Transcriptome-wide analysis of discriminating gene expression according to metabolic phenotype - PowerPoint Presentation

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Transcriptome-wide analysis of discriminating gene expression according to metabolic phenotype based on PET scan in resectable pancreatic cancer; Systematic In silico analysis using public database

Sung Hwan Lee1,4, Baek Gil Kim2,3, Ho Kyoung Hwang1,4, Woo Jung Lee1,4, Chang Moo Kang1,4** 1Department of Surgery, 2Department of pathology, 3Brain Korea 21 Plus Project for Medical Science Yonsei University College of Medicine4Pancreaticobiliary Cancer Clinic, Institute of Gastroenterology, Severance Hospital, Seoul, Korea

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BackgroundsPancreatic cancer is one of the most lethal malignant tumors arising from the gastrointestinal tract.Prognostic factors for pancreatic cancerTumor size

Lymph node metastasis & Positive lymph node ratio (LNR)Tumor grade (Differentiation)Lymphovascular & Perineural invasionSurgical margin statusFluorine 18-fluorodeoxyglucose (

FDG) positron emission

tomography (PET)

Differential diagnosis between benign and malignant massesPre-treatment stagingTherapeutic responseRecurrence

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Size-dependent propensity score matched analysis for DFS between high and low MTV (Volume based PET parameter) according to tumor size

Small (<2.5cm) tumor groupLarge (

≥

2.5cm) tumor group

Kang et al. Medicine 95(9):2595

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To identify differential gene expressions between opposite metabolic phenotype of pancreatic cancer

(high FDG uptake tumor vs. low FDG uptake tumor)The aim of this study

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MTV measurement in PET scan (Department of Nuclear medicine)MTV high vs low group database

Randomly selected 3 patients in each groupFFPE block (Tumor, A1)RNA extractionRNA quantification by OD value measurementHybridization by Affymetrix gene chip system (Affymetrix Human U133 plus 2.0)

Methods

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Results

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Scheme of the studyMTV high vs low

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Result I. Pattern analysis for gene expression by Hallmarks of cancer

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Hallmarks of Cancer: The Next GenerationCell (2011) 144;5;646–674

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MTV low

MTV high

Metabolism

Metastasis

EMT

Immune

Angiogenesis

ECM

Apoptosis

Proliferation

TSG

Oncogene

trefoil factor

2

TFF2

forkhead

box

F1

FOXF1

Sp3 transcription

factor

SP3

plakophilin

1

PKP1

microfibrillar

-associated protein

4

MFAP4

thiosulfate

sulfurtransferase

TST

SP3

Sp3

transcription

factor

NFKBIA

nuclear

factor of kappa light polypeptide gene enhancer

H19

imprinted

maternally expressed

transcript

TNK2

tyrosine

kinase, non-receptor,

2

SGK1

serum/glucocorticoid

regulated kinase

1

H19

imprinted maternally expressed transcript

GJB2

gap

junction protein, beta

2

SCGB3A1

secretoglobin

, family 3A, member

1

DMBT1

deleted

in malignant brain tumors 1

TFF2 trefoil factor 2

ALOX5AP arachidonate 5-lipoxygenase-activating protein

FOXF1 forkhead box F1

SP3 Sp3 transcription factor

PKP1 plakophilin 1 (ectodermal dysplasia/skin fragility syndrome)

PGC progastricsin (pepsinogen C)

NFKBIA nuclear factor of kappa light polypeptide gene enhancer

CRELD1 cysteine-rich with EGF-like domains 1

TFF2 trefoil factor 2

ALOX5AP arachidonate 5-lipoxygenase-activating protein

SP3 Sp3 transcription factor

NFKBIA nuclear factor of kappa light polypeptide gene enhancer

GJB2 gap junction protein, beta 2, 26kDa

TNK2 tyrosine kinase, non-receptor, 2

nuclear factor of kappa light polypeptide gene enhancer NFKBIA

gap junction protein, beta 2, 26kDa GJB2

ubiquitin B UBB

anterior gradient homolog 2 (Xenopus laevis) AGR2

trefoil factor 2 TFF2

forkhead box F1 FOXF1

Sp3 transcription factor SP3

plakophilin 1 (ectodermal dysplasia/skin fragility syndrome) PKP1

methyltransferase like 8 METTL8

kynureninase (L-kynurenine hydrolase) KYNU

apolipoprotein L, 1 APOL1

lymphocyte antigen 6 complex, locus E LY6E

trefoil factor 2 TFF2

arachidonate 5-lipoxygenase-activating protein ALOX5AP

Melanophilin MLPH

Landscape of discriminating gene expressions according to Metabolic phenotype

Unpublished data

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Result II.Pathway analysis for gene expression by Pancreatic cancer-specific pathway

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Pancreatic cancer pathway in KEGG. The pancreatic cancer pathway in KEGG is composed of K-Ras/Rho pathway, cell cycle pathway, p53 signaling pathway, TGF-β pathway, and Jak-STAT pathway.

Yunyan

Gu

et al.

Mol

Cancer

Ther

2010;9:2186-2195

Notch signaling pathway

Sonic Hedgehog signaling pathway

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Relative expression of pancreatic cancer specific pathway of MTV high tumorlogFCKRAS/Rho

pathwayCell cyclepathwayp53pathwayTGFbpathwayJak-STAT

pathway

Notch

pathwaySHHpathway

Unpublished data

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Relative expression of individual metabolic pathways of MTV high tumorUnpublished data

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Result III.Network analysis for gene expression using Differentially Expressed Genes (DEGs)

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Hierarchical clustering & Significant genes heatmap between MTV high vs. lowDown-regulation: 71 genesUp-regulation: 14 genes

Relative fold change (MTV high vs low) (log transformed, p-value<0.05)Unpublished data

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<Putative molecular networks between DEGs and major cancer-related gene>

Altered cytoskeletal regulation

Down-regulation of cell adhesion molecules

Up-regulation of mesenchymal phenotypic change

Down-regulation of ubiquitin related tumor suppression

Unpublished data

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Result IV.External validation of survival differences according to DEGs using Public database (NCBI GEO)

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Selection of Validation CohortPDAC (Pancreatic ductal adenocarcinoma)Homo Sapience (Excluding cell lines, Xenograft model)Primary Tissue (Whole Surgical specimen)Expression profiling by MicroarraySurvival data (Survival duration, Death)

31 dataset in NCBI GEO

Matched dataset

(GEO accession: GSE28735) (90 samples, tumor vs non-tumor)3 case excluded (No survival data)

Final enrollment to validation cohort:

42 patients of PDAC

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SurvivorCancer related deathHierarchical clustering of DEGs in validation cohort

Unpublished data

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DES gene

SYNPO2 gene

AGR2 gene

MUC6 gene

P=0.07

P=0.052

P=0.016

P=0.043

DES (+)

DES (-)

SYNPO2 (+)

SYNPO2 (-)

MUC6 (+)

MUC6 (-)

AGR2 (+)

AGR2 (-)

Unpublished data

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P=0.001Survival difference between Combination of DEGs genes and Control

Combination of DEGs genesControl gene setCombination of DEGs genes: Up-regulation of DES & SYNPO2 + Down-regulation of AGR2 & MUC6 Unpublished data

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Summary & ConclusionUp-regulation of glycolysis pathway in metabolic pathway analysis of MTV high groupNo significant result of pancreatic cancer specific signal transduction pathway Altered cell adhesion, cytoskeletal regulation and mesenchymal transition in network analysisSignificant survival prediction of DEGs combination in validation cohort

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Thank you for your attention!