Fang et al. 2002).
MOGAT2 was highly informative in the case of RNASeqV2. MOGAT2 is hyper-methylated in breast cancer
(Van der Auwera et al. 2010).
MT1G reported higher information gain in the domain of RNASeqV2. MT1G transcribes isoforms of
metallothioneins, a class of low molecular weight proteins with metal-binding and antioxidant properties. In
several human cancers, metallothionein expression was found to correlate with cell proliferation, tumor
progression, and drug resistance (Henrique et al. 2005).
CA3 was prominent when considering the combination of CNV and DNA methylation. The assessed result
of CA3 in the diagnosis of malignant effusions implied its value as tumor markers (Bramwell et al. 1985).
AQP2 was associated with high information gain in the combination of CNV and RNASeqV2. AQP2
mediates the estradiol-enhanced migration, invasion, and adhesion of endometrial carcinoma cells (Zou et al.
2011).
AKR1B10 reported higher information gain in the combination of CNV and RNASeqV2. Over-expression of
AKR1B10 was found to be a superior biomarker for non-small cell lung carcinoma. It is possible that AKR1B10
involves in carcinogen metabolism (Penning 2005).
PTPRO was selected in the combination of CNV and RNASeqV2. It was reported that PTPRO is often
silenced by DNA hyper-methylation in cancer cell lines and functions as a tumor suppressor (Xu et al. 2008).
The PTPRO gene expresses two major transcripts. The larger transcript is expressed abundantly in the brain and
kidney (Motiwala et al. 2004).
TTYH3 was selected in the combination of CNV and somatic mutations. TTYH3 was identified as one of top
markers for gastric cancer (Cui et al. 2011).
ACTB was highly informative in the combination of CNV and somatic mutations. TFRC and ACTB were
verified as the best combination of two genes among six housekeeping genes (TFRC, GUSB, GAPDH, ACTB,
HPRT1, and RPLP0) for breast cancer quantification (Majidzadeh et al. 2011).
CDH10 reported higher information gain in the combination of RNASeqV2 and somatic mutations. It was
reported that the mutation status of a five-gene-signature (CDH10, COL6A3, SMAD4, TMEM132D, VCAN)
could predict survival of patients with colorectal cancer (Yu et al. 2015).
Supplementary Table 1. The list of pathways associated with higher information gain genes
Target
cancer
Gene name
Pathway name
Amino acid transport across the plasma membrane, Na+/Cl- dependent
neurotransmitter transporters
Downstream signaling in naïve CD8+ T cells, IL12-mediated signaling events, TCR
CD8A
signaling in naïve CD8+ T cells
PYGM
Glycogen breakdown (glycogenolysis)
Chromatin remodeling by hswi/snf atp-dependent complexes, Control of gene
ARID1A
expression by vitamin d receptor, The information processing pathway at the IFN
beta enhancer
CMTM2
Coregulation of Androgen receptor activity
DRD4
G alpha (i) signalling events
Signaling events regulated by Ret tyrosine kinase, Direct p53 effectors,
ErbB2/ErbB3 signaling events, amb2 Integrin signaling, p75(NTR)-mediated
signaling, ATF-2 transcription factor network, Integrin-linked kinase signaling,
Angiopoietin receptor Tie2-mediated signaling, LKB1 signaling events, PDGFRVIT
alpha signaling pathway, RAC1 signaling pathway, CDC42 signaling events,
Nephrin/Neph1 signaling in the kidney podocyte, RhoA signaling pathway,
Signaling events mediated by Hepatocyte Growth Factor Receptor (c-Met), TRAIL
signaling pathway, Nongenotropic Androgen signalingRapid glucocorticoid
signaling, Rapid glucocorticoid signaling
SLC7A2
Amino acid transport across the plasma membrane
FOXG1
Regulation of nuclear SMAD2/3 signaling
JDP2
ATF-2 transcription factor network
KIRP2
TYRP1
Direct p53 effectors
DRD4
G alpha (i) signalling events
PTPRO
Signaling events mediated by Stem cell factor receptor (c-Kit)
EPO signaling pathway, HIF-1-alpha transcription factor network, HIF-2-alpha
transcription factor network, Signaling events mediated by Stem cell factor receptor
(c-Kit), CDC42 signaling events, CXCR4-mediated signaling events, EPHA
forward signaling, RAC1 signaling pathway, RhoA signaling pathway, CXCR3mediated signaling events, Lissencephaly gene (LIS1) in neuronal migration and
EPO
development, TCR signaling in naïve CD4+ T cells, LPA receptor mediated events,
PLK1 signaling events, Erythropoietin mediated neuroprotection through nf-kb,
Hypoxia-inducible factor in the cardivascular system, Rac1 cell motility signaling
pathway, Role of pi3k subunit p85 in regulation of actin organization and cell
migration
Chromatin remodeling by hswi/snf atp-dependent complexes, The information
ACTB
processing pathway at the ifn beta enhancer
PMS2CL
Direct p53 effectors
SLC12A3
Cation-coupled Chloride cotransporters
1
Bladder Urothelial Carcinoma
2
Kidney renal papillary cell carcinoma
The listed pathways are selected from the NCI-Nature Pathway Interaction Database
(http://pid.nci.nih.gov/index.shtml).
BLCA1
SLC6A6
Supplementary Table 2. Genes with significant levels of mutation and focal copy number changes in BLCA.
Genes with statistically significant levels of
mutation
Gene
Information gain
Genes with statistically significant focal copy
number changes
Gene
Information gain
TP53
0.359
CDKN2A
0.496
MLL2
0.151
E2F3
0.234
ARID1A
0.267
CCND1
0.0728
KDM6A
0.160
RB1
0.218
PIK3CA
0.207
EGFR
0.257
EP300
0.166
PPARG
0.436
CDKN1A
0.210
PVRL4
0.311
RB1
0.218
YWHAZ
0.311
ERCC2
0.0368
MDM2
0.162
FGFR3
0.333
ERBB2
0.210
STAG2
0.116
CREBBP
0.311
ERBB3
0.117
NCOR1
0.311
FBXW7
0.389
YAP1
0.261
RXRA
0.257
CCNE1
0.610
ELF3
0.309
MYC
0.311
NFE2L2
0.118
ZNF703
0.509
TSC1
0.257
FGFR3
0.333
KLF5
0.245
PTEN
0.207
TXNIP
0.117
MYCL
0.207
FOXQ1
0.371
BCL2L1
0.117
CDKN2A
0.496
RHOB
0.705
FOXA1
0.0367
PAIP1
0.116
BTG2
0.466
HRAS
0.381
ZFP36L1
0.261
RHOA
0.257
CCND3
0.257
*Above genes were produced in (TheCancerGenomeAtlasResearchNetwork 2014).
Supplementary Table 3. The list of the most differentially regulated genes in renal cell carcinoma.
Gene
Information gain
ZNF160
0.0493
BRD2
0.0754
SFRS18
0.102
ANKRD12
0.225
CYP3A5
0.371
SLC35E1
0.0493
PDE4C
0.405
GPATCH8
0.257
STAG3L1
0.371
BAT2D1
0.131
SMARCA4
0.0198
HNRNPL
0.0494
PTPRO
0.759
ANKRD36B
0.131
RBM25
0.160
PAX8
0.131
SRRM2
0.260
NKTR
0.191
LOC100132247
0.257
GYG1
0.223
ITFG1
0.223
VAMP3
0.191
MTFR1
0.0754
HSP90AB1
0.0754
LAPTM4B
0.0754
PNPLA4
0.0242
CRK
0.191
GNB5
0.227
MSH6
0.131
BXDC5
0.131
C19orf2
0.0493
PICALM
0.160
TSPAN3
0.102
MRPS28
0.0754
*Above genes were produced in (Beleut et al. 2012).
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