university malaya original literary work...

ii

UNIVERSITY MALAYA

ORIGINAL LITERARY WORK DECLARATION

Name of candidate: Khor Goot Heah

Matrix Number: HHC 090007

Name of Degree: Doctor of Philosophy

Title of Thesis (“this work”): Methylation Profiling in Oral Squamous Cell Carcinoma

Field of study: Molecular Biology

I do solemnly and sincerely declare that:

1) I am the sole author/writer of this Work;

2) This work is original;

3) Any use of any work in which copyright exists was done by way of fair dealing and for

permitted purposes and any excerpt or extract form, or reference to or reproduction of

any copyright work has been disclosed expressly and sufficiently and the title of the

Work and its authorship has been acknowledged in this Work;

4) I do not have any actual knowledge nor do I ought reasonably to know that the making of

this Work constitutes an infringement of any copyright work;

5) I hereby assign all and ever rights in the copyright to this Work to the University of

Malaya (“UM”), who henceforth shall be of the owner of the copyright in this Work and

that any reproduction or use in any form or by any means whatsoever is prohibited

without the written consent of UM having been first had and obtained;

6) I am fully aware that if in the course of making this Work I have infringed any copyright

whether internationally or otherwise, I may be subject to legal action or any other action

as may be determined by UM.

Candidate’s signature Date

Subscribed and solemnly declared before

Witness’ signature

Name: Date

Designation:

iii

ABSTRACT

Introduction: DNA methylation is an epigenetic phenomenon at molecular level that involves

gene expression regulation of cell development and differentiation, and diseases. DNA

hypermethylation in a gene promoter region shows dramatic effects on gene expression and is a

common phenomenon in initiation and progression of many solid tumours that includes Oral

Squamous Cell Carcinoma (OSCC). Silencing of hypermethylated genes in promoter regions is a

frequent phenomenon in different types of cancer and has achieved increasing diagnostic and

therapeutic importance since the changes are reversible. Thus, methylation analysis may provide

promising clinical applications that include the development of biomarkers, assessment of

prognosis and prediction of the therapeutic response in oral malignancy. For years, OSCC has

been amongst the leading cancers in developing countries. Despite considerable efforts in

research studies and cancer treatments, a 5-year survival rate for OSCC has not shown any

significant improvement. To improve on this situation, it is therefore necessary to understand the

fundamental biological processes and to identify appropriate prognostic factors of OSCC on

DNA hypermethylation-mediated silencing that leads to cancer progression.

Objectives: OSCC methylation profiling was investigated by microarray analysis followed by

identification and verification of significantly hypermethylated genes and their protein products.

To achieve this, methylation specific polymerase chain reaction (MSPCR) and

immunohistochemical (IHC) analysis were used. Both analyses were conducted on selected

promoter hypermethylation markers used for detecting the epigenetic alterations associated with

OSCC. In this study, the significant pathways of selected hypermethylated genes that are

involved in oral carcinogenesis were also elucidated. Finally, relations of demographic and

iv

clinicopathological characteristics along with these signature genes were conducted for

prognostic purposes of OSCC.

Materials and methods: Genome-wide analysis of 4 normal oral mucosa and 20 OSCC tissues

were conducted using Illumina methylation microarray. The specified differential genes were

selected from a gene list and their methylation statuses and protein expressions were further

verified by an independent cohort sample of 40 OSCC samples. Lastly, statistical analysis

conducted on demographic, clinicopathological data and gene hypermethylations for OSCC

prognostication.

Results: Unsupervised hierarchical clustering of methylation data revealed distinct methylation

patterns between the normal and the tumour tissues. For tumour tissues, high frequencies of

promoter hypermethylation were found in p16, DDAH2, DUSP1, PIKCR3, TP73, MEF2D,

RRM2 and CELSR3 genes in the MSPCR analysis; whereas low positive immunostaining of

DDAH2, DUSP1, MEF2D and RRM2 were demonstrated in the IHC analysis. Notably, an

inverse correlation was observed between hypermethylations and protein expressions of DDAH2,

DUSP1, MEF2D and RRM2. In addition to that, significant association was found between p16

and TP73 hypermethylation with patients’ tumour site, and CELSR3 and TP73 hypermethylation

with patients’ invasive stages. Furthermore, DDAH2 and CELSR3 hypermethylation, and RRM2

expression were correlated significantly with patients’ age. Finally, gender showed a significant

difference in the survival rate with 24.2% for males and 46.5% for females.

v

Conclusions: Multiple candidate genes were identified using computational and gene-specific

validation approaches in this study. The results provide a new insight into the molecular basis of

promoter hypermethylation and prognostic values of OSCC. Nevertheless, the identified

candidate genes revealed from the present research are worth making further investigations on

oral carcinogenesis.

vi

Abstrak

Pengenalan: Metilasi DNA adalah suatu fenomena epigenetik di peringkat molekul yang

melibatkan ekspresi gen dalam pembangunan dan pembahagian sel-sel, dan penyakit.

Hipermetilasi DNA dalam penganjur gen telah menunjukkan kesan dramatik ke atas gen regulasi

dan merupakan fenomena biasa dalam perkembangan banyak tumor pepejal termasuk karsinoma

sel skuamus mulut (OSCC). Penyenyapan dalam proses hipermetilasi adalah satu mekanisme

yang kerap berlaku dalam pelbagai jenis kanser dan menjadi semakin penting dalam bidang

diagnostik dan terapeutik kerana metilasi merupakan proses tindakbalas berbalik. Oleh itu,

analisis metilasi boleh menyediakan aplikasi klinikal yang menjamin, termasuk penemuan

penanda-penanda baru, penilaian prognosis dan pemberian terapeutik pada kanser mulut. Sejak

beberapa tahun yang dulu, OSCC telah menjadi salah satu kanser utama di negara-negara yang

sedang berkembang. Walaupun pelbagai usaha dalam penyelidikan dan pengubatan barah

berkembang telah dibuat , kadar kelangsungan hidup 5 tahun untuk pesakit-pesakit OSCC masih

tidak bertambah baik. Bagi memperbaiki keadaan ini, adalah perlu untuk memahami proses

biologi asas pada DNA hipermetilasi dalam penganjur gene yang merupakan punca kanser.

Objektif: Penyiasatan profil metilasi OSCC dijalankan oleh analisis microarray, diikuti dengan

pengenalan dan pengesahan status perbezaan gen metilasi yang signifikan dan ekspresi protein

menggunakan metilasi khusus “polymerase chain reaction” (MSPCR) dan immunohistokimia

(IHC) analisis. Kedua-dua analisis telah dijalankan ke atas gen-gen terpilih untuk mengesan

perubahan epigenetik yang berkaitan dengan OSCC. Dalam kajian ini, laluan sel-sel penting

untuk gen hipermetilasi yang terlibat dalam karsinogenesis mulut juga dijelaskan. Akhir sekali,

vii

hubungan ciri-ciri demografi dan klinikopathologikal dengan gen-gen penanda telah diadakan

untuk tujuan ramalan OSCC.

Material dan cara:

Untuk mengenal pasti kelainan metilasi DNA yang berkaitan dengan OSCC, kita menyiasat

analisis keseluruhan genom keatas 4 mukosa mulut biasa dan 20 tisu OSCC, dengan

menggunakan Illumina metilasi microarray. Status metilasi dan ekspresi protein untuk calon-

calon gen telah disahkan oleh sampel kohort berbeza daripada 40 sampel tumor. Akhir sekali,

analisis secara statistik juga dijalankan keatas data demografi dan klinikopathologika, dan tanda-

tanda hipermetilasi untuk nilai prognostik bagi pesakit OSCC.

Keputusan: Kelompok hierarki tanpa pengawasan data metilasi mendedahkan pola metilasi

berbeza antara tisu normal dan tisu tumor. Dalam kajian tisu tumor, frekuensi tinggi dalam

penganjur hipermetilasi ditemui pada gen-gen P16, DDAH2, DUSP1, PIKCR3, TP73, MEF2D,

RRM2, BCL2 dan CELSR3 dalam MSPCR analisis, begitu juga perwarnaan imuno dalam IHC

analisis yang rendah bagi DDAH2, DUSP1, MEF2D dan RRM2 telah ditunjukkan. Hubungan

songsang antara hipermetilasi dan imunoreaktiviti untuk DDAH2, DUSP1, MEF2D dan RRM2

telah dikesan dalam kajian ini. Tambahan pula, hubungan yang ketara ditemui antara

hipermetilasi P16 dan TP73 dengan lokasi tumor, hipermetilasi CELSR3 dan TP73 dengan

pesakit peringkat invasif. Tambahan pula, hipermetilasi bagi DDAH2 dan CELSR3, and RRM2

ekspresi adalah berkait rapat dengan ketara dengan usia pesakit. Akhir sekali, jantina

menunjukkan perbezaan yang signifikan dalam kadar jangkaan hidup dengan 24.2% bagi lelaki

dan 46.5% untuk wanita.

viii

Kesimpulan: Pelbagai calon gen telah dikenal pasti menggunakan pendekatan pengesahan

pengiraan dan gen khusus dalam kajian ini. Keputusan ini juga menyediakan wawasan baru ke

dalam asas molekul hIpermetilasi penganjur dan nilai prognostik OSCC. Lagipun, calon-calon

gen yang dikenal pasti dari kajian ini adalah bernilai untuk siasatan lanjut mengenai

karsinogenesis mulut.

ix

ACKNOWLEDGEMENTS

First and foremost, I would like to thank God for giving me the strength, patience and health

throughout the study tenure. I would like to express my deepest gratitude to my main supervisors,

Prof. Dr Thong Kwai Lin and Prof Dr Rosnah Mohd. Zain for their generous guidance,

enthusiastic supervision and constant encouragement. This also applies to my co-supervisor,

Associate Prof. Dr Gabriele Ruth Anisah Froemming for her continuous supports and invaluable

advices.

I appreciate the encouragement that I have received from my Dean of the Faculty of Dentistry,

Universiti Teknologi MARA (UiTM), Prof Dr Mohamed Bin Ibrahim Hassan. I would like to

acknowledge the grants, PS001/2010A and PV046-2011B provided by the University of Malaya,

600-RMI/DANA/5/3/RIF (633/2012) by the UiTM. and the Fundamental Research Grant

Scheme (FRGS) of the Ministry of Higher Education, Malaysia.

Many thanks to Asst. Prof Dr. Tan Aik Choon from the school of medicine, University of

Colorado, Denver, USA for his time and invaluable help in the microarray data analysis. I am

grateful to Dr Mariam Mohamad of Population Health and Preventive Medicine Department,

Medical Faculty, UiTM for her invaluable advice in the statistical analysis.

I would like to thank IMMB staffs and members who knowingly and unknowing-led me through

the life-challenges to our ability to thrive. Thank you to the OCRCC team for their support

despite the tight working schedule, especially to Nabilah, Karen Ng and Vincent Chong.

My deepest gratitude and special heartfelt thanks to my husband, Ir Teoh Teik Seng, my

wonderful sons, Teoh Khai Ping and Teoh Khai Jieh, and my loving daughter, Teoh Xian Ying

for being patient and supportive during my study. I am so lucky to have them with me all the

time sharing the great moments in our life. This thesis is dedicated to them.

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LIST OF CONTENTS Page No.

i. COVER PAGE

ii. ORIGINAL LITERARY WORK DECLARATION

iii. ABSTRACT

iv. ACKNOWLEDGEMENTS

v. LIST OF CONTENTS

vi. LIST OF FIGURES

vii. LIST OF TABLES

viii. LIST OF APPENDICES

ix. LIST OF ABBREVIATIONS

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xvi

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CHAPTER 1 INTRODUCTION

1.1 Background of the study

1.2 Rationale of the study

1.3 Significance of the study

1.4 Objectives of the study

CHAPTER 2 LITERARURE REVIEW

2.1 Oral cancer

2.1.1 The world scene

2.1.2 The Malaysian scene

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2.2 Carcinogenesis

2.3 Cancer staging and grading

2.4 Cancer prognosis

2.5 Risk factors of oral cancer

2.5.1 Lifestyle factors - Tobacco and betel quid chewing

2.5.1.1 Tobacco and betel quid chewing

2.5.1.2 Excessive alcohol consumption and mouthwash

2.5.2 Infectious factors

2.5.3 Genetic instability

2.5.4 Diet and nutrition

2.5.5 Host defences

2.5.6 Chemical carcinogenic substances

2.6 Molecular alterations in cancers

2.6.1 Oncogenes

2.6.2 Tumour suppressor genes

2.7 Epigenetic biomarkers for cancers

2.8 Cancer therapy

2.9 Epigenetic

2.9.1 Significance of epigenetic studies

2.9.2 Epigenetic interaction

2.9.2.1 Epigenetic and aging

2.9.2.2 Epigenetic and gender

2.9.2.3 Epigenetic and diets

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2.9.3 DNA methylation

2.9.3.1 DNA methylation mechanism

2.9.4 CpG islands

2.9.4.1 CpG methylation

2.9.4.2 DNA nucleotide methylatransferases

2.9.5 Effects of DNA methylation on gene transcription

2.10 Histone modifications

2.11 High throughput methylation analysis

2.11.1 Microarray

2.11.2 DNA methylation microarrays

2.11.3 Cytosine microarray

2.11.4 Microarray application in oral cancers

2.12 Methylation analysis

2.12.1 Bisulfite-conversion based method

2.12.2 Gene-specific methylation analysis

2.12.3 Methylation-specific polymerase chain reaction

2.12.4 Methylation-sensitive restriction enzymes methods

2.12.5 Methylation-sensitive high-resolution melting

2.13 Immunohistochemistry

2.13.1 Technical aspect of immunohistochemistry

CHAPTER 3 MATERIALS AND METHODS

3.1 Study design

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3.2 Study population

3.3 DNA extraction

3.3.1 Snap frozen tissues

3.3.2 Formalin fixed paraffin embedded tissues

3.3.3 Bisulfite converted DNA

3.4 Microarray assay

3.4.1 Microarray data analysis

3.4.1.1 Genome Studio data analysis

3.4.1.2 Partek Genomic Suite and Genego, MetacoreTM

analysis

3.5. Methylation-specific polymerase chain reaction analysis

3.5.1 Statistical analysis for comparisons between patients’ demographic profiles

and clinicopathological characteristics

3.5.2 Survival analysis

3.6 Immunohistochemical analysis

3.6.1 Protocol for detection of protein expression of selected genes using IHC

3.6.1.1 Dewaxing, deparafinization and rehydration

3. 6.1.2 Antigen retrieval

3. 6.1.3 Blocking

3. 6.1.4 Staining

3. 6.1.5 Counterstaining

3. 6.1.6. Tissue preservation

3. 6.2 Image scoring analysis of protein expressions of selected genes in IHC

stained specimens

3.6.3 Statistical analysis of protein expressions of selected genes

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CHAPTER 4 RESULTS

4.1 Methylation microarray analysis

4.1.1 Study population

4.1.2 Illumina’s Genome Studio software analysis

4.1.3 Partek Genomic Suite assay

4.1.4 Signalling pathway analysis of hypermethylated genes of OSCC

4.2 Methylation-specific polymerase chain reaction analysis

4.2.1 Demographic and clinicopathological parameters of OSCC

4.2.2 Association between patients’ demographic profiles, clinicopathological data

and methylation status

4.3 Survival analysis


4.4.1 Association between gene hypermethylation levels and protein expressions of

DDAH2, DUSP1, MEF2D and RRM2.

4.4.2 Correlation between protein expressions of DDAH2, DUSP1, MEF2D and

RRM2

4.4.3 Correlation between patients’ age and protein expressions of DDAH2,

DUSP1, MEF2D and RRM2.

CHAPTER 5 DISCUSSIONS

5.1 Methylation microarray analysis

5.2 Partek Genomic Suite assay

5.3 Methylation-specific polymerase chain reaction analysis

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5.4 Significant signaling pathway analysis of hypermethylated genes of MEF2D and

RRM2

5.4.1 MEF2D

5.4.2 RRM2

5.5 Survival analysis


5.6.1 Association of protein expression of DDAH2, DUSP1, MEF2D and RRM2

5.7 Association of gene hypermethylation levels with protein expression of

DDAH2, DUSP1, MEF2D and RRM2

5.8 Demographic profiles, clinicopathological characteristics, gene

hypermethylations and protein expressions of OSCC

5.9 Limitations of study

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CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusions

6.2 Recommendations

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BIBIOGRAPHY

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LIST OF FIGURES Page No.

Figure 2.1 Illustration of de novo methylation and maintenance methylation

processes

29

Figure 2.2

Schematic drawing of methylated CpG

30

Figure 2.3

Schematic drawing of cytosine methylation

32

Figure 2.4

Schematic drawing of known members of the DNMT super-family

34

Figure 2.5 Schematic chemical conversion of DNA methylation analysis 42

Figure 3.1 Project workflow 47

Figure 4.1 The line plot shows unsupervised hierarchical clustering of probes

of normal tissues and clinical samples

64

Figure 4.2 Mean methylation value difference between normal tissues with

different stages of OSCC cases

65

Figure 4.3.1 Histogram of group methylation profiles of p16 alleles average β

value between normal and 4 pathological stages

65

Figure 4.3.2 Histogram of group methylation profiles of DDAH2 alleles

average β value between normal and 4 pathological stages

66

Figure 4.3.3 Histogram of group methylation profiles of DUSP1 alleles average

β value between normal and 4 pathological stages

66

Figure 4.4 Heatmap methylation frequency of differentially methylated genes

in OSCC

68

Figure 4.5 Principle Component Analyses segregated the normal samples

from tumor samples

69

xvii

Figure 4.6 Scatter plot of fold change of normal and tumour tissues with gene

locus

69

Figure 4.7 Distribution chart of p value with locus for hypomethylated genes

and hypermethylated genes

70

Figure 4.8.1 Representative of Partek Genomic Suite visualization of P16 gene

in normal and four pathological stages

70

Figure 4.8.2 Representative of Partek Genomic Suite visualization of DUSP1

gene in normal and four pathological stages

71

Figure 4.8.3 Representative of Partek Genomic Suite visualization of DDAH2


71

Figure 4.8.4 Representative of Partek Genomic Suite visualization of PIKC3R5

gene in normal and four pathological stages 72

Figure 4.8.5 Representative of Partek Genomic Suite visualization of CELSR3


72

Figure 4.8.6 Representative of Partek Genomic Suite visualization of BCL2


73

Figure 4.9.1 Representative agarose gel electrophoretic images of methylation

status for gene of p16 in methylation control and tumour samples

76

Figure 4.9.2

Representative agarose gel electrophoretic images of methylation

status for gene of DDAH2 in methylation control and tumour

samples

77

Figure 4.9.3


status for gene of DUSP1 in methylation control and tumour

samples

77

Figure 4.9.4


status for gene of CELSR3 in methylation control and tumour

samples

78

Figure 4.9.5


status for gene of PIK3R5 in methylation control and tumour

samples

78


status for gene of TP73 in methylation control and tumour samples 79

Figure 4.9.7


status for gene of MEF2D in methylation control and tumour

sample.

79

xviii


status for gene RRM2 in methylation control and tumour samples

80

Figure 4.9.9


status for gene of BCL2 in methylation control and tumour

samples

80

Figure 4.10 Overall survival rate in OSCC patients showed in Kaplan-Meier

curve

89

Figure 4.11.1 Relative survival of ethnicity demonstrated in Kaplan-Meier

survival curve

89

Figure 4.11.2 Relative survival of age demonstrated in Kaplan-Meier survival

curve 90

Figure 4.11.3 Relative survival of gender demonstrated in Kaplan-Meier survival

curve 90

Figure 4.11.4 Relative survival of habits demonstrated in Kaplan-Meier survival

curve 91

Figure 4.11.5 Relative survival of tumour sites demonstrated in Kaplan-Meier

survival 91

Figure 4.11.6 Relative survival of pathological stages demonstrated in Kaplan-

Meier survival curve

92

Figure 4.11.7 Relative survival of invasive front demonstrated in Kaplan-Meier

survival curve

92

Figure 4.11.8 Relative survival of tumour grading demonstrated in Kaplan-Meier

survival curve

93

Figure 4.11.9 Relative survival of p16 methylation demonstrated in Kaplan-


93

Figure 4.11.10 Relative survival of DDAH2 methylation demonstrated in Kaplan-


94

Figure 4.11.11 Relative survival of DUSP1 methylation demonstrated in Kaplan-


94

Figure 4.11.12 Relative survival of CELSR3 methylation demonstrated in

Kaplan-Meier survival curve

95

xix

Figure 4.11.13 Relative survival of PIK3R5 methylation demonstrated in Kaplan-


95

Figure 4.11.14 Relative survival of TP73 methylation demonstrated in Kaplan-


96

Figure 4.11.15 Relative survival of MEF2D methylation demonstrated in Kaplan-


96

Figure 4.11.16 Relative survival of RRM2 methylation demonstrated in Kaplan-


97

Figure 4.11.17 Relative survival of BCL2 methylation demonstrated in Kaplan-


97

Figure 4.12.1 Negative immunostaining of DDAH2 shows in cytoplasm of the

tumour cells 99

Figure 4.12.2 Positive immunostaining of DDAH2 shows in the cytoplasm of the

normal epithelium 99

Figure 4.12.3 Positive staining of DDAH2 was detected in the cytoplasm of the

tumour cells 100

Figure 4.12.4 Negative cytoplasmic immunostaining of DUSP1 shows in the

tumour cells 100

Figure 4.12.5 Positive cytoplasmic immunostaining of DUSP1 shows in the

normal epithelium 101

Figure 4.12.6 Positive DUSP1 cytoplasmic staining was detected in the tumour

cells 101

Figure 4.12.7 Negative MEF2D nuclear staining shows in the tumour cells 102

Figure 4.12.8 Positive MEF2D nuclear staining shows in the normal epithelium 102

Figure 4.12.9 Positive MEF2D nuclear staining was detected in the tumour cells 103

Figure 4.12.10 Negative RRM2 cytoplasmic staining shows in the tumour cells 103

xx

Figure 4.12.11 Positive RRM2 cytoplasmic staining shows in the normal

epithelium 104

Figure 4.12.12 Positive RRM2 cytoplasmic staining was detected in the tumour

cells 104

LIST OF TABLES

Page No

Table 2.1

TNM clinical staging categories with 5-year survival rate for cancer

9

Table 3.1 Details of primers used in methylation-specific polymerase chain reaction

56

Table 3.2

Details of antibodies used in immunohistochemical assay

58

Table 4.1

Significant biological pathway associated with hypermethylated genes of

OSCC

75

Table 4.2

Methylation status and percentage for methylated genes

81

Table 4.3

Demographic profiles, clinicopathological characteristics and gene

methylations of OSCC patients

84

Table 4.4

Survival analysis of patients’ demographic profiles, clinicopathological

characteristics, and genes’ hypermethylation status

87

Table 4.5

Status and percentage of immunostaining for hypermethylated genes

105

xxi

LIST OF APPENDICES

Page No

Appendix A

Protocol of Haematoxylin and Eosin Staining

156

Appendix B

Primer design criteria

157

Appendix C

Protocol of immunohistochemical staining: Labelled Streptavidin

Biotin

157

Appendix D

Details of 34 promoter-associated hypermethylated genes of

OSCC with UCSC gene accession and CpG Island (Illumina’s

Genome Studio software)

158

Appendix E

Details of 89 promoter hypermethylated genes of OSCC with

island location and p value (Partek Genomic Suite software)

160

Appendix F

Correlation between protein expression and patients’ age

163

Appendix G List of publications and conference proceedings 164

xxii

LIST OF ABBREVIATIONS

5-MCs 5-Methylcytosines

A Adenine

ANOVA One-way analysis of variance

ASR Age standard ratio

BCL2 B-cell lymphoma 2

C Cytosine

Calmodulin Calcium-bound calmodulin 2

CaMK Calcium/calmodulin-dependent kinase

CELSR3 Cadherin EGF LAG seven-pass G-type receptor 3

CH3- Methyl

COBRA Combined bisulfite restriction analysis

CpG Cytosine-Guanine

DAB Diaminobenzidine

DAPK Death associated protein kinase

dd Dideoxynucleotides

DDAH2 Dimethylarginine dimethylaminohydrolase 2

ddH2O Double distilled water

ddNTPs Dideoxynucleotide triphosphate

DNMTs DNA nucleotide methyltransferases

DNP 2,4-dinitrophenol

dNTPs Deoxynucleoside triphosphates

xxiii

dRNs Deoxyribonucleotides

DUSP Dual specificity phosphatase

EBV Epstein–Barr virus

EGCG Epigallocatechin-3–gallate

FDR False discovery rate

FFPE Formalin fixed paraffin embedded

G Guanine

GSTP1 Glutathione S-transferase gene

H&E Haematoxylin and Eosin

H2O2 Hydrogen peroxide

HDACs Histone deacetylases

HNSCC Head and neck squamous cell carcinoma

HP1 Heterochromatin protein 1

HRP Horseradish peroxidase

lincRNAs Large intergenic non- coding RNAs

MAPKs Mitogen-activated protein kinase

MCs Methylcytosines

MEF2D Myocyte Enhancer Factor 2D

MGMT Methylguanine-DNA methyltransferase

Mins Minutes

ml Mililiter

mM Mili Molar

xxiv

MOCDTBS Malaysia Oral Cancer Database and Tissue Bank System

MS-HRM Methylation-sensitive high-resolution melting

MSPCR Methylation-specific polymerase chain reaction

MS-SNuPE Methylation-sensitive single nucleotide primer extension

nc RNAs Non-coding RNAs

NCR National Cancer Registry

ng Nanogram

NORs Nucleolar organizing regions

NTP Nucleoside triphosphate

OCRCC Oral Cancer Research and Coordinating Centre

OSCC Oral Squamous Cell Carcinoma

p101 PI3K regulation class IB

PCA Principle Component Analyses

PCP Planar cell polarity

Pik3r5 Phosphoinositide-3-kinase, regulatory subunit 5

piRNAs PIWI-interacting RNAs

RefSeq Reference sequence

RR Ribonucleotide reductase

RRM2 Ribonucleotide reductase small subunit M2

RT-PCR Real-time polymerase chain reaction

SAM S-Adenosyl-L-Methionin

SCC Squamous cell carcinoma

xxv

snoRNAs Small nucleolar RNAs

SPSS Statistical Package for Social Sciences

T Thymine

TBS Tris phosphate buffer

TP 73 Tumour protein 73

TSS Transcription start site

T-UCRs Transcribed ultra-conserved regions

U Uracil

ul Microliter

µg Microgram

μm Micrometer

USA United States of America

VEGF Vascular endothelial growth factor

WHO World Health Organization

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