Molecular Classification of Basal Cell Carcinoma of Skin by Gene Expression Profiling

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Molecular Classification of

Basal Cell Carcinoma of Skin

by Gene Expression Profiling

by

Hyoseob Lim

Major in Medicine

Department of Medical Sciences

The Graduate School, Ajou University

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Molecular Classification of

Basal Cell Carcinoma of Skin

by Gene Expression Profiling

by

Hyoseob Lim

A Dissertation Submitted to The Graduate School of

Ajou University in Partial Fulfillment of the Requirements

for the Degree of

Master of Medicine

Supervised by

Myong Chul Park, M.D., Ph.D.

Major in Medicine

Department of Medical Sciences

The Graduate School, Ajou University

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-ABSTRACT-

Molecular Classification of Basal Cell Carcinoma of Skin

by Gene Expression Profiling

Non-melanoma skin cancers (NMSC) including basal cell carcinoma (BCC) and

squamous cell carcinoma (SCC) are more common kinds of skin cancer. Although these

tumors share common pathological and clinical features, their similarity and heterogeneity at

molecular levels are not fully elaborated yet. Here, by performing comparative analysis of

gene expression profiling of BCC, SCC, and normal skin tissues, we could classify the BCC

into three subtypes of classical, SCC-like, and normal-like BCCs. Functional enrichment and

pathway analyses revealed the molecular characteristics of each subtype. The classical BCC

showed the enriched expression and transcription signature with the activation of Wnt and

Hedgehog signaling pathways, which were well known key features of BCC. By contrast,

the SCC-like BCC was enriched with immune-response genes and oxidative stress-related

genes. Network analysis revealed the PLAU/PLAUR as a key regulator of SCC-like BCC.

The normal-like BCC showed prominent activation of metabolic processes particularly the

fatty acid metabolism. The existence of these molecular subtypes could be validated in an

independent dataset, which demonstrated the three subgroups of BCC with distinct

functional enrichment. In conclusion, we suggest a novel molecular classification of BCC

providing insights on the heterogeneous progression of BCC.

Keywords: non-melanoma skin cancer, basal cell carcinoma, squamous cell carcinoma, gene

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LIST OF FIGURES

Fig. 1. Unsupervised clustering of gene expression profiles of NMSCs ··· 8

Fig. 2. The gene expression classifiers of the three groups ··· 11

Fig. 3. The enriched pathways Classical BCC subgroup ··· 12

Fig. 4. Gene expression profiles of the integrated data set ··· 19

Fig. 5. Comparison of the gene expression modules in independent data sets ··· 22

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LIST OF TABLES

Table 1. Clinicopathological Features of the Molecular Subtypes ··· 9

Table 2. Enrichment of gene functions in each group classifiers ··· 13

Table 3. Common transcription factor binding sites identified in each group ··· 17

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ABBREVIATION

NMSC: non-melanoma skin cancer;

BCC: basal cell carcinoma;

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I. INTRODUCTION

Skin cancer is the most common type of cancer in Caucasian population. The incidence of

both skin cancers of melanoma and that of non-melanoma are increasing over the past

decades (Leiter and Garbe, 2008; Rogers et al., 2010). Non-melanoma skin cancers (NMSCs)

are developed commonly in the Caucasian populations (Leiter and Garbe, 2008). The

incidence in Asian countries is increasing continuously as well (Kim et al., 2009). The

NMSCs are comprised with major two types, which are basal cell carcinoma (BCC) and

squamous cell carcinoma (SCC). These two types of NMSCs showed similar clinical and

pathological features. Common risk factors including sun exposure, ultraviolet radiation or

ionizing radiation, human papilloma virus, albinism, xeroderma pigmentosum,

immunosuppression, and chemical carcinogens (e.g., arsenic and coal tar) are known to

involve in both NMSC development (Alam and Ratner, 2001; Armstrong and Kricker, 2001;

Rubin et al., 2005). Signaling pathways including PPAR-γ, TGF-β, NF-κB, Ras, EGF

receptor/MAPKK/MAPK/AP-1, SHH/Wnt have been dysregulated in NMSCs (Serewko et

al., 2002; Lind et al., 2004; Heller et al., 2013). Moreover, recent genome-wide gene

expression profiling studies have successfully demonstrated the systematic aberration of

multiple signaling pathways in NMCSs. For example, Hedgehog, Wnt, and MAPK signaling

pathways have been shown to activate in BCC and SCC (Haider et al., 2006; Nindl et al.,

2006; Asplund et al., 2008; Heller et al., 2013). However, huge heterogeneity of pathological

and clinical features of NMSCs have been noticed (Backvall et al., 2005). Morphologically,

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BCC, which grows slowly, is locally invasive neoplasm that do not metastasizes, while SCC,

derived from flat shaped squamous cell in the skin surface, is aggressive, grows faster, and

has higher rates of recurrence, metastasis, and mortality, compared to those of BCC (Ulrich

et al., 2008; Quintana et al., 2013). In addition, based on the clinical and histopathological

features, BCC has been classified into several subtypes of nodular, superficial, micronodular,

infiltrative, and morpheaform (Crowson, 2006). The SCCs have been classified into three

subtypes, which are adenoid (acantholytic), adenosquamous, or desmoplastic SCC.

Furthermore, a recent study has shown that SCC can be further stratified into 10 subtypes

(Yanofsky et al., 2011). These studies imply the huge heterogeneity of BCC and SCC,

requiring further elaboration. Therefore, novel classification based on molecular profiles is

urgently needed to address the mechanistic and molecular backgrounds of the heterogeneous

progression of the NMSC.

Previously, gene expression profiling studies have successfully demonstrated the

heterogeneity of tumors at molecular level and have provided new clues for the underlying

mechanistic alteration in cancer genome (Kwon et al., 2012). Thus, we performed an

oncogenomic comparison of the gene expression profiling of SCC and BCC tumors to

delineate their molecular similarity and heterogeneity of NMSCs at molecular level.

Previously, our team had performed a similar approach to compare the gene expression

profiles of different tumor types. It could have provided not only the expression similarity

among those tissues but also the functional characteristics of the expression profiles,

identifying a new intermediate subtype between the two tumor types derived from similar

(12)

some of the BCC and SCC might share common genomic traits because both tumors are

derived from the resembling developmental origin cells. As expected, our gene expression

profiling analysis could reveal novel subtypes of BCC. This will provide a new mechanistic

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II. MATERIALS AND METHODS

A. Clinical Samples

A total of 21 cases of NMSCs and five normal skin tissue specimens were used for this

study. All the tumor and normal samples were obtained with patients’ consent, and ethical

approval obtained from the Institutional Review Board of Ajou University. Freshly collected

samples were snap-frozen liquid-nitrogen and stored at -70℃ before using.

B. Gene Expression Profiling

Total RNA was extracted from tumor and normal samples using the mirVana total RNA

extraction kit (Ambion, Austin, TX) according to manufacturer’s instruction. RNA

concentrations and purity were determined using the NanoDrop ND-100 spectrometer

(NanoDrop, Rockland, DE). In order to check the RNA integrity of the samples, the Agilent

2100 Bioanalyzer (Agilent Techologies, Santa Clara, CA) was used. Also, mRNAs samples,

given an RNA integrity number (RIN) value exceeding 7, were used in this experiment.

RNA prepared for microarray hybridization was amplified and purified using the Ambion

Illumina RNA amplification kit (Ambion) to yield biotinylated complementary RNA (cRNA)

according to the manufacturer’s suggested instructions. Briefly, total RNA was reverse

transcribed to synthesize first-strand cDNA using a T7 Oligo (dT) primer. The

single-stranded cDNA was converted into a double single-stranded DNA template using DNApolymerase

and RNase H. After purification, the cDNA quantity was assessed using the ND-100

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and labelled with biotin-NTP. The biotinylated cRNA samples were hybridized to

HumanHT-12 v4 expression beadchip array containing over 47,000 probes at 58℃ for 16–

18 h, and then washed, stained streptavidin-Cy3 according to the manufacturer’s protocol

(Illumina, Inc., San Diego, CA). The arrays were scanned with an Illumina bead array

Reader confocal scanner according to the manufacturer’s guidelines. Raw data were obtained

using the software, GenomeStudio.

C. Analysis of Microarray Data

The gene expression profiles of raw data were processed by log2 transformation and

quantile normalization. Then, gene and array centering was applied. All the data processing

was performed using R/Bioconductor packages. The cluster analysis was performed using

cluster 3.0 program (Eisen et al., 1998) and centroid linkage method was applied. In addition,

the functional profiling of biological function and signaling pathways were performed using

DAVID software (Dennis et al., 2003). Validation of the gene expression profile analysis

was performed by using a public gene expression profile, which was obtained from Gene

Expression Omnibus database (accession number, GSE7553) (Riker et al., 2008). Data

integration with the public data was performed by using the official gene symbol as a

common identifier to connect the two different platform data sets.

D. Immunohistochemistry

Ki67 immunostaining was performed on formalin fixed and paraffin-embedded tissues.

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graded ethanol. Endogeneous peroxidases were blocked with 3% hydrogen peroxide. After

antigen retrieval step with 10 mmol/L citrate buffer (pH 6.0), non-specific protein binding

was prevented by blocking with normal goat serum. The slides were then incubated with

monoclonal mouse anti-ki67 (clone MIB-1, DAKO) at a dilution of 1:100 for 30 min at room

temperature. After rinsing, sections were incubated with biotinylated secondary antibody and

avidin-biotin complex. After rinsing, sections were stained with 3,3'-diaminobenzidine and

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III. RESULTS

A. Comparison of Gene Expression Profiles of SCC and BCC

Gene expression profiling was performed using 16 cases of BCC, five cases of SCC, and

five cases of normal skin tissues. First, to evaluate the overall expression patterns, we

performed an unsupervised clustering analysis using the most variable gene sets with

standard deviation greater than 0.4 (n=7153). This revealed three cluster groups of G1, G2,

and G3, which have distinct gene expression patterns (Fig. 1). The G1 was comprised with

the BCC samples while the G2 was comprised with three cases of BCC and five cases of

SCC samples. The G3 was comprised with four cases of BCC and five normal tissues. This

result showed heterogeneous gene expressions of the BCCs. Based on the clustering results,

the BCCs in G1 can be designated as “classical BCC” and the BCCs in G2 as “SCC-like

BCC.” The BCCs in G3 were classified as the “normal-like BCC.” Clinical features including sex, age, tumor size, and stage showed no significant difference among the

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Fig. 1. Unsupervised clustering of gene expression profiles of NMSCs. Unsupervised

clustering of the gene expression profiles of NMSCs were performed, which reveals three

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Table 1. Clinicopathological Features of the Molecular Subtypes Variables Classical BCC (n = 9) SCC-like BCC (n = 3) Normal-like BCC (n = 4) SCC (n = 5) Sex (male/female) 2/7 1/2 1/3 3/2

Mean years (range) 69 (50–85) 68 (64–75) 75 (73–77) 78 (58–88) Tumor size, mean

(SD) 1.4 (0.7) 1.8 (0.2) 1.3 (2.4) 2.1 (0.8)

Cancer stage

T1N0M0 9 3 3 3

T2N0M0 0 0 1 2

Anatomical site

Head and neck 9 1 3 3

Limbs 0 0 0 2

Trunk 0 2 1 0

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B. The Functional Characteristics of Molecular Subtypes

Next, to identify the differentially expressed genes as classifiers for the subtypes, we

applied an ANOVA test with P<0.01, which yielded a total of 3234 genes as classifiers

including 1347 genes for G1, 913 genes for G2, and 974 genes for G3 (Fig. 2). The

functional characteristics of the each classifier were analyzed by performing gene ontology

analysis using DAVID software. The genes up-regulated in the G1 (i.e., classical BCC) but

not in G2 and G3 were enriched with the genes encoding the cell cycle, regulation of

transcription, and epidermis development. Wnt and Hedgehog signaling pathways were

activated only in the G1, which have previously known to activate in the BCCs (Yamazaki et

al., 2001). Consistently, pathway analysis using KEGG database also identified BCC

pathway, suggesting that the gene expression profile reflects the BCC characteristics (Fig. 3).

The genes up-regulated in the G2 (i.e., SCC-like BCC) were enriched with the functions of

defense and inflammatory response. By contrast, the genes up-regulated in the G3 were

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Fig. 2. The gene expression classifiers of the three groups. The three group classifiers are

identified by ANOVA test (P< 0.01) against the three subgroups of classical BCC, SCC-like

BCC, and normal-like BCC groups. Classical BCC (G1) SCC-like BCC (G2) Normal-like BCC (G3)

BCC

SCC

Normal

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Fig. 3. The enriched pathways Classical BCC subgroup. Pathway analysis using KEGG

database shows enrichment of (A) basal cell carcinoma signaling, (B) Hedgehog signaling, and (C) WNT signaling pathways in the classical BCC subgroup. The classifiers identified in the classical BCC are indicated with red star.

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Table 2. Enrichment of gene functions in each group classifiers

BCC subtypes Functions P-value

Classical BCC(G1)

Cell Cycle (ES =15.69)

GO:0022403~cell cycle phase 9.50ⅹ10-20

GO:0007049~cell cycle 1.56ⅹ10-19

GO:0022402~cell cycle process 4.84ⅹ10-18

Cytosekeleton and microtubule organization (ES: 8.60)

GO:0007010~cytoskeleton organization 7.42ⅹ10-10

GO:0007017~microtubule-based process 1.50ⅹ10-09

GO:0007051~spindle organization 5.34ⅹ10-09

GO:0000226~microtubule cytoskeleton organization 6.32ⅹ10-09 Transcription (ES: 6.19)

GO:0006350~transcription 1.63ⅹ10-09

GO:0045449~regulation of transcription 1.28ⅹ10-08

GO:0006357~regulation of transcription from RNA polymerase II promoter 2.15ⅹ10-08 Mesenchymal cell differentiation (ES: 5.50)

GO:0048762~mesenchymal cell differentiation 1.80ⅹ10-06

GO:0014031~mesenchymal cell development 1.80ⅹ10-06

GO:0060485~mesenchyme development 2.32ⅹ10-06

Interphase (ES: 5.36)

GO:0051329~interphase of mitotic cell cycle 2.87ⅹ10-07

GO:0051325~interphase 4.85ⅹ10-07

GO:0000082~G1/S transition of mitotic cell cycle 5.84ⅹ10-04 Metabolic process (ES: 5.32)

GO:0006259~DNA metabolic process 1.97ⅹ10-10

GO:0006974~response to DNA damage stimulus 3.29ⅹ10-05

GO:0006281~DNA repair 1.92ⅹ10-04

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Table 2. (Continued)

Hair and epidermis development (ES: 4.01)

GO:0001942~hair follicle development 3.74ⅹ10-05

GO:0022405~hair cycle process 3.74ⅹ10-05

GO:0022404~molting cycle process 3.74ⅹ10-05

GO:0042303~molting cycle 4.74ⅹ10-05

GO:0042633~hair cycle 4.74ⅹ10-05

GO:0007398~ectoderm development 6.45ⅹ10-04

GO:0008544~epidermis development 1.11ⅹ10-03

KEGG PATHWAY hsa05200:Pathways in cancer 4.12ⅹ10-15

hsa04110:Cell cycle 2.21ⅹ10-14

hsa04310:Wnt signaling pathway 2.39ⅹ10-07

hsa04115:p53 signaling pathway 3.41ⅹ10-07

hsa05217:Basal cell carcinoma 5.03ⅹ10-07

hsa04340:Hedgehog signaling pathway 1.68ⅹ10-03

SCC-like BCC(G2)

Defense response (ES: 2.72)

GO:0006952~defense response 3.63ⅹ10-05

GO:0009611~response to wounding 6.13ⅹ10-03

GO:0006954~inflammatory response 2.91ⅹ10-02

Epidermal development (ES: 2.51)

GO:0008544~epidermis development 4.63ⅹ10-04

GO:0007398~ectoderm development 4.71ⅹ10-04

GO:0009913~epidermal cell differentiation 1.12ⅹ10-03

GO:0030216~keratinocyte differentiation 2.23ⅹ10-03

GO:0030855~epithelial cell differentiation 2.74ⅹ10-03

GO:0031424~keratinization 3.88ⅹ10-02

GO:0060429~epithelium development 4.20ⅹ10-02

Cell adhesion (ES: 2.47)

GO:0022610~biological adhesion 1.39ⅹ10-03

GO:0007155~cell adhesion 1.39ⅹ10-03

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KEGG PATHWAY hsa04666:Fc gamma R-mediated phagocytosis 8.99ⅹ10-04

hsa04514:Cell adhesion molecules (CAMs) 3.27ⅹ10-03

hsa03010:Ribosome 2.58ⅹ10-02

hsa04670:Leukocyte transendothelial migration 3.63ⅹ10-02

Normal-like BCC(G3)

Fatty acid metabolism (ES: 8.06)

GO:0006631~fatty acid metabolic process 1.30ⅹ10-17

GO:0008610~lipid biosynthetic process 9.95ⅹ10-12

GO:0006633~fatty acid biosynthetic process 9.47ⅹ10-06

Steroid metabolism (ES: 6.65)

GO:0008202~steroid metabolic process 2.03ⅹ10-11

GO:0008203~cholesterol metabolic process 7.07ⅹ10-11

GO:0016125~sterol metabolic process 7.59ⅹ10-11

GO:0006694~steroid biosynthetic process 1.75ⅹ10-07

Catabolic process (ES: 4.05)

GO:0016054~organic acid catabolic process 1.77ⅹ10-08

GO:0046395~carboxylic acid catabolic process 1.77ⅹ10-08

GO:0009063~cellular amino acid catabolic process 4.27ⅹ10-05

KEGG PATHWAY hsa03320:PPAR signaling pathway 2.13ⅹ10-10

hsa00280:Valine, leucine and isoleucine degradation 3.19ⅹ10-08

hsa00900:Terpenoid backbone biosynthesis 7.52ⅹ10-05

hsa00620:Pyruvate metabolism 2.04ⅹ10-04

hsa00071:Fatty acid metabolism 2.04ⅹ10-04

hsa01040:Biosynthesis of unsaturated fatty acids 8.15ⅹ10-04

hsa00640:Propanoate metabolism 1.25ⅹ10-03

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To predict the key regulatory molecules, we next sought the common transcription binding

sites (TFBS) in the gene classifiers of each subtype. Transcription factors including BACH1,

BACH2, and AP-1 were identified as the common regulators for the differential expression

of G1 with statistical significance (P<1.06 x10-5) (Table 3). Similarly, the gene classifiers for G2 (i.e., SCC-like BCC) were also commonly bound to AP-1, BACH2, and STAT. These

transcription factors have been known to play important roles in cell differentiation,

proliferation, angiogenesis, malignant transformation, and survival in many cancer types.

Consistent with the previous reports, our findings from gene expression profile analyses

imply the aggressive behaviors of SCC-like BCC compared to those of the classical BCC

(Bowman et al., 2000; Vieira et al., 2001; Calo et al., 2003; Hoeben et al., 2004; Liang et al.,

2012). By contrast, the gene classifiers for the G3 (i.e., Normal-like BCC) were commonly

bound to PPARG, which might associated with the active lipid metabolism of the G3

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Table 3. Common transcription factor binding sites identified in each group

Group Term Count P-value

G1 BACH2 482 7.54ⅹ10-08 AP1 633 1.08ⅹ10-05 BACH1 534 1.06ⅹ₁₀-05 G2 AP1 423 5.47ⅹ10-03 BACH2 307 7.49ⅹ10-03 STAT 242 6.58ⅹ10-02 G3 PPARG 699 5.01ⅹ10-09

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C. External Validation of the Gene Expression Profiling Analysis.

To validate our analysis, we further performed a validation analysis by using a public gene

expression profile of NMSCs including 15 cases of BCC and 11 cases of SCC samples,

which were obtained from GEO database (GSE7553). To avoid possible effect of the

cross-platform difference, we applied an integrated analysis of the two data sets. We normalized

and integrated the data sets of ours (Set A) and the public data (Set B) together as described

in Materials and Methods Section. Then unsupervised clustering analysis was applied to the

integrated data set, which could reveal distinct three subtypes comprised with 24, 19, and 13

cases of samples, respectively (Fig. 4). The subgroups of Set A could be re-observed in the

integrated clustering analysis. This result may suggest that our classification of the tumors

with the three subtypes can be applied to the external dataset of Set B regardless of the

difference of platform.

In addition, unsupervised clustering analysis was separately performed on the Set A and Set

B, to evaluate the consistency of clustering result in individual data set. As expected, the

clustering of individual data sets also revealed same clustering results with that of the

integrated dataset (Fig. 5B). To address the uncertainty the clustering results, we applied

pvclust algorithm, which calculates the significance of the clustering results by applying

bootstrap resampling method (Suzuki and Shimodaira, 2006). The statistical significance of

the clustering results of Set A and B was evaluated by approximately unbiased (AU) P-value,

>0.88 and >0.67, respectively, which indicate the robustness of the clustering results in both

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Fig. 4. Gene expression profiles of the integrated data set.The data sets of ours and an independent dataset are integrated together as described in Materials and Methods. The

patients’ samples are stratified into three subgroups based on the gene expression similarity, which are independent of data platforms. Data platforms are indicated by black and grey bars.

BCC

SCC

Normal

GSE53462

GSE7553

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gene expression profiling could be observed in both Set A and Set B with consistency and

robustness regardless of cross-platform and cohort differences.

Next, we tried to compare the gene sets expressed in the subgroups of Set A and Set B. The

Set B classifiers for the three subtypes were determined by applying ANOVA test with the

same cutoff P<0.01, which yielded 3249 gene sets. Of these, only the 943 genes (29%) were

overlapped with the Set A classifiers for Set A, revealing substantial differences between the

classifiers of Set A and Set B. Indeed, when we applied the gene set classifiers identified in

the Set A to the Set B directly, the classifiers could not identify subgroups in Set B (data not

shown). This might be due to cross platform difference of the data sets. However, as like the

set A classifiers, the set B classifiers also showed five distinct modular expression patterns

across the subtypes (Fig. 5A). Remarkably, when we compared the functional enrichment of

the classifiers of Set A and Set B, the classifiers showed the shared functional enrichment in

both data sets. The modules of A1–A5 of Set A showed matched functional enrichment in

the modules of B1–B5 of Set B, respectively (Fig. 5A and Table 4). The module 1 (i.e., A1

and B1) expressed in the subtype G1 showed shared functions of transcription-related genes.

The module 2 (i.e., A2 and B2) was enriched with the cell cycle and DNA replication-related

genes, indicating the active proliferation of G1 and G2 tumors compared to the normal like

BCC,G3. The module 3 (i.e., A3 and B3), which was expressed in the G2 and G3 but not in

G1, was associated with oxidative stress and metabolism-related genes. This indicates the

decreased oxidative stress is one of key features in subtype G1. The module 4 (i.e., A4 and

B4) was expressed only in the subtype of SCC like BCC (G1), and was enriched with

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characterized with the active immune response. Interestingly, the proliferation module 2 (A2

and B2) was activated in the classical BCC and the SCC like BCC. By contrast, the

normal-like BCC and normal tissues in both data sets shared the expression of module 5 (i.e., A5 and

B5), which were featured with the enriched fatty acid metabolic process. To confirm this

observation, we analyzed the cell proliferation properties of the specimen by performing

immunohistochemistry analysis for ki67. As expected, the classical and the SCC-like BCCs

demonstrated more intense and higher number of positive cells for ki67, while the

normal-like BCC showed less positive cells (Fig. 5C). However, the statistical significance of the

group difference was not assessed due to limited availability of the sample specimens. Taken

together, our integrative analysis could reveal robust subgroups independent of data sets, and

each subtype could be characterized by the functionally enriched expression modules. This

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Fig. 5. Comparison of the gene expression modules in independent data sets. (A) The

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identified from the integrated independent data sets of GSE53462 and GSE7553,

respectively. Each data set shows five modules with the matched functional enrichment, that

is, A1–B1 (regulation of transcription), A2–B2 (cell cycle), A3–B3 (oxidation reduction),

A4–B4 (Immune response), and A5–B5 (metabolic process). (B) Unsupervised clustering

analysis was performed on the gene expression profiles of Set A (A) and Set B (B),

separately. BCC, SCC, and normal tissues are indicated with different colors. Using the

pvcluster algorithm implemented in R package, the uncertainty of the clustering results was

estimated. The approximately unbiased (AU) P-values and (bootstrap probability (BP)

values for each cluster are indicated with red and green colored numbers, respectively. (C)

All samples were immunohistochemically stained by antibody against ki67 antigen. The

final reaction product has a reddish brown color. The classical BCC and SCC-like BCC

illustrated more intense and higher number of positive cells for ki67 compared to the

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Table 4. The list of functional modules identified from two independent data sets

Module GO_Term P-value

A1 GO:0042127~regulation of cell proliferation 1.13ⅹ10-09

GO:0000902~cell morphogenesis 1.21ⅹ10-09

GO:0032989~cellular component morphogenesis 1.28ⅹ10-08

GO:0045449~regulation of transcription 1.36ⅹ10-08

GO:0030030~cell projection organization 3.03ⅹ10-08

GO:0006357~regulation of transcription from RNA polymerase II promoter 3.56ⅹ10-07

GO:0031175~neuron projection development 4.04ⅹ10-07

GO:0000904~cell morphogenesis involved in differentiation 4.28ⅹ10-07

GO:0048762~mesenchymal cell differentiation 4.35ⅹ10-07

B1 GO:0045449~regulation of transcription 3.51ⅹ10-18

GO:0051252~regulation of RNA metabolic process 1.34ⅹ10-12 GO:0006355~regulation of transcription, DNA-dependent 1.64ⅹ10-12

GO:0048858~cell projection morphogenesis 2.61ⅹ10-10

GO:0032990~cell part morphogenesis 1.26ⅹ10-09

GO:0030030~cell projection organization 1.78ⅹ10-09

GO:0060271~cilium morphogenesis 4.09ⅹ10-09

GO:0000902~cell morphogenesis 2.11ⅹ10-08

GO:0042384~cilium assembly 3.29ⅹ10-08

A2 GO:0007049~cell cycle 1.58ⅹ10-36

GO:0000279~M phase 8.50ⅹ10-36

GO:0022403~cell cycle phase 5.11ⅹ10-33

GO:0048285~organelle fission 8.43ⅹ10-30

GO:0000280~nuclear division 2.81ⅹ10-29

GO:0007067~mitosis 2.81ⅹ10-29

GO:0000087~M phase of mitotic cell cycle 5.64ⅹ10-29

GO:0000278~mitotic cell cycle 6.21ⅹ10-29

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B2 GO:0022403~cell cycle phase 3.14ⅹ10-39

GO:0000279~M phase 4.76ⅹ10-39

GO:0007049~cell cycle 4.33ⅹ10-38

GO:0000278~mitotic cell cycle 3.77ⅹ10-34

GO:0000280~nuclear division 7.05ⅹ10-32

GO:0007067~mitosis 7.05ⅹ10-32

GO:0000087~M phase of mitotic cell cycle 1.38ⅹ10-31

GO:0048285~organelle fission 3.13ⅹ10-31

GO:0051301~cell division 1.96ⅹ10-28

A3 GO:0055114~oxidation reduction 5.17ⅹ10-04

GO:0032689~negative regulation of interferon-gamma production 6.19ⅹ10-04

GO:0006690~icosanoid metabolic process 1.51ⅹ10-03

GO:0046638~positive regulation of alpha-beta T cell differentiation 1.75ⅹ10-03

GO:0015721~bile acid and bile salt transport 1.99ⅹ10-03

GO:0033559~unsaturated fatty acid metabolic process 2.32ⅹ10-03 GO:0046637~regulation of alpha-beta T cell differentiation 4.12ⅹ10-03

GO:0007242~intracellular signaling cascade 4.73ⅹ10-03

GO:0046635~positive regulation of alpha-beta T cell activation 4.77ⅹ10-03

GO:0008219~cell death 6.23ⅹ10-03

B3 GO:0055114~oxidation reduction 2.91ⅹ10-07

GO:0033559~unsaturated fatty acid metabolic process 2.11ⅹ10-05

GO:0010033~response to organic substance 3.06ⅹ10-05

GO:0006690~icosanoid metabolic process 9.81ⅹ10-05

GO:0006979~response to oxidative stress 1.21ⅹ10-04

GO:0016126~sterol biosynthetic process 1.40ⅹ10-04

(35)

A4 GO:0006955~immune response 3.72ⅹ10-15

GO:0006952~defense response 5.43ⅹ10-08

GO:0006968~cellular defense response 7.01ⅹ10-08

GO:0009615~response to virus 9.91ⅹ10-05

GO:0009611~response to wounding 2.78ⅹ10-04

GO:0006414~translational elongation 3.21ⅹ10-04

GO:0019882~antigen processing and presentation 5.20ⅹ10-04

GO:0042554~superoxide anion generation 1.22ⅹ10-03

GO:0050778~positive regulation of immune response 3.35ⅹ10-03

GO:0006954~inflammatory response 3.75ⅹ10-03

B4 GO:0006955~immune response 2.26ⅹ10-07

GO:0007264~small GTPase mediated signal transduction 2.97ⅹ10-05

GO:0016192~vesicle-mediated transport 1.49ⅹ10-04

GO:0009617~response to bacterium 1.60ⅹ10-04

GO:0044093~positive regulation of molecular function 2.15ⅹ10-04 GO:0002237~response to molecule of bacterial origin 2.20ⅹ10-04

GO:0007242~intracellular signaling cascade 3.23ⅹ10-04

GO:0006915~apoptosis 3.81ⅹ10-04

GO:0006916~anti-apoptosis 3.81ⅹ10-04

GO:0034097~response to cytokine stimulus 4.07ⅹ10-04

A5 GO:0055114~oxidation reduction 5.53ⅹ10-18

GO:0008203~cholesterol metabolic process 7.07ⅹ10-11

GO:0016125~sterol metabolic process 7.59ⅹ10-11

GO:0016054~organic acid catabolic process 1.77ⅹ10-08

GO:0046395~carboxylic acid catabolic process 1.77ⅹ10-08

GO:0006694~steroid biosynthetic process 1.75ⅹ10-07

(36)

B5 GO:0006631~fatty acid metabolic process 6.61ⅹ10-10

GO:0043434~response to peptide hormone stimulus 9.49ⅹ10-07 GO:0019216~regulation of lipid metabolic process 1.01ⅹ10-06

GO:0040008~regulation of growth 1.36ⅹ10-06

GO:0050873~brown fat cell differentiation 3.73ⅹ10-06

GO:0009725~response to hormone stimulus 4.33ⅹ10-06

GO:0009719~response to endogenous stimulus 5.88ⅹ10-06

GO:0010565~regulation of cellular ketone metabolic process 8.93ⅹ10-06

GO:0016042~lipid catabolic process 1.89ⅹ10-05

(37)

D. PLAU is a Putative Key Regulator of SCC-like BCC

Our analysis could reveal novel subtypes of BCC based on its similarity of gene expression.

That is SCC like BCC and normal-like BCC. Noticeably, we observed the prominent

expression of the module 4 in the SCC-like BCC, which represented the activation of

immune-related genes. This indicates the activated immune reaction might be associated

with the distinct progression of the subtype SCC-like BCC. In order to predict key regulators

for the module 4 expression, we performed network analysis of the module 4 genes by using

protein and genetic interaction and pathway networks implemented in GeneMANIA

software (Warde-Farley et al., 2010). The analysis revealed the most prominent and core

interaction of PLAU and PLAUR with multiple interaction partners of the module 4 genes

(Fig. 6A). Substantial difference of the PLAU and PLAUR gene expressions among the

subtypes could be observed (Fig. 6B). Consistent with our result, a previous study has shown

the differential expression of urokinase-type plasminogen activator (u-PA) in SCC but not in

BCC (Romer et al., 2001). Thus, we could suggest that the PLAU/PLAUR plays a pivotal

role in the development of the novel subtype SCC-like BCC. Also, the expression of

PLAU/PLAUR can be an indicative biomarker for predicting the aggressive subtype of

(38)

Fig. 6. PLAU and PLAUR network from the immune response module. (A) The

SCC-like BCC-specific expression of module A4 with the 368 genes shows common interaction

partner with PLAU and PLAUR. The network is constructed by using GeneMANIA software

with protein-protein interactions and pathway interactions. (B) The expression of PLAU and

PLAUR show the specific expression in the G2 (SCC-like) groups compared to those in G1

(39)

IV. DISCUSSION

In the present study, we performed a gene expression profiling analysis of NMSCs and

identified novel subtypes of BCC that is SCC-like BCC and normal-like BCC. Our findings

could be validated by demonstrating the existence of the same gene expression patterns in an

independent data set. Moreover, the distinct expression modules showed same functional

enrichment in the both independent data sets. We, thus, conclude that our molecular

classification of BCCs into three subgroups of SCC-like, normal-like, and classical-BCC is

robust and consistent regardless of platform and cohort difference of data set.

Our molecular classification also provides novel insight into the heterogeneity of BCCs.

Functional enrichment and pathway analyses revealed the molecular characteristics of each

subtype. The classical BCC showed the enriched expression of transcription signature with

the activation of Wnt and Hedgehog signaling pathways, which were well known key

features of BCC. The SCC-like BCC was characterized by the activation of

immune-response genes and oxidative stress-related genes. Previously, the functional relationship

between inflammation and cancer has been studied extensively (see details in review

(Coussens and Werb, 2002)). It has been suggested that inflammatory signals including the

release of chemokines and cytokines play regulatory roles in cancer progression. In addition,

network analysis showed the PLAU/PLAUR as a key regulator of SCC-like BCC. Congruent

with our results, PLAU, encoding a serine protease, has been known to degrade extracellular

matrix, promoting tumor cell development and metastasis in SCC and other cancer types

(40)

et al., 2006). Indeed, PLAU degrades the extracellular matrix and regulates intracellular

signal pathways for cancer cell survival (Santibanez, 2013). Thus, we could suggest that the

aberrant activation of immune response particularly the PLAU/PLAUR signaling might have

a pivotal role in the development of the aggressive subtype of SCC-like BCC. On the other

hand, the normal-like BCC showed the distinct expression of lipid metabolism-related genes

compared to those of classical and SCC-like BCCs. As an altered energy resource, lipid

metabolism has been known to play a critical role in caner development and progression

(Santos and Schulze, 2012). This could be confirmed by the expression of B2 module in an

independent data set (see Fig. 5A and Table 4). These altered expressions of metabolism

related genes were opposite to the expression of cell cycle-related genes in both classical and

SCC-like BCCs. This may indicate the imbalanced regulation of cell cycle regulators on the

cell cycle and lipid metabolism pathways as described previously (Fajas, 2013).

Like BCC, SCCs also have been noticed to have heterogeneous progression, but we could

not define the molecular heterogeneity of SCC, because only the five cases of SCC samples

were included in our present study. Large scaled study might be required for further

elaboration of the SCC heterogeneity. In conclusion, by comparing the gene expression

profiling of NMSCs, we could stratify BCC into three distinct molecular subtypes providing

(41)

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-국문요약-

유전자발현 프로파일링을 통한 피부 기저세포암의

분자학적 분류

아주대학교 대학원 의학과 임 효 섭 (지도교수: 박 명 철) 기저세포암과 편평세포암 등의 비흑색종피부암은 피부암 중에 가장 흔하다. 이 피부암이 병리학적으로 임상적으로 많은 공통점을 가지고 있음에도 불구하고, 분자 수준에서의 유사성과 이질성은 충분히 밝혀져 있지 않고 있다. 이에 기저세포암과 편평세포암 그리고 정상 피부조직을 유전자발현 프로파일링을 통하여 비교 분석 하였고, 이로부터 기저세포암을 classical, SCC-like, normal-like BCC 세 subtype으로 분류할 수 있었다. 실험 분석에서 Functional enrichment

analysis와 functional pathway analysis을 통해 각 subtype에서 분자학적 특성을

확인하였다. Classical BCC는 기저세포암에서 잘 표현되는 Wnt와 Hedgehog signaling pathway를 통한 활성화 산물을 잘 발현한 반면에 SCC-like BCC는

면역반응 유전자와 oxidative stress 관련 유전자가 잘 발현하였다. Network analysis를 통해 PLAU/PLAUR가 SCC-like BCC의 핵심유전자임을 알 수 있었다.

(48)

실험 분석에서 독립적인 자료로부터 이러한 분자학적 subtype이 있다는 것을 확인할 수 있었고, 명확한 functional enrichment로 입증할 수 있었다. 우리는 이 실험을 통해 기저세포암의 비균질적인 진행을 예측하는데 도움이 될 수 있는 분자학적인 분류를 제안하는 바이다. 핵심어: 비흑색종피부암, 기저세포암종, 편평세포암종, 유전자발현 프로파일링, 비균질성